Multi-modal data integration reveals functionally credible predictive biomarkers in ovarian cancer.

A tumor profiling resource for ovarian cancer: insights into chemotherapy-driven heterogeneity and personalized treatment strategy

In women with high-grade serous ovarian cancer, chemotherapy remains the primary standard treatment, despite growing recognition of the disease as highly heterogeneous. Here, we examine the feasibility and clinical utility of comprehensive multimodal molecular profiling to inform treatment decisions. We analyze blood, single-cell and bulk tumor tissue, and malignant ascites using up to eleven technologies (DNA, RNA, protein, and functional assays) within a four-week turnaround time. Hypothetical treatment recommendations are altered for 76% of patients, and multi-omics-guided maintenance therapy is associated with prolonged overall survival in a subset of patients. Subsequent cohort analysis reveals distinct cellular and molecular profiles in ascites-derived single-cells compared to solid tumor tissue, unique per-patient ex vivo drug responses, and a marked increase in cancer cell heterogeneity following chemotherapy exposure. This coincides with genomic signature alterations in whole-genome-amplified patients. Our data suggest that molecularly guided treatments should be tested as adjuvant therapies prior to chemotherapy in the future. High-grade serous ovarian cancer is clinically challenging due to marked molecular heterogeneity and variable treatment response. Here, the authors demonstrate that integrated multimodal tumor profiling can inform personalized maintenance treatment decisions and that chemotherapy reshapes tumor cell diversity.

Multi-omics identification and functional validation of signal regulatory protein gamma as a prognostic biomarker and immune regulator in head and neck squamous cell carcinoma

Head and neck squamous cell carcinoma (HNSCC) comprises biologically diverse tumors, and durable responses to immune-checkpoint blockade are achieved by only a subset of patients. There remains a need for markers that connect clinical outcome with malignant-cell phenotypes and tissue-level immune organization. We integrated The Cancer Genome Atlas HNSCC cohort (TCGA-HNSC), five Gene Expression Omnibus (GEO) validation cohorts, single-cell RNA sequencing, Visium spatial transcriptomics, cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq)-informed protein-potential inference, pharmacogenomic screening, genetic-risk analysis and experimental validation. A reconstructed 296-pipeline survival modelling framework was used to prioritize prognostic hub genes across validation-cohort-specific analyses. SIRPG was repeatedly ranked among the top ten selected genes in all five validation cohorts. At single-cell resolution, SIRPG-high tumor cells showed stronger malignant-cell features, immune-inhibitory and metabolic programs, Scissor-positive risk association, CLCA2/P53-related perturbation signals and inferred SIRPG-CD47/signal regulatory protein (SIRP) communication. Spatial analyses placed this axis within an immune-checkpoint-coupled niche, supported by Maxspin/multiview intercellular spatial modelling (MISTy) spatial coupling, communication analysis by optimal transport (COMMOT)-inferred CD47-SIRPG communication and scProTrans-inferred CD47/SIRPG protein-potential overlap. Functionally, SIRPG knockdown reduced HNSCC cell viability and increased apoptosis, whereas re-expression of short hairpin RNA (shRNA)-resistant SIRPG restored the CLCA2-BAX/BCL2 protein response. Together, these findings identify SIRPG as an immune-related prognostic hub and context-dependent tumor-cell regulator associated with apoptosis, immune communication and spatial microenvironmental organization in HNSCC.

Genomic profiling as an option for ovarian cancer diagnostics

ABSTRACT Introduction Ovarian cancer (OC) is a highly heterogeneous and lethal gynecological malignancy. Precision oncology has shifted the management paradigm to comprehensive molecular profiling. Genomic-based diagnostics are now a clinical necessity for accurate prognostic stratification and the rational selection of targeted therapeutics, such as PARP and immune checkpoint inhibitors. Areas covered This review evaluates current literature regarding the distinct genomic landscapes defining OC histotypes to underlined the role of molecular profiling in the diagnostic field of OC. We discuss the practical implementation, technical aspect, and clinical validity of the main molecular diagnostic platforms, focusing on tissue-based Comprehensive Genomic Profiling (CGP) and Homologous Recombination Deficiency (HRD). Furthermore, we explore emerging translational data on liquid biopsy (LBx) applications. Expert opinion While current tissue-based methodologies provide critical baseline data, the OC diagnostic paradigm must pivot from static testing to proactive and longitudinal tracking. Integrating advanced LBx approaches enables a real-time monitoring of dynamic parameters as minimal residual disease (MRD) and acquired resistance. Integrating these dynamic blood-based assays with multi-omic profiling and artificial intelligence (AI)-driven tools allows a full understanding of the complex tumor behavior.

Integrative profiling of diverse post-translational modifications for prognostic stratification and personalized therapy in papillary thyroid cancer

Post-translational modifications (PTMs) are pivotal in tumor biology, yet their role in papillary thyroid cancer (PTC) remains unclear. We integrated bulk and single-cell transcriptomes with clinical data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases to analyze 17 PTMs and construct a prognostic model using 12 machine learning algorithms for predicting the thyroid cancer-free interval (TCFi). Enrichment analysis, single-cell analysis, and immune-related analysis were performed to elucidate the biological role of PTMs. Therapeutic responses of PTC patients were predicted based on the model. We validated the expression of model genes and identified the key signature associated with the malignant phenotypes of PTC. We filtered out 12 genes to construct a post-translational modification index (PTMI) and identified three molecular clusters of PTC. Shapley additive explanations (SHAP) and nomogram models confirmed the predictive efficacy of PTMI. Integrated analyses revealed significant associations between PTMI and immune features. High-PTMI patients showed sensitivity to FDA-approved drugs and chemotherapeutics but resistance to radioactive iodine therapy. Notably, we identified TYMS as a key functional PTMI signature. The PTMI proposed in this study holds strong potential as a prognostic biomarker and therapeutic predictor, offering valuable insights for personalized management of PTC patients.

Assessing the Impact of Patient Selection and Analytical Methods on mRNA Biomarker Identification in Ovarian Cancer Using TCGA Data

Introduction The Cancer Genome Atlas (TCGA) data has been extensively used for differentially expressed genes (DEGs) discovery and validation in high-grade serous ovarian cancer (HGSOC), however platinum-sensitivity biomarkers have not yet reached clinical application. We aimed to propose a robust analytical framework for TCGA platinum sensitivity studies and systematically demonstrate how patient characteristics influence DEG analyses between platinum-sensitive and resistant groups, while also considering experimental platforms and analysis pipelines, as their impact is recognized but not comprehensively evaluated. Methods This retrospective TCGA cohort study used publicly available microarray and RNA-seq gene expression data. TCGA-derived datasets were identified through a literature review, including only studies enabling unambiguous patient identification. Three TCGA-based cohorts were assembled—two directly from published studies and one curated through own selection—differing in patient numbers (230, 201 and 142 patients, respectively) and stage distribution. Data were analyzed using DESeq2, edgeR, limma-voom, and Wilcoxon rank-sum test to compare the impact of patient selection and pipeline choice on DEGs outcome. Genes with an adjusted p-value < 0.05 and an |log2 fold change| > 2 were considered differentially expressed. Results The three cohorts showed partial overlap of DEGs across platforms and pipelines. Both patient selection and analytical workflow influenced which genes were identified, highlighting findings variability even when using the same TCGA dataset. We establish a robust analytical framework for TCGA HGSOC biomarker studies, including the provision of patient barcodes and openly shared workflows that enable straightforward replication of our analyses from data download to DEG analysis. Conclusions Reproducibility of TCGA studies is limited by variability in patient selection, platinum sensitivity definitions, data sources, and analysis pipelines. We addressed these factors by providing a robust framework that can serve as a template for data analysis and transparent reporting when identifying and validating predictive signatures of platinum sensitivity in HGSOC patients.

Spatially informed comprehensive tumor transcriptomic profiling stratifies clinical outcomes in early triple negative breast cancer