Journal of Experimental & Clinical Cancer Research

Background: Immune checkpoint inhibitors have transformed cancer treatment, yet a large number of patients fail to respond. Identifying molecular characteristics that predict response before treatment initiation remains an unmet need. Towards that end, this study presents a large-scale integrative analysis of existing single-cell and bulk tissue datasets, aimed at identifying predictive features while providing insights into their cellular origin and potential function within the tumor microenvironment. Methods: A stepwise analysis was performed using single-cell RNA-sequencing data from 60 melanoma patients at baseline, separated into discovery (n=41) and validation (n=19) sets. An integrated bulk transcriptomics dataset (n=128) from melanoma patients and a bladder cancer dataset (n=298) were used for further validation. Results: Integrative analysis of melanoma single-cell datasets revealed that responders exhibit distinct molecular profiles across multiple cell types compared to non-responders. Notably, these included downregulation of the TNFR superfamily and other immunosuppressive genes (TNFRSF18, TNFRSF9, TNFRSF4, LGALS1, BATF, IL12RB2, LINGO1, DUSP4, SDC4, VCAM1) in T-cells. By investigating the findings from the immune cell populations in the bulk tumor context, 13 transcripts were found to be consistently associated with response across all cohorts. These were differentially expressed in T-cells (SELL, EPB41, CD96, UHFR2, LINGO1, LGALS1), B-cells (ALDH5A1), NK cells (PLEC, PDGFRB) and Monocytes (TLR10, ST6GAL1, IKZF1, MPRIP). A predictive model based on these features effectively discriminated responders from non-responders in melanoma (AUC=0.73). The model maintained significant predictive power in an independent bladder cancer dataset (IMvigor210; AUC=0.64). Of high clinical relevance, it demonstrated enhanced performance in identifying responders among patients with low tumor mutational burden (AUC=0.75). Conclusion: Our study reveals pre-treatment molecular features related to immune-cancer crosstalk that are associated with response to immunotherapy. A 13-gene model demonstrates potential added clinical value in stratifying responders, particularly in patients with low tumor mutational burden, meriting further validation.

BackgroundStomach adenocarcinoma is driven by heterogeneity, limiting therapeutic success. Although ROS acts as a continuous "redox rheostat" for tumor evolution, it is categorized based on binary models that are masked by tumor-microenvironment (TME) confounders. Here, we have defined a continuous, TME-independent ROS axis to help identify intrinsic vulnerabilities and improve patient stratification. MethodsNon-negative matrix factorization (NMF) defined a ROS-Axis in TCGA-STAD which was validated in ACRG Cohort. Multivariate regression model isolated intrinsic signatures via "residual" ROS scores by adjusting for TME confounders. Survival was assessed using Cox hazard models. Drug sensitivities were mapped using GDSC2/ElasticNet modeling with cross-cohort replication. ResultsOur results define a reproducible ROS gradient, driven by effectors like NQO1 and SOD1, characterizing ROS-high tumors as proliferative, epithelial and "immune-cold". High residual ROS score was associated with an improved prognosis, regardless of TNM stage and age. Pharmacogenomic mapping revealed an overlapping sensitivity to mTOR inhibitors in ROS-high gastric cancer tumors which persisted after TME confounder adjustment. ConclusionThe continuous ROS axis provides a functional readout of metabolic dependency that refines traditional anatomical staging. By identifying mTOR dependent cold tumors, our framework offers a precision strategy for immunotherapy-resistant patients like those affected by microsatellite-stable gastric cancer.

Background: Immune-oncology has revolutionized cancer treatment, but some patients fail to benefit due to primary resistance and tumour-immune evasion. Extracellular vesicles (EVs) are secreted by both tumour and immune cells and mediate communication between cancer cells and the immune system. Our study used proteomic profiling of circulating EVs collected from NSCLC patients treated with immune checkpoint inhibitors (ICI) to identify predictive biomarkers of response as well as immune evasion mechanisms related to treatment resistance. Methods: EVs were isolated from plasma collected prior to ICI treatment using peptide-affinity purification and high-throughput proteomics was performed using Proximal Extension Assay. Differentially expressed EV proteins between durable (DR) and non-durable responders (NDR) were identified and evaluated using Cox proportional hazards regression, survival analysis, sex-stratified analysis, as well as pathway and network analysis. Results: Proteomics analysis identified 116 differentially expressed EV proteins between DR and NDR. NDR was characterized by enrichment of inflammatory, angiogenic, and immune-suppressive EV proteins, such as IL1RL1, TFRC, IL6ST, galectins, TNF superfamily death receptors, chemokines, and PCSK9. Pathway analysis revealed enrichment of angiogenesis, chemotaxis, ECM remodeling, and neutrophil degranulation associated with poor progression-free survival (PFS). In contrast, DR to ICI treatment was associated with EV proteins related to T- and B-cell activation and adaptive immunity. Sex-related differences in abundance and association with PFS was observed for certain EV proteins, including IL1RL1 and TFRC. A six protein EV model (IL1RL1, TFRC, ERI1, CCN5, IGFBPL1, and TNFRSF13C) demonstrated good prognostic performance for identifying NDR (AUC = 0.907) and stratified patients into three discrete risk groups. Conclusions: High-plex EV proteomics revealed biologically coherent tumour-immune signaling programs that are associated with ICI treatment resistance. Profiling circulating EVs may improve our understanding of EV-mediated immune evasion mechanisms and identify protein signatures that reflect the tumour immune microenvironment and predict response to immune checkpoint blockade.

BackgroundSurvivin is a mitotic regulator frequently overexpressed in human cancers and an attractive therapeutic target. However, how Survivin inhibition influences tumor immune regulation remains incompletely understood. This study aimed to investigate whether Survivin inhibition modulates antitumor immunity and to elucidate the underlying mechanisms. MethodsProgrammed death-ligand 1 (PD-L1) expression was evaluated in multiple tumor cell lines following pharmacological or genetic inhibition of Survivin. Mechanistic studies included RNA sequencing, immunoblotting, flow cytometry, cGAS knockdown, and NF-{kappa}B inhibition. Immune profiling was performed using CD8 T-cell cytotoxicity assays, mass cytometry, flow cytometry, immunofluorescence and single-cell RNA sequencing. Clinical relevance was assessed using patient tumor specimens and public immunotherapy cohorts. ResultsSurvivin inhibition, either by YM155 treatment or genetic depletion, increased PD-L1 expression at both mRNA and protein levels in tumor cells. Mechanistically, Survivin inhibition stabilized cyclic GMP - AMP synthase (cGAS) by reducing its ubiquitination and activated NF-{kappa}B signaling, thereby promoting transcriptional upregulation of PD-L1. Functionally, the induced PD-L1 enhanced PD-1 engagement and suppressed CD8 T-cell cytotoxicity, promoting immune evasion. In immunocompetent ovarian cancer models, pharmacological inhibition of Survivin increased PD-L1 expression in tumor and immune compartments and attenuated cytotoxic immune activity. PD-L1 blockade restored antitumor immunity and significantly enhanced the therapeutic efficacy of Survivin inhibition. In addition, analyses of patient samples and public single-cell datasets revealed an inverse association between Survivin and PD-L1 expression, and high Survivin expression was associated with reduced benefit from PD-1/PD-L1 blockade. ConclusionsThese findings identify a Survivin-cGAS-PD-L1 axis linking mitotic stress to immune suppression and provide a mechanistic rationale for combining Survivin-targeted therapy with immune checkpoint blockade.

Withdrawal StatementThe authors have withdrawn this manuscript to address issues related to data-use permission and authorship review. Therefore, the authors do not wish this work to be cited as reference for the project. If you have any questions, please contact the corresponding author.

Dendritic cells (DCs) are central orchestrators of antitumor immunity. Several DC subsets--including conventional type 1 (cDC1), conventional type 2 (cDC2), plasmacytoid DCs (pDCs), and mature DC populations--play distinct roles in immune surveillance, tumor control, immunotherapy response and prognosis. Recent findings suggest that cDC1 are spatially closed to CD8 T-cell and contribute to tertiary lymphoid structure formation in lung cancer. However, how other DC subsets interact with cDC1 to shape the tumor microenvironment (TME) remains largely unknown. Here, we analyzed the spatial distribution of major DC subsets, including cDC1, cDC2, mature DC and pDC, together with CD8 T cells in a cohort of anti-PD1-treated NSCLC patients and we deciphered the corresponding immune microenvironment behavior by paired transcriptomic analysis. We found that, while other DC subsets populated the stroma, cDC2 were localized both in the stroma and in tumor nests. Moreover, unlike other DC subsets, cDC2 abundancy did not affect ICB response both at transcriptomic and in situ analysis. We described spatial organization of DCs in megaclusters characterized by distinct proportions of DC subsets. Patients enriched in megaclusters involving variable proportion of pDC, cDC1 and mature DC, exhibited pro-inflammatory transcriptomic programs while those enriched in cDC2-based megaclusters showed limited immune activation features. Globally, DC in lung cancer were structured around three distinct DC spatial patterns, namely cDC1-driven, cDC2-driven and DC-Scattered, each defined by unique compositions of DC megaclusters, immune features and pathways activation profiles. Among them, the cDC1-driven pattern was associated to prolonged anti-PD1 response in two independent cohorts.

PurposeBladder cancer (BLCA) is a heterogeneous tumor type. Only one third of muscle-invasive (MIBC) patients respond to immune checkpoint inhibitors (ICIs). Reliable resistance markers are needed to guide clinical decisions. We investigated the nerve growth factor receptor (NGFR) in BLCA and analyzed its correlation with disease progression and response to immunotherapy. Experimental DesignWe analyzed NGFR expression in BLCA cell lines, organoids, mouse models and patient samples. The cohorts used were The Cancer Genome Atlas (TCGA), enriched in muscle-invasive bladder cancer (MIBC) (n=407); IMvigor210, representing MIBC patients treated with ICIs (n=348); and UROMOL2, as a non-muscle-invasive bladder cancer (NMIBC)-specific cohort (n=535). IMvigor010 was also included (n=728). Patients were stratified by NGFR expression quartiles. We analyzed survival and tumor subtypes and performed stromal deconvolution and functional profiling. We assessed stemness- and invasion-related features in SCaBER cells. ResultsNGFR marks a basal tumor cell subcluster and is independently associated with poor prognosis in TCGA and IMvigor210. NGFR-high tumors show stromal content enriched in cancer-associated fibroblasts, lower neoantigen burden, higher CD8+ T effector signature together with an immune-excluded phenotype, and a CAF-specific TGF{beta} signature. In the immunotherapy-treated cohort, high NGFR expression was also associated with poorer outcome. Functionally, NGFR appears to promote a stem-like/pro-invasive program in BLCA cells. ConclusionsNGFR identifies a basal-like BLCA subpopulation linked to poor survival, while its association with immunotherapy response requires further validation. In addition, our in vitro analyses support a role of NGFR in stem-like and invasive traits, highlighting its relevance as a biomarker in BLCA.

BackgroundMesothelioma (Me) is an aggressive cancer with limited response to conventional therapies. The tumors harsh microenvironment contributes to immune escape and therapy resistance and the effects of ICIs on Me are still unclear. Adenosine, an immunosuppressive molecule produced from AMP by the enzyme CD73, accumulates in hypoxic tumor areas. Elevated CD73 and adenosine receptor A2B (A2Br) levels on Me cells are linked to worse patient outcomes, indicating their important role in disease progression and potential as targets for treatment. AimThis study characterizes the Me-ME (micro environment) and evaluates the efficacy of TT-4 (A2B inibitor) and AB680 (CD73 inibitor), alone or with aPD-1, using 3D models in vitro and in vivo. MethodsCD73 and A2B receptor levels were quantified in tumor and normal samples using qRT-PCR and IHC. Cells lines were treated with CoCl2 to mimic hypoxia, then CD73, A2Br and related markers were analyzed. MSTO-211H and REN cells were silenced for CD73, grown as spheroids and adenosine release was measured. Co-culture spheroids of MSTO-211H and Jurkat cells were treated with AMP and CD73 inhibitor, then analyzed for viability and immune markers. An orthotopic Me model was established by injecting AB1-B/c-LUC cells and monitored by in vivo imaging. Proteomic analysis of spheroids was conducted to identify proteins and pathways involved. ResultsHypoxia boosts CD73 and A2Br expression in Me cells, leading to adenosine production via CD73. In 3D co-cultures, AB680 lowered Me cell viability and enhanced activation of Jurkat T cells. In mice, combining aPD-1 therapy with A2Br or CD73 inhibitors strongly reduced tumor growth. Proteomics identified 93 proteins influenced by adenosine signaling through A2B. ConclusionTargeting the adenosine pathway alongside PD-1 blockade offers a promising new immunotherapy strategy for Me.

O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=186 SRC="FIGDIR/small/717853v1_ufig1.gif" ALT="Figure 1"> View larger version (42K): org.highwire.dtl.DTLVardef@a696f3org.highwire.dtl.DTLVardef@1005c11org.highwire.dtl.DTLVardef@9c65bborg.highwire.dtl.DTLVardef@1dafb2d_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical abstractC_FLOATNO C_FIG This study establishes an integrative framework that combines paired mRNA/miRNA profiling with immune microenvironmental features to clarify how EML4-ALK fusions shape transcriptomic and post-transcriptional networks in Non-small cell lung cancer (NSCLC). Using paired mRNA-seq and miRNA-seq data generated from the same patients, we compared fusion-positive and fusion-negative NSCLC across three interconnected layers: (i) transcriptome architecture, including differential expression, pathway, and network analyses; (ii) the miRNA-mRNA regulatory axis, encompassing dysregulated miRNAs, target repression and sponging, and fusion-specific regulatory pairs; and (iii) the tumor microenvironment, with emphasis on immune and stromal infiltration, particularly cancer-associated fibroblast (CAF)-linked extracellular matrix (ECM) and adhesion programs. Our analyses revealed a distinct reprogramming pattern in fusion-positive NSCLC, marked by activation of metabolic and proteostasis pathways, including N-glycan metabolism coupled to ER export, together with attenuation of immune-stromal communication, adhesion, ECM, calcium signaling, and PI3K/VEGF-axis transcription relative to fusion-negative NSCLC. We also identified fusion-associated microRNA perturbations, including an exclusively upregulated miR-3065-centered regulatory hub predicted to repress ECM- and adhesion-related genes (PDGFRB, CTSK, COL4A2, SPARC, FBN1, and LUM) in fusion-positive tumors, in contrast to broader miRNA network rewiring in fusion-negative tumors targeting ciliary and mitotic hubs. Tumor microenvironment analysis further distinguished the subtypes, with fusion-positive tumors showing reduced CAF infiltration relative to fusion-negative tumors and concordant gene-CAF associations. By linking mechanistic insight with candidate biomarkers and targetable pathway nodes, this work provides a basis for precision strategies in both fusion-positive and fusion-negative cohorts and broadens the therapeutic perspective beyond kinase inhibition alone.

While prostate cancer (PC) is defined as immunologically cold, limiting the efficacy of immune checkpoint inhibitors, therapeutic vaccination targeting tumor-associated antigens represents an attractive strategy to promote disease control in low volume metastatic patients. The UV1 cancer vaccine is based on immunization with tripeptide fragments from human telomerase reverse transcriptase (hTERT) and a phase II clinical trial demonstrated induction of robust T cell response in men with de novo metastatic castration-sensitive prostate cancer (mCSPC). Comparison with long-term survival data of non-metastatic CSPC patients as reference showed that despite metastatic disease at diagnosis, UV1-treated patients who mounted an early vaccine-induced immune response achieved progression-free and overall survival comparable to non-metastatic patients. We examined biological determinants of clinical benefit following UV1 vaccination including tumor transcriptome and T cell receptor (TCR) profiling from circulating and tissue resident T-cells of the 22 men enrolled. Analysis of diagnostic and post-UV1 treatment biopsies revealed that low baseline exhaustion of T cells and higher CD8+ T cell abundance are associated with early immune response to the vaccine and longer survival. Moreover, we identified specific TCR motifs relative to early responders, that can indicate potential benefit from UV1 vaccination. These findings indicate that baseline intratumoral T cell exhaustion state and repertoire shape responsiveness to hTERT vaccination and long-term outcome. Overall, our study underlines how baseline immune profiling may be used as a companion biomarker to predict mCSPC patients most likely to benefit from therapeutic vaccination.

BackgroundTreatment optimization in HPV-associated oropharyngeal cancer (OPSCC) remains challenging, as recent de-escalation trials have shown limited success. Current patient selection strategies based on smoking history and TNM classification are insufficient, highlighting the need for robust, standardized prognostic biomarkers. We report the first validation of the Immunoscore (IS) for prognostic stratification in HPV-associated OPSCC. Patients and methodsWe analyzed 191 HPV-associated (p16+ and HPV DNA/RNA+) OPSCC patients from an international multicenter cohort (2015-2024), comprising a French monocentric retrospective training cohort (N = 48) and three validation cohorts: French monocentric retrospective (N = 48), French multicenter prospective (N = 50), and US multicenter retrospective (N = 45). IS is a standardized digital pathology assay quantifying CD3lJ and CD8lJ densities in tumor cores and invasive margins, with cut-offs defined in the training cohort and validated across cohorts. Associations with disease-free survival (DFS), time to recurrence (TTR) and overall survival (OS) were assessed, alongside 3RNA-seq and sequential immunofluorescence profiling of immune composition. ResultsMedian age 65; 80% male; 74% smokers; 66% T1-2; 82% N0-1 (AJCC8th). IS-High patients demonstrated superior 3-year DFS in the training and validation cohorts 1-3 (all log-rank P < 0.05). Multivariable analysis identified IS-Low as the strongest independent risk factor for DFS (HR 9.03; 95% CI: 4.02-20.31; P < 0.001). The model combining IS with clinical factors showed higher predictive accuracy for DFS (C-index 0.82) than clinical variables alone (0.7; P < 0.0001). Similar findings were observed for TTR and OS. IS-High tumors showed markedly higher enrichment of lymphoid and myeloid immune cell populations, contrasting with immune-poor signatures in IS-Low tumors. ConclusionsIS is a robust biomarker that outperforms standard clinical variables in both prognostic and predictive accuracy. The enriched cytotoxic immune infiltrate in IS-High tumors explains favorable outcomes and supports their suitability for treatment de-escalation. Prospective validation is warranted.

BackgroundThis study investigates the role of the pioneer transcription factor FOXA1 as a master gene in sustaining epithelial cell polarization in early-stage lung adenocarcinoma. The partial loss of FOXA1 is explored to determine if it will affect plasticity and progression of lung adenocarcinoma. The study also addresses the transcriptional circuitry that links polarity defects to lysosome homeostasis. MethodsA multiomics approach was used to define the status of the chromatin in epithelial and mesenchymal states of A549 adenocarcinoma cells obtained with a newly synthetized TGF-{beta} receptor inhibitor or TGF-{beta} respectively. The study leveraged ATAC-seq, RNA sequencing, Cut&Tag sequencing of FOXA1 and histone marks profiling. The functional impact of FOXA1 was examined by partial silencing in vitro and by heterozygous FOXA1 deletion in a KrasG12D mouse model. Three-dimensional organoid culture, high-resolution electron microscopy, spatial transcriptomics and multiplex immunohistochemistry assessed carcinoma cell polarity, proliferation, the tumor microenvironment and organelle content. Group differences were evaluated with two-tailed t tests or one-way analysis of variance. ResultsFOXA1 binding and expression were highest in cells harboring an epithelial phenotype. In mouse KrasG12D LUAD tumors FOXA1 marked polarized, CDH1-positive cells; heterozygous loss diminished CDH1, disrupted apical-basal architecture, lowered organoid-forming efficiency and remodeled the immune microenvironment. Spatial transcriptomics and ultrastructural analyses showed that FOXA1-deficient carcinoma cells accumulated lysosomes, down-regulated vesicle fusion genes of the SNARE family and activated the lysosomal CLEAR gene network. FOXA1 occupied enhancers of lysosome-associated genes and competed with the transcription factor TFE3, thereby suppressing transcription of cathepsin B and cathepsin C and restricting lysosome biogenesis. ConclusionsFOXA1 is a central regulator that preserves epithelial cell polarity and limits lysosome formation in lung adenocarcinoma. Targeting the FOXA1-TFE3-lysosome axis may affect tumor plasticity and provide new therapeutic opportunities.

Resistance to targeted therapy in HER2-positive breast cancer remains a clinical challenge, especially for patients with relapsed or metastatic disease. Particularly, persistent activation of hypoxia-inducible factor 1 (HIF-1) signalling is well documented in the context of trastuzumab and trastuzumab emtansine resistance. To achieve a deeper understanding of how HIF-1 activity modulates the response to anti-HER2 treatment, we functionally characterized a cellular model of hypoxia-induced drug resistance for HER2-positive breast cancer using shotgun proteomics. By global phosphoproteomics profiling, the Rac1 pathway was identified as one of the most enriched signalling networks under hypoxia. Furthermore, the selective Rac1 blockade with the 1A-116 small-molecule inhibitor sensitised HER2-positive cells to trastuzumab in both 2D and 3D culture systems. Altogether, our findings demonstrate that hypoxic conditions induce the resistance of HER2-positive breast cancer cells to targeted therapy and suggest the therapeutic potential of Rac1 inhibition to enhance trastuzumab efficacy. HighlightsO_LIHypoxic conditions induce trastuzumab resistance in HER2-positive breast cancer. C_LIO_LIRac1 signalling was mapped under hypoxia by phosphoproteomics profiling. C_LIO_LIRac1 inhibition sensitises HER2-positive cells to trastuzumab. C_LI

BackgroundCutaneous melanoma (CM) is an aggressive skin cancer with rising incidence, representing a growing public health concern. Despite the remarkable success of immune-checkpoint inhibitors (ICIs) in the management of advanced disease, mortality remains high due to therapy resistance. Identifying reliable prognostic and predictive biomarkers is therefore essential to improve patient stratification, optimize treatment selection, and minimize unnecessary toxicity. MethodsWe comprehensively profiled the circulating immune landscape of 54 treatment-naive CM patients by integrating flow cytometry immunophenotyping with clinicopathological data, and performed tumor gene expression analysis in a subset of 26 patients. ResultsElevated HLA-DR and CD69 expression on circulating CD4+ T cells, together with reduced circulating CD8+ T cell frequency, emerged as candidate prognostic biomarkers associated with improved survival. Prognostic models combining these immune variables with clinical covariates accurately stratified patients by overall survival (89.5% sensitivity, 72.7% specificity; AUC = 0.872, p < 0.0001) and progression/recurrence risk (75% sensitivity and 71.4% specificity; AUC = 0.763, p = 0.001). In a subset of 43 patients subsequently treated with ICIs, elevated baseline HLA-DR and CD69 expression on circulating CD4+ T cells was also associated with therapeutic benefit. A predictive model integrating these markers with clinical covariates achieved good discriminatory performance (65.2% sensitivity, 88.9% specificity; AUC = 0.775, p = 0.0027). Tumor gene expression profiling supported the role of IFN-{gamma}-related signatures, previously linked to ICI response, as complementary prognostic and predictive tools. ConclusionThese findings highlight systemic CD4+ T cell activation status as a promising, easily measurable biomarker in CM, laying the foundation for future strategies to refine patient stratification and guiding immunotherapy decisions.

Purpose: The prognostic role of tumor-infiltrating lymphocytes in luminal breast cancer remains uncertain, partly because density-based metrics do not capture spatial interactions between immune cell subsets. We developed a density-independent spatial metric quantifying macrophage-T cell proximity and assessed its prognostic value. Experimental Design: Using multiplex immunohistochemistry across three breast cancer cohorts (exploratory, n = 17; discovery, n = 687; validation, n = 305), we measured nearest-neighbor distances from T cells to M1-like and M2-like macrophages, benchmarked against a randomly subsampled total macrophage pool. We defined the Macrophage Spatial Polarity Index (MSPI) as the difference between M2-to-T cell and M1-to-T cell affinity scores, where higher values reflect an M2-dominated spatial phenotype. Cox regression was used to assess associations with distant disease-free survival (discovery) and overall survival (validation). Results: M2-like macrophages preferentially localized near T cells, independent of cell density. Higher MSPI was associated with shorter survival in luminal cancers (discovery: HR = 1.45, p < 0.001), with the strongest effect in young women with early-stage disease (HR = 2.16, p < 0.0001). MSPI remained independently prognostic after adjustment for stage, systemic treatment, and diagnosis period (HR = 2.31, 95% CI 1.73-3.09, p < 0.0001) and was non-significant in HER2-positive and triple-negative subtypes. Validation in an independent ER-positive cohort confirmed the finding (HR = 1.30, p = 0.004). Pooled analysis yielded HR = 2.13 (95% CI 1.68-2.70, p = 3.45 x 10-10). Conclusions: MSPI is a robust prognostic biomarker in luminal breast cancer, particularly in young women with early-stage disease, warranting further validation for risk stratification and therapeutic guidance.

High grade serous ovarian cancer patients initially respond to platinum-based chemotherapy, but usually relapse within two years and ultimately develop therapy resistance. Management of response and effective clinical decisions are currently based on unspecific biomarkers and limited imaging techniques, illustrating the clear clinical need for reliable predictors of response. In this work, we evaluated the performance of patient-derived organoids generated from ascitic fluid and functionally tested in parallel to the patients clinical course, in the prediction of treatment response, and guiding clinical decision-making in a patient-specific manner. Ascites derived organoids reliably recapitulated the histological and molecular features of a paradigmatic HGSOC patient with an apparent dissociated response, and demonstrated chemoresistance months before laparoscopy confirmed persistent inoperable disease with poor pathological response. Drug screening identified alternative therapeutic options, while multi-omics provided additional insights into the tumor-specific biological features, to assist in the personalized clinical management in ovarian cancer.

Background: Prostate cancer (PCa) presents a formidable clinical paradox. It is immunologically cold and resistant to immune checkpoint blockade (ICB), yet bulk genomic analyses consistently reveal low and non-prognostic expression of CD274 (PD-L1), the primary molecular target of such therapies. We hypothesised that this paradox arises from a failure of current methodologies to account for two critical, interacting dimensions: the granular heterogeneity of basal gene expression (the static engine) and the spatiotemporal dynamics of adaptive resistance mediated by interferon-gamma (the adaptive engine). Methods: We developed a rigorous, multi-phase computational framework integrating clinical genomics with hybrid agent-based modelling. In Phase I, we extracted and normalized CD274 mRNA expression from the TCGA-PRAD cohort (n = 554) to define the empirical landscape of basal resistance. In Phase II, we developed a spatial Agent-Based Model (ABM) parameterized by this distribution to simulate clonal selection. In Phase III, we extended this into a Hybrid Discrete-Continuum model, coupling discrete agents with a reaction-diffusion Partial Differential Equation (PDE) representing the IFN-{gamma} field. We simulated 50 stochastic replicates per arm across four experimental arms, including Diffusion and Induction knockouts. Results: Bulk TCGA analysis confirmed low average PD-L1 expression (Median Transcripts Per Million (TPM) = 1.48; Interquartile Range (IQR): 0.91-2.14) with no prognostic value (Hazard Ratio (HR) = 1.15; 95% Confidence Interval (CI): 0.67-1.97; log-rank p = 0.605). However, the static ABM revealed that rare, high-expressing genomic outliers (>9.0 TPM) drive persistence through Darwinian immunoediting, enriching the surviving population's resistance by 3.86-fold. The hybrid adaptive model demonstrated a far superior survival strategy: the IFN-{gamma}/PD-L1 feedback loop facilitated the emergence of "protective sanctuaries"-localised regions of high resistance at the tumour-immune interface. This mechanism increased final tumour burden by ~4.5-fold compared to static selection alone (p<0.001). Spatiotemporal analysis confirmed that resistance is not a fixed trait but a dynamic state induced by immune pressure. Diffusion knockout (D = 0) abolished sanctuary formation, reducing final burden by 65% (p<0.001), while induction knockout (Pmax = 0) reverted to static outcomes. Conclusions: This study resolves the cold tumour paradox by demonstrating that PCa resistance is driven by a twin engine of rare genomic outliers and adaptive spatial dynamics. The failure of biomarkers in PCa is due to their inability to capture the dynamic mirage of adaptive sanctuaries. Our validated framework offers a platform for testing synchronised therapeutic disruptions targeting both the static genomic landscape and the dynamic cytokine signalling axis.

High-Grade Serous Ovarian Cancer (HGSOC) is the most lethal gynecological malignancy due to aggressive growth, widespread metastases, and high intra-tumoral heterogeneity. Poor prognosis is largely due to late diagnosis, hence there is an urgent need to identify novel biomarkers for screening, diagnosis, and monitoring. Here, we propose the voltage-dependent calcium channel hCaV1.2 encoded by CACNA1C as a potential biomarker and therapeutic target in HGSOC. Using IHC analysis for ten ovarian cancer patients, cytotoxicity assay, TCGA gene expression and survival analyses, homology modeling, molecular docking, Calcium channel membrane assembly and molecular dynamics simulations, we tested CACNA1Cs role in HGSOC progression and the effect of blocking on cancer cell survival. We show that nifedipine (NIFE), a calcium channel blocker (CCB), had a tumor suppressive effect based on binding models predicted by three-dimensional computer assisted molecular modeling and in vitro validation using human HGSOC cell line. Using The Cancer Genome Atlas ovarian public cohort, we found CACNA1C mRNA expression strongly correlated with poor patient survival for late-stage and metastasis than primary. We also show strong correlation of CACNA1C protein expression using immunohistochemistry correlating with COH ovarian carcinomas patients disease progression. This research demonstrates that targeting HGSOC via CCBs may be therapeutically beneficial. By establishing further in vitro, in vivo, and clinical trials using FDA approved NIFE may be repurposed to target CACNA1C for HGSOC. Novelty and ImpactHigh-grade serous ovarian cancer (HGSOC) remains lethal due to late diagnosis and drug resistance. This study identifies CACNA1C (Cav1.2) as a novel prognostic biomarker and therapeutic target in HGSOC, showing that elevated expression correlates with metastatic/recurrent disease and poor survival. Using molecular dynamics and in vitro models, we demonstrate that the FDA-approved calcium channel blocker nifedipine binds stably to Cav1.2 and suppresses tumor cell growth more effectively than cisplatin. These findings support repurposing nifedipine for biomarker-driven HGSOC therapy. Translational RelevanceLate diagnosis and progressive relapses significantly contribute to the poor prognosis of ovarian cancer. Identification of a tumor biomarker that can be used for screening, diagnosis, and monitoring is critical for improving clinical outcome. Our findings demonstrate that CACNA1C is a viable diagnostic marker for HGSOC and that its blockade with CCBs reduces tumor progression, highlighting their therapeutic potential.

Cancer remains a major therapeutic challenge despite substantial advances in diagnosis and treatment, including immune checkpoint blockade. Among emerging immunotherapeutic approaches, adoptive cell transfer (ACT) has attracted growing interest. Human peripheral V{gamma}9V{delta}2 T cells are promising candidates for ACT because they combine rapid and potent antitumor functions with major histocompatibility complex (MHC)-independent tumor recognition, enabling allogeneic use with limited risk of graft-versus-host disease. This raises the possibility of generating standardized V{gamma}9V{delta}2 T-cell banks from healthy donors for off-the-shelf immunotherapy. Here, we provide preclinical evidence supporting the suitability of allogeneic human V{gamma}9V{delta}2 T cells for ACT. We characterized peripheral blood V{gamma}9V{delta}2 T cells from healthy donors after successive antigen-specific and non-specific amplification steps, assessing their phenotype, effector functions, and metabolic state. Amplified cells maintained a strong pro-inflammatory Th1-like profile, preserved cytotoxic activity, and did not produce immunoregulatory cytokines. They also displayed high purity, a predominant effector memory phenotype, reduced expression of several inhibitory immune checkpoints, and sustained antitumor reactivity. Altogether, these findings support the development of allogeneic V{gamma}9V{delta}2 T-cell products as a scalable platform for next-generation cancer immunotherapies.

Thoracic malignancies, including lung adenocarcinoma (ADC) and malignant pleural mesothelioma (MPM), remain associated with poor prognosis and limited durable therapeutic responses in advanced stages. Although targeted therapies and immunotherapy have improved outcomes in selected patients, systemic chemotherapy continues to play a central role in routine clinical practice. However, treatment response is highly heterogeneous, and reliable predictive biomarkers of chemotherapy sensitivity are lacking. Both ADC and MPM frequently involve the pleural cavity and are commonly associated with malignant pleural effusion (MPE), which contributes to symptoms such as dyspnea and chest pain and requires therapeutic drainage. Importantly, MPE represents a clinically accessible source of viable tumor cells obtained through minimally invasive procedures. In this study, we established patient-derived organoids (PDOs) from malignant pleural effusion samples obtained from five patients with advanced lung adenocarcinoma and, as an exploratory extension, from one patient with malignant pleural mesothelioma. Organoids were characterized by immunohistochemistry and subjected to systematic chemotherapy drug screening. Inter-model variability in treatment response was assessed, and selected drug sensitivities were further validated through dose-response assays. Pleural effusion-derived organoids successfully recapitulated tumor-specific phenotypic features and revealed marked heterogeneity in chemotherapy sensitivity across models. Secondary validation confirmed the reproducibility of selected responses. Our findings support the feasibility of generating functional organoid models from malignant pleural effusions and highlight their potential as translational platforms for individualized chemotherapy profiling in advanced thoracic malignancies.