The overall goal of the HMS LINCS Center is to delineate the fundamental principles of cellular response to perturbagens at the level of single-cells and cell populations and to make response data routinely available via web-based browse, query and programmatic interfaces. This will involve the development and testing of new measurement methods, computational algorithms, and response signatures for diverse human cell types exposed to perturbations individually and in combination. We will emphasize the systematic collection of data not currently available in existing public databases including live and fixed-cell imaging, biochemical data on signaling proteins and multi-factorial drug-response phenotypes. A focus on diverse transformed and primary cells, including those derived from healthy and diseased donors, and on clinical grade small molecules (kinase inhibitors and epigenome drugs) will increase the translational impact of our work. The proposed LINCS Center represents a continuation of a program in operation for ~3.5 years under a LINCS pilot phase U54 award. We will expand the scope and sophistication of our Center, devoting significant effort to (i) improving the quality of data analysis and visualization, particularly wih respect to the complexities of perturbagen polypharmacy (ii) accelerating the release of perturbagen-response signatures using methods that have been demonstrated to yield reliable and informative data, with a particular emphasis on primary and non-transformed cells (iii) developing and applying new measurement methods, particularly mass spectrometry for analysis of cell populations and live-cell imaging for analysis of single cells. The work will involve nine complementary but independent aims. In Data Generation, Aim 1 will perform systematic analysis of perturbagen responses at a single-cell level.
Aim 2 will collect multiplex protein and mRNA data on perturbagen response using a set of complementary imaging, mass spectrometry and bead-based assays.
Aim 3 will apply LINCS methods to non-transformed immune cells and induced pluripotent stem cells, and explore if signatures can guide a detailed medicinal chemistry campaign. In Data Analysis, Aim 4 will construct perturbagen-response signatures using statistical modeling, network inference and machine learning methods.
Aim 5 will develop new approaches to understanding and analyzing drug interactions on multiple phenotypes in single cells.
Aim 6 will develop a novel compressed sensing framework for analyzing the poly-pharmacology of kinase inhibitors.
Aim 7 will enhance the query, browse and explore functions of the HMS LINCS website and database and its integration with the UCSC Genome Browser. In Community Interaction and Outreach, Aim 8 will implement diverse training and outreach activities, including collaboration with LINCS and non-LINCS research groups. In Administration, Aim 9 will ensure effective management of the Center, sustained access to tools and data produced within the LINCS Project, and compliance with program goals.
Disease networks are complex interactions between multiple proteins. Precise understanding of the multifactorial perturbations imposed on multi-component networks is a crucial step in the development of a rational approach to drug discovery and personalization of therapy. The LINCS Center we propose will develop innovative computational and experimental methods for understanding cellular perturbagen-response networks and will make the resulting data and new knowledge accessible to the diverse community of molecular and computational biologists, medicinal chemists and clinical investigators through outreach.
|Lee, John K; Phillips, John W; Smith, Bryan A et al. (2016) N-Myc Drives Neuroendocrine Prostate Cancer Initiated from Human Prostate Epithelial Cells. Cancer Cell 29:536-47|
|Sokolov, Artem; Paull, Evan O; Stuart, Joshua M (2016) ONE-CLASS DETECTION OF CELL STATES IN TUMOR SUBTYPES. Pac Symp Biocomput 21:405-16|
|Sokolov, Artem; Carlin, Daniel E; Paull, Evan O et al. (2016) Pathway-Based Genomics Prediction using Generalized Elastic Net. PLoS Comput Biol 12:e1004790|
|Shi, Tujin; Niepel, Mario; McDermott, Jason E et al. (2016) Conservation of protein abundance patterns reveals the regulatory architecture of the EGFR-MAPK pathway. Sci Signal 9:rs6|
|Jones, Douglas S; Jenney, Anne P; Swantek, Jennifer L et al. (2016) Profiling drugs for rheumatoid arthritis that inhibit synovial fibroblast activation. Nat Chem Biol :|
|Hafner, Marc; Niepel, Mario; Chung, Mirra et al. (2016) Growth rate inhibition metrics correct for confounders in measuring sensitivity to cancer drugs. Nat Methods 13:521-7|
|Zhang, Jinwei; Gao, Geng; Begum, Gulnaz et al. (2016) Functional kinomics establishes a critical node of volume-sensitive cation-Cl(-) cotransporter regulation in the mammalian brain. Sci Rep 6:35986|
|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|
|Lin, Jia-Ren; Fallahi-Sichani, Mohammad; Sorger, Peter K (2015) Highly multiplexed imaging of single cells using a high-throughput cyclic immunofluorescence method. Nat Commun 6:8390|
|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|