Cancer is a long-term illness that involves an imbalance in cellular and immune functions. It can be caused by a range of factors, including exposure to environmental carcinogens, poor diet, infections, and genetic alterations. Maintaining a healthy gut microbiome is crucial for overall health, and short-chain fatty acids (SCFAs) produced by gut microbiota play a vital role in this process. Recent research has established that alterations in the gut microbiome led to decreased production of SCFA's in lumen of the colon, which associated with changes in the intestinal epithelial barrier function, and immunity, are closely linked to colorectal cancer (CRC) development and its progression. SCFAs influence cancer progression by modifying epigenetic mechanisms such as DNA methylation, histone modifications, and non-coding RNA functions thereby affecting tumor initiation and metastasis. This suggests that restoring SCFA levels in colon through microbiota modulation could serve as an innovative strategy for CRC prevention and treatment. This review highlights the critical relationship between gut microbiota and CRC, emphasizing the potential of targeting SCFAs to enhance gut health and reduce CRC risk.

The potential of a novel thiazole-modified covalent triazine framework (S-CTF) as surface for the adsorption and sensing of the carcinogenic metabolites acrylamide (AM), 2-amino-3,8-dimethylimidazo-[4,5-f]quinoxaline (MEIQX), 2-amino-1-methyl-6-phenylimidazole[4,5-f]pyridine (PhlP) and 3-amino-1,4-dimethyl-5H-pyrido[4,3-b]indole (Trp-P-1) is explored. The selectivity, sensitivity, and adsorption properties of the S-CTF surface are investigated through noncovalent interaction (NCI), quantum theory of atoms in molecules (QTAIM) and symmetry adapted perturbation theory (SAPT0) analyses. All the analytes were found to be physiosorbed on the surface of the sensor with the following strength of interaction: MEIQX@S-CTF = PhlP@S-CTF > Trp-P-1@S-CTF > AM@S-CTF. Evaluation of the electronic properties was done by natural bond orbital (NBO), electron density difference (EDD), frontier molecular orbital (FMO) and density of states (DOS) analyses. Through SAPT0 analysis, MEIQX@S-CTF has shown to have the highest ESAPT0 energy data (-24.58 kcal/mol) whereas FMO analysis reveals that the S-CTF surface shows the highest sensing power for Trp-P-1 among all analytes.

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.

The notion of clonal cell populations in human atherosclerosis has been suggested but not demonstrated. Somatic mutations are used to define cellular clones in tumors. Here, we characterized the mutational landscape of human carotid plaques through whole-exome sequencing to explore the presence of clonal cell populations. Somatic mutations were identified in 12 of 13 investigated plaques, while no mutations were detected in 11 non-atherosclerotic arteries. Mutated clones often constituted over 10% of the sample cell population, with genes related to the contractile apparatus enriched for mutations. In carriers of clonal hematopoiesis of indeterminate potential (CHIP), hematopoietic clones had infiltrated the plaque tissue and constituted substantial fractions of the plaque cell population alongside locally expanded clones. Our findings establish somatic mutations as a common feature of human atherosclerosis and demonstrate the existence of mutated clones expanding locally, as well as CHIP clones invading from the circulation. While our data do not support plaque monoclonality, we observed a pattern suggesting the coexistence of multiple mutated clones of considerable size spanning different regions of plaques. Mutated clones are likely to be relevant to disease development, and somatic mutations will serve as a convenient tool to uncover novel pathological processes of atherosclerosis in future studies.

Most solid tumors harbor somatic mutations attributed to off-target activities of APOBEC3A (A3A) and/or APOBEC3B (A3B). However, how APOBEC3A/B enzymes affect tumor evolution in the presence of exogenous mutagenic processes is largely unknown. Here, multi-omics profiling of 309 lung cancers from smokers identifies two subtypes defined by low (LAS) and high (HAS) APOBEC mutagenesis. LAS are enriched for A3B-like mutagenesis and KRAS mutations; HAS for A3A-like mutagenesis and TP53 mutations. Compared to LAS, HAS have older age at onset and high proportions of newly generated progenitor-like cells likely due to the combined tobacco smoking- and APOBEC3A-associated DNA damage and apoptosis. Consistently, HAS exhibit high expression of pulmonary healing signaling pathway, stemness markers, distal cell-of-origin, more neoantigens, slower clonal expansion, but no smoking-associated genomic/epigenomic changes. With validation in 184 lung tumor samples, these findings show how heterogeneity in mutational burden across co-occurring mutational processes and cell types contributes to tumor development.

Immune checkpoint inhibition (ICI) has revolutionized oncology, offering extended survival and long-term remission in previously incurable cancers. While highly effective in tumors with high mutational burden, lowly mutated cancers, including pediatric malignancies, present low response rate and limited predictive biomarkers.

We present a framework for the identification and validation of tumor-reactive T cells as a biomarker to quantify ICI efficacy and as candidates for a personalized TCR-T cell therapy. Therefore, we profiled a pediatric malignant rhabdoid tumor patient with complete remission after ICI therapy using deep single-cell T cell receptor (TCR) repertoire sequencing of the tumor microenvironment (TME) and the peripheral blood.

Tracking T cell dynamics longitudinally from the tumor to cells in circulation over a time course of 12 months revealed a systemic response and durable clonal expansion of tumor-resident and ICI-induced TCR clonotypes. We functionally validated tumor reactivity of TCRs identified from the TME and the blood by co-culturing patient-derived tumor cells with TCR-engineered autologous T cells. Here, we observed unexpectedly high frequencies of tumor-reactive TCR clonotypes in the TME and confirmed T cell dynamics in the blood post-ICI to predict tumor-reactivity.

These findings strongly support spatio-temporal tracking of T cell activity in response to ICI to inform therapy efficacy and to serve as a source of tumor-reactive TCRs for personalized TCR-T designs.

Genome-wide association studies (GWASs) of melanoma risk have identified 68 independent signals at 54 loci. For most loci, specific functional variants and their respective target genes remain to be established. Capture-HiC is an assay that links fine-mapped risk variants to candidate target genes by comprehensively mapping chromatin interactions. We performed a melanoma GWAS region-focused capture-HiC assay in human primary melanocytes to identify physical interactions between fine-mapped risk variants and potential causal melanoma-susceptibility genes. Overall, chromatin-interaction data alone nominated potential causal genes for 61 of the 68 melanoma risk signals, identifying many candidates beyond those reported by previous studies. We further integrated these data with epigenomic (chromatin state, accessibility), gene expression (expression quantitative trait locus [eQTL]/transcriptome-wide association study [TWAS]), DNA methylation (methylation QTL [meQTL]/methylome-wide association study [MWAS]), and massively parallel reporter assay (MPRA) data generated from melanoma-relevant cell types to prioritize potentially cis-regulatory variants and their respective candidate gene targets. From the set of fine-mapped variants across these loci, we identified 140 prioritized credible causal variants linked to 195 candidate genes at 42 risk signals. In addition, we developed an integrative scoring system to facilitate candidate gene prioritization, integrating melanocyte and melanoma datasets. Notably, at several GWAS risk signals, we observed long-range chromatin connections (500 kb to >1 Mb) with distant candidate target genes. We validated several such cis-regulatory interactions using CRISPR inhibition, providing evidence for known cancer driver genes MDM4 and CBL, as well as the SRY-box transcription factor SOX4, as likely melanoma risk genes.

Caspases (CASPs) are attractive targets for cancer therapy. Many prognostic models based on gene signatures include genes from the CASPs family in diffuse glioma. CASP3, CASP4 and CASP6 in glioma have been studied individually. However, specialized comprehensive analysis of the roles of CASPs family in glioma is lacking. Therefore, this study utilized bioinformatics methods to investigate this issue. CASP1-10 expressionlevels were significantly up-regulated in LGG and GBM and glioma, and varied significantly across different clinical subgroups of glioma and LGG and various cell types, and most of CASP1-10 members showed significant differences in recurrence status of LGG. 10 signatures (CASP1-10) were associated with poor overall survival (OS) in glioma and LGG and GBM. However, pan-cancer survival analysis showed that CASP1-10 were associated with the prognosis of LGG, but not GBM. CASP1-10 were related to poor prognosis of glioma and LGG, except for CASP9, which was the opposite of a protective factor. CASP1-10 were independent prognostic factors for OS in glioma and LGG, except for CASP5, and also for recurrence-free survival (RFS) in LGG. Most of CASP1-10 were also independent prognostic factors for disease-specific survival (DSS) and progression-free interval (PFI) and had diagnostic value in glioma and LGG. Genetic alterations of CASP1-10 genes set were associated with poor prognosis in LGG. CASP1-10 were involved in immune infiltration and programmed cell death in glioma and LGG and GBM, and might promote the apoptosis of immune cells. Compared to GBM, CASP1-10 had a more significant impact on the prognosis, cancer-related pathways, and immune infiltration in LGG, indicating that CASP1-10 might play important roles in the recurrence and progression of LGG, and might be promising therapeutic targets for LGG. Therefore, it is speculated that natural caspase inhibitor p35 may be a promising drug for the treatment of glioma, especially for LGG.

De novo germline mutation is an important factor in the evolution of allelic diversity and disease predisposition in a population. Here, we study the influence of genetically-inferred ancestry and environmental factors on de novo mutation rates and spectra. Using a genetically diverse sample of ~10 K whole-genome sequenced trios, one of the largest de novo mutation catalogues to date, we found that genetically-inferred ancestry is associated with modest but significant changes in both germline mutation rate and spectra across continental populations. These effects may be due to genetic or environmental factors correlated with ancestry. We find epidemiological evidence that cigarette smoking is significantly associated with increased de novo mutation rate, but it does not mediate the observed ancestry effects. Investigation of several other potential mutagenic factors using Mendelian randomisation showed no consistent effects, except for age at  menopause, where factors increasing this corresponded to a reduction in de novo mutation rate. Overall, our study sheds light on factors influencing de novo mutation rates and spectra.

Breast cancer remains a formidable global health challenge, with tumor-infiltrating lymphocytes (TILs) serving as pivotal biomarkers associated with disease progression, therapeutic response, and survival. While research typically focused on stromal TILs (sTILs), we hypothesize that intratumoral TILs (iTILs), which are in direct contact with tumor cells, have a more profound role in the immune-tumor interactions. In light of this, we have developed an iTIL-centric model for breast cancer patient stratification and prognostic prediction.

We sourced RNA-seq data and clinical profiles of breast cancer patients from The Cancer Genome Atlas (TCGA) and the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) to form our training dataset. Testing datasets, including GSE20685, GSE42568, GSE48390, and GSE88770, were retrieved from Gene Expression Omnibus (GEO). Employing consensus clustering and Weighted Correlation Network Analysis (WGCNA), we identified iTIL-associated hub genes. Our iTIL-centric signature was developed using a machine learning framework integrating 101 algorithms, validated across independent testing sets. Kaplan-Meier analysis and a nomogram model were utilized to evaluate the prognostic accuracy and clinical correlation of our model. GO and KEGG analyses elucidated the biological processes and pathways related to the iTIL signature. The immune profiling provided a comprehensive assessment of the immunological landscape. Moreover, potential drugs for high-risk patients were identified using CTRP v.2.0 and PRISM databases.

Our study constructed a pioneering prognostic model based on iTIL-centric signature via a machine learning framework that evaluated 101 algorithm combinations. This model revealed significant differences in the immune landscape among stratified patient cohorts, and demonstrated robust predictive capabilities across multiple datasets. The model showed excellent predictive performance with area under the curve (AUC) values of 0.940, 0.959, and 0.973 for 3-, 5-, and 10-year survival predictions, respectively. Additionally, it was identified as a significant risk factor for overall survival (OS) in the univariate analysis, with a hazard ratio (HR) > 1 and a p-value < 0.001.

Our prognostic model, founded on machining learning algorithms and anchored by an iTIL-centric signature, stands out as an invaluable tool for breast cancer patients, offering advanced prognostic insights and facilitating the development of personalized therapeutic strategies.

Prostate cancer (PCa) remains a leading cause of cancer-related mortality, necessitating robust prognostic models and personalized therapeutic strategies. This study integrated bulk RNA sequencing, single-cell RNA sequencing (scRNA-seq), and spatial transcriptomics to construct a prognostic model based on genes shared between ferroptosis and fatty acid metabolism (FAM). Using the TCGA-PRAD dataset, we identified 73 differentially expressed genes (DEGs) at the intersection of ferroptosis and FAM, of which 19 were significantly associated with progression-free survival (PFS). A machine learning-based prognostic model, optimized using the Lasso + Random Survival Forest (RSF) algorithm, achieved a high C-index of 0.876 and demonstrated strong predictive accuracy (1-, 2-, and 3-year AUCs: 0.77, 0.75, and 0.78, respectively). The model, validated in the DFKZ cohort, stratified patients into high- and low-risk groups, with the high-risk group exhibiting worse PFS and higher tumor mutation burden (TMB). Functional enrichment analysis revealed distinct pathway activities, with high-risk patients showing enrichment in immune-related and proliferative pathways, while low-risk patients were enriched in metabolic pathways. Immune microenvironment analysis revealed heightened immune activity in high-risk patients, characterized by increased infiltration of CD8 + T cells, regulatory T cells, and M2 macrophages, alongside elevated TIDE scores, suggesting immune evasion and resistance to immunotherapy. In contrast, low-risk patients exhibited higher infiltration of plasma cells and neutrophils and demonstrated better responses to immune checkpoint inhibitors (ICIs). Spatial transcriptomics and scRNA-seq further elucidated the spatial distribution of model genes, highlighting the central role of macrophages in mediating risk stratification. Additionally, chemotherapy sensitivity analysis identified potential therapeutic agents, such as Erlotinib and Picolinic acid, for low-risk patients. In vitro experiments showed that overexpression of CD38 in the PC-3 cell line led to elevated lipid peroxidation (C11-BODIPY) and reactive oxygen species (ROS), suggesting increased cell ferroptosis. These findings provide a comprehensive framework for risk stratification and personalized treatment in PCa, bridging molecular mechanisms with clinical outcomes.

Activating mutations in the rat sarcoma (RAS) genes HRAS, NRAS and KRAS collectively represent the most frequent oncogenic driver in human cancer1. They have previously been considered undruggable, but advances in the past few years have led to the clinical development of agents that target KRAS(G12C) and KRAS(G12D) mutants, yielding promises of therapeutic responses at tolerated doses2. However, clinical agents that selectively target NRAS(Q61*) mutants (* represents 'any'), the second-most-frequent oncogenic driver in melanoma, are still lacking. Here we identify SHOC2, a component of the SHOC2-MRAS-PP1C complex, as a dependency of RAS(Q61*) tumours in a nucleotide-state-dependent and isoform-agnostic manner. Mechanistically, we found that oncogenic NRAS(Q61R) forms a direct interaction with SHOC2, evidenced by X-ray co-crystal structure. In vitro high-throughput screening enabled the discovery of small molecules that bind to SHOC2 and disrupt the interaction with NRAS(Q61*). Structure-based optimization led to a cellularly active tool compound that shows inhibition of mitogen-activated protein kinase (MAPK) signalling and proliferation in RAS-mutant cancer models, most notably in NRAS(Q61*) settings. These findings provide evidence for a neomorph SHOC2-(canonical)RAS protein interaction that is pharmacologically actionable and relevant to cancer sustenance. Overall, this work provides the concept validation and foundation for developing new therapies at the core of the RAS signalling pathway.

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.

Despite recent advances in understanding disease biology, treatment of group 3/4 medulloblastoma remains a therapeutic challenge in paediatric neuro-oncology1. Bulk-omics approaches have identified considerable intertumoural heterogeneity in group 3/4 medulloblastoma, including the presence of clear single-gene oncogenic drivers in only a subset of cases, whereas in most cases, large-scale copy number aberrations prevail2,3. However, intratumoural heterogeneity, the role of oncogene aberrations, and broad copy number variation in tumour evolution and treatment resistance remain poorly understood. To dissect this interplay, we used single-cell technologies (single-nucleus RNA sequencing (snRNA-seq), single-nucleus assay for transposase-accessible chromatin with high-throughput sequencing (snATAC-seq) and spatial transcriptomics) on a cohort of group 3/4 medulloblastoma with known alterations in the oncogenes MYC, MYCN and PRDM6. We show that large-scale chromosomal aberrations are early tumour-initiating events, whereas the single-gene oncogenic events arise late and are typically subclonal, but MYC can become clonal upon disease progression to drive further tumour development and therapy resistance. Spatial transcriptomics shows that the subclones are mostly interspersed across tumour tissue, but clear segregation is also present. Using a population genetics model, we estimate medulloblastoma initiation in the cerebellar unipolar brush cell lineage starting from the first gestational trimester. Our findings demonstrate how single-cell technologies can be applied for early detection and diagnosis of this fatal disease.

Neuroblastoma, the most prevalent extracranial pediatric solid tumor, arises from neural crest progeny cells. It exhibits substantial developmental plasticity and intratumoral heterogeneity, leading to survival rates below 50% in high-risk cases. The regulatory mechanisms underlying this plasticity remain largely elusive. In this integrative study, we used single-cell MultiOmics from a mouse spontaneous tumor model and spatial transcriptomics from human patient samples to dissect the transcriptional and epigenetic landscapes that govern developmental states in neuroblastoma. We identified developmental intermediate states in high-risk neuroblastomas critical for malignant transitions and uncovered extensive epigenetic priming with latent capacity for diverse state transitions. Furthermore, we mapped enhancer gene regulatory networks (eGRNs) and tumor microenvironments sustaining these aggressive states. State transitions and malignancy could be interfered with by targeting transcription factors controlling the eGRNs.

Allele Frequency (AF) is the percentage of sequence reads with a specific mutation relative to the read depth at that locus, reflecting the proportion of gene mutation. This review explores the AF characteristics of different mutations in thyroid cancer, investigating their connection with tumor features and clinical characteristics. BRAF mutation AF is associated with tumour malignancy and prognosis, exhibiting a relatively low peak value. TERT mutations in AF are associated with invasive characteristics, and the combination between BRAF and TERT mutations AF improved the diagnostic value in identifying patients' risk of recurrence and tumour malignancy. RET mutation is frequently observed in medullary carcinoma, and RET mutation AF is associated with partial tumour characteristics. RAS mutation is prevalent in follicular tumors, but the association between RAS mutation AF and tumour characteristics is relatively weak. TP53 mutation is more frequently occurred in poorly differentiated and anaplastic carcinoma, and its AF might be associated with the dedifferentiation process. We also concentrated on the mutually exclusive and synergistic effect between different mutations. The mutation rate of TERT increases with the elevation of BRAF mutation AF. Finally, the detection and assessment of AF by NGS in clinical practice helps to provide a reference for individualised targeted therapy plans.

This study demonstrates that environmental mutagenic exposure during pregnancy can increase somatic mutation accumulation in the fetus. Given that detrimental early life exposures can adversely affect health outcomes later in life, our study highlights the need for further research into the impact of environmentally induced genomic insults during the perinatal period. See related commentary by Furudate and Takahashi, p. 870.

The two-parametric Avrami-Dobrzyński model, originally based on the condensed matter physics for phase transitions, was applied to the cumulative populational mammary cancer data of laboratory rats. The joint effect of parity, irradiation and BRCA1 mutation on breast cancer incidence was analysed. The study showed that the proposed model fits well with the data points, however, the values of parameters differ regarding the investigated group of animals. It was concluded that both model's parameters, which relate to the dimension of carcinogenesis dynamics and the age distribution, are good candidates for cancer risk assessment regarding different risk factors.

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.

SWI/SNF (switch/sucrose non-fermentable) chromatin remodelers possess unique functionalities difficult to dissect. Distinct cancers harbor mutations in specific subunits, such as the polybromo-associated BAF (PBAF)-specific component ARID2 in melanoma. Here, we perform epigenomic profiling of SWI/SNF complexes and their associated chromatin states in melanocytes and melanoma. Time-resolved approaches reveal that PBAF regions are generally less sensitive to ATPase inhibition than BAF sites. We further uncover a subset of PBAF-exclusive regions within Polycomb-repressed chromatin that are enriched for REST (RE1 silencing transcription factor), a transcription factor that represses neuronal genes. In turn, PBAF complex disruption via ARID2 loss hinders REST's ability to bind and inactivate its targets, leading to upregulation of synaptic transcripts. Remarkably, this gene signature is conserved in melanoma patients with ARID2 mutations and correlates with an expression program enriched in melanoma brain metastases. Overall, we demonstrate a unique role for PBAF in generating accessibility for a silencing transcription factor at repressed chromatin, with important implications for disease.

Somatic mutations could influence critical cellular processes, leading to uncontrolled cell growth and tumor formation. Understanding the intricate interactions between somatic mutations and drugs was crucial for advancing our knowledge of the underlying biological mechanisms of cancer. This knowledge, in turn, could drive advancements in cancer detection, diagnosis, and treatment. Exploring the relationships between specific somatic mutations and drug responses held the potential to identify targeted therapeutic interventions and improve personalized treatment strategies for cancer patients. In this study, we introduced a computational model, the signed graph comparison learning for mutation-drug associations (SGCLMD), designed to predict signs of somatic mutation-drug associations. Initially, we leveraged clinical data to construct a benchmark dataset encompassing somatic mutation-drug associations. We proposed a graph enhancement method, employing a random perturbation strategy, to expand the signed graph. This approach not only preserved interaction information across the two perspectives of the signed graph but also retained implicit relationships between these perspectives. Furthermore, we devised a multi-view comparison loss algorithm to learn node representations for the graph generated post-random perturbation. Through parameter optimization using 5-fold cross-validation, our SGCLMD model achieves optimal area under the curve (AUC) and area under the precision-recall curve (AUPR) values of 0.8306 and 0.8751, respectively, representing improvements of 3 % and 3.1 % over the state-of-the-art method. Through ablation experiments and case studies, we validated the importance of graph enhancement methods and multi-view contrast learning modules, demonstrating SGCLMD's potential in predicting somatic mutation-drug associations. The code and dataset for SGCLMD are available at https://github.com/wangxiaosong96/SGCLMD.

Luminal breast cancer (BC) is generally associated with a lower risk of recurrence compared with other subtypes. However, patients with luminal BC can still experience recurrence, which remains a significant concern and contributes to BC-related mortality. Current clinical practice for recurrence risk prognosis relies on prognostic tests based on tumor gene expression profiles.

In this study, we aimed to investigate the association between different genetic alterations with the likelihood of recurrence and gene expression prognostic prediction (Oncotype DX®, MammaPrint®, and PAM50-ROR) in luminal BC patients. We constructed three transcriptome-based predictive models, based on these widely used clinical tests, to evaluate the recurrence risk of patients with luminal BC, using RNA-seq data from 1527 samples across 11 datasets. We further classified 1780 patients from the TCGA and METABRIC datasets into risk groups and detected distinct recurrence risk patterns.

Our analysis revealed that low-risk groups had higher frequencies of mutations in PIK3CA, MAP3K1, CDH1, KMT2C, and CBFB, as well as co-mutations in PIK3CA-MAP3K1, PIK3CA-CBFB, and KMT2C-MAP3K1. In contrast, high-risk groups showed enrichment of TP53, RB1, and PTPN22 mutations compared with the whole cohort, with notable co-mutations in TP53-PIK3CA and TP53-KMT2C. Furthermore, mutations in TP53 and BRCA2, and deletions in the 7p22.3 region were at least threefold more frequent in high-risk patients compared with low-risk patients. Using an independent dataset, we validated our finding of higher frequency of BRCA2 mutations in Oncotype DX® high-risk patients. Notably, PIK3CA mutations had an unexpected negative impact on recurrence and survival among high-risk patients.

Our study reveals key genetic factors associated with recurrence risk in luminal BC. Identifying these mutations and copy number alterations provides a basis for refined prognostic models and suggests avenues for further research, potentially improving treatment strategies and follow-up care for patients with luminal BC.

In healthy cells, cyclin D1 is expressed during the G1 phase of the cell cycle, where it activates CDK4 and CDK6. Its dysregulation is a well-established oncogenic driver in numerous human cancers. The cancer-related function of cyclin D1 has been primarily studied by focusing on the phosphorylation of the retinoblastoma (RB) gene product. Here, using an integrative approach combining bioinformatic analyses and biochemical experiments, we show that GTSE1 (G-Two and S phases expressed protein 1), a protein positively regulating cell cycle progression, is a previously unrecognized substrate of cyclin D1-CDK4/6 in tumor cells overexpressing cyclin D1 during G1 and subsequent phases. The phosphorylation of GTSE1 mediated by cyclin D1-CDK4/6 inhibits GTSE1 degradation, leading to high levels of GTSE1 across all cell cycle phases. Functionally, the phosphorylation of GTSE1 promotes cellular proliferation and is associated with poor prognosis within a pan-cancer cohort. Our findings provide insights into cyclin D1's role in cell cycle control and oncogenesis beyond RB phosphorylation.

Lynch syndrome (LS), caused by germline pathogenic variants in the mismatch repair genes, leads to high rates of frameshift-derived neopeptide (FSDN) expression due to microsatellite instability (MSI). While colorectal cancer (CRC) prevention is effective, most LS-related tumors lack such strategies. Cancer vaccines targeting FSDNs offer a promising approach for immune interception in LS. This study aimed to identify and validate LS-related FSDNs to develop vaccines for cancer prevention.

We identified LS-related coding MS mutations and predicted FSDN with high coverage on common Human Leukocyte Antigen (HLA)-I and II alleles. We validated FSDN-associated mutations in colorectal adenomas (CrAD), endometrial cancers (EC), and CRC samples from patients with LS, non-LS tumors, and cell lines. Immunogenicity was assessed through interferon (IFN)-γ enzyme-linked immunospot and flow cytometry analysis of tissue-infiltrating lymphocytes (TILs) from LS carriers.

We prioritized 53 HLA-I and 45 HLA-II FSDNs in MSI tumors using in silico predictions. Validation revealed 86.7% of FSDN-associated mutations present in LS-CRC samples, with a median of 7.67 (6.5-9) mutations in CrADs and 6.02 (2-10) in CRCs. Sequencing of CrAD and EC samples showed 95% and 77.5% of predicted FSDN-associated mutations, respectively. MSI cancer cell lines transcribed 69.8% of FSDNs. Immunogenicity assays showed that 71% of potential FSDNs elicited IFN-γ responses, with a median of 7.37 (1-10.75) HLA-I and 6 (2-5.75) HLA-II FSDNs per patient. After prioritizing 24 FSDN, in a cohort of 19 LS-derived samples (4 CrAD and 15 normal mucosa), 52% (10/19) demonstrated T-cell reactivity to an HLA-I neoantigen pool. CD8+CD137+ activation markers increased significantly (p=0.037) over time and peptide-specific cells were detected by pentamer staining.

Our predicted FSDN set has optimal coverage among LS carriers and can induce IFN-γ inflammatory responses in LS-derived TILs, offering an opportunity for vaccine development.

TP53 mutations are the main drivers of aggressive, high-risk endometrial carcinomas commonly diagnosed in Black individuals. However, TP53 mutations have also been identified in benign, non-cancerous tissues. We sought to understand the TP53 mutational landscape in benign endometrium throughout the lifespan of Black and White individuals, accounting for structural socioeconomic context.

Ultra-sensitive TP53 mutation detection was performed with high-depth duplex sequencing (∼13,000×) in DNA extracted from histologically normal endometrium collected at autopsy (69 % of cases) or surgery (31 % of cases) from 83 individuals ages 0 to 81 (31 Black and 52 White, median age 35 years) without endometrial cancer. Histologically normal endometrium was also collected from 10 White individuals with endometrial cancer.

We identified 266 coding TP53 mutations in the normal endometrium of individuals without endometrial cancer, 57 % of which were pathogenic. The number, pathogenicity, and size of TP53 mutant clones in normal endometrium increased with age. Multivariable models showed no significant association between race or socioeconomic metrics and TP53 mutation frequency in normal endometrium. An exploratory analysis on the histologically normal endometrium of White individuals with endometrial cancer identified the tumor mutations at low levels in the normal biopsy of 5 out of 6 cases.

Our study revealed prevalent TP53 somatic evolution in benign endometrium across human lifespan and no racial differences in this cohort of predominantly younger individuals. Future studies should consider the analysis of larger cohorts with older individuals to detect potential effects of racial disparities on TP53 somatic evolution later in life.

Laminin-γ2 fusion gene (Lm-γ2F), formed by translocation between LAMC2 and NR6A1, functions as an epidermal growth factor receptor (EGFR) ligand. However, its expression and impact on cancers beyond the initially studied contexts remain unclear. This study focused on Lm-γ2F protein secretion and its role in non-small-cell lung carcinoma (NSCLC), where EGFR signaling plays a pivotal role in malignancy progression. Lm-γ2F secretion was confirmed in serum-free conditioned medium from six NSCLC cell lines by Western blot analysis and further validated in NCI-H1650 cells. Hypothesizing that Lm-γ2F functions as an EGFR ligand, its effects in NSCLC cells lacking EGFR mutations were explored. In EKVX and RERF-LC-KJ cell lines, Lm-γ2F overexpression significantly enhanced cell growth, survival, motility, and invasiveness through EGFR signaling activation compared with controls. Conversely, no effects were observed in VMRC-LCD cells lacking EGFR expression. Additionally, increased membrane-type 1 matrix metalloproteinase expression was detected in Lm-γ2F-expressing EKVX cells. In vivo, these cells exhibited elevated metastatic activity in a lung metastasis model. These findings suggested that ectopic Lm-γ2F expression contributes to malignant progression in NSCLC cells without EGFR mutations. Furthermore, EGFR tyrosine kinase inhibitors may suppress metastasis in these contexts. This study provides novel insights into the oncogenic role of Lm-γ2F in NSCLC, highlighting its potential as a therapeutic target to mitigate tumor progression and metastasis.

Clear cell renal cell carcinoma (ccRCC) is the most prevalent type of renal cell carcinoma. However, our understanding of ccRCC risk genes remains limited. This gap in knowledge poses challenges to the effective diagnosis and treatment of ccRCC. To address this problem, we propose a deep reinforcement learning-based computational approach named RL-GenRisk to identify ccRCC risk genes. Distinct from traditional supervised models, RL-GenRisk frames the identification of ccRCC risk genes as a Markov Decision Process, combining the graph convolutional network and Deep Q-Network for risk gene identification. Moreover, a well-designed data-driven reward is proposed for mitigating the limitation of scant known risk genes. The evaluation demonstrates that RL-GenRisk outperforms existing methods in ccRCC risk gene identification. Additionally, RL-GenRisk identifies eight potential ccRCC risk genes. We successfully validated epidermal growth factor receptor (EGFR) and piccolo presynaptic cytomatrix protein (PCLO), corroborated through independent datasets and biological experimentation. This approach may also be used for other diseases in the future.

Thyroid cancer, a prevalent endocrine malignancy, has an age-standardized incidence rate of 9.1 per 100,000 people and a mortality rate of 0.44 per 100,000 as of 2024. Despite significant advances in precision oncology driven by large-scale international consortia, gaps persist in understanding the genomic landscape of thyroid cancer and its impact on therapeutic efficacy across diverse populations.

To address this gap, we performed comprehensive data mining and in silico analyses to identify pathogenic variants in thyroid cancer driver genes, calculate allele frequencies, and assess deleteriousness scores across global populations, including African, Amish, Ashkenazi Jewish, East and South Asian, Finnish and non-Finnish European, Latino, and Middle Eastern groups. Additionally, pharmacogenomic profiling, in silico drug prescription, and clinical trial data were analyzed to prioritize targeted therapeutic strategies.

Our analysis examined 56,622 variants in 40 thyroid cancer-driver genes across 76,156 human genomes, identifying 5,001 known and predicted oncogenic variants. Enrichment analysis revealed critical pathways such as MAPK, PI3K-AKT-mTOR, and p53 signaling, underscoring their roles in thyroid cancer pathogenesis. High-throughput validation strategies confirmed actionable genomic alterations in RET, BRAF, NRAS, KRAS, and EPHA7. Ligandability assessments identified these proteins as promising therapeutic targets. Furthermore, our findings highlight the clinical potential of targeted drug inhibitors, including vandetanib, dabrafenib, and selumetinib, for improving treatment outcomes.

This study underscores the significance of integrating genomic insights with pharmacogenomic strategies to address disparities in thyroid cancer treatment. The identification of population-specific oncogenic variants and actionable therapeutic targets provides a foundation for advancing precision oncology. Future efforts should focus on including underrepresented populations, developing population-specific prevention strategies, and fostering global collaboration to ensure equitable access to pharmacogenomic testing and innovative therapies. These initiatives have the potential to transform thyroid cancer care and align with the broader goals of personalized medicine.