Biology subjects

Reyna, M. A.

Publications and source records attributed to Reyna, M. A..

4 recordsLinked to original sources

bioRxiv2026

MOSAIC: Model-based, Subgroup-Aware Identification of Driver Mutations in Cancer

In cancer genomics, recurrent patterns of mutual exclusivity within a gene set can indicate shared biological context and involvement in tumorigenesis. However, existing methods are not designed to distinguish between mutual exclusivity arising from meaningful biological interactions from those influenced by heterogeneity between underlying patient subpopulations. In this work, we introduce MOSAIC, a novel statistical framework that models patient subgroup heterogeneity in mutual exclusivity analyses. In experiments with simulated data and real data from The Cancer Genome Atlas, we show that MOSAIC amplifies subgroup-specific mutual exclusivity signals, including between IDH1 and IDH2 in young low grade glioma patients, while reducing the effect of signals produced by underlying subgroup structures, such as distinct genomic lineages associated with histological subtypes of endometrial cancer. Finally, we demonstrate that MOSAIC is more powerful than existing p-value combination methods for patient subgroup stratification. MOSAIC is available as an open-source tool at https://github.com/reynalab/mosaic.

bioinformatics↗

bioRxiv2021

Systematic discovery of mutation-directed neo-protein-protein interactions in cancer

Comprehensive sequencing of patient tumors reveals numerous genomic mutations across tumor types that enable tumorigenesis and progression. A subset of oncogenic driver mutations results in neomorphic activity where the mutant protein mediates functions not engaged by the parental molecule. Here, we identify prevalent variant-enabled neomorph-protein-protein interactions (neoPPI) with a quantitative High Throughput differential Screening (qHT-dS) platform. Coupling of highly sensitive BRET biosensors with miniaturized co-expression in an ultra-HTS format allows large-scale monitoring of interactions of wild-type and mutant variant counterparts with a library of cancer-associated proteins in live cells. Screening of 13,392 interactions with 1,474,560 data points revealed a landscape of gain-of-interactions encompassing both oncogenic and tumor suppressor mutations. For example, the recurrent BRAF V600E lesion mediates KEAP1 neoPPI, rewiring a BRAFV600E-KEAP1 signaling axis and creating collateral vulnerability to NQO1 substrates, offering a combination therapeutic strategy. Thus, cancer genomic alterations can create neo-interactions, informing variant-directed therapeutic approaches for precision medicine.

cancer biology↗

bioRxiv2020

NetMix: A network-structured mixture model for reduced-bias estimation of altered subnetworks

A classic problem in computational biology is the identification of altered subnetworks: subnetworks of an interaction network that contain genes/proteins that are differentially expressed, highly mutated, or otherwise aberrant compared to other genes/proteins. Numerous methods have been developed to solve this problem under various assumptions, but the statistical properties of these methods are often unknown. For example, some widely-used methods are reported to output very large subnetworks that are difficult to interpret biologically. In this work, we formulate the identification of altered subnetworks as the problem of estimating the parameters of a class of probability distributions which we call the Altered Subset Distribution (ASD). We derive a connection between a popular method, jActiveModules, and the maximum likelihood estimator (MLE) of the ASD. We show that the MLE is statistically biased, explaining the large subnetworks output by jActiveModules. We introduce NetMix, an algorithm that uses Gaussian mixture models to obtain less biased estimates of the parameters of the ASD. We demonstrate that NetMix outperforms existing methods in identifying altered subnetworks on both simulated and real data, including the identification of differentially expressed genes from both microarray and RNA-seq experiments and the identification of cancer driver genes in somatic mutation data. AvailabilityNetMix is available online at https://github.com/raphael-group/netmix. Contactbraphael@princeton.edu

bioinformatics↗

bioRxiv2018

Pathway and network analysis of more than 2,500 whole cancer genomes

The catalog of cancer driver mutations in protein-coding genes has greatly expanded in the past decade. However, non-coding cancer driver mutations are less well-characterized and only a handful of recurrent non-coding mutations, most notably TERT promoter mutations, have been reported. Motivated by the success of pathway and network analyses in prioritizing rare mutations in protein-coding genes, we performed multi-faceted pathway and network analyses of non-coding mutations across 2,583 whole cancer genomes from 27 tumor types compiled by the ICGC/TCGA PCAWG project. While few non-coding genomic elements were recurrently mutated in this cohort, we identified 93 genes harboring non-coding mutations that cluster into several modules of interacting proteins. Among these are promoter mutations associated with reduced mRNA expression in TP53, TLE4, and TCF4. We found that biological processes had variable proportions of coding and non-coding mutations, with chromatin remodeling and proliferation pathways altered primarily by coding mutations, while developmental pathways, including Wnt and Notch, altered by both coding and non-coding mutations. RNA splicing was primarily targeted by non-coding mutations in this cohort, with samples containing non-coding mutations exhibiting similar gene expression signatures as coding mutations in well-known RNA splicing factors. These analyses contribute a new repertoire of possible cancer genes and mechanisms that are altered by non-coding mutations and offer insights into additional cancer vulnerabilities that can be investigated for potential therapeutic treatments.

cancer biology↗