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At least 19 recordsLinked to original sources

PubMed2026

Using cancer profiles to identify synthetic lethal therapeutic targets and predictive biomarkers in cancer gene dependency data.

MOTIVATION: Large scale loss-of-function screens utilising CRISPR or siRNA can provide profound insights into the importance of individual genes for the survival of a cancer cell and can drive the identification of therapeutic targets and biomarkers, and the development of targeted drugs. However, the analysis of these data and the substantial bodies of metadata that relate to them, is technically challenging and typically requires substantial expertise in data science and computer coding. RESULTS: To facilitate the analysis of cancer gene dependency data by cancer biologists and clinical scientists, we have developed DepMine-a computational toolkit providing a powerful system for framing complex queries relating cancer gene dependency to the underlying genetic changes that occur in cancer cells. DepMine identifies synthetic lethal relationships between putative target genes and complex 'cancer profiles' built from user-specified combinations of mutations, copy-number variation, and expression levels, and can refine these to optimal biomarker definitions for target dependency. AVAILABILITY: The Python implementation of DepMine and associated data files can be obtained at https://github.com/UOSbioinformaticslab/depmine and is free to academics and Not-For-Profit organisations. The DepMine release referenced in this paper is archived as DOI: 10.5281/zenodo.19570601.

Humans↗

PubMed2024

SJPedPanel: A Pan-Cancer Gene Panel for Childhood Malignancies to Enhance Cancer Monitoring and Early Detection.

PURPOSE: The purpose of the study was to design a pan-cancer gene panel for childhood malignancies and validate it using clinically characterized patient samples. EXPERIMENTAL DESIGN: In addition to 5,275 coding exons, SJPedPanel also covers 297 introns for fusions/structural variations and 7,590 polymorphic sites for copy-number alterations. Capture uniformity and limit of detection are determined by targeted sequencing of cell lines using dilution experiment. We validate its coverage by in silico analysis of an established real-time clinical genomics (RTCG) cohort of 253 patients. We further validate its performance by targeted resequencing of 113 patient samples from the RTCG cohort. We demonstrate its power in analyzing low tumor burden specimens using morphologic remission and monitoring samples. RESULTS: Among the 485 pathogenic variants reported in RTCG cohort, SJPedPanel covered 86% of variants, including 82% of 90 rearrangements responsible for fusion oncoproteins. In our targeted resequencing cohort, 91% of 389 pathogenic variants are detected. The gene panel enabled us to detect ∼95% of variants at allele fraction (AF) 0.5%, whereas the detection rate is ∼80% at AF 0.2%. The panel detected low-frequency driver alterations from morphologic leukemia remission samples and relapse-enriched alterations from monitoring samples, demonstrating its power for cancer monitoring and early detection. CONCLUSIONS: SJPedPanel enables the cost-effective detection of clinically relevant genetic alterations including rearrangements responsible for subtype-defining fusions by targeted sequencing of ∼0.15% of human genome for childhood malignancies. It will enhance the analysis of specimens with low tumor burdens for cancer monitoring and early detection.

Humans↗

PubMed2003

1-Mb resolution array-based comparative genomic hybridization using a BAC clone set optimized for cancer gene analysis.

Array-based comparative genomic hybridization (aCGH) is a recently developed tool for genome-wide determination of DNA copy number alterations. This technology has tremendous potential for disease-gene discovery in cancer and developmental disorders as well as numerous other applications. However, widespread utilization of a CGH has been limited by the lack of well characterized, high-resolution clone sets optimized for consistent performance in aCGH assays and specifically designed analytic software. We have assembled a set of approximately 4100 publicly available human bacterial artificial chromosome (BAC) clones evenly spaced at approximately 1-Mb resolution across the genome, which includes direct coverage of approximately 400 known cancer genes. This aCGH-optimized clone set was compiled from five existing sets, experimentally refined, and supplemented for higher resolution and enhancing mapping capabilities. This clone set is associated with a public online resource containing detailed clone mapping data, protocols for the construction and use of arrays, and a suite of analytical software tools designed specifically for aCGH analysis. These resources should greatly facilitate the use of aCGH in gene discovery.

Cell Line, Tumor↗

PubMed2026

Landscape of genetic alterations affecting cancer genes in primary and advanced malignant phyllodes tumours.

BACKGROUND: Malignant phyllodes tumours (MPT) are aggressive breast fibroepithelial neoplasms. Their rarity has limited their genetic characterization, and associations with genetic ancestry and progression drivers remain poorly understood. Prior studies suggest two evolutionary pathways according to MED12 mutational status. We sought to determine the repertoire of somatic genetic alterations in cancer genes in primary versus metastatic/recurrent MPTs, and according to MED12 mutational status and genetic ancestry. MATERIALS AND METHODS: We analysed the paired tumour-normal targeting sequencing data (up to 505 cancer-related genes) of 31 MPTs (primary, n = 20; metastatic/recurrent, n = 11). RESULTS: Metastatic/recurrent MPTs harboured a numerically higher frequency of genetic alterations in CDKN2A/2B (55% vs. 25%). Moreover, analysis of an MPT case with paired primary and metastatic samples revealed a CDKN2A/2B homozygous deletion restricted to the metastatic sample, suggesting a role for CDKN2A/2B in progression. Compared with MED12-wild type MPTs, MED12-mutant MPTs had higher tumour mutation burden (P = 0.002), frequency of TERT promoter (82% vs. 35%; P = 0.02) and RB1 mutations (45% vs. 5%; P = 0.01). Genetic alterations in the PI3K pathway, including PIK3CA and PTEN, were only present in MED12-wild type MPTs, and absent in MED12-mutant cases (15% vs. 0%; P > 0.05). Furthermore, genetic alterations in EGFR were restricted to MPTs from patients of European genetic ancestry and absent in those of Asian ancestry (43% vs. 0%; P > 0.05). CONCLUSIONS: Taken together, the repertoire of genetic alterations in primary and metastatic/recurrent MPTs shows overlap, and CDKN2A/2B homozygous deletions may play a role in progression. Additionally, molecular profiles of MPTs may vary according to genetic ancestry and MED12 mutational status.

Humans↗

PubMed2026

Syncytium-forming HSV-1 in cancer gene therapy: From molecular mechanisms to clinical translation.

Gene therapy has emerged as a promising strategy for cancer treatment, yet challenges in efficient gene delivery remain a major barrier. Herpes simplex virus type 1 (HSV-1), as an oncolytic virus, has garnered attention for its potential in cancer therapy due to its replicative capacity, large genomic payload, and relatively low toxicity. Notably, syncytium-forming HSV-1 (SF-HSV-1) not only exhibits enhanced and sustained antitumor efficacy but also triggers profound immune responses. However, the exact molecular mechanisms orchestrating HSV-1-induced syncytium formation, its resulting cytotoxicity, and its precise role in immune modulation remain incompletely understood. This review aims to provide an in-depth exploration of the mechanisms underlying HSV-1 syncytium formation and its therapeutic implications in cancer gene therapy.

Humans↗

PubMed2022

Reversal of cancer gene expression identifies repurposed drugs for diffuse intrinsic pontine glioma.

Diffuse intrinsic pontine glioma (DIPG) is an aggressive incurable brainstem tumor that targets young children. Complete resection is not possible, and chemotherapy and radiotherapy are currently only palliative. This study aimed to identify potential therapeutic agents using a computational pipeline to perform an in silico screen for novel drugs. We then tested the identified drugs against a panel of patient-derived DIPG cell lines. Using a systematic computational approach with publicly available databases of gene signature in DIPG patients and cancer cell lines treated with a library of clinically available drugs, we identified drug hits with the ability to reverse a DIPG gene signature to one that matches normal tissue background. The biological and molecular effects of drug treatment was analyzed by cell viability assay and RNA sequence. In vivo DIPG mouse model survival studies were also conducted. As a result, two of three identified drugs showed potency against the DIPG cell lines Triptolide and mycophenolate mofetil (MMF) demonstrated significant inhibition of cell viability in DIPG cell lines. Guanosine rescued reduced cell viability induced by MMF. In vivo, MMF treatment significantly inhibited tumor growth in subcutaneous xenograft mice models. In conclusion, we identified clinically available drugs with the ability to reverse DIPG gene signatures and anti-DIPG activity in vitro and in vivo. This novel approach can repurpose drugs and significantly decrease the cost and time normally required in drug discovery.

Humans↗

PubMed2016

Adeno-associated virus (AAV) vectors in cancer gene therapy.

Gene delivery vectors based on adeno-associated virus (AAV) have been utilized in a large number of gene therapy clinical trials, which have demonstrated their strong safety profile and increasingly their therapeutic efficacy for treating monogenic diseases. For cancer applications, AAV vectors have been harnessed for delivery of an extensive repertoire of transgenes to preclinical models and, more recently, clinical trials involving certain cancers. This review describes the applications of AAV vectors to cancer models and presents developments in vector engineering and payload design aimed at tailoring AAV vectors for transduction and treatment of cancer cells. We also discuss the current status of AAV clinical development in oncology and future directions for AAV in this field.

Capsid Proteins↗

PubMed2026

Comprehensive Genomic Profiling Reveals the Mutational Spectrum and Clinical Significance of BRCA1/2 and Other Cancer-Susceptibility Genes in Breast Cancer Patients from Southern Tunisia.

BACKGROUND/OBJECTIVES: This study aims to investigate the mutational spectrum of BRCA1 and BRCA2 genes in a cohort of breast cancer (BC) patients from southern Tunisia, and to evaluate their clinical and prognostic significance. Additionally, this study explores the contribution of other cancer predisposition genes and the prevalence of variants of uncertain significance (VUS). RESULTS: Among the 165 patients included, pathogenic or likely pathogenic variants (P/LPVs) in BRCA1/BRCA2 were identified in 19 cases (11.51%), including 8 in BRCA1 and 11 in BRCA2. The presence of BRCA P/LPVs associated with young patients (p = 0.006) and those with TNBC (p = 0.036). Beyond BRCA1/2, PV/LPVs were detected in other cancer-related genes, including TP53 (n = 3), CHEK2, RAD50 (n = 2 cases each), and MUTYH, BARD1, and BRIP1 (one case each). Furthermore, 56 VUS were identified; among them, 7 were prioritized based on in silico predictive analyses, suggesting a potential deleterious effect. However, these VUS should not be used for clinical decision-making without additional evidence from functional and familial segregation studies. CONCLUSIONS: Our findings provide novel insights into the genetic landscape of breast cancer in southern Tunisia, highlighting the clinical relevance of BRCA1/2 mutations and the contribution of other susceptibility genes. These results support the personalized management of breast cancer patients and the implementation of expanded multigene panel testing in routine clinical practice to improve genetic counseling.

BRCA1↗

PubMed2025

Established Cancer Predisposition Genes in Single and Multiple Cancer Diagnoses.

IMPORTANCE: Much of the understanding of cancer risk associated with rare pathogenic variants (RPVs) is derived from family-based studies or clinically ascertained samples, which may be limited by ascertainment and selection bias. OBJECTIVE: To quantify associations between RPVs in previously implicated cancer predisposition genes and single and multiple cancer diagnoses in a large population-based study. DESIGN, SETTING, AND PARTICIPANTS: In this genetic association study, whole-exome sequencing data were used from the UK Biobank, a UK population-based cohort that enrolled participants aged 40 to 69 years between 2006 and 2010. Participants who were involved in the whole-exome sequencing release of 200 000 genomes in 2020 were included in this study. This analysis included White participants only, as findings in other racial and ethnic groups had small sample sizes. Participants were diagnosed before or after biobank enrollment until March 2024. EXPOSURES: The sequencing data of a set of 96 previously implicated cancer predisposition genes were analyzed and compared using 2 methods. To determine the statistical significance of an association, a robust optimal sequence kernel association test was used, while odds ratios (ORs) and 95% CIs were obtained through Firth logistic regression. MAIN OUTCOMES AND MEASURES: The primary study outcome was the diagnosis of 1 of 11 cancers (bladder, breast, central nervous system, colorectal, lung, melanoma, ovary, pancreatic, prostate, renal, thyroid) defined by relevant diagnosis codes in inpatient hospital diagnosis, cancer registry, and/or death registry data. RESULTS: Data from 183 627 participants (101 414 [55.2%] female) were analyzed, including 25 824 participants with at least 1 cancer diagnosis, of whom 23 704 (91.8%) had a single cancer diagnosis and 2130 (8.2%) had 2 or more cancer diagnoses. A total of 157 793 controls had no cancer diagnosis. The median (IQR) age was 62 (56-65) years in participants with at least 1 cancer diagnosis, compared to 57 (50-63) years in those without a cancer diagnosis. Genetic variation in 16 genes was significantly associated with at least 1 cancer of interest (ATM, BARD1, BRCA1, BRCA2, BRIP1, CDKN2A, CHEK2, HOXB13, MITF, MLH1, MSH2, MSH6, NF1, PALB2, RAD51C, and RAD51D). The presence of an RPV in 1 of these 16 genes was associated with increased odds of at least 1 cancer (OR, 1.87; 95% CI, 1.76-1.98) and multiple primary cancers (OR, 2.56; 95% CI, 2.18-2.99). Carrier frequency was 6.28% and 8.36%, respectively. CONCLUSIONS AND RELEVANCE: This genetic association study demonstrates several established associations between cancer predisposition genes and cancer diagnoses in an unselected population-based study. These results also demonstrate that RPVs in cancer predisposition genes are associated with multiple primary cancer diagnoses, suggesting that multigene panel testing may be warranted in these individuals.

Humans↗

PubMed2026

CanVar-UK: A collaborative platform for germline interpretation in cancer susceptibility genes.

Germline variants in cancer susceptibility genes (CSGs) are typically inherited rather than arising de novo. Hence, wide cascade testing of families across geographies is common, meaning consistency in variant classification is particularly critical. Variant interpretation requires collation of variant-level data from diverse sources, as well as assembly of comprehensive clinical data, often necessitating sharing of information between genomic testing centers. Here, we describe CanVar-UK, a freely accessible web platform bespoke designed to support interpretation of germline CSG variants. CanVar-UK contains variant-level data for over 1.1 million single-nucleotide variants (SNVs), comprising all possible coding SNVs in 116 established CSGs. The data sources with which variants are annotated include in silico scores from 11 clinically relevant tools, population allele frequencies from gnomAD v4.1, case counts from multiple cohorts, including National Health Service (NHS) clinical laboratory testing, variant-level readouts from 47 selected functional and splicing datasets across 19 CSGs, genetic epidemiology studies, and live linkage to existing consensus classifications in the ClinVar database. The diagnostic discussion forum is only available to registered diagnostic scientist users. Through this, a variant-tagged email message can be dispatched in real time across the diagnostic forum community of >1,500 users, with all exchanges and classifications captured and stored in the platform. Already widely used by NHS diagnostic clinical scientists in the UK, CanVar-UK has a rapidly growing international diagnostic user base (>800 UK and >600 non-UK registered users). Survey of the NHS diagnostic user community illustrates the wide-ranging utility of CanVar-UK within their clinical workflows for interpretation of germline CSG variants.

Journal Article↗

PubMed2026

ORBIT: Oncogenic Representation Learning via Bi-Prototype Contrastive Learning in Hyperbolic Space for cancer driver gene identification.

Accurate identification of cancer driver genes is crucial for precision oncology but remains challenging due to the complexity of integrating heterogeneous data and modeling dynamic biological systems. To address these limitations, we propose ORBIT (Oncogenic Representation Learning via Bi-Prototype Contrastive Learning in Hyperbolic Space). Our framework synergistically fuses multi-omics profiles with functional network data using a context-adaptive graph reweighting mechanism to capture cancer-specific dynamics. The model employs a bi-prototype contrastive learning strategy within hyperbolic space, which aligns gene representations around distinct driver and non-driver semantic anchors while preserving the intrinsic hierarchy of biological networks. Comprehensive evaluations demonstrate that ORBIT achieves highly competitive stability in pan-cancer analysis while consistently outperforming state-of-the-art methods in cancer-specific predictions. Furthermore, functional enrichment analysis confirms that the model effectively segregates core cancer pathways, and drug sensitivity profiling validates the clinical relevance of the identified drivers. By integrating hyperbolic geometry with context-adaptive learning, ORBIT offers a robust and interpretable paradigm for precision medicine. The source codes and datasets are publicly accessible at https://github.com/spcho-dev/ORBIT.

Humans↗

PubMed2026

Characteristics of fusion genes in breast cancer.

Fusion genes, arising from aberrant genomic rearrangements, represent critical oncogenic drivers with distinct oncogenic functions. Although relatively uncommon in breast cancer, accumulating evidence suggests that fusion genes contribute to tumor initiation, progression, and therapeutic resistance. This review first summarizes the molecular mechanisms underlying fusion gene formation, their frequency and subtype distribution, and advances in detection technologies in breast cancer. We then discuss how fusion genes reprogram oncogenic signaling pathways and mediate resistance to conventional and targeted therapies. Finally, we evaluate their translational potential as diagnostic biomarkers and therapeutic targets, emphasizing opportunities for precision oncology. By integrating current insights, this review underscores the multifaceted roles of fusion genes in breast cancer biology and highlights their promise for guiding the development of more effective, personalized treatment strategies.

Breast cancer↗

PubMed2025

Revealing cancer driver genes through integrative transcriptomic and epigenomic analyses with Moonlight.

Cancer involves dynamic changes caused by (epi)genetic alterations such as mutations or abnormal DNA methylation patterns which occur in cancer driver genes. These driver genes are divided into oncogenes and tumor suppressors depending on their function and mechanism of action. Discovering driver genes in different cancer (sub)types is important not only for increasing current understanding of carcinogenesis but also from prognostic and therapeutic perspectives. We have previously developed a framework called Moonlight which uses a systems biology multi-omics approach for prediction of driver genes. Here, we present an important development in Moonlight2 by incorporating a DNA methylation layer which provides epigenetic evidence for deregulated expression profiles of driver genes. To this end, we present a novel functionality called Gene Methylation Analysis (GMA) which investigates abnormal DNA methylation patterns to predict driver genes. This is achieved by integrating the tool EpiMix which is designed to detect such aberrant DNA methylation patterns in a cohort of patients and further couples these patterns with gene expression changes. To showcase GMA, we applied it to three cancer (sub)types (basal-like breast cancer, lung adenocarcinoma, and thyroid carcinoma) where we discovered 33, 190, and 263 epigenetically driven genes, respectively. A subset of these driver genes had prognostic effects with expression levels significantly affecting survival of the patients. Moreover, a subset of the driver genes demonstrated therapeutic potential as drug targets. This study provides a framework for exploring the driving forces behind cancer and provides novel insights into the landscape of three cancer sub(types) by integrating gene expression and methylation data.

Humans↗

PubMed2024

Classification of variants of reduced penetrance in high-penetrance cancer susceptibility genes: Framework for genetics clinicians and clinical scientists by CanVIG-UK (Cancer Variant Interpretation Group-UK).

PURPOSE: Current practice is to report and manage likely pathogenic/pathogenic variants in a given cancer susceptibility gene as though having equivalent penetrance, despite increasing evidence of intervariant variability in risk associations. Using existing variant interpretation approaches, largely based on full-penetrance models, variants in which reduced penetrance is suspected may be classified inconsistently and/or as variants of uncertain significance. We aimed to develop a national consensus approach for such variants within the Cancer Variant Interpretation Group UK (CanVIG-UK) multidisciplinary network. METHODS: A series of surveys and live polls were conducted during and between CanVIG-UK monthly meetings on various scenarios potentially indicating reduced penetrance. These informed the iterative development of a framework for the classification of variants of reduced penetrance by the CanVIG-UK Steering and Advisory Group working group. RESULTS: CanVIG-UK recommendations for amendment of the 2015 ACMG/AMP variant interpretation framework were developed for variants in which (A) active evidence suggests a reduced-penetrance effect size (eg, from case-control or segregation data) and (B) reduced penetrance effect is inferred from weaker/potentially inconsistent observed data. CONCLUSION: CanVIG-UK propose a framework for the classification of variants of reduced penetrance in high-penetrance genes. These principles, although developed for cancer susceptibility genes, are potentially applicable to other clinical contexts.

Humans↗

PubMed2026

Updated ENIGMA recommendations for reporting germline variants in cancer susceptibility genes and their translation into twenty languages.

Genetic testing for cancer susceptibility underpins precision cancer prevention and care. Gaps in the healthcare providers' genetic literacy and an ambiguous lexicon for variant description may hinder proper delivery and clinical application of consistently trustworthy test results. The Evidence-based Network for the Interpretation of Germline Mutant Alleles (ENIGMA) international consortium supports controlled terminology and recommends a framework for reporting germline variants in cancer susceptibility genes, using breast cancer as an exemplar. Moving forward towards terminological coherence across disciplines and borders, the ENIGMA Clinical Working Group launched a multinational effort to release consortium-approved translations of the published recommendations. The herein reported Vocabulary Translation Project offered an opportunity to reappraise and align the reference text to the recent BRCA1 and BRCA2 specifications to the American College of Medical Genetics and Genomics/Association for Molecular Pathology rules by the ENIGMA Variant Curation Expert Panel and to highlight country-specific differences in breast cancer risk assessment and management. The updated recommendations and their 20 translations are now provided as easy to handle documents, covering 11 of the most widely spoken languages in the world. They will contribute to minimised erroneous inferences, more informed decision-making, improved health outcomes and equity in the use of genetic testing for cancer predisposition and in translational oncology.

Humans↗

PubMed2025

Identification of MMP14 and MKLN1 as colorectal cancer susceptibility genes and drug-repositioning candidates from a genome-wide association study.

BACKGROUND: Genome-wide association studies (GWAS) and subsequent functional interpretation have been used to identify susceptible genes and potential drug-repositioning candidates. This study aimed to identify genes associated with colorectal cancer (CRC) and potential drug-repositioning candidates. METHODS: Patients with CRC at Seoul National University Hospital (SNUH, discovery study) and Chonnam National University Hospital (CNUH, replication study) were included as case groups. The Korean Genome and Epidemiology Study (KoGES) participants were included as a control group. Single-nucleotide polymorphisms (SNPs) were extracted from blood-derived DNA (N = 409,063). A SNP-based logistic regression model was applied. Furthermore, post-GWAS analysis was conducted. Drug-repositioning candidates were identified using a pre-trained deep neural network and the druggability assessment tool. RESULTS: In the discovery study, we conducted a 1:3 age- and sex-matched case-control study that included 500 CRC cases (mean age 63.0 ± 7.15 years) and 1,500 healthy controls (mean age 62.9 ± 7.07 years), each group comprising 50% males and 50% females. The replication study enrolled 4,860 patients with CRC and 46,384 healthy controls. The two-stage GWAS revealed statistically significant associations among MKLN1 (rs75170436, 7q32.3, beta (log odds ratio) = - 0.90, Pmeta = 5.90 × 10-13), MMP14 (rs3751489, 14q11.2, beta (log odds ratio) = - 1.91, Pmeta = 2.31 × 10-12). Post-GWAS functional analysis revealed strong associations on two genes highlighting deleterious effects and increased gene expression. Drug-repositioning analysis identified GW0742 (PPARβ/δ agonist) with the highest binding score and druggability score for MMP14 with a reference allele (12.06, 0.85). CONCLUSIONS: Using GWAS, MKLN1 and MMP14 were found to be associated with CRC development and we identified GW0742 (PPARβ/δ agonist) as a potential drug-repositioning candidate for CRC based on MKLN1 and MMP14. These findings improve the understanding of CRC development and provide insights into novel therapeutic targets and candidates for CRC treatment.

Humans↗

PubMed2025

Breast Cancer Risk Stratification in Black Women: Current Status and Potential Solutions to Improve Accuracy.

Breast cancer risk stratification models identify individuals at increased risk, allowing earlier screening than for those at average risk and potentially improving health outcomes. Due to the increasing rates of breast cancer in individuals aged <40 years, especially among Black females, the American College of Radiology now recommends all females initiate breast cancer risk assessment by age 25 years. Several breast cancer risk prediction models are readily available, including the Gail Model, Breast Cancer Surveillance Consortium Risk Calculator, BOADICEA, and Tyrer-Cuzick Model. However, because these models were primarily developed using data from White women of European ancestry, they may underestimate risk in Black women. Indeed, current evidence suggests that these models underpredict breast cancer risk among Black women, particularly those of African ancestry. Although cancer risk prediction models typically incorporate personal characteristics, family history of cancer, and hormonal and lifestyle factors, inherited breast cancer genes can also increase risk for breast cancer. Beyond monogenic inherited breast cancer genes that increase breast cancer risk, emerging data suggest that single nucleotide polymorphisms identified through genome-wide association studies (GWAS) may be used to generate polygenic risk scores, which may further refine breast cancer risk. However, GWAS data are also primarily gathered from European ancestry females, further reducing the ability to accurately stratify breast cancer risk in non-European ancestry populations. Current data highlight the importance of ensuring representation from all populations in developing cancer risk prediction models, conducting genomics research, and designing effective implementation strategies to enhance the use of these models in routine clinical care. Although new analytic methods and models are being developed to improve breast cancer risk stratification across populations, it remains critical to assess the utility and calibration of existing and new models to ensure applicability across non-European ancestry populations.

Humans↗

PubMed2024

Co-expression in tissue-specific gene networks links genes in cancer-susceptibility loci to known somatic driver genes.

BACKGROUND: The genetic background of cancer remains complex and challenging to integrate. Many somatic mutations within genes are known to cause and drive cancer, while genome-wide association studies (GWAS) of cancer have revealed many germline risk factors associated with cancer. However, the overlap between known somatic driver genes and positional candidate genes from GWAS loci is surprisingly small. We hypothesised that genes from multiple independent cancer GWAS loci should show tissue-specific co-regulation patterns that converge on cancer-specific driver genes. RESULTS: We studied recent well-powered GWAS of breast, prostate, colorectal and skin cancer by estimating co-expression between genes and subsequently prioritising genes that show significant co-expression with genes mapping within susceptibility loci from cancer GWAS. We observed that the prioritised genes were strongly enriched for cancer drivers defined by COSMIC, IntOGen and Dietlein et al. The enrichment of known cancer driver genes was most significant when using co-expression networks derived from non-cancer samples of the relevant tissue of origin. CONCLUSION: We show how genes within risk loci identified by cancer GWAS can be linked to known cancer driver genes through tissue-specific co-expression networks. This provides an important explanation for why seemingly unrelated sets of genes that harbour either germline risk factors or somatic mutations can eventually cause the same type of disease.

Humans↗