TITLE: “Efficient, Multi Sample Inference of Cancer Phylogeny.”
ABSTRACT: Cancer is characterized by the ongoing accumulation of somatic mutations that may lead to dysregulated cellular proliferation. The selection of advantageous mutations leads to clonal expansions of progressively more aberrant and fit cancer cells. Reconstructing the evolutionary history of a tumor allows us to understand key events in disease progression and mechanisms that lead to disease lethality. Sequencing multiple tumor samples provide an opportunity to study tumor evolution in much greater detail and accuracy than was previously feasible through single-sample datasets. However, current reconstruction methods utilize stochastic-search algorithms, which iterates through a parameter space to jointly infer clone populations and their phylogeny. As tumor phylogenetic topology increases in complexity, the parameter space grows exponentially, and stochastic-search algorithms become computationally intractable. To circumvent current computational limitations, we developed a heuristic-based algorithm for subclonal reconstruction that leverages fundamental principles of cancer biology to encode heuristics that reduce the solution space to biologically plausible phylogenies. Benchmarking on real and simulated datasets are ongoing, and preliminary results indicate a ten-fold reduction in runtime. We have thus presented a novel method for rapid and optimized reconstruction of tumor evolutionary histories from multi-sample datasets.
