Biology subjects

Hess, J. M.

Publications and source records attributed to Hess, J. M..

4 recordsLinked to original sources

bioRxiv2025

SVelfie: A method for discovering cancer drivers based on enrichment of likely functional structural variants

Structural variants (SVs) can drive tumorigenesis, yet discovering SV cancer drivers remains challenging1. Here, we present SVelfie (Structural Variants enriched with likely functional/impactful events), a statistical method to infer driver genes from SVs detected across a cohort of cancer genomes, based on enrichment of likely functional events. When testing SVelfie on lymphoma samples from the Pan-Cancer Analysis of Whole Genomes (PCAWG)2, it corroborates known tumor suppressor genes and also yields novel driver candidates.

bioinformatics↗

bioRxiv2021

MIAAIM: Multi-omics image integration and tissue state mapping using topological data analysis and cobordism learning

High-parameter tissue imaging enables detailed molecular analysis of single cells in their spatial environment. However, the comprehensive characterization and mapping of tissue states through multimodal imaging across different physiological and pathological conditions requires data integration across multiple imaging systems. Here, we introduce MIAAIM (Multi-omics Image Alignment and Analysis by Information Manifolds) a modular, reproducible computational framework for aligning data across bioimaging technologies, modeling continuities in tissue states, and translating multimodal measures across tissue types. We demonstrate MIAAIMs workflows across diverse imaging platforms, including histological stains, imaging mass cytometry, and mass spectrometry imaging, to link cellular phenotypic states with molecular microenvironments in clinical biopsies from multiple tissue types with high cellular complexity. MIAAIM provides a robust foundation for the development of computational methods to integrate multimodal, high-parameter tissue imaging data and enable downstream computational and statistical interrogation of tissue states.

systems biology↗

bioRxiv2019

Passenger Hotspot Mutations in Cancer

Hotspots, or mutations that recur at the same genomic site across multiple tumors, have been conventionally interpreted as strong universal evidence of somatic positive selection, unequivocally pinpointing genes driving tumorigenesis. Here, we demonstrate that this convention is falsely premised on an inaccurate statistical model of background mutagenesis. Many hotspots are in fact passenger events, recurring at sites that are simply inherently more mutable rather than under positive selection, which current background models do not account for. We thus detail a log-normal-Poisson (LNP) background model that accounts for variation in site-specific mutability in a manner consistent with models of mutagenesis, use this model to show that the tendency to generate passenger hotspots pervades all common mutational processes, and apply it to a ~10, 000 patient cohort from The Cancer Genome Atlas to nominate driver hotspots with far fewer false positives compared to conventional methods. As the biomedical community faces critical decisions in prioritizing putative driver mutations for deep experimental characterization to assess therapeutic potential, we offer our findings as a guide to avoid wasting valuable scientific resources on passenger hotspots.

genomics↗

bioRxiv2017

Discovery and characterization of coding and non-coding driver mutations in more than 2,500 whole cancer genomes

Discovery of cancer drivers has traditionally focused on the identification of protein-coding genes. Here we present a comprehensive analysis of putative cancer driver mutations in both protein-coding and non-coding genomic regions across >2,500 whole cancer genomes from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium. We developed a statistically rigorous strategy for combining significance levels from multiple driver discovery methods and demonstrate that the integrated results overcome limitations of individual methods. We combined this strategy with careful filtering and applied it to protein-coding genes, promoters, untranslated regions (UTRs), distal enhancers and non-coding RNAs. These analyses redefine the landscape of non-coding driver mutations in cancer genomes, confirming a few previously reported elements and raising doubts about others, while identifying novel candidate elements across 27 cancer types. Novel recurrent events were found in the promoters or 5UTRs of TP53, RFTN1, RNF34, and MTG2, in the 3UTRs of NFKBIZ and TOB1, and in the non-coding RNA RMRP. We provide evidence that the previously reported non-coding RNAs NEAT1 and MALAT1 may be subject to a localized mutational process. Perhaps the most striking finding is the relative paucity of point mutations driving cancer in non-coding genes and regulatory elements. Though we have limited power to discover infrequent non-coding drivers in individual cohorts, combined analysis of promoters of known cancer genes show little excess of mutations beyond TERT.

genomics↗