Comprehensive and Robust Tools for Analysis of Tumor Heterogeneity and Evolution

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Project Summary/Abstract In recent years, precision medicine approaches based on molecular changes in an individual patient’s tumor have become a promising strategy for diagnosis and treatment of cancer. These approaches are challenged by the fact that tumors are a heterogeneous collection of cells that change over time and in response to treatment. At the DNA sequence level, changes range in scale from single-nucleotide mutations to large chromosomal rearrangements and whole-genome duplications. New DNA/RNA sequencing technologies enable measurement of this heterogeneity and provide data to infer the evolutionary history of a tumor. However, the algorithms and software necessary to analyze the complexities of tumor heterogeneity and evolution remain limited in scope. We propose to develop a comprehensive software toolkit to analyze tumor heterogeneity and tumor evolution across space, time, and genomic scale. This toolkit will be based on advanced combinatorial and statistical algorithms developed by PI over the past several years. These algorithms will be unified into a robust, computationally efficient, and statistically sound software package. This toolkit will incorporate modules for different types of tumor samples including single tumor samples, multiple tumor regions, multiple anatomical sites (e.g. primary tumor and metastasis), and multiple time points. The software will also analyze data from different sequencing approaches (whole-genome, whole-exome, and targeted sequencing) and different sequencing technologies including bulk tumor, single-cell, short-read, and long-read. The software package will be open source and will be released to run on individual computers, computing clusters, or in cloud computing environments. Extensive documentation and training will be provided to facilitate use by a wide range of users from expert bioinformaticians to clinicians. These powerful data analytic tools will enable researchers to characterize the heterogeneity within tumors with high accuracy, enabling greater precision in cancer diagnosis and treatment.