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SU2C-ACS Lung Cancer Dream Team Investigator
Cyril Benes, PhDAssistant Professor of MedicineHarvard Medical School
The Benes laboratory, known as The Center for Molecular Therapeutics, is engaged in the design and application of personalized therapies for cancer. Targeted cancer treatments have emerged from research studies showing that the biology of cancer cells differs from that of healthy cells, and that each person’s cancer has a unique genetic signature. Our goal is to pinpoint the cancer cells’ biological weak points and then to attack those weak points with smart drugs that are speciﬁcally designed for such an attack. We use a very large collection of previously established tumor cell lines derived from many different cancers as well as newly established lines from patients treated at MGH. We are focused on developing molecular diagnostics that will reveal the best treatment course for each patient and on discovering how gene mutations in cancer can be exploited to develop new treatments.
Cyril Benes, PhDPrincipal Investigator
Genetics of Cancer Therapeutic Response
Clinical responses to anticancer therapeutics are often restricted to a subset of cases treated. In some instances, clear evidence is available that correlates clinical responses with speciﬁc tumor genotypes. Our goal is to identify tumor cell states (i.e., genotypes, gene expression) that predict sensitivity to anticancer agents. To accomplish this goal, we use high-throughput screening and expose 1,000 cell lines derived from a broad spectrum of cancers to known and potential anticancer therapeutic agents. We characterize the activity of single agents and combinations to discover therapeutic applications and biomarkers of response that could be used to select patients most likely to benefit.
The use of a very large cell line collection allows us to capture some mutational events that—although relatively rare—are very important for therapeutic response. In addition, while some patient selection strategies have proven quite successful, a wide range of variation in response to treatment exists in almost all cases. Similar to this clinical observation—and perhaps related mechanistically—our large cell line collection allows us to observe important variation in drug response within a given sensitizing genotype. For example, among BRAF-mutant cell lines which are, as a group, remarkably sensitive to BRAF inhibitors, some lines do not respond signiﬁcantly. Based on these observations, we aim to identify additional biomarkers that will permit more accurate prediction of drug response in the clinic.
Resistance to Cancer Therapies
Even for the most successful anticancer therapies, drug resistance invariably emerges and limits the impact on patient lives. The molecular mechanisms underlying acquired resistance to cancer therapeutics are not well deﬁned but are likely to be different for each therapy and cancer. We are investigating how drug combinations could overcome resistance, and within this context, studying how changes in intracellular signaling pathways affect drug response.
We are tackling the problem of therapeutic resistance using cell lines made resistant in the laboratory or isolated from resistant tumors. Previous results have shown that these cell line models do recapitulate at least some of the mechanisms of resistance at play in patients. We interrogate combinations of a panel of clinically relevant anticancer drugs as a way to quickly identify candidate therapeutic strategies and to jumpstart mechanistic studies that will help characterize the molecular basis of acquired resistance. To complement genomic guided therapeutic decisions we are developing approaches to rapidly grow cells from tumor and identify clinically relevant drugs with potential for clinical efficacy in the patients from which the cells were obtained.
In recent studies we have explored the role of cells present in the tumor together with the cancer cells. Tumor contain fibroblasts, endothelial cells and immune cells among others. We are studying the impact that the fibroblasts in the tumor have on response to therapy. We use biopsy derived fibrobalasts and cancer cells to study their relationship and understand how fribroblasts might provide cancer cells with some protection against drug treatment.
We are also approaching the problem of resistance using a very different and complementary approach. We systematically identify genes that can cause resistance to a given drug in a given context using a transposon-based genetic screen. Transposons are mobile genetic elements that can insert into a host genome—in our case, the genome of cancer cells. We use an engineered version of a transposon so we can control its mobility and identify genes with expressions that are modiﬁed by its insertion, leading to drug resistance.
Ember SW et. al. Potent Dual BET Bromodomain-Kinase Inhibitors as Value-Added Multitargeted Chemical Probes and Cancer Therapeutics. Mol Cancer Ther. June 2017.
Iorio F, Knijnenburg TA, Vis DJ, Bignell GR, Menden MP, Schubert M, Aben N, Gonçalves E, Barthorpe S, Lightfoot H, Cokelaer T, Greninger P, van Dyk E, Chang H, de Silva H, Heyn H, Deng X, Egan RK, Liu Q, Mironenko T, Mitropoulos X, Richardson L, Wang J, Zhang T, Moran S, Sayols S, Soleimani M, Tamborero D, Lopez-Bigas N, Ross-Macdonald P, Esteller M, Gray NS, Haber DA, Stratton MR, Benes CH, Wessels LF, Saez-Rodriguez J, McDermott U, Garnett MJ. A Landscape of Pharmacogenomic Interactions in Cancer. Cell. July 2016.
Benes CH. Direct pharmacological assessment of clinically acquired models as a strategy to overcome resistance to tyrosine kinase inhibitors. Mol Cell Oncol. May 2015.
Saha SK, Gordan JD, Kleinstiver BP, Vu P, Najem MS, Yeo JC, Shi L, Kato Y, Levin RS, Webber JT, Damon LJ, Egan RK, Greninger P, McDermott U, Garnett MJ, Jenkins RL, Rieger-Christ KM, Sullivan TB, Hezel AF, Liss AS, Mizukami Y, Goyal L, Ferrone CR, Zhu AX, Joung JK, Shokat KM, Benes CH, Bardeesy N. Isocitrate Dehydrogenase Mutations Confer Dasatinib Hypersensitivity and SRC Dependence in Intrahepatic Cholangiocarcinoma. Cancer Discov. July 2016.
Crystal AS, Shaw AT, Sequist LV, Friboulet L, Niederst MJ, Lockerman EL, Frias RL, Gainor JF, Amzallag A, Greninger P, Lee D, Kalsy A, Gomez-Caraballo M, Elamine L, Howe E, Hur W, Lifshits E, Robinson HE, Katayama R, Faber AC, Awad MM, Ramaswamy S, Mino-Kenudson M, Iafrate AJ, Benes CH, Engelman JA. Patient-derived models of acquired resistance can identify effective drug combinations for cancer. Science. December 2014.
Chen L, Stuart L, Ohsumi TK, Burgess S, Varshney GK, Dastur A, Borowsky M, Benes C, Lacy-Hulbert A, Schmidt EV. Transposon activation mutagenesis as a screening tool for identifying resistance to cancer therapeutics. BMC Cancer. February 2013.
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