Pathway, Network and Spatiotemporal Integration of Cancer Genomics Data

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PROJECT SUMMARY Cancer sequencing projects have demonstrated that tumors are tremendously heterogeneous, reflecting the large diversity of perturbations in the cellular machinery that promote tumor growth and metastasis. Tumors from different patients with the same type of cancer have a diverse collection of genomic, epigenomic, and transcriptomic aberrations. Moreover, a tumor from a single patient is a mixture of cell types including normal cells, stroma, and multiple subpopulations of cancerous cells. We propose a Genome Data Analysis Center (GDAC) that will develop and apply novel computational approaches to address the challenges of inter-tumor and intra-tumor heterogeneity. This GDAC will integrate data from multiple genome characterization platforms, multiple sequencing technologies -- including bulk, single-cell, and spatial sequencing technologies – and leverage prior knowledge of pathways and interaction networks to explain clinical phenotypes and inform treatment strategies. The GDAC will perform pathway and network integration of genome characterization data, spatial analysis of tumor microenvironment, and temporal analysis of intra-tumor heterogeneity, tumor evolution, and network rewiring. These analyses will enable more precise translation of cancer genome characterization efforts into clinical utility for a larger fraction of cancer patients. The GDAC will continue the ongoing contributions of the PIs to the current Genome Data Analysis Network (GDAN) and previous efforts in The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) projects.