Biology subjects

Parsons, H. A.

Publications and source records attributed to Parsons, H. A..

5 recordsLinked to original sources

bioRxiv2026

A pan-cohort transcriptional landscape of breast cancer maps subtype and microenvironmental programs

Breast cancer comprises heterogeneous transcriptional states that are incompletely captured by discrete clinical or molecular subtype labels. To visualize this heterogeneity in a unified framework, we integrated bulk RNA-seq data from 2,284 patient samples across 13 studies using 18,089 protein coding genes, a harmonized processing pipeline, batch correction, consensus clustering and PaCMAP dimensionality reduction to construct an interactive breast cancer transcriptional landscape. Consensus clustering identified five major regions, which were annotated using PAM50 scores calculated for each sample: Luminal A, Luminal B, HER2 enriched, and two basal associated clusters. The basal clusters separated into an immune rich region marked by T cell-inflamed, tumor-associated macrophages (TAM), and low-purity signatures, and a cell-cycle driven region enriched for proliferation and DNA replication programs. Overlay of marker genes, pathways, kinases, neuronal like signaling programs, cancer associated fibroblasts (CAF) states, and TAM programs revealed spatially organized subtype biology and microenvironmental heterogeneity. Finally, projection of therapy associated resistance signatures identified landscape regions linked to predicted resistance to HER2-targeted therapy and hormone receptor directed endocrine therapies. By enabling interactive exploration of transcriptional states, marker genes, pathways, and therapeutic response programs, this resource provides a community framework for biomarker discovery in breast cancer.

bioinformatics↗

bioRxiv2025

MEDUSA: Maintaining Entire DNA Duplexes for Utmost Sequencing Accuracy

Highly accurate DNA sequencing is essential for many applications. Duplex sequencing-based methods achieve unparalleled accuracy by requiring reads from both strands of the original DNA duplex to match. Yet, methods to prepare dsDNA for sequencing may resynthesize portions of each DNA duplex and cause base damage errors on one strand to become indistinguishable from true mutations on both strands. Here, we report MEDUSA (Maintaining Entire DNA Duplexes for Utmost Sequencing Accuracy) which minimizes dsDNA resynthesis to maximize duplex sequencing accuracy and yield. MEDUSA carefully repairs and blunts fragmented dsDNA, then employs apyrase to digest residual dNTPs, followed by restricted dA-tailing to prevent resynthesis. MEDUSA affords full genome coverage in a simplified protocol that is broadly compatible with dsDNA fragmentation and library preparation kits. We benchmarked MEDUSA on sheared genomic DNA and cell-free DNA and found a residual SNV frequency within a median 1.23-fold (range 0.92-1.88; p < 0.001) of what was expected if resynthesis was mostly blocked, but with full genome coverage and duplex yields within a median 1.02-fold (range 0.33 - 1.46; p = 0.258) of traditional methods that do not limit resynthesis. In all, MEDUSA could enable high breadth or depth of duplex sequencing while limiting false discovery. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=111 SRC="FIGDIR/small/687202v1_ufig1.gif" ALT="Figure 1"> View larger version (12K): org.highwire.dtl.DTLVardef@1ddbc77org.highwire.dtl.DTLVardef@802ca9org.highwire.dtl.DTLVardef@f443ecorg.highwire.dtl.DTLVardef@974bdb_HPS_FORMAT_FIGEXP M_FIG C_FIG

genomics↗

bioRxiv2024

DirectHRD enables sensitive scar-based classification of homologous recombination deficiency (HRD)

Homologous recombination deficiency (HRD) is a predictive biomarker for efficacy of PARP inhibition and platinum chemotherapy but remains challenging to detect from low tumor fraction samples such as liquid biopsies. Here, we describe DirectHRD, a whole-genome sequencing (WGS) scar-based classifier that is 10x more sensitive than state-of-the-art methods. DirectHRD can detect HRD at >=1% tumor fraction using 50x WGS of cell-free DNA.

bioinformatics↗

bioRxiv2024

FinaleMe: Predicting DNA methylation by the fragmentation patterns of plasma cell-free DNA

Analysis of DNA methylation in cell-free DNA (cfDNA) reveals clinically relevant biomarkers but requires specialized protocols and sufficient input material that limits its applicability. Millions of cfDNA samples have been profiled by genomic sequencing. To maximize the gene regulation information from the existing dataset, we developed FinaleMe, a non-homogeneous Hidden Markov Model (HMM), to predict DNA methylation of cfDNA and, therefore, tissues-of-origin directly from plasma whole-genome sequencing (WGS). We validated the performance with 80 pairs of deep and shallow-coverage WGS and whole-genome bisulfite sequencing (WGBS) data.

bioinformatics↗

bioRxiv2021

MAESTRO affords 'breadth and depth' for mutation testing

The ability to assay large numbers of low-abundance mutations is crucial in biomedicine. Yet, the technical hurdles of sequencing multiple mutations at extremely high depth and accuracy remain daunting. For sequencing low-level mutations, its either depth or breadth but not both. Here, we report a simple and powerful approach to accurately track thousands of distinct mutations with minimal reads. Our technique called MAESTRO (minor allele enriched sequencing through recognition oligonucleotides) employs massively-parallel mutation enrichment to empower duplex sequencing--one of the most accurate methods--to track up to 10,000 low-frequency mutations with up to 100-fold less sequencing. In example use cases, we show that MAESTRO could enable mutation validation from cancer genome sequencing studies. We also show that it could track thousands of mutations from a patients tumor in cell-free DNA, which may improve detection of minimal residual disease from liquid biopsies. In all, MAESTRO improves the breadth, depth, accuracy, and efficiency of mutation testing.

molecular biology↗