An analysis of genomic changes in high grade serous ovarian cancer provides the most comprehensive and integrated view of cancer genes for any cancer type to date. Ovarian serous adenocarcinoma tumors from 489 patients were examined by The Cancer Genome Atlas (TCGA) Research Network and its analyses are reported in the June 30, 2011 issue of Nature.
An analysis of genomic changes in high grade serous ovarian cancer has provided the most comprehensive and integrated view of cancer genes for any cancer type to date. Ovarian serous adenocarcinoma tumors from 500 patients were examined by The Cancer Genome Atlas (TCGA) Research Network and analyses are reported in the June 30, 2011 issue of Nature. 
To enhance educational learning, we recommend that readers who are less familiar with the topics presented in this article, review the seven appendices provided below, prior to reading the main article text. The topics covered in this article and accompanying appendices are set forth below in the table of contents.
Table of Contents
Introduction – The Cancer Genome Atlas
The Cancer Genome Atlas Analysis of High-Grade Serous Ovarian Cancer
- Samples & Clinical Data
- Demographics and Histopathology
- Overall & Progression‐Free Survival
- Surgical Outcome is Associated with Overall Survival, Progression-Free Survival & Platinum Status
- Gene Mutation Analysis
- Gene Copy Number Analysis
- High-Grade Serous Ovarian Cancer Subtypes & Patient Outcome Prediction.
- Four HG-OvCa Transcriptional Subtypes Based Upon mRNA Analysis
- Three HG-OvCa subtypes Based Upon miRNA Analysis
- Four HG-OvCa Subtypes Based Upon Epigentic DNA Promoter Methylation Analysis
- Transcriptional Signature Predictive of HG-OvCa Patient Survival
- Cellular/Biological Pathways Influencing Disease
- p53 Signaling Pathway
- RB & RAS/PI3K Signaling Pathways
- NOTCH Signaling Pathway
- Homologous Recombination DNA Repair Pathway
- Forkhead Signaling Pathway/FOXM1 Transcriptional Factor Network
Potential Therapeutic Approaches
- PARP Inhibitor Use Against DNA Repair Pathway Defects
- Targeted Therapies Based Upon Deregulated Cellular/Biological Pathways
- Potential p53 Signaling Pathway Therapeutic Approaches
- Potential RB Signaling Pathway Therapeutic Approaches
- Potential RAS/PI3K Signaling Pathway Therapeutic Approaches
- Potential FOXM1 Transcriptional Factor Network Therapeutic Approaches
- Potential NOTCH Signaling Pathway Therapeutic Approaches
- Targeted Therapies Based Upon Common Gene Amplifications
The Road Ahead
- Limitations of The “One Target- One Drug” Approach
- First Steps on the Road to Personalized Medicine.
- Creation of an Ovarian Cancer Molecular Disease Model
- Chemotherapy Sensitivity & Resistance Assay Testing: A “Bridge” or Adjunct to the Ovarian Cancer Molecular Disease Model?
About the National Cancer Institute
About the National Human Genome Research Institute
About the National Institutes of Health
Appendix 1- DNA & RNA
Appendix 2 – Genes, Chromosomes & Proteins
Appendix 3 – Specific Types of Gene Mutations
Appendix 4 – Epigenetics & Gene Silencing
Appendix 5 – Cellular/Biological Pathways
Appendix 6 – Targeting DNA Repair Through PARP Inhibition
Appendix 7 – PARP Inhibitors: A New Class of Targeted Therapies
A catalogue of molecular aberrations that cause ovarian cancer is critical for the development and ultimate use of therapies that will improve patients’ lives. TCGA analyzed messenger RNA (mRNA) expression, microRNA (miRNA) expression, promoter DNA (deoxyribonucleic acid) methylation and DNA copy number variations in 489 high-grade, serous ovarian adenocarcinomas (HGS-OvCa), as well as the DNA sequences of exons from coding genes in 316 of these tumors. The TCGA study findings and some of our observations are summarized below.
- Mutations in the TP53 gene are highly prevalent, occurring in at least 96% of HGS-OvCa samples.
- Low prevalence (2% – 6% of samples) but statistically recurrent somatic (i.e., lifetime acquired) mutations were identified in nine additional genes, including NF1, BRCA1, BRCA2, RB1 and CDK12.
- Several HGS-OvCa genes that were less commonly mutated, include BRAF, PIK3CA, KRAS, and NRAS. It is believed that these mutations are rare but important drivers in HGS-OvCa.
- 113 significant focal DNA copy number variations, and DNA promoter methylation events involving 168 genes were identified.
- Analyses delineated (i) four mRNA ovarian cancer transcriptional subtypes, (ii) three miRNA subtypes, (iii) four promoter methylation subtypes, and (iv) a transcriptional signature associated with survival duration.
- Survival duration did not differ significantly among mRNA transcriptional subtypes. Survival duration differed significantly between miRNA subtypes. Patients identified within the four DNA methylation subtypes differ significantly in overall 5-year survival. Patients whose tumors possessed a gene expression signature associated with poor survival lived for a period that was 23 percent shorter than patients whose tumors did not possess such a signature.
- BRCA1 and/or BRCA2 were mutated in 20% of HGS-OvCa tumors, owing to a combination of germline (i.e., inherited) and somatic (i.e., lifetime acquired) gene mutations. Analysis of these tumors confirmed prior observations that patients with mutated BRCA1 and BRCA2 genes have better survival odds than patients without mutations in these genes. Importantly, the researchers determined that the mechanism through which the BRCA1 and BRCA2 genes become defective, also relates to survival. If the BRCA1 or BRCA2 gene is mutated, there is improved survival duration. In contrast, if BRCA1 activity is reduced by methylation, there is no improved survival duration.
- Cellular/biological pathway analyses suggested that a primary DNA repair process — called “homologous recombination” — is defective in about half of the HGS-OvCa tumors analysed, thereby suggesting potential use of PARP inhibitors in patients whose tumors possess such defects.
- Alterations to the p53, FOXM1, RB, RAS/PI3K, and NOTCH signaling pathways were identified in 87%, 84%, 67%, 45%, and 22%, respectively, of the samples analyzed. Based upon this finding, the TCGA researchers suggested that current and future therapeutic opportunities exist. We provide below potential therapeutic approaches for each deregulated pathway.
- 22 recurrently amplified genes were identified in at least 10% of the HGS-OvCa samples. The TCGA researchers provided a list of existing experimental and U.S. Food & Drug Administration (FDA)-approved drugs or compounds which could theoretically target the recurrent problematic genes. We added additional experimental and FDA-approved drugs or compounds to the list provided by the TCGA researchers.
- The gene mutation spectrum identified in the TCGA study confirms that HGS-OvCa is completely distinct from other epithelial ovarian cancer histological subtypes. For example, clear-cell ovarian cancer tumors possess few TP53 gene mutations but have recurrent ARID1A and PIK3CA gene mutations; endometrioid ovarian cancer tumors possess frequent CTNNB1, ARID1A and PIK3CA mutations, but possess a lower rate of TP53 gene mutations; and mucinous ovarian cancer tumors have prevalent KRAS gene mutations. The differences between epithelial ovarian cancer subtypes represent an opportunity to improve ovarian cancer outcomes through subtype-stratified care.
- There are significant limitations to the “one drug-one target” approach to personalized medicine; however, scientific progress is being made through molecular/genomic tumor profile testing used to identify individualized cancer therapy.
- The creation and continual refinement of an online ovarian cancer molecular disease model (MDM), similar to the melanoma MDM published recently as part of the Cancer Commons project, should be made a priority based upon the high mortality rate associated with the disease.
- Chemotherapy sensitivity and resistance assay (CSRA) technology should be further developed through clinical testing. CSRAs could act as a “bridge” or adjunct to the ovarian cancer molecular disease model.
“This landmark study is producing impressive insights into the biology of this type of cancer. It will significantly empower the cancer research community to make additional discoveries that will help us treat women with this deadly disease. It also illustrates the power of what’s to come from our investment in TCGA.”
– Francis Collins, M.D., Ph.D., Director, National Institutes of Health
Introduction – The Cancer Genome Atlas
The TCGA is a comprehensive and coordinated effort to accelerate our understanding of the molecular basis of cancer through the application of genome analysis technologies, including large-scale genome sequencing. TCGA is a joint effort of the National Cancer Institute (NCI) and the National Human Genome Research Institute (NHGRI), which are both part of the National Institutes of Health (NIH), U.S. Department of Health and Human Services (HHS). Started in 2006, the mission of the TCGA pilot programs is to map the genomic changes in brain, lung and ovarian cancers, and assess the feasibility of a full-scale effort to systematically explore the entire spectrum of genomic changes involved in every major type of human cancer. The ultimate goal is to create a resource that will be used to develop new strategies for preventing, diagnosing and treating cancer.
Ovarian cancer is the fifth-leading cause of cancer death among women in the United States; in 2011, an estimated 21,990 new cases will be diagnosed and 15,460 deaths will occur.  Most patient deaths (i.e., approximately 70%) occur in those women presenting with advanced-stage, high-grade serous ovarian cancer. [3-4] The standard treatment for HGS-OvCa is aggressive surgery followed by platinum–taxane chemotherapy (e.g., carboplatin and paclitaxel). After therapy, platinum-resistant cancer recurs in approximately 25% of patients within six months,  and the overall five-year survival probability is 31% . Approximately 13% of HGS-OvCa is attributable to germline (i.e., inherited) mutations in the BRCA 1 or BRCA 2 gene [7-8], and a smaller percentage can be accounted for by other germline mutations. However, most ovarian cancer can be attributed to a growing number of somatic (i.e., lifetime acquired) aberrations.
The lack of successful ovarian cancer treatment strategies led the TCGA researchers to (i) measure comprehensively genomic and epigenomic abnormalities with respect to clinically-annotated HGS-OvCa tumor samples, and (ii) identify molecular abnormalities that influence pathophysiology, affect outcome, and constitute therapeutic targets. Microarray analyses produced high-resolution measurements of mRNA expression, miRNA expression, DNA copy number variations, and DNA promoter methylation for 489 HGS-OvCa tumors. Massive parallel sequencing coupled with hybrid affinity capture [10-11] provided whole-exome DNA sequence information for 316 of these samples. [See Appendices 1 & 2]
“The new knowledge of the genomic changes in ovarian cancer has revealed that the molecular catalysts of this disease are not limited to small changes affecting individual genes. Also important are large structural changes that occur in these cancer genomes. Cancer researchers can use this comprehensive body of information to better understand the biology of ovarian cancer and improve the diagnosis and treatment of this dreaded disease.”
– Harold E. Varmus, M.D, Director, National Cancer Institute; Co-Recipient, 1989 Nobel Prize in Physiology or Medicine for discovery of the cellular origin of retroviral oncogenes.
The Cancer Genome Atlas Analysis of High-Grade Serous Ovarian Cancer
- Samples & Clinical Data
In this study, TCGA researchers report the analysis of 489 clinically-annotated stage II-IV, HGS-OvCa tumor samples and corresponding normal DNA. Serous adenocarcinoma is the most prevalent form of ovarian cancer, accounting for approximately 85 percent of all ovarian cancer deaths. TCGA researchers completed whole-exome sequencing, which examines the protein-coding regions of the genome, on an unprecedented 316 tumors. They also completed other genomic characterizations with respect to these tumors and an additional 173 samples. The HGS-OvCa tumor samples were surgically resected before systemic treatment, but all patients received a platinum drug and 94% of patients received a taxane drug. The median progression-free survival (PFS) and overall survival (OS) of the cohort are similar to those identified in previously published trials. [12-13] Twenty-five percent of the patients remained free from disease and 45% were alive at the time of last follow-up, whereas 31% experienced disease progression within six months of completing platinum-based therapy. The median follow-up time was 30 months (range, 0–179 months).
1. Demographics and Histopathology
As noted above, the characteristics of the HGS-OvCa cases reflect the general population of women with advanced ovarian cancer. Patients reflected the age at diagnosis, stage, tumor grade and surgical outcome of individuals typically diagnosed with HGS-OvCa. Specifically, the average age of women at diagnosis was 60.2 years; all cases possessed serous histology; 79% of cases were FIGO (International Federation of Gynecology and Obstetrics) stage III (with 73% of those cases designated stage IIIC); 16% of cases were FIGO stage IV; 88% of tumors were grade 3; 72% of patients experienced recurrent disease; and 73% of patients had optimal surgical cytoreduction. “Optimal cytoreduction” was defined as ≤ 1 centimetre (cm) of residual disease remaining after surgery. In addition, 69% of the cases represented platinum-sensitive disease, whereas 21% of cases were platinum-resistant. Generally, platinum status was defined as “resistant” when the platinum free interval was less than six months and the patient had progressed or recurred. Platinum status was defined as “sensitive” when the platinum free interval was six months or greater.
2. Overall & Progression‐Free Survival
Patient age at diagnosis was associated with OS. Platinum status was associated with both PFS and OS. Stage III and optimal surgical cytoreduction cases demonstrated a trend toward improved OS. The median PFS and OS was 16.7 and 43.4 months for FIGO stage III patients, and 14 and 32.9 months for FIGO stage IV patients, respectively (p value (p) = 0.12 for PFS and p = 0.07 for OS). The median PFS and OS was 16.7 and 44.2 months for optimally debulked patients, and 15.4 and 36.2 months, respectively, for suboptimally debulked patients (p = 0.34 for PFS and p = 0.06 for OS). The median OS was 57.9 months for platinum-sensitive patients and 33.2 months for platinum-resistant patients (p =3.5‐19).
In a multivariate analysis, the patient’s age at diagnosis and platinum status were independently associated with OS (hazard ratio (HR) = 1.02, 95% confidence interval (CI):1.00‐1.03; HR = 3.69, 95% CI: 2.60‐5.21, respectively). Stage and platinum status were independently associated with PFS (HR = .80, 95% CI: 0.66‐0.96; HR = 25.6, 95% CI: 15.9‐41.7, respectively).
3. Surgical Outcome is Associated with Overall Survival, Progression-Free Survival & Platinum Status
Many recent studies have demonstrated that patients left with microscopic residual disease after surgical cytoreduction have an improved outcome when compared with other optimally or suboptimally debulked patients. [14-17] Accordingly, the researchers examined the PFS and OS of the TCGA ovarian cancer patients in relation to the size of residual disease after surgical cytoreduction.
Size of residual disease was microscopic in 90 (21%) cases; between 1 and 10mm (i.e., .1 cm to 1 cm) in 223 (52%) cases; between 11 and 20mm (i.e., 1.1 cm to 2cm) in 30 (7%) cases; and more than 20mm (i.e., 2cm) in 89 (20%) cases. TCGA ovarian cancer patients left with microscopic residual disease had improved PFS and OS when compared to patients left with optimal, macroscopic residual disease or suboptimal residual disease. The median PFS was 21.8 months for patients with microscopic residual disease, and 15.0 months for patients with more than microscopic residual disease (p = 0.001). The median OS was 57.4 months for patients with microscopic residual disease and 38.1 months for patients with more than microscopic residual disease (p = 3‐4). Microscopic residual disease was found to be independently associated with OS in a multivariate analysis.
An association between surgical cytoreduction and platinum sensitivity has also been previously reported.  Considering the improved PFS and OS identified in patients with microscopic residual disease, the researchers explored the relationship between surgical cytoreduction and platinum sensitivity in the TCGA ovarian cases. They found no association between platinum status and surgical cytoreduction when defined traditionally as “optimal” or “suboptimal” based upon the measurement of macroscopic residual disease. However, when considering microscopic residual disease separately from optimally or suboptimally debulked patients, there was an association between surgical outcome and platinum status. Patients with microscopic residual disease were more likely to be platinum-sensitive than patients with macroscopic residual disease (p = 0.02; p = 0.003, odds ratio = 3.1, 95% CI: 1.44‐6.68). These data suggest that surgical cytoreduction may have a direct impact on the patient’s platinum status. Moreover, logistic regression analyses indicate that microscopic residual disease is independently associated with platinum status (p = 0.005).
- Gene Mutation Analysis
The DNA segments that carry genetic information are called “genes.” [See Appendix 1] The “genome” is the entire set of genetic instructions found in a human cell. [See Appendix 2] The genome consists of 23 pairs of chromosomes. These chromosomes are composed of six billion individual DNA letters. In the English alphabet there are 26 letters: A through Z. In the alphabet of our genes there are four letters: A, C, G and T.
A gene is essentially a sentence made up of the chemical bases — A (adenine), C (cytosine), G (guanine), or T (thymine) — which describes how to make a protein. Any change in the sequence of bases — and therefore in the protein instructions — is called a “mutation.” Just like changing a letter in a word (or a word in a sentence) can change the meaning of the word (or sentence), a mutation can change the instruction contained in the gene. Any changes to those instructions can alter the gene’s meaning and change the protein that is made, or how or when a cell makes that protein.
“Gene expression” is the process by which the information encoded in a gene is used to direct the assembly of a protein. The sequence of the gene is read in groups of three bases. Each group of three bases — referred to as a “codon” — corresponds to one of 20 different amino acids used to build the protein. Gene mutations can (i) result in a protein that cannot carry out its normal function in the cell, (ii) prevent the protein from being made at all, or (iii) cause too much or too little of a normal protein to be made.
Gene mutations are broadly divided into two groups — “germline” (i.e., inherited) mutations, and “somatic” (i.e., lifetime acquired) mutations. [See Appendix 3] Gene mutations have varying effects on health, depending on where they occur and whether they alter the function of essential proteins.
The TCGA researchers performed exome capture and sequencing on (i) DNA isolated from 316 HGS-OvCa samples, and (ii) matched normal samples obtained from each individual. On average, 76% of gene coding bases were covered in sufficient depth in both the HGS-OvCa tumor samples and the matched normal samples to allow confident mutation detection. Through application of two algorithms, the researchers identified nine genes that exhibited significant mutations.
Specifically, the TP53 (tumor protein p53) gene was mutated in 303 of 316 HGS-OvCa samples (95.8%), and the BRCA1 (BReast CAncer -1) gene and BRCA2 (BReast CAncer-2) gene possessed germline (inherited) mutations in 9% and 8% of cases, respectively, and exhibited somatic (lifetime acquired) mutations in 3% of cases. The TP53 gene encodes the p53 tumor suppressor protein that normally aids in the prevention of cancer formation. Mutations in the gene disrupt the functionality of the protein, which in turn, contributes to uncontrolled growth of ovarian cancer cells. Mutations in the BRCA1 or BRCA2 gene increase a woman’s lifetime breast and ovarian cancer risk.
The researchers also identified six other statistically recurrent mutated genes: RB1 (retinoblastoma 1), NF1 (neurofibromin 1), FAT3 (FAT tumor suppressor homolog 3), CSMD3 (CUB and Sushi multiple domains 3), GABRA6 (gamma-aminobutyric acid (GABA) A receptor, alpha 6), and CDK12 (cyclin-dependent kinase 12). Five of the nine CDK12 mutations suggested potential loss of function, and four mutations were clustered in its protein kinase domain. According to the researchers, GABRA6 and FAT3 appeared significantly mutated, but such mutations were expressed in HGS-OvCa samples and matching normal fallopian tube tissue. Thus, it is less likely that the GABRA6 and FAT3 gene mutations play a significant role in HGS-OvCa.
Through additional analyses, the TCGA researchers identified several HGS-OvCa genes that were less commonly mutated, including mutations in BRAF (v-raf murine sarcoma viral oncogene homolog B1), PIK3CA (phosphoinositide-3-kinase, catalytic, alpha polypeptide), KRAS (v-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog), and NRAS (neuroblastoma RAS viral (v-ras) oncogene homolog). It is believed that these mutations are rare but important drivers in HGS-OvCa.
- Gene Copy Number Analysis
A “chromosome” is an organized package of DNA found in the nucleus of the cell. [See Appendix 2] Humans have 23 pairs of chromosomes–22 pairs of numbered chromosomes, called “autosomes,” and one pair of sex chromosomes, “X “and “Y.” Each parent contributes one chromosome to each pair so that offspring get half of their chromosomes from their mother and half from their father.
“Copy-number variations” (CNVs)—a form of structural variation—are alterations of DNA that result in the cell having an abnormal number of copies of one or more sections of DNA (i.e., genes). CNVs can correspond to relatively large regions of the genome that have been deleted (fewer than the normal number) or duplicated/amplified (more than the normal number) on certain chromosomes. For example, the chromosome that normally possesses sections in the order of “A-B-C-D” could instead possess sections “A-B-C-C-D” (a duplication of “C”) or “A-B-D” (a deletion of “C”).
The TCGA researchers identified somatic copy number alterations in the 489 HGS-OvCa tumor genomes. The most common gene amplifications encoded CCNE1 (cyclin E1), MYC (v-myc myelocytomatosis viral oncogene homolog ) and MECOM (MDS1 and EVI1 complex locus), each of which was highly amplified in more than 20% of tumors.
Additional gene amplifications were identified in ZMYND8 (zinc finger, MYND-type containing 8), the receptor for activated C-kinase; IRF2BP2 (interferon regulatory factor 2 binding protein 2), the p53 target gene; ID4 (inhibitor of DNA binding 4, dominant negative helix-loop-helix protein), the DNA-binding protein inhibitor; PAX8 (paired box 8), the embryonic development gene; and TERT (telomerase reverse transcriptase), the telomerase catalytic subunit.
Fifty mutations in the form of deletions were also identified by the researchers. The known tumor suppressor genes PTEN (phosphatase and tensin homolog), RB1 and NF1 exhibited deletions in at least 2% of the HGS-OvCa tumors.
- High-Grade Serous Ovarian Cancer Subtypes & Patient Outcome Prediction.
The researchers combined expression measurements for 11,864 genes, which were used for various subtype identification and patient outcome predictions. This portion of the HG-OvCa analysis involved four steps which led to the identification of (i) four transcriptional subtypes based on mRNA testing; (ii) three miRNA subtypes, (iii) four DNA promoter methylation subtypes, and (iv) a transcriptional signature that is predictive of HG-OvCa patient survival.
1. Four HGS-OvCa Transcriptional Subtypes Based Upon mRNA Analysis
RNA is the genetic material that transcribes (i.e., copies) DNA instructions and translates them into proteins. It is RNA’s job to transport the genetic information out of the cell’s nucleus and use it as instructions for building proteins. The “transcriptome” consists of all RNA molecules within our cells, including mRNA, transfer RNA (tRNA), and ribosomal RNA (rRNA). The sequence of RNA mirrors the sequence of the DNA from which it was transcribed or copied. Consequently, by analyzing the entire collection of RNAs (i.e., the transcriptome) in a cell, researchers can determine when and where each gene is turned on or off in our cells and tissues. Unlike DNA, the transcriptome can vary with external environmental conditions. Because it includes all mRNA transcripts in the cell, the transcriptome reflects the genes that are being actively expressed at any given time.
As noted above, there are various types of RNA. A major type, called “messenger RNA” or “mRNA,” plays a vital role in making proteins. In this process, mRNA transcribed or copied from genes (which include the protein-coding parts of the genome) is delivered outside the cell nucleus (where DNA is generally stored) to ribosomes, which are molecular machines located in the cell’s cytoplasm (within the cell but outside the nucleus). The ribosomes read, or “translate,” the sequence of the chemical base letters (A, C, G, or T) imprinted in mRNA to assemble protein building blocks (called “amino acids“) into proteins. Accordingly, each mRNA is “transcribed” (copied) from a gene and then “translated” (synthesized) into a specific protein. When the process is uninterrupted, and a protein is produced, the original encoding gene is considered “expressed.”
The mRNA transcriptional analysis of the combined data set identified approximately 1,500 variable genes. Based on the gene expression analysis, the TCGA researchers concluded that at least four robust gene expression subtypes exist in HGS-OvCa. The four HGS-OvCa transcriptional subtypes were identified as “immunoreactive,” “differentiated,” “proliferative” and “mesenchymal” on the basis of gene content in the clusters and observations made in prior studies. 
The immunoreactive subtype was characterized by T-cell chemokine ligands CXCL11 (chemokine (C-X-C motif) ligand 11) and CXCL10 (chemokine (C-X-C motif) ligand 10), and the receptor CXCR3 (chemokine (C-X-C motif) receptor 3).
The proliferative subtype was characterized by (i) high expression of transcription factors — a protein that binds to specific DNA sequences, thereby controlling the flow (or transcription) of genetic information from DNA to mRNA — such as HMGA2 (high mobility group AT-hook 2) and SOX11 (SRY (sex determining region Y)-box 11); (ii) low expression of ovarian tumor markers MUC1 (mucin 1, cell surface associated) and MUC16 (mucin 16, cell surface associated); and (iii) high expression of cell growth proliferation markers such as MCM2 (minichromosome maintenance complex component 2) and PCNA (proliferating cell nuclear antigen).
The differentiated subtype was associated with high expression of MUC16 and MUC1, in tandem with expression of the secretory fallopian tube maker SLPI (secretory leukocyte peptidase inhibitor), thereby suggesting a more mature stage of development.
The mesenchymal subtype was characterized by high expression of HOX genes and markers suggestive of increased stromal components such as myofibroblasts (FAP or fibroblast activation protein, alpha) and microvascular pericytes (ANGPTL2 (angiopoietin-like 2) and ANGPTL1 (angiopoietin-like 1)).
Survival duration did not differ significantly for transcriptional subtypes in the TCGA data set. The proliferative group did exhibit a decrease in the rate of MYC (v-myc myelocytomatosis viral oncogene homolog (avian)) gene amplification and RB1 gene deletion, whereas the immunoreactive subtype showed an increased frequency of MECOM gene amplification.
2. Three HGS-OvCa Subtypes Based Upon miRNA Analysis
Once ignored completely and overlooked in humans until 2001, microRNAs (miRNAs) are encoded in the human genome and can function as natural regulators of global gene expression. miRNAs are a newly-recognized class of small, non-coding RNAs, which have emerged as a central player in controlling cell survival, growth, differentiation, and death. To date, more than 700 human miRNAs have been identified, which are estimated to regulate more than 30% of all human genes.
miRNAs regulate gene expression by binding to complementary protein-related DNA sequences encoded on mRNA. The binding action of miRNA usually results in mRNA translational repression which, in turn, prevents the manufacture or synthesis of the encoded protein. The overall effect produced by miRNAs is the failure of a gene to be expressed, which represents a form of “gene silencing.”
Each miRNA appears to regulate the expression of tens to hundreds of genes, thereby functioning a “master-switch” that regulates and coordinates multiple cellular/biological pathways involving important processes such as embryonic development and immune response, as well as cellular growth and proliferation. Mounting evidence from both basic and clinical studies indicates that miRNAs are expressed in cancer, and different types of cancer have different miRNA expression profiles. The identification of unique miRNA cancer profiles could lead to a novel cancer type (or even subtype)-selective treatment strategy in the future.
The TCGA researchers discovered three miRNA subtypes or clusters in HGS-OvCa, which were identified in the study as “C1,” “C2,” and “C3.”
Notably, miRNA subtype C1 overlapped the mRNA proliferative subtype (discussed above), and miRNA subtype C2 overlapped the mRNA mesenchymal subtype.
Survival duration differed significantly between miRNA subtypes: patients with miRNA subtype C1 tumors survived significantly longer, compared to patients with miRNA subtype C2 or C3.
3. Four HGS-OvCa Subtypes Based Upon Epigentic DNA Promoter Methylation Analysis
A “genome” is the complete set of DNA code or instructions located in the nucleus of each human cell. DNA provides the blueprint instructions for building all proteins. The protein-coding parts of the genome do not make proteins all of the time in each human cell. Instead, different sets of genes are turned on or off in various kinds of cells at different points in time. Differences in the types and amounts of proteins produced determine how cells look, grow and act.
Derived from the Greek word “epi,” the “epigenome” means “above” the genome. The epigenome consists of chemical compounds that modify, or mark, the genome in a way that tells it what to do, where to do it and when to do it. In sum, the epigenome influences which genes are active — and which proteins are produced — in a particular cell. The epigenome is what tells your skin cells to behave like skin cells, heart cells like heart cells and so on.
The epigenome marks, which are not part of the DNA itself, can be passed on from cell to cell during cell division, and from one human generation to the next. The epigenome is made up of chemical compounds, some of which come from natural sources like food and others from man-made sources like medicines or pesticides. As it marks the genome with these chemical tags, the epigenome serves as the intersection between the human genome and the environment. The study of the epigenome is referred to as “epigenetics.”
The epigenome marks the human genome in two primary ways, both of which play a role in turning genes off or on.
The first and primary form of epigentic marking is referred to as “DNA methylation,” which directly affects the DNA in the human genome. In this process, chemical tags called “methyl groups” attach to the backbone of the DNA molecule in specific places. The methyl groups turn genes off or on by affecting interactions between DNA and the cell’s protein-making machinery. For example, if a methyl group is added to areas of the DNA where there is a C (cytosine) followed by a G (guanine), the resulting structural changes to the DNA can prevent the DNA from being transcribed (i.e., copied), or translated (i.e., synthesized) into a protein. In this example, DNA methylation causes the “silencing” of certain genes.
There is a class of drugs currently in clinical development which is designed to counteract the effects of DNA methylation. This drug class is generally referred to as “DNA methyltransferase (DNMT) inhibitors” or “hypomethylating agents.” Examples of DNMT inhibitors include SGI-110, decitabine (Dacogen™), and azacitidine (Vidaza®). As of this writing, the latter two drugs are being tested in ovarian cancer and solid tumor clinical trials. The importance of DNMT inhibitors relates to their potential ability to turn tumor suppressor genes back on, which have been silenced previously through DNA methylation.
The second form of epigentic marking is referred to as “histone modification.” Histones are spool-like proteins that enable DNA’s very long molecules to be wound up neatly into chromosomes inside the cell nucleus. A variety of chemical tags can grab the tails of histones, thereby changing how tightly or loosely they package DNA. If the wrapping is tight, a gene may be hidden from the cell’s protein-making machinery, and consequently be switched off. In contrast, if the wrapping is loosened, a gene that was formerly hidden may be turned on.
There is a class of drugs currently in clinical development which are designed to counteract the effects of histone modification. This drug class is generally referred to as “histone deacetylase (HDAC) inhibitors.” Examples of HDAC inhibitors include vorinostat (Zolinza®), entinostat (a/k/a MS-275), panobinostat, belinostat (a/k/a PDX 101), and SB939. Several HDAC inhibitors are being tested in ovarian cancer and solid tumor clinical trials.
In addition to gene deletion and mutation, epigenetic silencing is increasingly recognized as an important form of gene function inactivation with respect to cancer. [20-21] The TCGA researchers believe that identification of epigenetically-silenced genes in a cancer-specific manner is critical to a complete understanding of the molecular alterations which contribute to the creation of cancer. This type of analysis, according to the researchers, can shed light on the early detection, prevention, treatment, and prognosis of the disease.
The researchers reported that increased DNA methylation and reduced tumor expression implicated 168 genes as epigenetically silenced in the HGS-OvCa samples, as compared with the fallopian tube tissue controls.  DNA methylation was correlated with reduced gene expression across all tumor samples. The researchers noted that several genes — AMT (aminomethyltransferase), CCL21 (chemokine (C-C motif) ligand 21), RAB25 (member RAS oncogene family), and SPARCL1 (SPARC-like 1 (hevin)) — showed DNA promoter hypermethylation in the vast majority of tumors. A “gene promoter” is a region of DNA that facilitates the transcription or copying of a particular gene.
According to the researchers, RAB25 is ranked highest among the 168 genes, based on the level of DNA methylation. Unexpectedly, RAB25, previously reported to be amplified and overexpressed in ovarian cancer,  seems to be epigenetically silenced in a subset of HG-OvCa tumors.
SPARCL1 was originally shown to be down-regulated in many epithelium-derived cancers. [24-25] A gene closely related to SPARCL1 (i.e., SPARC (secreted protein, acidic, cysteine-rich (osteonectin)) was previously shown to have tumor-suppressor activity in human ovarian epithelial cells,  and one driver mutation of SPARC was observed in the TCGA study. Loss of SPARCL1 expression was previously shown to be associated with increased proliferation and cell cycle progression,  highlighting its role in tumor formation.
CCL21 has been shown to be a chemo-attractant for T-cells and dendritic cells.  Anti-tumor properties of this gene have been attributed to its role as a chemo-attractant  and as as a regulator of angiostasis. 
BRCA1 gene silencing via promoter hypermethylation has been reported previously in breast and ovarian cancer,  and recent studies have reported BRCA1 hypermethylation in 10% to 20% of ovarian cancer patients. [32-33; Ref. 31]
The TCGA researchers determined that BRCA1 was hypermethylated and silenced in 56 of 489 (11.5%) of the HGS-OvCa tumors, as reported in previous studies. [Ref. 31]. According to the researchers, the presence of BRCA1 epigenetic silencing was mutually exclusive with all identified BRCA1 and BRCA2 gene mutations. This finding is consistent with a previous population-based study that reported BRCA1 epigenetic silencing only in ovarian cancer patients without a family history associated with a hereditary breast and ovarian cancer syndrome. The findings of the TCGA study, as well as the previous population-based study, suggest that BRCA1 promoter hypermethylation is unlikely to be inherited; rather, it is a lifetime acquired somatic change that leads to BRCA1 inactivation in sporadic ovarian cancers. The researchers also noted that BRCA1 hypermethylated cases are considerably younger and occur more frequently than the BRCA1 somatic gene mutation cases. Accordingly, epigenetic silencing of BRCA1, rather than mutation, could be a more efficient somatic mechanism of gene inactivation.
Based on the totality of the DNA methylation analyses, the researchers identified four HGS-OvCa tumor subtypes referred to as “MC1,” “MC2,” “MC3,” and “MC4,” which were significantly associated with differences in age, BRCA inactivation events, and survival.
The average patient ages in the four DNA methylation clusters are 59.1, 65.8, 57.1, and 62.1, for MC1, MC2, MC3, and MC4, respectively.
The four DNA methylation clusters also differed significantly in their frequencies of BRCA inactivation events, which include BRCA1 and BRCA2 gene mutation, and BRCA1 epigenetic silencing. Altogether, MC1 and MC3 possessed the highest frequencies of BRCA inactivation (46.6% and 44.5%, respectively), while MC2 possessed the lowest frequency (13.2%).
The patients in the four DNA methylation clusters differed significantly in overall 5-year survival (median survival time: MC1 = 48.9 months, MC2 = 35.8 months; MC3 = 40.9 months; MC4 =43.6 months; p=0.04.). After adjusting for age, the researchers determined that MC1 had the best survival and MC3 had a significantly worse survival compared to MC1 (HR = 1.43; p=0.04). MC2 had a marginally significant lower age-adjusted survival (HR = 1.42, p=0.09) compared to MC1.
According to the TCGA researchers, the DNA methylation clusters demonstrated only modest stability, which indicates weak definition with substantial intra-group variations. The researchers note that other clustering methods yielded varying subgroupings of the tumors based on their DNA methylation profiles, and recommend that the DNA methylation cluster memberships should be considered preliminary. Nevertheless, there is a moderate, but statistically significant, overlap between the DNA methylation clusters and gene expression subtypes, thereby suggesting some validity to the subdivisions.
4. Transcriptional Signature Predictive of HGS-OvCa Patient Survival
The TCGA researchers also identified sets of expressed genes that can predict patient survival. A 193-gene transcriptional signature predictive of overall survival was defined using the data set from 215 HGS-OvCa samples. Upon analysis completion, the researchers identified 108 genes that were correlated with poor survival, while 85 genes were correlated with good survival (p = 0.01). Patients whose tumors possessed a gene-expression signature associated with poor survival lived for a period that was 23 percent shorter than patients whose tumors did not possess such a signature.
The researchers validated the predictive power of this gene expression signature on an independent set of 255 HGS-OvCa samples, as well as on three independent expression data sets. Each of the validation samples was assigned a prognostic gene score, reflecting the similarity between its expression profile and the prognostic gene signature. The validation survival analysis of this signature showed statistically significant association with survival in all validation data sets.
- Cellular/Biological Pathways Influencing Disease
A cellular/biological pathway is a series of actions among molecules in a cell that leads to a certain product or a change in a cell. Such a pathway can trigger the assembly of new molecules, such as a protein. Pathways can also turn genes on and off. [See Appendix 5]
There are several types of cellular pathways. Some of the most common are involved in metabolism, the regulation of genes, and the transmission of signals. Metabolic pathways make possible the chemical reactions that occur in our bodies. Gene regulation pathways turn genes on and off. Signal transduction pathways move a signal from a cell’s exterior to its interior.
Researchers are learning that cellular pathways are far more complicated than once thought. Most pathways do not start at point A and end at point B. In fact, many pathways have no real boundaries, and they often work together to accomplish tasks. When multiple cellular pathways interact with each other, it is referred to as a “network.”
Several TCGA analyses integrated data obtained from 316 tumors to identify biology that contributes to HGS-OvCa. The researchers analyzed several cellular pathways which are generally altered in various cancer types, including the RAS/PI3K, RB, and p53 signaling pathways, as well as the primary DNA repair pathway which can possess germline (inherited) and somatic (lifetime acquired) alterations in ovarian cancer.
[Click Title Above to View]
1. p53 Signaling Pathway
The p53 protein is encoded by the TP53 gene. Often referred to as the “guardian of the genome,” p53 is crucial because it regulates the cell cycle and functions as a tumor suppressor in preventing cancer. The TCGA researchers observed a mutation rate of 96% with respect to the TP53 protein. This finding raises the possibility that TP53 gene mutations in HGS-OvCa are essentially universal. Amplifications of MDM2 (Mdm2 p53 binding protein homolog) and MDM4 (Mdm4 p53 binding protein homolog) were uncommon in the p53 signaling pathway, and only occurred in approximately 4% and 3% of cases, respectively.
2. RB & RAS/PI3K Signaling Pathways
The analyses of the frequency with which known cancer-associated pathways harbored one or more mutations, copy number variations or changes in gene expression revealed that the RB pathway and RAS/PI3K pathway were deregulated or altered in 67% and 45% of cases, respectively.
Cancer researchers are interested in the RB pathway because it is altered consistently in cancer cells and promotes deregulated cell proliferation. In this pathway, the Ink4-family and the RB-family proteins function as tumor suppressors, whereas the D-cyclins, cdk4/cdk6, and E2F promote tumor cell proliferation. Within the RB signaling pathway, the TCGA researchers determined that the alterations affected CDKN2A (cyclin-dependent kinase inhibitor 2A), with 32% of altered samples downregulated or deleted; CCNE1 (cyclin E1), with 20% amplified; CCND1 (cyclin D1), with 4% amplified; CCND2 (cyclin D2), with 15% upregulated; and RB1, with 10% deleted or mutated.
RAS operates in a complex signaling network that regulates many cellular functions such as cell proliferation, differentiation, apoptosis, and senescence. There are two well-known RAS-regulated pathways.
The first pathway consists, in part, of RAS-PI3K (phosphatidylinositol 3-kinases)-PTEN (phosphatase and tensin homolog; tumor suppressor gene)-AKT (protein kinase B)-mTOR(mammalian target of rapamycin)(collectively, the RAS-PI3K-PTEN-AKT-mTOR pathway).
The second pathway consists, in part, of RAS-RAF (Raf kinases)-MEK (a/k/a mitogen activated protein kinase kinase or MAP2K)-ERK (extracellular-signal-regulated kinases; a/k/a MAPK (mitogen-activated protein kinases))(collectively, the RAS-RAF-MEK-ERK pathway).
Within the RAS/PI3K signaling pathway, the alterations affected PTEN (phosphatase & tensin homolog), with 7% of altered samples deleted; PIK3CA with 18% amplified; AKT1 (v-akt murine thymoma viral oncogene homolog 1), with 3% amplified; AKT2 (v-akt murine thymoma viral oncogene homolog 2), with 6% amplified; NF1 (neurofibromin 1), with 12% deleted or mutated; KRAS with 11% amplified; and BRAF with .5% mutated.
3. NOTCH Signaling Pathway
The TCGA researchers performed a search for altered subnetworks in a large protein-to–protein interaction network, which contains genes with significant numbers of mutations and copy number variations. By using HOTNET (an algorithm for finding significantly altered subnetworks within a large gene interaction network), they identified several known pathways, including the NOTCH signaling pathway, which was altered in 22% of HGS-OvCa samples. Among its many functions, the NOTCH signaling pathway is important for cell-to-cell communication, which involves gene regulation mechanisms that control multiple cell differentiation processes during embryonic and adult life.
4. Homologous Recombination DNA Repair Pathway
Approximately 10-15% of ovarian cancers are hereditary, and the majority of these cases are due to germline gene mutations in BRCA1 or BRCA2.  A subset of sporadic ovarian tumors appear to share distinctive DNA-repair defects with BRCA1/BRCA2 germline mutation carriers; a phenomenon broadly described as “BRCAness.” [36-38] DNA-repair defects can be caused by germline or somatic alterations to the primary DNA repair pathway called “homologous recombination” (HR). [See Appendix 6]. The types of HR DNA repair pathway alterations examined by the TCGA researchers included: (i) germline and somatic mutation of BRCA1/BRCA2; (ii) epigenetic silencing of BRCA1 through DNA hypermethylation; (iii) changes to the core set of Fanconi Anemia genes; and (iv) additional genetic changes to other key components of the HR pathway.
Previous published studies reported that cells with a mutated or methylated BRCA1 gene, or mutated BRCA2 gene, possess defective HR and are highly responsive to PARP (poly (ADP-ribose) polymerase) inhibitors. [39-42] [See Appendix 7] Notably, PARP inhibitors proved effective in recent breast and ovarian cancer clinical trials. [43-44; Ref. 40] Moreover, a U.K. research team developed a new assay which predicted that 50% of ovarian cancers would respond to in vitro PARP inhibition.[45-48] Several PARP inhibitors are being tested currently in ovarian cancer and solid tumor clinical trials. [49-50]
The researchers in the TCGA study found that 20% of the HGS-OvCa samples possessed germline or somatic gene mutations in BRCA1 (11%) or BRCA2 (9%). Of the studied samples, 11% of HGS-OvCa tumors lost BRCA1 expression through DNA hypermethylation. Importantly, the presence of epigenetic silencing in BRCA1, in combination with BRCA1 or BRCA2 gene mutations, was mutually exclusive.
The survival analysis performed by researchers regarding the impact of BRCA1 or BRCA2 gene status revealed better overall survival for BRCA1 and BRCA2-mutated HGS-OvCa cases, as compared to non-BRCA gene-mutated cases (median overall survival of 66.5 months vs. 41.9 months). Notably, epigenetically silenced BRCA1 cases experienced survival similar to non-BRCA1 or BRCA2-mutated HGS-OvCa cases (median overall survivals of 41.5 and 41.9 months, respectively, p = 0.69). The researchers believe that BRCA1 is inactivated by mutually exclusive genomic and epigenomic mechanisms; therefore, patient survival appears to depend on the specific mechanism of inactivation.
The genomic alterations in other HR DNA repair pathway-involved genes that could render HG-OvCa cells sensitive to PARP inhibitors discovered in this study include: (i) amplification or mutation of EMSY (chromosome 11 open reading frame 30 or C11orf30) (8%); (ii) deletion or mutation of PTEN (7%); (iii) hypermethylation of RAD51C (RAD51 homolog C) (3%); (iv) mutation of ATM (ataxia telangiectasia mutated) or ATR (ataxia telangiectasia and Rad3 related) (2%); and mutation of Fanconi anemia genes (5%).
The TCGA researchers noted that on an overall basis, the HR DNA repair pathway defects may be present in approximately 50% of all HGS-OvCa cases, thereby providing a rationale for additional clinical trials involving PARP inhibitors, which target HGS-OvCa tumors with these HR-related aberrations.
A comparison between the complete set of BRCA inactivation events to all recurrently altered gene copy number peaks, revealed an unexpectedly low frequency of CCNE1 gene amplification in cases with BRCA inactivation (8% of BRCA gene-altered cases possessed CCNE1 amplification vs. 26% of non-altered BRCA cases). In past studies, overall survival was reported to be lower for patients with CCNE1 amplification, as compared to patients in all other cases. In the TCGA study, a survival disadvantage was not established for CCNE1-amplified cases (p = 0.24) when looking only at BRCA non-altered cases, thereby suggesting that the previously reported CCNE1 survival difference can be explained by the higher survival of BRCA-mutated cases.
5. Forkhead Signaling Pathway/FOXM1 Transcriptional Factor Network
The role of the FOXM1 (forkhead box M1) pathway in ovarian cancer has not been established. FOXM1 is a multifunctional transcription factor with three known dominant splice forms, each regulating distinct subsets of genes with a variety of roles in cell proliferation and DNA repair. For example, the FOXM1b isoform regulates a subset of genes that include the DNA repair genes BRCA2 and XRCC1 (X-ray repair complementing defective repair in Chinese hamster cells 1).  CHEK2 (CHK2 checkpoint homolog), which is under the indirect control of ATM (ataxia telangiectasia mutated), directly regulates FOXM1’s expression level. The FOXM1c isoform directly regulates several targets with known roles in cell proliferation, including AURKB (aurora kinase B), PLK1 (polo-like kinase 1), CDC25C (cell division cycle 25 homolog C), and BIRC5 (baculoviral IAP repeat containing 5). 
To identify significantly altered pathways through an integrated analysis of both copy number variations and gene expression, the TCGA researchers used a probabilistic graphical model (referred to as “PARADIGM”) and searched the U.S. National Cancer Institute Pathway Interaction Database (NCI-PID). The PARADIGM computational model incorporates gene copy number changes, gene expression data, and pathway structures to produce an integrated pathway activity (IPA) for every gene, complex, and genetic process present in the pathway database. The TCGA researchers applied PARADIGM to the HGS-OvCa samples and found alterations in many different genes and processes present in pathways contained in the NCI-PID.
Importantly, the researchers determined that the FOXM1 transcription factor network was significantly altered (i.e., activated) in 87% of the HGS-OvCa cases. FOXM1 and its growth proliferation-related target genes, AURKB, CCNB1, BIRC5, CDC25C, and PLK1, were consistently overexpressed but not altered by DNA copy number variations, thereby indicative of transcriptional regulation. Notably, the TP53 gene represses FOXM1 after DNA damage, suggesting that the high rate of TP53 mutation in HGS-OvCa contributes to FOXM1 overexpression. In other data sets, the FOXM1 pathway is significantly activated in tumors relative to adjacent epithelial tissue [54-56] and is associated with HGS-OvCa. 
In sum, the FOXM1 transcription factor network was altered in the largest number of HGS-OvCa samples among all NCI-PID pathways tested. In comparison, pathways with the next highest level of altered activities in the HGS-OvCa cohort included PLK1 signaling events (27%), Aurora B signaling (24%), and Thromboxane A2 receptor signaling (20%).
“Like all cancers, ovarian cancer results from genomic derangements. The efforts of TCGA are confirming that the more we learn about genomic changes in tumor cells, the more we will be able to care for the people affected by cancer.”
– Eric D. Green, M.D., Ph.D., Director, National Human Genome Research Institute
Potential Therapeutic Approaches
The HGS-OvCa mutation spectrum, as identified by the TCGA researchers, indicates that HGS-OvCa is completely distinct from other epithelial ovarian cancer histological subtypes. For example, clear-cell ovarian cancer tumors possess few TP53 mutations but do possess recurrent ARID1A and PIK3CA mutations; endometrioid ovarian cancer tumors have frequent CTNNB1 (catenin (cadherin-associated protein), beta 1, 88kDa), ARID1A, and PIK3CA mutations and a lower rate of TP53 gene mutations; and mucinous ovarian cancer tumors possess prevalent KRAS mutations. [58-61] These differences between ovarian cancer subtypes represents an opportunity to improve ovarian cancer outcomes through subtype-stratified care.
Identification of new therapeutic approaches is a central goal of the TCGA. Overall, the discoveries described above set the stage for approaches to the treatment of HGS-OvCa in which aberrant genes or cellular/biological networks are detected through molecular testing, and targeted with therapies selected to be effective against specific aberrations. Based upon the TCGA study findings, there are three general approaches that could be used to target select HGS-OvCa tumor aberrations: (i) PARP inhibitor use against tumors with HR DNA repair pathway deficiencies; (ii) targeted therapy use against deregulated cellular pathways; and (iii) targeted therapy use against recurrent amplified gene mutations. Each therapeutic approach is addressed below.
- PARP Inhibitor Use Against DNA Repair Pathway Defects
The researchers highlighted the fact that approximately 50% of HGS-OvCa tumors in this study possessed HR DNA repair pathway defects. As noted above, PARP inhibitors could be used in an attempt to take advantage of the primary DNA repair defect observed in half of the HGS-OvCa tumors studied. PARP inhibitors exploit the genetic instability of the tumors and cause the cancer cells to die. [See Appendices 6 & 7] While researchers knew that these drugs could be effective against HGS-OvCa tumors possessing germline (inherited) BRCA1 and BRCA2 gene mutations, this study revealed that 50 percent of HGS-OvCa tumors — including those with HR DNA repair pathway defects — could be responsive to this drug class. [Ref. 49-50]
The key challenges underlying the use of PARP inhibitors include: (i) determination of the extent of DNA repair defects in sporadic ovarian cancers; (ii) development of biomarkers to identify those DNA repair defects and the likelihood of drug response to PARP inhibitors; and (iii) application of this knowledge to identify patients likely to benefit from PARP inhibition therapy.
- Targeted Therapies Based Upon Deregulated Cellular/Biological Pathways
The commonly deregulated cellular pathways identified by the TCGA researchers — p53 (87% of samples), RB (67% of samples), RAS/PI3K (45% of samples), FOXM1 (87% of samples), and NOTCH (22% of samples) — also provide current and future opportunities for therapeutic treatment. Unfortunately, the TCGA researchers did not provide specific therapeutic approaches that target the HGS-OvCa deregulated pathways, which were identified in the study. Despite that fact, we thought it would be helpful to address potential treatment approaches for each altered pathway based upon existing preclinical and clinical research.
1. Potential p53 Signaling Pathway Therapeutic Approaches
Considerable energy has been focused on the p53 pathway because nearly all cancers show defects in this system, and over 50% of those cancers have mutations in the TP53 gene. The TCGA researchers observed a 96% mutation rate in the TP53 gene, and related alterations in the associated p53 protein.
To date, there have been several preclinical and clinical therapeutic approaches to treating cancer cells possessing a mutated TP53 gene and impaired p53 protein functionality. These approaches include the following:
(i) restoration of the TP53 gene function through gene therapy;
(ii) generation of an immune system response through vaccination;
(iii) use of inhibitors to target MDM2;
(iv) administration of “cyclotherapy;” and
(v) inhibition of the G2 cell cycle checkpoint. [62-65]
“Gene therapy” is the insertion, alteration, or removal of genes within an individual’s cells for the purpose of treating disease. This technique has already been used in the U.S. (only through clinical trials) and China (through government-approved drugs) in an attempt to correct defective genes such as TP53. The most common form of gene therapy involves the insertion of functional genes into an unspecified genomic location in order to replace a mutated gene.
Gendicine™ (a/k/a rAd-p53, SCH-58500, and INGN-201), an adenoviral p53-based gene therapy was approved by the Chinese State Drug and Food Administration in 2003 for the treatment of head and neck cancer, along with radiotherapy. Advexin (a/k/a INGN-201), a p53 tumor suppressor gene therapy agent manufactured by Introgen Therapeutics, Inc., was tested in past U.S. clinical trials. Unfortunately, the U.S. Food and Drug Administration (FDA) refused to approve Advexin in 2008, and shortly thereafter, the company filed for bankruptcy.
As of this writing, Shenzhen SiBiono GeneTech Co., Ltd., the company that apparently owns the rights to the prior U.S. formulations of Gendicine™ (i.e., SCH-58500 and Advexin), is currently conducting several phase IV clinical trials involving the use of Gendicine™ against advanced head and neck tumors.  Despite a review of over 2,500 patients that have received Gendicine™ therapy in China, a definitive report addressing the clinical trial phase IV safety and efficacy of the drug has not been published.  We should note, however, that the early U.S. formulations of Gendicine™ were clinically tested by U.S. companies (i.e., former Schering-Plough, Merck & Co. & former Introgen) in ovarian cancer patients. Those drugs demonstrated anti-cancer activity in ovarian cancer clinical trials involving intraperitoneal administration of the drug used in combination with chemotherapy. [68-71]
Another Chinese p53 gene-based therapy known as “H-101″ (brand name: Ocorine; manufactured by Shanghai Sunway Biotech Co., Ltd) was also approved by the Chinese government in 2005.
The second potential therapeutic approach to treating cancer that possesses a TP53 gene mutation involves the use of a vaccine to produce a patient immune system response capable of destroying the mutated cancer cells. Recent advances in our understanding of the regulation of the immune system, based upon detailed analyses of regulatory T cells and the signaling pathways that initiate and restrain the immune response, have revitalized the field of cancer immunology. Vaccines utilizing a p53-derived peptide(s) are currently being tested in ovarian cancer clinical trials.  One of the more advanced p53 vaccines being clinically tested in ovarian cancer is the p53 synthetic long peptide (p53-SLP®) vaccine. To date, the ovarian cancer clinical trials involving the p53 synthetic long peptide (p53-SLP®) vaccine have demonstrated safety and produced a patient immune response. In phase 2 clinical testing, the vaccine produced a low rate (10%) of stable disease; however, a connection could not be made between the clinical response and the vaccine-induced immunity. [73-74]
At this point, it does not appear that p53-SLP®, as a monotherapy, can produce a patient immune response strong enough to create a clinical response in TP53 gene-mutated ovarian cancer. Accordingly, new p53 vaccine approaches (e.g., DNA-based or dendritic cell-delivered) are needed for HGS-OvCa. In the area of small cell lung cancer (SCLC), an adenovirus p53 autologous dendritic cell vaccine (e.g., INGN-225) increased the effectiveness of subsequent chemotherapy.  Specifically, in a phase 1/2 clinical study, 78% of SCLC patients who showed an immune response to the INGN-225 p53 vaccine benefited from subsequent chemotherapy. In contrast, only 33% of vaccinated SCLC patients, who did not experience an immune response, benefited from the same chemotherapy.
The third potential therapeutic approach to treating TP53 gene-mutated cancer involves the use of MDM2 inhibitors. MDM2 can bind to the p53 protein, and degrade its tumor suppressor function. By way of example, JNJ-26854165 and RO5045337 represent two small-molecule MDM2 inhibitors in clinical development. The general concept is to use these inhibitor drugs to suppress MDM2 and restore some p53 protein tumor suppression functionality. [76-78]
There are two caveats associated with potential use of MDM2 inhibitors in HGS-OvCa.
First, many researchers believe that MDM2 inhibitors are not effective against TP53 gene-mutated cancers which do not possess functional p53 protein.  Based upon the results of the TCGA study, there is little doubt that HGS-OvCa is a TP53 gene-mutated cancer which possesses significant p53 protein inactivation or low functionality. Despite that fact, recent preclinical research findings suggest that use of MDM2 inhibitors in TP53 gene-mutated cancer could still be warranted based upon the potential ability of that inhibitor drug class, possibly in combination with a chemotherapeutic agent, to reactivate p73 tumor suppressor protein functionality. [80-82] The TP53 gene family consists of TP53, TP63 and TP73, as well as the associated encoded proteins p53, p63, and p73.
Second, assuming arguendo that MDM2 inhibitors could be effectively used in TP53 gene-mutated HGS-OvCa, the TCGA researchers determined that only 4% of those tumors possessed MDM2 gene amplification, thereby making the gene a potential drug target. Despite the two caveats raised with respect to the use of MDM2 inhibitors against TP53 gene-mutated cancers such as HGS-OvCa, a novel preclinical approach, known as “cyclotherapy,” could nevertheless allow MDM2 inhibitors to play a role in fighting HGS-OvCa.
The fourth potential therapeutic approach to treating TP53 gene-mutated cancer involves administration of so-called “cyclotherapy.” This approach could take advantage of the TP53 gene mutation in HGS-OvCa cancer cells, while protecting normal (non-TP53 gene-mutated) cells from the toxic effect of chemotherapy, as described below. [Ref. 62-64; 83]
The “cell cycle” involves a series of events that take place in the human cell which lead to its division and duplication. The cell cycle consists of five phases:
(i) “Gap 0″ (G0), a resting phase where the cell leaves the cycle and stops dividing;
(ii) “Gap 1″ (G1), in which the cell increases in size, and the “G1 checkpoint” ensures that the cell is is ready for DNA synthesis;
(iii) “Synthesis” (S), in which DNA replication occurs while the “intra-S checkpoint” monitors proper replication in the event of DNA damage or replication stress;
(iv) “Gap 2″ (G2), in which the cell continues to grow, and the “G2 checkpoint” ensures that the cell is ready to enter the Mitosis phase and divide; and
(v) “Mitosis” (M), in which cell growth stops and cellular energy is focused on the orderly division of the cell into two daughter cells, while the “metaphase checkpoint” ensures that the cell is ready to complete division.
An important function of the G1 and G2 checkpoints is to assess DNA damage. When damage is found, the checkpoints use a signal mechanism to stall (or arrest) the cell cycle until repairs are made, or alternatively, if repairs cannot be made, to target the cell for destruction via apoptosis (i.e., a form of programmed cell death).
Most active cancer cytotoxic agents preferentially target rapidly dividing cells. Cyclotherapy exploits the difference between the abilities of normal and cancer cells to undergo G1 or G2 checkpoint arrest. In essence, nonstop cycling cancer cells could be killed preferentially through taxane-based chemotherapy,  while normal cells enter a state of reversible cell-cycle checkpoint arrest and subsequently recover with minimal toxic effects. The key to this approach is the identification of major cellular/biological pathways involved in cell-cycle arrest that are altered in cancer cells, but not in normal cells. This requirement seems to be fulfilled by the altered p53 signaling pathway in cancer cells, and therefore, cyclotherapy could be well suited to killing TP53-mutated HGS-OvCa tumor cells.
Pursuant to the cyclotherapy preclinical model, a reversible TP53-dependent G1 cell-cycle arrest is first induced in normal cells bearing no TP53 mutations by a MDM2 inhibitor. HGS-OvCa cells harboring TP53 gene mutations possess a defective G1 checkpoint and continue to cycle despite the presence of a checkpoint-inducing signal created artificially by the MDM2 inhibitor. Next, a mitotic inhibitor (e.g., a taxane drug such as paclitaxel (Taxol)) is introduced while the HGS-OvCa cancer cells continue to divide. In the presence of a mitotic inhibitor, which targets the S phase or M phase of the cell cycle, the HGS-OvCa cancer cells die. In contrast, normal cells with “wild-type” (normal) TP53 gene function are placed in a reversible (p53-dependent) arrest throughout the period of the therapeutic drug treatment, and retain their proliferative capacity after treatment completion.
Proof of concept preclinical studies have determined that cyclotherapy is effective against select non-ovarian TP53-mutated cancers, when combining a MDM2 inhibitor (e.g., nutlin-3a) with paclitaxel (Taxol). These important results stem from the basic biological fact that the mitotic inhibitor class of drugs (which include taxanes) cannot cause mitotic arrest in cells that do not enter the M phase of the cell cycle. Thus, a temporary arrest in the G1 phase produced by the MDM2 inhibitor nutlin-3 treatment, will protect normal cells from the catastrophic mitotic arrest induced by a taxane drug. In preclinical studies, the use of a MDM2 inhibitor has been shown to protect normal cells from mitotic agents such as the aurora kinase inhibitor VX680 , taxanes [Ref. 82-84], and polo-like kinase inhibitors . Most interestingly, nutlin-3 protects mice from PLK1 (polo-like kinase 1) inhibitor–induced neutropenia without abating the anticancer potency of the mitotic inhibitor. [Ref. 86]
In a provocative preclinical study by Apontes et al, the use of rapamycin (a/k/a sirolimus; Rapamune®) and metformin (Glucophage®), in combination with a MDM2 inhibitor, potentiated the killing of TP53-mutated breast cancer cells, while providing additive and longer-term protection to normal cells stimulated into cell cycle arrest by the MDM2 inhibitor. [Ref. 83] Several potential cyclotherapy clinical use implications stem from the findings of Apontes et al. First, the protective agents rapamycin and metformin are already widely used in the clinic, and therefore, the toxicity and pharmacokinetics of these drugs are well-known. The MDM2 inhibitor, nutlin-3a, while not advanced into clinical trials yet, appears to mimick the activity of low concentrations of actinomycin D (a/k/a dactinomycin; Cosmegen®) , a chemotherapy drug with recognized clinical application. Thus, the two additional agents that can be used to protect normal cells under this approach have already been clinically tested. The second observation relates to the anticancer effect of rapamycin and metformin. Notably, while Apontes et. al. demonstrated that rapamycin and metformin seem to provide added protection to normal cells under their approach, those same drug have produced anticancer activity against ovarian cancer in past preclinical studies. The third observation of clinical importance relates to the fact that Apontes et. al. offer specific recommendations on the timing and sequence of administration of these protective agents (i.e., rapamycin and metformin) and mitotic inhibitors in the treatment of TP53-mutated cancer.
Although the promising concept of cyclotherapy appears to work in the lab by using the combination of a MDM2 inhibitor and a taxane drug, the opposite appears to be true for other ovarian cancer drugs such as platinums (e.g., carboplatin and cisplatin) and doxorubicin. [88-89] In fact, MDM2 inhibitors appear to be synergistic with, and increase the power of, these drugs in non-ovarian cancer preclinical studies. Based on the foregoing, it appears that cyclotherapy in platinum-resistant HGS-OvCa patients receiving a single agent “dose dense” taxane drug is theoretically possible. Moreover, the use of cyclotherapy, by administering a MDM2 inhibitor in combination with a platinum drug, could be used in an attempt to reestablish chemosensitivity in platinum-resistant patients, albeit without the protection of normal cells otherwise provided under the general cyclotherapy approach, and with additional toxicity.
The fifth potential therapeutic approach to treating TP53 gene-mutated cancer involves inhibition of the G2 cell cycle checkpoint. [90-92] TP53 is a key regulator of the G1 checkpoint, and according to the TCGA study, is one of the most frequently mutated genes in HGS-OvCa. Due to TP53 gene mutation, HGS-OvCa cancer cells lack the G1 checkpoint, but can retain the G2 checkpoint. As a result, researchers believe that TP53-mutated cells tend to be more dependent on the G2 checkpoint for DNA damage repair. Wee 1 is a tyrosine kinase that can inactivate cdc2 (i.e., human form of cdk1 (cyclin-dependent kinase 1)), and therefore, plays a pivotal role in the G2 DNA damage checkpoint. In theory, the knockout of the G2 checkpoint with a Wee 1 inhibitor could leave TP53-mutated HGS-OvCa cells with significantly impaired options for DNA damage repair. Thus, the administration of DNA damaging chemotherapy in combination with a Wee 1 inhibitor could selectively target TP53-mutated HGS-OvCa cells, while normal cells are ultimately spared albeit with some toxicity.
MK-1775 is a specific and potent inhibitor of Wee-1 and is currently under investigation in a multi-center phase 1 study (ClinicalTrials.gov ID: NCT00648648) as a monotherapy, and in combination with gemcitabine, carboplatin or cisplatin, in patients with advanced solid tumors. The phase 1 study results show good tolerability and promising anti-cancer activity.  In fact, four of six ovarian cancer patients enrolled in the phase 1 study experienced stable disease for four or more months. To view the MK-1775 phase 1 study presentation made at the 2009 American Society of Clinical Oncology (ASCO) annual meeting, click here. It appears that the MK-1775 phase 1 study is still ongoing in the U.S., Canada, and The Netherlands, with a scheduled completion date of July 2012.
As of this writing, there are two separate MK-1775 phase 2 studies underway. [94-95] The first open phase 2 study involves MK-1775 combined with carboplatin in platinum-resistant patients with TP53 gene-mutated epithelial ovarian cancer, who experienced early recurrence (< 3 months), or progression during first line of treatment. The only stated location for this study is The Netherlands Cancer Institute.
The second phase 2 randomized study (announced, but not yet recruiting; study locations unidentified) is evaluating MK-1775 in combination with paclitaxel and carboplatin, versus paclitaxel and carboplatin alone, in platinum-sensitive, TP53-mutated ovarian cancer patients, who progressed after treatment with paclitaxel and carboplatin. For purposes of this phase 2 MK-1775 study, “platinum-sensitivity” is defined as a recurrence that occurs six or more months after first line treatment.
2. Potential RB Signaling Pathway Therapeutic Approaches
The TCGA researchers discovered that the RB pathway was deregulated or altered in 67% of the HGS-OvCa tumors tested. As noted above, the RB pathway consists of five families of proteins: (i) CDKN2A (cyclin-dependent kinase inhibitor 2A; a/k/a p16Ink4); (ii) D-type cyclins (cyclin protein family — cyclin D1, cyclin D2 and cyclin D3 — is involved in regulating cell cycle progression); (iii) cyclin-dependent protein kinases (cdk4 and cdk6); (iv) RB-family of pocket proteins (RB, p107, and p130); and (v) the E2F-family of transcription factors.  In this pathway, the Ink4-family (CDKN2A) and the RB-family proteins function as tumor suppressors, whereas the D-cyclins, cdk4/cdk6, and several (but not all) E2F transcription factors promote tumor cell proliferation.
Several components of the RB pathway (e.g., CDKN2A, cyclin D1, and RB1) are frequently altered in cancer cells, including the deletion and/or silencing of CDKN2A, the amplification of the cyclin D1, and the mutation of the RB1 gene. In the case of HGS-OvCa, the TCGA researchers discovered that CDKN2A was downregulated or deleted in 32% of altered samples; CCNE1 (cyclin E1) was amplified in 20% of altered samples; CCND1(cyclin D1) was amplified in 4% of altered samples; CCND2 (cyclin D2) was upregulated in 15% of altered samples; and RB1 was deleted or mutated in 10% of altered samples. Thus, components of the RB pathway are rational targets in HGS-OvCa therapy.
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There are currently two distinct approaches that are gaining traction in directly targeting the RB pathway. [Ref. 96] The first approach is designed to take advantage of RB pathway deregulation. The second approach is designed to activate the tumor suppression function of the RB pathway. Based on these two general approaches, potential therapeutic strategies that directly target the RB pathway defects include:
(i) the reactivation of CDKN2A expression when that gene is silenced but not mutated;
(ii) the inhibition of cdk4/cdk6 kinase activity; and
(iii) the enhancement of E2F-dependent cell death (apoptosis).
It is well-known that reactivating a compromised tumor suppressor gene such as CDKN2A is a major pharmacological challenge. In terms of the RB pathway, the closest approach to achieving this goal is the use of inhibitors of DNA methylation (DNA methyltransferase (DNMT) inhibitors) and/or histone deacetylases (HDAC inhibitors), which although cytotoxic with potential application against other molecular targets, could also lead to the activation of epigenetically silenced CDKN2A.  DNMT inhibitors that are currently in clinical testing include SGI-110, decitabine (Dacogen™), and azacitidine. Several HDAC inhibitors are also being tested in ovarian cancer and solid tumor clinical trials. 
A second therapeutic strategy involves the use of cyclin-dependent kinase (CDK) inhibitors. [Ref. 96] Specifically, dual inhibition of cdk4 and cdk6 could be used against HGS-OvCa tumors in which the CDKN2A gene is downregulated and the RB1 gene is not deleted. [99-102] Several compounds are currently being tested in clinical trials including flavopiridol (a/k/a alvocidib), R-roscovitine (a/k/a seliciclib and CYC202), UCN-01 (7-hydroxystaurosporine), and BMS-387032 (a/k/a SNS-032). Most of these compounds, however, inhibit multiple cyclin-dependent kinases, with cdk2 being a common target in many drug discovery programs. Despite that fact, several recent preclinical studies suggest that mammalian cells can continue to proliferate in the absence of cdk2/cyclin E activity, possibly attributable to compensation by cdk4 and/or cdk6. [103-105] These preclinical studies suggest that cdk2 could be a less attractive anticancer target than cdk4.
CDK inhibitors that specifically target cdk4 and cdk6 have been developed and appear to be highly selective with no off-target effects based on biochemical and cell-based assays. Of these agents, PD-0332991 is the most broadly deployed in clinical studies. The phase 1 trial of PD-0332991 represented the first targeted use of an agent that specifically attempts to activate the RB pathway.  In keeping with the preclinical data, the PD-0332991 phase 1 trial used RB protein deficiency as an exclusion criterion.  For example, the TCGA researchers found that the RB1 gene is deleted or mutated in 10% of the HGS-OvCa tumors tested. It is likely that HGS-OvCa patients, whose tumors do not possess a RB1 gene due to deletion, would not qualify for PD-0332991 clinical trials. There are several phase 1/2 single-agent or combination PD-0332991 clinical trials in progress. 
In a preclinical study conducted by researchers at the University of California, Los Angeles (UCLA), PD-0332991 was tested against 40 ovarian cancer cell lines. [Ref. 102] The anti-cancer effects of PD-0332991 were observed in all ovarian cancer cell lines, but varied significantly between individual lines. Consistent with past preclinical studies, the UCLA researchers determined that RB gene-proficient cell lines with low p16 protein (encoded by the CDKN2A gene) expression were most responsive to dual cdk4/cdk6 inhibition. In addition, gene copy number variations of CDKN2A, RB, CCNE1, and CCND1 were associated with response to PD-0332991. The UCLA researchers concluded that cdk4/cdk6 inhibition induced G1 cell cycle arrest and enhanced the effects of chemotherapy. Importantly, the UCLA researchers noted that RB gene proficiency with low p16 protein expression was observed in 37% of ovarian cancer patients and was independently associated with poor progression-free survival.
A third potential therapeutic strategy involves harnessing the pro-apoptotic activity of E2F. [Ref. 96] Specifically, there are cellular signaling pathways that can protect cancer cells with deregulated E2F-activity from death. Inhibitors of these pathways, by definition, could be “synthetically lethal” in tandem with the pre-existing disruption of the RB pathway in cancer cells. [109-110] In these preclinical studies, the epidermal growth factor receptor (EGFR; a/k/a HER1) and apoptosis inhibitor 5 (API5, a/k/a AAC11) were required to suppress apoptosis arising from deregulated E2F activity. The studies suggest that EGFR inhibitors (e.g., gefitinib/Iressa®, afatinib, and vandetanib/Zactima®) could have utility in cancer tumors which harbor loss of RB function. Furthermore, API5 is upregulated in a number of tumor cell types, and depletion of API5 mediated cell death of tumor cells. Accordingly, the therapeutic targeting of these “survival” factors could be effective in RB-deficient tumors.
3. Potential RAS/PI3K Signaling Pathway Therapeutic Approaches
RAS (rat sarcoma) operates in a complex signaling network that regulates many cellular functions such as cell proliferation, differentiation, apoptosis, and senescence. There are two well-known RAS-regulated pathways.
The first RAS signaling pathway cascade consists, in part, of RAS-PI3K-PTEN-AKT-mTOR.
The second RAS signaling pathway cascade consists, in part, of RAS-RAF-MEK-ERK.
The TCGA researchers discovered that the RAS/PI3K pathways were altered in 45% of the HGS-OvCa samples. There are several classes of therapeutic drugs currently being studied in ovarian and solid tumor clinical trials, which target one or more components of the RAS/PI3K pathways, including RAS (e.g., farnesyltransferase inhibitors (FTI) such as Zarnestra (a/k/a tipifarnib and R115777), and Sarasar (a/k/a lonafarnib)), PI3K , AKT , mTOR , RAF (e.g., sorafenib (Nexavar®)), BRAF , and MEK/ERK .
For example, the TCGA researchers determined that the PIK3CA gene and the KRAS gene were amplified in 18% and 11% of the RAS/PI3K pathway-altered samples tested, respectively. The PTEN tumor suppressor gene was deleted in 7% of the altered samples.
In a phase 1 study conducted by The University of Texas M.D. Anderson Cancer Center, patients with tumors carrying PIK3CA gene mutations and/or PTEN tumor suppressor gene loss were preferentially treated with drugs targeting the PI3K-PTEN-AKT-mTOR pathway. 
The M.D. Anderson researchers reported that the heavily pretreated patients with PIK3CA mutations and/or PTEN loss, and normal (non-mutated) KRAS gene status had a significantly higher partial response (PR) rate (31%) on protocols incorporating PI3K/mTOR inhibitors compared to patients on the same protocols without known PIK3CA gene mutations/PTEN loss (PR = 6%), or patients with PIK3CA mutations and/or PTEN loss and simultaneous KRAS mutations (PR = 6%). Based on these findings, the researchers suggested that screening for PIK3CA mutations, PTEN loss and KRAS mutations is warranted in patients who are treated with PI3K/mTOR inhibitor drugs.
Notably, it appears that ovarian cancer patients with PIK3CA gene mutation or PTEN gene loss could respond to PI3K-AKT-mTOR pathway inhibitors, even if they possess KRAS or BRAF gene mutations. [117-118] Nevertheless, preclinical studies suggest that use of a MEK inhibitor in combination with a PI3K-AKT-mTOR pathway inhibitor could produce the best response in KRAS gene-mutated ovarian cancer tumors. [119-120]
Over the past 25 years, there has been significant progress made in identifying the involvement of the RAS-RAF-MEK-ERK and RAS-PI3K-PTEN-AKT-mTOR pathway cascades in promoting cell growth, regulating apoptosis, and causing chemotherapeutic drug resistance.  There are several pros and cons, however, with respect to targeting one or both RAS signaling pathways.
There are three potential benefits from targeting one or both RAS pathways. First, these pathways are frequently activated in HGS-OvCa and many other human cancers, and therefore, in many cases, targeting one or both RAS pathways will suppress cell growth, in the absence of knowing the precise gene mutation(s) responsible for the cancer. Second, although the biochemical interactions of these pathways are quite complex, researchers possess a great deal of knowledge regarding how these pathways function. Third, several inhibitors that target key components in these pathways (e.g., rapamycin, which targets mTOR) have already undergone extensive evaluation in humans.
There are also three potential obstacles associated with targeting one or both RAS pathways. First, an obvious problem arises from the fact that these pathways control the expression of many downstream molecular and genetic targets (easily in the 1000’s), and therefore, inhibiting these pathways will be detrimental in certain normal cells, unless it is possible to deliver the inhibitor specifically to the cancer cells.
Second, the two RAS pathways discussed above cross regulate (or “crosstalk” to) each other, and affect other cellular pathways including the Wnt/β-cateinin pathway, which is critical for many aspects of cellular growth and differentiation including the epithelial-mesenchymal transition (EMT). In addition, the two RAS pathways also interact with several other pathways, including: (i) the Jak (Janus kinase)/STAT (Signal Transducer and Activator of Transcription) pathway, (ii) the NF-κB (nuclear factor kappa-light-chain-enhancer of activated B cells) pathway, and (iii) the transforming growth factor-β (TGF-β) pathway, all of which can be directly and indirectly regulated by ERK and AKT phosphorylation (i.e., a process capable of turning enzymes, like kinases, and receptors on or off). The cross-talk between the two RAS pathways, as well as their interaction with other cellular pathways, creates a sort of “Yin-Yang” effect. That is, when one pathway is inhibited by a drug or drug combination, components of the second pathway could be deregulated by, or compensate for, such inhibition.
Third, inhibitors that target one or both RAS pathways are often cytostatic (i.e., stop further cancer growth) but not cytotoxic (i.e., cause cancer regression). In an attempt to solve the third issue, it could be possible to combine the inhibitors used to target these pathways with cytotoxic chemotherapeutic drugs which destroy rapidly growing cancer cells.
4. Potential FOXM1 Transcriptional Factor Network Therapeutic Approaches
The TCGA researchers identified the deregulation or alteration of the FOXM1 signaling pathway in 87% of the HGS-OvCa samples. FOXM1, is a transcription factor that induces the expression of genes involved in cell cycle progression and maintenance of genomic stability. It is generally known that FOXM1 is strongly upregulated in a variety of human solid tumors. Because FOXM1 suppression appears to inhibit cancer proliferation, the identification of anti-cancer drugs or compounds that target FoxM1 could prove beneficial in fighting HGS-OvCa.
For example, past preclinical studies have shown that select antibiotics (e.g., Siomycin A and thiostrepton), as well as proteosome inhibitors (e.g., MG-132, and bortezomib (a/k/a P-341; Velcade®)), inhibit FoxM1 transcriptional activity and expression.[123-125] One team of researchers reported that docetaxel (Taxotere®), alone or in combination with estramustine (Emcyt®), down-regulated the expression of FoxM1 in prostate cancer leading to cell growth inhibition and induction of apoptosis. Another research group determined that genistein (i.e., an isoflavone found in lupin, fava beans, soybeans, kudzu, and psoralea) inhibited pancreatic cell growth, induced apoptosis, and attenuated the activity of FoxM1 and its downstream genes, such as survivin, CDC25a (cell division cycle 25 homolog A ), MMP-9 (atrix metallopeptidase 9), and VEGF (vascular endothelial growth factor).  Similarly, the chemopreventive agent diindolylmethane –an anticarcinogen compound derived from the digestion of indole-3-carbinol found in broccoli, cauliflower, and collard greens — inhibited FoxM1 activation in breast cancer cells, thereby leading to apoptotic cell death. [Ref. 126]
The genes directly affected by FOXM1 activity include CCNB1 (cyclin B1), AURKB (Aurora B), BIRC5 (survivin), PLK1 (polo-like kinase 1), and CDC25B (cell division cycle 25 homolog B). Various preclinical and clinical drugs and compounds are capable of targeting the genes “downstream” from FOXM1. Cyclin B1 could be targeted with CDK inhibitors [128-130] TAK-901 and GSK1070916A are clinical trial drugs designed to target Aurora B. Clinical trial drugs designed to target the polo-like kinase 1 include TAK-960, volasertib (a/k/a BI 6727), and NMS-1286937. YM155 (a/k/a sepantronium bromide) and EZN-3042 are among the clinical trial drugs designed to target survivin. Several vaccines are also in development to target survivin-expressing cancer cells (e.g., survivin peptide vaccine and survivin Sur1M2 peptide vaccine). Drugs and compounds designed to target CDC25B are also in development. 
Researchers believe that the “cross-talk” between FoxM1 and other cellular/biological signaling pathways plays an important role in cancer tumor aggressiveness. Specifically, potential cancer-causing signaling pathways that have been reported to cross-talk with the FoxM1 pathway include: (i) PI3K-AKT, (ii) NF-κB, (iii) Sonic hedgehog (Shh), (iv) ERK, (v) mitogen-activated protein kinase (MAPK), (vi) cyclooxygenase-2 (COX-2), (vii) EGFR, (viii) estrogen receptor (ER), (ix) VEGF, (x) matrix metalloproteinases (MMP), (xi) c-myc (v-myc myelocytomatosis viral oncogene homolog), (xii) p53, (xiii) reactive oxygen species (ROS), and (xiv) hypoxia inducible factor-1 (HIF-1). [Ref. 126]
Because FOXM1 interacts with many cellular pathways, additional preclinical and clinical studies will be needed to fully understand how FOXM1 induces HGS-OvCa tumor growth.
5. Potential NOTCH Signaling Pathway Therapeutic Approaches
The NOTCH pathway, with its family of four receptors (Notch-1, -2, -3, -4) and their numerous ligands of the Delta and Jagged/Serrate group, plays an important role in cell fate determination, and organ development during embryonic development. It also carries out cell differentiation and “maintenance” functions during adult life. Because the NOTCH signaling pathway involves ligand-receptor interactions, it can initiate signal transduction –a protein-to-protein interaction between the ligand and cell membrane receptor which runs from cell surface to cell interior — that results in modulation of a set of downstream genes, which mediate the ultimate functions of the pathway. Aberrant activation of the NOTCH signaling pathway could confer a growth advantage to tumor cells.
The TCGA researchers determined that the NOTCH signaling pathway was altered in 22% of the HGS-OvCa samples. Within the pathway, the researchers observed gene amplification in NOTCH 3 (11% of pathway-altered samples), JAG1 (2%), and JAG2 (3%).
This result is consistent with other preclinical studies that found active NOTCH pathway signaling in ovarian cancer. A preclinical study conducted by a team of Johns Hopkins researchers determined that NOTCH pathway activation (i) reprograms HGS-OvCa tumor cells to assume an array of embryonic stem cell markers, and (ii) participates in the development of carboplatin chemoresistance. 
John Hopkins researchers also determined that expression of NOTCH3 activation enhances the expression level of its ligand, Jagged1, a finding that had not been previously reported in cancer cells.  In addition, the Johns Hopkins researchers also demonstrated that the Wnt/β-catenin pathway was essential to Jagged1 expression regulation, indicating that Jagged1 expression is regulated by at least two signaling pathways in ovarian cancer. The Johns Hopkins researchers had determined previously that (i) Jagged-1 is the primary Notch3 ligand in ovarian cancer, and (ii) the Jagged-1/Notch3 interaction constitutes a cell-to-cell communication loop that promotes proliferation and dissemination of ovarian cancer cells within the intraperitoneal cavity.  Notably, Jagged 1 has also been associated with vascular development, but it is unknown whether it promotes angiogenesis in cancer tumors.
The fact that ovarian cancer utilizes dual signaling pathways (NOTCH 3 & Wnt/β-catenin) to control Jagged1 expression indicates the importance of maintaining active Notch signaling in epithelial cancer cells. There are several drugs in clinical development that are designed to act as NOTCH inhibitors, including MK0752 and OMP-59R5.  A recent preclinical study reported that xanthohumol (i.e., the primary flavonoid found in hops, an herb used to make beer) was a potent inhibitor of ovarian cancer cell growth, while noting that it seemed to influence the NOTCH pathway.
It is also possible that gamma-secretase (GS) inhibitors can target the NOTCH signaling pathway. The gamma-secretase complex consists of four known proteins that act as “gatekeepers” to the interior of a cell after signal transduction initiation caused by NOTCH receptor-ligand binding. In concept, if the signal can be blocked, the associated signal activity can also be blocked. GS inhibitors (also generally referred to as “NOTCH inhibitors”) that are being tested in clinical trials include BMS-708163, PF-03084014, and RO4929097.
Based upon the Johns Hopkins finding that the NOTCH and Wnt/β-catenin pathways contribute to Jagged-1 expression, the dual targeting of both pathways could be necessary. The Wnt signaling pathway is a network of proteins associated with embryonic development and cancer, as well as normal human physiological processes. Wnt inhibitors that are being tested in clinical trials include PRI-724 and OMP-18R5.
- Targeted Therapies Based Upon Common Gene Amplifications
To identify opportunities for specific targeted treatment, the TCGA researchers searched for existing drugs that might inhibit amplified or overexpressed genes which could play a role in ovarian cancer. The search identified 68 genes that could be targeted by existing FDA-approved or experimental therapeutic drugs or compounds. Within the supplemental information that accompanied the TCGA study, the researchers provided a list of therapeutic inhibitors that already exist for 22 frequently amplified HGS-OvCa genes, which warrant initial or further clinical assessment in HGS-OvCa cases.
Each of the 22 genes identified by the researchers, including MECOM, MAPK1, CCNE1 and KRAS, is amplified in at least 10% of the HGS-OvCa cases.
The 22 frequently amplified HGS-OvCa genes and the corresponding therapeutic inhibitors indentified by TCGA researchers are listed below. Additional drugs/compounds identified by Libby’s H*O*P*E* (LH) are listed within brackets directly below the researcher-provided therapeutic inhibitors.
EPCAM: tucotuzumab celmoleukin (a/k/a EMD 273066)
[LH ERBB3/HER-3 additions: MM-111]
HSP90AB1: 17-dimethylaminoethylamino-17-demethoxygeldanamycin (a/k/a alvespimycin hydrochloride or KOS-1022), IPI-504 (a/k/a retaspimycin hydrochloride), cisplatin
[LH HSP90AB1 additions: AUY922, AT 13387, BIIB021 (a/k/a CNF2024), CNF2024 (a/k/a BIIB021), debio 0932 (Zeven®; formerly CUDC-305), DS-2248, HSP990, MPC-3100, SNX-5422, ganetespib (a/k/a STA-9090, tanespimycin (a/k/a KOS-953), XL-888]
[LH IGF-1R additions: AMG-479 (a/k/a ganitumab), AVE 1642, AXL1717, BIIB022, BMS-754807, dalotuzumab (a/k/a MK-0646), GSK1904529A, figitumumab (a/k/a CP-751,871), KW-2450, , NVP-ADW742, NVP-AEW541, R1507, robatumumab (a/k/a SCH-717454)]
[LH KRAS additions: AZD8330 (a/k/a ARRY-424704), CI-1040 (a/k/a PD184352), GDC-0623, GDC-0973 (a/k/a XL518), MEK162 (a/k/a ARRY-162), RDEA119 (a/k/a BAY 86-9766), RO4987655, RO5126766, TAK-733, U0126].
[LH MAPK15 additions: AZD8330 (a/k/a ARRY-424704), CI-1040 (a/k/a PD184352), GDC-0623, GDC-0973 (a/k/a XL518), MEK162 (a/k/a ARRY-162), RDEA119 (a/k/a BAY 86-9766), RO4987655, RO5126766, TAK-733, U0126].
MSTN: stamulumab (a/k/a MYO-029)
RHEB: BKM120, BEZ235, MK2206, GDC0941, GSK2141795, GSK690693, ridaforolimus (a/k/a AP23573, MK8669 or deforolimus), OSI-027, temsirolimus (a/k/a CCI-779; Torisel®), tacrolimus (a/k/a FK 506; Prograf®, Protopic® & Advagraf®), everolimus (a/k/a RAD001; Afinitor®).
[LH RHEB additions: AMG 319, AT7867, AZD-6482, BAY 80-6946, BGT 226, BYL719, CAL-101, CCT128930, GSK1059615, GSK2126458, LY294002, PI-103, PP-121, PHT-427, PX-866, TG100-115, TGX-221, XL147, ZSTK-474, DS 7423, GDC-0980, Palomid 529, PF-04691502, PKI-587 (a/k/a PF-05212384), SF1126, , XL765, AZD-2014, AZD-8055, CC-115, CC-223 (TORKi®), KU-0063794, INK-128, PP242, sirolimus (Rapamune®), WYE-354]
RICTOR: ridaforolimus (a/k/a AP23573, MK8669, or deforolimus), OSI-027, temsirolimus (a/k/a CCI-779; Torisel®), tacrolimus(a/k/a FK 506; Prograf®, Protopic® & Advagraf®), everolimus (a/k/a RAD001; Afinitor®)
[LH RICTOR additions: DS 7423, GDC-0980, Palomid 529, PF-04691502, PKI-587 (a/k/a PF-05212384), SF1126, , XL765, AZD-2014, AZD-8055, CC-115, CC-223 (TORKi®), KU-0063794,INK-128, PP242, sirolimus (Rapamune®), WYE-354]
RPTOR: ridaforolimus (a/k/a AP23573, MK8669, or deforolimus), OSI-027, temsirolimus (a/k/a CCI-779; Torisel®), tacrolimus(a/k/a FK 506; Prograf®, Protopic® & Advagraf®), everolimus (a/k/a RAD001; Afinitor®)
[LH RPTOR additions: DS 7423, GDC-0980, Palomid 529, PF-04691502, PKI-587 (a/k/a PF-05212384), SF1126, , XL765, AZD-2014, AZD-8055, CC-115, CC-223 (TORKi®), KU-0063794,INK-128, PP242, sirolimus (Rapamune®), WYE-354]
STAT1: simvastin (Zocor®)
STAT4: simvastin (Zocor®)
TERT: GRN163L (a/k/a imetelstat sodium)
[LH VEGFA additions: cediranib (Recentin®; a/k/a AZD2171), E-3810, KRN951, icrucumab (a/k/a IMC-18F1), pegdinetanib (Angiocept®; a/k/a CT-322), ramucirumab (a/k/a IMC-1121B), tivozanib (a/k/a AV-951)]
“The integration of complex genomic data sets enabled us to discover an intricate array of genomic changes and validate one specific change that occurs in the vast majority of all ovarian cancers. Significantly, we have also found new information regarding the role that the BRCA1 and BRCA2 genes play in determining survival.”
– Paul T. Spellman, Ph.D., Lead TCGA HGS-OvCa study author; Prinicipal Scientist, Lawrence Berkeley National Laboratory; and Co-Director, Berkeley Cancer Genome Center.
The Road Ahead
- Limitations of The “One Target- One Drug” Approach
Chemotherapy still remains one of the most effective anti-cancer therapies. The effectiveness of chemotherapy, which targets DNA replication and mitosis, is limited by the side effects resulting from killing normal cells that also proliferate.
The discovery of cancer-related molecular abnormities stimulated research to develop targeted therapies aimed at selectively inhibiting the growth of cancer by interfering with specific molecules or signal transduction pathways that are overexpressed (overactive) in cancer cells. It was anticipated that targeted cancer therapies would be more effective than current treatments and less harmful to normal cells.
Imatinib mesylate (Gleevec®, STI–571), the drug developed to treat chronic myelogenous leukemia (CML), exemplifies the first successful application of a targeted therapy. The success, however, is due to the fact that the target (Bcr (breakpoint cluster region)-Abl (c-abl oncogene 1, non-receptor tyrosine kinase)) tyrosine kinase is (i) unique to this leukemia but absent in normal cells, and (ii) essential for cell growth. The same “one target-one drug” approach supported the development and use of trastuzumab (Herceptin®) in the highly successful fight against HER-2 positive breast cancer.
Unfortunately, the “one-target, one-drug approach” has not held up for most cancer types. This result is not surprising because the target is usually the component of a cell signaling pathway that, while overexpressed in cancer, is also essential for the survival of normal cells. Recent projects, including the TCGA study, have deciphered the genomes of cancer cells and discovered that an array of different genetic mutations can lead to the same cancer in different patients. The long-term goal of these genome studies is the development of “personalized medicine.” That is, each cancer patient would receive the drug or drug combination which is most likely to work based upon his or her genomic and molecular tumor profile.
The complexity of the personalized medicine approach is daunting. Instead of discovering ways to attack one well-defined genetic enemy, researchers are now faced with the prospect of fighting lots of little enemies. Fortunately, this complex view could be simplified by looking at which cellular/biological pathways are disrupted by the genetic mutations. Rather than designing dozens of drugs to target dozens of mutations, drug developers could focus their attentions on just two or three cellular/biological pathways. Patients could then receive the drugs most likely to work for them based upon the pathways affected in their particular tumors.
- First Steps on the Road to Personalized Medicine.
Although scientific researchers and medical clinicians are just beginning to understand the complexity of the human genome, epigenome, and cellular pathways, progress is being made. At the 2011 ASCO annual meeting, the M.D. Anderson Cancer Center reported that customizing targeted therapies to the molecular characteristics of a patient’s tumor — instead of using a “one-size-fits-all” approach based upon broad cancer type — could be more effective for some types of cancer. In patients with various forms of end-stage cancer, patients matched with a clinical trial therapy based upon the molecular characteristics of their tumor achieved a 27% response rate, as compared to a 5% response rate in those patients who were not matched with a personalized therapy. 
In addition to M.D. Anderson, there are many other world-renowned U.S. and international cancer research/treatment institutions pursuing a path to personalized medicine, including Massachusetts General Hospital Cancer Center , Dana-Farber Cancer Institute , and the British Columbia Cancer Agency/Ovarian Cancer Research Program of British Columbia.  In addition, there are private companies that provide genomic/molecular profiling testing to oncologists and their patients (e.g., Caris Life Sciences), and at least one U.S. nonprofit organization that helps patients subsidize such testing (i.e., The Clearity Foundation). 
In September 2008, the Genomics of Drug Sensitivity In Cancer Project was launched with funding from a five-year Wellcome Trust Sanger Institute strategic award. The U.K.–U.S. collaboration harnesses the experience in experimental molecular therapeutics at Massachusetts General Hospital Cancer Center and the expertise in large scale genomics, sequencing, and bioinformatics at the Wellcome Trust Sanger Institute.  In July 2010, the researchers working on the the study identified the responses of 350 cancer samples (including ovarian cancer) to 18 anticancer therapeutics based upon an initial dataset.
The TCGA study also represents a significant step ahead with respect to glioblastoma multiforme (i.e., an aggressive form of brain cancer) and HGS-OvCa. As noted above, the researchers identified existing experimental or FDA-approved drugs which could target frequently amplified genes associated with HGS-OvCa. The TCGA is in the process of collecting samples for many other forms of cancer which will be analyzed in similar fashion to glioblastoma and HGS-OvCa.
- Creation of an Ovarian Cancer Molecular Disease Model
There is a long road ahead for ovarian cancer scientific researchers in their attempt to fully understand HGS-OvCa, as well as other subtypes of epithelial ovarian cancer such as clear cell, endometrioid and mucinous. As ovarian cancer researchers and clinicians move ahead, there is a greater need for better application of current ovarian cancer preclinical scientific research to related preclinical drug development, clinical trial testing of developed drugs, and ultimately, patient drug use.
An interesting research and treatment model is being developed in the area of melanoma; it is the creation of a melanoma molecular map to assist clinicians in making personalized therapy treatment decisions. We believe that the general principles underlying the creation of this innovative melanoma treatment model could provide a general roadmap or framework for future ovarian cancer scientific research and personalized therapy.
In this regard, the mission of CollabRx® is to save lives by using information technology (IT) to personalize cancer treatments and accelerate research. The CollabRx® web-based applications and services power Cancer Commons, a family of open-science cancer communities in which physicians, patients, and scientists collaborate on models of cancer subtypes towards the goal of more accurately predicting responses to therapy. These web-based applications allow scientific researchers and clinicians to find tests and therapies, discuss cases and treatment recommendations, report outcomes, analyze data, provide laboratory and analytic services, and ultimately refine the models.
Each molecular disease model (MDM) is supported by scientific and medical experts with respect to a particular form of cancer. These experts are responsible for creating and maintaining a MDM for their designated cancer, which enumerates the known genomic subtypes and molecular pathways, hyperlinked to the latest and most relevant diagnostic tests, treatments, trials, literature, and news. At each point in time, a patient can be treated with the best available experimental therapies for that individual’s tumor molecular subtype. The subtypes and associated therapies are continually refined within the model based upon how individual patients respond. In sum, the MDMs serve as living review articles, which are maintained online and continuously updated by the experts who created them.
In March 2011, the first MDM created by the Cancer Commons project was published.  The information contained in that MDM addresses the treatment of melanoma and is embodied in the “Targeted Therapy Finder — Melanoma” online application. The melanoma MDM consists of a set of “actionable” molecular subtypes and proposed practice guidelines for treating each disease subtype; that is, which therapies (approved or experimental) should be considered by a patient’s oncologist and which are contraindicated. A “molecular subtype of melanoma” is loosely defined as those tumors containing the same set of molecular (primarily genetic) defect(s) and their associated cellular/biological pathways. A subtype is deemed actionable if there is both a CLIA-approved assay to determine whether a given tumor fits that classification, and at least one FDA-approved or experimental targeted therapy with potential effectiveness for that subtype.
Notably, the bold CollabRx®/Cancer Commons initiative is supported by ASCO through a partnership with CollabRx®, which was announced in April 2011. 
We took the liberty of writing to the Cancer Commons initiative to encourage the existing scientific research and medical team to form a team or network of ovarian cancer experts who could oversee the creation of an ovarian cancer MDM. Although the list of all proposed cancer types to be addressed by the Cancer Commons initiative has not been stated, it appears that expert networks in the area of sarcoma, breast cancer and lung cancer have already been formed or will be formed in the near future.
Although there is a great deal of preclinical and clinical research that must be performed in the future with respect to ovarian cancer, the creation of an ovarian cancer MDM should become a priority within the gynecologic scientific and medical community. We urge that community to undertake this important goal, especially given the high mortality rate associated with ovarian cancer.
- Chemotherapy Sensitivity & Resistance Assay Testing: A “Bridge” or Adjunct to the Ovarian Cancer Molecular Disease Model?
If we assume arguendo that a future ovarian cancer MDM is warranted, the model development process should incorporate all existing technologies which allow us to transition from the current standard of care treatments to the emerging ovarian cancer MDM. This transition period is attributable to the considerable time that it will take for scientific researchers and clinicians to fully “blueprint” all of the significant molecular and genomic workings of the ovarian cancer cell.
We decided to address this issue after reading an expert commentary published as part of the 2011 ASCO annual meeting Daily News publication. The commentary is entitled, Utility of Chemotherapy Sensitivity and Resistance Assays for Optimizing Treatment for Patients with Solid Tumors.  The commentary was written by Drs. Harold J. Burstein, M.D., Ph.D., and Jaffer A. Ajani, M.D. Dr. Burstein is an Assistant Professor of Medicine at Harvard Medical School, and a medical oncologist in the Breast Oncology Center at the Dana-Farber Cancer Institute. He is also the Chair-Elect of the ASCO Cancer Education Committee. Dr. Ajani is a medical oncologist and Professor of Medicine at the M. D. Anderson Cancer Center. In the commentary, the authors raise several excellent points about the development of personalized medicine and highlight the frustrations experienced along the way by patients and clinicians alike.
The primary focus of the commentary is the future viability of chemotherapy sensitivity and resistance assays (CSRAs), in light of the rapid development of new molecular and genomic profile testing methods. The basic premise of CSRAs involves: (i) procurement of fresh tumor samples (primary or metastatic) and/or malignant effusions (including abdominal ascites fluid) from the cancer patient through primary/secondary surgery or biopsy; (ii) disaggregation of tumor cells from the sample; and (iii) exposure of these tumor cells to one or more therapeutic agents under various in vitro conditions (including 3-D lab culture models). The overarching goal of using a CRSA is to identify a drug or drug combination that will work for a cancer patient with the least amount of toxicity, prior to actual administration of the the drug to the patient.
Older forms of CSRAs measured chemotherapy resistance only; that is, the identification of drugs that are unlikely to benefit the cancer patient. Newer forms of CSRAs also include the measurement of chemotherapy sensitivity; that is, the identification of drugs that are likely to benefit the cancer patient. One of the newest forms of CSRA testing identifies potential cancer patient treatment benefit based upon the measurement of actual cancer cell death (i.e., apoptosis) after in vitro drug(s) application, in lieu of the older measurement involving cancer cell proliferation or growth. The difference is subtle but important.
This newer form of CSRA testing is provided by private U.S. companies such as Precision Therapeutics (ChemoFx® bioassay), Rational Therapeutics (Ex-Vivo Analysis—Programmed Cell Death (EVA-PCD®) bioassay)), DiaTech Oncology (the MiCK (microculture kinetic) assay™), and the Weisenthal Cancer Group (Functional Tumor Cell Profiling bioassay).  We should note that Precision Therapeutics (BioSpeciFx®) and Rational Therapeutics (EVA-PCD TARxGET™ assay) are also offer molecular/cellular pathway analyses in addition to their chemosensitivity testing.
In regard to CSRAs, Drs. Burstein and Ajani state: “It might be that the era of CSRA is coming to an end.” The reasons provided by the authors are as follows:
The academic oncology community has been reviewing the extent of clinical development of CSRA, as well as their utility in treatment selection and other benefits, such as improving overall survival of those patients treated based on the assay results compared with empiric approaches. ASCO addressed the issue of CSRA utility in 2004 and is updating the guideline in 2011. Each time the subject has been addressed, ASCO commissioned a number of experts to conduct detailed reviews of existing English literature on CSRA and to establish the level of evidence based on randomized trials.
In 2004 ASCO found that, although there were many commercially available CSRA, there was insufficient evidence of benefit to patients; therefore, ASCO did not recommend the use of any CSRA for selection of chemotherapeutic agents for individual patients with cancer outside of clinical trials.
The major setback for CSRA lies in the lack of rigorous clinical testing (i.e., small sample sizes and lack of prospective comparisons) necessary to document patient benefit, and most published studies significantly fall short of this requirement.
The authors further explain that advances in biotechnology provide us with a much deeper understanding of how cancer is organized, how it maintains itself (i.e., tumor-initiating cells with self-renewal potential), and how it acquires several survival advantages under stressful circumstances. Moreover, Drs. Burstein and Ajani emphasize that detailed sequencing of tumor DNA has already provided us with a greater understanding of activating gene mutations, amplifications, deletions, and translocations. These types of functional studies have led to discrimination between genetic “driver” mutations (which push cells towards cancer) and “passenger” mutations (which are a by-product of cancer development). The authors further state that without the advancement of molecular/genomic science, cancer researchers would not have identified drugs like imatinib (Gleevec®) (e.g., for select chronic myeloid leukemias and gastrointestinal stromal tumors), trastuzumab (Herceptin®) (e.g., for HER-2 positive breast cancers), crizonib (e.g., for certain lung cancers), crizonib (PF-02341066 )(e.g., for certain lung cancers), and PLX4032 (vemurafenib) (e.g., for select melanomas) through use of CSRAs.
The authors close the article by pointing out the continued importance of, and need for, prospective, randomized studies when evaluating new technologies (including CRSAs) and drugs.
We agree with many of the points raised in the ASCO expert commentary; however, we believe that new technologies (e.g., modern CRSAs based upon measurement of actual cell death) should not be abandoned or considered a mutually exclusive alternative to molecular and genetic tumor profile analyses. In fact, we believe that the newest forms of CSRAs should be evaluated as a means to steer ovarian cancer patients into potentially beneficial clinical trials, with possible use in combination with molecular/gemonic tumor profile testing. We base our position on several important factors.
First, Drs. Burstein and Ajani support their position with studies that do not address the newest forms of CRSAs. [147-148]
Second, the more recent CRSA studies in the area of ovarian cancer indicate that such testing can be predictive of progression-free and overall survival. [149-154] That fact alone warrants further evaluation of CRSA in ovarian cancer clinical trials.
Third, several ovarian cancer studies suggest CRSAs are capable of increasing patient response rates, and progression-free survival; [155-158] albeit no study has demonstrated an increase in overall survival (with one possible exception[Ref. 156]). One study even reports that CRSAs could decrease recurrent ovarian cancer treatment costs. 
Fourth, past CSRA studies have reported novel drug combination recommendations in the treatment of ovarian cancer. [160-161; Ref. 154, 158]
Fifth, although neither ASCO or the National Comprehensive Cancer Network (NCCN) recommend CRSA use outside of a clinical trial, some NCCN member institutions are utilizing such testing. 
Sixth, a CRSA should be viewed as another medical oncology test (similar to computed tomography (CT) or magnetic resonance imaging (MRI)) which can assist a doctor in making optimal cancer treatment recommendations, as opposed to a drug treatment to be approved or disapproved based solely upon overall survival data.
Seventh, CRSAs enhance our understanding as to the general ability of a drug or drug combination to cause apoptosis in a cancer cell. Simply put, either the drug kills the cancer cell or it does not. Although it is true that many factors come into play within the human body (which are not reproducible through an in vitro 3-D lab culture) and affect cancer cell proliferation or death (e.g., the immune system), it is still helpful to know whether a drug possesses the ability to kill a patient’s tumor cells in vitro prior to use of that drug in the patient’s body.
Eighth, we are not aware of any published studies that suggest CRSAs should not be further tested in clinical trials. In fact, ChemoFx®  and the MiCK apoptosis assay™  were recently evaluated through ovarian and non-ovarian cancer clinical trials.
Ninth, researchers are engaging in preclinical research with the working hypothesis that tandem use of CRSAs and molecular/genomic tumor profiling could enhance our understanding of more targeted cancer treatment. [165-167]
It is true that CSRA testing does not result in the discovery of new drugs, but it should enhance the drug developmental process. Specifically, cancer drug development should be enhanced by assessing the value of CSRAs in combination with molecular genomic techniques. This action should allow better stratification and assignment of patients to clinical trials which are predicted to be of greatest beneﬁt to the individual patient. In turn, such studies may better deﬁne the optimal diagnostic criteria for treatment selection and accelerate the approval of new rationally-designed drugs. These drugs may be effective in small subsets of patients that have developed tumors which possess the requisite molecular drug targets. Accordingly, the integration of CRSAs and molecular genomic diagnostics into the clinical trial design should be a high priority.
It is also true that the use of the most advanced forms of CRSAs in everyday oncology practice remains a controversial topic. [168-169] Nevertheless, we should not allow that fact to stop the clinical evaluation of CRSAs, which could advance basic ovarian cancer scientific knowledge, especially knowing that the road of discovery to personalized medicine will likely be a long one with many obstacles and detours along the way. Stated in the vernacular, let’s not throw the baby out with the bathwater!
About the National Cancer Institute
The National Cancer Institute (NCI) is part of the National Institutes of Health (NIH), which is one of 11 agencies that compose the U.S. Department of Health and Human Services (HHS). The NCI, established under the National Cancer Institute Act of 1937, is the U.S. Government’s principal agency for cancer research and training. The National Cancer Act of 1971 broadened the scope and responsibilities of the NCI and created the National Cancer Program. Over the years, legislative amendments have maintained the NCI authorities and responsibilities and added new information dissemination mandates as well as a requirement to assess the incorporation of state-of-the-art cancer treatments into clinical practice.
NCI takes the lead in coordinating the National Cancer Program, which conducts and supports (i) research, training, and health information dissemination; (ii) programs with respect to the cause, diagnosis, prevention, and treatment of cancer; (iii) programs with respect to rehabilitation from cancer; and (iv) the continuing care of cancer patients and the families of cancer patients.
For more information about cancer, please visit the NCI website at www.cancer.gov or call NCI’s Cancer Information Service at 1-800-4-CANCER (1-800-422-6237).
About the National Human Genome Research Institute
The National Human Genome Research Institute (NHGRI) began as the National Center for Human Genome Research (NCHGR), which was established in 1989 to carry out the role of the NIH in the International Human Genome Project (HGP).
In 1993, NCHGR expanded its role on the NIH campus by establishing the Division of Intramural Research to apply genome technologies to the study of specific diseases. In 1996, the Center for Inherited Disease Research (CIDR) was also established (co-funded by eight NIH institutes and centers) to study the genetic components of complex disorders.
In 1997, the HHS renamed NCHGR as the “National Human Genome Research Institute”, officially elevating it to the status of research institute – one of 27 institutes and centers that make up the NIH.
The NHGRI Division of Extramural Research supports grants for research and training and career development at sites nationwide. Additional information about NHGRI can be found at www.genome.gov. The activities described above are being funded through the American Recovery and Reinvestment Act (ARRA).
More information about NIH’s ARRA grant funding opportunities can be found at www.grants.nih.gov/recovery/. To track the progress of HHS activities funded through the ARRA, visit www.hhs.gov/recovery. To track all federal funds provided through the ARRA, visit www.recovery.gov.
About the National Institutes of Health
The National Institutes of Health (NIH), the nation’s medical research agency, includes 27 Institutes and Centers and is a component of the HHS. NIH is the primary federal agency conducting and supporting basic, clinical, and translational medical research. NIH also investigates the causes, treatments, and cures for both common and rare diseases. For more information about NIH and its programs, visit www.nih.gov.
1/Spellman PT, et al. Integrated Genomic Analyses of Ovarian Carcinoma. Nature. 2011 Jun 29; 474(7353):609-15. doi: 10.1038/nature10166. PMID: 21720365.
2/Siegel R, et. al. Cancer statistics, 2011: The impact of eliminating socioeconomic and racial disparities on premature cancer deaths. CA Cancer J Clin 2011; 61:212-236 (published online before print June 17, 2011). PubMed PMID: 21685461.
3/ Koonings PP, et. al. Relative frequency of primary ovarian neoplasms: a 10-year review. Obstet Gynecol. 1989 Dec;74(6):921-6. PubMed PMID: 2685680.
4/Seidman, JD, et al. The histologic type and stage distribution of ovarian carcinomas of surface epithelial origin. Int J Gynecol Pathol. 2004 Jan;23(1):41-4. PubMed PMID: 14668549.
5/Miller DS, et al. Phase II evaluation of pemetrexed in the treatment of recurrent or persistent platinum-resistant ovarian or primary peritoneal carcinoma: a study of the Gynecologic Oncology Group. J Clin Oncol. 2009 Jun 1;27(16):2686-91. Epub 2009 Mar 30. PubMed PMID: 19332726; PubMed PMCID: PMC2690393.
6/Jemal A, et al. Cancer statistics, 2009. CA Cancer J Clin. 2009 Jul-Aug; 59(4):225-49. Epub 2009 May 27. PubMed PMID: 19474385.
7/Pal T, et al. BRCA1 and BRCA2 mutations account for a large proportion of ovarian carcinoma cases. Cancer. 2005 Dec 15;104(12):2807-16. PubMed PMID: 16284991.
8/Risch HA, et al. Population BRCA1 and BRCA2 mutation frequencies and cancer penetrances: a kin-cohort study in Ontario, Canada. J Natl Cancer Inst. 2006 Dec 6;98(23):1694-706. PubMed PMID: 17148771.
10/Gnirke A., et al. Solution hybrid selection with ultra-long oligonucleotides for massively parallel targeted sequencing. Nat Biotechnol. 2009 Feb;27(2):182-9. Epub 2009 Feb 1. PubMed PMID: 19182786; PubMed PMCID: PMC2663421.
11/Hodges E, et al. Hybrid selection of discrete genomic intervals on custom-designed microarrays for massively parallel sequencing. Nat Protoc. 2009;4(6):960-74. Epub 2009 May 28. PubMed PMID: 19478811; PubMed PMCID: PMC2990409.
12/Bookman MA., et al. Evaluation of new platinum-based treatment regimens in advanced-stage ovarian cancer: a phase III trial of the Gynecologic Cancer Intergroup. J Clin Oncol. 2009 Mar 20;27(9):1419-25. Epub 2009 Feb 17. PubMed PMID: 19224846; PubMed PMCID: PMC2668552.
13/Muggia FM, et al. Phase III randomized study of cisplatin versus paclitaxel versus cisplatin and paclitaxel in patients with suboptimal stage III or IV ovarian cancer: a gynecologic oncology group study. J Clin Oncol. 2000 Jan;18(1):106-15. PubMed PMID: 10623700.
14/Chi, DS., et al. What is the optimal goal of primary cytoreductive surgery for bulky stage IIIC epithelial ovarian carcinoma (EOC)? Gynecol Oncol. 2006 Nov;103(2):559-64. Epub 2006 May 22. PubMed PMID: 16714056.
15/Armstrong, DK, et al. Intraperitoneal cisplatin and paclitaxel in ovarian cancer. N Engl J Med. 2006 Jan 5;354(1):34-43. PubMed PMID: 16394300.
16/Bookman MA, et al. Evaluation of new platinum‐based treatment regimens in advanced‐stage ovarian cancer: a Phase III Trial of the Gynecologic Cancer Intergroup. J Clin Oncol. 2009 Mar 20;27(9):1419-25. Epub 2009 Feb 17. PubMed PMID: 19224846; PubMed PMCID: PMC2668552.
17/Winter WE, 3rd et al. Prognostic factors for stage III epithelial ovarian cancer: a Gynecologic Oncology Group Study. J Clin Oncol. 2007 Aug 20;25(24):3621-7. PubMed PMID: 17704411.
18/Eisenhauer EL, et al. The effect of maximal surgical cytoreduction on sensitivity to platinum-taxane chemotherapy and subsequent survival in patients with advanced ovarian cancer. Gynecol Oncol. 2008 Feb;108(2):276-81. PubMed PMID: 18063020.
19/Tothill RW, et al. Novel molecular subtypes of serous and endometrioid ovarian cancer linked to clinical outcome. Clin Cancer Res. 2008 Aug 15;14(16):5198-208. PubMed PMID: 18698038.
20/Jones PA, et. al. Cancer epigenetics comes of age. Nat Genet. 1999 Feb;21(2):163-7. PubMed PMID: 9988266.
21/Jones PA, et. al. The fundamental role of epigenetic events in cancer. Nat Rev Genet. 2002 Jun;3(6):415-28. PubMed PMID: 12042769.
22/Dubeau L. The cell of origin of ovarian epithelial tumours. Lancet Oncol. 2008 Dec;9(12):1191-7. PubMed PMID: 19038766.
23/ Cheng KW, et al. The RAB25 small GTPase determines aggressiveness of ovarian and breast cancers. Nat Med. 2004 Nov;10(11):1251-6. PubMed PMID: 15502842.
24/ Bendik I, et. al. Characterization of MAST9/Hevin, a SPARC-like protein, that is down-regulated in non-small cell lung cancer. Cancer Res. 1998 Feb 15;58(4):626-9. PubMed PMID: 9485012.
25/Nelson PS, et. al. Hevin,an antiadhesive extracellular matrix protein, is down-regulated in metastatic prostate adenocarcinoma. Cancer Res. 1998 Jan 15;58(2):232-6. PubMed PMID: 9443398.
26/Mok SC, et. al. SPARC, an extracellular matrix protein with tumor-suppressing activity in human ovarian epithelial cells. Oncogene. 1996 May 2;12(9):1895-901. PubMed PMID: 8649850.
27/Claeskens A, et. al. Hevin is down-regulated in many cancers and is a negative regulator of cell growth and proliferation. Br J Cancer. 2000 Mar;82(6):1123-30. PubMed PMID: 10735494; PubMed Central PMCID: PMC2363342.
30/Vicari AP, et. al. Antitumor effects of the mouse chemokine 6Ckine/SLC through angiostatic and immunological mechanisms. J Immunol. 2000 Aug 15;165(4):1992-2000. PubMed PMID: 10925282.
31/Esteller M, et. al. Promoter hypermethylation and BRCA1 inactivation in sporadic breast and ovarian tumors. J Natl Cancer Inst. 2000 Apr 5;92(7):564-9. PubMed PMID: 10749912.
32/Baldwin RL, et. al. BRCA1 promoter region hypermethylation in ovarian carcinoma: a population-based study. Cancer Res. 2000 Oct 1;60(19):5329-33. PubMed PMID: 11034065.
33/Press JZ, et. al. Ovarian carcinomas with genetic and epigenetic BRCA1 loss have distinct molecular abnormalities. BMC Cancer. 2008 Jan 22;8:17. PubMed PMID: 18208621; PubMed Central PMCID: PMC2245962.
34/Esteller, M. et al. Promoter hypermethylation and BRCA1 inactivation in sporadic breast and ovarian tumors. J. Natl. Cancer Inst. 92, 564–569 (2000).
36/Turner N., et. al. Hallmarks of “BRCAness” in sporadic cancers. Nat Rev Cancer. 2004 Oct;4(10):814-9. PubMed PMID: 15510162.
37/Bast RC Jr., et. al. Personalizing therapy for ovarian cancer: BRCAness and beyond. J Clin Oncol. 2010 Aug 1;28(22):3545-8. PubMed PMID: 20547987.
38/Hennessy BT, et al. Somatic mutations in BRCA1 and BRCA2 could expand the number of patients that benefit from poly (ADP ribose) polymerase inhibitors in ovarian cancer. J Clin Oncol. 2010 Aug 1;28(22):3570-6. PubMed PMID: 20606085.
39/Farmer H, et al. Targeting the DNA repair defect in BRCA mutant cells as a therapeutic strategy. Nature. 2005 Apr 14;434(7035):917-21. PubMed PMID: 15829967.
40/Fong, PC, et al. Inhibition of poly(ADP-ribose) polymerase in tumors from BRCA mutation carriers. N Engl J Med. 2009 Jul 9;361(2):123-34. PubMed PMID: 19553641.
41/Veeck J, et. al. BRCA1 CpG island hypermethylation predicts sensitivity to poly(adenosine diphosphate)-ribose polymerase inhibitors. J Clin Oncol. 2010 Oct 10;28(29):e563-4. PubMed PMID: 20679605.
42/Yap TA, et al. Reply to J. Veeck et al. J. Clin. Oncol. 2010 Oct 10;28(29): e565–e566. PubMed PMID: 20679603.
43/Audeh MW, et. al. Oral poly(ADP-ribose) polymerase inhibitor olaparib in patients with BRCA1 or BRCA2 mutations and recurrent ovarian cancer: a proof-of-concept trial. Lancet. 2010 Jul 24;376(9737):245-51. PubMed PMID: 20609468.
44/Fong PC, et. al. Poly(ADP)-ribose polymerase inhibition: frequent durable responses in BRCA carrier ovarian cancer correlating with platinum-free interval. J Clin Oncol. 2010 May 20;28(15):2512-9. PubMed PMID: 20406929.
45/Mukhopadhya A., et. al. Functional evaluation of homologous recombination pathway in epithelial ovarian cancer and the potential for targeted chemotherapy with PARP inhibitors. 2010 National Cancer Research Institute Conference, Poster Session C, Abstract C129.
46/Mukhopadhyay A, et. al. Development of a functional assay for homologous recombination status in primary cultures of epithelial ovarian tumor and correlation with sensitivity to poly(ADP-ribose) polymerase inhibitors. Clin Cancer Res 2010; 16(8):2344-51. PubMed PMID: 20371688.
47/New Assay Test Predicts That 50% of Ovarian Cancers Will Respond To In Vitro PARP Inhibition, by Paul Cacciatore, Libby’s H*O*P*E*™ website, November 11, 2010.
48/10 Exciting Ovarian Cancer Research Topics from 2010 — PARP Inhibitors & BRCA Gene-Mutated Ovarian Cancer (Topic #2 of 10), Episode #2, WORD of HOPE Ovarian Cancer Podcast, April 11, 2011.
49/A list of open ovarian cancer clinical trials involving the study of PARP inhibitors.
50/A list of open solid tumor clinical trials involving the study of PARP inhibitors.
51/Tan Y, et. al. Chk2 mediates stabilization of the FoxM1 transcription factor to stimulate expression of DNA repair genes. Mol Cell Biol. 2007 Feb;27(3):1007-16. PubMed PMID: 17101782; PubMed Central PMCID: PMC1800696.
52/Ma RY, et. al. Raf/MEK/MAPK signaling stimulates the nuclear translocation and transactivating activity of FOXM1c. J Cell Sci. 2005 Feb 15;118(Pt 4):795-806. PubMed PMID: 15671063.
54/Tone AA, et. al. Gene expression profiles of luteal phase fallopian tube epithelium from BRCA mutation carriers resemble high-grade serous carcinoma. Clin Cancer Res. 2008 Jul 1;14(13):4067-78. PubMed PMID: 18593983.
55/Myatt SS, et. al. The emerging roles of forkhead box (Fox) proteins in cancer. Nat Rev Cancer. 2007 Nov;7(11):847-59. PubMed PMID: 17943136.
56/Wang IC, et. al. Deletion of Forkhead Box M1 transcription factor from respiratory epithelial cells inhibits pulmonary tumorigenesis. PLoS One. 2009 Aug 12;4(8):e6609. PubMed PMID: 19672312; PubMed PMCID: PMC2720537.
57/Etemadmoghadam D, et al. Integrated genome-wide DNA copy number and expression analysis identifies distinct mechanisms of primary chemoresistance in ovarian carcinomas. Clin Cancer Res. 2009 Feb 15;15(4):1417-27. PubMed PMID: 19193619; PubMed PMCID: PMC2670486.
58/Ho ES, et al. p53 mutation is infrequent in clear cell carcinoma of the ovary. Gynecol Oncol. 2001 Feb;80(2):189-93. PubMed PMID: 11161858.
61/Cuatrecasas M, et al. K-ras mutations in mucinous ovarian tumors: a clinicopathologic and molecular study of 95 cases. Cancer. 1997 Apr 15;79(8):1581-6. PubMed PMID: 9118042.
62/Essmann F, et. al. Translational approaches targeting the p53 pathway for anticancer therapy. Br J Pharmacol. 2011 Jun 30. doi:10.1111/j.1476-5381.2011.01570.x. [Epub ahead of print] PubMed PMID: 21718309.
63/Cheok CF,et. al. Translating p53 into the clinic. Nat Rev Clin Oncol. 2011 Jan;8(1):25-37. Epub 2010 Oct 26. PubMed PMID: 20975744.
64/Lane DP, et al. p53-based cancer therapy. Cold Spring Harb Perspect Biol. 2010 Sep;2(9):a001222. Epub 2010 May 12. PubMed PMID: 20463003.
65/Stathis A, et. al. Targeting Wee1-like protein kinase to treat cancer. Drug News Perspect. 2010 Sep;23(7):425-9. Review. PubMed PMID: 20862394.
66/A list of open clinical trials involving Gendicine (a/k/a rAd-p53).
67/Shi J, et. al. An update on gene therapy in China. Curr Opin Mol Ther. 2009 Oct;11(5):547-53. PubMed PMID: 19806503.
68/Wolf JK, et. al. A phase I study of Adp53 (INGN 201; ADVEXIN) for patients with platinum- and paclitaxel-resistant epithelial ovarian cancer. Gynecol Oncol. 2004 Aug;94(2):442-8. PubMed PMID: 15297186.
69/Wen SF, et. al. Assessment of p53 gene transfer and biological activities in a clinical study of adenovirus-p53 gene therapy for recurrent ovarian cancer. Cancer Gene Ther. 2003 Mar;10(3):224-38. PubMed PMID: 12637944.
70/Buller RE, et. al. Long term follow-up of patients with recurrent ovarian cancer after Ad p53 gene replacement with SCH 58500. Cancer Gene Ther. 2002 Jul;9(7):567-72. PubMed PMID: 12082456.
71/Buller RE, et. al. A phase I/II trial of rAd/p53 (SCH 58500) gene replacement in recurrent ovarian cancer. Cancer Gene Ther. 2002 Jul;9(7):553-66. PubMed PMID: 12082455.
72/A list of ovarian cancer clinical trials involving use of a p53 vaccine.
73/Leffers N, et. al. Long-term clinical and immunological effects of p53-SLP® vaccine in patients with ovarian cancer. Int J Cancer. 2011 Feb 16. doi: 10.1002/ijc.25980. [Epub ahead of print] PubMed PMID: 21328579.
74/Leffers N, et. al. Immunization with a P53 synthetic long peptide vaccine induces P53-specific immune responses in ovarian cancer patients, a phase II trial. Int J Cancer. 2009 Nov 1;125(9):2104-13. PubMed PMID: 19621448.
75/Chiappori AA, et. al. INGN-225: a dendritic cell-based p53 vaccine (Ad.p53-DC) in small cell lung cancer: observed association between immune response and enhanced chemotherapy effect. Expert Opin Biol Ther. 2010 Jun;10(6):983-91. PubMed PMID: 20420527.
77/Millard M, et. al. Small-molecule inhibitors of p53-MDM2 interaction: the 2006-2010 update. Curr Pharm Des. 2011;17(6):536-59. PubMed PMID: 21391905.
78/Kojima K, et. al. The novel tryptamine derivative JNJ-26854165 induces wild-type p53- and E2F1-mediated apoptosis in acute myeloid and lymphoid leukemias. Mol Cancer Ther. 2010 Sep;9(9):2545-57. PubMed PMID: 20736344.
80/Ray RM, et. al. Mdm2 inhibition induces apoptosis in p53 deficient human colon cancer cells by activating p73- and E2F1-mediated expression of PUMA and Siva-1. Apoptosis. 2011 Jan;16(1):35-44. PubMed PMID:20812030.
81/Lau LM, et. al. HDM2 antagonist Nutlin-3 disrupts p73-HDM2 binding and enhances p73 function. Oncogene. 2008 Feb 7;27(7):997-1003. Epub 2007 Aug 13. PubMed PMID: 17700533.
82/Ambrosini G, et. al. Mouse double minute antagonist Nutlin-3a enhances chemotherapy-induced apoptosis in cancer cells with mutant p53 by activating E2F1. Oncogene. 2007 May 24;26(24):3473-81. Epub 2006 Dec 4. PubMed PMID: 17146434.
83/Apontes P, et. al. Exploring long-term protection of normal human fibroblasts and epithelial cells from chemotherapy in cell culture. Oncotarget. 2011 Mar;2(3):222-33. PubMed PMID: 21447859.
84/Carvajal D, et. al. Activation of p53 by MDM2 antagonists can protect proliferating cells from mitotic inhibitors. Cancer Res. 2005 Mar 1;65(5):1918-24. PubMed PMID: 15753391.
85/Cheok CF, et. al. Combination of nutlin-3 and VX-680 selectively targets p53 mutant cells with reversible effects on cells expressing wild-type p53. Cell Death Differ 2010; 17:1486-500. PubMed PMID: 20203688.
86/Sur S, et. al. A panel of isogenic human cancer cells suggests a therapeutic approach for cancers with inactivated p53. Proc Natl Acad Sci U S A 2009; 106:3964-9. PubMed PMID: 19225112. PubMed PMCID: PMC2656188.
87/Choong ML, et. al. Specific activation of the p53 pathway by low dose actinomycin D: a new route to p53 based cyclotherapy. Cell Cycle. 2009 Sep 1;8(17):2810-8. Epub 2009 Sep 13. PubMed PMID: 19657224.
88/Ohnstad HO, et. al. MDM2 antagonist Nutlin-3a potentiates antitumour activity of cytotoxic drugs in sarcoma cell lines. BMC Cancer. 2011 May 30;11:211. PubMed PMID: 21624110.
89/Zheng T, et. al. Nutlin-3 cooperates with doxorubicin to induce apoptosis of human hepatocellular carcinoma cells through p53 or p73 signaling pathways. J Cancer Res Clin Oncol. 2010. PubMed PMID: 20174822.
90/Rajeshkumar NV, et al. MK-1775, a potent Wee1 inhibitor, synergizes with gemcitabine to achieve tumor regressions, selectively in p53-deficient pancreatic cancer xenografts. Clin Cancer Res. 2011 May 1;17(9):2799-806. Epub 2011 Mar 9. PubMed PMID: 21389100.
91/Stathis A, et. al. Targeting Wee1-like protein kinase to treat cancer. Drug News Perspect. 2010 Sep;23(7):425-9. Review. PubMed PMID: 20862394.
92/Leijen S, et. al. Abrogation of the G2 checkpoint by inhibition of Wee-1 kinase results in sensitization of p53-deficient tumor cells to DNA-damaging agents. Curr Clin Pharmacol. 2010 Aug;5(3):186-91. Review. PubMed PMID: 20406171.
93/Schellens JH, et. al. A phase I and pharmacological study of MK-1775, a Wee1 tyrosine kinase inhibitor, in both monotherapy and in combination with gemcitabine, cisplatin, or carboplatin in patients with advanced solid tumors. J Clin Oncol 27:15s, 2009 (suppl; abstr 3510). [2009 ASCO Annual Meeting]
94/Phase II Pharmacological Study With Wee-1 Inhibitor MK-1775 Combined With Carboplatin in Patients With p53 Mutated Epithelial Ovarian Cancer and Early Relapse (< 3 Months) or Progression During Standard First Line Treatment, ClinicalTrials.gov Identifier: NCT01164995.
95/A Randomized, Phase II Study Evaluating MK-1775 in Combination With Paclitaxel and Carboplatin Versus Paclitaxel and Carboplatin Alone in Adult Patients With Platinum Sensitive p53 Mutant Ovarian Cancer, ClinicalTrials.gov Identifier: NCT 01357161.
97/Egger G, et. al. Inhibition of histone deacetylation does not block resilencing of p16 after 5-aza-2′-deoxycytidine treatment. Cancer Res 2007;67:346–53. PubMed PMID: 17210717.
98/A list of open ovarian cancer and solid tumor clinical trials utilizing HDAC inhibitors.
99/Lapenna S, et. al. Cell cycle kinases as therapeutic targets for cancer. Nat Rev Drug Discov 2009;8:547–66. PubMed PMID: 19568282.
100/Toogood PL, et. al. Discovery of a potent and selective inhibitor of cyclin-dependent kinase 4/6. J Med Chem 2005;48:2388–406. PubMed PMID: 15801831.
101/Fry DW, et. al. Specific inhibition of cyclin–dependent kinase 4/6 by PD 0332991 and associated antitumor activity in human tumor xenografts. Mol Cancer Ther 2004;3:1427–38. PubMed PMID: 15542782.
102/Konecny GE, et. al. Expression of p16 and retinoblastoma determines response to CDK4/6 inhibition in ovarian cancer. Clin Cancer Res. 2011 Mar 15;17(6):1591-602. Epub 2011 Jan 28. PubMed PMID: 21278246.
103/Tetsu O, et. al. Proliferation of cancer cells despite CDK2 inhibition. Cancer Cell 2003;3:233–45. PubMed PMID: 12676582.
104/Ortega SO, et. al. Cyclin-dependent kinase 2 is essential for meiosis but not for mitotic cell division in mice. Nat Genet 2003;35:25–31. PubMed PMID: 12923533.
105/Geng Y, et. al. Cyclin E ablation in the mouse. Cell 2003;114:431–43. PubMed PMID: 12941272.
106/Schwartz GK, et. al. Phase I study of PD 0332991, a cyclin-dependent kinase inhibitor, administered in 3-week cycles (Schedule 2/1). Br J Cancer. 2011 Jun 7;104(12):1862-8. doi: 10.1038/bjc.2011.177. Epub 2011 May 24. PubMed PMID: 21610706.
107/A Phase I Clinical, Pharmacokinetic, And Pharmacodynamic Evaluation Of 2 Schedules Of Oral PD 0332991, A Cyclin-Dependent Kinase Inhibitor, In Patients With Advanced Cancer. ClinicalTrials.gov Identifier: NCT00141297.
108/A list of open clinical trials utilizing PD 0332991.
110/Moon NS, et. al. A gradient of epidermal growth factor receptor signaling determines the sensitivity of rbf1 mutant cells to E2F-dependent apoptosis. Mol Cell Biol 2006;26:7601–15. PubMed PMID: 16954388; PubMed PMCID: PMC1636876.
111/A list of open ovarian cancer and solid tumor clinical trials utilizing PI3K inhibitors.
112/A list of open ovarian cancer and solid tumor clinical trials utilizing AKT inhibitors.
113/A list of open ovarian cancer and solid tumor clinical trials utilizing mTOR inhibitors.
114/A list of open ovarian cancer and solid tumor clinical trials utilizing BRAF inhibitors.
115/A list of open ovarian cancer and solid tumor clinical trials utilizing MEK inhibitors.
116/Janku F, et. al. Screening for PIK3CA mutations, PTEN loss, and RAS/RAF mutations in early-phase protocols with PI3K/mTOR pathway inhibitors. J Clin Oncol 29: 2011 (suppl; abstr 10507). [2011 American Society of Clinical Oncology (ASCO) Annual Meeting].
117/Janku F, et. al. PIK3CA mutations in patients with advanced cancers treated with PI3K/AKT/mTOR axis inhibitors. Mol Cancer Ther. 2011 Mar;10(3):558-65. Epub 2011 Jan 7. PubMed PMID: 21216929.
118/Experimental Drug NVP-BEZ235 Slows Ovarian Cancer Growth in Mice; Solid Tumor Clinical Trials Ongoing, by Paul Cacciatore, Libby’s H*O*P*E*™, May 2, 2011.
119/Kinross KM, et. al. In Vivo Activity of Combined PI3K/mTOR and MEK Inhibition in a KrasG12D;Pten Deletion Mouse Model of Ovarian Cancer. Mol Cancer Ther. 2011 Jul 19. [Epub ahead of print] PubMed PMID: 21632463.
120/Wee S, et. al. PI3K pathway activation mediates resistance to MEK inhibitors in KRAS mutant cancers. Cancer Res. 2009 May 15;69(10):4286-93. Epub 2009 Apr 28. PubMed PMID: 19401449.
121/Steelman LS, et. al. Roles of the Raf/MEK/ERK and PI3K/PTEN/Akt/mTOR pathways in controlling growth and sensitivity to therapy-implications for cancer and aging. Aging (Albany NY). 2011 Mar;3(3):192-222. PubMed PMID: 21422497; PubMed PMCID: PMC3091517.
122/Pilarsky C et. al. Identification and validation of commonly overexpressed genes in solid tumors by comparison of microarray data. Neoplasia. 2004 Nov-Dec;6(6):744-50. PubMed PMID: 15720800; PubMed PMCID: PMC1531678.
123/Gartel AL. et. al. A new target for proteasome inhibitors: FoxM1. Expert Opin Investig Drugs. 2010 Feb;19(2):235-42. Review. PubMed PMID: 20074015.
124/Pandit B, et. al. New potential anti-cancer agents synergize with bortezomib and ABT-737 against prostate cancer. Prostate. 2010 Jun 1;70(8):825-33. PubMed PMID: 20058240.
128/Feldmann G, et. al. Cyclin-dependent kinase inhibitor Dinaciclib (SCH727965) inhibits pancreatic cancer growth and progression in murine xenograft models. Cancer Biol Ther. 2011 Oct 1;12(7). [Epub ahead of print] PubMed PMID: 21768779.
129/Santo L, et. al. A novel small molecule multi-cyclin-dependent kinase inhibitor, induces apoptosis in multiple myeloma via GSK-3beta activation and RNA polymerase II inhibition. Oncogene. 2010 Apr 22;29(16):2325-36. PubMed PMID: 20101221.
130/DePinto W, et. al. In vitro and in vivo activity of R547: a potent and selective cyclin-dependent kinase inhibitor currently in phase I clinical trials. Mol Cancer Ther. 2006 Nov;5(11):2644-58. PubMed PMID: 17121911.
131/Lavecchia A, et. al. CDC25A and B dual-specificity phosphatase inhibitors: potential agents for cancer therapy. Curr Med Chem. 2009;16(15):1831-49. Review. PubMed PMID: 19442149.
132/Rose SL, et. al. Notch 1 signaling is active in ovarian cancer. Gynecol Oncol. 2010 Apr;117(1):130-3. Epub 2010 Jan 8. PubMed PMID: 20060575.
133/Park JT, et. al. Notch3 overexpression is related to the recurrence of ovarian cancer and confers resistance to carboplatin. Am J Pathol. 2010 Sep;177(3):1087-94. Epub 2010 Jul 29. PubMed PMID: 20671266.
134/Chen X, et. al. Jagged1 expression regulated by Notch3 and Wnt/β-catenin signaling pathways in ovarian cancer. Oncotarget. 2010 Jul;1(3):210-8. PubMed PMID: 20953350; PubMed Central PMCID: PMC2953807.
136/A list of open ovarian cancer and solid tumor clinical trials utilizing NOTCH inhibitors.
137/2011 ASCO: Matching Targeted Therapies To Specific Tumor Gene Mutations Key to Personalized Cancer Treatment, by Paul Cacciatore, Libby’s H*O*P*E*™, June 3, 2011.
138/Massachusetts General Hospital Cancer Center To Genetically Profile All Patient Tumors, by Paul Cacciatore, Libby’s H*O*P*E*™, March 14, 2009.
139/Dana-Farber Researchers “OncoMap” The Way To Personalized Treatment For Ovarian Cancer, by Paul Cacciatore, Libby’s H*O*P*E*™, November 16, 2010.
140/British Columbian Researchers Make Groundbreaking Genetic Discovery In Endometriosis-Associated Ovarian Cancers, by Paul Cacciatore, Libby’s H*O*P*E*™, September 8, 2010.
141/Caris Life Sciences Launches Molecular Profiling Service For Ovarian Cancer Patients, by Paul Cacciatore, Libby’s H*O*P*E*™, January 14, 2011.
142/Largest Study Matching Genomes To Potential Anticancer Treatments Releases Initial Results, by Paul Cacciatore, Libby’s H*O*P*E*™, August 3, 2010.
144/The American Society of Clinical Oncology and CollabRx Partner to Develop Online Apps for Planning Cancer Treatment, News Release, The American Society of Clinical Oncology, April 5, 2011.
145/Utility of Chemotherapy Sensitivity and Resistance Assays for Optimizing Treatment for Patients with Solid Tumors, by Harold J. Burstein, MD, PhD, and Jaffer A. Ajani, MD, American Society of Clinical Oncology Annual Meeting, Expert Editorial, ASCO Daily News (Issue #3; Section C), June 6, 2011.
146/We should note that Libby’s H*O*P*E*™ does not receive any direct or indirect renumeration from the aforementioned private companies.
147/Schrag D, et. al. (ASCO Working Group on Chemotherapy Sensitivity and Resistance Assays). American Society of Clinical Oncology Technology Assessment: chemotherapy sensitivity and resistance assays. J Clin Oncol. 2004 Sep 1;22(17):3631-8. Epub 2004 Aug 2.PubMed PMID: 15289488.
148/Samson DJ, et. al. Chemotherapy sensitivity and resistance assays: a systematic review. J Clin Oncol. 2004 Sep 1;22(17):3618-30. Epub 2004 Aug 2. Review. PubMed PMID: 15289487.
149/Herzog TJ, et. al. Chemosensitivity testing with ChemoFx and overall survival in primary ovarian cancer. Am J Obstet Gynecol. 2010 Jul;203(1):68.e1-6. Epub 2010 Mar 12. PubMed PMID: 20227055.
150/Zhao D, et. al. Application of ATP-tumor chemosensitivity assay in primary epithelial ovarian cancer. Zhonghua Zhong Liu Za Zhi. 2010 May;32(5):368-72. Chinese. PubMed PMID: 20723436.
151/Zhao D, et. al. Application of ATP-tumor chemosensitivity assay in recurrent epithelial ovarian cancer. Zhonghua Zhong Liu Za Zhi. 2010 Nov;32(11):855-8. Chinese. PubMed PMID: 21223693.
152/Gallion H, et. al. Progression-free interval in ovarian cancer and predictive value of an ex vivo chemoresponse assay. Int J Gynecol Cancer. 2006 Jan-Feb;16(1):194-201. PubMed PMID: 16445633.
153/Taylor CG, et. al. Chemosensitivity testing predicts survival in ovarian cancer. Eur J Gynaecol Oncol. 2001;22(4):278-82. PubMed PMID: 11695809.
154/Konecny G, et. al. Correlation of drug response with the ATP tumorchemosensitivity assay in primary FIGO stage III ovarian cancer. Gynecol Oncol. 2000 May;77(2):258-63. PubMed PMID: 10785475.
155/Cree IA, et. al. A prospective randomized controlled trial of tumour chemosensitivity assay directed chemotherapy versus physician’s choice in patients with recurrent platinum-resistant ovarian cancer. Anticancer Drugs. 2007 Oct;18(9):1093-101. PubMed PMID: 17704660.
156/Salom EM, et. al. Can we increase response rate (RR) and overall survival (OS) by individualizing chemotherapy in ovarian cancer (OC): The role of a new chemotherapy (CT) induced apoptosis assay. J Clin Oncol 28:15s, 2010 (suppl; abstr 5112)(2010 American Society of Clinical Oncology Annual Meeting).
157/Loizzi V, et. al. Survival outcomes in patients with recurrent ovarian cancer who were treated with chemoresistance assay-guided chemotherapy. Am J Obstet Gynecol. 2003 Nov;189(5):1301-7. PubMed PMID: 14634558.
158/Kurbacher CM, et. al. Use of an ex vivo ATP luminescence assay to direct chemotherapy for recurrent ovarian cancer. Anticancer Drugs. 1998 Jan;9(1):51-7. PubMed PMID: 9491792.
159/Havrilesky LJ, et. al. Impact of a chemoresponse assay on treatment costs for recurrent ovarian cancer. Am J Obstet Gynecol. 2010 Aug;203(2):160.e1-7. Epub 2010 Apr 24. PubMed PMID: 20417480.
160/Nagourney RA, et. al. Phase II trial of gemcitabine plus cisplatin repeating doublet therapy in previously treated, relapsed ovarian cancer patients. Gynecol Oncol. 2003 Jan;88(1):35-9. PubMed PMID: 12504624.
161/Di Nicolantonio F, et. al. Use of an ATP-based chemosensitivity assay to design new combinations of high-concentration doxorubicin with other drugs for recurrent ovarian cancer. Anticancer Drugs. 2002 Jul;13(6):625-30. PubMed PMID: 12172508.
162/National Comprehensive Cancer Network Clinical Practice Guidelines in Oncology™ – Ovarian cancer including fallopian tube cancer and primary peritoneal cancer. V2.2011. National Comprehensive Cancer Network, Inc. [free NCCN website registration required to view Adobe Reader PDF document]
163/A list of clinical studies evaluating ChemoFx®.
164/A list of clinical studies evaluating the MiCK apoptosis assay™.
165/Arienti C, et. al. Peritoneal carcinomatosis from ovarian cancer: chemosensitivity test and tissue markers as predictors of response to chemotherapy. J Transl Med. 2011 Jun 20;9:94. PubMed PMID: 21689426; PubMed PMCID: PMC3141502.
166/Glaysher S, et. al. NHS Collaborative Research Programme for Predictive Oncology. Molecular basis of chemosensitivity of platinum pre-treated ovarian cancer to chemotherapy. Br J Cancer. 2010 Aug 24;103(5):656-62. Epub 2010 Aug 10. PubMed PMID: 20700122.
167/Michalski CW, et. al. Ex vivo chemosensitivity testing and gene expression profiling predict response towards adjuvant gemcitabine treatment in pancreatic cancer. Br J Cancer. 2008 Sep 2;99(5):760-7. PubMed PMID: 18728667; PubMed Central PMCID: PMC2528151.
168/Schink JC, et. al. Point: chemosensitivity assays have a role in the management of recurrent ovarian cancer. J Natl Compr Canc Netw. 2011 Jan;9(1):115-20. PubMed PMID: 21233247.
169/Markman M. Counterpoint: chemosensitivity assays for recurrent ovarian cancer. J Natl Compr Canc Netw. 2011 Jan;9(1):121-4. PubMed PMID: 21233248.
Appendix 1 – DNA & RNA
Often compared to a recipe or a code, DNA (deoxyribonucleic acid) is a set of blueprints that contains the instructions our cells require to construct other cell components, such as proteins and RNA (ribonucleic acid) molecules. The DNA segments that carry this genetic information are called “genes.” DNA is the genetic material that contains the instructions used in the development and functioning of our cells. DNA consists of two strands that wind around each other like a twisted ladder. Each strand has a backbone made of alternating sugar (deoxyribose) and phosphate groups. Attached to each sugar is one of four bases–adenine (A), cytosine (C), guanine (G), or thymine (T). DNA is generally stored in the nucleus of our cells. The primary purpose of DNA molecules is the long-term storage of genomic information.
RNA is the genetic material that “transcribes” (i.e., copies) DNA instructions and “translates” (i.e., synthesizes) them into proteins. It is RNA’s job to transport the genetic information out of the cell’s nucleus and use it as instructions for building proteins. The so-called “transcriptome” consists of all RNA molecules within our cells, including messenger RNA (mRNA), transfer RNA (tRNA), and ribosomal RNA (rRNA). The sequence of RNA mirrors the sequence of the DNA from which it was transcribed or copied. Consequently, by analyzing the entire collection of RNAs (i.e., the transcriptome) in a cell, researchers can determine when and where each gene is turned on or off in our cells and tissues. Unlike DNA, the transcriptome can vary with external environmental conditions. Because it includes all mRNA transcripts in the cell, the transcriptome reflects the genes that are being actively expressed at any given time.
Messenger RNA (mRNA) is a single-stranded RNA molecule that is complementary to one of the DNA strands of a gene. The mRNA is an RNA version of the gene that leaves the cell nucleus (where DNA is stored) and moves to the cell cytoplasm where proteins are made. During protein synthesis, a “ribosome” moves along the mRNA, reads its base sequence, and uses the genetic code to translate each three-base triplet, or “codon,” into its corresponding amino acid.
Once the protein is manufactured, the gene (i.e., the DNA code segment) which originally encoded the protein, via mRNA, is considered “expressed.” Gene expression should not be confused with gene “amplification.” Gene amplification is an increase in the number of copies of a gene sequence. Cancer cells sometimes produce multiple copies of genes in response to signals from other cells or their environment. When gene amplification results in the excessive production of an encoded protein, it is often referred to as gene “overexpression.”
Once ignored completely, and overlooked until 1993, microRNAs (miRNAs), a newly-recognized class of small, non-coding RNAs, have emerged as a central player in controlling cell survival, growth, differentiation and death. MiRNAs regulate gene expression after transcription (i.e., copying) by interacting with protein-encoding mRNAs. MiRNAs are encoded in the human genome and function as natural regulators of global gene expression. To date, more than 700 human miRNAs have been identified, which are estimated to regulate 30% of all human genes. MiRNAs are expressed and processed by proteins within the cell’s nucleus and cytoplasm, and negatively regulate the expression of genes with sequences that are complementary to specific miRNAs. Each miRNA appears to regulate the expression of tens to hundreds of genes, thereby functioning as a “master-switch” which regulates and coordinates multiple cellular/biological pathways (See Appendix 5) in important processes such as embryonic development, immune response, and cellular growth and proliferation.
Appendix 2 – Genes , Chromosomes & Proteins
As noted in Appendix 1, the DNA segments that carry genetic information are called “genes.” The “genome” is the entire set of genetic instructions found in a human cell. The genome consists of 23 pairs of chromosomes. These chromosomes are composed of six billion individual DNA letters. In the English alphabet there are 26 letters: A through Z. In the alphabet of our genes there are four letters: A, C, G and T. “Genomics” is the study of the sequence of these letters in our DNA and how each string of letters passes information to help each cell in your body work properly.
A gene is essentially a sentence made up of the bases — A , C , G , and T — which describes how to make a protein. Any change in the sequence of bases — and therefore in the protein instructions — is called a “mutation.” Just like the changing of a letter in a word or a word in a sentence can change the meaning of that word or sentence, a mutation can change the instruction contained in the gene. Any changes to those instructions can alter the gene’s meaning and change the protein that is made, or how or when a cell makes that protein.
“Gene expression” is the process by which the information encoded in a gene is used to direct the assembly of a protein. The cell reads the sequence of the gene in groups of three bases. Each group of three bases (or codon) corresponds to one of 20 different amino acids used to build the protein. Gene mutations can (i) result in a protein that cannot carry out its normal function in the cell, (ii) prevent the protein from being made at all, or (iii) cause too much or too little of a normal protein to be made. Gene mutations occur in two general ways as noted below.
- Inherited or Germline Mutations — This form of general mutation is inherited from a parent. Mutations that are passed from parent to child are called “hereditary” mutations or “germline” mutations (because they are present in the egg and sperm cells, which are also called “germ cells”). This type of mutation is present throughout a person’s life in virtually every cell in the body.
- Acquired or Somatic Mutations — This form of general mutation occurs in the DNA of individual cells during a person’s lifetime. These changes can be caused by environmental factors such as ultraviolet radiation from the sun, or can occur if a mistake is made as DNA copies itself during cell division. Acquired mutations in somatic cells (i.e., other than sperm and egg cells) generally can not be passed on to the next generation.
A “chromosome” is an organized package of DNA found in the nucleus of the cell. Humans have 23 pairs of chromosomes–22 pairs of numbered chromosomes, called “autosomes,” and one pair of sex chromosomes, referred to as “X” and “Y.” Each parent contributes one chromosome to each pair so that offspring get half of their chromosomes from their mother and half from their father.
An “exon” is the portion of a gene that codes for amino acids. The parts of the gene sequence that are expressed in the protein are called “exons,” because they are expressed, while the parts of the gene sequence that are not expressed in the protein are called “introns,” because they come in between–or interfere with–the exons. The “exome” is the part of the genome formed by exons.
“Proteins” participate in virtually every process within cells. A protein is composed of one or more long chains of amino acids, the sequence of which corresponds to the DNA sequence of the gene that encodes it. The synthesis or production of a protein requires the transcription or copying of DNA code by mRNA, which travels from the cell’s nucleus to its cytoplasm. The mRNA code that is transcribed or copied from DNA is subsequently translated or synthesized into a protein built from amino acids. When this manufacturing process works uninterrupted, and a protein is produced, the gene (i.e., the original DNA code segment) is considered expressed.
Proteins play a variety of roles in the cell, including structural (cytoskeleton), mechanical (muscle), biochemical (enzymes), and cell signaling (hormones). Proteins are also an essential part of a healthy diet.
Appendix 3 – Specific Types of Gene Mutations
The DNA sequence of a gene can be altered by a mutation in several ways. Gene mutations have varying effects on health, depending on where they occur and whether they alter the function of essential proteins. The various types of gene mutations include:
- Missense mutation
A “missense” mutation is a change in one DNA base pair that results in the substitution of one amino acid for another in the protein made by a gene.
- Nonsense mutation
A “nonsense” mutation is also a change in one DNA base pair. Instead of substituting one amino acid for another, however, the altered DNA sequence prematurely signals the cell to stop building a protein. This type of mutation results in a shortened protein that may function improperly or not at all.
An “insertion” mutation changes the number of DNA bases in a gene by adding a piece of DNA. As a result, the protein made by the gene may not function properly.
A “deletion” mutation changes the number of DNA bases by removing a piece of DNA. Small deletions may remove one or a few base pairs within a gene, while larger deletions can remove an entire gene or several neighboring genes. The deleted DNA may alter the function of the resulting protein(s).
A “duplication” mutation consists of a piece of DNA that is abnormally copied one or more times. This type of mutation may alter the function of the resulting protein.
- Frameshift mutation
A “frameshift” mutation occurs when the addition or loss of DNA bases changes a gene’s “reading frame.” A reading frame consists of 3 base groups that each code for one amino acid. A frameshift mutation shifts the grouping of these bases and changes the code for amino acids. The resulting protein is usually nonfunctional. Insertions, deletions, and duplications can all be frameshift mutations.
- Repeat expansion
“Nucleotide repeats” are short DNA sequences that are repeated a number of times in a row. For example, a trinucleotide repeat is made up of 3-base-pair sequences, and a tetranucleotide repeat is made up of 4-base-pair sequences. A “repeat expansion” mutation increases the number of times that the short DNA sequence is repeated. This type of mutation can cause the resulting protein to function improperly.
Apendix 4 – Epigenetics & Gene Silencing
A genome is the complete set of DNA in a cell. DNA carries the instructions for building all of the proteins that make each living creature unique. Derived from the Greek word “epi,” the term “epigenome” means “above” the genome. The epigenome consists of chemical compounds that modify, or mark, the genome in a way that tells it what to do, where to do it and when to do it. The marks, which are not part of the DNA itself, can be passed on from cell to cell as cells proliferate, and from one generation to the next.
- What does the epigenome do?
Each person’s body contains trillions of cells, all of which have essentially the same genome. Yet some cells are optimized for use in muscles, others for bones, the brain, the stomach and the remainder of the human body. What makes these cells different? The protein-coding parts of our genome (i.e., genes) do not make proteins all of the time in all of your cells. Instead, different sets of genes are turned on or off in various kinds of cells at different points in time. Differences in the types and amounts of proteins produced determine how cells look, grow and act. The epigenome influences which genes are active — and which proteins are produced — in a particular cell. So, the epigenome is what tells your skin cells to behave like skin cells, heart cells like heart cells, and so on.
- What makes up the epigenome?
The epigenome is made up of chemical compounds, some of which come from natural sources like food and others from man-made sources like medicines or pesticides. As it marks the genome with these chemical tags, the epigenome serves as the intersection between the genome and the environment. The epigenome marks your genome in two primary, both of which play a role in turning genes off or on.
The first type of mark, called “DNA methylation,” directly affects the DNA in your genome. In this process, chemical tags called “methyl groups” attach to the backbone of the DNA molecule in specific places. The methyl groups turn genes off or on by affecting interactions between DNA and the cell’s protein-making machinery.
The second kind of mark, called “histone modification,” indirectly affects the DNA in your genome. Histones are spool-like proteins that enable DNA’s very long molecules to be wound up neatly into chromosomes inside the cell nucleus. A variety of chemical tags can grab hold of the tails of histones, changing how tightly or loosely they package DNA. If the wrapping is tight, a gene may be hidden from the cell’s protein-making machinery, and consequently be switched off. In contrast, if the wrapping is loosened, a gene that was formerly hidden may be turned on.
- Is the epigenome inherited?
Just as the genome is passed along from parents to their offspring, the epigenome can also be inherited. The chemical tags found on the DNA and histones of eggs and sperm can be conveyed to the next generation.
- What is imprinting?
As noted above, our genome contains two copies of every gene — one inherited from our mother and one from our father. For some genes, only the copy from the mother ever gets switched on, and for others, only the copy from the father. This pattern is called “imprinting.” The epigenome serves to distinguish between the two copies of an imprinted gene. For example, only the father’s copy of a gene called IGF2 (insulin-like growth factor 2) is able to make its protein. That is because marks in the epigenome keep the mother’s IGF2 gene copy switched off in every cell of the body. Some diseases are caused by abnormal imprinting.
- Can the epigenome change?
While all cells in your body contain essentially the same genome, the chemical tags on the DNA and histones get rearranged in different cell types. The epigenome can also change throughout a person’s lifetime. A good example relates to identical twins. Although they share nearly the same genome, their bodies may not be exactly identical. One twin may weigh more, for example, or develop arthritis. Researchers think that at least some of these differences are due to changes in the epigenome.
- What makes the epigenome change?
Lifestyle and environmental factors can expose a person to chemical tags that change the epigenome. In other words, our epigenome may change based on what we eat and drink, whether we smoke, what medicines we take, what pollutants we encounter and even how quickly our body ages. There is also some evidence from animal and human studies that indicates that what a female eats and drinks during pregnancy may change the epigenome of her offspring. Most epigenomic changes are probably harmless, but some changes may trigger or increase the severity of disease. Already, researchers have linked changes in the epigenome to various cancers, diabetes, autoimmune diseases, and mental illnesses.
- How do changes in the epigenome contribute to cancer?
Cancers are caused by a combination of changes to the genome and the epigenome. Adding or removing methyl groups can switch genes involved in cell growth off or on. If such changes occur at the wrong time or in the wrong cell, they can wreak havoc, thereby converting normal cells into cancer cells.
For example, in a type of brain tumor called “glioblastoma,” doctors have had some success in treating patients with a drug called “temozolomide,” which kills cancer cells by adding methyl groups to DNA. But that’s only part of a very complex picture. Cells also contain a gene called “MGMT” (O-6-methylguanine-DNA methyltransferase), which produces a protein that subtracts methyl groups — an action that counteracts the effects of temozolomide. In some glioblastomas, however, the switch for the MGMT gene has been turned off by methylation, which blocks production of the protein that counteracts temozolomide. Consequently, glioblastoma patients whose tumors have methylated MGMT genes are far more likely to respond to temozolomide than those with unmethylated MGMT genes.
Changes in the epigenome also activate growth-promoting genes in stomach cancer, colon cancer and the most common type of kidney cancer. In other cancers, changes in the epigenome silence genes that normally serve to keep cell growth in check. Moreover, it is now recognized that in addition to genetic alterations, epigenetic mechanisms, such as DNA methylation and histone modifications play an important role in the development and progression of ovarian cancer by affecting chromatin structure, as well as gene and miRNA expression.
- How are researchers exploring the epigenome?
Researchers are exploring the epigenome through the research field known as “epigenomics,” which is the study of all chemical tags on the genome that control the activities of genes. This is different from genomics, which is the study of all the changes that occur in the order, or sequence, of the DNA building blocks that make up the genome. Experts once thought that diseases were caused mainly by changes, or mutations, in DNA sequence – changes that either disrupt protein production or lead to abnormal proteins. Recently, researchers have learned that changes in the epigenome may cause or contribute to many diseases, making epigenomics a vital part of efforts to better understand the human body and improve human health.
To come up with a complete list of all possible changes that can lead to cancer, the National Institutes of Health (NIH) started “The Cancer Genome Atlas” (TCGA”). As noted above, these researchers are comparing the genomes and epigenomes of normal cells to those of cancer cells. They are looking for any changes (i.e., mutations) in the DNA sequence; changes in the number and structure of chromosomes; changes in the amounts of proteins produced by genes; and changes in the number of methyl groups on the DNA. Understanding all of the changes that turn a normal cell into a cancer cell could speed efforts to develop new and better ways of diagnosing, treating and preventing cancer. To learn more about this effort, visit www.cancergenome.nih.gov.
As part of its Roadmap for Medical Research Epigenomics Program, NIH also plans to develop a map of the epigenomic marks that occur on the human genome. The effort will require the development of better technologies to quickly and efficiently detect epigenomic marks, as well as improved understanding of the factors that drive these changes.
Internationally, the Human Epigenome Project (HEP) is working with the stated aim to “identify, catalog, and interpret genome-wide DNA methylation patterns of all human genes in all major tissues.” The current HEP consortium members include The Wellcome Trust Sanger Institute, Epigenomics AG, and The Centre National de Génotypage. To learn more about this project, visit www.epigenome.org.
Appendix 5 – Cellular/Biological Pathways
- What is a biological pathway?
A cellular/biological pathway is a series of actions among molecules in a cell that leads to a certain product or a change in a cell. Such a pathway can trigger the assembly of new molecules, such as a fat or protein. Cellular pathways can also turn genes on and off, or spur a cell to move.
- How do biological pathways work?
For our bodies to develop properly and stay healthy, many things must work together at many different levels – from organs to cells to genes.
Cells are constantly receiving cues from both inside and outside the body, which are prompted by such things as injury, infection, stress or even food. To react and adjust to these cues, cells send and receive signals through biological pathways. The molecules that make up biological pathways interact with signals, as well as with each other, to carry out their designated tasks.
Cellular pathways can act over short or long distances. For example, some cells send out signals to nearby cells to repair localized damage from a scratch on the knee. Other cells produce substances such as hormones, which that travel through your blood to distant target cells.
Cellular pathways can also produce small or large outcomes. For example, some pathways subtly affect how the body processes drugs, while others play a major role in how a fertilized egg develops into a baby.
There are many other examples of how biological pathways help our bodies work. The pupil in our eye opens or closes in response to light. If our skin senses that the temperature is rising, the body sweats to cool down.
It is important to keep in mind that cellular pathways do not always work properly. When something goes wrong in a pathway, the result can be a disease such as cancer or diabetes.
- What are some types of cellular/biological pathways?
There are many types of cellular pathways. Some of the most common are involved in metabolism, the regulation of genes, and the transmission of signals.
“Metabolic pathways” make possible the chemical reactions that occur in our bodies. An example of a metabolic pathway is the process by which our cells break down food into energy molecules, which can be stored for later use. Other metabolic pathways help to build molecules.
“Gene regulation pathways” turn genes on and off. Such action is vital because genes produce proteins, which are the key components needed to carry out nearly every task in our bodies.
“Signal transduction pathways” or signaling pathways move a signal from the cell exterior to the cell interior. Different cells are able to receive specific signals through structures on their surface, called “receptors.” After interacting with a receptor, the signal travels through the cell where its message is transmitted by specialized proteins, which, in turn, trigger a specific action in the cell. For example, a chemical signal from outside the cell could be turned into a protein signal inside the cell. In turn, that protein signal could be converted into a signal that prompts the cell to move.
- What is a cellular/biological network?
Researchers are learning that cellular pathways are far more complicated than once thought. Most pathways do not start at point “A” and end at point “B.” In fact, many pathways have no real boundaries, and they often work together (i.e., “cross-talk”) to accomplish tasks or provide redundancy. When multiple cellular pathways interact with each other, it is called a “biological network.”
- How do researchers find cellular pathways?
Researchers discovered many important cellular pathways through laboratory studies of cultured cells, bacteria, fruit flies, mice and other organisms. Many of the pathways identified in these model systems are the same or have similar counterparts in humans.
Still, many biological pathways remain unknown. It will take many years of research to identify and understand the complex connections among all of the molecules in all cellular pathways, as well as to understand how these pathways work together.
- What can biological pathways tell us about disease?
Researchers are able to learn a lot about human disease from studying cellular pathways. Identifying what genes, proteins and other molecules are involved in a cellular pathway can provide clues about what goes wrong when a disease occurs.
For example, researchers may compare certain cellular pathways in a healthy person to the same pathways in a person with a disease to discover the root(s) of the disorder. Keep in mind that problems in any number of steps along a cellular pathway can often lead to the same disease.
- How can biological pathway information improve health?
Finding out what cellular pathway is involved in a disease — and identifying which step of the pathway is affected in each patient — could lead to more personalized strategies for diagnosing, treating and preventing disease.
Currently, researchers are using information about cellular pathways to develop new and better drugs. It likely will take some time before we routinely see drugs that are specifically designed to target cellular pathways. Nevertheless, doctors are already beginning to use pathway information to more effectively choose and combine existing drugs.
- Why are cancer researchers excited about cellular/biological pathways?
Until recently, many scientific researchers hoped that most types of cancers were driven by a single genetic error and could be treated by designing drugs to target those specific errors. Much of that hope was based on the success of imatinib (Gleevec®), a drug that was specifically designed to treat a blood cancer called “chronic myeloid leukemia” (CML). CML occurs because of a single genetic glitch that leads to the production of a defective protein, which in turn, spurs uncontrolled cell growth. Gleevec binds to that protein, thereby stopping its activity and producing dramatic results in many CML patients. A similar approach was used in the development of the blockbuster drug trastuzumab (Herceptin®), which is used to treat an aggressive subtype of breast cancer. In both cases, the cancer cell possessed the gene defect, however, most or all of the normal cells did not.
Unfortunately, the “one-target, one-drug” approach has not held up for most other types of cancer. Recent projects that deciphered the genomes of cancer cells have found an array of different genetic mutations that can lead to the same cancer in different patients. Based upon the genetic profile of a patient’s particular tumor, we hope that patients could receive the drug or drug combination that is most likely to work for them.
The complexity of this findings is clearly daunting. Instead of attempting to discover ways to attack one well-defined genetic enemy, researchers are now faced with the prospect of fighting lots of little enemies. Fortunately, this complex view can be simplified by looking at which cellular pathways are disrupted by the genetic mutations. Rather than designing dozens of drugs to target dozens of mutations, drug developers could focus their attentions on just two or three cellular pathways. Patients could then receive the drug combination that is most likely to work for them based on the pathways affected in their particular tumors.
Example: Imagine a thousand people from all across the U.S. traveling towards the front door of a single building in Los Angeles. How would you keep all of these people from entering the building?
If you had limitless resources, you could hire workers to go out and stop each person as he or she drove down the highway, arrived at the train station or waited at the airport. Such actions would represent the one-target, one-drug approach.
But if you wanted to save a lot of time and money, you could just block the door to the building. This action represents the pathway-based strategy which many researchers are now pursuing to design drugs for cancer and other common diseases.
The cellular pathway approach is further complicated by the fact that many pathways are essential for the proliferation and survival of both cancer and normal cells. In this case, the hope is to design a drug that is capable of targeting only the defective pathways in cancer cells.
Appendix 6 – Targeting DNA Repair Through PARP Inhibition
Normally functioning BRCA1 (BReast CAncer-1) and BRCA2 (BReast CAncer-2) genes are necessary for major DNA repair through a process known as “homologous recombination” (HR). HR is a form of genetic recombination in which two similar DNA strands exchange genetic material. This process is critical to a cell’s ability to repair its DNA in the event that it becomes severely damaged, so the cell can continue to function. For discussion purposes, it is helpful to think of HR as the cell’s primary or major DNA “repair kit.”
A cell’s DNA structure can be damaged by a wide variety of intentional factors (e.g., select cancer treatments) or unintentional factors (e.g., ultraviolet light, ionizing radiation, man-made chemicals, etc.). For example, chemotherapy regimens used in the treatment of cancer, including alkylating agents, topoisomerase inhibitors, and platinum drugs, are designed to damage DNA and prevent cancer cells from reproducing.
In approximately 10% to 15% percent of germline (i.e., inherited) ovarian cancers, the BRCA 1 or BRCA2 gene is damaged or mutated, and therefore, the HR DNA repair kit is defective. The TCGA researchers determined that up to 50% of HGS-OvCa tumors possess BRCA gene-related or other defects in the HR DNA pair pathway. When the BRCA1 or BRCA2 gene is mutated, a secondary or minor DNA repair mechanism called “base-excision repair” usually compensates for the lack of HR DNA repair. As such, base-excision repair represents a secondary or minor DNA “repair kit.”
DNA repair enzymes such as PARP (poly (ADP-ribose) polymerase), whose activity and expression are upregulated in tumor cells, are believed to dampen the intended effect of chemotherapy and generate drug resistance. When the PARP1 protein – which is necessary for base-excision repair of “single-strand” DNA breaks – is inhibited in ovarian cancer cells possessing a BRCA gene mutation, DNA repair is drastically reduced. Ovarian cancer tumors that are caused by a mutation in the BRCA1 or BRCA2 gene are susceptible to cell death through PARP inhibition because BRCA-mutated genes are functionally incapable of repairing “double-strand” DNA breaks via HR. Without the ability to repair single strand breaks (due to therapeutic PARP inhibition) or subsequently resulting double strand DNA breaks (due to the lack of HR DNA repair by BRCA-mutated genes), the cancer cell dies through so-called “synthetic lethality.” Healthy cells are unaffected if PARP is blocked because they either contain one or two working BRCA1/BRCA2 genes which can perform DNA repair via HR. Accordingly, PARP inhibitors enhance the potential of chemotherapy (and radiation therapy) to induce ovarian cancer cell death.
Appendix 7 – PARP Inhibitors: A New Class of Targeted Therapy
PARP inhibitors represent a new, targeted approach to treating certain types of cancers. PARP inhibition has the potential to overwhelm cancer cells with lethal DNA damage by exploiting impaired DNA repair function inherent in some cancers, including breast and ovarian cancers with defects in the BRCA1 gene or BRCA 2 gene, and other DNA repair molecules. Inhibition of PARP leads to the cell’s failure to repair single strand DNA breaks, which, in turn, causes double strand DNA breaks. These effects are particularly detrimental to BRCA-mutated cancer cells that are deficient in repairing double strand DNA breaks due to lack of HR, and ultimately lead to cancer cell death. PARP inhibitors are the first targeted treatment to be developed for women with inherited forms of breast and ovarian cancer carrying faults or mutations in the BRCA-1 or BRCA-2 gene. Early results from clinical trials are showing promise for patients with these rare inherited forms of cancers.
Early preclinical research, including the findings above published by the The Cancer Genome Atlas, suggest that PARP inhibitors could provide benefit to women with germline (i.e., inherited) or somatic (i.e., lifetime acquired) BRCA gene mutations, as well as other defects in the HR DNA repair pathway. If ultimately proven true, the PARP class of drugs could provide benefit for up to 50% of high-grade, serous ovarian cancer patients.