Translational Oncology

Background Patients with advanced pancreatic ductal adenocarcinoma (aPDAC) often experience general health decline at diagnosis due to a high-symptom burden. The optimal management of symptoms and/or poor performance status (PS) in these patients remains an unmet medical need. Patients and Methods In this multicenter study, patients with PS[≥]2 and pathologically confirmed or imaging-suspected aPDAC were included at first oncology visit in a personalized 14-day emergency integrative supportive care program (14-EISCP) to manage pain, nutrition, diagnostics, and stenting procedures. The primary endpoint was the 14-EISCP success in feasibility of planned procedures and clinical benefit defined as post-EISCP PS[≤]1, [≥]5 points improvement in fatigue, pain, global health-related quality of life (HRQoL) scores (EORTC QLQ-C15-PAL), or chemotherapy initiation within 30 days. Results A total of 106 patients were included; 93 evaluable patients considered for primary endpoint analysis (median age: 76 years [68-80], PS3: 20.9%, metastases: 61.3%). The median overall survival was 4.1 months (IC95% 2.6-5.6). The 14-EISCP was successful in 59.1% (n=55) of patients, meeting the primary objective (clinically relevant). The 14-EISCP feasibility was achieved in 70.9% of cases. Post-EISCP clinical benefit was observed in 79.6% of patients, with PS improvement to 0/1 in 13.2%, HRQoL improvement in 23.9%, and chemotherapy initiation [≤]30 days in 73.1%. Among evaluable patients, 17.2% received mFOLFIRINOX or gemcitabine-nab-paclitaxel, 35.4% received FOLFOX, 25.3% had gemcitabine or 5-fluorouracil alone, and 22.2% received best supportive care. In patients with PS2 at baseline, the administration of doublet/triplet chemotherapy was associated with improved overall survival compared to single-agent. Discussion These results offer a promising framework for improving outcomes in aPDAC patients, bridging the gap between symptom management and systemic therapy administration. Conclusions In patients with PS[≥]2 and aPDAC, the personalized 14-EISCP was feasible and lead to meaningful clinical benefit, allowing doublet or triplet chemotherapy in half the patients.

Introduction. TAC/SARIFA has been introduced as a new robust and easy-to-evaluate biomarker in several cancer entities, including colorectal cancer. It is defined by direct contact between at least five tumour cells and one adipocyte and is believed to indicate metabolic reprogramming associated with adverse outcome. However, the mechanism that leads to TAC/SARIFA positivity remains unclear. To investigate whether there is an individual component, we conducted a study on double and triple cancers, establishing a within patient design. Methods. We retrospectively analysed a total of 135 cases with 276 colorectal cancers from two academic medical centres. The TAC/SARIFA status was evaluated, as were the basic histopathological factors. The median follow-up time was 120 months. Results. Cases with any TAC/SARIFA positive tumours showed significantly reduced overall survival (62 vs. 88 months; p = 0.011). Analysing the entire cohort, the rates of concordant and discordant cases followed a random distribution. However, restricting the analysis to synchronous pT3/4 cases revealed a significant deviation from a random distribution (p = 0.016). Conclusion. This study reveals significant concordance of TAC/SARIFA status in synchronous locally advanced colorectal double/triple carcinomas, supporting the concept that tumour adipocyte interaction reflects a host related microenvironmental condition linked to metabolic reprogramming rather than a purely tumour intrinsic event.

Triple Negative Breast Cancer (TNBC) is the deadliest and most aggressive subtype of breast cancer, with poor prognosis and high rates of metastasis. Despite knowledge of metabolic rewiring in TNBC, the systems-level coordination of these adaptive pathways remains unmapped. This integrative systems-level analysis reveals key metabolic hub genes and identifies ATP1A2 as a significant prognostic marker. Analysis identified 764 differentially expressed genes, with 89 enriched biological processes predominantly involving metabolic pathways. Co-expression network analysis of 261 genes identified metabolic hub genes including LEP, ADIPOQ, and ATP1A2. To evaluate the prognostic framework, survival analysis of the top 10 hubs was performed on synthetic survival data, revealing ATP1A2 as a significant marker (p = 0.03) under Cox regression, with elevated expression associating with altered survival outcomes. By systematically mapping metabolic rewiring in TNBC, this work identifies ATP1A2 as an actionable therapeutic target and establishes a systems-level framework for rational drug discovery and patient stratification in this aggressive malignancy.

Colorectal cancer (CRC) is challenging to track because its molecular changes are very complex as the disease progresses, creating significant challenges for robust biomarker discovery. In this study, we developed a machine learning framework by integrating monotonic progression and the StepMiner approach. We conducted external validation to identify reproducible, consistent transcriptomic biomarkers associated with CRC progression. Gene expression datasets were analyzed across four disease states from publicly available GEO: normal colon, adenoma, primary colorectal cancer, and metastasis. First, we identified genes with monotonic expression, then used the StepMiner approach to identify genes that act as switches between stages. A balanced 74-gene signature was used for machine-learning classification with a Random Forest. External validation showed strong performance in tissue-based datasets. However, tissue-derived signatures and plasma and blood-based datasets showed poor performance, highlighting biological differences between transcriptomic profiles. Cross-filtering between tissue-derived genes and blood expression datasets was performed, which resulted in the selection of 62 blood-compatible gene signatures. Leakage-free retraining on GSE164191 achieved a mean AUC of 0.868 with balanced precision. Functional enrichment analysis showed that these genes are highly active in cancer growth. Specifically, genes CBX3, S100A11, PDK4, NCOR1, and SOX4 demonstrated stable and reliable performance across the validation fold. Overall, our study presents a progression-aware transcriptomic framework for CRC biomarker discovery and demonstrates the importance of external validation. Additionally, we evaluate whether tissue-derived signatures can predict blood profiles. This proposed approach may help the future development of tissue-based diagnostics and minimally liquid-biopsy strategies for CRC. To ensure reproducibility, our proposed workflow was automated as a Nextflow pipeline. The tissue-derived model was deployed as an application utilizing Angular, ASP.NET Core, and Plumber (R).

Background: Cutaneous squamous cell carcinoma (cSCC) accounts for a significant proportion of skin malignancies in India, yet data on patterns of failure, particularly for extremity and truncal primaries remain scarce. We audited a decade of surgically treated cSCC at a tertiary cancer center to characterize failure patterns and associated risk factors. Methods: This retrospective study included 161 patients with histopathologically confirmed cSCC treated surgically between January 2013 and December 2023, comprising 127 upfront/residual and 34 recurrent presentations. Primary sites were extremities (64%), head and neck (26%) and torso (10%). 21 patients had Marjolin's ulcer. Outcomes included local, regional and distant failure, recurrence-free survival and overall survival. Brigham and Women's Hospital (BWH) staging was applied to assess prognostic utility. Statistical analysis was done using Kaplan-Meier and competing-risk methods. Results: Median follow-up was 2.4 years. Regional recurrence was the predominant failure pattern seen in 26 patients, local recurrence was seen in 14 patients and distant metastasis in 13. The 3-year cumulative incidences of local, regional and distant failure were 11%, 19% and 8.4% respectively. Rates of regional recurrence were substantially higher than Western series. Extremity primaries accounted for 19/26 regional recurrences. BWH T2b disease showed the highest regional failure rate (27.6%), exceeding T3 (17.8%) and T2a (6%) with perineural invasion significantly associated with regional failure in T2b/T3 tumors (p<0.001). Median time to regional metastasis was 8.4 months. At 3 years, overall survival was 77% and progression-free survival was 64%. Conclusion: Regional recurrence is the dominant mode of failure in this cohort, at rates higher than most published series, with extremity primaries and BWH T2b staging identifying particularly high-risk subgroups. These findings highlight the need for a comprehensive staging system encompassing non head and neck cSCC and support prospective evaluation of elective nodal staging and adjuvant radiotherapy in high-risk patients, alongside intensified surveillance.

Background: For patients with metastatic gastrointestinal cancers, chemotherapy resistance is a common phenomenon that, if known in advance, would allow for individualized treatment decisions. This study aimed to test the feasibility of developing a deep learning computer vision system that uses laparoscopy images depicting peritoneal surface metastases (i.e., capturing the in-vivo optical appearance of metastases as a summary of their molecular makeup) to predict whether a patient is resistant to standard chemotherapy. Methods: The retrospective observational feasibility study included 35 adult patients who underwent staging laparoscopy for non-colon gastrointestinal adenocarcinoma with biopsy-confirmed peritoneal surface metastases and who underwent chemotherapy as their only treatment modality. Chemotherapy resistance was determined based on each patient's observed cancer-specific survival after controlling for confounders. Results: Of 35 patients, 17 were assigned to the chemotherapy sensitive group and 18 to the chemotherapy resistant group. The study cohort provided 1010 laparoscopy image patches of 101 biopsy-confirmed metastases. A densely connected convolutional neural network with cross-validation provided the best results for correctly predicting chemotherapy resistance at the patient level (accuracy 0.80 (95%CI 0.63-0.92), sensitivity 0.72, specificity 0.88, AUC-ROC 0.78). Saliency maps demonstrated the system's trustworthiness. Conclusion: In this study, a prototype surgical computer vision system designed to determine chemotherapy resistance from operative images of peritoneal surface metastases demonstrated its technical feasibility. Further development and validation in a multi-institutional clinical study are pending.

Abstract Background: The lack of effective biomarkers and therapeutic targets to overcome radioresistance in cervical cancer remains a major clinical challenge. Tumor necrosis factor receptor-associated factor 6 (TRAF6), an E3 ubiquitin ligase pivotal in immune and inflammatory signaling, has been implicated in various malignancies. However, its role in radioresistance in cervical cancer remains unclear. Methods: TRAF6 expression was evaluated in cervical cancer tissues from 162 patients who underwent postoperative radiotherapy at our institution and in 304 cases from the TCGA-CESC cohort. The prognostic significance of TRAF6 was assessed using Kaplan-Meier and Cox regression analyses. A nomogram integrating TRAF6 expression with clinicopathological factors was constructed to predict overall survival (OS) and progression-free survival (PFS). The functional role of TRAF6 in malignant phenotypes and radiosensitivity was investigated using shRNA-mediated knockdown in HeLa and C33A cervical cancer cells. Immune cell infiltration patterns associated with TRAF6 expression were analyzed using ssGSEA and xCELL algorithms based on TCGA data. Results: TRAF6 expression was significantly elevated in cervical cancer tissues compared with adjacent normal tissues (70.99% vs. control, P < 0.001) and was higher in radioresistant than in radiosensitive patients (P < 0.001). High TRAF6 expression was associated with shorter OS (HR = 18.73, P = 0.004) and PFS (HR = 8.44, P < 0.001) and was identified as an independent risk factor for radiotherapy resistance (OR = 8.44, P < 0.001). The TRAF6-integrated nomogram demonstrated good predictive accuracy for OS (C-index = 0.7351) and PFS (C-index = 0.7444). TRAF6 knockdown in cervical cancer cells significantly suppressed proliferation, migration, and invasion, while substantially enhancing radiosensitivity of tumor cells. Functional enrichment analysis revealed that TRAF6-related genes were enriched in autophagy, mitophagy, and HPV infection pathways. Immune cell infiltration analysis showed that TRAF6 expression correlated with distinct immune cell profiles, characterized by enrichment of activated dendritic cells, M1 macrophages, and regulatory T cells, alongside depletion of cytotoxic effectors such as CD8+ T cells and {gamma}{delta} T cells. Conclusions: TRAF6 could be a prognostic biomarker associated with poor outcomes and indicator of radiotherapy resistance in cervical cancer, TRAF6 represents a potential therapeutic target for overcoming radioresistance in cervical cancer.

Background: Despite advances in circulating tumor DNA analysis, reliable detection of oncological disease from ultra-low coverage whole genome sequencing (ulcWGS) remains challenging, particularly at low tumor fractions. This study leverages cell-free DNA (cfDNA) characteristics to develop and evaluate a robust, integrative binary predictive model for ovarian cancer (OC) status screening. OC represents a growing global burden and is often diagnosed at advanced stages due to the lack of specific early symptoms and effective screening strategies, highlighting the need for sensitive and broadly applicable early detection approaches.Methods and Findings: We analyzed plasma cfDNA from OC patients (N = 85) and cancer-free controls (N = 41) using ulcWGS (~1x). Participation in the study was voluntary, and all participants provided written informed consent before any study-related procedures under study approval No. 16119-8/2022/EUIG. Within an integrated workflow combining standardized laboratory processing, bioinformatic pipelines, and machine learning (ML), we extracted 21 features capturing copy number variations (CNVs) and fragmentomic characteristics to identify complementary signatures distinguishing OC from controls. Predictive models were developed using XGBoost with hyperparameter optimization and evaluated on an independent test set (n = 25% of the cohort). A dual-threshold classification strategy was applied to define an uncertainty zone and optimize screening performance.CNV-derived and fragmentomic features assessed in exploratory analysis on the training-validation set showed moderate discriminative power (AUC 0.569 - 0.946) but substantial overlap between groups. On the test set, the CNV-only model achieved an AUC of 0.855 (sensitivity 85%, specificity 50%), while the fragmentomics-only model reached an AUC of 0.8825 (sensitivity 95%, specificity 30%). Both feature domains captured complementary aspects of tumor-derived cfDNA, with fragmentomics favoring sensitivity and CNV-derived metrics improving specificity. Integration of both feature classes improved performance, yielding an AUC of 0.900, sensitivity of 85.00%, and specificity of 90.00%. SHAP analysis confirmed contributions from both feature types without a single dominant predictor.Conclusions: We present an integrative cfDNA framework for OC detection based on ulcWGS that combines CNV and fragmentomic signals to improve diagnostic performance over single-feature approaches. By enabling robust detection of tumor-associated patterns at ultra-low sequencing depth, this approach demonstrates that meaningful cancer discrimination can be achieved without reliance on deep sequencing. This highlights the potential of cost-effective and scalable liquid biopsy strategies for population-level cancer screening their integration into personalized and preventive oncology. Keywords: Liquid biopsy, ovarian cancer, ultra-low coverage whole genome sequencing, cell-free DNA, cell-free tumor DNA, cancer detection, copy number variations, insert size, fragmentomics, machine learning

Background. Duodenal adenocarcinoma (DA) is a rare gastrointestinal malignancy with limited DA-specific evidence on the prognostic role of perineural invasion (PNI) and the performance of AJCC 8th-edition staging. We described PNI, AJCC 8 discrimination, and adjuvant-chemotherapy subgroup associations in a single-center cohort. Methods. We retrospectively analyzed 51 patients with curatively resected, histologically invasive DA (2013-2025). Overall survival (OS) and disease-free survival (DFS) were estimated by Kaplan-Meier analysis; prognostic associations by Cox regression. Model discrimination was quantified by the C-index; adjuvant-chemotherapy associations were explored across pre-specified subgroups. Analyses were exploratory and hypothesis-generating. Results. Median follow-up was 34.9 months; 26 patients (51%) died and 32 (63%) recurred, with the highest recurrence frequency 6-12 months after surgery. AJCC 8 staging discriminated modestly (C-index 0.595); no adjacent stage pair differed significantly after Bonferroni correction (minimum raw pairwise P = 0.051). Among candidate factors, PNI had the largest effect estimate (univariable HR 2.08, 95% CI 0.88-4.90, P = 0.095) and the largest incremental discrimination when added to a stage + T + N base model (delta C-index +0.062; likelihood-ratio P = 0.145), exceeding the increments from tumor size (+0.046), differentiation (+0.034), and sex (+0.016). Adjuvant chemotherapy was associated with hazard-ratio reductions in PNI-positive (HR 0.19, 95% CI 0.02-1.59), stage III (HR 0.36, 95% CI 0.12-1.15), and node-positive (HR 0.36, 95% CI 0.12-1.15) subgroups; none reached statistical significance, consistent with limited power in subgroups of 8-22 patients. Conclusion. In this small single-center cohort, PNI showed the largest prognostic effect estimate among candidate variables and the largest incremental discrimination beyond AJCC 8 stage, although neither reached statistical significance and AJCC 8 discriminated only modestly. These hypothesis-generating findings - directionally concordant with larger published DA series - support formal evaluation of PNI as a stratification variable in multicenter cohorts and in individual-patient-data meta-analyses of duodenal adenocarcinoma, rather than a change in current practice.

BackgroundTumor-associated neutrophils (TANs) are emerging as functionally heterogeneous and plastic cells in the tumor microenvironment. In immunologically cold tumors, elevated neutrophil abundance correlates with poor prognosis and resistance to immune checkpoint inhibition (ICI). Whether distinct anti-tumoral neutrophil states can be induced by different immunotherapies and how they relate to treatment efficacy remains unclear. MethodsUsing the syngeneic MOC2-huEGFR (M2h) mouse model of head and neck squamous cell cancer (HNSCC), we treated tumor-bearing mice with agonistic anti-CD40 monoclonal antibody (mAb) (aCD40), TNF, Cetuximab, or a combination of all three, designated Neutrophil Activating Therapy (NAT). In addition to evaluating anti-tumor efficacy, we performed single-cell multiomics RNA and protein sequencing, followed by bioinformatics analyses and flow cytometry validation. NAT-induced anti-tumor efficacy and related neutrophil states were also assessed in another cold tumor model, 9464D-GD2 neuroblastoma. Murine treatment-induced neutrophil gene signatures were then evaluated using clinical, proteomic, and transcriptomic data from HNSCC patients. ResultsFive transcriptionally distinct neutrophil states (N0-N4), including precursor state CD49d+ N4, were identified using the M2h model. N0 neutrophils (immunosuppressive/quiescent) dominated untreated tumors, but not in successful treatments. ISG+ N1 neutrophils and CCR3+ N3 neutrophils expanded by aCD40, TNF, and NAT treatment with anti-tumoral gene signatures and found more interacting with CD8+ T cells from bioinformatics analysis. N2 neutrophils reflected a recently established hypoxia-adapted state found in all treatments. ICAM1 (CD54) emerged as a marker of treatment-induced neutrophil activation, discriminating N1, N2, and N3 neutrophils from N0 neutrophils, validated by flow cytometry. In the 9464D-GD2 neuroblastoma model, NAT treatment also reduced the N0 dominance seen in untreated tumors in the HNSCC model but failed to induce anti-tumoral neutrophil states. In 23 HNSCC patients who received ICI therapy, ICAM1 protein expression in neutrophils trended toward association with responder status (TMA-level p=0.029), and ICAM1 neutrophil gene expression also trended toward association with improved overall survival in TCGA data (HR=0.75, p=0.059). ConclusionsDistinct immunotherapy-induced neutrophil states are defined by transcriptional profiles enriched in different functional pathways, associated with both anti-tumor and pro-tumor signatures. ICAM1 identifies activated neutrophils and potentially serves as a biomarker of ICI response in HNSCC, warranting further clinical validation. WHAT IS ALREADY KNOWN ON THIS TOPICNeutrophil heterogeneity has received increasing attention, with studies identifying antitumoral neutrophil populations, either at baseline or induced by treatment. Several effective treatment regimens involve an anti-CD40 agonist (aCD40) antibody, among them Neutrophil Activating Therapy (NAT), which combines aCD40, TNF, and a tumor antigen binding antibody designed to reprogram neutrophils. NAT could thus be particularly effective in cold, myeloid-rich tumors that are largely unresponsive to conventional immunotherapies such as checkpoint blockade, enacting these anti-tumoral effects through similar and different mechanisms; however, this has not been tested. WHAT THIS STUDY ADDSThis study adds a single-cell multi-omics framework for defining treatment-induced neutrophil heterogeneity in MOC2-huEGFR and 9464D-GD2 tumors, two immunologically cold models. It highlights ICAM1/CD54 and interferon-stimulated genes as markers of a dominant antitumor neutrophil state, while showing that neutrophil state composition variy across tumor models. HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE, OR POLICYThese results support the efficacy of a myeloid-modulating therapy built around aCD40 and TNF in a cold murine head and neck cancer model, and to a lesser extent in a cold murine neuroblastoma model. ICAM1/CD54 expression in neutrophils was also identified as a promising marker of antitumor activity and treatment response. More broadly, this work suggests that incorporating aCD40 and/or TNF into existing treatment regimens could improve outcomes, while ICAM1/CD54-high neutrophils may serve as a useful therapeutic readout.

Merkel cell carcinoma (MCC) is a rare and aggressive neuroendocrine skin cancer that frequently carries integrated Merkel cell polyomavirus DNA and expresses oncogenic viral small T antigen (sTAg) and truncated large T antigen (tLTAg). We previously reported a mouse model of MCC with skin-targeted expression of sTAg, tLTAg, and the Merkel cell transcription factor ATOH1, combined with deletion of Trp53. Here, we optimized this model to achieve 100% tumor penetrance with lymph node metastases, established four mouse MCC cell lines, and selected one line, mMCC2, for pilot preclinical trials. In immunocompetent C57BL/6J mice, mMCC2 cells reliably produce MCCs and lymph node metastases following subcutaneous or intradermal (orthotopic) injection, and liver and lung metastases after tail vein injection. Mouse MCC allografts resemble parental tumors histologically and express a full complement of MCC differentiation markers. Treatment of allografted mice with anti-PD-1 resulted in variable inhibition of tumor growth. In contrast, treatment with lysine-specific histone Wdemethylase 1 (LSD1) inhibitors, with or without anti-PD-1, led to consistently lower tumor volumes by 5.7-fold in both groups (P < 0.0001) and smaller or undetectable lymph node metastases. Growth-inhibited tumors in all groups showed a marked reduction in proliferating tumor cells and increased infiltration by F4/80+ macrophages and CD8+ T cells. These findings support a role for immune-cell recruitment in treatment response and underscore the importance of immunocompetent preclinical models, even in studies using targeted therapies. This unique virus-positive MCC allograft model, which produces local tumors as well as regional and distant metastases in immunocompetent hosts, provides a critical platform for preclinical evaluation of new therapeutic strategies and sets the stage for much-needed translational studies to inform future clinical trials.

IntroductionThe RAS(ON) multi-selective daraxonrasib (RMC-6236) inhibitor is effective in patients with NSCLC KRAS mutant cancers. Tolerance to daraxonrasib invariably develops by mechanisms that remain unclear. There is no known involvement of the M1C oncogenic protein in daraxonrasib resistance. MethodsNSCLC H358 KRAS(G12C), H2122 KRAS(G12C) and patient derived MGH1112 KRAS(G12C) cells with acquired daraxonrasib resistance were investigated for M1C dependence in studies of SHP2, STAT1/3 and NF-KB activation, clonogenicity, and self-renewal capacity. ResultsWe demonstrate that M1C is induced as a protective response in NSCLC KRAS(G12C) mutant cells treated with daraxonrasib. We report that M1C forms novel cell membrane-associated biomolecular condensates with the SHP2 protein tyrosine phosphatase in driving daraxonrasib resistance. M1C integrates SHP2 activation with induction of (i) oncostatin-m/gp130/STAT3 signaling, and (ii) the NF-{kappa}B-mediated epithelial-mesenchymal transition (EMT) pathway. The functional significance of this M1C-driven pathway is supported by the demonstration that targeting STAT3 and NF-{kappa}B reverses daraxonrasib resistance. Consistent with M1C dependence, we also show that targeting M1C is effective against daraxonrasib-resistant NSCLC KRAS mutant cell line and tumor models. In contrast, M1C drives sotorasib resistance by STAT1-mediated inflammatory signaling, demonstrating that M1C confers resistance to KRAS(G12C)-selective and RAS(ON) tri-complex inhibitors by noncongruent mechanisms. ConclusionsThese findings demonstrate that M1C is required for daraxonrasib tolerance and is a potential target for the treatment of patients with NSCLC KRAS(G12C) mutant tumors refractory to this agent.

Lung squamous cell carcinoma (LUSC) is the second most common type of lung cancer, yet therapeutic options remain limited. A deeper understanding of its biology and molecular pathogenesis is essential for developing new treatment strategies. Here, we investigated the mechanisms of phenotypic plasticity in LUSC by comparing organoid-derived orthotopic lung models (ODOLs) and subcutaneous xenograft models (ODXs). ODXs showed greater tumor growth, squamous differentiation, and extracellular matrix (ECM) organization compared to ODOLs. Transcriptomic analyses revealed upregulation of multiple HIF1 and SOX2 target genes together with enhanced hypoxia signaling in ODXs. CRISPR/Cas9-mediated HIF1-knockout ODXs showed reduced SOX2 expression, tumor growth, and ECM organization, whereas SOX2-knockout ODXs reduced tumor growth without affecting HIF1 and ECM organization. These results indicate that HIF1 regulates squamous lineage maintenance through SOX2 and ECM remodeling. Spatial transcriptomics revealed enrichment of basal cell-like and squamous-differentiated tumor states in ODXs, whereas ODOLs displayed less differentiated phenotypes. These findings identify the tumor microenvironment as a critical determinant of lineage plasticity in LUSC and provide mechanistic insight into how hypoxia shapes tumor differentiation.

Long non-coding RNAs (lncRNAs) have attracted increasing interest because of their roles as modulators of tumor progression, acting either as oncogenic drivers or tumor suppressors, depending on the cellular context. LINC01133 has been implicated in regulation of multiple tumor-related mechanisms; however, its role in breast cancer, particularly in the triple-negative subtype, remains poorly characterized. In this study, we investigated the impact of LINC01133 depletion on malignant phenotypes and on the expression of migration- and invasion-associated genes using the Hs578T triple-negative breast cancer (TNBC) cell line, through comparative analyses of parental, control, and LINC01133-knockout cell lines, namely Hs578T_wt, Hs578T_ctr, and Hs578T_ko. Functional characterization included morphological analysis, growth assays, anchorage-independent colony formation, migration, invasion, and quantitative biomolecular experiments. Depletion of LINC01133 led to reduction of cell diameter, a significant increase in colony-forming capacity, and marked enhancement of migratory and invasive potential. At the molecular level, LINC01133 loss induced the expression of genes associated with extracellular matrix remodeling and cellular plasticity, including fibronectin, vimentin, integrins, FOXC1, and TWIST1, concomitant with reduced expression of ZEB1, TWIST2, and N-cadherin. Collectively, these data indicate that LINC01133 acts as a potential fine regulator of in vitro migration and invasion processes in TNBC, with its expression favoring a more asymptomatic mode of tumor progression, whereas its loss markedly enhances tumor malignancy.

Background and Aims: Endoscopic artificial intelligence is commonly validated on selected single images, whereas gastric cancer interpretation requires integrating whole examinations. We developed GutCore and evaluated whether whole-case endoscopic images could be used for patient-level assessment of gastric cancer depth, biomarkers, and prognosis. Methods: GutCore was pretrained on 5.6 million de-identified endoscopic images from more than ten hospitals. We compared it with five general, medical, and endoscopy-specific foundation models using open image-level datasets and an internal tertiary-center cohort of 11,035 de-identified endoscopic examinations (2019-2023): 8,049 with early or advanced gastric cancer and 2,986 with benign gastritis or intestinal metaplasia. All examination images were aggregated for patient-level assessment of cancer status, invasion depth, molecular biomarkers, and overall survival. Results: Aggregating all stored images from each examination enabled patient-level gastric cancer assessment without selecting representative frames. GutCore achieved AUCs of 0.995 for cancer detection, 0.960 for muscularis propria invasion, and 0.804 for SM2-or-deeper invasion. Prediction of tissue-defined biomarker status was strongest for Epstein-Barr virus status and MLH1 loss (AUC, 0.831 and 0.854), with lower HER2 performance (AUC, 0.673). In the held-out advanced gastric cancer test set, GutCore-derived risk groups showed marked survival separation (log-rank P < .0001; high-risk vs low-risk hazard ratio, 13.18; 95% CI, 6.06-28.66), with stratification persisting within pathological stage II and III disease. External frame-level benchmarks showed strong performance for anatomical landmark recognition, disease grading, and segmentation. Conclusions: GutCore supported whole-case patient-level gastric cancer assessment using routinely stored endoscopic images. Further validation in independent clinical cohorts is needed to establish generalizability and clinical utility.

Pancreatic cancer remains a highly lethal malignancy with limited therapeutic options. Chimeric antigen receptor (CAR) therapy has revolutionized the treatment of hematological cancers but still faces major limitations in solid tumors, particularly due to the scarcity of tumor-specific targets. Cutaneous lymphocyte antigen (CLA) recently emerged as a promising PDAC target due to its high tumor expression and limited presence in healthy tissues. However, previously reported CLA-directed CAR constructs lacked antitumor functionality. Here, we investigated multiple strategies to generate functional CLA-targeting CAR approaches. We first hypothesized that impaired activity resulted from fratricide caused by CLA expression on activated T cells. CLA knockout was successfully achieved through deletion of fucosyltransferase-7, but not by knockout of the major CLA carrier backbones CD162, CD44 or CD43, suggesting additional CLA carriers or compensatory regulation. As CLA knockout alone did not restore CAR-mediated killing, we explored whether insufficient binding affinity limited CAR activity. Affinity maturation was performed in silico and in vitro using yeast surface display, identifying 39 candidate mutations, although none restored cytotoxicity. We finally switched to an AdCAR strategy using anti-biotin CAR T cells combined with biotinylated anti-CLA scFv-Fc adapters. This approach enabled efficient, concentration-dependent cytotoxicity with both CLA-targeting binders. Additionally, we identified a dynamic, cell density-dependent regulation of CLA expression. Finally, glycan profiling of CLA binders further revealed broader-than-expected glycan interactions, suggesting a potentially wider definition of the CLA family. Overall, our findings establish CLA as a functional PDAC immunotherapy target while revealing unexpected complexity in its regulation and molecular presentation.

Pancreatic ductal adenocarcinoma (PDAC) is dependent on autophagy for growth. Chloroquine/Hydroxychloroquine (CQ/HCQ), the sole FDA-approved autophagy inhibitors, have shown limited clinical efficacy as cancer therapies. To identify approaches to improve PDAC response to CQ, we performed a CQ-anchored, CRISPR-Cas9 mediated loss-of-function screen. We identified that the loss of genes encoding proteins upstream in the autophagy pathway enhanced CQ-mediated growth suppression. This indicated that simultaneous targeting of two distinct nodes of the same pathway, vertical inhibition, may be a more effective strategy than single node inhibition. We demonstrated that genetic loss or pharmacological inhibition of VPS34, a protein necessary for autophagosome nucleation, sensitized PDAC cells to inhibitors of the terminal stage of the autophagy pathway, including CQ and an inhibitor of PIKfyve. We extended this concept to the initiation complex and demonstrated that ULK1/2 inhibition synergized with CQ and PIKfyve inhibition to impair PDAC cell growth and increase apoptosis. Anticipating mechanisms of resistance to vertical autophagy inhibition, we performed reverse-phase protein array profiling and identified that vertical inhibition of the autophagy pathway resulted in enhanced activation of the PI3K-AKT-mTORC1 signaling pathway. Increased mTORC1 signaling resulted in heightened sensitivity to bi-steric mTORC1 inhibition in both cell line and organoid models of PDAC. This study identifies novel anti-autophagy inhibitor combinations that may improve the clinical efficacy of autophagy inhibition for PDAC treatment. IMPLICATIONSVertical inhibition of the autophagy pathway reduces pancreatic cancer cell growth, increases apoptosis, and enhances sensitivity to mTORC1 inhibition; thereby representing a novel therapeutic strategy for autophagy-driven pancreatic cancer.

The intensification of neoadjuvant therapy for early triple-negative breast cancer (eTNBC) - through the addition of carboplatin to standard chemotherapy and the incorporation of pembrolizumab - has markedly improved prognosis in recent years. However, this escalation carries a substantial risk of toxicity, and not all patients require the full regimen to achieve benefit. Realizing individualized treatment strategies will therefore depend on prognostic and predictive biomarkers that can forecast treatment response and long-term outcome. In the present study, we interrogated public gene expression datasets to develop transcriptomic signatures predicting response to neoadjuvant treatment and risk of recurrence. To validate these signatures, we used the Multiplex8+ platform for spatially informed comprehensive transcriptomic profiling in a real-world, multicenter, retrospective cohort of 590 patients diagnosed with eTNBC and treated with neoadjuvant chemotherapy with or without immunotherapy. The diagnostic Multiplex8+ test uses H&E and multiplexed RNA-FISH to guide the selection of specific tumor areas for the whole transcriptome sequencing and signature analysis. In the real-world cohort, the Multiplex8+ signatures were associated with both response and prognosis, remaining highly significant in multivariable models that included clinical parameters. The signatures were complementary to established biomarkers such as stromal tumor-infiltrating lymphocytes. These findings warrant prospective integration of the signatures into risk-stratified clinical trials to support future de-escalation and escalation strategies, enabling a better balance of efficacy, toxicity, cost, and drug availability.

The therapeutic landscape for hepatocellular carcinoma (HCC) is evolving rapidly, necessitating scalable approaches to synthesize the expanding scientific literature. We characterized thematic shifts in HCC treatment and prognosis research by conducting a retrospective bibliometric analysis of influential publications from 2023 and 2024. Using the OpenAlex database, we identified the 50 most highly cited papers from each year based on eighteen-month post-publication citation counts. Large language models were deployed to extract, normalize, and classify concepts from unstructured text into canonical topics and parent themes, enabling quantitative year-over-year frequency comparisons. Analysis of these 100 papers revealed a distinct maturation in research focus. Although broad categories like general immunotherapy remained prevalent, their relative frequency declined in favor of specific dual immune checkpoint regimens, notably CTLA-4 inhibition and the durvalumab plus tremelimumab combination. Concurrently, parent themes related to radiomics, imaging, and health systems exhibited significant growth in the 2024 cohort. These findings demonstrate a thematic transition in high-impact HCC research from foundational immuno-oncology toward optimized combination therapies and precision diagnostics. Furthermore, this study highlights the utility of artificial intelligence-driven bibliometrics for objectively tracking dynamic conceptual shifts in oncology. A web interface for exploring the data is available at https://pri.pepkio.com/.

Background & Aims: Hepatocellular carcinoma (HCC) remains a leading cause of cancer mortality, with most patients presenting at advanced stages requiring systemic therapy. Despite promising outcomes with immune checkpoint inhibitors (ICI), responses remain variable due to an immunosuppressive tumor microenvironment. Y90 radioembolization offers potential immune priming, but only a subset of patients benefit. Here, we apply spatial multi-omics to delineate baseline and treatment-induced immune features and identify predictive signatures of progressive disease (PD) for early detection of patients unlikely to benefit from therapy. Approach & Results: Paired baseline (Day 0) and on-treatment (Day 35) biopsies were obtained from 33 patients, following Y90 radioembolization (Day 14) and nivolumab. Multiplex immunohistochemistry (mIHC) was used for cell-cell interaction analysis. A subset was further profiled using Visium (n=13) for tissue category-specific analysis and NanoString GeoMx DSP (n=12) for cell type-resolved transcriptomic and pathway analyses. Global spatial transcriptomics analysis revealed minimal baseline immune activity in PD, indicating an intrinsically immune-deficient TME. Despite treatment-induced activation, PD exhibited reduced CD8+ T cell abundance and limited reinvigoration of exhausted subsets, and persistent LAG-3-associated exhaustion. DSP showed downregulation of antigen presentation and T cell activation pathways. Macrophage profiling revealed enrichment of CD38+ phenotypes, contrasting CXCL9-CXCR3-associated responses in responders. Furthermore, a 72-gene PD signature was identified and validated in TCGA, associating with poorer survival. Conclusions: Integrated spatial multi-omics reveals that PD in HCC is associated with an immune-deficient TME, characterized by LAG-3-associated CD8+ exhaustion and immunosuppressive macrophages. A 72-gene signature enables early identification and supports alternative therapeutic strategies.