Search Results (426)

Diffuse pleural mesothelioma (PM) is a rare thoracic malignancy with historically limited treatment options and poor outcomes. Despite the recent breakthrough of dual immune checkpoint blockade (ICB)—notably the combination of anti-PD-1 and anti-CTLA-4 therapies—clinical responses remain variable and overall survival gains modest. Consequently, there is an urgent need for multidimensional biomarkers and adaptive trial designs to unravel the complexity of PM immune biology. This review provides a comprehensive overview of current evidence on how histological subtypes (epithelioid vs. non-epithelioid) influence ICB efficacy, highlighting distinct genetic landscapes (e.g., BAP1, CDKN2A, NF2 mutations) and tumor microenvironment (TME) features, including immune infiltration patterns and PD-L1 or VISTA expression, that underlie differential responses. We further examine intrinsic tumor factors—such as mutational burden and checkpoint ligand expression—and extrinsic determinants, including immune cell composition, stromal architecture, patient immune status, and microbiota, as modulators of immunotherapy outcomes. We also discuss the rationale behind emerging strategies designed to enhance ICB efficacy, currently under clinical evaluation. These include combination regimens with chemotherapy, radiotherapy, surgery, epigenetic modulators, anti-angiogenic agents, and novel immunotherapies such as next-generation checkpoint inhibitors (LAG-3, VISTA), immune-suppressive cell–targeting agents, vaccines, cell-based therapies, and oncolytic viruses. Collectively, these advancements underscore the importance of integrating histological classification with molecular and microenvironmental profiling to refine patient selection and guide the development of combination strategies aimed at transforming “cold” mesotheliomas into “hot,” immune-responsive tumors, thereby enhancing the efficacy of ICB. Full article

Background/Objectives: Mesothelioma is a highly aggressive tumor with a poor prognosis that typically develops after a long latency period following asbestos exposure. Although immunotherapy combined with chemotherapy is increasingly used, the efficacy of standard treatments remains limited. This study aimed to explore ferroptosis induction as a potential therapeutic strategy for mesothelioma. Methods: We first screened microbial culture extracts collected from soil and marine environments to identify compounds with selective cytotoxicity against mesothelioma cells. Gene expression profiling was performed to investigate the mechanism of cell death induced by the identified compound. To assess intrinsic ferroptosis susceptibility, patient-derived mesothelioma cell lines and immortalized mesothelial cell lines were treated with RSL3, a GPX4 inhibitor. Results: Screening identified brefeldin A as a compound that selectively induces cell death in mesothelioma cells. Gene expression profiling revealed transcriptional changes consistent with ferroptosis induction. Treatment with RSL3 demonstrated marked variability in ferroptosis sensitivity across cell lines; the subgroup showing high sensitivity to RSL3 did not exhibit significant genetic alterations in NF2 or BAP1, but contained a significantly higher proportion of epithelioid tumors in histological classification. Conclusions: Our findings highlight ferroptosis induction as a promising antitumor mechanism in mesothelioma, particularly in the epithelioid subtype. While GPX4 inhibitors such as RSL3 are effective in vitro, further studies are needed to overcome pharmacological limitations and define molecular determinants of ferroptosis susceptibility, which may inform future personalized therapeutic strategies. Full article

Renal cell carcinoma (RCC) features a complex tumor microenvironment, where cancer-associated fibroblasts (CAFs) play key roles in tumor progression, epithelial–mesenchymal transition (EMT), immune evasion, and resistance to treatment. This article updates our understanding of CAF origins, diversity, and functions in RCC, incorporating recent single-cell RNA sequencing (scRNA-seq) data that refine CAF subtypes. The paper explores the mechanistic interactions between CAFs and EMT, focusing on CAF-derived signaling pathways like TGF-β, IL-6/STAT3, HGF/c-MET, and Wnt/β-catenin, as well as extracellular-vesicle-mediated transfer of miRNAs and lncRNAs that promote metastatic behavior in RCC. It also addresses how CAF-driven remodeling of the extracellular matrix, metabolic changes, and activation of YAP/TAZ contribute to invasion and resistance to therapies, particularly in relation to tyrosine kinase inhibitors, mTOR inhibitors, and immune checkpoint blockade. The review highlights emerging therapeutic strategies targeting CAFs, such as inhibiting specific signaling pathways, disrupting CAF–tumor cell communication, and selectively depleting CAFs. In conclusion, it identifies limitations in current CAF classification systems and proposes future research avenues to improve RCC-specific CAF profiling and exploit the CAF–EMT axis for therapeutic gain. Full article

Breast cancer (BC) is increasingly recognized as a heterogeneous disease, with complexity that extends beyond the classical luminal A/B, HER2-enriched, and triple-negative framework. Advances in molecular and functional profiling have uncovered emerging subpopulations, including HER2-low, claudin-low, BRCA-deficient (“BRCAness”), and refined TNBC subsets, such as luminal AR (LAR) and basal-like immune variants, that extend beyond traditional taxonomies. These novel classifications provide additional resolutions, offering both prognostic insight and therapeutic opportunities. In this comprehensive review, we integrate evidence from genomic, epigenetic, proteomic, immune-related, and liquid biopsy biomarkers, underscoring how they define the biology of these subgroups and predict responses to targeted therapies, such as antibody–drug conjugates, PARP inhibitors, and immune checkpoint blockade. We further highlight the role of the tumor microenvironment (TME) and intratumoral heterogeneity in shaping these entities. Collectively, recognition of emerging subtypes as clinically actionable groups represents a paradigm shift from static receptor-based models to dynamic, biomarker-driven frameworks that refine prognosis, enable patient stratification, and support precision oncology in aggressive BC. Full article

The 2022 WHO classification gives more importance to the integration of morphological and molecular characteristics of tumors, introducing new diagnostic criteria for papillary thyroid carcinoma (PTC). The invasive encapsulated follicular variant of PTC (IEFVPTC) is now considered a separate entity and no longer a subtype of PTC, while the infiltrative follicular variant (IFVPTC) is still considered a PTC subtype. The separation of invasive encapsulated follicular variants from PTCs implies that differential diagnosis between IEFVPTC and IFVPTC can be difficult. We performed a gene expression analysis by NanoString technology on 23 PTCs, divided into 11 IEFVPTCs and 12 IFVPTCs. We focused our attention on the possible role of the tumor microenvironment (TME) and, in particular, the role of the extracellular matrix (ECM). IFVPTC, compared to IEFVPTC, showed a statistically significant downregulation of 2 genes and an upregulation of 45. Among these genes, we focused our attention on TIMP2 and COL1A2, whose high upregulation was statistically significant in IFVPTC. TIMP2 and COL1A2 are involved in ECM degradation and synthesis and in collagen biosynthesis and modification. The ECM and collagen alterations in IFVPTC could reflect the different tumor behavior of FVPTCs, allowing the identification of new biomarkers to distinguish IEFVPTC from IFVPTC. Full article

Background/Objectives: Colorectal cancer (CRC) exhibits significant molecular heterogeneity, as reflected in Consensus Molecular Subtype (CMS) classification, and demonstrates extensive crosstalk with the microbiome. However, the role of the microbiome in determining subtypes of CRC, and CMS4 in particular, which represents an aggressive, stromal-rich variant associated with poor prognosis, remains poorly understood. Here, we reveal the role of the tumor microbiome in shaping the tumor microenvironment (TME) and its impact on CMS4 determination. Methods: A total of 25 CRC tissues were analyzed using RNA sequencing and classified with CMScaller to identify significantly enriched microbial species. Functional studies were performed using these CMS-specific microbial species and CMS2 organoids co-cultured with stromal (18Co) and immune (THP-1) cells. Results: 16S rRNA profiling of matched CRC tissues showed that Bacteroides fragilis was significantly enriched in CMS4 tumors (linear discriminant analysis score = 4.7). Functional studies revealed that exposure to enterotoxigenic Bacteroides fragilis (ETBF) induced CMS4-like features, including enhanced growth and gene expression patterns resembling those of primary CMS4 tumors. Conclusions: These findings suggest that ETBF contributes to the development of CMS4 and may facilitate the acquisition of aggressive phenotype associated with this CRC subtype. Full article

Background: Colorectal cancer is a multifactorial malignancy implicating a wide variety of risk factors, such as genetic, environmental, nutritional, and lifestyle factors, leading to a certain heterogeneity in the development of the disease. Colorectal cancer is generally classified in terms of a Warburg metabolic phenotype, characterized by an excess of glycolytic axes as compared to oxidative phosphorylation. It is therefore important to better characterize the metabolic heterogeneity of these tumors in relation to their mitochondrial activity. Materials and Methods: Two R-packages (keggmetascore and mitoscore) were developed to explore metabolism, based on KEGG metabolism pathways, and mitochondrial activities, based on mitocarta V3 annotations, for the investigation of diverse transcriptomics data such as bulk or single cell experiments at the single-sample level. Results: Using the two R-packages, we functionally confirmed both regulation of metabolism and mitochondrial activities in LOVO cells after stimulation with metformin. At the single-cell level, in single-cell RNA-sequencing of colorectal tumors, we conjointly observed an activation of metabolism and mitochondrial activities in tumor cells from MSI-high tumors, in contrast to a conjoint repression of metabolism and mitochondrial activity in tumor cells from POLE-mutated tumors. These two types of tumors have distinct responses to immune checkpoint blockade therapy. At the bulk transcriptome level, colorectal tumors present less metabolism/mitochondria activities as compared to normal tissues. Multi-modal integration by co-expression network analysis showed that metabolism/mitochondrial activities are associated with a consensus molecular subtype (CMS) classification of colorectal cancer. Regarding KRAS, BRAF, and TP53 driver gene mutation status, strong repression of metabolism pathways was observed, mainly associated with fewer intra-mitochondrial membrane interactions in tumors harboring a BRAF-V600E mutation. Machine learning using Elastic-net allowed us to build a mixed metabolism/mitochondrial activity score, which was found to be increased in the CMS1-MSI subtype and metastatic samples and to be an independent parameter predictive of BRAF-V600E mutation status in colorectal cancer. Conclusions: These findings underscore the pivotal role of mitochondrial metabolism in colorectal cancer subtyping and highlight its value as a predictive biomarker for personalized therapeutic strategies. Full article

High-grade serous ovarian cancer (HGSOC) is the most common (~80%) and lethal ovarian cancer subtype in the United States, characterized by TP53 mutations and DNA repair defects causing chromosomal instability (CIN). KIF18A is an essential cytoskeletal motor protein for cell division in CIN+ cancer cells, but it is not necessary for cell division in normal cells. Therefore, KIF18A represents a promising target for therapeutic interventions in CIN+ cancers. We investigated the use of a novel KIF18A inhibitor ATX020, for selectively targeting CIN+ HGSOC cells using growth inhibition assays, invasion assays, immunoassays, cell cycle analysis, and immunofluorescence techniques. Using DepMap and flow cytometry, we classified a panel of HGSOC cell lines based on aneuploidy scores (AS) and ploidy levels and identified a correlation between these classifications and sensitivity against ATX020. ATX020 induced cytotoxicity through mitotic arrest and DNA damage, and reduced tumor growth in HGSOC with high aneuploidy scores (AS). Mechanistically, ATX020 blocks KIF18A’s plus-end movement on spindle fibers, increasing spindle length, resulting in chromosomal mis-segregation, aneuploidy, and DNA damage. Our findings suggest that ATX020 inhibits CIN+ HGSOC cells mainly by inducing mitotic arrest and DNA damage, disrupting KIF18A’s function crucial for mitosis. Full article

Deep learning has demonstrated significant promise in medical image analysis; however, standard CNNs frequently encounter challenges in detecting subtle and intricate features vital for accurate diagnosis. To address this limitation, we systematically integrated attention mechanisms into five commonly used CNN backbones: VGG16, ResNet18, InceptionV3, DenseNet121, and EfficientNetB5. Each network was modified using either a Squeeze-and-Excitation block or a hybrid Convolutional Block Attention Module, allowing for more effective recalibration of channel and spatial features. We evaluated these attention-augmented models on two distinct datasets: (1) a Products of Conception histopathological dataset containing four tissue categories, and (2) a brain tumor MRI dataset that includes multiple tumor subtypes. Across both datasets, networks enhanced with attention mechanisms consistently outperformed their baseline counterparts on all measured evaluation criteria. Importantly, EfficientNetB5 with hybrid attention achieved superior overall results, with notable enhancements in both accuracy and generalizability. In addition to improved classification outcomes, the inclusion of attention mechanisms also advanced feature localization, thereby increasing robustness across a range of imaging modalities. Our study established a comprehensive framework for incorporating attention modules into diverse CNN architectures and delineated their impact on medical image classification. These results provide important insights for the development of interpretable and clinically robust deep learning-driven diagnostic systems. Full article

Background: Glioblastoma (GB) is the most common primary brain tumor, with poor prognosis, significant neurological symptoms, and near-universal recurrence. Biomarker development is often limited by the scarcity of tumor tissue available for study. Noninvasive serum-based profiling offers potential to improve outcomes. Purpose: This study examined serum proteomic and metabolomic profiles pre- and post-concurrent chemoirradiation (CRT) to identify associations with patient outcomes and molecular classification, and to explore relevant signaling and metabolic pathways. Methods: Serum samples from 109 GB patients, obtained prior to and following completion of CRT, were analyzed with each patient serving as their own control, using a SOMAScan^® proteomic assay (7289 proteins) and metabolomics (SECIM, 6015 compounds). Clinical data were obtained through chart review. Proteomic and metabolomic changes were examined at baseline (prior to CRT) and in alteration (pre- vs. post-CRT) for their association with overall survival (OS), progression-free survival (PFS), MGMT, and IDH status. Cox models, gene set enrichment analysis (Hallmark, GSEA), and Kaplan–Meier survival analysis were used. Results: Several hundred proteins and metabolites were associated with OS and PFS. MGMT status was known in 60% and IDH in 38% of patients. Pre-CRT DLST (HR 11.7, p < 0.001, adj p = 0.01) was the only protein significantly associated with OS. Pre-CRT, and higher 7-HOCA was linked to worse OS (HR 1.3) and PFS (HR 1.5), while increased p-cresol was associated with improved OS (HR 0.8) and PFS (HR 0.9). Kaplan–Meier analysis based on signal alteration post-CRT vs. pre-CRT, revealed superior OS with lower DLST and MSR1 and superior PFS with higher PGAM2 and ATG5, and lower 7-HOCA. Pathway analysis linked improved PFS to fatty acid metabolism, citric acid cycle, and purine biosynthesis. MGMT and IDH class comparisons revealed associations primarily with amino acid and fatty acid metabolism. Both MGMT methylation and IDH mutation correlated with increased PLAG12B expression, with significance only for MGMT (p < 0.001). IDH mutation was associated with decreased MSR1 (p = 0.047) and p-cresol (p < 0.001). Conclusions: Serum-based fatty acid and purine metabolism pathways are associated with OS and PFS in GB. 7-HOCA and p-cresol emerged as potential biomarkers linked to treatment response and molecular subtype. These findings support further investigation of noninvasive biospecimens for clinically actionable biomarkers in GB. Full article

Precision oncology is broadly defined as cancer prevention, diagnosis, and treatment specifically tailored to the patient based on his/her genetics and molecular profile. In simple terms, the goal of precision medicine is to deliver the right cancer treatment to the right patient, at the right dose, at the right time. Precision oncology is the most studied and widely applied subarea of precision medicine. Now, precision oncology has expanded to include modern technology (big data, single-cell spatial multiomics, molecular imaging, liquid biopsy, CRISPR gene editing, stem cells, organoids), a deeper understanding of cancer biology (driver cancer genes, single nucleotide polymorphism, cancer initiation, intratumor heterogeneity, tumor microenvironment ecosystem, pan-cancer), cancer stratification (subtyping of traditionally defined cancer types and pan-cancer re-classification based on shared properties across traditionally defined cancer types), clinical applications (cancer prevention, early detection, diagnosis, targeted therapy, minimal residual disease monitoring, managing drug resistance), lifestyle changes (physical activity, smoking, alcohol consumption, sunscreen), cost management, public policy, and more. Despite being the most developed area in precision medicine, precision oncology is still in its early stages and faces multiple challenges that need to be overcome for its successful implementation. In this review, we examine the history, development, and future directions of precision oncology by focusing on emerging technology, novel concepts and principles, molecular cancer stratification, and clinical applications. Full article

Colorectal cancer (CRC) classification has progressed from consensus molecular subtypes (CMS) to epithelial–intrinsic consensus molecular subtypes (iCMS) and the layered intrinsic subtype-MSI-fibrosis (IMF) system that combines intrinsic state, MSI status, and fibrosis. This article reviews biological underpinnings of iCMS/IMF, their relationships to tumor-microenvironment crosstalk, and how single-cell and spatial transcriptomics refine therapeutic stratification by resolving tumor microenvironment heterogeneity and its impact on fibrosis. Prognostic and therapeutic implications are covered, including PD-1 blockade in MSI-high (MSI-H), MAPK-directed therapy in BRAF-mutant disease, and EGFR targeting in selected RAS wild-type (WT) left-sided tumors, and we suggest decision points specifically informed by the activity of the fibrosis axis. A step-by-step procedure is presented for the analysis of bulk and single-cell RNA and formalin-fixed, paraffin-embedded (FFPE) resources, along with open-source tools and reporting standards to make iCMS/IMF calling reproducible in clinics and trials. Future outlooks are outlined with near-term biomarker–drug hypotheses for microsatellite-stable (MSS)-iCMS3 and high fibrosis tumors and key gaps to close for clinical translation. This review outlines a roadmap for precision medicine in colorectal cancer by leveraging the iCMS/IMF framework to integrate pathology and digital pathology, molecular diagnostics, and therapy mapping with FAP-targeted imaging and therapy. Full article

Background/Objectives: Lower-grade gliomas (LGGs) are a biologically and clinically heterogeneous group of brain tumors, for which molecular stratification plays essential role in diagnosis, prognosis, and therapeutic decision-making. Conventional unimodal classifiers do not necessarily describe cross-layer regulatory dynamics which entail the heterogeneity of glioma. Methods: This paper presents a protein–protein interaction (PPI)-informed hybrid model that combines multi-omics profiles, including RNA expression, DNA methylation, and microRNA expression, with a Graph Attention Network (GAT), Random Forest (RF), and logistic stacking ensemble learning. The proposed model utilizes ElasticNet-based feature selection to obtain the most informative biomarkers across omics layers, and the GAT module learns the biologically significant topological representations in the PPI network. The Synthetic Minority Over-Sampling Technique (SMOTE) was used to mitigate the class imbalance, and the model performance was assessed using a repeated five-fold stratified cross-validation approach using the following performance metrics: accuracy, precision, recall, F1-score, ROC-AUC, and AUPRC. Results: The findings illustrate that a combination of multi-omics data increases subtype classification rates (up to 0.984 ± 0.012) more than single-omics methods, and DNA methylation proves to be the most discriminative modality. In addition, analysis of interpretability using attention revealed the major subtype-specific biomarkers, including UBA2, LRRC41, ANKRD53, and WDR77, that show great biological relevance and could be used as diagnostic and therapeutic tools. Conclusions: The proposed multi-omics based on a biological and explainable framework provides a solid computational approach to molecular stratification and biomarker identification in lower-grade glioma, bridging between predictive power, biological clarification, and clinical benefits. Full article

Background: The combination of multi-omics data, including genomics, transcriptomics, and epigenomics, with medical imaging modalities (PET, CT, MRI, histopathology) has emerged in recent years as a promising direction for the advancement of precision oncology. Many researchers have contributed to this domain, exploring the multi-modality aspect of using both multi-omics and image data for better cancer identification, subtype classifications, cancer prognosis, etc. Methods: We present an umbrella review summarizing the state of the art in fusing imaging modalities with omics and artificial intelligence, focusing on existing reviews and meta-analyses. The analysis highlights early, late, and hybrid fusion strategies and their advantages and disadvantages, mainly in tumor classification, prognosis, and treatment prediction. We searched review articles until 25 May 2025 across multiple databases following PRISMA guidelines, with registration on PROSPERO (CRD420251062147). Results: After identifying 56 articles from different databases (i.e., PubMed, Scopus, Web of Science and Dimensions.ai), 35 articles were screened out based on the inclusion and exclusion criteria, keeping 21 studies for the umbrella review. Discussion: We investigated prominent fusion techniques in various contexts of cancer types and the role of machine learning in model performance enhancement. We address the problems of model generalizability versus interpretability within the clinical context and argue how these multi-modal issues can facilitate translating research into actual clinical scenarios. Conclusions: Lastly, we recommend future work to define clearer and more reliable validation criteria, address the need for integration of human clinicians with the AI system, and describe the trust issue with AI in cancer care, which requires more standardized approaches. Full article

Integrins, cell-surface adhesion receptors involved in tumor progression, invasion, and metastasis, serve as crucial biomarkers for cancer diagnosis and therapeutic targeting. Multiplexed detection of integrins and cancer cell classification at the single-cell level allows for comprehensive profiling, facilitating precise identification and categorization of tumor cells that are heterogeneous in integrin expression and cell subtype. In this study, we developed a five-plex detection platform and demonstrated integrin profile for cancer cell classification leveraging surface-enhanced Raman scattering (SERS) with gap-enhanced gold nanorods (GENRs) in conjunction with advanced computational analysis. Specifically, we synthesized GENRs bearing five distinct Raman nanotags, each producing a unique spectral fingerprint upon targeting a specific integrin subtype expressed on cancer cell surfaces. SERS signals from single cancer cells—after labeling simultaneously with the five-color SERS nanotags—were collected on single cells and subsequently analyzed with classical least squares regression to reliably deconvolute and quantify expression level of five different integrin monomers. Utilizing a random forest classifier trained on integrin profiles from individual cancer cell lines, we achieved simultaneous detections of three different breast cancer cell lines, with exceptional classification accuracy of 99.9%. The feasibility of this method for multiplexed detection of circulating tumor cells was tested using peripheral blood mononuclear cells (PBMCs) spiked with mixed breast cancer cells from three cell lines. By integrating GENRs, multiplexed SERS nanotag technology, and machine learning, our platform significantly advances cancer diagnostics through accurate integrin-based cell profiling and classification. These findings highlight the potential of multiplexed integrin detection using SERS technology as a powerful diagnostic approach, ultimately supporting improved cancer subtype characterization, personalized diagnostics, and more targeted therapeutic strategies. Full article