The overall goal of the Mutographs project is to advance our understanding of what causes cancer. We will do this by identifying what factors are creating the distinctive DNA signatures that are associated with cancer a. In order to achieve this goal, our consortium is collecting 5,000 cancer samples from more than 25 countries around the world, developing a next-generation of machine learning approaches for analyzing these data, and performing extensive validation using multiple synergistic model systems.

All cancers originate from a single cell that undergoes a transformation from a normally functioning somatic cell into a malignant neoplasm. In most cases, this transformation follows a stepwise process with the somatic cell first expanding into a precancer and, subsequently, becoming an advanced invasive cancer. In this proposal, we are providing a comprehensive genomic characterization of 2,561 samples from 37 types of precancer found in 21 distinct tissues. The analysis will reveal the mutational burden, driver mutations, copy number changes, mutational signatures, and subclonal architecture of pre-malignant lesions and compare them to molecular events previously identified in advanced invasive cancers.

Our Lab has developed and maintains the SigProfiler suite of computational machine learning tools for analysis of next-generation generation cancer sequencing data. SigProfiler tools allow researchers to analyze their genomics data and gain a predictive-understanding of the basic molecular processes that contribute to cancer initiation and cancer evolution. These tools are designed to work seamlessly together and can also be used individually for specialized types of analyses. To learn more about each of the tools, please visit our Lab's GitHub repository.