The long-term goal of this Program Project is to understand, in precise quantitative terms, how individual cancer cells and tumors respond to drug treatment, from target engagement to induction of apoptosis to eventual tumor regression. This will improve patient care by allowing improved prediction of drug responses and rational design of combination therapies, and by identifying targets for better future drugs. We will address this goal in the context of two drug classes that trigger apoptosis in cancer cells, anti-mitotic drugs, and targeted apoptosis inducers, including TRAIL and ABT737. Experiments will be performed in cell culture and mouse tumors.
We aim for an understanding of the cellular response to these drugs that is (i) mechanistic in explaining cellular phenotypes in terms of interactions among specific proteins and other bio-molecules (ii) quantitative in applying mass-action kinetics and other mathematical formalisms to predicting the behavior of ensembles of interacting proteins from knowledge of their individual biochemistry (iii) probabilistic in accounting for the variability from one cell to the next in responses to drugs with the attendant likelihood that only a fraction of tumor cells will arrest or die in response to treatment with a chemotherapeutic drug (iv) post-genomic in analyzing diverse cell lines (and ultimately patient samples) with knowledge of their genetic differences and with the possibility of applying powerful knock-out/in and RNAi strategies to alter genotype (v) integrative in assuming that determinants of drug response are multi-factorial and that multiple interacting pathways rather than single genes or proteins must be studied. We will address these goals in four Program Specific Aims:
In aim 1 we will determine the molecular mechanisms that regulate MOMP in response to anti-mitotic drugs and ABT737.
In aim 2 we will investigate the causes of variation in cell responses to anti-mitotics and targeted inducers of apoptosis.
In aim 3 we will ask to what extent drug responses are the same in cell culture and mouse tumors, using intravital imaging and other methods.
In aim 4 we will pursue several approaches towards translating mechanistic understanding from aims 1-3 into improved patient care.
Cancer chemotherapy drugs only work for some patients, and we often do not know why. We will investigate how drugs kill cancer cells, and why drugs kill some cancer cells but not others. The knowledge we gain will help us predict which patients will be cured by a particular dnjg or drug combination, so the most effective drugs can be selected for that patient. It will also help us design more effective future drugs.
|Lee, Robin E C; Qasaimeh, Mohammad A; Xia, Xianfang et al. (2016) NF-ÎºB signalling and cell fate decisions in response to a short pulse of tumour necrosis factor. Sci Rep 6:39519|
|Sarosiek, Kristopher A; Letai, Anthony (2016) Directly targeting the mitochondrial pathway of apoptosis for cancer therapy using BH3 mimetics - recent successes, current challenges and future promise. FEBS J 283:3523-3533|
|Giedt, Randy J; Fumene Feruglio, Paolo; Pathania, Divya et al. (2016) Computational imaging reveals mitochondrial morphology as a biomarker of cancer phenotype and drug response. Sci Rep 6:32985|
|Bhola, Patrick D; Mar, Brenton G; Lindsley, R Coleman et al. (2016) Functionally identifiable apoptosis-insensitive subpopulations determine chemoresistance in acute myeloid leukemia. J Clin Invest 126:3827-3836|
|Roux, JÃ©rÃ©mie; Hafner, Marc; Bandara, Samuel et al. (2015) Fractional killing arises from cell-to-cell variability in overcoming a caspase activity threshold. Mol Syst Biol 11:803|
|Fallahi-Sichani, Mohammad; Moerke, Nathan J; Niepel, Mario et al. (2015) Systematic analysis of BRAF(V600E) melanomas reveals a role for JNK/c-Jun pathway in adaptive resistance to drug-induced apoptosis. Mol Syst Biol 11:797|
|Chittajallu, Deepak R; Florian, Stefan; Kohler, Rainer H et al. (2015) In vivo cell-cycle profiling in xenograft tumors by quantitative intravital microscopy. Nat Methods 12:577-85|
|Montero, Joan; Sarosiek, Kristopher A; DeAngelo, Joseph D et al. (2015) Drug-induced death signaling strategy rapidly predicts cancer response to chemotherapy. Cell 160:977-89|
|Miller, Miles A; Zheng, Yao-Rong; Gadde, Suresh et al. (2015) Tumour-associated macrophages act as a slow-release reservoir of nano-therapeutic Pt(IV) pro-drug. Nat Commun 6:8692|
|Flusberg, Deborah A; Sorger, Peter K (2015) Surviving apoptosis: life-death signaling in single cells. Trends Cell Biol 25:446-58|
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