Phase II clinical and immune correlate study of adjuvant nivolumab plus ipilimumab for high-risk resected melanoma.

BACKGROUND: Adjuvant therapy for high-risk resected melanoma with programmed cell-death 1 blockade results in a median relapse-free survival (RFS) of 5 years. The addition of low dose ipilimumab (IPI) to a regimen of adjuvant nivolumab (NIVO) in CheckMate-915 did not result in increased RFS. A pilot phase II adjuvant study of either standard dose or low dose IPI with NIVO was conducted at two centers to evaluate RFS with correlative biomarker studies. METHODS: Patients with resected stages IIIB/IIIC/IV melanoma received either IPI 3 mg/kg and NIVO 1 mg/kg (cohort 4) or IPI 1 mg/kg and NIVO 3 mg/kg (cohorts 5 and 6) induction therapy every 3 weeks for 12 weeks, followed by maintenance NIVO. In an amalgamated subset of patients across cohorts, peripheral T cells at baseline and on-treatment were assessed by flow cytometry and RNA sequencing for exploratory biomarkers. RESULTS: High rates of grade 3-4 adverse events precluded completion of induction therapy in 50%, 35% and 7% of the patients in cohorts 4, 5 and 6, respectively. At a median of 63.9 months of follow-up, 16/56 patients (29%) relapsed. For all patients, at 5 years, RFS was 71% (95% CI: 60 to 84), and overall survival was 94% (95% CI: 88 to 100). Expansion of CD3+CD4+CD38+CD127-GARP- T cells, an on-treatment increase in CD39 expression in CD8+ T cells, and T-cell expression of phosphorylated signal-transducer-and-activator-of-transcription (STAT)2 and STAT5 were associated with relapse. CONCLUSIONS: Adjuvant IPI/NIVO at the induction doses used resulted in promising relapse-free and overall survival, although with a high rate of grade 3-4 adverse events. Biomarker analyses highlight an association of ectoenzyme-expressing T cells and STAT signaling pathways with relapse, warranting future validation. TRIAL REGISTRATION NUMBER: NCT01176474 and NCT02970981.

PhenoComb: a discovery tool to assess complex phenotypes in high-dimensional single-cell datasets.

Motivation: High-dimensional cytometry assays can simultaneously measure dozens of markers, enabling the investigation of complex phenotypes. However, as manual gating relies on previous biological knowledge, few marker combinations are often assessed. This results in complex phenotypes with the potential for biological relevance being overlooked. Here, we present PhenoComb, an R package that allows agnostic exploration of phenotypes by assessing all combinations of markers. PhenoComb uses signal intensity thresholds to assign markers to discrete states (e.g. negative, low, high) and then counts the number of cells per sample from all possible marker combinations in a memory-safe manner. Time and disk space are the only constraints on the number of markers evaluated. PhenoComb also provides several approaches to perform statistical comparisons, evaluate the relevance of phenotypes and assess the independence of identified phenotypes. PhenoComb allows users to guide analysis by adjusting several function arguments, such as identifying parent populations of interest, filtering of low-frequency populations and defining a maximum complexity of phenotypes to evaluate. We have designed PhenoComb to be compatible with a local computer or server-based use. Results: In testing of PhenoComb's performance on synthetic datasets, computation on 16 markers was completed in the scale of minutes and up to 26 markers in hours. We applied PhenoComb to two publicly available datasets: an HIV flow cytometry dataset (12 markers and 421 samples) and the COVIDome CyTOF dataset (40 markers and 99 samples). In the HIV dataset, PhenoComb identified immune phenotypes associated with HIV seroconversion, including those highlighted in the original publication. In the COVID dataset, we identified several immune phenotypes with altered frequencies in infected individuals relative to healthy individuals. Collectively, PhenoComb represents a powerful discovery tool for agnostically assessing high-dimensional single-cell data. Availability and implementation: The PhenoComb R package can be downloaded from https://github.com/SciOmicsLab/PhenoComb. Supplementary information: Supplementary data are available at Bioinformatics Advances online.

Targeting Fat Oxidation in Mouse Prostate Cancer Decreases Tumor Growth and Stimulates Anti-Cancer Immunity.

Lipid catabolism represents an Achilles heel in prostate cancer (PCa) that can be exploited for therapy. CPT1A regulates the entry of fatty acids into the mitochondria for beta-oxidation and its inhibition has been shown to decrease PCa growth. In this study, we examined the pharmacological blockade of lipid oxidation with ranolazine in TRAMPC1 PCa models. Oral administration of ranolazine (100 mg/Kg for 21 days) resulted in decreased tumor CD8+ T-cells Tim3 content, increased macrophages, and decreased blood myeloid immunosuppressive monocytes. Using multispectral staining, drug treatments increased infiltration of CD8+ T-cells and dendritic cells compared to vehicle. Functional studies with spleen cells of drug-treated tumors co-cultured with TRAMPC1 cells showed increased ex vivo T-cell cytotoxic activity, suggesting an anti-tumoral response. Lastly, a decrease in CD4+ and CD8+ T-cells expressing PD1 was observed when exhausted spleen cells were incubated with TRAMPC1 Cpt1a-KD compared to the control cells. These data indicated that genetically blocking the ability of the tumor cells to oxidize lipid can change the activation status of the neighboring T-cells. This study provides new knowledge of the role of lipid catabolism in the intercommunication of tumor and immune cells, which can be extrapolated to other cancers with high CPT1A expression.

CPT1A Over-Expression Increases Reactive Oxygen Species in the Mitochondria and Promotes Antioxidant Defenses in Prostate Cancer.

Cancers reprogram their metabolism to adapt to environmental changes. In this study, we examined the consequences of altered expression of the mitochondrial enzyme carnitine palmitoyl transferase I (CPT1A) in prostate cancer (PCa) cell models. Using transcriptomic and metabolomic analyses, we compared LNCaP-C4-2 cell lines with depleted (knockdown (KD)) or increased (overexpression (OE)) CPT1A expression. Mitochondrial reactive oxygen species (ROS) were also measured. Transcriptomic analysis identified ER stress, serine biosynthesis and lipid catabolism as significantly upregulated pathways in the OE versus KD cells. On the other hand, androgen response was significantly downregulated in OE cells. These changes associated with increased acyl-carnitines, serine synthesis and glutathione precursors in OE cells. Unexpectedly, OE cells showed increased mitochondrial ROS but when challenged with fatty acids and no androgens, the Superoxide dismutase 2 (SOD2) enzyme increased in the OE cells, suggesting better antioxidant defenses with excess CPT1A expression. Public databases also showed decreased androgen response correlation with increased serine-related metabolism in advanced PCa. Lastly, worse progression free survival was observed with increased lipid catabolism and decreased androgen response. Excess CPT1A is associated with a ROS-mediated stress phenotype that can support PCa disease progression. This study provides a rationale for targeting lipid catabolic pathways for therapy in hormonal cancers.