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PubMed2026

Graph neural network-based risk stratification of prostate cancer using gene expression and SHAP interpretability.

Accurate risk stratification is essential for guiding treatment decisions and preventing over treatment of prostate cancer, which remains one of the most prevalent cancers among adult men. While the Gleason score, obtained from prostate biopsies, is routinely used to assess tumor aggressiveness, the biopsy procedure carries risks such as pain, infection, and, in some cases, serious complications such as sepsis. In this study, we proposed an artificial intelligence-based framework that integrates mRNA expression profiles with functional interaction networks to classify prostate cancer patients into low-, medium-, and high-risk groups defined by Gleason scores. The pipeline comprised five steps: (1) data collection from The Cancer Genome Atlas (TCGA), (2) preprocessing of gene expression data, (3) two-stage feature selection to identify informative biomarkers, (4) risk classification using a dual-branch graph neural network (GNN) that combines gene-gene interaction graphs with sample-level expression features, and (5) model interpretation using SHAP to quantify feature contributions. Differentially expressed genes were identified in the High (ASPN, GMNN, PEBP4, C2, KNCK17), Medium (C2, IGSF1, ASPN, CDKN3, AMH), and Low (TNMD, VWA5B2, ST6GALNAC5, CYP3A5, PHGR1) risk groups, underscoring the molecular heterogeneity of disease progression. On an independent held-out test set, the model achieved AUCs of 0.86, 0.88, and 0.95 for the low-, medium-, and high-risk groups, respectively, with an overall accuracy of 80%. These results suggest that combining GNN-based modeling with explainable AI can capture both global and local molecular patterns relevant to tumor aggressiveness. However, as the model was developed and evaluated solely on the TCGA cohort, the findings should be regarded as exploratory, and external validation will be required to establish generalizability. Within these limitations, the proposed framework highlights the potential of molecular profiling and graph-based deep learning to support more precise, potentially less invasive, risk assessment and individualized treatment planning in prostate cancer.

Prostatic Neoplasms↗

PubMed2025

Molecular analysis of lung adenocarcinomas from the SAFIR02-Lung cohort reveals new metastasis-associated copy-number alterations including frequent mutant-specific KRAS-allelic imbalance and identifies CDKN2A homozygous deletions as an independent biomarker of poor prognosis.

BACKGROUND: Identifying molecular alterations specific to advanced lung adenocarcinomas could provide insights into tumour progression and dissemination mechanisms. METHOD: We analysed tumour samples, either from locoregional lesions or distant metastases, from patients with advanced lung adenocarcinoma from the SAFIR02-Lung trial by targeted sequencing of 45 cancer genes and comparative genomic hybridisation array and compared them to early tumours samples from The Cancer Genome Atlas. RESULTS: Differences in copy-number alterations frequencies suggest the involvement in tumour progression of LAMB3, TNN/KIAA0040/TNR, KRAS, DAB2, MYC, EPHA3 and VIPR2, and in metastatic dissemination of AREG, ZNF503, PAX8, MMP13, JAM3, and MTURN. Conversely, no meaningful difference was found in pathogenic single-nucleotide variant frequencies, reinforcing the notion that they are early events in tumorigenesis. CDKN2A homozygous deletion was linked to poor clinical outcome in patients with early tumours (overall survival hazard ratio 2.17, 95% CI: 1.43-3.28, corrected p-value = 0.01). Furthermore, we found that KRAS mutant allele specific imbalance, i.e. focal amplification of the mutant allele, is more prevalent in locoregional or distant samples of metastatic patients than in early lesions (8.4%, 13% and 2.8% respectively). This observation was replicated in three public cohorts. Tumours with KRAS mutant allele specific imbalance show specific patterns of co-occurrence and mutual exclusion with alterations in key cancer genes like CDKN2A, TP53, STK11 and NKX2-1, often in a tumour type dependent manner. CONCLUSION: Advanced LUAD tumours exhibit higher copy-number alteration burden, with distinct alterations associated with tumour progression and metastasis. CDKN2A homozygous deletions predict poor prognosis in early disease, while KRAS mutant allele-specific imbalance is enriched in advanced tumours.

Humans↗

PubMed2024

Deep-Learning Model for Tumor-Type Prediction Using Targeted Clinical Genomic Sequencing Data.

UNLABELLED: Tumor type guides clinical treatment decisions in cancer, but histology-based diagnosis remains challenging. Genomic alterations are highly diagnostic of tumor type, and tumor-type classifiers trained on genomic features have been explored, but the most accurate methods are not clinically feasible, relying on features derived from whole-genome sequencing (WGS), or predicting across limited cancer types. We use genomic features from a data set of 39,787 solid tumors sequenced using a clinically targeted cancer gene panel to develop Genome-Derived-Diagnosis Ensemble (GDD-ENS): a hyperparameter ensemble for classifying tumor type using deep neural networks. GDD-ENS achieves 93% accuracy for high-confidence predictions across 38 cancer types, rivaling the performance of WGS-based methods. GDD-ENS can also guide diagnoses of rare type and cancers of unknown primary and incorporate patient-specific clinical information for improved predictions. Overall, integrating GDD-ENS into prospective clinical sequencing workflows could provide clinically relevant tumor-type predictions to guide treatment decisions in real time. SIGNIFICANCE: We describe a highly accurate tumor-type prediction model, designed specifically for clinical implementation. Our model relies only on widely used cancer gene panel sequencing data, predicts across 38 distinct cancer types, and supports integration of patient-specific nongenomic information for enhanced decision support in challenging diagnostic situations. See related commentary by Garg, p. 906. This article is featured in Selected Articles from This Issue, p. 897.

Humans↗

PubMed2026

EPIC: Event Prototyping via Information Constrained graph learning for personalized cancer driver gene prediction.

MOTIVATION: Precision oncology relies on accurately distinguishing patient-specific driver mutations from the vast background of passenger alterations. While graph-based computational methods have emerged as powerful tools for this task, they often struggle to preserve the distinct genomic context of individual mutations within complex biological networks. Consequently, subtle patient-specific driver signals are frequently obscured by dominant topological patterns, critically impeding the identification of individualized oncogenic events essential for personalized cancer therapy. RESULTS: To address this, we propose EPIC, a novel framework for Event Prototyping via Information Constrained Graph Learning. Unlike traditional node-centric approaches, EPIC redefines driver prediction as a metric learning task in an event embedding space. We introduce an information-constrained learning strategy that imposes explicit geometric constraints on feature variance, effectively preventing feature collapse and ensuring that low-frequency driver signals are distinctively preserved. Experiments on large-scale cancer cohorts demonstrate that EPIC significantly outperforms established baselines. Notably, the model prioritizes low-frequency driver variants typically overlooked by population-based methods, mapping them to critical oncogenic mechanisms associated with drug resistance and metastasis. Furthermore, clinical actionability analysis confirms that EPIC substantially expands the patient population eligible for targeted therapies. EPIC provides a robust and context-aware solution for personalized cancer driver discovery, bridging the gap between genomic data and actionable therapeutic insights. AVAILABILITY AND IMPLEMENTATION: The source code and datasets are available at https://github.com/spcho-dev/EPIC.

Humans↗

PubMed2026

Genomic characterization of aggressiveness in pituitary neuroendocrine tumors.

BACKGROUND: Aggressive evolution of PitNETs is rare; metastatic spread is even more. Defining aggressiveness and malignancy is challenging, subsequently hard to predict, and to understand. The aim was to provide a molecular definition of aggressiveness using genomic approaches. METHODS: PitNETs from 206 patients were included. Associations between 9 clinicopathological features of aggressiveness and PitNETs' omics were explored. Omics included transcriptome, DNA methylation, chromosomal alterations, and mutations. Clonal tumor evolution was monitored in 7 patients. RESULTS: Among the 9 clinicopathological features of aggressiveness, only rapid progression, progression after radiotherapy, Ki67/MIB1 proliferation index ≥10%, temozolomide treatment, metastases, and specific death were associated with specific omics signatures, while tumour maximal diameter ≥40 mm, cavernous, and sphenoid invasion were not. The omic signatures associated with these features of aggressiveness overlapped but remained distinct between corticotroph and mammo-somato-thyrotroph lineages. For each lineage, a common signature of aggressiveness was identified, associating a proliferative transcriptome signature and DNA hypermethylation. Alterations in specific genes were associated with aggressive features, including a novel PitNET gene, LRP1B, and known cancer genes (TP53, CDKN2A), while USP8 and GNAS alterations were not. Integration of gene alterations with methylome and transcriptome signatures isolated a subset of molecularly aggressive PitNETs. Molecular signatures were stable during the course of the disease, despite evolution toward aggressiveness and potential clonal divergence. CONCLUSION: This systematic analysis of clinicopathological features of aggressiveness using an integrated multiomic approach establishes a histomolecular definition of aggressiveness in PitNETs. Prospective cohort studies are needed to validate these molecular signatures and establish their prognostic value.

Humans↗

PubMed2026

Pan-Cancer Quantification of Driver Alteration Transmission Across Molecular Layers Reveals Limited Propagation to Protein Abundance.

Precision oncology relies primarily on DNA-level alterations for therapeutic decisions, but the extent to which driver mutations propagate to protein abundance has not been systematically evaluated. Here, I developed a regression-based transmission score (TS_R 2) to quantify driver alteration signal propagation across DNA, mRNA, and protein layers. Applying this framework to matched genomic, transcriptomic, proteomic, and phosphoproteomic data from 754 Clinical Proteomic Tumor Analysis Consortium (CPTAC) tumors across seven cancer types, I analyzed 86 driver gene-cancer type pairs, of which 83 were evaluable for the full two-layer transmission score. I employed covariate-adjusted regression for each molecular transition, assessing significance via permutation testing (n = 1000). Mixed-effects modeling then partitioned gene-intrinsic from cancer-type-dependent effects. Only 5 of 83 evaluable pairs (6%) demonstrated high transmission (TS_R 2 > 0.05), with receptor tyrosine kinases (EGFR, FGFR2) exemplifying this class. The primary bottleneck occurred at the mutation-mRNA transition, not mRNA-protein translation. Gene identity accounted for 49% of transmission efficiency variance, nearly double the contribution of cancer type (29%). Copy number alterations transmitted signals 13.8-fold more efficiently than point mutations, and truncating mutations showed higher transmission than missense variants (Wilcoxon p = 0.005). Microsatellite instability attenuated mRNA-protein transmission in UCEC and COAD. These findings demonstrate that many driver alterations show limited propagation to protein abundance. This challenges DNA-only interpretations in precision oncology and provides a framework for integrated functional driver prioritization.

Humans↗

PubMed2025

Identification and validation of prognostic genes associated with mitochondrial nuclear genes in gastric cancer.

Mitochondrial-related nuclear genes (MNGs) have shown great importance in cancer diagnosis and prognosis, but their role in gastric cancer (GC) remains unclear. GC-related transcriptome data from the gene expression omnibus and cancer genome atlas databases were analyzed to identify differentially expressed MNGs. A prognostic risk model was constructed through univariate Cox and least absolute shrinkage and selection operator regression, validated by Kaplan-Meier (K-M) survival curve and receiver operating characteristic curve. This was followed by immune infiltration analysis, independent prognostic analysis, functional enrichment analysis, drug sensitivity analysis, drug prediction, molecular docking and construction of regulatory networks. Three prognostic genes (ATP8A2, COX15 and TARS2) were identified. The expression of TARS2 and COX15 was positively correlated with CNV, while ATP8A2 was unaffected. The risk model and nomogram, integrating risk score and clinicopathological factors, exhibited excellent predictive performance. A significant correlation was observed between prognostic genes and differential immune cells, such as T cells, B cells, and NK cells. BMS-754807, Gefitinib, JQ1, Lapatinib, and Sapitinib exhibited significant differences in sensitivity between the high-risk group and the low-risk group. The results of molecular docking showed TP8A2 has stable binding ability with cytosine, COX15 with indomethacin, and TARS2 with bisacodyl. RT-qPCR revealed downregulation of ATP8A2 and upregulation of COX15 and TARS2 in GC samples. MNGs, including ATP8A2, COX15, and TARS2, demonstrated significant associations with immune infiltration, CNV, and prognostic outcomes of GC.

Humans↗

PubMed2025

Asymmetric integration of various cancer datasets for identifying risk-associated variants and genes.

MOTIVATION: Cancer genomic research provides an opportunity to identify cancer risk-associated genes, but often suffers from undesirable low statistical power due to a limited sample size. Integrated analysis with different cancers has the potential to enhance statistical power for identifying pan-cancer risk genes. However, substantial heterogeneity across various cancers makes this challenging. RESULTS: Recently, a novel asymmetric integration method was developed that can deal with data heterogeneity and exclude unhelpful datasets from the analysis. We adapted and applied this method to integrate genotype datasets with matched case and control individuals from the Michigan Genomics Initiative, using each cancer as the primary dataset of interest and the other cancers as auxiliary datasets, respectively. Conditional logistic regression models were coupled with the asymmetric integrated framework to handle the matched case-control study design and permutation tests were performed to control for false discovery rates (FDRs). At the same FDR level, the integrated analysis found more potential genetic variants and genes that are associated with the risks of various cancers, showcasing the promise of the proposed approach for integrated analysis of cancer datasets. AVAILABILITY AND IMPLEMENTATION: Our method is available as source code at https://github.com/rxxwang/integrate_cancer.

Journal Article↗

PubMed2026

Recurrent mechanisms of biallelic epigenetic inactivation reveal new putative tumour suppressor genes in prostate cancer.

The inactivation of tumour suppressor genes is a key step in cancer development, and is usually achieved by homozygous loss. In prostate cancer, however, large genomic regions are often hemizygously lost, which complicates the identification of putative tumour suppressors in these regions. Here, we develop Epi2Hit, an integrative computational method that leverages whole genome sequencing, epigenomic profiling and gene expression to identify biallelic inactivation of tumour suppressor genes involving DNA methylation of promoter and enhancer regions of one allele and genomic loss of the other allele. We apply Epi2Hit to a cohort of 2,021 prostate cancers to discover tumour suppressor genes. In particular, we identify epigenetic biallelic inactivation of ZFHX3 at a recurrence level similar to TP53. Biallelic inactivation of ZFHX3, a transcriptional repressor, leads to upregulation of oncogenes, including MYC and a shorter time to metastasis. Finally, we provide evidence that epigenetic silencing as 2nd hit is particularly enriched in regions with nearby essential genes, precluding homozygous loss.

Prostatic Neoplasms↗

PubMed2026

CRISPR/Cas in gynecologic cancers: A review of experimental and therapeutic applications.

Gynecological malignancies-including cervical, ovarian, and endometrial cancers-remain a major global health challenge, contributing significantly to cancer-related morbidity and mortality among women. Despite advances in conventional treatments such as surgery, chemotherapy, radiotherapy, and immunotherapy, issues such as drug resistance, tumor recurrence, and limited efficacy in advanced-stage disease necessitate novel therapeutic strategies. The emergence of CRISPR/Cas-based genome editing has revolutionized cancer research by enabling precise, efficient, and programmable modifications of specific genomic loci. In gynecologic oncology, CRISPR/Cas systems have been employed to dissect oncogenic mechanisms, identify therapeutic targets, and develop innovative treatment modalities. In cervical cancer, CRISPR-mediated targeting of HPV E6 and E7 oncogenes has shown potential in restoring tumor suppressor pathways and enhancing chemosensitivity. In ovarian cancer, gene editing has been used to modulate chemoresistance, tumor angiogenesis, and metastasis through the knockout of key regulators such as DNMT1, EGFL6, and BRCA1/2. Similarly, in endometrial cancer, CRISPR tools have elucidated mechanisms of hormonal resistance and facilitated the development of in vivo models via somatic gene editing. This review highlights recent advances in the application of CRISPR/Cas technology to gynecologic malignancies, discussing its potential as both a therapeutic and research platform while acknowledging current limitations and translational hurdles.

Humans↗

PubMed2025

A multifaceted investigation into the impact of m6A methylation-related genes on pancreatic cancer, integrating insights from various databases and foundational experimental research.

BACKGROUND: Despite advances in surgical techniques, immunotherapy, the mortality rate associated with pancreatic cancer (PC) has been on the rise in recent years. Understanding the importance of RNA N6-methyladenosine (m6A) in PC is critical for prognosis, tumor microenvironment, and immunotherapy efficacy. The study aims to identify m6A methylation regulators that play an important role in the development and progression of PC by mining databases. The effect of insulin-like growth factor-binding protein 3 (IGFBP3) on pancreatic tumors was explored, and the related mechanisms were explored. METHODS: We analyzed the expression of m6A regulators in PC by digging deeper into the datasets of The Cancer Genome Atlas and Gene Expression Omnibus (GEO) databases, and analyzed its relationship with the prognosis of patients with PC, looking for m6A methylation regulators that play an important role in the development and progression of PC. Reuse the ConsensusClusterPlus package, Cox analysis, and unsupervised clustering to delineate three distinct m6A clusters - designated as m6A cluster A, m6A cluster B, and m6A cluster C single-sample gene set enrichment analysis, gene set variation analysis, Gene Ontology, and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses evaluated the different pathway roles of these clusters in the development and progression of PC. Finally, the cell lines with IGFBP3 overexpression and knockdown were constructed by lentivirus transfection, the transfection effect was identified by WB, and the effects of IGFBP3 overexpression/knockdown on the survival and growth of PC cell lines were verified by cell cloning experiments and cell counting kit-8 experiments, and the possible related pathways were explored by KEGG. RESULTS: Most m6A regulatory factors are highly expressed in PC, and their high expression is negatively correlated with the prognosis of patients with PC. Furthermore, m6A regulatory factors may influence the occurrence and development of PC through metabolic pathways, stroma activation pathways, immune regulatory processes, and the immune microenvironment. Finally, the overexpression of IGFBP3 promoted the growth of PC cells, and vice versa. CONCLUSIONS: Most m6A regulatory factors are differentially expressed in PC and are associated with the prognosis of patients with PC, potentially influencing the occurrence and development of PC through pathways such as the immune microenvironment. The overexpression of IGFBP3 can promote the growth of PC cells and vice versa.

IGFBP3↗

PubMed1976

Genetics and the etiology of childhood cancer.

A consideration of the world-wide incidences of childhood cancer and of hereditary subgroups leads to the conclusion that two successive mutations can initiate cancer cells and that such cells usually proceed to develop into detectable cancers in a period of time which is short compared with the time required for most adult cancers. Environmental carcinogens could hypothetically increase the rates at which these mutations occur, but they probably, in fact, contribute little to the incidences. Certain exceptions, notably leukemia and lymphoma, are noteworthy, and a viral origin for them has been widely hypothesized. If most solid tumors of childhood are indeed correctly attributable to mutations in germ and/or somatic cells, then the prospect for the prevention of childhood cancer becomes very dim. In fact, the incidence of the germinal forms may increase as treatment improves (18). In theory, one might be able to identify individuals harboring cancer genes germinally and even to identify them prenatally. But even if the burden of cancer attributable to the hereditary subgroups were elimanted, there would still remain the larger nonhereditary group resulting from somatic mutations. If this hypothesis is correct, then childhood cancer cannot be prevented. With this conclusion goes the admonition, however, that environmental mutagens might significantly increase the burden of childhood cancer. One such mutagen, therapeutic radiation, is known to increase the prospect that second tumors will occur in patients who carry a germinal cancer mutation. The major effort to reduce the incidence of childhood cancer by prevention should be spent in examining the possibility that leukemia and lymphoma are viral in origin. If the arguments presented are correct, then the main effort against childhood cancer must be that of early diagnosis and treatment. I realize that many have already argued for that strategy in the approach to cancer generally, but I now believe that it is particularly relevant to any program against cancer in children.

Child↗

PubMed2026

Functions and mechanisms of BRCA1 in early embryonic development.

Breast Cancer Gene 1 (BRCA1) is a critical regulator of genome integrity whose dysfunction greatly increases lifetime risk of breast and ovarian cancers. While BRCA1 has been extensively studied in the contexts of adult biology and cancer, its diverse functions, including homologous recombination-mediated DNA repair, cell cycle checkpoint activation, protein ubiquitination, and transcriptional regulation, have many underexplored implications. In early embryonic development, the maternal-to-zygotic transition (MZT) and subsequent developmental processes place extraordinary demands on DNA replication fidelity, cell cycle regulation, transcriptional activation, and chromatin remodeling. These critical processes overlap strikingly with canonical functions of BRCA1, yet its function in early development is poorly characterized. In this review, we investigate BRCA1 conservation across species and connect its well-established functions to findings from developmental studies to assess its role in development. We highlight evidence of BRCA1 mitigating genome integrity loss from diverse sources, maintaining the proliferative activity needed for successful germ layer formation and early tissue morphogenesis, and regulating transcription and epigenetic modifications. Together, this synthesis supports a model where BRCA1 acts as a multi-functional and dynamic regulator of early embryogenesis. Building on this, we propose outstanding questions that could further illuminate these developmental roles. Characterization of BRCA1 in early development may not only provide important insight into the origin and progression of cancer susceptibility but may also elucidate fundamental mechanisms shaping early development.

BRCA1 Protein↗

PubMed2026

Tumor-Intrinsic Blood and Imaging Correlatives in Advanced Prostate Cancer Treated with Combination Radiopharmaceutical Therapy and Immunotherapy.

The PRINCE trial showed the clinical activity for 177Lu-PSMA-617 in combination with pembrolizumab for metastatic castration-resistant prostate cancer. To refine patient selection and improve response monitoring strategies to this combination, we investigated candidate tumor-intrinsic biomarkers of treatment response and resistance. Methods: We performed circulating tumor DNA (ctDNA), circulating tumor cell (CTC), and PET imaging analyses at baseline, 12 wk on-treatment, and disease progression in participants enrolled in PRINCE (n = 37). We performed targeted sequencing for ctDNA quantification and genomic analysis of more than 70 prostate cancer genes. CTC enumeration was performed on the EpicSciences platform and was combined with selective single-cell whole-genome sequencing. PET imaging included serial PSMA PET as well as 18F-FDG PET imaging at baseline. Results: A low baseline ctDNA fraction and high PSMA avidity in metastatic lesions were linked to superior treatment responses and may have composite biomarker value. Genomic alterations in tumor suppressor genes TP53, RB1, or PTEN were associated with higher 18F-FDG avidity and metabolic tumor volume on 18F-FDG PET imaging and worse prognosis. At 12-wk on-treatment, both ctDNA detection and PSMA PET imaging were strong indicators of response depth and durability. At disease progression, PSMA expression on PET imaging was lower compared with baseline and supported by subclonal remodeling of ctDNA and CTC copy number profiles and by clonal expansions of tumor suppressor gene mutations. Conclusion: We provide the first integrated molecular and imaging insights into determinants of response and resistance to combined radiopharmaceutical therapy and immunotherapy in prostate cancer and propose biomarker strategies to inform future clinical development.

177Lu-PSMA-617↗

PubMed2026

Long-Read Haplotype Phasing Resolves Allelic Configuration as a Missing Layer of Precision Oncology.

Short-read sequencing cannot determine whether co-occurring variants within a cancer gene lie on the same allele (cis) or opposing alleles (trans), a distinction with direct therapeutic consequences: trans configurations confirm biallelic tumor suppressor inactivation, whereas cis configurations generate compound oncogenic alleles with enhanced activity. Among 768 patients with prostate, breast, or ovarian cancers, we used mutational signatures to nominate cryptic genomic instability cases lacking a causative biallelic event on short-read sequencing. Long-read nanopore sequencing resolved 32 of 46 cryptic cases (69.6%) through methylation detection, long insertion resolution, and structural variant characterization, confirming trans inactivation in every resolved tumor suppressor case. Analysis of 4,496 MiOncoSeq samples identified 17,519 multi-hit gene pairs, 78.7% of which exceeded the 500 bp short-read phasing limit, and long-read phasing revealed recurrent compound cis alleles in NOTCH1, PIK3CA, PDGFRB, and KIT. Haplotype phasing addresses an overlooked gap in cancer variant interpretation and warrants integration into precision oncology.

Journal Article↗

PubMed2026

Origin and evolution of colorectal mixed neuroendocrine-non-neuroendocrine neoplasms (MiNEN).

Colorectal neuroendocrine carcinoma (NEC) is a rare and aggressive cancer and in a subset of patients associated with an adenocarcinoma (AC) component. When both components exceed 30% of the tumour, it is classified as mixed neuroendocrine-non-neuroendocrine neoplasm (MiNEN), although there is an ongoing debate about whether any presence of two distinct components should be sufficient for a MiNEN diagnosis. This study aimed to investigate the origin and subsequent genetic changes of these two components. Ten colorectal cases suitable for sampling of an AC and a poorly differentiated NEC component were identified from the NORDIC NEC 2 study and sequenced across a 360-cancer gene panel. Mock phylogenetic trees were constructed from the molecular profiles of each sample within a patient. All ten cases revealed a common trunk of shared somatic mutations, including well-known colorectal cancer driver mutations such as BRAF, KRAS, APC, and TP53. In all cases, a single branching point separated the AC and NEC components. Private AC and NEC mutations generally had low variant allele frequencies, indicating that most AC and NEC cells were genetically similar. NEC, when compared with AC samples, demonstrated a higher frequency of private mutations (P = 0.009), indicating a higher mutation rate and greater ploidy (P = 0.012), suggesting an association between genomic duplication and AC-to-NEC transition. Shared mutations indicate a common clonal origin, underscoring the role of established colorectal driver mutations in the early development of these tumours, while the mechanisms underlying NEC differentiation remain poorly understood and may involve non-genetic factors.

Humans↗

PubMed2026

Long-Read Haplotype Phasing Resolves Allelic Configuration as a Missing Layer of Precision Oncology.

Conventional short-read sequencing cannot determine whether co-occurring variants within a cancer gene reside on the same allele (cis) or on opposing alleles (trans), a distinction with direct biological and therapeutic consequences. Trans configurations confirm biallelic tumor suppressor inactivation and inform therapy selection, while cis configurations generate compound oncogenic alleles with enhanced activity. We analyzed 768 patients with prostate, breast, or ovarian cancers in the PROBLEM cohort, using mutational signatures to nominate cryptic genomic instability cases where the causative biallelic event was not apparent from short-read sequencing. Long-read nanopore sequencing resolved 32 of 46 cryptic cases (69.6%), leveraging its unique advantages in direct methylation detection, long insertion resolution, and complex structural variant characterization, confirming trans biallelic inactivation in all resolved tumor suppressor cases. Systematic analysis of 4,496 MiOncoSeq samples identified 17,519 multi-hit gene pairs, of which 78.7% exceeded the 500 bp short-read phasing limit. Long-read phasing further revealed recurrent compound cis oncogenic alleles in NOTCH1, PIK3CA, PDGFRB, and KIT with functionally synergistic activity. Haplotype phasing resolves a systematically overlooked gap in cancer variant interpretation and warrants broader integration into precision oncology workflows.

Journal Article↗

PubMed2026

PRDM16 Regulates Prostate Cancer Cell Dormancy and Prevents Bone Metastatic Outgrowth.

UNLABELLED: Understanding dormancy in prostate cancer is challenging because of model availability. In this study, using murine and human prostate cancer cell lines, we generated a stress-induced model of dormancy in vitro and demonstrated that the phenotype could be sustained upon intrailiac artery delivery into the bone marrow microenvironment. RNA sequencing analysis revealed that the transcription factor positive regulatory domain-containing 16 (PRDM16) was commonly upregulated in dormant prostate cancer cells compared with controls. Furthermore, bone marrow-disseminated prostate cancer cells from primary orthotopic tumors were largely positive for PRDM16. Genetic ablation and forced ectopic expression supported a role for PRDM16 in maintaining prostate cancer dormancy in vitro and in vivo. Clinically, PRDM16 negatively correlated with disease recurrence and with the E2F cell-cycle program in disseminated tumor cells derived from the bone marrow of patients with prostate cancer. Gene enrichment and characterization studies implicated PRDM16 as a regulator of metabolic and cell-cycle pathways. Chromatin immunoprecipitation-qPCR further revealed that PRDM16 binds upstream of the promoter of RB1, a potent repressor of E2F activity. Overall, this study developed a straightforward method for inducing cancer cell dormancy and applied this approach to find that PRDM16 governs an intrinsic dormancy program in prostate cancer. SIGNIFICANCE: PRDM16 initiates a dormancy program in prostate cancer cells that is sustained in the bone marrow microenvironment, highlighting PRDM16 as a potential biomarker for relapse and target for eliminating dormant cancer cells.

Male↗