Featuring Computational and Systems Biology Program at Memorial Sloan Kettering Cancer Center, Sloan Kettering Institute (SKI), The Dana Pe’er Lab

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Featuring Computational and Systems Biology Program at Memorial Sloan Kettering Cancer Center, Sloan Kettering Institute (SKI), The Dana Pe’er Lab

June 16, 2019 by 2012pharmaceutical

Featuring Computational and Systems Biology Program at Memorial Sloan Kettering Cancer Center, Sloan Kettering Institute (SKI), The Dana Pe’er Lab

Reporter: Aviva Lev-Ari, PhD, RN

4.2.2 Featuring Computational and Systems Biology Program at Memorial Sloan Kettering Cancer Center, Sloan Kettering Institute (SKI), The Dana Pe’er Lab, Volume 2 (Volume Two: Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS and BioInformatics, Simulations and the Genome Ontology), Part 4: Single Cell Genomics

A lecture by Dana Pe’er is included, below in the eProceedings which I generated in Real Time on 6/14/2019 @MIT

eProceeding 2019 Koch Institute Symposium – 18th Annual Cancer Research Symposium – Machine Learning and Cancer, June 14, 2019, 8:00 AM-5:00 PM ET MIT Kresge Auditorium, 48 Massachusetts Ave, Cambridge, MA

https://pharmaceuticalintelligence.com/2019/03/12/2019-koch-institute-symposium-machine-learning-and-cancer-june-14-2019-800-am-500-pmet-mit-kresge-auditorium-48-massachusetts-ave-cambridge-ma/

Memorial Sloan Kettering Cancer Center, Sloan Kettering Institute (SKI

https://www.mskcc.org/research/ski/about

Cancer Biology & Genetics Program

Our scientists study the molecular and genetic determinants of cancer predisposition, tumor development, and metastasis.

Cell Biology Program

Our researchers explore the molecular mechanisms that control normal cell behavior and how these mechanisms are disrupted in cancer.

Chemical Biology Program

Our scientists use chemical principles to investigate cutting-edge topics in biology and medicine.

Computational & Systems Biology Program

The goal of our research is to build computer models that simulate biological processes, from the molecular level up to the organism as a whole.

Developmental Biology Program

Our investigators study the mechanisms that control cell proliferation, cell differentiation, tissue patterning, and tissue morphogenesis.

Immunology Program

Our research is geared toward understanding how the immune system functions in all its complexity and how it can be harnessed to fight disease.

Molecular Biology Program

Our research is directed at understanding how cell growth is regulated and how the integrity of the genome is maintained.

Molecular Pharmacology Program

Our research program serves as a conduit for bringing basic science discoveries to preclinical and clinical evaluation.

Structural Biology Program

Our researchers are dedicated to understanding biology at the structural and mechanistic levels, and aiding the development of new cancer therapies.

The Pe’er lab combines single cell technologies, genomic datasets and machine learning algorithms to address fundamental questions in biomedical science. Empowered by recent breakthrough technologies like massive parallel single cell RNA-sequencing, we ask questions such as: How do multi-cellular organisms develop from a single cell, resulting in the vast diversity of progenitor and terminal cell types? How does a cell’s regulatory circuit control the dynamics of signal processing and how do these circuits rewire over the course of development? How does an ensemble of cells function together to execute a multi-cellular response, such as an immune response to pathogen or cancer? We will also address more medically oriented questions such as: How do regulatory circuits go awry in disease? What is the consequence of intra-tumor heterogeneity? Can we characterize the tumor immune eco-system to gain a better understanding of when or why immunotherapy works or does not work? A key goal is to use this characterization of the tumor immune eco-system to personalize immunotherapy.

Dana Pe’er, PhD

Chair, Computational and Systems Biology Program, SKI; Scientific Director, Metastasis & Tumor Ecosystems Center

Research Focus

Computational Biologist Dana Pe’er combines single cell technologies, genomic datasets and machine learning techniques to address fundamental questions addressing regulatory cell circuits, cellular development, tumor immune eco-system, genotype to phenotype relations and precision medicine.

Education

PhD, Hebrew University, Jerusalem Israel

Kathleen Sadowski (admin) 646-888-2277
Kathleen Sadowski sadowskk@mskcc.org
Dry Lab Phone 646-888-3486
Wet Lab Phone 646-888-2319

View a full listing of Dana Pe’er’s journal articles.

Palantir characterizes cell fate continuities in human hematopoiesis. Setty M, Kiseliovas V, Levine J, Gayoso A, Mazutis L, Pe’er D. 2019, in press. Nature Biotechnology.

Single-cell map of diverse immune phenotypes in the breast tumor microenvironment. Azizi E, Carr AJ, Plitas G, Cornish AE, Konopacki C, Prabhakaran S, Nainys J, Wu K, Kiseliovas V, Setty M, Choi K, Fromme RM, Dao P, McKenney PT, Wasti RC, Kadaveru K, Mazutis L, Rudensky AY, Pe’er D. Cell. 2018 Aug 23;174(5):1293-1308.e36. doi: 10.1016/j.cell.2018.05.060. PMID: 29961579

Recovering gene interactions from single-cell data using data diffusion. van Dijk D, Sharma R, Nainys J, Yim K, Kathail P, Carr AJ, Burdziak C, Moon KR, Chaffer CL, Pattabiraman D, Bierie B, Mazutis L, Wolf G, Krishnaswamy S, Pe’er D. Cell. 2018 Jul 26;174(3):716-729.e27. doi: 10.1016/j.cell.2018.05.061. PubMed PMID: 29961576

The Human Cell Atlas. Regev A et al. Elife. 2017 Dec 5;6. pii: e27041. doi: 10.7554/eLife.27041. PubMed PMID: 29206104

Distinct cellular mechanisms underlie anti-CTLA-4 and anti-PD-1 checkpoint blockade. Wei SC, Levine JH, Cogdill AP, Zhao Y, Anang NAS, Andrews MC, Sharma P, Wang J, Wargo JA, Pe’er D, Allison JP. Cell. 2017 Sep 7;170(6):1120-1133.e17. doi: 10.1016/j.cell.2017.07.024. PMID: 28803728

Wishbone identifies bifurcating developmental trajectories from single-cell data. Setty M, Tadmor MD, Reich-Zeliger S, Angel O, Salame TM, Kathail P, Choi K, Bendall S, Friedman N, Pe’er D. Nat Biotechnol. 2016 Jun;34(6):637-45. doi: 10.1038/nbt.3569. PMID: 27136076

Data-driven phenotypic dissection of AML reveals progenitor-like cells that correlate with prognosis. Levine JH, Simonds EF, Bendall SC, Davis KL, Amir el-AD, Tadmor MD, Litvin O, Fienberg HG, Jager A, Zunder ER, Finck R, Gedman AL, Radtke I, Downing JR, Pe’er D, Nolan GP. Cell. 2015 Jul 2;162(1):184-97. doi: 10.1016/j.cell.2015.05.047. PMID: 26095251

Interferon α/β enhances the cytotoxic response of MEK inhibition in melanoma. Litvin O, Schwartz S, Wan Z, Schild T, Rocco M, Oh NL, Chen BJ, Goddard N, Pratilas C, Pe’er D. Mol Cell. 2015 Mar 5;57(5):784-796. doi: 10.1016/j.molcel.2014.12.030. PMID: 25684207

Integration of genomic data enables selective discovery of breast cancer drivers. Sanchez-Garcia F, Villagrasa P, Matsui J, Kotliar D, Castro V, Akavia UD, Chen BJ, Saucedo-Cuevas L, Rodriguez Barrueco R, Llobet-Navas D, Silva JM, Pe’er D. Cell. 2014 Dec 4;159(6):1461-75. doi: 10.1016/j.cell.2014.10.048. PMID: 25433701

Conditional density-based analysis of T cell signaling in single-cell data. Krishnaswamy S, Spitzer MH, Mingueneau M, Bendall SC, Litvin O, Stone E, Pe’er D, Nolan GP. Systems biology. Science. 2014 Nov 28;346(6213):1250689. doi: 10.1126/science.1250689. PMID: 25342659

Single-cell trajectory detection uncovers progression and regulatory coordination in human B cell development. Bendall SC, Davis KL, Amir el-AD, Tadmor MD, Simonds EF, Chen TJ, Shenfeld DK, Nolan GP, Pe’er D. Cell. 2014 Apr 24;157(3):714-25. doi: 10.1016/j.cell.2014.04.005. PMID: 24766814

SOURCE

https://www.mskcc.org/research/ski/labs/dana-pe-er/publications

Computational biologists combine findings in biology with computer algorithms and databases to conduct biological research on powerful computers, using sophisticated software — so-called “dry” laboratories — in ways that complement and strengthen traditional laboratory and clinical research. The aim is to build computer models that simulate biological processes from the molecular level up to the organism as a whole and to use these models to make useful predictions.

Computational biology can help interpret detailed molecular profiles of cancerous and noncancerous cells, molecular response profiles of therapeutic agents, and a person’s genetic profile to assist in the development of better diagnostics and prognostics, as well as improved therapies. Intelligent use of computational methods using detailed molecular and genomic data is expected to reduce the trial and error of drug development and possibly lead to shorter, more accurate clinical trials.