In Aim 2 we will construct computational models of cellular responses to drugs across genetically diverse cancer and normal cell lines. Data will be collected using a variety of single-cell and multiplex biochemical assays including sandwich immunoassays, protein and metabolite mass spectrometry, immunofluorescence microscopy and live-cell imaging of cells carrying fluorescent reporter proteins. These data will be integrated in computational models using a three-part strategy. First, significant connections between data on signaling molecules (e.g. Akt inhibition) and phenotypes (e.g. senescence v. apoptosis) will be discovered using statistical techniques such as partial least squares regression (PLSR), discriminant PLSR and Random Forest analysis. Second, network inference involving logical modeling or dynamic Bayes nets and literature-based priors will be used to determine the approximate topology of drug response networks in specific cell types. Finally, information from statistical modeling and network inference will be used to construct dynamic models in which the biochemistry of drug-target binding and of interacting response networks is rendered in mechanistic detail sufficient to reproduce and explain the observed variation in drug sensitivity and resistance from one tumor to the next. We have consciously chosen to model drug response networks for which genomic data provides clear evidence about which molecules and networks to focus on, and for which multiple precedents exist for translating cell-based studies into drug development and clinical care.
Aim 2. 1 will focus on measuring and modeling the PI3K/mTOR/Akt kinase network in triple negative breast cancer (TNBC), a disease in which this pathway is frequently mutated and being targeted by multiple kinase inhibitors in clinical development or use. Our translational goal is developing signatures and biomarkers predictive of patient response to mono and combination therapy.
Aim 2. 2 will develop new approaches to the poly-pharmacology of kinase inhibitors based on compressed algorithms that integrate diverse biochemical and structural data. We will use this information to analyze drug responses as multi-factorial perturbations of multi-component networks. Our translational goal is development of rational approaches to multi-kinase targeting.
Aim 2. 3 will focus on measuring and modeling the responses of BRAF-V600E melanoma and colon cancers to drugs such as vemurafinib with the primary aim of understanding diversity of genes implicated in acquired drug resistance. Our translational goal is overcoming or mitigating acquired resistance through design of patient-specific combination therapies using new or existing drugs.
Aim 2. 4 will compare the responses of normal and transformed cells directly with the aim of understanding the mechanistic basis of therapeutic index. We will focus on readily available """"""""normal"""""""" human cells and on stem-cell derived cardiomyocytes, an area of interest for the FDA.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Specialized Center (P50)
Project #
1P50GM107618-01A1
Application #
8769536
Study Section
Special Emphasis Panel (ZGM1)
Project Start
Project End
Budget Start
2014-09-01
Budget End
2015-05-31
Support Year
1
Fiscal Year
2014
Total Cost
Indirect Cost
Name
Harvard Medical School
Department
Type
DUNS #
City
Boston
State
MA
Country
United States
Zip Code
02115
Fallahi-Sichani, Mohammad; Becker, Verena; Izar, Benjamin et al. (2017) Adaptive resistance of melanoma cells to RAF inhibition via reversible induction of a slowly dividing de-differentiated state. Mol Syst Biol 13:905
Flier, Jeffrey S; Loscalzo, Joseph (2017) Categorizing biomedical research: the basics of translation. FASEB J 31:3210-3215
Gyori, Benjamin M; Bachman, John A; Subramanian, Kartik et al. (2017) From word models to executable models of signaling networks using automated assembly. Mol Syst Biol 13:954
Handy, Diane E; Loscalzo, Joseph (2017) Responses to reductive stress in the cardiovascular system. Free Radic Biol Med 109:114-124
Hafner, Marc; Niepel, Mario; Subramanian, Kartik et al. (2017) Designing Drug-Response Experiments and Quantifying their Results. Curr Protoc Chem Biol 9:96-116
Silbersweig, David; Loscalzo, Joseph (2017) Precision Psychiatry Meets Network Medicine: Network Psychiatry. JAMA Psychiatry 74:665-666
Cokol, Murat; Kuru, Nurdan; Bicak, Ece et al. (2017) Efficient measurement and factorization of high-order drug interactions in Mycobacterium tuberculosis. Sci Adv 3:e1701881
Everley, Robert A; Huttlin, Edward L; Erickson, Alison R et al. (2017) Neutral Loss Is a Very Common Occurrence in Phosphotyrosine-Containing Peptides Labeled with Isobaric Tags. J Proteome Res 16:1069-1076
Boswell, Sarah A; Snavely, Andrew; Landry, Heather M et al. (2017) Total RNA-seq to identify pharmacological effects on specific stages of mRNA synthesis. Nat Chem Biol 13:501-507
Tan, Li; Gurbani, Deepak; Weisberg, Ellen L et al. (2017) Structure-guided development of covalent TAK1 inhibitors. Bioorg Med Chem 25:838-846

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