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Jun 15, 2010 (Vol. 30, No. 12)

Personalizing Cancer Treatment

Answers Likely to Be Found in Unraveling Genomic and Epigenomic Complexities

  • Coping with Complexity

    Bert Vogelstein, M.D., Clayton professor of oncology and pathology at Johns Hopkins University, summarized the information collected to date from the first 100 cancer genomes that have been analyzed, 78 of which are published. Across various tumor types, the number of genetic alterations—primarily point mutations—typically ranges from 30 to 80. In pancreatic cancer, for example, the median number of altered genes is 44, with a range of 36 to 60.

    For lung and melanoma tumors, the number of mutations increases to 100–200, which can be explained by the addition of a variety of alterations caused by carcinogens associated with tobacco and sun exposure. Leukemia and medulloblastoma, which develop more quickly, typically contain about 10 cancer-associated mutations.

    The data indicates that mutations tend to accumulate as a tumor ages and evolves, and that most alterations are “passenger” mutations, rather than “driver” mutations, the latter of which are present in tumor suppressor genes or oncogenes and are directly responsible for driving oncogenic transformation. Differentiating driver from passenger mutations is a key challenge and the focus of much current research.

    Of the 3,142 mutated (and potential) cancer genes found in these 100 genomes, about 286 are in tumor suppressor genes. This has important implications for drug discovery, noted Dr. Vogelstein, as “you can’t target something that is being suppressed.” Nearly all of the driver genes are part of 12 core signaling pathways.

    Dr. Vogelstein described tumor heterogeneity as the “elephant in the room.” For example, two different people may have the same type of tumor with mutations affecting the same signaling pathway, but the mutations may be in different genes.

    Most cancer genome studies to date have been done in model organisms, and Dr. Vogelstein emphasized the need to perform large-scale genome analysis studies in human cancer cells and the importance of developing an understanding of the pathways through which cancer genes operate in human cancer cells. This information will be essential for translating genomic information to drug discovery and for targeting pathways such as DNA repair pathways, angiogenesis pathways, and metabolic pathways—rather than individual mutant proteins. 

    Over the past 30 years, scientists have come to understand “most of the genes and all of the pathways in cancer,” said Dr. Vogelstein, and he does not expect any surprises such as the discovery of new cancer genes. “We know the landscape,” and it is sobering, he said, noting the complexity and challenges the cancer genome presents. Yet he is optimistic that “the mitigation of much disease caused by cancer is within our grasp.”

  • Translational Genomics

    The development of personalized medicine will depend on the merging of clinical and molecular data, according to Laura van ’t Veer, Ph.D., head of molecular pathology at The Netherlands Cancer Institute, who is studying gene-expression profiles in women with breast cancer and discovering how this information can be used to guide treatment decisions and predict prognoses.

    Dr. van ’t Veer described a 6,000-patient trial being conducted in 21 countries in which the MammaPrint Profile, a 70-gene prognostic signature from Agendia, is being used as a prognostic tool to predict cancer recurrence risk and to identify patients with early-stage breast cancer who are likely to benefit from adjuvant chemotherapy. The protocol includes banking of FFPE and frozen tumor tissue as well as blood for analysis.

    Described by the study investigators, from the University of Texas M.D. Anderson Cancer Center, as “the first completed biopsy-mandated study in pretreated NSCLC,” the BATTLE trial assessed disease control after eight weeks of drug treatment. Results of the Phase II trial were revealed at the AACR meeting.

    Biopsy specimens from 255 patients with non-small-cell lung cancer (NSCLC) were analyzed for 11 biomarkers from four molecular pathways implicated in NSCLC, and this information was used in the second phase of the trial to randomize patients into four drug treatment groups. Each of the drugs targets one of these four pathways. In the first phase of the trial, patients were equally randomized to receive any one of the four study drugs (or drug combinations), whereas in the second phase, patients were adaptively randomized for drug treatment based on their biomarker profiles.

    The results supported the pretrial hypotheses. Among patients with a mutation in the KRAS pathway, for example, 61% of those treated with the drug sorafenib, which targets the KRAS pathway, exhibited disease control at eight weeks, compared to 32% of those who received one of the other three drugs. Overall, 46% of the patients in the trial had disease control at eight weeks, compared to the historical experience of about 30% among patients with late-stage lung cancer.


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