Dataset Information

Machine learning enables pan-cancer identification of mutational hotspots at persistent CTCF binding sites.

ABSTRACT: CCCTC-binding factor (CTCF) is an insulator protein that binds to a highly conserved DNA motif and facilitates regulation of three-dimensional (3D) nuclear architecture and transcription. CTCF binding sites (CTCF-BSs) reside in non-coding DNA and are frequently mutated in cancer. Our previous study identified a small subclass of CTCF-BSs that are resistant to CTCF knock down, termed persistent CTCF binding sites (P-CTCF-BSs). P-CTCF-BSs show high binding conservation and potentially regulate cell-type constitutive 3D chromatin architecture. Here, using ICGC sequencing data we made the striking observation that P-CTCF-BSs display a highly elevated mutation rate in breast and prostate cancer when compared to all CTCF-BSs. To address whether P-CTCF-BS mutations are also enriched in other cell-types, we developed CTCF-INSITE-a tool utilising machine learning to predict persistence based on genetic and epigenetic features of experimentally-determined P-CTCF-BSs. Notably, predicted P-CTCF-BSs also show a significantly elevated mutational burden in all 12 cancer-types tested. Enrichment was even stronger for P-CTCF-BS mutations with predicted functional impact to CTCF binding and chromatin looping. Using in vitro binding assays we validated that P-CTCF-BS cancer mutations, predicted to be disruptive, indeed reduced CTCF binding. Together this study reveals a new subclass of cancer specific CTCF-BS DNA mutations and provides insights into their importance in genome organization in a pan-cancer setting.

SUBMITTER: Chen W

PROVIDER: S-EPMC11317138 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Machine learning enables pan-cancer identification of mutational hotspots at persistent CTCF binding sites.

Chen Wenhan W Zeng Yi C YC Achinger-Kawecka Joanna J Campbell Elyssa E Jones Alicia K AK Stewart Alastair G AG Khoury Amanda A Clark Susan J SJ

Nucleic acids research 20240801 14

CCCTC-binding factor (CTCF) is an insulator protein that binds to a highly conserved DNA motif and facilitates regulation of three-dimensional (3D) nuclear architecture and transcription. CTCF binding sites (CTCF-BSs) reside in non-coding DNA and are frequently mutated in cancer. Our previous study identified a small subclass of CTCF-BSs that are resistant to CTCF knock down, termed persistent CTCF binding sites (P-CTCF-BSs). P-CTCF-BSs show high binding conservation and potentially regulate cel ...[more]

PMID: 38950902

Similar Datasets

Project description:MotivationBenzo[a]pyrene, a notorious DNA-damaging carcinogen, belongs to the family of polycyclic aromatic hydrocarbons commonly found in tobacco smoke. Surprisingly, nucleotide excision repair (NER) machinery exhibits inefficiency in recognizing specific bulky DNA adducts including Benzo[a]pyrene Diol-Epoxide (BPDE), a Benzo[a]pyrene metabolite. While sequence context is emerging as the leading factor linking the inadequate NER response to BPDE adducts, the precise structural attributes governing these disparities remain inadequately understood. We therefore combined the domains of molecular dynamics and machine learning to conduct a comprehensive assessment of helical distortion caused by BPDE-Guanine adducts in multiple gene contexts. Specifically, we implemented a dual approach involving a random forest classification-based analysis and subsequent feature selection to identify precise topological features that may distinguish adduct sites of variable repair capacity. Our models were trained using helical data extracted from duplexes representing both BPDE hotspot and nonhotspot sites within the TP53 gene, then applied to sites within TP53, cII, and lacZ genes.ResultsWe show our optimized model consistently achieved exceptional performance, with accuracy, precision, and f1 scores exceeding 91%. Our feature selection approach uncovered that discernible variance in regional base pair rotation played a pivotal role in informing the decisions of our model. Notably, these disparities were highly conserved among TP53 and lacZ duplexes and appeared to be influenced by the regional GC content. As such, our findings suggest that there are indeed conserved topological features distinguishing hotspots and nonhotpot sites, highlighting regional GC content as a potential biomarker for mutation.Availability and implementationCode for comparing machine learning classifiers and evaluating their performance is available at https://github.com/jdavies24/ML-Classifier-Comparison, and code for analysing DNA structure with Curves+ and Canal using Random Forest is available at https://github.com/jdavies24/ML-classification-of-DNA-trajectories.

Dataset Information

Machine learning enables pan-cancer identification of mutational hotspots at persistent CTCF binding sites.

Publications

Machine learning enables pan-cancer identification of mutational hotspots at persistent CTCF binding sites.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets