Biology subjects

Klein, Z. P.

Publications and source records attributed to Klein, Z. P..

5 recordsLinked to original sources

bioRxiv2026

Therapy-associated mutagenesis at CTCF binding sites is shaped by chromatin context and DNA repair capacity

Genotoxic cancer therapies introduce DNA damage that can be fixed as somatic mutations in surviving tumor cells. However, the impact of therapy-associated mutagenesis on regulatory elements remains unclear. CTCF binding sites (CBS) are chromatin architectural elements that exhibit recurrent localized mutation enrichment in cancer genomes. We asked whether treatment exposure is associated with increased mutagenesis at CBS in 4,870 whole-genome sequences from metastatic tumors across 17 cancer types and 45 therapies. Radiotherapy and trifluridine exposure in metastatic colorectal cancer were associated with increased mutation enrichment at CBS. This enrichment was pronounced at motif-containing sites and in low-expression or late-replicating genomic contexts. Alterations in DNA damage response genes, including BRCA2, were associated with increased CBS mutation enrichment following radiotherapy. Together, these findings indicate that therapy-associated mutagenesis at CTCF binding sites is shaped by chromatin context and DNA repair capacity, extending the mutational consequences of cancer treatment to regulatory genome architecture.

cancer biology↗

bioRxiv2026

Mutational signatures in cancer genomes alter protein sequence motifs in cellular signaling networks

Somatic mutations in cancer genomes arise from distinct, context-specific mutational processes, yet their functional consequences at the protein and network level remain incompletely understood. Here, we show that mutational processes of single-nucleotide variants (SNVs) can systematically rewire signaling networks by inducing amino acid substitutions in short linear motifs (SLiMs) that mediate interactions with kinases and other signaling proteins. By analysing 11,000 cancer genomes and 144 classes of SLiMs, we identify motif-rewiring SNVs (rwSNVs) that create or disrupt SLiMs or remove phosphorylated residues. Mutational processes of methylcytosine deamination, APOBEC activity, and ultraviolet light exposure emerge as major contributors to motif rewiring. rwSNVs are enriched in cancer driver genes and pathways, linking mutation etiology to functional consequences. rwSNVs at the BRAF V600E hotspot associated with UV-related mutagenesis are predicted to generate a phosphorylation motif recognized by PLK1 kinase. Together, these findings reveal how mutational processes shape oncogenic signaling and tumor heterogeneity.

cancer biology↗

bioRxiv2024

Combinations of genomic alterations and immune microenvironmental features associate with patient survival in multiple cancer types

Oncogenesis and tumor progression are shaped by somatic alterations in the cancer genome and features of the tumor immune microenvironment (TME). How interactions of these two systems influence tumor development and clinical outcomes remains incompletely understood. To address this challenge, we developed the multi-omics analysis framework PACIFIC to systematically integrate genetic cancer drivers and infiltration profiles of immune cells with clinical information. In an analysis of 8500 cancer samples, we report 34 immunogenomic interactions (IGXs) in 13 cancer types in which context-specific combinations of genomic alterations and immune cell activities associate with disease outcomes. Risk associations of IGXs are potentially explained by tumor-intrinsic and microenvironmental metrics of immunogenicity and differential expression of therapeutic targets. In luminal-A breast cancer, MEN1 deletion combined with reduced neutrophils is associated with poor prognosis and deregulation of immune signalling pathways. These findings help elucidate how cancer drivers interact with TME to contribute to tumorigenesis.

cancer biology↗

bioRxiv2024

Topoisomerase IIb binding underlies frequently mutated elements in cancer genomes

Type-II topoisomerases resolve topological stress in DNA through controlled double-strand breaks. While TOP2A is a chemotherapy target in proliferating cells, the ubiquitously expressed TOP2B is a potential off-target. Here we explore roles of TOP2B in mutagenesis by generating DNA-binding maps of TOP2B, CTCF, and RAD21 in human cancer samples and analysing these maps for driver mutations and mutational processes in 6500 whole cancer genomes. TOP2B-CTCF-RAD21 and TOP2B-RAD21 sites are enriched in somatic mutations and structural variants (SVs), especially at evolutionary conserved sites displaying high transcription and long-range chromatin interactions. TOP2B binding underlies SVs and hotspot mutations in cancer-driving genes such as TP53, MYC, FOXA1, and VHL, and many cis-regulatory elements. We show that the TOP2B-bound mutational hotspot at RMRP drives tumor initiation and growth in vivo. These data highlight TOP2B as a protector of the genome from topological challenges whose aberrant activity promotes driver and passenger mutations in cancer genomes.

cancer biology↗

bioRxiv2023

Directional integration and pathway enrichment analysis for multi-omics data

Omics techniques generate comprehensive profiles of biomolecules in cells and tissues. However, a holistic understanding of the data requires joint multi-omics analyses that are challenging. Here we present DPM, a data fusion method for combining multiple omics datasets using directionality and significance estimates of genes, transcripts, or proteins. DPM allows users to define how the input datasets are expected to interact directionally, reflecting the initial experimental design or regulatory relationships between the datasets. DPM statistically prioritises genes and pathways that change consistently across the datasets, while penalising those violating the constraints. Joint analyses of transcriptomic, proteomic, DNA methylation, and clinical datasets of cancer samples demonstrate how directional integration identifies genes and pathways modulated across omics datasets, highlights those with inconsistent evidence, and reveals candidate biomarkers with prognostic signals in multiple datasets. DPM is implemented in the ActivePathways method and provides a general framework for testing detailed hypotheses in multi-omics data.

bioinformatics↗