Biology subjects

Krasnitz, A.

Publications and source records attributed to Krasnitz, A..

3 recordsLinked to original sources

bioRxiv2026

A versatile set of computational tools for inference of genetic ancestry from human molecular data

Molecular properties of human diseases and conditions often exhibit dependence on the genetic ancestry of the donor. In order to make more human molecular data available for ancestry-informed research, we developed a set of software tools termed Robust Ancestry Inference using Data Synthesis (RAIDS) for inference of genetic ancestry from sequence data that pose challenges to genotyping. Here we describe a recent expansion and refinement of RAIDS, applicable to nucleic-acid sequence data resultant from a broad range of experimental strategies. These strategies currently include whole-genome, whole-exome, and targeted gene panel sequencing, RNA-Seq, including single-cell RNA-Seq, chromatin-accessibility (ATAC-Seq), and protein-binding (ChIP-Seq) assays. RAIDS inferential tools perform robustly in the presence of somatic nucleic-acid alterations caused by cancer and under the conditions of extreme non-uniformity of sequence read coverage, yielding both global ancestry and ancestral admixtures of the donor. RAIDS is available as an eponymous Bioconductor package.

bioinformatics↗

bioRxiv2026

Comprehensive Molecular Characterization of High-Grade Endometrial Cancer in an Ancestrally-Diverse Cohort

Endometrial cancer (EC) exhibits one of the most striking racial disparities in oncology with black women disproportionately affected by aggressive high-grade subtypes that have poorer outcomes. While social and environmental factors undoubtedly contribute, the molecular underpinnings of these disparities remain critically understudied. To bridge this knowledge gap, we performed matched tumor-normal whole-genome sequencing and tumor transcriptome sequencing on 71 predominantly high-grade EC patient samples from an ancestrally diverse cohort of women recruited at a large hospital system in the New York metropolitan area. Our analysis characterized the germline and somatic mutation landscape, identifying ancestry-associated molecular differences. Notably, focal amplification of the EVI1 transcription factor (encoded at the MECOM locus) was significantly more frequent in African ancestry patients and associated with poorer clinical outcomes in an external validation cohort. Additionally transcriptome analysis revealed decreased CD8+ T cell infiltration with increasing African ancestry, suggesting tumor immune microenvironment differences with potential therapeutic implications. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=163 SRC="FIGDIR/small/721962v1_ufig1.gif" ALT="Figure 1"> View larger version (63K): org.highwire.dtl.DTLVardef@12125b9org.highwire.dtl.DTLVardef@133c787org.highwire.dtl.DTLVardef@707af0org.highwire.dtl.DTLVardef@97615c_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIThis study represents the most ancestrally diverse whole-genome sequencing characterization of high-grade endometrial cancer, with 62% of patients of African ancestry. C_LIO_LIMECOM focal amplification preferentially targets the oncogenic short isoform (EVI1) and is more frequent in patients of African ancestry. C_LIO_LIAfrican ancestry is associated with reduced CD8+ T cell infiltration and differential activation of immune and metabolic pathways in copy-number high endometrial tumors. C_LI

cancer biology↗

bioRxiv2022

Accurate and robust inference of genetic ancestry from cancer-derived molecular data across genomic platforms

Genetic ancestry-oriented cancer research requires the ability to perform accurate and robust genetic ancestry inference from existing cancer-derived data, including whole exomes, transcriptomes and targeted gene panels, very often in the absence of matching cancer-free genomic data. Here we examine the feasibility and accuracy of such computation. In order to optimize and assess the performance of the ancestry inference for any given input cancer-derived molecular profile, we have developed a data synthesis framework. In its core procedure, the ancestral background of the profiled patient is replaced with one of any number of individuals with known ancestry. Data synthesis is applicable to multiple profiling platforms and makes it possible to assess the performance of inference specifically for a given molecular profile, and separately for each continental-level ancestry. This ability extends to all ancestries, including those without statistically sufficient representation in the existing cancer data. We further show that our inference procedure is accurate and robust in a wide range of sequencing depths. Testing our approach for three representative cancer types, and across three molecular profiling modalities, we demonstrate that global, continental-level ancestry of the patient can be inferred with high accuracy, as quantified by its agreement with the golden standard of the ancestry derived from matching cancer-free molecular data. Our study demonstrates that vast amounts of existing cancer-derived molecular data potentially are amenable to ancestry-oriented studies of the disease, without recourse to matching cancer-free genomes or patients self-identification by ancestry.

bioinformatics↗