CAMILLA is a basket trial (NCT03539822) evaluating cabozantinib plus the ICI durvalumab in chemorefractory gastrointestinal cancer. Herein, are the phase II colorectal cohort results. 29 patients were evaluable. 100% had confirmed pMMR/MSS tumors. Primary endpoint was met with ORR of 27.6% (95% CI 12.7-47.2%). Secondary endpoints of 4-month PFS rate was 44.83% (95% CI 26.5-64.3%); and median OS was 9.1 months (95% CI 5.8-20.2). Grade≥3 TRAE occurred in 39%. In post-hoc analysis of patients with RAS wild type tumors, ORR was 50% and median PFS and OS were 6.3 and 21.5 months respectively. Exploratory spatial transcriptomic profiling of pretreatment tumors showed upregulation of VEGF and MET signaling, increased extracellular matrix activity and preexisting anti-tumor immune responses coexisting with immune suppressive features like T cell migration barriers in responders versus non-responders. Cabozantinib plus durvalumab demonstrated anti-tumor activity, manageable toxicity, and have led to the activation of the phase III STELLAR-303 trial.

Gastrointestinal cancers are highly aggressive malignancies with significant mortality rates. Recent research emphasizes the critical role of the tumor microenvironment (TME) in these cancers, which includes cancer-associated fibroblasts (CAFs), a key component of the TME that have diverse origins, including fibroblasts, mesenchymal stem cells, and endothelial cells. Several markers, such as α-SMA and FAP, have been identified to label CAFs, and some specific markers may serve as potential therapeutic targets. In this review article, we summarize the literature on the multifaceted role of CAFs in tumor progression, including their effects on angiogenesis, immune suppression, invasion, and metastasis. In addition, we highlight the use of single-cell transcriptomics to understand CAF heterogeneity and their interactions within the TME. Moreover, we discuss the dynamic interplay between CAFs and the immune system, which contributes to immunosuppression in the TME, and the potential for CAF-targeted therapies and combination approaches with immunotherapy to improve cancer treatment outcomes.

Purpose: The study aimed to create a machine learning model (MLM) to predict the stone-free status (SFS) of patients undergoing percutaneous nephrolithotomy (PCNL) and compare its performance to the S.T.O.N.E. and Guy’s stone scores. Patients and Methods: This is a retrospective study that included 320 PCNL patients. Pre-operative and post-operative variables were extracted and entered into three MLMs: RFC, SVM, and XGBoost. The methods used to assess the performance of each were mean bootstrap estimate, 10-fold cross-validation, classification report, and AUC. Each model was externally validated and evaluated by mean bootstrap estimate with CI, classification report, and AUC. Results: Out of the 320 patients who underwent PCNL, the SFS was found to be 69.4%. The RFC mean bootstrap estimate was 0.75 and 95% CI: [0.65– 0.85], 10-fold cross-validation of 0.744, an accuracy of 0.74, and AUC of 0.761. The XGBoost results were 0.74 [0.63– 0.85], 0.759, 0.72, and 0.769, respectively. The SVM results were 0.70 [0.60– 0.79], 0.725, 0.74, and 0.751, respectively. The AUC of Guy’s stone score and the S.T.O.N.E. score were 0.666 and 0.71, respectively. The RFC external validation set had a mean bootstrap estimate of 0.87 and 95% CI: [0.81– 0.92], an accuracy of 0.70, and an AUC of 0.795, While the XGBoost results were 0.84 [0.78– 0.91], 0.74, and 0.84, respectively. The SVM results were 0.86 [0.80– 0.91], 0.79, and 0.858, respectively. Conclusion: MLMs can be used with high accuracy in predicting SFS for patients undergoing PCNL. MLMs we utilized predicted the SFS with AUCs superior to those of GSS and S.T.O.N.E scores.

Background: While the prognostic role of tertiary lymphoid structures (TLS) has been well studied in solid cancers, the prevalence and impact of immature precursor lymphoid structures known as lymphoid aggregates (LA) remain unresolved in relation to the disease process. In this study, we examined characteristics and the prognostic utility of LA and TLS status in histological samples from patients with melanoma. Methods: We assessed The Cancer Genomic Atlas-skin cutaneous melanoma digital slides and melanoma specimens from the University of Pittsburgh for the presence of LA and TLS using H&E staining, multiplex immunofluorescence (mIF) and transcriptomic analyses. Cox proportional hazard regression models were used to assess the prognostic value associated with the presence of lymphoid structures in melanomas. Results: A total of 278 evaluable samples were analyzed and split into primary melanomas in skin (N=195) and metastatic melanomas involving skin/subcutaneous/soft tissue sites (N=83). 72% of tumor specimens contained histologically defined LA located in peritumoral (34%), intratumoral (5.6%) or stromal (6.1%) locations, with the remaining samples (54.3%) exhibiting LA in multiple locations. In contrast to LA which tended to form more commonly in primary melanoma samples, TLS with germinal centers predominantly formed in peritumoral (45.2%) or stromal (35.5%) locations in metastatic melanomas (p=0.02), with TLS observed in 11% of all melanoma specimens evaluated. mIF analyses revealed cellular heterogeneity of lymphoid structures, with CD20⁺ (B) cells present in nodule-shaped and stromal locations where they exhibited a high degree of colocalization with CD4⁺ and CD8⁺ T cells. A previously defined 12-chemokine gene expression score was significantly higher in samples with evidence of LA versus none (p<0.001), and samples without LA/TLS were enriched with pigmentation/neural network gene signatures. The presence of LA was significantly associated with tumor-free regional lymph node status (p=0.002). In multivariable analysis, after adjusting for age, sex, sample type, and stage, the presence of LA was associated with improved patient overall survival (OS) (HR=0.52, 95% CI 0.31 to 0.87, p=0.01). Conclusion: Melanoma frequently contains LA, which tends to form in diverse locations in the tumor microenvironment in association with improved overall survival and tumor-free regional lymph node status in patients with primary disease.

Immune checkpoint inhibitors (ICIs) became one of the most revolutionary cancer treatments, especially in melanoma. While they have been proven to prolong survival with lesser side effects compared to chemotherapy, the accurate prediction of response remains to be an unmet gap. Thus, we aim to identify accurate clinical and transcriptomic biomarkers for ICI response in melanoma. We also provide mechanistic insight into how high-performing markers impose their effect on the tumor microenvironment (TME). Clinical and transcriptomic data were retrieved from melanoma studies administering ICIs from cBioportal and GEO databases. Four machine learning models were developed using random-forest classification (RFC) entailing clinical and genomic features (RFC7), differentially expressed genes (DEGs, RFC-Seq), survival-related DEGs (RFC-Surv) and a combination model. The xCELL algorithm was used to investigate the TME. A total of 212 ICI-treated melanoma patients were identified. All models achieved a high area under the curve (AUC) and bootstrap estimate (RFC7: 0.71, 0.74; RFC-Seq: 0.87, 0.75; RFC-Surv: 0.76, 0.76, respectively). Tumor mutation burden, GSTA3, and VNN2 were the highest contributing features. Tumor infiltration analyses revealed a direct correlation between upregulated genes and CD8+, CD4+ T cells, and B cells and inversely correlated with myeloid-derived suppressor cells. Our findings confirmed the accuracy of several genomic, clinical, and transcriptomic-based RFC models, that could further support the use of TMB in predicting response to ICIs. Novel genes (GSTA3 and VNN2) were identified through RFC-seq and RFC-surv models that could serve as genomic biomarkers after robust validation.