Abstract Background Despite its viral etiology and immunogenic features, cervical cancer shows limited and often short-lived benefit from programmed cell death protein 1 blockade, currently the only approved immunotherapy for this disease. This limitation highlights the need for a deeper understanding of its immune microenvironment to uncover alternative or complementary immunotherapeutic targets that may improve outcomes. Methods We integrated spatial proteomic and bulk transcriptomic profiling of the cervical cancer immune landscape. Immune subset markers (CD8, CD4, CD68, FoxP3, NKp46) and clinically actionable immune checkpoint molecules (HLA-E, CD47, CD73, CD276, CD155, Gal-9, PD-L1, CD70, LAG-3) were assessed by immunohistochemistry in 65 resected tumors, spatially resolved across tumor stroma and tumor epithelium niches. Expression patterns and correlations with clinicopathological variables, immunotypes, and survival were systematically analyzed. Key findings were cross-validated in The Cancer Genome Atlas cohort, and tumor-killing assays were conducted to evaluate the therapeutic potential of identified checkpoint axes. Results Immune infiltration was predominantly localized to the tumor stroma, with CD8+ and CD4+ T cells as dominant subsets. Within the tumor epithelium, CD8+ T cells and CD68+ macrophages were most abundant. Among checkpoints, HLA-E and CD47 showed the highest and most widespread expression, whereas CD276 and CD155 were enriched in the tumor epithelium, and CD73 and CD70 in the tumor stroma. Squamous cell carcinoma showed a stronger immunologic profile than adenocarcinoma. Immunotype stratification revealed distinct expression profiles and prognostic patterns. Elevated niche-defined expression of CD8, CD4, CD4-FoxP3, and NKp46 associated with improved survival. CD155 emerged as the only checkpoint consistently linked with poor survival across niches and cohorts, and associated with chemotherapy resistance. Notably, CD155 was the most promising functional target and was highly and selectively enriched in the tumor epithelium across all immunotypes, including immune-desert tumors where other immune markers were scarce. Conclusion This study reveals a complex, niche-specific and immunotype-specific immunoregulatory architecture in cervical cancer that extends well beyond programmed death-ligand 1. CD155 stands out as a compelling and underused therapeutic target, supporting a paradigm shift in targeting the T cell immunoreceptor with Ig and ITIM domains (TIGIT) axis. Its functional impact and selective enrichment in the tumor epithelium positions CD155 as a promising therapeutic target for both checkpoint inhibition and epithelial-directed approaches in cervical cancer.

Purpose In pancreatic ductal adenocarcinoma (PDAC), immune checkpoint inhibitors have shown limited efficacy, and the role of the TIGIT axis remains underexplored. This study aimed to characterize TIGIT axis components on protein level and their relationship to PD-1/PD-L1 expression in matched blood and tumor samples from PDAC patients to identify immunosuppressive mechanisms and fuel future strategies for immune checkpoint co-targeting in PDAC patients. Experimental design Fresh tumor and peripheral blood samples were collected from PDAC patients undergoing surgical resection. Flow cytometry was performed on tumor-infiltrating lymphocytes and PBMCs to assess expression of TIGIT, DNAM-1, TACTILE, and PD-1. Ligands CD111, CD112, CD113, and CD155 were analyzed using immunohistochemistry. Additional RNA expression analysis (TCGA/GTEx) was used to evaluate ligand distribution and gene expression profiles. Results TIGIT was highly upregulated on intratumoral CD8⁺ T cells and regulatory T cells, frequently co-expressed with PD-1. DNAM-1 expression was significantly reduced in tumors. However, contrasting pattern emerges with Tregs, which uniquely upregulate DNAM-1 in the PDAC TME. In addition, CD112 and CD155 were broadly expressed, including novel stromal CD112 localization. NK cells were nearly absent intratumorally, correlating with DNAM-1 downregulation. Conclusions Our findings identify TIGIT as a promising immunotherapeutic target in PDAC and suggest that dual checkpoint blockade (TIGIT/PD-1), alongside restoration of DNAM-1 signaling, may overcome immune suppression. These results provide mechanistic rationale to inform future clinical trials in PDAC. Supplementary Information The online version contains supplementary material available at 10.1007/s00262-026-04343-w.

KRAS mutations are among the most prevalent oncogenic alterations in colorectal, lung, and pancreatic cancer, yet their detection remains analytically challenging in the presence of an overwhelming wild-type (WT) background. Here, we report a photoelectrochemical (PEC) genotyping platform that integrates clamp-inhibited loop-mediated isothermal amplification (C-LAMP) with enzyme-free singlet oxygen (1O2)-driven PEC transduction for mutation-selective KRAS detection. Locked nucleic acid (LNA) clamp probes selectively suppress WT amplification during isothermal amplification, enriching mutant alleles and enabling single-nucleotide variant (SNV) discrimination with high selectivity. Amplified products are magnetically captured and transduced into photocurrent via visible-light-induced 1O2 redox cycling, eliminating enzymatic reporters and reducing background interference. The C-LAMP/PEC platform achieves a limit of detection of 35 copies µL-1 (58 aM) and a minimum detectable variant allele frequency (VAF) of 4.8% in heterogeneous mutant/WT genomic DNA mixtures. Analytical performance was validated in cancer cell lines and in patient-derived fresh frozen tissues, showing complete concordance with Nanopore sequencing and droplet digital PCR (ddPCR) within the evaluated cohort (n = 16). This work introduces a robust and modular PEC biosensing strategy that combines molecular WT suppession with enzyme-free photoelectrochemistry, offering an economically competitive and instrumentation-simplified approach for clinically relevant KRAS mutation analysis toward decentralized testing.

Ki67 is a well-established proliferation marker in breast cancer. Current clinical use focuses on the proportion of Ki67-positive cells, ignoring spatial heterogeneity in expression. However intra-tumoral heterogeneity has demonstrated to be associated with worse outcome. We hypothesized that spatial Ki67 heterogeneity carries clinical information beyond conventional scoring and aimed to evaluate its added value for predicting pathological complete response (pCR) after neoadjuvant chemotherapy (NACT) and for stratifying recurrence risk using genomic expression profiling (GEP). Using digital image analysis (DIA), precise and spatial quantification of biomarker distribution is possible. We analyzed two retrospective breast cancer cohorts using an AI-assisted DIA pipeline. Tumor sections stained for ER, PR, Ki67, and HER2 were digitized and analyzed in QuPath. Using AI, individual tumor cells were recognized and four tumor regions (0.5mm x 0.5mm) with the highest tumor/stroma ratio were selected for analysis. Spatial Ki67 heterogeneity was quantified using the Morisita-Horn Index (MHI) after the Ki67-positive and Ki67-negative tumor cells were mapped using XY-coordinates and square tessellation (100×100 µm tiles) was applied. The MHI was used to compare the similarity in cell composition between all pairs of tiles within a region. MHI values range from 0 to 1, with higher values indicating a more uneven distribution of Ki67+ cells. We used logistic regression and model comparison with Akaike Information Criterion (AIC), likelihood ratio test (LRT) or Vuong test, to evaluate the predictive value of Ki67 heterogeneity. In the first cohort (n=45), spatial heterogeneity was assessed on pretreatment biopsies from patients treated with NACT. In the second cohort (n=79), heterogeneity was evaluated in HR+/HER2- breast cancer patients stratified as high or low risk of recurrence based on GEP. In the GEP cohort, both a higher proportion of Ki67- positive cells and greater Ki67 heterogeneity were significantly associated with high genomic risk. The median MHI was 0.24 (0.03–0.35) in the high-risk group compared to 0.14 (0.01–0.43) in the low-risk group (P = 0.008). This higher MHI indicates more heterogeneous regionally clustered Ki67 expression, suggesting biologically distinct proliferative zones. In multivariate models, Ki67 heterogeneity remained a significant predictor of high-risk classification (OR 0.22, P = 0.036). Furthermore, in nested model comparison using LRT, addition of Ki67 heterogeneity significantly improved the model for predicting genomic risk (P = 0.034). These findings were consistent across biopsy and resection specimens, highlighting the robustness of heterogeneity measures. In the NACT cohort, Ki67 heterogeneity was higher in patients who achieved pCR (median MHI 0.26 [0.17–0.35]) compared to those who did not (median MHI 0.22 [0.12–0.40], P = 0.023). In multivariate modeling, Ki67 heterogeneity emerged as an independent predictor of pCR (OR 23.5, P = 0.038), outperforming Ki67 density and improving model fit (AIC 31.6 vs. 36.1; P = 0.038). Finally, in both cohorts, DIA-derived Ki67 models slightly outperformed traditional pathologist scoring, although Vuong tests did not show a statistically significant difference. Spatial Ki67 heterogeneity provides additional prognostic and predictive value beyond conventional Ki67 scoring. This heterogeneity indicates distinct areas of higher proliferation, clinically relevant biological variation, not captured by simple percentage positivity. Although validation in larger, prospective cohorts is necessary before clinical implementation, DIA provides a more objective and reproducible alternative to manual scoring, particularly when incorporating spatial heterogeneity. C. Van Berckelaer, K. Zwaenepoel, L. Cox, D. Charlotte, D. Julie, E. Louise, H. Fleur, L. Evy, G. R. Devi, A. Ramadhan, S. Koljenovic, P. Van Dam. Ki67 Spatial Heterogeneity as a Predictive and Prognostic Marker in Breast Cancer: A Spatial Image Analysis Approach [abstract]. In: Proceedings of the San Antonio Breast Cancer Symposium 2025; 2025 Dec 9-12; San Antonio, TX. Philadelphia (PA): AACR; Clin Cancer Res 2026;32(4 Suppl):Abstract nr PS2-08-19.

PURPOSE OF REVIEW Achieving adequate resection margins (i.e., ≥5 mm of healthy tissue surrounding the tumor) in oral cavity squamous cell carcinoma (OCSCC) is difficult. This review discusses recent developments to guide surgical resection. It highlights the transition from the subjective conventional approaches to emerging, objective photonics-based methods. RECENT FINDINGS Specimen-driven intraoperative assessment of resection margins (IOARM) has significantly improved surgical outcomes. However, IOARM is subjective; moreover, it lacks widespread adoption due to reliance on a dedicated team of specialists. Raman spectroscopy is an objective, fast, nondestructive, and label-free technique that is suitable for IOARM. The latest Raman-based prototype demonstrates high precision. SUMMARY Integrating Raman-guided IOARM into the surgical-pathological workflow offers a practical, scalable approach to real-time, objective margin assessment, thereby improving patient outcomes.

Early identification of the risk of malignant transformation in oral potentially malignant disorders (OPMDs) is critical for improving outcomes in oral squamous cell carcinoma (OSCC). This comprehensive review examines immunological biomarkers obtained from minimally invasive oral cytobrush (OCB) specimens for the early detection of OSCC within a precision medicine framework. The objectives were to (1) identify and characterise key immunological biomarkers associated with early oral carcinogenesis; (2) evaluate the diagnostic utility of OCB sampling for detecting these biomarkers; and (3) explore the potential of OCB-based profiling to support personalised screening and patient management. The review highlights the potential advantages of OCB compared with conventional diagnostic methods, as reported in the literature, particularly its ability to capture early malignant changes through immunological analysis. Evidence is discussed for biomarker pathways related to cell-cycle and differentiation dysregulation (p53, Ki-67, CKs), inflammation-driven epithelial transformation (IL-1β, IL-6, IL-8, TNF-α), and immune suppression and checkpoint activation (PD-L1, B7-H6). OCB provides reliable and patient-friendly cyto-salivary samples that are suitable for immunological and molecular analyses. Aberrant biomarker expression detected in OCB specimens correlates with epithelial dysplasia and reflects early non-invasive neoplastic transformation, supporting the diagnostic value of integrated biomarker panels. Overall, OCB-based immunoanalysis represents a practical, non-invasive approach for the early detection of OSCC. Emerging technologies, including AI and multi-omics approaches, may further support the precision and predictive values of immunological analysis for OSCC. When combined with relevant biomarker pathways reflecting tumour biology and host immune responses, this strategy could offer a strong foundation for precision-medicine screening. It may also support personalised monitoring in patients with OPMDs.

Background Delayed diagnosis of brain cancer leads to two-thirds of patients receiving a diagnosis only after presenting to the emergency department with more severe symptoms or neurological deficits. A simple, rapid, liquid biopsy implemented in primary care could enable more efficient triage of patients with non-specific symptoms potentially related to brain cancer, prioritising patients for urgent brain imaging, and accelerating diagnosis. Patients and methods Presented is the international, multi-centre, observational Early and tiMely detection of BRAin CancEr (EMBRACE) study. Patients were prospectively recruited across seven sites in Europe, from the United Kingdom, Belgium, Sweden and Switzerland. The target population consisted of patients with symptoms potentially associated with brain cancer. Blood serum samples were analysed by the Dxcover® Brain Cancer Liquid Biopsy Platform. Test performance was assessed by comparison of the liquid biopsy result to diagnostic imaging. Results Two thousand five hundred and fifty-four patients were enrolled across the seven collection sites; 2324 were deemed eligible and taken forward for test assessment. There were 697 brain tumours in total, of which 395 were malignant, and 1627 non-tumour diagnoses. Overall diagnostic performance for the primary objective was 86% sensitivity for brain cancer detection with 99% negative predictive value (NPV). Sensitivity for all brain tumours combined (malignant and benign) was 77%. Notably, for the most prevalent and most aggressive brain cancer, glioblastoma, 86% of cases were successfully identified. Additionally, 94% of patients with central nervous system lymphoma, and 90% of brain metastases were predicted correctly as having tumours. Conclusions Existing symptom-based referral pathways are ineffective for the detection of brain cancer, and there is an urgent need for new tests to help with clinical decision making. With a NPV of 99%, the Dxcover Liquid Biopsy test could assist in primary care for efficient stratification of patients toward diagnostic imaging.

Background: Advances in computational pathology using deep learning have enabled the prediction of molecular biomarkers and therapy response directly from hematoxylin and eosin (H&E) whole slide images (WSIs), offering a potential complement to molecular assays. Methods: Following PRISMA guidelines, we searched Embase, Web of Science, and PubMed (01/2020–04/2025) for studies applying H&E-based WSI computational analysis to predict prognosis or treatment response in solid tumors, with molecular pathology comparators. Data extraction and quality assessment with a 7-item QUADAS-2/PROBAST-adapted tool was performed. Results: Of 322 records screened, 64 met inclusion. Many studies focused on breast, colorectal, lung, gastric, or prostate cancer. Targets included Microsatellite Instability, mutations (e.g., EGFR, TP53), protein biomarkers (e.g., PD-L1, HER2), and gene expression profiles. Convolutional neural networks were most common, alongside Vision Transformers and foundation models. Median dataset size for model development was 509 cases (range: 13–12,592); 64% used independent external validation. AUROCs ranged from 0.58 to 0.97. Clinical readiness varied: 41% were proof-of-concept only, while 30% had multi-cohort validation. Median quality score was 71.4%, with frequent gaps in cohort representativeness and method reporting. Conclusions: H&E-based WSI computational analysis can accurately predict diverse biomarkers and therapy-relevant endpoints, sometimes nearing molecular assay performance. Dataset quality, external validation, and reporting variability currently limit clinical adoption; standardized benchmarking and multi-institutional validation are needed for integration into precision oncology workflows.