AI and Cancer Research ── 2026-11-05
New papers on cancer and AI, collected from journal feeds, PubMed (searched across all journals) and OpenAlex (a public index used instead of Google Scholar), judged by Jev (an AI that makes language judgements): original research, AI central, which of 13 themes, treatment or trials, and then ranked by importance up to a daily limit. Journals are not filtered by impact factor; each paper shows its journal's IF-equivalent (OpenAlex two-year mean citedness) and the date of that value. Each entry shows the full original abstract.
All cancers: Cancer biology（2）
Hematologic malignancies: AML（0） · CML（0） · ALL（0） · CLL（0） · Lymphoma（0） · Myeloma（0）
Solid tumors: Breast（2） · Lung（1） · Colorectal（2） · Pancreatic（1） · Prostate（1） · Brain（0） · Ovarian（0） · Endometrial（0） · Gastric（0） · Liver（2） · Kidney（0） · Bladder（1）
Rare cancers: ACC（0）
Cancer molecular biology and AI
- Abstract
Precision oncology requires accurate molecular insights, yet obtaining these directly from genomics is costly and time-consuming for broad clinical use. Predicting complex molecular features and patient prognosis directly from routine whole-slide images (WSI) remains a major challenge for current deep learning methods. Here we introduce PathLUPI, which uses transcriptomic privileged information during training to extract genome-anchored histological embeddings, enabling effective molecular prediction using only WSIs at inference. Through extensive evaluation across 49 molecular oncology tasks using 11,257 cases among 20 cohorts, PathLUPI demonstrated superior performance compared to conventional methods trained solely on WSIs. Crucially, it achieves AUC $\geq$ 0.80 in 14 of the biomarker prediction and molecular subtyping tasks and C-index $\geq$ 0.70 in survival cohorts of 5 major cancer types. Moreover, PathLUPI embeddings reveal distinct cellular morphological signatures associated with specific genotypes and related biological pathways within WSIs. By effectively encoding molecular context to refine WSI representations, PathLUPI overcomes a key limitation of existing models and offers a novel strategy to bridge molecular insights with routine pathology workflows for wider clinical application.
Journal IF-equivalent: 18.1 (OpenAlex 2-year mean citedness, value as of 2026-10-06, retrieved 2026-10-07; not the official Clarivate IF)Reference: Jin C, Zhou F, Yu Y, Ma J, Wang Y, Xu Y, et al. Genome-anchored foundation model embeddings improve molecular prediction from histology images. Nat Commun. 2026 Nov 2 [Epub ahead of print]. doi:10.1038/s41467-026-78947-2.Checked: Abstract only - Abstract
Major Finding: Proximity mapping identifies CDCP1 as a tumor-associated proximity antigen partner of EGFR, wherein co-targeting of EGFR and CDCP1 boosts tumor cell killing across multiple modalities. Concept: Photocatalytic proximity labeling and graph-based machine learning map tumor surface protein networks. Impact : The concept of tumor-associated proximity antigens is a promising tool to identify novel and tumor-specific therapeutic co-targets.
Journal IF-equivalent: 8.0 (OpenAlex 2-year mean citedness, value as of 2026-11-03, retrieved 2026-11-04; not the official Clarivate IF)Reference: [No authors listed]. Spatial Proximity Mapping Identifies Vulnerabilities for Therapeutic Co-Targeting. Cancer Discov. 2026 Oct 8;16(11):OF1-OF1. doi:10.1158/2159-8290.cd-rw2026-090. PMID: 42847768.Checked: Abstract only
See all 2 Cancer biology papers →
Hematologic malignancies
Acute myeloid leukemia (AML): treatment, trials and AI
No new qualifying paper for this issue.
Chronic myeloid leukemia (CML): treatment, trials and AI
No new qualifying paper for this issue.
Acute lymphoblastic leukemia (ALL): treatment, trials and AI
No new qualifying paper for this issue.
Chronic lymphocytic leukemia (CLL): treatment, trials and AI
No new qualifying paper for this issue.
Lymphoma: treatment, trials and AI
No new qualifying paper for this issue.
Multiple myeloma: treatment, trials and AI
No new qualifying paper for this issue.
Solid tumors
Breast cancer: treatment, trials and AI
- Abstract
PURPOSE: Accurate assessment of low levels of human epidermal growth factor receptor 2 (HER2) expression has become increasingly important with the expansion of HER2-targeted therapies to patients with HER2-low and HER2-ultralow breast cancer (BC). Following the introduction of revised College of American Pathologists (CAP) guidelines incorporating the HER2-ultralow (0+) category, we conducted a nationwide multireader study to evaluate interobserver variation in HER2 interpretation and to investigate the impact of artificial intelligence (AI)-assisted image analysis. MATERIALS AND METHODS: A total of 168 BC cases stained using the VENTANA HER2 (4B5) assay were digitized as whole-slide images (WSIs). Thirty-four board-certified breast pathologists independently reviewed all cases without AI assistance and repeated the assessment after a four-week washout period using AI support. Interobserver agreement, classification performance, and reading time were compared between the two phases. RESULTS: AI assistance significantly improved interobserver agreement (Krippendorff's alpha, 0.7415 to 0.8849; Fleiss' kappa, 0.4492 to 0.7524; p<0.001). The largest gains in diagnostic performance were observed in HER2-ultralow and HER2 IHC 1+ BCs, with sensitivities improving from 48.5% to 73.9% and from 64.9% to 87.7%, respectively (p<0.001). Although overall reading time was not reduced in the unadjusted analysis, AI assistance shortened interpretation time in HER2-ultralow BCs by 3.12 seconds (5.1% reduction). CONCLUSION: AI-assisted assessment improved the reproducibility and diagnostic performance of HER2 interpretation, with the greatest benefit observed in clinically relevant HER2 low-expression categories. The reduced interpretation time in HER2-ultralow BCs further suggests that AI can improve efficiency in diagnostically challenging cases encountered in routine practice.
Journal IF-equivalent: 3.6 (OpenAlex 2-year mean citedness, value as of 2026-11-03, retrieved 2026-11-04; not the official Clarivate IF)Reference: Lee H, Kim GY, Cho YA, Gong G, Lee HJ, Jeong BK, et al. A Nationwide Study of Interobserver Agreement and Accuracy in HER2 Immunohistochemistry and the Impact of Artificial Intelligence on HER2 Interpretation. Cancer Res Treat. 2026 Nov 2 [Epub ahead of print]. doi:10.4143/crt.2026.0805. PMID: 42971167.Checked: Abstract only - Abstract
BACKGROUND: The inherent biological heterogeneity of tumors continues to pose a challenge for achieving accurate survival status prediction in breast cancer. To address this, the current study seeks to improve the prognostic accuracy by developing an integrated framework that synthesizes clinical parameters with specific genetic variables derived from the comprehensive METABRIC (Molecular Taxonomy of Breast Cancer International Consortium) dataset. METHODS: Several machine learning (ML) models were applied under overall survival month (OSM)-dropped scenarios to prevent information leakage and develop clinically applicable predictive models. A diverse array of feature selection strategies, including SelectKBest and an integrated SHapley Additive exPlanations-XGBoost (SHAP-XGB) framework, were implemented. The limma package was used to identify differentially expressed genes (DEGs) with thresholds of |logFC|≥ 0.25 and adjusted p-value < 0.05. High-contribution genes associated with survival were further determined using a Leave-One-Out AUC (LOO-AUC) reduction approach. Gene Ontology (GO) enrichment analysis was performed to investigate biological pathways associated with the identified genes. Monte Carlo permutation testing, Kaplan-Meier survival analysis, multivariate Cox proportional hazards regression, and external validation using the TCGA-BRCA cohort were implemented to validate consensus biomarkers. RESULTS: Integration of clinical data with a 10-gene subset selected by the SHAP-XGB model achieved the highest predictive accuracy of 0.7133 and a balanced accuracy of 0.7217 by the Random Forest model. Integrated analyses of SHAP-XGB/RF, SelectKBest, DEG, and high-contribution gene results consistently identified six prognostic genes: JAK1, JAK2, CASP8, KIT, STAT5A, and GSK3B. GO enrichment analysis revealed significant immune-inflammatory pathways, particularly those related to cytokine production and regulation. CONCLUSION: This study presents a biologically interpretable framework that integrates explainable machine learning, transcriptomic analyses, and survival modeling for breast cancer prognosis. The identified six-gene consensus signature was consistently validated through multiple independent analytical approaches, external cohort validation, and survival analyses, highlighting its potential as a promising prognostic biomarker panel for breast cancer risk stratification.
Journal IF-equivalent: 3.3 (OpenAlex 2-year mean citedness, value as of 2026-11-03, retrieved 2026-11-04; not the official Clarivate IF)Reference: Oguz O, Aydın Z, Gungor BB, Goksuluk D. Explainable machine learning and multi-layer transcriptomic analysis identify and validate prognostic biomarkers for breast cancer survival. BMC Med Genomics. 2026 Aug 1;19(1):166. doi:10.1186/s12920-026-02430-6; PMCID: PMC13726301.Checked: Full text checked
Lung cancer: treatment, trials and AI
- Abstract
BACKGROUND: Ischemic cardiomyopathy (ICM) and lung adenocarcinoma (LUAD) are distinct diseases but may share fibroblast mediated mechanisms involving tissue remodeling and inflammation. However, their common molecular features at single cell resolution remain insufficiently defined. METHODS: We integrated single cell RNA sequencing datasets from ICM and LUAD with bulk transcriptomic profiles from TCGA LUAD, GSE18842, and GSE48166. Differential expression analysis identified 36 shared core genes. A total of 127 combinations of machine learning algorithms and feature selection methods were benchmarked to identify six hub genes: KRTCAP3, RUNX2, DBF4B, C11orf80, COL3A1, and ZFP64. UCell, scMetabolism, SCISSOR, Monocle 3, CellChat, scTenifoldNet/scTenifoldKnk, virtual drug screening, and molecular docking were used for functional characterization. RESULTS: Fibroblasts were the dominant cellular source of the six hub genes in both ICM and LUAD. COL3A1 progressively increased along fibroblast pseudotime trajectories. Virtual overexpression and knockout analyses showed convergence on extracellular matrix organization, inflammatory response, and immune regulation. Cell communication analysis identified fibroblasts as major senders of COLLAGEN signaling, with recipient cells shifting from inflammatory monocytes under normal conditions to proliferating macrophages in ICM and tumor cells in LUAD. Molecular docking nominated Digoxin, Ergotamine, and Ledipasvir as candidate RUNX2-binding compounds with favorable predicted docking scores. CONCLUSIONS: This study provides a single cell framework linking ICM and LUAD through fibroblast driven extracellular matrix remodeling and inflammation, and identifies potential molecular targets and therapeutic candidates for further validation.
Journal IF-equivalent: 4.3 (OpenAlex 2-year mean citedness, value as of 2026-10-04, retrieved 2026-10-05; not the official Clarivate IF)Reference: Pu Z, Liu Z, Zhang H, Han Y. Integrative single cell and machine learning analysis identifies fibroblast centric hub genes and therapeutic candidates in lung adenocarcinoma and ischemic cardiomyopathy. Transl Oncol. 2026 Dec;74:103096. doi:10.1016/j.tranon.2026.103096. PMID: 42971051.Checked: Abstract only
Colorectal cancer: screening, treatment, trials and AI
- Abstract
Purpose: Colorectal cancer (CRC) prognosis is influenced by the tumor microenvironment (TME), including spatial immune cell organization and spatial immune aggregates (SIAs). However, the prognostic relevance of spatially resolved immune subsets, particularly B-cell-related phenotypes, remains incompletely defined and requires careful evaluation against established clinicopathological predictors.
Methods: A cohort of 786 CRC patients was analyzed using multiplex fluorescence immunohistochemistry (mIHC) targeting 10 immune phenotypes, coupled with deep learning-based single-cell segmentation and spatial statistics. A biologically informed density-based spatial clustering of applications with noise (DBSCAN) algorithm was used for automated SIA detection. Prognostic modeling was restricted to 636 patients with complete survival information and was evaluated using a leakage-free patient-level training/validation split. Benchmark models included TNM-based, clinicopathological, spatial-only, and integrated XGBoost models. Spatial heterogeneity, immune subset distributions, region-specific associations, and prognostic performance were systematically evaluated.
Results: Non-proliferating CD3 + CD8−FOXP3 − T cells dominated the TME (64.9%), and immune composition varied with disease stage and metastatic status. Automated SIA detection showed high concordance with pathologist-reviewed annotations and enabled region-specific profiling of immune phenotypes. SIA-region proliferating CD8 + T cells were associated with favorable outcome, whereas non-proliferating CD21 + B cells in non-SIA regions were associated with poorer outcome. Spatial proximity analysis identified metastasis-associated distance patterns, including shortened distances between CD21 − B cells and CD3 + CD8−FOXP3 − T cells. In prognostic benchmarking, clinicopathological variables remained the strongest predictors in the current cohort, whereas spatial features alone showed limited standalone discrimination. An integrated XGBoost model incorporating spatial and clinicopathological variables provided complementary prognostic information but did not materially outperform the clinicopathological benchmark.
Conclusion: Our study provides a high-resolution spatial immune atlas of CRC and identifies spatially resolved immune features associated with patient prognosis. The automated SIA detection framework and prognostic benchmarking analyses offer an exploratory basis for future validation of spatial biomarkers, while the current data support a complementary rather than dominant role for spatial features alongside established clinicopathological variables.
Journal IF-equivalent: 4.5 (OpenAlex 2-year mean citedness, value as of 2026-11-03, retrieved 2026-11-04; not the official Clarivate IF)Reference: Lai Y, Wang J, Yong X, Wu Z, Guo G, Sun Y, et al. Prognostic architecture of spatially resolved immune ecosystems in colorectal cancer: a multiplex immunohistochemistry and machine learning study. Cell Oncol. 2026 Nov 2 [Epub ahead of print]. doi:10.1007/s13402-026-01309-z.Checked: Abstract only - Abstract
BACKGROUND: Tumor-infiltrating lymphocyte (TIL) density provides prognostic information in colon adenocarcinoma (COAD), but the additional value of TIL spatial organization remains unclear. METHODS: We developed CoATIL, a computational pathology framework quantifying TIL spatial architecture from routine H&E-stained whole-slide images. The study included 461 patients with microsatellite-stable or MSI-low COAD. The model was trained using 140 patients from University Hospitals Cleveland Medical Center, validated internally in 52 patients, and externally in The Cancer Genome Atlas (n = 130), and Guangdong Provincial People's Hospital (n = 139). Prognostic performance was assessed using Kaplan-Meier analysis and multivariable Cox regression. CoATIL was compared with a clinical model and eight foundation-model-based multiple-instance learning approaches. RESULTS: CoATIL was associated with progression-free survival (HR per standard-deviation increase, 1.32; 95% CI 1.13-1.55; p < 0.001; C-index=0.66) and remained prognostic after adjustment for clinicopathologic factors in each validation cohort (adjusted HRs, 2.00-4.40; all p < 0.05). High-versus-low-risk stratification was retained within stage II (HR 3.07; 95% CI 1.83-5.15; p < 0.001) and stage III disease (HR 3.24; 95% CI 1.28-8.19; p = 0.013). The combined clinical-CoATIL model achieved a higher C-index than the clinical model alone (0.69 versus 0.61; paired bootstrap difference, 0.08; 95% CI 0.03-0.14). Exploratory genomic analyses associated AMER1 and FBXW7 mutations with lower and higher CoATIL risk scores, respectively. CONCLUSIONS: Spatial immune architecture extracted from routine histology provides complementary prognostic information in COAD. Prospective validation is warranted.
Journal IF-equivalent: 3.1 (OpenAlex 2-year mean citedness, value as of 2026-11-03, retrieved 2026-11-04; not the official Clarivate IF)Reference: Song B, Maurya H, Khalighi S, Aqeel A, Corredor G, Hammouda K, et al. Computational image features of immune architecture reveal molecular patterns associated with outcome and risk of recurrence in microsatellite-stable or MSI-low colon cancers. Eur J Cancer. 2026 Dec;249:117810. doi:10.1016/j.ejca.2026.117810. PMID: 42966031.Checked: Abstract only
Pancreatic cancer: treatment, trials and AI
- Abstract
Preoperative assessment of pancreatic cancer resectability critically relies on accurate joint segmentation of tumors and peripancreatic vessels from contrast-enhanced CT. However, beyond segmentation accuracy alone, effective decision support in practice further requires task-specific characterization of peri‑tumoral infiltration risk and topological discontinuity risk. To address this, we propose a clinical risk-aware uncertainty alignment framework that achieves reliable risk characterization while maintaining high segmentation accuracy. The framework is built upon a dual-decoder architecture with evidential deep learning for joint segmentation and uncertainty estimation. Two clinically grounded alignment strategies are further designed to transform this uncertainty into task-specific risk indicators. Specifically, an infiltration-aware boundary uncertainty alignment module is proposed, which synthesizes texture, spatial, and vascular proximity factors into an infiltration-inspired prior and uses its ordinal structure to guide alignment, yielding infiltration-risk-aware uncertainty from which infiltration-aware tumor boundaries are derived for downstream invasion analysis. A topology-aware discontinuity uncertainty alignment module is further designed, which uses skeleton-mismatch regions as a connectivity-related proxy and constrains uncertainty by a three-tier ordinal relation with region-weighted supervision, yielding discontinuity-risk-aware uncertainty and improved vessel connectivity. Multi-center validation on 1058 patients from five medical centers demonstrates favorable and stable cross-institutional performance, and downstream experiments explore the utility of the aligned uncertainty for tumor-vessel invasion analysis. Overall, this study couples accurate tumor and vessel delineation with clinically grounded risk characterization, with the potential to support tumor-vessel assessment in preoperative planning. Our source code will be released at https://github.com/SJTUBME-QianLab/CRUA-Net.
Journal IF-equivalent: 12.5 (OpenAlex 2-year mean citedness, value as of 2026-10-25, retrieved 2026-10-26; not the official Clarivate IF)Reference: Fu W, Zhu H, Chen T, Qian X. Joint segmentation of pancreatic tumors and peripancreatic vessels via a clinical risk-aware uncertainty alignment framework. Med Image Anal. 2027 Feb;116:104378. doi:10.1016/j.media.2026.104378. PMID: 42966317.Checked: Abstract only
Prostate cancer: treatment, trials and AI
- Abstract
INTRODUCTION: Artificial intelligence (AI)-based models can quantify tumour volume in positron emission tomography (PET) examinations using prostate specific membrane antigen (PSMA). It is, however, unknown whether this measurement of tumour volume contains prognostic information. The purpose of this study was to examine whether tumour volume measured by AI is associated with overall survival in patients with prostate cancer. METHODS: Patients who had undergone [ 18 F]PSMA-1007 PET-CT due to primary staging or biochemical recurrence (BCR) of prostate cancer at our centre were retrospectively included and followed for 5 years. Automatic quantification of tumour volume was performed using a previously developed fully automated AI model from our research group for PSMA PET-CT analysis, without manual corrections. The tumour volume for the prostate, lymph nodes, and bone was calculated and summarized as Total Lesion Volume (TLV) and divided categorically (no detectable tumour, low, and high) as well as aggregated to whole-body TLV and divided into tertiles (low, intermediate, and high). Mortality rates were compared using Cox-regression and adjusted for age. RESULTS: Among 267 patients in the final analysis, 48 (18%) died during follow-up. Whole-body TLV was significantly associated with mortality in the combined cohort (p = 0.001) and in the primary staging cohort (p = 0.014) but did not reach statistical significance in the BCR cohort (p = 0.061). CONCLUSIONS: Fully automated AI-derived TLV from PSMA PET-CT was associated with overall survival. These findings support the potential role of AI-based tumour quantification as a potential prognostic biomarker in prostate cancer.
Journal IF-equivalent: 1.2 (OpenAlex 2-year mean citedness, value as of 2026-11-03, retrieved 2026-11-04; not the official Clarivate IF)Reference: Wittgren J, Ingvar J, Enqvist O, Larsson M, Minarik D, Edenbrandt L, et al. Fully automated AI‐based quantification of tumour volume on PSMA PET‐CT images is associated with overall survival in prostate cancer. Clin Physio Funct Imaging. 2026 Nov 2;46(6):e70104. doi:10.1111/cpf.70104.Checked: Abstract only
Brain tumours: treatment, trials and AI
No new qualifying paper for this issue.
Ovarian cancer: treatment, trials and AI
No new qualifying paper for this issue.
Endometrial cancer: treatment, trials and AI
No new qualifying paper for this issue.
Gastric cancer: treatment, trials and AI
No new qualifying paper for this issue.
Liver cancer: treatment, trials and AI
- Abstract
Objective: Microvascular invasion (MVI) predicts postoperative recurrence in hepatocellular carcinoma (HCC), yet preoperative prediction remains challenging. This study compared a Transformer, a convolutional neural network (CNN), and a multiple instance learning (MIL) framework for predicting MVI from contrast-enhanced CT.
Methods: We retrospectively enrolled 236 patients with pathologically confirmed HCC. Tumor regions of interest were semi-automatically delineated on arterial-phase CT images, and slices centered on the largest tumor cross-section were sampled to build a 2.5D dataset. Four models—Transformer, MIL, CNN, and a clinical baseline—were trained. Models were compared using the area under the receiver operating characteristic curve (AUC) with the DeLong test; bootstrap resampling assessed the stability of AUC estimates.
Results: The Transformer achieved the highest AUC of 0.827 (95% CI 0.7585–0.8962), with an accuracy of 0.775, sensitivity of 0.577, and specificity of 0.889, followed by MIL (0.766; 95% CI 0.6875–0.8441), CNN (0.723), and the clinical model (0.715). Bootstrap validation confirmed stable AUC estimates for all models, with the Transformer ranking first (mean 0.827; SD 0.034). The Transformer significantly outperformed the CNN (adjusted P = 0.001) and the clinical model (adjusted P = 0.039); after correction for multiple comparisons, its difference from MIL was of borderline significance (adjusted P = 0.097). No significant differences were observed among the CNN, MIL, and clinical models.
Conclusion: Among the four models evaluated, the contrast-enhanced CT-based Transformer predicted MVI most accurately, and its advantage persisted after bootstrap internal validation. These findings concern the ranking of discriminative performance only; the clinical utility of the model is not established, and prospective validation in larger cohorts is required before any clinical application.
Journal IF-equivalent: 2.8 (OpenAlex 2-year mean citedness, value as of 2026-11-03, retrieved 2026-11-04; not the official Clarivate IF)Reference: Lin M, Zheng X, Zhao C, Lin T, Zhao Q. Predicting microvascular invasion in hepatocellular carcinoma using contrast-enhanced CT-based deep learning models: a comparative study of transformer, CNN, and MIL. Front Oncol. 2026 Nov 2;16:1988882. doi:10.3389/fonc.2026.1988882.Checked: Abstract only - Abstract
INTRODUCTION: Hepatocellular carcinoma (HCC) exhibits substantial molecular heterogeneity, complicating treatment selection and clinical management. OBJECTIVES: We developed an artificial intelligence-based histopathological framework to predict HCC biological characteristics from routine hematoxylin and eosin (H&E)-stained slides. METHODS: Using The Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC) cohort, transcriptomic phenotypes associated with angiogenesis, stemness, and autophagy were identified through gene set analysis, differential expression analysis, and consensus clustering, and evaluated for overall survival and drug sensitivity. Whole-slide images were processed into 224 × 224-pixel tumor tiles for phenotype prediction using feature extraction (UNI, ResNet50, CTrans, GigaPath, and Virchow2) combined with classic multiple instance learning and clustering-constrained attention-based multiple instance learning.. Models were externally validated at Renmin Hospital of Wuhan University and Tongji Hospital. Interpretability was assessed using attention heatmaps, and predictions were evaluated against molecular markers and tumor differentiation grades. RESULTS: Unsupervised clustering identified two subgroups per phenotype, with significant difference in survival (all p < 0.004) and sensitivities to sorafenib or transcatheter arterial chemoembolization (all p < 0.0001). The tumor detection model achieved an area under the receiver operating characteristic curve (AUC) of 97.53%. The phenotype prediction models achieved internal optimal AUCs of 85.51%-91.05% and accuracies of 79.63%-88.89%. External optimal AUCs were 70.94%, 88.64%, and 91.17% for angiogenesis, stemness, and autophagy, with accuracies of 62.00%, 78.00%, and 84.00%, respectively. Attention heatmaps revealed distinct morphological features among subgroups, including neovascularization, increased nuclear-to-cytoplasmic ratios, and cytoplasmic vacuolization. Model predictions showed concordance with CD31/CD105 (κ = 0.398-0.432), CD133/LGR5 (κ = 0.428-0.629), Beclin-1/p62-LC3B (κ = 0.592-0.762) (all p < 0.05), and tumor differentiation grade (κ = - 0.167 to - 0.175, p < 0.05). CONCLUSION: This proof-of-concept AI framework predicted transcriptome-defined HCC phenotypes from routine histology, with predictions associated with prognosis and therapeutic response. It may provide a cost-effective approach for assessing HCC heterogeneity and complement molecular profiling.
Journal IF-equivalent: 9.9 (OpenAlex 2-year mean citedness, value as of 2026-11-03, retrieved 2026-11-04; not the official Clarivate IF)Reference: Zhang H, Li J, Sheng J, Li Z, Jiang W, Xu K, et al. Rapid and label-free analysis of biological characteristics of hepatocelluar carcinoma tissues based on histological images. J Adv Res. 2026 Nov [Epub ahead of print]. doi:10.1016/j.jare.2026.10.067. PMID: 42967526.Checked: Abstract only
Kidney cancer: treatment, trials and AI
No new qualifying paper for this issue.
Bladder cancer: treatment, trials and AI
- Abstract
Bladder cancer (BC) presents a significant clinical challenge, with a poor prognosis in its advanced stages. Although immunotherapy has improved outcomes, patient response rates remain limited due to complex immune evasion mechanisms. Robust biomarkers are urgently needed for prognostic stratification and to help identify patients who might benefit from immunotherapy. We developed an immune evasion-related signature (IRS) using transcriptomic data from The Cancer Genome Atlas (TCGA) and validated it across four independent Gene Expression Omnibus (GEO) cohorts. An integrative machine learning framework based on ten algorithms, fitted as 60 candidate model configurations, was used to construct the IRS from differentially expressed immune-related genes. The signature’s prognostic value, association with the tumor immune microenvironment (TME), and predictive power for immunotherapy response were assessed, and the key gene DOT1L was functionally validated in vitro. The IRS, comprising five genes (DOT1L, DVAC2, SRRT, PPP2R3C, and IRF9), effectively stratified BC patients into high-risk and low-risk groups. A high IRS score was an independent prognostic factor for poorer overall survival and was associated with an immunosuppressive TME, characterized by reduced CD8 + T cell infiltration and higher Tumor Immune Dysfunction and Exclusion (TIDE) scores. Conversely, low IRS scores correlated with enhanced response to anti-PD-1/L1 therapy in validation cohorts. Furthermore, DOT1L knockdown suppressed BC cell proliferation and migration. We developed and retrospectively validated a machine learning-derived IRS that showed consistent associations with prognosis and immunotherapy benefit across multiple independent bladder cancer cohorts. The IRS is a promising research biomarker, and prospective, multicenter validation is required before it can guide personalized treatment decisions.
Journal IF-equivalent: 2.4 (OpenAlex 2-year mean citedness, value as of 2026-10-12, retrieved 2026-10-13; not the official Clarivate IF)Reference: Huang B, Quan J, Xue W, Ma X. Development of a machine learning-based immune evasion signature for prognostic stratification and its association with immunotherapy response in bladder cancer. Discov Onc. 2026 Nov 2 [Epub ahead of print]. doi:10.1007/s12672-026-06185-5.Checked: Abstract only
Rare cancers
Adenoid cystic carcinoma: treatment, trials and AI
No new qualifying paper for this issue.