The largest study to correlate genetics with response to anticancer drugs released its first results on July 15. The researchers behind the study, based at Massachusetts General Hospital Cancer Center and the Wellcome Trust Sanger Institute, describe in this initial dataset the responses of 350 cancer samples (including ovarian cancer) to 18 anticancer therapeutics.
U.K.–U.S. Collaboration Builds a Database For “Personalized” Cancer Treatment
The Genomics of Drug Sensitivity in Cancer project released its first results on July 15th. Researchers released a first dataset from a study that will expose 1,000 cancer cell lines (including ovarian) to 400 anticancer treatments.
The largest study to correlate genetics with response to anticancer drugs released its first results on July 15. The researchers behind the study, based at Massachusetts General Hospital Cancer Center and the Wellcome Trust Sanger Institute, describe in this initial dataset the responses of 350 cancer samples (including ovarian cancer) to 18 anticancer therapeutics.
These first results, made freely available on the Genomics of Drug Sensitivity in Cancer website, will help cancer researchers around the world to obtain a better understanding of cancer genetics and could help to improve treatment regimens.
Dr. Andy Futreal, co-leader of the Cancer Genome Project at the Wellcome Trust Sanger Institute, said:
Today is our first glimpse of this complex interface, where genomes meet cancer medicine. We will, over the course of this work, add to this picture, identifying genetic changes that can inform clinical decisions, with the hope of improving treatment. By producing a carefully curated set of data to serve the cancer research community, we hope to produce a database for improving patient response during cancer treatment.
How a patient responds to anticancer treatment is determined in large part by the combination of gene mutations in her or his cancer cells. The better this relationship is understood, the better treatment can be targeted to the particular tumor.
The aim of the five-year, international drug-sensitivity study is to find the best combinations of treatments for a wide range of cancer types: roughly 1000 cancer cell lines will be exposed to 400 anticancer treatments, alone or in combination, to determine the most effective drug or combination of drugs in the lab.
The therapies include known anticancer drugs as well as others in preclinical development.
To make the study as comprehensive as possible, the researchers have selected 1000 genetically characterized cell lines that include common cancers such as breast, colorectal and lung. Each cell line has been genetically fingerprinted and this data will also be publicly available on the website. Importantly, the researchers will take promising leads from the cancer samples in the lab to be verified in clinical specimens: the findings will be used to design clinical studies in which treatment will be selected based on a patient’s cancer mutation spectrum.
The new data released today draws on large-scale analyses of cancer genomes to identify genomic markers of sensitivity to anticancer drugs.
The first data release confirms several genes that predict therapeutic response in different cancer types. These include sensitivity of melanoma, a deadly form of skin cancer, with activating mutations in the gene BRAF to molecular therapeutics targeting this protein, a therapeutic strategy that is currently being exploited in the clinical setting. These first results provide a striking example of the power of this approach to identify genetic factors that determine drug response.
Dr. Ultan McDermott, Faculty Investigator at the Wellcome Trust Sanger Institute, said:
It is very encouraging that we are able to clearly identify drug–gene interactions that are known to have clinical impact at an early stage in the study. It suggests that we will discover many novel interactions even before we have the full complement of cancer cell lines and drugs screened. We have already studied more gene mutation-drug interactions than any previous work but, more importantly, we are putting in place a mechanism to ensure rapid dissemination of our results to enable worldwide collaborative research. By ensuring that all the drug sensitivity data and correlative analysis is freely available in an easy-to-use website, we hope to enable and support the important work of the wider community of cancer researchers.
Further results from this study should, over its five-year term, identify interactions between mutations and drug sensitivities most likely to translate into benefit for patients: at the moment we do not have sufficient understanding of the complexity of cancer drug response to optimize treatment based on a person’s genome.
Professor Daniel Haber, Director of the Cancer Center at Massachusetts General Hospital and Harvard Medical School, said:
We need better information linking tumor genotypes to drug sensitivities across the broad spectrum of cancer heterogeneity, and then we need to be in position to apply that research foundation to improve patient care. The effectiveness of novel targeted cancer agents could be substantially improved by directing treatment towards those patients that genetic study suggests are most likely to benefit, thus “personalizing” cancer treatment.
The comprehensive results include correlating drug sensitivity with measurements of mutations in key cancer genes, structural changes in the cancer cells (copy number information) and differences in gene activity, making this the largest project of its type and a unique resource for cancer researchers around the world.
Professor Michael Stratton, co-leader of the Cancer Genome Project and Director of the Wellcome Trust Sanger Institute, said:
“This is one of the Sanger Institute’s first large-scale explorations into the therapeutics of human disease. I am delighted to see the early results from our partnership with the team at Massachusetts General Hospital. Collaboration is essential in cancer research: this important project is part of wider efforts to bring international expertise to bear on cancer.”
Ovarian Cancer Sample Gene Mutation Prevalence
As part of the Cancer Genome Project, researchers identified gene mutations found in 20 ovarian cancer cell lines and the associated prevalence of such mutations within the sample population tested. For purposes of this project, a mutation — referred to by researchers as a “genetic event” in the project analyses description — is defined as (i) a coding sequence variant in a cancer gene, or (ii) a gene copy number equal to zero (i.e., a gene deletion) or greater than or equal to 8 (i.e., gene amplification). The ovarian cancer sample analysis thus far, indicates the presence of mutations in twelve genes. The genes that are mutated and the accompanying mutation prevalence percentage are as follows: APC (5%), CDKN2A (24%), CTNNB1 (5%), ERBB2/HER-2 (5%), KRAS (10% ), MAP2K4 (5%), MSH2 (5%), NRAS (10%), PIK3CA (10%), PTEN (14%), STK11 (5%), and TP53 (62%). Accordingly, as of date, the top five ovarian cancer gene mutations occurred in TP53, CDKN2A, CDKN2a(p14)(see below), PTEN, and KRAS.
Click here to view the Ovary Tissue Overview. Click here to download a Microsoft Excel spreadsheet listing the mutations in 52 cancer genes across tissue types. Based upon the Ovary Tissue Overview chart, the Microsoft Excel Chart has not been updated to include the following additional ovarian cancer sample mutations and associated prevalence percentages: CDKN2a(p14)(24%), FAM123B (5%), FBXW7 (5%), MLH1 (10%), MSH6 (5%).
18 AntiCancer Therapies Tested; Next 9 Therapies To Be Tested Identified
As presented in the initial study results, 18 drugs/preclinical compounds were tested against various cancer cell lines, including ovarian. The list of drugs/preclinical compounds that were tested for sensitivity are as follows: imatinib (brand name: Gleevec), AZ628 (C-Raf inhibitor), MG132 (proteasome inhibitor), TAE684 (ALK inhibitor), MK-0457 (Aurora kinase inhibitor), sorafenib (C-Raf kinase & angiogenesis inhibitor) (brand name: Nexavar), Go 6976 (protein kinase C (PKC) inhibitor), paclitaxel (brand name: Taxol), rapamycin (mTOR inhibitor)(brand name: Rapamune), erlotinib (EGFR inhibitor)(brand name: Tarceva), HKI-272 (a/k/a neratinib) (HER-2 inhibitor), Geldanamycin (Heat Shock Protein 90 inhibitor), cyclopamine (Hedgehog pathway inhibitor), AZD-0530 (Src and Abl inhibitor), sunitinib (angiogenesis & c-kit inhibitor)(brand name: Sutent), PHA665752 (c-Met inhibitor), PF-2341066 (c-Met inhibitor), and PD173074 (FGFR1 & angiogenesis inhibitor).
Click here to view the project drug/preclinical compound sensitivity data chart.
The additional drugs/compounds that will be screened by researchers in the near future are metformin (insulin)(brand name: Glucophage), AICAR (AMP inhibitor), docetaxel (platinum drug)(brand name: Taxotere), cisplatin (platinum drug)(brand name: Platinol), gefitinib (EGFR inhibitor)(brand name: Iressa), BIBW 2992 (EGFR/HER-2 inhibitor)(brand name: Tovok), PLX4720 (B-Raf [V600E] inhibitor), axitinib (angiogenesis inhibitor)(a/k/a AG-013736), and CI-1040 (PD184352)(MEK inhibitor).
Ovarian cancer cells dividing. (Source: ecancermedia)
Ovarian Cancer Therapy Sensitivity
Targeted molecular therapies that disrupt specific intracellular signaling pathways are increasingly used for the treatment of cancer. The rational for this approach is based on our ever increasing understanding of the genes that are causally implicated in cancer and the clinical observation that the genetic features of a cancer can be predictive of a patient’s response to targeted therapies. As noted above, the goal of the Cancer Genome Project is to discover new cancer biomarkers that define subsets of drug-sensitive patients. Towards this aim, the researchers are (i) screening a wide range of anti-cancer therapeutics against a large number of genetically characterized human cancer cell lines (including ovarian), and (ii) correlating drug sensitivity with extensive genetic data. This information can be used to determine the optimal clinical application of cancer drugs as well as the design of clinical trials involving investigational compounds being developed for the clinic.
When the researchers tested the 18 anticancer therapies against the 20 ovarian cancer cell lines, they determined that the samples were sensitive to many of the drugs/compounds. The initial results of this testing indicate that there are at least six ovarian cancer gene mutations that were sensitive to eight of the anticancer therapies, with such results rising to the level of statistical significance. We should note that although most (but not all) of the ovarian cancer gene mutations were sensitive to several anticancer therapies, we listed below only those which were sensitive enough to be assigned a green (i.e., sensitive) heatmap code by the researchers.
Click here to download a Microsoft Excel spreadsheet showing the effect of each of the 51 genes on the 18 drugs tested. Statistically significant effects are highlighted in bold and the corresponding p values for each gene/drug interaction are displayed in an adjacent table. A heatmap overlay for the effect of the gene on drug sensitivity was created, with the color red indicating drug resistance and the color green indicating drug sensitivity.
The mutated genes present within the 20 ovarian cancer cell line sample that were sensitive to anticancer therapies are listed below. Again, only statistically significant sensitivities are provided.
- CDKN2A gene mutation was sensitive to TAE684, MK-0457, paclitaxel, and PHA665752.
- CTNNB1 gene mutation was sensitive to MK-0457.
- ERBB2/HER-2 gene mutation was sensitive to HKI-272.
- KRAS gene mutation was sensitive to AZ628.
- MSH2 gene mutation was sensitive to AZD0530.
- NRAS gene mutation was sensitive to AZ628.
We will provide you with future updates regarding additional ovarian cancer gene mutation findings, and new anticancer therapies tested, pursuant to the ongoing Cancer Genome Project.
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About The Genomics of Drug Sensitivity In Cancer Project
The Genomics of Drug Sensitivity In Cancer Project was launched in December 2008 with funding from a five-year Wellcome Trust 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 informatics at the Wellcome Trust Sanger Institute. The scientists will use their skills in high-throughput research to test the sensitivity of 1000 cancer cell samples to hundreds of known and novel molecular anticancer treatments and correlate these responses to the genes known to be driving the cancers. The study makes use of a very large collection of genetically defined cancer cell lines to identify genetic events that predict response to cancer drugs. The results will give a catalogue of the most promising treatments or combinations of treatments for each of the cancer types based on the specific genetic alterations in these cancers. This information will then be used to empower more informative clinical trials thus aiding the use of targeted agents in the clinic and ultimately improvements in patient care.
Project leadership includes Professor Daniel Haber and Dr. Cyril Benes at Massachusetts General Hospital Cancer Center and Professor Mike Stratton and Drs. Andy Futreal and Ultan McDermott at the Wellcome Trust Sanger Institute.
About Massachusetts General Hospital
Massachusetts General Hospital (MGH), established in 1811, is the original and largest teaching hospital of Harvard Medical School. The MGH conducts the largest hospital-based research program in the United States, with an annual research budget of more than $600 million and major research centers in AIDS, cardiovascular research, cancer, computational and integrative biology, cutaneous biology, human genetics, medical imaging, neurodegenerative disorders, regenerative medicine, systems biology, transplantation biology and photomedicine.
About The Wellcome Trust Sanger Institute
The Wellcome Trust Sanger Institute, which receives the majority of its funding from the Wellcome Trust, was founded in 1992 as the focus for U.K. gene sequencing efforts. The Institute is responsible for the completion of the sequence of approximately one-third of the human genome as well as genomes of model organisms such as mouse and zebrafish, and more than 90 pathogen genomes. In October 2005, new funding was awarded by the Wellcome Trust to enable the Institute to build on its world-class scientific achievements and exploit the wealth of genome data now available to answer important questions about health and disease. These programs are built around a Faculty of more than 30 senior researchers. The Wellcome Trust Sanger Institute is based in Hinxton, Cambridge, U.K.
About The Wellcome Trust
The Wellcome Trust is a global charity dedicated to achieving extraordinary improvements in human and animal health. It supports the brightest minds in biomedical research and the medical humanities. The Trust’s breadth of support includes public engagement, education, and the application of research to improve health. It is independent of both political and commercial interests.
Required Cancer Genome Project Disclaimer:
The data above was obtained from the Wellcome Trust Sanger Institute Cancer Genome Project web site, http://www.sanger.ac.uk/genetics/CGP. The data is made available before scientific publication with the understanding that the Wellcom Trust Sanger Institute intends to publish the initial large-scale analysis of the dataset. This publication will include a summary detailing the curated data and its key features. Any redistribution of the original data should carry this notice: Please ensure that you use the latest available version of the data as it is being continually updated. If you have any questions regarding the sequence or mutation data or their use in publications, please contact cosmic@sanger.ac.uk so as to obtain any updated or additional data. The Wellcome Trust Sanger Institute provides this data in good faith, but makes no warranty, express or implied, nor assumes any legal liability or responsibility for any purpose for which the data are used.
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