Untangling a complex web: Computational analyses of tumor molecular profiles to decode driver mechanisms

A Mouse-Specific Model to Detect Genes under Selection in Tumors

The mouse is a widely used model organism in cancer research. However, no computational methods exist to identify cancer driver genes in mice due to a lack of labeled training data. To address this knowledge gap, we adapted the GUST (Genes Under Selection in Tumors) model, originally trained on human exomes, to mouse exomes via transfer learning. The resulting tool, called GUST-mouse, can estimate long-term and short-term evolutionary selection in mouse tumors, and distinguish between oncogenes, tumor suppressor genes, and passenger genes using high-throughput sequencing data. We applied GUST-mouse to analyze 65 exomes of mouse primary breast cancer models and 17 exomes of mouse leukemia models. Comparing the predictions between cancer types and between human and mouse tumors revealed common and unique driver genes. The GUST-mouse method is available as an open-source R package on github.

Increased Cell Proliferation as a Key Event in Chemical Carcinogenesis: Application in an Integrated Approach for the Testing and Assessment of Non-Genotoxic Carcinogenesis

In contrast to genotoxic carcinogens, there are currently no internationally agreed upon regulatory tools for identifying non-genotoxic carcinogens of human relevance. The rodent cancer bioassay is only used in certain regulatory sectors and is criticized for its limited predictive power for human cancer risk. Cancer is due to genetic errors occurring in single cells. The risk of cancer is higher when there is an increase in the number of errors per replication (genotoxic agents) or in the number of replications (cell proliferation-inducing agents). The default regulatory approach for genotoxic agents whereby no threshold is set is reasonably conservative. However, non-genotoxic carcinogens cannot be regulated in the same way since increased cell proliferation has a clear threshold. An integrated approach for the testing and assessment (IATA) of non-genotoxic carcinogens is under development at the OECD, considering learnings from the regulatory assessment of data-rich substances such as agrochemicals. The aim is to achieve an endorsed IATA that predicts human cancer better than the rodent cancer bioassay, using methodologies that equally or better protect human health and are superior from the view of animal welfare/efficiency. This paper describes the technical opportunities available to assess cell proliferation as the central gateway of an IATA for non-genotoxic carcinogenicity.

SYSMut: decoding the functional significance of rare somatic mutations in cancer

Current tailored-therapy efforts in cancer are largely focused on a small number of highly recurrently mutated driver genes but therapeutic targeting of these oncogenes remains challenging. However, the vast number of genes mutated infrequently across cancers has received less attention, in part, due to a lack of understanding of their biological significance. We present SYSMut, an extendable systems biology platform that can robustly infer the biologic consequences of somatic mutations by integrating routine multiomics profiles in primary tumors. We establish SYSMut's improved performance vis-à-vis state-of-the-art driver gene identification methodologies by recapitulating the functional impact of known driver genes, while additionally identifying novel functionally impactful mutated genes across 29 cancers. Subsequent application of SYSMut on low-frequency gene mutations in head and neck squamous cell (HNSC) cancers, followed by molecular and pharmacogenetic validation, revealed the lipidogenic network as a novel therapeutic vulnerability in aggressive HNSC cancers. SYSMut is thus a robust scalable framework that enables the discovery of new targetable avenues in cancer.