Largest Study Matching Genomes To Potential Anticancer Treatments Releases Initial Results

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.



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, 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 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.

5 thoughts on “Largest Study Matching Genomes To Potential Anticancer Treatments Releases Initial Results

    • Hi Dee,

      Your welcome. I will keep everybody updated regarding future findings. For those readers who do not know Dee, she is an inspirational ovarian cancer survivor who founded the Women of Teal weblog. Dee is also involved with the Lance Armstrong Foundation, Northern NJ National Ovarian Cancer Coalition, Kaleidoscope of Hope (Board Member), and the Wellness Community Central NJ.

      I would encourage our readers to stop by Dee’s weblog to learn more.

      Thanks again Dee for your kind words.

      Regards, Paul


  1. Genes do not operate alone within the cell but in an intricate network of interactions. The cell is a system, an integrated, intereacting network of genes, proteins and other cellular constituents that produce functions. One needs to analyze the systems’ response to drug treatments, not just one or a few targets (pathways/mechanisms).

    There are many pathways/mechanisms to the altered cellular (forest) function, hence all the different “trees” which correlate in different situations. Improvement can be made by measuring what happens at the end (the effects on the forest), rather than the status of the indivudal trees.

    Dealing with genome-scale data in this context requires of its functional profiling, but this step must be taken within a systems biology framework, in which the collective properties of groups of genes are considered.

    The importance of mechanistic work around targeted therapy as a starting point should be downplayed in favor of a systems biology approach were compounds are first screened in cell-based assays, with mechanistic understanding of the target coming after validation of its impact on the biology of the cancer cells.

    Functional profiling measures the response of the tumor cells to drug exposure. Following this exposure, it measures both cell metabolism and cell morphology. The integrated effect of the drugs on the whole cell, resulting in a cellular response to the drug, measuring the interaction of the entire genome. No matter which genes are being affected, functional profiling is measuring them through the surrogate of measuring if the cell is alive or dead.

    Functional profiling is not intended to be a scale model of chemotherapy in the patient, anymore than the barometric pressure is a scale model of the weather. But it’s always more likely to rain when the barometer is falling than when it is rising, and chemotherapy is more likely to work in the patient when it kills the patient’s cancer cells in the laboratory.

    Real life 3D analysis makes functional profiling indicative of what will happen in the body. It tests fresh “live” cells in their three dimensional (3D), floating clusters (in their natural state). Solid tumor specimens are cultured in concical polypropylene microwells for 96 hours to increase the proportion of tumor cells, relative to normal cells.

    Polypropylene is a slippery material which prevents the attachment of fibroblasts and epithelial cells and encourages the tumor cells to remain in the form of three dimensional (3D), floating clusters. Our body is 3D, not 2D in form, undoubtedly, making this novel step better replicate that of the human body.

    Many of these drugs cry out for validated clinical biomarkers to help set dosage and select people likely to respond. And optimal and reproducible gene expression testing continues to evade the diagnositcs of the disease. Numerous other genes, tumor, and patient factors contribute to the risk of the cancer coming back and the effectiveness of chemotherapy for solid tumors.

    It could be vastly more beneficial to measure the net effect of all processes (systems) instead of just individual molecular targets. The cell is a system, an integrated, interacting network of genes, proteins, and other cellular constituents that produce functions. One needs to analyze the systems’ response to drug treatments, not just one or a few targets (pathways/mechanisms).

    There are many pathways/mechanisms to the altered cellular (forest) function, hence all the different “trees” which correlate in different situations. Improvement can be made by measuring what happens at the end (the effects on the forest), rather than the status of the indivudal trees.

    As you can see, just selecting the right test to perform in the right situation is a very important step on the road to personalizing cancer therapy.

    A number of cell-based assay labs across the country have data from tens of thousands of fresh human tumor specimens, representing virtually all types of human solid and hematologic neoplasms. They have the database necessary to define sensitivity and resistance for virtually all of the currently available drugs in virtually all types of human solid and hematologic neoplasms.

    Two of them have the most extensive experience in this field. What they do is a technique called “functional profiling,” capable of examining the nuances of cellular response to drugs. It looks at the entire genome, not just an individual (or a few) genetic mutation mechanism.

    If you believe in the molecular approach, you at least need a gold standard. You have patients not just with lung cancer, but with virtually all solid tumors who are potential candidates for EGFR targeted therapy. How are you ever going to validate a predictive test for breast, colon, pancreatic or melanoma? Do single agent trials correlate assay results?

    There are advantages in having cells which you can define as mutation-targeted sensitive or resistant and studying what exactly is different. Then there is testing to circumvent resistance. Then there is testing combinations.

    The importance of mechanistic work around targeted therapy as a starting point should be downplayed in favor of a systems biology approach were compounds are first screened in cell-based assays, with mechanistic understanding of the target coming after validation of its impact on the biology of the cancer cells.


    • Hi Greg,

      Thank you for your comment. I was going to respond to your comment in full. Instead, due to the importance of the subject matter, I am going to write an article that addresses the history of, and pros and cons associated with, chemoresistance and chemosensitivity assays. There is a long and bewildering history associated with this subject that should be briefly summarized for our readers along with proper factual and medical citation.

      It is unfortunate Greg that you did not take this opportunity to explain both sides of this issue in your comment. It seems that in your world, medical research is a “zero sum game,” i.e., someone must win and someone must lose. The fact of the matter is that both technologies (sensitivity based on genome and functional cell analysis) are needed. Neither method is perfect, but I believe that both methodologies serve different purposes.

      Your post also implies that we know everything there is to know about cellular function and cellular pathways. We do not, and a considerable amount of time will pass before we ever do. In fact, this past week, UCLA discovered a new pathway into the mitochondria of cells. Any attempt to compare the overarching genome work being performed pursuant to the U.S.-U.K. Cancer Genome Project to the area of chemo resistance and sensitivity assays is like comparing apples and oranges.

      Because you “cut and paste” this same description all over the internet, you should at least provide full citation (above and beyond the two citations that you typically post in support of functional cell analysis) in support of your scientific conclusions and you should disclose all of your relationships, if any, with companies involved in this area. The two experts that you refer to need all of the support that we can muster, but I am afraid that you do them a disservice with your ubiquitous “cut and paste” approach.

      But don’t worry, I will be citing medical abstracts and commentaries as part of my future article.

      Regards, Paul


    • My oh my, you are getting me wrong Paul. My post does not imply we know everything there is to know about cellular function and cellular pathways. On the contrary, I am saying that molecular profiling is an indirect approache to chemotherapy selection which examines a single process (pathway) within the cell or a relatively small number of processes (pathways). It’s aim is to determine only if there is evidence of a theoretical predisposition to drug susceptibility. In this regard, molecular profiling is a “static” profiling approach.

      In contrast, the functional profiling approach involves real-time assessment of ‘fresh’ living cancer and endothelial cell behaviors in the presence or absence of anti-cancer or anti-angiogenic drugs. This method accounts not only for the existence of genes and proteins but also for their functionality and for their interaction with other genes, other proteins and other processes occurring within the cell. You can take advantage of profiling the entire cell to measure the interaction of the entire genome (not just one pathway or a couple of pathways).

      So, you can study all the pathways you want. Although molecular testing currently is limited in its reliability as a clinical tool, the testing can be important in research settings such as in helping to identify rational targets for development of new anti-cancer drugs. However, for drug selection, there are many pathways to altered cellular (forest) function (hence all the different “trees” which correlate in different situations). Functional profiling the whole cell measures what happens at the end (the effects on the forest), rather than the status of the individual trees.

      And again, while I do not feel that medical research is a “zero sum game,” I feel that there is a concept of chemotherapy being a “zero sum game” in certain situations which is very intriguing (e.g. metastatic breast cancer, platinum resistant ovarian cancer). You can push response rates higher with more intensive therapy, but you don’t improve survival for the group as a whole. So every month of life you gain on one patient is a month lost in other patients.

      The problem is that ineffective, aggressive chemotherapy can diminish not just the quality of life but also the quantity of life. Organ toxicity. Sepsis. Bleeding. Immunosuppression. Mutagenesis in genetically unstable tumor to more aggressive phenotypes. Perhaps mood lowering, with resultant changes in cytokines. So the real challenge is to kill tumor, while avoiding as much of the above as possible. It seems obvious, but that’s not the paradigm being used.

      In regards to all my relationships Paul, I receive no financial support from any drug company, laboratory, medical equipment manufacturer, insurance carrier, professional organization, or hospital grroup. To paraphrase Martin Luther King, Jr., “a scientific communication should be judged on the quality of its content and only secondarily, or not at all, on the qualifications of its author.

      As a cancer patient advocate, I’ve been interested in and studied the aspects of cell function analysis (harken back to my college days studying biology) for a number of years, like anyone would have an interest in molecular science or biological science. My point with respect to cell function analysis is to educate patients and others that such science and technology exists, and might be very valuable. I get nothing out of my endeavors except the satisfaction of knowing that I’ve helped to increase the knowledge of informed consent. I get no pay, no lectureships, no junkets, not even any free meals.
      It is important to address the history of so-called chemoresistance and chemosensitivity assays. There is so much misinformation about them being perpetuated. All the important progress in them has come outside of NCI-sponsored university-based research and it strikes at the heart of the standard NCI/university clinical research paradigms. That’s why many doctors have not yet made the effort to learn about them. I hope you are not going to try and refer to ASCO’s feeble tech assessment in 2004.

      Because of the efforts of a dedicated private sector (like the two experts you allude to) and the availability of media such as the Internet, the NCI and NCI-oriented institutions will soon find themselves in the position of having to make the effort to learn and to be forced to provide sound reasons if they choose not to use these tests in the management of individual patients.


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