DEA Annual Report 2011
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Division of Extramural Activities Annual Report 2011 National Cancer Institute U.S. DEPARTMENT OF HEALTH AND HUMAN SERVICES National Institutes of Health Deciphering the Complexity of Drug Resistance in Cancer For more than 30 years, the Laboratory of Cell Biology has focused on understanding the mechanisms by which cancer cells elude the toxic effects of chemotherapy. This work was initially stimulated by the observation that while some cancers go into remission following treatment with anti-cancer drugs, others fail to respond at all (intrinsic resistance), or once in remission relapse with tumors that are difficult to treat with chemotherapy (acquired resistance). New, targeted anti-cancer drugs are not exempt from the problem of both intrinsic and acquired multidrug resistance (MDR). This functional complexity of drug resistance suggests that the mechanisms underlying resistance are likely to be complex as well. It was something of a surprise when we and others discovered in 1985 that the product of a single gene (ABCB1 or MDR1), an energy-dependent multidrug efflux pump termed P-glycoprotein (P-gp), could account for both intrinsic and acquired resistance in cultured cancer cells. We showed that at least 50 percent of drug-resistant human cancers expressed P-gp at levels sufficient to confer MDR, and strategies to inhibit P-gp in cancers and restore sensitivity to anti-cancer drugs were devised. Along the way, we discovered that common polymorphisms in the ABCB1 gene, including single nucleotide changes that do not alter the amino acids in P-gp (so-called “silent” mutations), can significantly alter the pattern of MDR and sensitivity to inhibitors. It soon became apparent that we were not curing most MDR cancers with strategies that inhibit P-gp, even in cases where P-gp was expressed and functional. We decided to reconsider some of the assumptions that had gone into studies on MDR in cancer. First and foremost was the underlying hypothesis that resistance mechanisms that operated in cultured cancer cells reflected mechanisms of resistance in cancers in patients. Growth conditions of such cells differ dramatically from the environment in vivo. Furthermore, most cultured cancer cells have been grown outside of the body for many years and have adapted to these highly altered culture conditions by expressing many genes involved in environ- mental adaptation and traditionally associated with the development of drug resistance. Although cultured cells could be used to catalog possible mechanisms of MDR, they by no means reflected the likely mechanisms active in clinical cancers. Using the tools of modern molecular biology, it was possible to interrogate samples from human cancers, such as ovarian cancer, acute myelogenous leukemia, and hepatoma, and correlate expression of specific drug-resistance genes with clinical outcome. This analysis of clinical cancers revealed robust, but complex, signals predicting clinical outcome involving multiple genes that could independently confer drug-resistance, but seemed to be working together in clinical cancers to thwart effective chemotherapy. P-gp expression was embedded in some of these signals as a potential predictor of poor response to chemotherapy, but was by no means the only MDR gene capable of predicting poor response. It appears that resistance derives from a reprogramming of cellular pathways that regulate growth and response to environmental adversity, and that the cancer cell can draw from many different built-in mechanisms to survive chemotherapy. Our challenge now is to use system and computational approaches to define the pathways involved in drug resis- tance, and to develop better ex vivo systems to dissect the contribution of the many gene products and pathways involved in the development of MDR in cancer. This would allow development and testing of new approaches to circumventing or targeting resistance, such as RNAi and small molecule inhibitors of some pathways, targeting the Achilles’ heel of some MDR cancers that overexpress P-gp (and other MDR cancers) with agents that specifically kill such cells, and strategies that alter resistance pathways through epigenetic changes leading to new patterns of gene expression. References: Shen DW, Pouliot LM, Hall MD, and Gottesman MM. Cisplatin resistance: a cellular self-defense mechanism resulting from multiple epigenetic and genetic changes. [Review] Pharm. Rev. 2012:64(3);706-721. Gillet JP, Calcagno AM, Varma S, Davidson B, Bunkholt Elstrand M, Ganapathi R, Kamat AA, Sood AK, Ambudkar SV, Seiden MV, Rueda BR, and Gottesman MM. Multidrug resistance-linked gene signature predicts overall survival of patients with primary ovarian serous carcinoma. Clin. Cancer Res. 2012:18;3197-3206. Gillet JP, Calcagno AM, Varma S, Marino M, Green LJ, Vora M, Patel C, Orina JN, Eliseeva T, Singal V, Padmanabhan R, Davidson B, Ganapathi R, Sood AK, Rueda BR, Ambudkar SV, and Gottesman MM. Redefining the relevance of established cancer cell lines to the study of mechanisms of clinical anti-cancer drug resistance. Proc. Natl. Acad. Sci. U.S.A. 2011:108;18708-18713. Hall MD, Handley MD, and Gottesman MM. Is resistance useless? Multidrug resistance and collateral sensitivity. [Review] Trends Pharma- col. Sci. 2009:30;546-555. Fung KL and Gottesman MM. A synonymous polymorphism in a common MDR1 (ABCB1) haplotype shapes protein function. Biochim Biophys Acta 2009:1794;860-871. Division of Extramural Activities Annual Report 2011 National Cancer Institute Cover Images: Cellular and molecular approaches to the study of multidrug resistance in cancer. The outer circle demonstrates the polarized expression of the multidrug transporter, P-glycoprotein, on the apical membrane of kidney epithelial cells. The left panel uses fluorescently labeled drug-resistant cells (green) to show how resistant ovarian cancer cells overgrow sensitive cells (red) following selection in the anti-cancer drug paclitaxel. The right panel illustrates the use of expression arrays to identify genes whose expression is associated with poor response to chemotherapy. Images and narrative are courtesy of Michael M. Gottesman, M.D., Chief, Laboratory of Cell Biology, Center for Cancer Research, National Cancer Institute, National Institutes of Health U.S. DEPARTMENT OF HEALTH AND HUMAN SERVICES National Institutes of Health ii NCI DEA 2011 Annual Report Contents Introduction ..................................................................... 1 Overview of the Division of Extramural Activities.......................................3 Special Activities in the Office of the Director, DEA ....................................4 Program Coordination: A Resource for New Funding Initiatives ............................6 Grant Referral: A First Point of Contact for NCI Grant Applicants and Receipt of Applications ................................................................... 7 Peer Review—The Next Step .......................................................9 NCI Grant and RFA Funding ......................................................17 Supporting Peer Review Consultants.................................................19 DEA’s Role in Advisory Activities ...................................................20 Committee Management Activities ..................................................24 Portfolio Tracking and Analysis .....................................................26 Information Resources Management . 30 Organizational Structure of the Division of Extramural Activities..........................32 Figures ______________________________________________________________________________ Figure 1. Receipt and Referral of NCI Grant Applications, FY2007 - 2011.....................7 Figure 2. DEA Review Workload, FY2007 - 2011........................................10 Figure 3. P01, SPORE, and Other Multi-Project Research Applications Reviewed, FY2007 - 2011............................................................12 Figure 4. Numbers of Career Development (CD) and Training and Education (T&E) Applications Reviewed, FY2007 - 2011 ........................................14 Figure 5. Technology Initiatives Applications Reviewed, FY2007 - 2011 ......................16 Figure 6. NCI Grant and RFA Funding Percentages by Concept Area, FY2010 . 17 Figure 7. NCI Grant and RFA Funding Percentages by Concept Area, FY2011 . 18 Figure 8. BSA Approved RFA Concept Set-Asides by Division/Office/Center .................18 Figure 9. FY2011 Success Rates for Applications in High Incidence Cancers ..................28 Figure 10. FY2011 Success Rates for Applications in Selected Special Interest Categories .........29 Tables ______________________________________________________________________________ Table 1a. Requests for Applications (RFAs) Published by the NCI in FY2011, Sorted by Date of Publication................................................42 Table 1b. Requests for Applications (RFAs) Published by the NCI in FY2011, Sorted by Division, Office, and Center .........................................42 Table 2. NCI Participation in Trans-NIH Requests for Applications (RFAs) in FY2011, Sorted by Date of Publication................................................43 Table 3a. Program Announcements (PAs) Published by the NCI in FY2011, Sorted by Date of Publication................................................44 Table 3b. Program Announcements (PAs) Published by the NCI in FY2011, Sorted by Division, Office, and Center .........................................45 Table 4. NCI Participation