# Bigpicc: a graph-based approach to identifying carcinogenic gene combinations from mutation data

## TL;DR

This paper introduces a new method to find gene mutation combinations that cause cancer, using a more efficient approach than traditional methods.

## Contribution

The novel contribution is a parameter-free heuristic approach for identifying carcinogenic gene combinations without exhaustive search.

## Key findings

- The method achieves 80.1% sensitivity and 91.6% specificity in predicting tumors for 16 cancer types.
- It identifies higher-hit carcinogenic combinations that would take years to find using exhaustive search.
- Classifiers built from the identified combinations perform as well as those from exhaustive search.

## Abstract

Genome data from cancer patients represents relationships between the presence of a gene mutation and cancer occurrence in a patient. Different types of cancer in human are thought to be caused by combinations of two to nine gene mutations. Identifying these combinations through traditional exhaustive search requires the amount of computation that scales exponentially with the combination size and in most cases is intractable even for cutting-edge supercomputers. We propose a parameter-free heuristic approach that leverages the intrinsic topology of gene-patient mutations to identify carcinogenic combinations. The biological relevance of the identified combinations is measured by using them to predict the presence of tumor in previously unseen samples. The resulting classifiers for 16 cancer types perform on par with exhaustive search results, and score the average of 80.1% sensitivity and 91.6% specificity for the best choice of hit range per cancer type. Our approach is able to find higher-hit carcinogenic combinations targeting which would take years of computations using exhaustive search.