Looking for (Genomic) Needles in a Haystack: Sparsity-Driven Search for Identifying Correlated Genetic Mutations in Cancer

TL;DR

This paper introduces P-DFS, a sparsity-exploiting algorithm that significantly accelerates the identification of multi-gene mutation combinations in cancer, enabling feasible analysis of higher-order hits.

Contribution

The paper presents P-DFS, a novel pruning algorithm leveraging data sparsity to efficiently search for correlated genetic mutations in cancer, reducing computational complexity dramatically.

Findings

Achieves 90-98% reduction in search space for 4-hit combinations.

Provides approximately 183x speedup over exhaustive search methods.

Handles higher-order gene hits efficiently on high-performance clusters.

Abstract

Cancer typically arises not from a single genetic mutation (i.e., hit) but from multi-hit combinations that accumulate within cells. However, enumerating multi-hit combinations becomes exponentially more expensive computationally as the number of candidate hit gene combinations grow, i.e. on the order of 20,000 choose h, where 20,000 is the number of genes in the human genome and h is the number of hits. To address this challenge, we present an algorithmic framework, called Pruned Depth-First Search (P-DFS) that leverages the high sparsity in tumor mutation data to prune large portions of the search space. Specifically, P-DFS (the main contribution of this paper) - a pruning technique that exploits sparsity to drastically reduce the otherwise exponential h-hit search space for candidate combinations used by Weighted Set Cover - which is grounded in a depth-first search backtracking technique, prunes infeasible gene subsets early, while a weighted set cover formulation systematically scores and selects the most discriminative combinations. By intertwining these ideas with optimized bitwise operations and a scalable distributed algorithm on high-performance computing clusters, our algorithm can achieve approximately 90 - 98% reduction in visited combinations for 4-hits, and roughly a 183x speedup over the exhaustive set cover approach(which is algorithmically NP-complete) measured on 147,456 ranks. In doing so, our method can feasibly handle four-hit and even higher-order gene hits, achieving both speed and resource efficiency.