Biology subjects

Martinez-Fundichely, A.

Publications and source records attributed to Martinez-Fundichely, A..

6 recordsLinked to original sources

bioRxiv2025

cfOncoXpress: Tumor gene expression prediction from cell-free DNA whole-genome sequences

Cell-free DNA (cfDNA) fragments in the plasma capture cellular nucleosomal profiles since nucleosome-protected regions escape enzymatic degradation while nucleosome-depleted regions can not. We developed cfOncoXpress, a machine learning framework that uses fragmentation patterns to predict oncogene expression from cfDNA WGS. cfOncoXpress incorporates gene copy number aberrations inferred from cfDNA, including those associated with extrachromosomal DNA. Its application in prostate and breast cancers shows it can predict tumor subtype based on expression of signature genes and activated pathways. cfOncoXpress shows superior performance relative to other state-of-the-art methods and can be used to predict tumor gene expression when tissue biopsies are infeasible.

genomics↗

bioRxiv2024

Three-dimensional regulatory hubs support oncogenic programs in glioblastoma

Dysregulation of enhancer-promoter communication in the context of the three-dimensional (3D) nucleus is increasingly recognized as a potential driver of oncogenic programs. Here, we profiled the 3D enhancer-promoter networks of primary patient-derived glioblastoma stem cells (GSCs) in comparison with neuronal stem cells (NSCs) to identify potential central nodes and vulnerabilities in the regulatory logic of this devastating cancer. Specifically, we focused on hyperconnected 3D regulatory hubs and demonstrated that hub-interacting genes exhibit high and coordinated expression at the single-cell level and strong association with oncogenic programs that distinguish IDH-wt glioblastoma patients from low-grade glioma. Epigenetic silencing of a recurrent 3D enhancer hub--with an uncharacterized role in glioblastoma--was sufficient to cause concordant downregulation of multiple hub-connected genes along with significant shifts in transcriptional states and reduced clonogenicity. By integrating published datasets from other cancer types, we also identified both universal and cancer type-specific 3D regulatory hubs which enrich for varying oncogenic programs and nominate specific factors associated with worse outcomes. Genetic alterations, such as focal duplications, could explain only a small fraction of the detected hyperconnected hubs and their increased activity. Overall, our study provides computational and experimental support for the potential central role of 3D regulatory hubs in controlling oncogenic programs and properties. HIGHLIGHTS- 3D regulatory "hubs" in glioblastoma enrich for highly coregulated genes at a single-cell level and expand oncogenic regulatory networks. - Targeted perturbation of a highly recurrent 3D regulatory hub in GSCs results in altered transcriptional states and cellular properties. - 3D regulatory hubs across cancer types associate with tumor-specific and universal oncogenic programs and worse outcomes. - The majority of hyperconnected hubs do not overlap with structural variants, suggesting epigenetic mechanisms. eTOCHere we profile the 3D enhancer connectomes of primary patient-derived human glioblastoma stem cells (GSCs), identify hyperconnected 3D regulatory "hubs", and examine the impact of 3D hub perturbation on the transcriptional program and oncogenic properties.

genomics↗

bioRxiv2021

Modeling tissue-specific breakpoint proximity of structural variations from 2,382 whole-genomes to identify cancer drivers

Structural variations (SVs) in cancer cells often impact large genomic regions with functional consequences. However, little is known about the genomic features related to the breakpoint distribution of SVs in different cancers, a prerequisite to distinguish loci under positive selection from those with neutral evolution. We developed a method that uses a generalized additive model to investigate the breakpoint proximity curves from 2,382 whole-genomes of 32 cancer types. We find that a multivariate model, which includes linear and nonlinear partial contributions of various tissue-specific features and their interaction terms, can explain up to 57% of the observed deviance of breakpoint proximity. In particular, three-dimensional genomic features such as topologically associating domains (TADs), TAD-boundaries and their interaction with other features show significant contributions. The model is validated by identification of known cancer genes and revealed putative drivers in novel cancers that have previous evidence of therapeutic relevance in other cancers.

cancer biology↗

bioRxiv2021

Systems Biology Analysis of Human Genomes Points to Key Pathways Conferring Spina Bifida Risk

Spina bifida (SB) is a debilitating birth defect caused by multiple gene and environment interactions. Though SB shows non-Mendelian inheritance, genetic factors contribute to an estimated 70% of cases. Nevertheless, identifying human mutations conferring SB risk is challenging due to its relative rarity, genetic heterogeneity, incomplete penetrance and environmental influences that hamper GWAS approaches to untargeted discovery. Thus, SB genetic studies may suffer from population substructure and/or selection bias introduced by typical candidate gene searches. We report a population based, ancestry-matched whole-genome sequence analysis of SB genetic predisposition using a systems biology strategy to interrogate 298 case-control subject genomes (149 pairs). Genes that were enriched in likely gene disrupting (LGD), rare protein-coding variants were subjected to machine learning analysis to identify genes in which LGD variants occur with a different frequency in cases vs. controls and so discriminate between these groups. Those genes with high discriminatory potential for SB significantly enriched pathways pertaining to carbon metabolism, inflammation, innate immunity, cytoskeletal regulation and essential transcriptional regulation, indicating their impact on the pathogenesis of human SB. Additionally, interrogation of conserved non-coding sequences identified robust variant enrichment in regulatory regions of several transcription factors critical to embryonic development. This genome-wide perspective offers an effective approach to interrogation of coding and non-coding sequence variant contributions to rare complex genetic disorders.

genomics↗

bioRxiv2020

CNCDatabase: a database of non-coding cancer drivers

Most mutations in cancer genomes occur in the non-coding regions with unknown impact to tumor development. Although the increase in number of cancer whole-genome sequences has revealed numerous putative non-coding cancer drivers, their information is dispersed across multiple studies and thus it is difficult to bridge the understanding of non-coding alterations, the genes they impact and the supporting evidence for their role in tumorigenesis across multiple cancer types. To address this gap, we have developed CNCDatabase, Cornell Non-Coding Cancer driver Database (https://cncdatabase.med.cornell.edu/) that contains detailed information about predicted non-coding drivers at gene promoters, 5 and 3 UTRs (untranslated regions), enhancers, CTCF insulators and non-coding RNAs. CNCDatabase documents 1,111 protein-coding genes and 90 non-coding RNAs with reported drivers in their non-coding regions from 32 cancer types by computational predictions of positive selection in whole-genome sequences; differential gene expression in samples with and without mutations; or another set of experimental validations including luciferase reporter assays and genome editing. The database can be easily modified and scaled as lists of non-coding drivers are revised in the community with larger whole-genome sequencing studies, CRISPR screens and further experimental validations. Overall, CNCDatabase provides a helpful resource for researchers to explore the pathological role of non-coding alterations and their associations with gene expression in human cancers.

cancer biology↗

bioRxiv2018

Passenger mutations in 2500 cancer genomes: Overall molecular functional impact and consequences

The Pan-cancer Analysis of Whole Genomes (PCAWG) project provides an unprecedented opportunity to comprehensively characterize a vast set of uniformly annotated coding and non-coding mutations present in thousands of cancer genomes. Classical models of cancer progression posit that only a small number of these mutations strongly drive tumor progression and that the remaining ones (termed \"putative passengers\") are inconsequential for tumorigenesis. In this study, we leveraged the comprehensive variant data from PCAWG to ascertain the molecular functional impact of each variant. The impact distribution of PCAWG mutations shows that, in addition to high- and low-impact mutations, there is a group of medium-impact putative passengers predicted to influence gene activity. Moreover, the predicted impact relates to the underlying mutational signature: different signatures confer divergent impact, differentially affecting distinct regulatory subsystems and gene categories. We also find that impact varies based on subclonal architecture (i.e., early vs. late mutations) and can be related to patient survival. Finally, we note that insufficient power due to limited cohort sizes precludes identification of weak drivers using standard recurrence-based approaches. To address this, we adapted an additive effects model derived from complex trait studies to show that aggregating the impact of putative passenger variants (i.e. including yet undetected weak drivers) provides significant predictability for cancer phenotypes beyond the PCAWG identified driver mutations (12.5% additive variance). Furthermore, this framework allowed us to estimate the frequency of potential weak driver mutations in the subset of PCAWG samples lacking well-characterized driver alterations.

genomics↗