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Results for “intratumoral heterogeneity”

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PubMed2026

Topologically distinct intratumoral heterogeneity scores for predicting high-risk pathological grades in invasive lung adenocarcinoma: A multicenter study across four institutions.

High-risk subtypes of invasive lung adenocarcinoma (IAC), particularly micropapillary- or solid-predominant patterns, are closely associated with poor prognosis. This multicenter retrospective study developed and validated a predictive model for the preoperative identification of these high-risk subtypes using topologically distinct intratumoral heterogeneity (ITH) scores derived from CT images. The study included 1,051 patients with IAC. Two complementary ITH scores were developed: a two-dimensional ITH score, which integrated local radiomics features with global pixel distribution patterns on the largest cross-sectional CT slice, and a three-dimensional ITH score, which extended this quantification across the entire tumor volume. Clinicoradiological features and ITH scores were incorporated as model inputs to construct six base machine learning classifiers and a final stacking ensemble classifier. Model interpretability and robustness were evaluated using SHapley Additive exPlanations (SHAP)-based ablation analyses. An independent dataset from The Cancer Imaging Archive (TCIA) was used for external validation to investigate associations between ITH scores and pathological characteristics, genomic features, recurrence-free survival, and overall survival. The stacking ensemble classifier achieved the best predictive performance, with an area under the receiver operating characteristic curve of 0.875, outperforming models based solely on radiomics features (0.834) or clinicoradiological features (0.792). SHAP analysis identified the 3D ITH score as the most influential contributor to model output, and TCIA validation showed that higher 3D ITH scores were associated with more aggressive tumor biology and poorer survival outcomes. The topologically distinct 3D ITH score may provide a clinically meaningful imaging biomarker for preoperative risk stratification in IAC.

Journal Article↗

PubMed2026

Clonotypic characterization defines B-cell drivers of clonal expansion and intratumor heterogeneity in IgM monoclonal gammopathies.

Waldenström macroglobulinemia (WM) and IgM monoclonal gammopathy of undetermined significance (MGUS) share the same cell of origin but differ in clonal size. Compared with other B-cell neoplasms, the lymphoplasmacytic clone in WM can be rather small, limiting our understanding of clonal expansion. We applied an integrative approach using single-cell RNA with B-cell receptor (BCR) sequencing, the assay for transposase-accessible chromatin, and whole-genome sequencing to characterize the tumor clone in patients with IgM MGUS, smoldering WM (SWM), and symptomatic WM (WM). IgM MGUS and low- or intermediate-risk SWM harbored multiple B-cell clones compared to WM. CD9, JCHAIN, RASSF6, and DUSP22 were the main markers of the dominant B-cell clone at gene expression and chromatin activity levels, with CD9 preferentially expressed in plasma cell-like tumor cells. POU2F2 had high activity in the tumor clone and was linked to CD9 regulatory regions. MYD88 and IGLL5 mutations, mainly associated with the mutational signature SBS5, were present in minor clones, whereas the MYD88 mutation was also detected in nonexpanded B-cells. The 6q deletion was present in tumor cells from high-risk patients, which harbored fitness advantage over copy-neutral tumor cells. Coding mutations clustered tumor and minor clones from oligoclonal patients and were associated with abnormal transcriptional programs. The B-cell clones also showed enriched predicted interactions with monocytes. Our integrative single-cell approach reveals the importance of clone size in IgM gammopathy and identifies key markers promoting clonal expansion.

Journal Article↗

PubMed2026

EGFR-mutant transformed small cell lung cancer harbors intratumoral heterogeneity targetable with MEK inhibitor combination therapy.

Small cell lung cancer (SCLC) transformation is an incompletely characterized mechanism of resistance to epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) in EGFR-mutant cancers, limiting development of optimal treatment approaches. Through single-cell RNA sequencing of malignant pleural effusions from patients who underwent SCLC transformation, we identified heterogeneity and diversity, including distinct neuroendocrine (NE) and mesenchymal non-NE cancer cell subsets, which were maintained in patient-derived cell lines. We demonstrate that EZH2 regulates EGFR expression in NE cells where EGFR expression is silenced at baseline. Although neither epigenetic derepression nor exogenous overexpression of mutant EGFR sensitized the cells to EGFR inhibition, non-NE cells exhibited selective sensitivity to MEK inhibitors. Combined MEK inhibitor and chemotherapy effectively inhibited growth of both NE and non-NE cells in vitro and in vivo. Our findings demonstrate that EGFR-mutant SCLC is composed of mixed cell states with distinct therapeutic vulnerabilities and offer a therapeutic strategy to target tumor heterogeneity in highly plastic and treatment-resistant malignancies such as transformed SCLC.

Humans↗

PubMed2026

A machine learning-derived intratumoral heterogeneity-related signature predicts the prognosis for and therapeutic response in patients with skin cutaneous melanoma.

BACKGROUND: Reliable biomarkers for predicting prognosis and therapeutic response in skin cutaneous melanoma (SKCM) remain limited. This study aimed to develop an intratumoral heterogeneity (ITH)-related prognostic signature for SKCM using integrative machine learning. METHODS: RNA sequencing (RNA-seq) data from 472 SKCM patients in The Cancer Genome Atlas (TCGA) and 214 patients in the GSE65904 cohort were analyzed. ITH scores were calculated using the DEPTH2 algorithm. Differentially expressed genes (DEGs) were identified between high- and low-ITH groups [|log2fold change (FC)| ≥1, false discovery rate (FDR) <0.05]. Based on 38 prognostic DEGs identified by univariate Cox regression, we employed an integrative framework of 101 machine learning algorithm combinations to construct prognostic models in the TCGA training cohort. The model with the highest average concordance index (C-index) was validated in the GSE65904 cohort and selected as the prognostic ITH-related signature (PIRS). Associations of the PIRS risk score with tumor mutational burden (TMB), immune cell infiltration, immune checkpoint gene expression, and drug sensitivity were systematically evaluated. Model performance was assessed using receiver operating characteristic (ROC) curves and Cox regression analyses. RESULTS: A 38-gene PIRS was constructed using the plsRcox algorithm. Patients with high PIRS risk scores exhibited significantly poorer overall survival (OS) in both the TCGA and Gene Expression Omnibus (GEO) cohorts. The PIRS was identified as an independent prognostic factor, with area under the curve (AUC) values of 0.779, 0.734, and 0.756 for 1-, 3-, and 5-year survival, respectively. High-risk samples displayed significantly lower TMB (P<0.05), reduced immune and stromal cell infiltration (P<0.001), downregulated immune function, and decreased expression of immune checkpoint genes. Additionally, high- and low-PIRS risk score groups exhibited distinct sensitivity patterns to different classes of targeted agents. CONCLUSIONS: The machine learning-derived PIRS robustly predicts prognosis in SKCM patients. Its clinical application is promising for optimizing patient risk stratification and treatment decisions, though further prospective validation is warranted.

Skin cutaneous melanoma (SKCM)↗

PubMed2026

Competing subclones and fitness diversity shape tumor evolution across cancer types.

MOTIVATION: Intratumor heterogeneity arises from ongoing somatic evolution and complicates cancer diagnosis, prognosis, and treatment. Reconstructing evolutionary dynamics typically requires spatiotemporal samples, which are often unavailable in clinical settings. Computational approaches that can infer tumor evolutionary history from single-timepoint bulk sequencing data remain limited. RESULTS: We present estimating evolutionary events through single-timepoint sequencing (TEATIME), a novel computational framework that models tumors as mixtures of two competing cell populations: an ancestral clone with baseline fitness and a derived subclone with elevated fitness. Using cross-sectional bulk sequencing data, TEATIME estimates mutation rates, timing of subclone emergence, relative fitness, and number of generations of growth. To quantify intratumor fitness asymmetries, we introduce a novel metric-fitness diversity-which captures the imbalance between competing cell populations and serves as a measure of functional intratumor heterogeneity. Applying TEATIME to 33 tumor types from The Cancer Genome Atlas, we revealed divergent as well as convergent evolutionary patterns. Notably, we found that immune-hot microenvironments constraint subclonal expansion and limit fitness diversity. Moreover, we detected temporal dependencies in mutation acquisition, where early driver mutations in ancestral clones epistatically shape the fitness landscape, predisposing specific subclones to selective advantages. These findings underscore the importance of intratumor competition and tumor-microenvironment interactions in shaping evolutionary trajectories, driving intratumor heterogeneity. Lastly, we demonstrate that TEATIME-derived evolutionary parameters and fitness diversity offer novel prognostic insights across multiple cancer types. AVAILABILITY AND IMPLEMENTATION: R implementation of TEATIME is available on GitHub (https://github.com/liliulab/TEATIME) and Zenodo (https://zenodo.org/records/17422174).

Neoplasms↗

PubMed2026

Multiregion profiling of genomic and transcriptional heterogeneity in head and neck squamous-cell carcinoma.

BACKGROUND: Intratumoral heterogeneity (ITH) is thought to contribute to tumour evolution and treatment resistance but its biological and clinical significance in localised head and neck squamous-cell carcinoma (HNSCC) remains incompletely understood. PATIENTS AND METHODS: In the prospective SCANDARE study, we analysed 87 patients with resectable HNSCC treated with upfront surgery. Two to five spatially distinct tumour regions per patient underwent pathological evaluation, targeted DNA sequencing, and bulk RNA sequencing. Genomic ITH (gITH) was quantified using clonal deconvolution and Shannon diversity indices, whereas transcriptional heterogeneity (tITH) was assessed using the intratumour expression distance metric. Associations between ITH, molecular features, tumour microenvironment composition, and clinical outcomes were explored using multivariable statistical models. RESULTS: Pathology-based spatial heterogeneity showed limited prognostic value. gITH was common, with 37% of tumours displaying regionally heterogeneous pathogenic variants, including spatially actionable alterations in 10% of patients. In an initial multivariable Cox model, higher gITH was associated with shorter disease-free survival. However, after Ridge-penalised modelling and bootstrap internal validation, the effect size was attenuated [corrected hazard ratio 1.42, 95% confidence interval (CI) 0.91-2.75]. The overall model retained moderate discriminative performance (optimism-corrected C-index 0.69, 95% CI 0.59-0.79). gITH was associated with tumour cellularity, reduced estimated endothelial cell infiltration, and alterations in KMT2C and PIK3CA. tITH differed according to human papillomavirus (HPV) status, with lower tITH in HPV-positive tumours, and was associated with distinct biological pathways and genomic alterations. Genomic and tITH were not correlated. CONCLUSIONS: This prospective multiregion study provides a comprehensive characterisation of genomic and tITH in localised HNSCC. Our findings highlight substantial spatial molecular diversity within primary tumours and suggest potential associations between heterogeneity, tumour biology, and clinical outcome that warrant validation in independent cohorts.

head and neck squamous-cell carcinoma (HNSCC)↗

PubMed2025

Deep Learning on Histologic Slides Accurately Predicts Consensus Molecular Subtypes and Spatial Heterogeneity in Colon Cancer.

Colon cancer (CC) is the third most prevalent cancer type. It is highly heterogeneous, particularly in terms of molecular profiles, which have both prognostic and predictive impacts on the treatment efficacy. However, CC treatment in adjuvant situations is currently guided solely by T and N staging. In this context, consensus molecular subtypes (CMSs) were introduced to stratify patients with CC based on molecular profiles. Recent studies have shown that CMS can be heterogeneous in CC, leading to a worse prognosis. This study focused on predicting CMS and its heterogeneity in CC using deep learning on digitized hematoxylin and eosin ± saffron-stained whole-slide images. Data and whole-slide images of 1996 patients from the PETACC-8, The Cancer Genome Atlas-COAD, and PRODIGE-13 cohorts were used. The model is trained to predict a 4-dimensional CMS vector, reflecting intratumor heterogeneity (ITH). It comprises a self-supervised model for embedding image patches into vectors and a weakly supervised model predicting CMS calls. Ground-truth CMS scores are obtained with the CMSclassifier package. Interpretability analyses are performed at the slide and patch levels. For homogeneous tumors, the model trained on PETACC-8 achieves 93.0% (±1.4%) macroaverage area under the curve in internal cross-validation and 94.4% macroaverage area under the curve in external validation over PRODIGE-13, whereas the The Cancer Genome Atlas-COAD model reaches 85.4% (±3.0%) in cross-validation and 92.4% over PRODIGE-13. The trained models also provide spatial distributions of CMS across tumor slides and associate specific histologic features with each CMS. Finally, the models are able to predict ITH. The results show that a deep learning model trained on routine histology slides is capable of providing an efficient and robust method for predicting CMS and characterizing a patient's ITH, paving the way for the routine consideration of CMS/ITH in clinical decision making in the adjuvant setting.

Humans↗

PubMed2026

Deconvolution of evolutionary architecture unmasks a high-risk, subclonal-rich subtype in treatment-naive small cell lung cancer.

BACKGROUND: Intratumoral heterogeneity (ITH) drives therapeutic resistance in small cell lung cancer (SCLC). However, conventional single-sample analysis has limited horizontal, cross-patient comparisons, leaving the overarching evolutionary architecture in treatment-naive tumors poorly understood. This study aims to deconvolve these architectures to identify clinically relevant evolutionary subtypes. METHODS: We analyzed whole-exome sequencing data from 41 treatment-naive SCLC patients. To overcome the cross-patient comparability bottleneck, we developed a novel probabilistic framework using a refined Gaussian Mixture Model (GMM). This standardized subclonal structures into four hierarchical strata, enabling the identification of evolutionary subtypes via unsupervised clustering. To address the scarcity of SCLC public data, prognostic concordance was robustly explored in The Cancer Genome Atlas (TCGA) lung squamous cell carcinoma (LUSC) based on shared smoking etiology, with lung adenocarcinoma (LUAD) serving as a negative control. RESULTS: The cohort robustly segregated into "Clonal-dominant" (Group 1, n=28) and "Subclonal-rich" (Group 2, n=13) subtypes. Group 1 evolution was primarily driven by tobacco signatures (SBS4). Conversely, Group 2 exhibited late-stage acquisition of a DNA mismatch repair deficiency (MMRd) signature (SBS15), fueling trace subclonal diversification. Clinically, Group 2 demonstrated a significantly lower objective response rate (ORR) to platinum-based regimens (25.0% vs. 81.3%, P=0.02). Furthermore, the Subclonal-rich architecture independently predicted inferior overall survival (OS) [adjusted hazard ratio (adj. HR) =2.93, P=0.02], driven predominantly by limited-stage disease. Cross-cancer analysis validated this histology-dependent, high-heterogeneity adverse pattern in early-stage LUSC but not in LUAD. CONCLUSIONS: This hypothesis-generating study demonstrates that a "Subclonal-rich" architecture, driven by acquired MMRd, identifies high-risk, chemo-resistant SCLC. Our GMM approach suggests that pre-existing heterogeneity may serve as a potential, histology-dependent prognostic marker that warrants prospective validation for tailoring future therapeutic regimens.

Gaussian Mixture Model (GMM)↗

PubMed2026

Non-coding RNAs as regulators of chromosomal instability in breast cancer.

Breast cancer is a highly heterogeneous disease characterized by extensive genomic and chromosomal instability (CIN), a hallmark that drives tumor evolution, intratumoral heterogeneity, therapeutic resistance, and poor clinical outcomes. Increasing evidence indicates that non-coding RNAs (ncRNAs) are important regulators of genome maintenance and chromosome stability. However, their specific contributions to CIN and the strength of the available evidence remain incompletely understood. This review examines the role of the major ncRNA classes, including circular RNAs, microRNAs, PIWI-interacting RNAs, small nucleolar RNAs, and long non-coding RNAs, in the regulation of CIN-related processes in breast cancer. We discuss the molecular mechanisms by which these ncRNAs regulate key pathways involved in CIN, while critically evaluating the strength of the experimental evidence supporting their functional roles. We also examine their associations with distinct breast cancer molecular subtypes and assess their potential as biomarkers and therapeutic targets, highlighting current limitations and knowledge gaps that hinder clinical translation. Collectively, the available evidence supports an emerging role for ncRNAs as regulators of CIN while underscoring the need for further mechanistic and subtype-specific studies to validate their clinical utility.

DNA repair↗

PubMed2024

Genomic and the tumor microenvironment heterogeneity in multifocal hepatocellular carcinoma.

BACKGROUND AND AIMS: Ambiguous understanding of tumors and tumor microenvironments (TMEs) hinders accurate diagnosis and available treatment for multifocal hepatocellular carcinoma (HCC) covering intrahepatic metastasis (IM) and multicentric occurrence (MO). Here, we characterized the diverse TMEs of IM and MO identified by whole-exome sequencing at single-cell resolution. APPROACH AND RESULTS: We performed parallel whole-exome sequencing and scRNA-seq on 23 samples from 7 patients to profile their TMEs when major results were validated by immunohistochemistry in the additional cohort. Integrative analysis of whole-exome sequencing and single-cell RNA sequencing found that malignant cells in IM showed higher intratumor heterogeneity, stemness, and more activated metabolism than those in MO. Tumors from IM shared similar TMEs while distinct TMEs were noticed in those from MO. Furthermore, CD20+ B cells, plasma cells, and conventional type II dendritic cells (cDC2s) were decreased in IM relative to MO while T cells in IM exhibited a more terminally exhausted capacity with a higher proportion of proliferative/exhausted T cells than that in MO. Both CD20 and CD1C correlated with better prognosis in multifocal HCC. Additionally, MMP9+ tumor-associated macrophages were enriched across IM and MO, which formed cellular niches with regulatory T cells and proliferative/exhausted T cells. CONCLUSIONS: Our findings deeply decipher the heterogeneous TMEs between IM and MO, which provide a comprehensive landscape of multifocal HCC.

Humans↗

PubMed2026

Molecular Landscape and Advanced Diagnostic Technologies for BRAF Mutations in Cancer: From Quantitative PCR and ddPCR to CRISPR-Based Platforms.

BRAF mutations are key oncogenic alterations across multiple malignancies, including melanoma, thyroid carcinoma, colorectal cancer, non-small cell lung cancer, glioma, and hairy cell leukemia. The most prevalent variant, BRAF-V600E, induces constitutive activation of the MAPK signaling pathway, promoting tumor progression and influencing therapeutic responsiveness. Accurate detection of BRAF alterations is therefore essential for molecular classification, prognostic assessment, treatment selection, and resistance surveillance. This review summarizes the molecular heterogeneity of BRAF mutations and critically evaluates current diagnostic methodologies. Conventional approaches such as allele-specific PCR and Sanger sequencing are compared with advanced quantitative platforms, including high-resolution melting analysis, droplet digital PCR, and next-generation sequencing, with emphasis on analytical sensitivity, mutation coverage, and clinical applicability. Emerging technologies such as CRISPR-based assays, rolling circle amplification systems, and nanoparticle-based biosensors and point-of-care diagnostic platforms are also discussed for their potential to enhance ultra-sensitive detection, particularly in liquid biopsy settings. These emerging tools are highlighted for their potential to enable ultra-sensitive, rapid, and decentralized mutation detection, particularly in liquid biopsy settings. Key challenges, including intratumoral heterogeneity, low allele-frequency variants, FFPE-associated artifacts, and clonal evolution under therapeutic pressure, are examined within a translational framework. In addition, we examine critical barriers to clinical implementation, including standardization, cost, and global accessibility of molecular diagnostics, and outline potential solutions through scalable technologies and decentralized testing strategies. We propose that optimal BRAF testing requires a mutation subclass-informed and clinically integrated strategy combining comprehensive baseline profiling with longitudinal molecular monitoring. Future diagnostic paradigms will likely integrate multi-omics data and artificial intelligence (AI)-assisted interpretation to refine precision oncology implementation. Looking forward, we propose that optimal BRAF testing will require integration of multi-omics profiling with AI-assisted interpretation, enabling automated variant classification, real-time clinical decision support, and improved prediction of therapeutic response and resistance.

Humans↗

PubMed2026

Clinicopathologic Spectrum and Intraprostatic Heterogeneity of Primary Mismatch Repair-Deficient Prostate Cancer.

Mismatch repair deficiency (MMRd) is identified in a small subset of prostate cancers and has implications for therapy selection and germline testing. The clinicopathologic spectrum of primary MMRd prostate cancer remains incompletely characterized. We evaluated 32 primary treatment-naive MMRd prostatic adenocarcinomas identified at a single institution. Grade group distribution included GG5 (n=12, 38%), GG4 (n=4, 13%), GG3 (n=7, 22%), and GG2 (n=9, 28%). Intraductal and/or invasive cribriform carcinoma was identified in 78% of cases overall and in 78% of GG2 tumors. MMR protein loss predominantly involved MSH2/MSH6 (78%), with isolated MSH6 loss in 16% and MLH1/PMS2 loss in 6%. Concordant pathogenic MMR gene alterations were identified on targeted NGS in all cases, while concurrent Tier 1/2 HRR gene alterations were present in 11 cases (35%), including ATM, BRCA1, and BRCA2. Germline testing performed in 15 cases identified Lynch syndrome in 5 (33%). MMR immunohistochemistry performed on multiple tissue blocks in 14 cases revealed discordant MMR status between tumor foci in 8 cases (57%), with MMR deficiency consistently restricted to the highest-grade focus and retained expression in spatially separate lower-grade foci. One additional case demonstrated apparent intratumoral heterogeneity within a single biopsy core, with MMR deficiency restricted to a higher-grade component and retained expression in the adjacent lower-grade tumor. These findings demonstrate a broader clinicopathologic spectrum of primary MMRd prostate cancer than previously recognized. Frequent discordance of MMR status between tumor foci highlights the importance of evaluating the highest-grade tumor focus when assessing multifocal primary prostate cancer.

cribriform↗

PubMed2026

A Multi-omics Regulated Cell Death Framework Defines Immune Phenotypes and Guides Precision Therapy in Colorectal Cancer.

Colorectal cancer (CRC) is molecularly and immunologically heterogeneous, contributing to variable treatment response. Because regulated cell death (RCD) intersects with tumor metabolism, immune regulation, and therapeutic susceptibility, we built an RCD-centered framework for CRC stratification. Multi-cohort transcriptomic data were used to infer RCD subtypes with non-negative matrix factorization (NMF) and non-negative least squares (NNLS). Genomic, bulk RNA-seq, single-cell RNA-seq, and spatial transcriptomic datasets were integrated to characterize subtype-associated biology. Machine-learning models were developed for immunotherapy response and survival-risk estimation. Candidate compounds were screened by GDSC2-based drug-sensitivity modeling and molecular docking, and FSTL3 was functionally assessed in vitro. The framework separated CRC samples into two RCD-related phenotypes resembling immune-hot and immune-cold states. RCD1 showed immune activation and higher mutational burden, whereas RCD2 showed immune-suppressed features, intratumoral heterogeneity, and aggressive biology. RCD-associated signatures showed potential for predicting immunotherapy response and survival risk. Dasatinib was prioritized for immune-cold, high-risk tumors, with preliminary evidence supporting its activity in CRC cells, while functional assays suggested a role for FSTL3 in growth, invasion, epithelial-mesenchymal transition, and apoptosis regulation. These findings suggest that RCD-based multi-omics analysis may refine CRC stratification and help generate therapeutic hypotheses.

Colorectal cancer↗

PubMed2026

Targeting cancer stem cells predicts response and reverses chemoresistance in ascites-derived ovarian cancer organoids.

BACKGROUND: Ovarian cancer (OC) is frequently diagnosed at an advanced stage, where tumor heterogeneity and rapid development of chemoresistance contribute to a poor prognosis. The lack of reliable predictive biomarkers further hinders the development of effective treatment strategies. Patient-derived organoids (PDOs) have recently emerged as promising preclinical models with the potential to predict therapeutic responses. METHODS: OC PDOs were generated from ascites samples representing diverse histological subtypes. Histological and genomic fidelity to parental tumors was confirmed through histopathological analysis and whole-exome sequencing. Drug sensitivity to cisplatin and poly (ADP-ribose) polymerase (PARP) inhibitors was evaluated and correlated with 1-year clinical outcomes. We also investigated the therapeutic efficacy of oncolytic herpes simplex virus 2 (OH2) both as a single agent and in combination with cisplatin. The expression of cancer stem cell (CSC) markers CD44 and ALDH1A1 under treatment conditions was analyzed using immunohistochemistry and flow cytometry. RESULTS: PDOs were successfully established with an 86.2% success rate. These PDOs faithfully recapitulated the histopathological and genomic features of their corresponding tumors, maintaining intratumoral heterogeneity, and were amenable to xenotransplantation. Drug sensitivity assays demonstrated that PDOs accurately predicted patient-specific responses to cisplatin and PARP inhibitors. OH2 exhibited direct cytotoxicity in both cisplatin-sensitive and cisplatin-resistant PDOs, reducing cell viability by 20-60%. Notably, the combination treatment with OH2 and cisplatin enhanced antitumor efficacy, resulting in a significant reduction of the CD44+CSC subpopulation. CONCLUSIONS: Ascites-derived OC PDOs represent a robust platform for individualized drug testing. The combination of OH2 and cisplatin offers a novel and effective strategy for circumventing chemoresistance in OC.

Female↗

PubMed2026

The mechanism of malignant progression in extramammary Paget's disease (EMPD): hallmarks of EMPD.

The pathogenesis of cancer is characterized by the acceleration of tumor growth, inhibition of tumor suppression, genetic and epigenetic alteration, lubricative transformation and tumor microenvironment. Extramammary Paget's disease (EMPD) is a rare skin cancer that originates from apocrine glands in genital and axillary area. Although the pathogenesis of EMPD is still poorly understood, increasing evidence reveals that the mechanism of EMPD progression is regulated by the acquired ability of EMPD cells and tumor microenvironment. HER2/PI3K/AKT signaling and hormone receptor pathways are activated. Whereas tumor mutation burden is low, numerous driver genes such as ERBB2 and PIK3CA are detected. Tumor evolution in EMPD is characterized by high genetic intratumor heterogeneity with shared background factors. Tumor microenvironment in EMPD promotes immune evasion through the reduction of reduced CD4 + and CD8 + T cells and the increase of Treg cells and CD163 + macrophages. Enhanced Warburg effect and S. aureus contribute to the suppression of antitumor immunity. This review focuses on the mechanism of malignant progression in EMPD (hallmarks of EMPD).

Genome mutation↗

PubMed2026

Identification of molecular subtypes in clear cell renal cell carcinoma based on chromatin regulators and tumor immune microenvironment profiling.

In the histological classification of renal cell carcinoma, clear cell renal cell carcinoma (ccRCC) accounts for the highest proportion and is the most common subtype. Despite advances in management, it continues to be associated with considerable incidence and mortality. Although surgery and systemic therapies are available, their efficacy is constrained by pronounced intratumoral heterogeneity and treatment resistance. Identifying robust biomarkers and clarifying the underlying biological mechanisms are therefore essential to improving diagnosis, risk stratification and therapeutic decision-making. In this work, we identified two ccRCC molecular subtypes displaying divergent chromatin regulator (CR) profiles and different clinical prognoses. Using the genes differentially expressed between these subgroups, we constructed a CR-related score (CRS) that effectively stratified patients according to survival. More analysis concluded that the low expression of CR was more linked with the immune-activated tumors, which encompassed the immune pathway enrichment, as well as the elevation of numerous immune cell subtypes. Moreover, elevated CRS was associated with improved immunotherapy responsiveness. Drug-sensitivity analyses nominated several candidate agents, and SMARCD3 knockdown in 786-O cells inhibited proliferation and migration and reduced sensitivity to masitinib. Collectively, these findings support the prognostic and therapeutic relevance of CR-related states in ccRCC and provide a framework for future experimental validation of chromatin-regulated tumor-immune interactions.

Humans↗

PubMed2026

Stress-driven strategic games in cancer.

Tumor cells face chronic genotoxic, metabolic, hypoxic, and immune stress that shapes their evolution. While stress-response molecular pathways are well characterized, cancer biology lacks a predictive framework for how cells select among alternative adaptive strategies and how these selections interact to produce tumor-level behavior. We propose that evolutionary game theory, previously applied to cooperation in cancer, should be extended to position stress adaptation itself as the organizing principle of tumor evolution. In this framework, stress-adaptive strategies constitute frequency-dependent games whose payoffs depend on population composition. We introduce a three-level distinction between cell states (transcriptional snapshots), game states (local configurations of stress and neighbor composition that define the active payoff structure), and cell strategies (conditional behavioral policies mapping game states to fitness-relevant outputs). This perspective explains the maintenance of intratumor heterogeneity through frequency-dependent selection, the reversibility of resistance through bet-hedging dynamics, and therapy resistance as an equilibrium outcome rather than genetic inevitability. Integrating insights from single-cell genomics, spatial profiling, and lineage tracing, we outline testable predictions and experimental approaches for measuring payoff structures. Therapeutically, the framework suggests exploiting adaptive trade-offs, restricting phenotypic plasticity, and reshaping competitive landscapes. Re-framing cancer as an evolving game of stress adaptation provides a unifying structure for predictive oncology.

Animals↗

PubMed2026

Novel insights into retinoblastoma: From oncogenic circuitry to precision diagnosis and eye-preserving therapies.

Retinoblastoma (RB) represents the most common primary intraocular malignancy in childhood and stands as a paradigm for translating molecular oncology into precision clinical management. This review synthesizes the comprehensive evolution in the understanding and treatment of RB. First, we deconstruct the intricate oncogenic circuitry that extends far beyond Knudson's classic "two-hit" RB1 inactivation model, describing non-classical MYCN-driven pathogenesis, multi-layered epigenetic reprogramming (including chromatin, RNA and histone changes), and distinct histological subtypes with defined clinical correlates, such as the favorable-prognosis cavitary RB. Single-cell genomics has elucidated the cellular origin from cone precursor cells and intratumoral heterogeneity. Risk stratification has been refined through well-defined classification systems, from the therapy-guiding International Intraocular Retinoblastoma Classification (IIRC) to the comprehensive American Joint Committee on Cancer Tumor-Node-metastasis (AJCC TNM) staging. Furthermore, the diagnostic paradigm has advanced from conventional anatomical imaging to liquid biopsies, enabling non-invasive molecular staging and monitoring via tumor-derived cell-free DNA analysis. Concurrently, the therapeutic landscape has undergone a radical shift, moving from enucleation and external-beam radiotherapy to an era dominated by local sight-preserving strategies. We provide a critical synthesis of the evidence for intravenous chemotherapy and the transformative role of super-selective intra-arterial chemotherapy (IAC), and describe essential randomized controlled trials, technical innovations, and optimized drug regimens. Finally, we explore emerging targeted molecular therapies and future directions. By integrating cutting-edge molecular insights with robust, high-level clinical evidence, this review offers the framework for achieving patient and eye survival as well as vision preservation in children with Retinoblastoma.

Intra-arterial chemotherapy↗