Tri-Institutional Faculty



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Chris Sander, Ph.D.

Tri-Institutional Professor
Memorial Sloan-Kettering Cancer Center
Program Chairman and Director, Computational Biology Center
sanderc@mskcc.org

Dr. Sander combines theory and experiment to develop predictive models of biological systems and build tools that translate genomic data into biomedical knowledge and practice.

Lab members and their collaborators infer quantitative models of signaling in normal or cancer cells by performing combinatorial perturbation experiments with rich molecular readout using perturbants, such as targeted drugs, and then optimizing predictive accuracy and model simplicity. The resulting computational models can be used to design combinatorial interventions for investigational or therapeutic purposes, identify novel drug synergies, discover the specificity spectrum of drugs or redesign cellular circuits for synthetic biology.

To discover molecular processes characteristic of cancer subtypes and indicative of prognosis and response to therapy, lab members use high throughput data on genome structure, genetic and epigenetic variation and gene expression from massively parallel sequencing and proteomic technologies. They map these molecular profiles to pathway and interaction networks for analysis. The group actively participates in The Cancer Genome Atlas project and the International Cancer Genome Consortium, which is delivering, in tremendous detail, information on the molecular characteristics of human cancers. When combined with clinical information, patient genomics becomes a powerful basis for the design of clinical trials and personalized choice of therapy. 

In protein science, Dr. Sander and his collaborators have developed a powerful approach, adapted from statistical physics, to calculate evolutionary couplings between amino acid residues in a protein family. The couplings can be used to identify functionally constrained residue interactions and, using distance geometry and simulated annealing, to predict the three-dimensional structure of proteins and protein complexes (www.EVfold.org) to unprecedented accuracy. A new theory of evolutionary couplings based on this work is currently under development.

SELECTED PUBLICATIONS

  • Rajasethupathy, P. et al. Characterization of small RNAs in Aplysia reveals a role for miR-124 in constraining synaptic plasticity through CREB. Neuron 63, 803–817 (2009).
  • Chitale, D. et al. An integrated genomic analysis of lung cancer reveals loss of DUSP4 in EGFR-mutant tumors. Oncogene 28, 2773–2783 (2009).
  • Nelander, S. et al. Models from experiments: Combinatorial drug perturbations of cancer cells. Mol. Syst. Biol. 4, 216 (2008).