Pan-cancer analysis of whole genomes

The landscape of malignant transition: Unraveling cancer cell-of-origin and heterogeneous tissue microenvironment

Understanding disease progression and sophisticated tumor ecosystems is imperative for investigating tumorigenesis mechanisms and developing novel prevention strategies. Here, we dissected heterogeneous microenvironments during malignant transitions by leveraging data from 1396 samples spanning 13 major tissues. Within transitional stem-like subpopulations highly enriched in precancers and cancers, we identified 30 recurring cellular states strongly linked to malignancy, including hypoxia and epithelial senescence, revealing a high degree of plasticity in epithelial stem cells. By characterizing dynamics in stem-cell crosstalk with the microenvironment along the pseudotime axis, we found differential roles of ANXA1 at different stages of tumor development. In precancerous stages, reduced ANXA1 levels promoted monocyte differentiation toward M1 macrophages and inflammatory responses, whereas during malignant progression, upregulated ANXA1 fostered M2 macrophage polarization and cancer-associated fibroblast transformation by increasing TGF-β production. Our spatiotemporal analysis further provided insights into mechanisms responsible for immunosuppression and a potential target to control evolution of precancer and mitigate the risk for cancer development.

Charting the landscape of intratumoral microbiota in lung cancer: From bench to bedside

The intratumoral microbiota plays a critical role in lung cancer development, metastasis, and treatment response, offering valuable insights into the tumor microenvironment (TME) and revealing new therapeutic opportunities. Lung cancer remains the leading cause of cancer-related deaths worldwide, with the intratumoral microbiota exhibiting unique characteristics and functions within this disease. In this review, we summarized the origin of the intratumoral microbiota, its entry into the tumor, its detailed composition, functions, and its potential clinical applications in lung cancer. For the first time, we estimate the absolute abundance of different microbes in lung cancer, highlight the specific differences in microorganisms, and track their dynamic changes from health to disease. We also describe the overall landscape of intratumoral microbiota. Finally, we discuss the challenges and implications of this emerging field, offering insights for future research and therapeutic strategies.

Chromothripsis-associated chromosome 21 amplification orchestrates transformation to blast-phase MPN through targetable overexpression of DYRK1A

Chromothripsis, the chaotic shattering and repair of chromosomes, is common in cancer. Whether chromothripsis generates actionable therapeutic targets remains an open question. In a cohort of 64 patients in blast phase of a myeloproliferative neoplasm (BP-MPN), we describe recurrent amplification of a region of chromosome 21q ('chr. 21amp') in 25%, driven by chromothripsis in a third of these cases. We report that chr. 21amp BP-MPN has a particularly aggressive and treatment-resistant phenotype. DYRK1A, a serine threonine kinase, is the only gene in the 2.7-megabase minimally amplified region that showed both increased expression and chromatin accessibility compared with non-chr. 21amp BP-MPN controls. DYRK1A is a central node at the nexus of multiple cellular functions critical for BP-MPN development and is essential for BP-MPN cell proliferation in vitro and in vivo, and represents a druggable axis. Collectively, these findings define chr. 21amp as a prognostic biomarker in BP-MPN, and link chromothripsis to a therapeutic target.

A transient mutational burst occurs during yeast colony development

Characterizing the contribution of mutators to mutation accumulation is essential for understanding cellular adaptation and diseases like cancer. By measuring single and double mutation rates, including point mutations, segmental duplications, and reciprocal translocations, we found that wild-type yeast colonies exhibit double mutation rates up to 17 times higher than expected from experimentally determined single mutation rates. These double mutants retained wild-type mutation rates, indicating they originated from genetically normal cells that transiently expressed a mutator phenotype. Numerical simulations suggest that transient mutator subpopulations likely consist of less than a few thousand cells, and experience high-intensity mutational bursts for less than five generations. Most double mutations accumulated sequentially across cell cycles, with simultaneous acquisition being rare and likely linked to systemic genomic instability. Additionally, we explored the genetic control of transient hypermutation and found that the excess of double mutants can be modulated by replication stress and the DNA damage tolerance pathway. Our findings suggest that transient mutators play a significant role in genomic instability and contribute to the mutational load accumulating in growing isogenic populations.

Epithelial-to-mesenchymal transition (EMT) and cancer metastasis: the status quo of methods and experimental models 2025

Epithelial-to-mesenchymal transition (EMT) is a crucial cellular process for embryogenesis, wound healing, and cancer progression. It involves a shift in cell interactions, leading to the detachment of epithelial cells and activation of gene programs promoting a mesenchymal state. EMT plays a significant role in cancer metastasis triggering tumor initiation and stemness, and activates metastatic cascades resulting in resistance to therapy. Moreover, reversal of EMT contributes to the formation of metastatic lesions. Metastasis still needs to be better understood functionally in its major but complex steps of migration, invasion, intravasation, dissemination, which contributes to the establishment of minimal residual disease (MRD), extravasation, and successful seeding and growth of metastatic lesions at microenvironmentally heterogeneous sites. Therefore, the current review article intends to present, and discuss comprehensively, the status quo of experimental models able to investigate EMT and metastasis in vitro and in vivo, for researchers planning to enter the field. We emphasize various methods to understand EMT function and the major steps of metastasis, including diverse migration, invasion and matrix degradation assays, microfluidics, 3D co-culture models, spheroids, organoids, or latest spatial and imaging methods to analyze complex compartments. In vivo models such as the chorionallantoic membrane (CAM) assay, cell line-derived and patient-derived xenografts, syngeneic, genetically modified, and humanized mice, are presented as a promising arsenal of tools to analyze intravasation, site specific metastasis, and treatment response. Furthermore, we give a brief overview on methods detecting dissemination and MRD in carcinomas, highlighting its significance in tracking the course of disease and response to treatment. Enhanced lineage tracking tools, dynamic in vivo imaging, and therapeutically useful in vivo models as powerful preclinical tools may still better reveal functional interdependencies between metastasis and EMT. Future directions are discussed in light of emerging views on the biology, diagnosis, and treatment of EMT and metastasis.

Improving outcomes of patients with pancreatic cancer

Research studies aimed at improving the outcomes of patients with pancreatic ductal adenocarcinoma (PDAC) have brought about limited progress, and in clinical practice, the optimized use of surgery, chemotherapy and supportive care have led to modest improvements in survival that have probably reached a plateau. As a result, PDAC is expected to be the second leading cause of cancer-related death in Western societies within a decade. The development of therapeutic advances in PDAC has been challenging owing to a lack of actionable molecular targets, a typically immunosuppressive microenvironment, and a disease course characterized by rapid progression and clinical deterioration. Yet, the progress in our understanding of PDAC and identification of novel therapeutic opportunities over the past few years is leading to a strong sense of optimism in the field. In this Perspective, we address the aforementioned challenges, including biological aspects of PDAC that make this malignancy particularly difficult to treat. We explore specific areas with potential for therapeutic advances, including targeting mutant KRAS, novel strategies to harness the antitumour immune response and approaches to early detection, and propose mechanisms to improve clinical trial design and to overcome various community and institutional barriers to progress.

Cancer type and survival prediction based on transcriptomic feature map

This study achieved cancer type and survival time prediction by transforming transcriptomic features into feature maps and employing deep learning models. Using transcriptomic data from 27 cancer types and survival data from 10 types in the TCGA database, a pan-cancer transcriptomic feature map was constructed through data cleaning, feature extraction, and visualization. Using Inception network and gated convolutional modules yielded a pan-cancer classification accuracy of 91.8 %. Additionally, by extracting 31 differential genes from different cancer feature maps, an interaction network diagram was drawn, identifying two key genes, ANXA5 and ACTB. These genes are potential biomarkers related to cancer progression, angiogenesis, metastasis, and treatment resistance. Survival prediction analysis on 10 cancer types, combined with feature maps and data amplification, cancer survival prediction accuracy reached from 0.75 to 0.91. This transcriptomic feature map provides a novel approach for cancer omics analysis, to facilitate personalized treatments and reflecting individual differences.

Association of proteomics with lymph node metastasis in early gastric cancer patients

Surgical decision making for early gastric cancer (EGC) is heavily influenced by its metastasis into the lymph nodes. Currently, the clinicopathological features of EGC cannot be used to accurately distinguish between EGC patients with and without lymph node metastasis. Our retrospective case-matching study included a total of 132 samples from 66 pairs of EGC patients with or without lymph node metastasis and conducted proteomic assays. By comparing the lymph node metastasis group and the nonmetastasis group, we found that two proteins, GABARAPL2 and NAV1, were significantly associated with lymph node metastasis in EGC patients. Our prediction model using protein biomarkers had good prediction accuracy, with an area under the curve (AUC) of 0.87, a sensitivity of 0.78, a specificity of 0.89, and an accuracy of 0.84, which can help distinguish between EGC patients with and without lymph node metastasis and guide the decision-making process for performing tailored surgery.

A systematic evaluation of the therapeutic potential of endogenous-ADAR editors in cancer prevention and treatment

Adenosine deaminases acting on RNA (ADAR) enzymes constitute a natural cellular mechanism that induces A-to-I(G) editing, introducing genetic changes at the RNA level. Recently, interest in the endogenous-ADAR editor has emerged for correcting genetic mutations, consisting of a programmed oligonucleotide that attracts the native ADAR, thereby offering opportunities for medical therapy. Here, we systematically chart the scope of cancer mutations that endogenous-ADAR can correct. First, analyzing germline single nucleotide variants in cancer predisposition genes, we find that endogenous-ADAR can revert a fifth of them, reducing the risk of cancer development later in life. Second, examining somatic mutations across various cancer types, we find that it has the potential to correct at least one driver mutation in over a third of the samples, suggesting a promising future treatment strategy. We also highlight key driver mutations that are amenable to endogenous-ADAR, and are thus of special clinical interest. As using endogenous-ADAR entails delivering relatively small payloads, the prospects of delivering endogenous-ADAR to various cancers seem promising. We expect that the large scope of correctable mutations that are systematically charted here for the first time will pave the way for a new era of cancer treatment options.

Targeting metastasis in paediatric bone sarcomas

Paediatric bone sarcomas (e.g. Ewing sarcoma, osteosarcoma) comprise significant biological and clinical heterogeneity. This extreme heterogeneity affects response to systemic therapy, facilitates inherent and acquired drug resistance and possibly underpins the origins of metastatic disease, a key component implicit in cancer related death. Across all cancers, metastatic models have offered competing accounts on when dissemination occurs, either early or late during tumorigenesis, whether metastases at different foci arise independently and directly from the primary tumour or give rise to each other, i.e. metastases-to-metastases dissemination, and whether cell exchange occurs between synchronously growing lesions. Although it is probable that all the above mechanisms can lead to metastatic disease, clinical observations indicate that distinct modes of metastasis might predominate in different cancers. Around 70% of patients with bone sarcoma experience metastasis during their disease course but the fundamental molecular and cell mechanisms underlying spread are equivocal. Newer therapies such as tyrosine kinase inhibitors have shown promise in reducing metastatic relapse in trials, nonetheless, not all patients respond and 5-year overall survival remains at ~ 50%. Better understanding of potential bone sarcoma biological subgroups, the role of the tumour immune microenvironment, factors that promote metastasis and clinical biomarkers of prognosis and drug response are required to make progress. In this review, we provide a comprehensive overview of the approaches to manage paediatric patients with metastatic Ewing sarcoma and osteosarcoma. We describe the molecular basis of the tumour immune microenvironment, cell plasticity, circulating tumour cells and the development of the pre-metastatic niche, all required for successful distant colonisation. Finally, we discuss ongoing and upcoming patient clinical trials, biomarkers and gene regulatory networks amenable to the development of anti-metastasis medicines.

Discovery of candidate functional non-coding mutations in acute myeloid leukemia using single-cell chromatin accessibility sequencing

Mutations and gene rearrangements are crucial for the diagnosis and subtyping of acute myeloid leukemia (AML). However, the contribution of non-coding genetic variants, particularly those within cis-regulatory elements (CREs), to AML pathophysiology and heterogeneity remains poorly understood. In this study, we characterize the single-cell chromatin accessibility landscapes of 10 bone marrow samples from AML patients at diagnosis. Additionally, we develop eMut, an integrated computational pipeline for detecting, imputing, and functionally characterizing non-coding mutations in CREs at the single-cell level. Our analysis identifies 2878 potential somatic non-coding mutations, highlighting the extensive mutational heterogeneity in the non-coding genome of AML patients, with recurrent non-coding mutations displaying cell type-specific patterns. We show that mutated CREs are enriched with blood-related genetic variants, potentially linked to AML-associated genes, and harbor a higher abundance of functional CREs, suggesting their functional relevance in leukemogenesis. Importantly, we pinpoint candidate functional non-coding mutations that associate with alteration of target gene expression in AML. Collectively, our work provides a comprehensive resource of single-cell chromatin accessibility in AML and introduces an integrative approach to identify candidate functional non-coding mutations contributing to cellular heterogeneity in AML.

Modulating immune cells within pancreatic ductal adenocarcinoma via nanomedicine

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive malignancy characterized by a dense extracellular matrix (ECM) and a uniquely immunosuppressive tumor microenvironment (TME), which together form a formidable barrier that hinders deep drug penetration, limiting the efficacy of conventional therapies and leading to poor patient outcomes. Nanocarrier technology emerges as a promising strategy to improve treatment efficacy in PDAC. Nanocarriers can not only improve drug penetration through their adjustable physicochemical properties but also effectively regulate immune cell function in pancreatic cancer TME and promote anti-tumor immune response. This mini-review discusses the effects of nanocarriers on the immune microenvironment of PDAC, analyzing their mechanisms in modulating immune cells, overcoming ECM barriers, and reshaping the TME.

POCALI: Prediction and Insight on CAncer LncRNAs by Integrating Multi-Omics Data with Machine Learning

Long non-coding RNAs (lncRNAs) are receiving increasing attention as biomarkers for cancer diagnosis and therapy. Although there are many computational methods to identify cancer lncRNAs, they do not comprehensively integrate multi-omics features for predictions or systematically evaluate the contribution of each omics to the multifaceted landscape of cancer lncRNAs. In this study, an algorithm, POCALI, is developed to identify cancer lncRNAs by integrating 44 omics features across six categories. The contributions of different omics are explored to identifying cancer lncRNAs and, more specifically, how each feature contributes to a single prediction. The model is evaluated and benchmarked POCALI with existing methods. Finally, the cancer phenotype and genomics characteristics of the predicted novel cancer lncRNAs are validated. POCALI identifies secondary structure and gene expression-related features as strong predictors of cancer lncRNAs, and epigenomic features as moderate predictors. POCALI performed better than other methods, especially in terms of sensitivity, and predicted more candidates. Novel POCALI-predicted cancer lncRNAs have strong relationships with cancer phenotypes, similar to known cancer lncRNAs. Overall, this study facilitates the identification of previously undetected cancer lncRNAs and the comprehensive exploration of the multifaceted feature contributions to cancer lncRNA prediction.

Small intestinal neuroendocrine tumors lack early genomic drivers, acquire DNA repair defects and harbor hallmarks of low REST expression

The tumorigenesis of small intestinal neuroendocrine tumors (siNETs) is not understood and comprehensive genomic and transcriptomic data sets are limited. Therefore, we performed whole genome and transcriptome analysis of 39 well differentiated siNET samples. Our genomic data revealed a lack of recurrent driver mutations and demonstrated that multifocal siNETs from individual patients can arise genetically independently. We detected germline mutations in Fanconi anemia DNA repair pathway (FANC) genes, involved in homologous recombination (HR) DNA repair, in 9% of patients and found mutational signatures of defective HR DNA repair in late-stage tumor evolution. Furthermore, transcriptomic analysis revealed low expression of the transcriptional repressor REST. Summarizing, we identify a novel common transcriptomic signature of siNETs and demonstrate that genomic alterations alone do not explain initial tumor formation, while impaired DNA repair likely contributes to tumor evolution and represents a potential pharmaceutical target in a subset of patients.

Global DNA methylation differences involving germline structural variation impact gene expression in pediatric brain tumors

The extent of genetic variation and its influence on gene expression across multiple tissue and cellular contexts is still being characterized, with germline Structural Variants (SVs) being historically understudied. DNA methylation also represents a component of normal germline variation across individuals. Here, we combine germline SVs (by short-read sequencing) with tumor DNA methylation across 1292 pediatric brain tumor patients. For thousands of methylation probes for CpG Islands (CGIs) or enhancers, rare and common SV breakpoints upstream or downstream associate with differential methylation in tumors spanning various histologic types, a significant subset involving genes with SV-associated differential expression. Cancer predisposition genes involving SV-associated differential methylation and expression include MSH2, RSPA, and PALB2. SV breakpoints falling within CGIs or histone marks H3K36me3 or H3K9me3 associate with differential CGI methylation. Genes with SVs and CGI methylation associated with patient survival include POLD4. Our results capture a class of normal phenotypic variation having disease implications.

Tumor diagnosis recharacterization enabled by comprehensive genomic profiling to guide precision medicine strategy

Comprehensive genomic profiling (CGP) via next-generation sequencing is standard clinical practice for advanced and metastatic cancers in the U.S. and can help identify clinically actionable alterations in patients who may benefit from targeted therapies. CGP can also complement clinicopathological findings and in certain cases, may lead to diagnostic recharacterization resulting in more precise therapeutic strategies. Here, we highlight examples where molecular findings resulted in tumor re-evaluation and subsequent recharacterization. Twenty-eight cases where CGP results were inconsistent with initial pathological diagnosis and clinical presentation were selected for secondary clinicopathological review to explore alternative diagnostic explanations more consistent with the genomic results. Genomic profiling identified clinically actionable and prognostic variants leading to more accurate therapeutic recommendations based on the updated diagnoses highlighting the value of CGP beyond biomarker detection for therapy selection and supporting its complementary use in diagnostic confirmation to unveil opportunities for precision medicine strategies.

GEPIA3: Enhanced drug sensitivity and interaction network analysis for cancer research

The GEPIA series has provided robust and widely used tools for pan-cancer analysis of gene expression data. In the post-genomic era, a major challenge lies in deconvoluting complex regulatory relationship influenced by multiple factors and discovering gene-based precision therapeutics. Here we present GEPIA3, an advanced version of GEPIA that provides a comprehensive analysis of gene/protein interactions across various cancer types. This version facilitates the investigation of treatment sensitivity utilizing both real-world patient data and cell line screens for over 1000 therapeutic agents, as well as the integration of RNA alterations derived from the pan-cancer analysis of whole genomes project. GEPIA3 represents a significant enhancement of the original platform, enabling in-depth exploration of gene regulation and cancer phenotypes, thereby supporting the identification of novel biomarkers and therapeutic targets. GEPIA3 is publicly accessible at https://gepia3.bioinfoliu.com.

WorkflowHub: a registry for computational workflows

The rising popularity of computational workflows is driven by the need for repetitive and scalable data processing, sharing of processing know-how, and transparent methods. As both combined records of analysis and descriptions of processing steps, workflows should be reproducible, reusable, adaptable, and available. Workflow sharing presents opportunities to reduce unnecessary reinvention, promote reuse, increase access to best practice analyses for non-experts, and increase productivity. In reality, workflows are scattered and difficult to find, in part due to the diversity of available workflow engines and ecosystems, and because workflow sharing is not yet part of research practice. WorkflowHub provides a unified registry for all computational workflows that links to community repositories, and supports both the workflow lifecycle and making workflows findable, accessible, interoperable, and reusable (FAIR). By interoperating with diverse platforms, services, and external registries, WorkflowHub adds value by supporting workflow sharing, explicitly assigning credit, enhancing FAIRness, and promoting workflows as scholarly artefacts. The registry has a global reach, with hundreds of research organisations involved, and more than 800 workflows registered.

Evaluating topography of mutational signatures with SigProfilerTopography

The mutations found in a cancer genome are shaped by diverse processes, each displaying a characteristic mutational signature that may be influenced by the genome's architecture. While prior analyses have evaluated the effect of topographical genomic features on mutational signatures, there has been no computational tool that can comprehensively examine this interplay. Here, we present SigProfilerTopography, a Python package that allows evaluating the effect of chromatin organization, histone modifications, transcription factor binding, DNA replication, and DNA transcription on the activities of different mutational processes. SigProfilerTopography elucidates the unique topographical characteristics of mutational signatures, unveiling their underlying biological and molecular mechanisms.

Immune checkpoint inhibitor (ICI) therapy in central nervous system cancers: State-of-the-art and future outlook

Invasive central nervous system (CNS) cancers are an area where the development of breakthrough therapies is urgently needed. For instance, conditions such as glioblastoma multiforme (GBM) are associated with poor clinical prognosis, with the majority of trials offering no improvement to marginally enhanced survival. Unleashing the potential of targeting the immune system in CNS cancers has gained attention in recent years. Inhibition of immune checkpoints such as CTLA-4, PD-1/PD-L1, TIM-3, and LAG-3 has been attempted in recent trials. While potentially offering a notable edge over other immunotherapies, multi-organ adverse events have been found with the administration of immune checkpoint inhibitors (ICIs). The present review captures the state-of-the-art evidence on ICI treatments in different CNS cancers. Also, we discuss the value of combinational therapies involving ICIs as well as next-generation therapeutics such as bispecific antibodies targeting PD-1/LAG-3/TIM-3 and CRISPR-Cas9-edited PD-1-knock-out checkpoint-resistant CAR T-cells.

Macromolecular interactions dictate Polycomb-mediated epigenetic repression

The dynamic regulation of epigenetic states relies on complex macromolecular interactions. PRC2, the methyltransferase complex responsible for depositing H3K27me3, interacts with distinct accessory proteins to form the mutually exclusive subcomplexes PHF1-PRC2.1, MTF2-PRC2.1, PHF19-PRC2.1, and PRC2.2. The functions of these subcomplexes are unclear and thought to be highly redundant. Here we show that PRC2 subcomplexes have distinct roles in epigenetic repression of lineage-specific genes and stem cell differentiation. Using a human pluripotent stem cell model, we engineered a comprehensive set of separation-of-function mutants to dissect the roles of individual protein-protein and DNA-protein interactions. Our results show that PRC2.1 and PRC2.2 deposit H3K27me3 locus-specifically, resulting in opposing outcomes in cardiomyocyte differentiation. We find that MTF2 stimulates PRC2.1-mediated repression in stem cells and cardiac differentiation through its interaction with DNA and H3K36me3, while PHF19 antagonizes it. Furthermore, MTF2-PRC2.1 maintains normal cardiomyocyte function. Together, these results reveal the importance and specificity of individual macromolecular interactions in Polycomb-mediated epigenetic repression in human stem cells and differentiation.

FADD Functions as an Oncogene in Chr11q13.3-Amplified Head and Neck Squamous Cell Carcinoma

Chromosomal 11q13.3 amplification is the most common gene copy-number variation event in head and neck squamous cell carcinoma (HNSCC) that corresponds with poor prognosis. Although cyclin D1, a G1/S phase cell-cycle regulatory protein at this locus, is considered as a key driver of malignant progression, further exploration is needed to develop more effective targets for cases with this amplification. Using CRISPR-based gene knockout screening of genes located in chr11q13.3, we found that loss of the gene encoding the Fas-associated death domain (FADD) protein, a well-recognized adapter to caspase-8 that induces cell apoptosis, significantly reduced cancer cell proliferation. FADD expression was elevated in chr11q13.3-amplified tumors and correlated with poor prognosis. RNA sequencing, mass spectrometry, and proteomics analyses revealed a direct relationship between FADD and the DNA helicase MCM5 in the S phase. FADD and cyclin D1 acted at different stages of the cell cycle to synergistically induce proliferation, and caspase-8 deficiency was required for the oncogenic activity of FADD. In a patient-derived xenograft model with chr11q13.3 amplification, combined administration of the DNA helicase complex inhibitor and CDK4/6 inhibitor effectively curtailed tumor growth. Overall, this study identified a nonclassic oncogenic role for FADD in mediating tumor progression in HNSCC and provided a feasible treatment option for patients with chr11q13.3 amplification. Significance: FADD promotes progression of tumors with chr11q13.3 amplification by binding to the DNA helicase complex, which can be targeted in combination with cyclin D1 as a viable therapeutic strategy for HNSCC patients.

Dynamic clustering of genomics cohorts beyond race, ethnicity-and ancestry

BACKGROUND

Recent decades have witnessed a steady decrease in the use of race categories in genomic studies. While studies that still include race categories vary in goal and type, these categories already build on a history during which racial color lines have been enforced and adjusted in the service of social and political systems of power and disenfranchisement. For early modern classification systems, data collection was also considerably arbitrary and limited. Fixed, discrete classifications have limited the study of human genomic variation and disrupted widely spread genetic and phenotypic continuums across geographic scales. Relatedly, the use of broad and predefined classification schemes-e.g. continent-based-across traits can risk missing important trait-specific genomic signals.

METHODS

To address these issues, we introduce a dynamic approach to clustering human genomics cohorts based on genomic variation in trait-specific loci and without using a set of predefined categories. We tested the approach on whole-exome sequencing datasets in ten cancer types and partitioned them based on germline variants in cancer-relevant genes that could confer cancer type-specific disease predisposition.

RESULTS

Results demonstrate clustering patterns that transcend discrete continent-based categories across cancer types. Functional analysis based on cancer type-specific clusterings also captures the fundamental biological processes underlying cancer, differentiates between dynamic clusters on a functional level, and identifies novel potential drivers overlooked by a predefined continent-based clustering.

CONCLUSIONS

Through a trait-based lens, the dynamic clustering approach reveals genomic patterns that transcend predefined classification categories. We propose that coupled with diverse data collection, new clustering approaches have the potential to draw a more complete portrait of genomic variation and to address, in parallel, technical and social aspects of its study.

SMARCA4 Inhibits Breast Cancer Progression and Metastasis through RHOA Suppression

Triple-negative breast cancer (TNBC) is the most challenging subtype of the disease due to its aggressive nature and lack of targeted therapy options. To identify regulators of TNBC, we conducted a genome-wide CRISPR knockout screen in both three-dimensional (3D) tumor spheroid and two-dimensional cell culture models. The 3D spheroid model displayed unique potential in identifying putative tumor suppressors because of its closer mimicry of in vivo tumor growth conditions. Notably, the chromatin remodeling SWI/SNF complex emerged as a potent suppressor of tumor spheroid growth. Specifically, loss of the SWI/SNF ATPase subunit SMARCA4 promoted tumor spheroid growth with reduced compactness and enhanced primary tumor growth and metastasis across multiple TNBC models. SMARCA4 was required for the transcription of the Rho GTPase-activating factor ARHGAP29 by enhancing DNA accessibility through direct binding to its promoter. SMARCA4 loss resulted in reduced ARHGAP29 levels and hyperactive RHOA signaling, subsequently disrupting cell adhesion, facilitating the formation of a loose spheroid structure in vitro, and enhancing breast cancer growth and metastasis in vivo. These results establish SMARCA4 and SWI/SNF as tumor suppressors of TNBC through suppression of RHOA activity. Significance: CRISPR-knockout screen in 3D tumor spheroid revealed that SMARCA4, a SWI/SNF ATPase subunit, suppresses triple-negative breast cancer growth and metastasis by increasing ARHGAP29 transcription and inhibiting the RHOA signaling pathway.

Tumor break load quantitates structural variant-associated genomic instability with biological and clinical relevance across cancers

While structural variants (SVs) are a clear sign of genomic instability, they have not been systematically quantified per patient since declining costs have only recently enabled large-scale profiling. Therefore, the biological and clinical impact of high numbers of SVs in patients is unknown. We introduce tumor break load (TBL), defined as the sum of unbalanced SVs, as a measure for SV-associated genomic instability. Using pan-cancer data from TCGA, PCAWG, and CCLE, we show that a high TBL is associated with significant changes in gene expression in 26/31 cancer types that consistently involve upregulation of DNA damage repair and downregulation of immune response pathways. Patients with a high TBL show a higher risk of recurrence and shorter median survival times for 5/15 cancer types. Our data demonstrate that TBL is a biologically and clinically relevant feature of genomic instability that may aid patient prognostication and treatment stratification. For the datasets analyzed in this study, TBL has been made available in cBioPortal.

ncRNAs as Key Regulators in Gastric Cancer: From Molecular Subtyping to Therapeutic Targets

Gastric cancer (GC) poses a major global health challenge, underscoring the need for advanced diagnostic and therapeutic approaches. Non-coding RNAs (ncRNAs), including microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and circular RNAs (circRNAs), have emerged as pivotal regulators in GC, with their dysregulated expression driving key processes such as tumorigenesis, metastasis, immune evasion, and chemoresistance. The functional diversity of ncRNAs across different GC molecular subtypes highlights their potential as biomarkers for improved subtype classification and patient stratification. Beyond their diagnostic value, ncRNAs demonstrate critical regulatory functions in tumor biology, establishing these RNA molecules as promising targets for therapeutic development. Strategies based on RNA hold considerable promise for addressing critical challenges such as immune escape and drug resistance by modulating key signaling pathways. These approaches can enhance immune responses, reprogram the tumor microenvironment, and reverse resistance mechanisms that compromise treatment efficacy, thereby improving clinical outcomes. Although ncRNAs represent a promising frontier in GC precision medicine, further research is required to fully harness their clinical potential.

Telomeres and Telomerase: Targets for Chemoprevention of Hepatocellular Carcinoma With Bioactive Food Compounds

The maintenance of telomere length by telomerase plays an essential role in senescence, aging, and cancer. Mutations in the TERT promoter, a telomerase subunit, are frequent in human cancers. In hepatocellular carcinoma (HCC), telomere shortening contributes to preneoplastic conditions such as cirrhosis. Telomerase activation during cirrhosis may reduce chromosomal instability, while its suppression in early dysplastic nodules may prevent hepatocarcinogenesis. Evidence suggests that bioactive food compounds (BFCs) can reduce the incidence and/or delay the onset of HCC by modulating telomerase activity. A systematic review was conducted on the role of BFCs in telomerase activity during hepatocarcinogenesis. BFCs were analyzed in isolated form or as part of extracts and categorized into fatty acids, isoprenoids, isothiocyanates, and phenolic compounds. Despite structural diversity, BFCs modulate telomerase through common mechanisms, including inhibition of activating proteins at the TERT promoter, activation of nuclear receptors, or histone H3 hyperacetylation. Indirectly, telomerase can also be modulated via activation of antioxidant defense pathways. Understanding telomerase reactivation and its modulation by BFCs is key to establishing effective HCC chemoprevention strategies targeting telomerase.

RNA transcripts serve as a template for double-strand break repair in human cells

Double-strand breaks (DSBs) are toxic lesions that lead to genome instability. While canonical DSB repair pathways typically operate independently of RNA, growing evidence suggests that RNA:DNA hybrids and nearby transcripts can influence repair outcomes. However, whether transcript RNA can directly serve as a template for DSB repair in human cells remains unclear. In this study, we develop fluorescence and sequencing-based assays to show that RNA-containing oligonucleotides and messenger RNA can serve as templates during DSB repair. We conduct a CRISPR/Cas9-based genetic screen to identify factors that promote RNA-templated DSB repair (RT-DSBR). Of the candidate polymerases, we identify DNA polymerase zeta (Polζ) as a potential reverse transcriptase that facilitates RT-DSBR. Furthermore, analysis of cancer genome sequencing data reveals whole intron deletions - a distinct genomic signature of RT-DSBR that occurs when spliced mRNA guides repair. Altogether, our findings highlight RT-DSBR as an alternative pathway for repairing DSBs in transcribed genes, with potential mutagenic consequences.

ProgModule: A novel computational framework to identify mutation driver modules for predicting cancer prognosis and immunotherapy response

BACKGROUND

Cancer originates from dysregulated cell proliferation driven by driver gene mutations. Despite numerous algorithms developed to identify genomic mutational signatures, they often suffer from high computational complexity and limited clinical applicability.

METHODS

Here, we presented ProgModule, an advanced computational framework designed to identify mutation driver modules for cancer prognosis and immunotherapy response prediction. In ProgModule, we introduced the Prognosis-Related Mutually Exclusive Mutation (PRMEM) score, which optimizes the balance between exclusive mutation coverage and the incorporation of mutation combination mechanisms critical for cancer prognosis.

RESULTS

Applying to BLCA and HNSC cohorts, ProgModule successfully identified driver modules that stratify patients into distinct prognostic subgroups, and the combination of these modules could serve as an effective prognostic biomarker. Extending our method to diverse cancers, ProgModule presented robust prognostic performance and stability across model parameters, including stopping criteria and network topology. Moreover, our analysis suggested that driver modules can predict immunotherapeutic benefit more effectively than existing signatures. Further analyses based on published CRISPR data indicated that genes within these modules may serve as potential therapeutic targets.

CONCLUSIONS

Altogether, ProgModule emerges as a powerful tool for identifying mutation driver modules as prognostic and immunotherapy response biomarkers, and genes within these modules may be used as potential therapeutic targets for cancer, offering new insights into precision oncology.

Evoking the Cancer-immunity cycle by targeting the tumor-specific antigens in Cancer immunotherapy

Cancer-related deaths continue to rise, largely due to the suboptimal efficacy of current treatments. Fortunately, immunotherapy has emerged as a promising alternative, offering new hope for cancer patients. Among various immunotherapy approaches, targeting tumor-specific antigens (TSAs) has gained particular attention due to its demonstrated success in clinical settings. Despite these advancements, there are still gaps in our understanding of TSAs. Therefore, this review explores the life cycle of TSAs in cancer, the methods used to identify them, and recent advances in TSAs-targeted cancer therapies. Enhancing medical professionals' understanding of TSAs will help facilitate the development of more effective TSAs-based cancer treatments.

Potential shared neoantigens from pan-cancer transcript isoforms

Isoform switching in cancer is a prevalent phenomenon with significant implications for immunotherapy, as actionable neoantigens derived from these cancer-specific events would be applicable to broad categories of patients, reducing the necessity for personalized treatments. By integrating five large-scale transcriptomic datasets comprising over 19,500 samples across 29 cancer and 54 normal tissue types, we identified cancer-associated isoform switching events common to multiple cancer types, several of which involve genes with established mechanistic roles in oncogenesis. The presence of neoantigen-containing peptides derived from these transcripts was confirmed in broad cancer and normal tissue proteome datasets and the binding affinity of predicted neoantigens to the human leukocyte antigen (HLA) complex via molecular dynamics simulations. The study presents strong evidence that isoform switching in cancer is a significant source of actionable neoantigens that have the capability to trigger an immune response. These findings suggest that isoform switching events could potentially be leveraged for broad immunotherapeutic strategies across various cancer types.

Genomic landscape of diffuse glioma revealed by whole genome sequencing

Diffuse gliomas are the commonest malignant primary brain tumour in adults. Herein, we present analysis of the genomic landscape of adult glioma, by whole genome sequencing of 403 tumours (256 glioblastoma, 89 astrocytoma, 58 oligodendroglioma; 338 primary, 65 recurrence). We identify an extended catalogue of recurrent coding and non-coding genetic mutations that represents a source for future studies and provides a high-resolution map of structural variants, copy number changes and global genome features including telomere length, mutational signatures and extrachromosomal DNA. Finally, we relate these to clinical outcome. As well as identifying drug targets for treatment of glioma our findings offer the prospect of improving treatment allocation with established targeted therapies.

Role of noncoding RNA and protein interaction in pancreatic cancer

ABSTRACT

Noncoding RNAs (ncRNAs) are a class of RNA molecules with little or no protein-coding potential. Emerging evidence indicates that ncRNAs are frequently dysregulated and play pivotal roles in the pathogenesis of pancreatic cancer. Their aberrant expression can arise from chromosomal abnormalities, dysregulated transcriptional control, and epigenetic modifications. ncRNAs function as protein scaffolds or molecular decoys to modulate interactions between proteins and other biomolecules, thereby regulating gene expression and contributing to pancreatic cancer progression. In this review, we summarize the mechanisms underlying ncRNA dysregulation in pancreatic cancer, emphasize the biological significance of ncRNA-protein interactions, and highlight their clinical relevance. A deeper understanding of ncRNA-protein interactions is essential to elucidate molecular mechanisms and advance translational research in pancreatic cancer.

The Germline and Somatic Origins of Prostate Cancer Heterogeneity

SIGNIFICANCE

This study uncovered 223 recurrently mutated driver regions using the largest cohort of prostate tumors to date. It reveals associations between germline SNPs, somatic drivers, and tumor aggression, offering significant insights into how prostate tumor evolution is shaped by germline factors and the timing of somatic mutations.

A 3D Genome Atlas of Genetic Variants and Their Pathological Effects in Cancer

The hierarchical organization of the eukaryotic genome is crucial for nuclear activities and cellular development. Genetic aberrations can disrupt this 3D genomic architecture, potentially driving oncogenesis. However, current research often lacks a comprehensive perspective, focusing on specific mutation types and singular 3D structural levels. Here, pathological changes from chromosomes to nucleotides are systematically cataloged, including 10 789 interchromosomal translocations (ICTs), 18 863 structural variants (SVs), and 162 769 single nucleotide polymorphisms (SNPs). The multilayered analysis reveals that fewer than 10% of ICTs disrupt territories via potent 3D interactions, and only a minimal fraction of SVs disrupt compartments or intersect topologically associated domain structures, yet these events significantly influence gene expression. Pathogenic SNPs typically show reduced interactions within the 3D genomic space. To investigate the effects of variants in the context of 3D organization, a two-phase scoring algorithm, 3DFunc, is developed to evaluate the pathogenicity of variant-gene pairs in cancer. Using 3DFunc, IGHV3-23's critical role in chronic lymphocytic leukemia is identified and it is found that three pathological SNPs (rs6605578, rs7814783, rs2738144) interact with DEFA3. Additionally, 3DGAtlas is introduced, which provides a highly accessible 3D genome atlas and a valuable resource for exploring the pathological effects of genetic mutations in cancer.

A multi-omics analysis of effector and resting treg cells in pan-cancer

Regulatory T cells (Tregs) are critical for maintaining the stability of the immune system and facilitating tumor escape through various mechanisms. Resting T cells are involved in cell-mediated immunity and remain in a resting state until stimulated, while effector T cells promote immune responses. Here, we investigated the roles of two gene signatures, one for resting Tregs (FOXP3 and IL2RA) and another for effector Tregs (FOXP3, CTLA-4, CCR8 and TNFRSF9) in pan-cancer. Using data from The Cancer Genome Atlas (TCGA), The Cancer Proteome Atlas (TCPA) and Gene Expression Omnibus (GEO), we focused on the expression profile of the two signatures, the existence of single nucleotide variants (SNVs) and copy number variants (CNVs), methylation, infiltration of immune cells in the tumor and sensitivity to different drugs. Our analysis revealed that both signatures are differentially expressed across different cancer types, and correlate with patient survival. Furthermore, both types of Tregs influence important pathways in cancer development and progression, like apoptosis, epithelial-to-mesenchymal transition (EMT) and the DNA damage pathway. Moreover, a positive correlation was highlighted between the expression of gene markers in both resting and effector Tregs and immune cell infiltration in adrenocortical carcinoma, while mutations in both signatures correlated with enrichment of specific immune cells, mainly in skin melanoma and endometrial cancer. In addition, we reveal the existence of widespread CNVs and hypomethylation affecting both Treg signatures in most cancer types. Last, we identified a few correlations between the expression of CCR8 and TNFRSF9 and sensitivity to several drugs, including COL-3, Chlorambucil and GSK1070916, in pan-cancer. Overall, these findings highlight new evidence that both Treg signatures are crucial regulators of cancer progression, providing potential clinical outcomes for cancer therapy.

Tracking single-cell evolution using clock-like chromatin accessibility loci

Single-cell chromatin accessibility sequencing (scATAC-seq) reconstructs developmental trajectory by phenotypic similarity. However, inferring the exact developmental trajectory is challenging. Previous studies showed age-associated DNA methylation (DNAm) changes in specific genomic regions, termed clock-like differential methylation loci (ClockDML). Age-associated DNAm could either result from or result in chromatin accessibility changes at ClockDML. As cells undergo mitosis, the heterogeneity of chromatin accessibility on clock-like loci is reduced, providing a measure of mitotic age. In this study, we developed a method, called EpiTrace, that counts the fraction of opened clock-like loci from scATAC-seq data to determine cell age and perform lineage tracing in various cell lineages and animal species. It shows concordance with known developmental hierarchies, correlates well with DNAm-based clocks and is complementary with mutation-based lineage tracing, RNA velocity and stemness predictions. Applying EpiTrace to scATAC-seq data reveals biological insights with clinically relevant implications, ranging from hematopoiesis, organ development, tumor biology and immunity to cortical gyrification.

Exploring the link between low germline mutational load and low breast cancer incidence: Lessons from the Xavante Indians

The study of cancer, its initiation, and its mechanisms of progression has been a focal point in science for more than a century. Despite controversies among scientists, there is a growing consensus to determine the moment when a cell gains the capacity to be transformed and whether this mechanism is to be attributed to germinal or somatic events, or possibly both. The case of the Xavante Indians is a beacon for this journey, pointing toward the importance of genetic diversity in shaping our approach to cancer research and treatment. As we incorporated these lessons into clinical practice, we embarked on a new era of personalized preventative healthcare strategies against cancer. Based on recent data, we comment on the low germinal mutational load and low cancer incidence. Statistical analyses reveal a significantly lower mutation burden in Xavante women compared to global populations (p < 0.0001), including rare deleterious variants in cancer-associated genes. Additionally, polygenic risk scores (PRS) for breast cancer are markedly lower in Xavante (mean PRS ∼35) compared to TCGA cohorts (∼80-90) (p < 0.0001). The absence of breast cancer cases in Xavante is statistically significant when compared to expected rates (p < 0.001), reinforcing the hypothesis of a protective genetic landscape.

Genomic and fragmentomic landscapes of cell-free DNA for early cancer detection

Genomic analyses of cell-free DNA (cfDNA) in plasma are enabling noninvasive blood-based biomarker approaches to cancer detection and disease monitoring. Current approaches for identification of circulating tumour DNA typically use targeted tumour-specific mutations or methylation analyses. An emerging approach is based on the recognition of altered genome-wide cfDNA fragmentation in patients with cancer. Recent studies have revealed a multitude of characteristics that can affect the compendium of cfDNA fragments across the genome, collectively called the 'cfDNA fragmentome'. These changes result from genomic, epigenomic, transcriptomic and chromatin states of an individual and affect the size, position, coverage, mutation, structural and methylation characteristics of cfDNA. Identifying and monitoring these changes has the potential to improve early detection of cancer, especially using highly sensitive multi-feature machine learning approaches that would be amenable to broad use in populations at increased risk. This Review highlights the rapidly evolving field of genome-wide analyses of cfDNA characteristics, their comparison to existing cfDNA methods, and recent related innovations at the intersection of large-scale sequencing and artificial intelligence. As the breadth of clinical applications of cfDNA fragmentome methods have enormous public health implications for cancer screening and personalized approaches for clinical management of patients with cancer, we outline the challenges and opportunities ahead.

Precision proteogenomics reveals pan-cancer impact of germline variants

We investigate the impact of germline variants on cancer patients' proteomes, encompassing 1,064 individuals across 10 cancer types. We introduced an approach, "precision peptidomics," mapping 337,469 coding germline variants onto peptides from patients' mass spectrometry data, revealing their potential impact on post-translational modifications, protein stability, allele-specific expression, and protein structure by leveraging the relevant protein databases. We identified rare pathogenic and common germline variants in cancer genes potentially affecting proteomic features, including variants altering protein abundance and structure and variants in kinases (ERBB2 and MAP2K2) impacting phosphorylation. Precision peptidome analysis predicted destabilizing events in signal-regulatory protein alpha (SIRPA) and glial fibrillary acid protein (GFAP), relevant to immunomodulation and glioblastoma diagnostics, respectively. Genome-wide association studies identified quantitative trait loci for gene expression and protein levels, spanning millions of SNPs and thousands of proteins. Polygenic risk scores correlated with distal effects from risk variants. Our findings emphasize the contribution of germline genetics to cancer heterogeneity and high-throughput precision peptidomics.

Genomic landscape of cancer in racially and ethnically diverse populations

Cancer incidence and mortality rates can vary widely among different racial and ethnic groups, attributed to a complex interplay of genetic, environmental and social factors. Recently, substantial progress has been made in investigating hereditary genetic risk factors and in characterizing tumour genomes. However, most research has been conducted in individuals of European ancestries and, increasingly, in individuals of Asian ancestries. The study of germline and somatic genetics in cancer across racial and ethnic groups using omics technologies offers opportunities to identify similarities and differences in both heritable traits and the molecular features of cancer genomes. An improved understanding of population-specific cancer genomics, as well as translation of those findings across populations, will help reduce cancer disparities and ensure that personalized medicine and public health approaches are equitable across racial and ethnic groups.

A redefined InDel taxonomy provides insights into mutational signatures

Despite their deleterious effects, small insertions and deletions (InDels) have received far less attention than substitutions. Here we generated isogenic CRISPR-edited human cellular models of postreplicative repair dysfunction (PRRd), including individual and combined gene edits of DNA mismatch repair (MMR) and replicative polymerases (Pol ε and Pol δ). Unique, diverse InDel mutational footprints were revealed. However, the prevailing InDel classification framework was unable to discriminate these InDel signatures from background mutagenesis and from each other. To address this, we developed an alternative InDel classification system that considers flanking sequences and informative motifs (for example, longer homopolymers), enabling unambiguous InDel classification into 89 subtypes. Through focused characterization of seven tumor types from the 100,000 Genomes Project, we uncovered 37 InDel signatures; 27 were new. In addition to unveiling previously hidden biological insights, we also developed PRRDetect-a highly specific classifier of PRRd status in tumors, with potential implications for immunotherapies.

A Super-Assembled Synergistically Nanoplatform AP@ZIF-8Pt for Hepatocarcinoma Therapy

Introduction

Intensive cancer treatment with nanoplatform is widely exploited in the clinic, the emerging nanomedicine offers an unparalleled opportunity for encapsulating potential antitumor drugs in a nano-carrier. Apoptin (AP), a coding protein of VP3 gene, stem from the chicken anemia virus (CAV), can be activated in malignant cells selectively and prevents the dividing cancer cells from repairing their DNA lesions, thereby forcing them to undergo apoptosis. Herein, a three-step intelligent biodegradable drug delivery nanoplatform was designed.

Methods

First, a hollow ZIF-8 was synthesized, embedded with platinum nanoparticle to form ZIF-8Pt, and then loaded with AP, and lastly formed AP@ZIF-8Pt, which possess pH-responsive drug release and cancer-targeted ability.

Results

As expected, both in vitro and in vivo experiment demonstrated that AP@ZIF-8Pt performed treatment effects in hepatocarcinoma through relieving tumor-hypoxic microenvironment, inhibiting cell proliferation and promoting cell apoptosis. Further transcriptomic analysis showed that the specific mechanism of the AP@ZIF-8Pt was thermogenesis, signaling pathways regulating pluripotency of stem cells, ribosome, prion disease and PI3K-Akt signaling pathway.

Discussion

This work highlights a new strategy for liver cancer treatment and provides a reference for treating malignant tumors.

Error-corrected flow-based sequencing at whole-genome scale and its application to circulating cell-free DNA profiling

Differentiating sequencing errors from true variants is a central genomics challenge, calling for error suppression strategies that balance costs and sensitivity. For example, circulating cell-free DNA (ccfDNA) sequencing for cancer monitoring is limited by sparsity of circulating tumor DNA, abundance of genomic material in samples and preanalytical error rates. Whole-genome sequencing (WGS) can overcome the low abundance of ccfDNA by integrating signals across the mutation landscape, but higher costs limit its wide adoption. Here, we applied deep (~120×) lower-cost WGS (Ultima Genomics) for tumor-informed circulating tumor DNA detection within the part-per-million range. We further leveraged lower-cost sequencing by developing duplex error-corrected WGS of ccfDNA, achieving 7.7 × 10-7 error rates, allowing us to assess disease burden in individuals with melanoma and urothelial cancer without matched tumor sequencing. This error-corrected WGS approach will have broad applicability across genomics, allowing for accurate calling of low-abundance variants at efficient cost and enabling deeper mapping of somatic mosaicism as an emerging central aspect of aging and disease.

Advances in personalized medicine: translating genomic insights into targeted therapies for cancer treatment

Background

Personalized medicine has revolutionized cancer treatment by utilizing genomic insights to tailor therapies based on individual molecular profiles. This approach enhances therapeutic efficacy, minimizes adverse effects, and addresses tumor heterogeneity through precision-targeted interventions.

Methods

A scoping review was conducted through a comprehensive literature search in PubMed, using MeSH terms and keywords related to genomic profiling and targeted cancer therapies. Eligible studies included original research involving cancer patients who underwent genomic profiling and targeted therapies from January 1, 1950, to February 9, 2025.

Results

Advances in next-generation sequencing (NGS) and bioinformatics have accelerated the identification of clinically relevant mutations-such as epidermal growth factor receptor (EGFR) in non-small cell lung cancer (NSCLC) and BRAF V600E in melanoma-enabling the development of effective targeted therapies. Emerging technologies like clustered regularly interspaced short palindromic repeats (CRISPR) gene editing and artificial intelligence (AI) are further refining treatment selection by enabling more precise and adaptive therapeutic strategies. Despite these innovations, challenges persist regarding data interpretation, equitable access, costs, regulatory frameworks, and integration into routine clinical workflows.

Conclusions

Genomic profiling is central to the advancement of precision oncology. The convergence of genomics, gene editing, and AI is paving the way toward more personalized, efficient, and inclusive cancer care. Realizing the full potential of personalized medicine will require interdisciplinary collaboration, investment in infrastructure, and ethical oversight to ensure broad, equitable, and responsible implementation in clinical practice.

Epigenomic disorder and partial EMT impair luminal progenitor integrity in Brca1-associated breast tumorigenesis

In breast cancer related to the BRCA1 mutation, luminal progenitor cells are believed to be the cells of origin, yet how these cells transform into invasive cancer cells remain poorly understood. Here, we combine single-cell epigenomic and transcriptomic data to reconstitute sequences of events in luminal cells that lead to tumorigenesis. Upon deletion of Trp53 and Brca1, we find that luminal progenitors display an extensive epigenomic disorder associated with a loss of cell identity. These cells then progress to tumor formation through a partial epithelial-to-mesenchymal transition, orchestrated by Snail and the timely activation of immunosuppressive and FGF signaling with their microenvironment. In human samples, pre-tumoral changes can be detected in early stage, basal-like tumors, which rarely recur, as well as in normal-like mammary glands of BRCA1 mutation carriers who have had cancer. Our study fills critical gaps in our understanding of BRCA1-driven tumorigenesis, opening perspectives for the early monitoring of individuals with high cancer risk.

Serine metabolism in tumor progression and immunotherapy

Serine plays a vital role in various metabolic processes including the synthesis of proteins and other amino acids, which are essential for the cell proliferation and growth. Cancer cells either absorb exogenous serine or produce it through the serine synthesis pathway, enabling the generation of intracellular glycine and one-carbon units, which are crucial for nucleotide biosynthesis. This metabolic process, referred to as serine-glycine-one-carbon (SGOC) metabolism, is essential for tumorigenesis and exhibits considerable complexity and clinical significance. Enzymes involved in the SGOC pathway are linked to tumor growth, metastasis, and resistance to therapies. The SGOC pathway is a vital metabolic network that facilitates cell survival and proliferation, especially in aggressive cancers. Understanding how this network is regulated is crucial for tackling tumor heterogeneity and recurrence. This review emphasizes recent advancements in understanding the roles and effects of the SGOC metabolic pathway in the context of cancer progression. Additionally, it outlines the complex influences of the SGOC metabolic pathway on the tumor microenvironment (TME), offering potential strategies to enhance cancer immunotherapy.

Understanding the IDH1 missense SNPs on expression of genes involved in Glioblastoma multiforme

The IDH1 gene encodes isocitrate dehydrogenase 1 enzyme (IDH1), crucial in the citric acid cycle that converts isocitrate to alpha-ketoglutarate. Mutations in IDH1 at R132 lead to the production of the oncometabolite 2-hydroxyglutarate, which impacts cellular metabolism, differentiation, and epigenetic regulation, and is associated with GBM. This study utilized in silico methods (SIFT, PROVEAN, PolyPhen2, Predict SNP, MutPred2, InterPro, MusiteDeep, I-Mutant 3.0, MUpro, and INSP-MD) to identify high-risk missense SNPs in IDH1, located in highly conserved regions and overlapping with protein-ligand and PTM sites and assessed their impact on the structure and function of IDH1 protein. A total of 12 high-risk missense SNPs were found at T77, N96, S94, and K260 leading to the gain or loss of catalytic and allosteric sites, alteration in the metal binding site, as well as the gain or loss of PTMs such as acetylation, methylation, N-linked glycosylation, and ubiquitylation at different residues within the active sites of mutated IDH1 enzymes. These changes may significantly impact the structure and function of the IDH1 protein. Furthermore, GEPIA and survival analysis were performed to evaluate IDH1 expression in GBM and LGG and survival outcomes. GEPIA analysis showed significant (p < 0.05) upregulation of IDH1 expression in GBM and LGG. Survival analysis indicated that GBM patients with low IDH1 expression group had better survival outcomes, while LGG patients with low IDH1 expression group showed poorer survival rates. Overall, this study highlights the diagnostic and prognostic potential biomarker of IDH1 in glioblastoma. However, additional in vitro and in vivo studies will be valuable in confirming the role of IDH1 proteins in GBM.

Metapipeline-DNA: A Comprehensive Germline & Somatic Genomics Nextflow Pipeline

Summary

The price, quality and throughout of DNA sequencing continue to improve. Algorithmic innovations have allowed inference of a growing range of features from DNA sequencing data, quantifying nuclear, mitochondrial and evolutionary aspects of both germline and somatic genomes. To automate analyses of the full range of genomic characteristics, we created an extensible Nextflow meta-pipeline called metapipeline-DNA. Metapipeline-DNA analyzes targeted and whole-genome sequencing data from raw reads through pre-processing, feature detection by multiple algorithms, quality-control and data- visualization. Each step can be run independently and is supported robust software engineering including automated failure-recovery, robust testing and consistent verifications of inputs, outputs and parameters. Metapipeline-DNA is cloud-compatible and highly configurable, with options to subset and optimize each analysis. Metapipeline-DNA facilitates high-scale, comprehensive analysis of DNA sequencing data.

Availability

Metapipeline-DNA is an open-source Nextflow pipeline under the GPLv2 license and is available at https://github.com/uclahs-cds/metapipeline-DNA .