A Population of Tumor-Infiltrating CD4+ T Cells Co-Expressing CD38 and CD39 Is Associated with Checkpoint Inhibitor Resistance.

PURPOSE: We previously showed that elevated frequencies of peripheral blood CD3+CD4+CD127-GARP-CD38+CD39+ T cells were associated with checkpoint immunotherapy resistance in patients with metastatic melanoma. In the present study, we sought to further investigate this population of ectoenzyme-expressing T cells (Teee). EXPERIMENTAL DESIGN: Teee derived from the peripheral blood of patients with metastatic melanoma were evaluated by bulk RNA-sequencing (RNA-seq) and flow cytometry. The presence of Teee in the tumor microenvironment was assessed using publically available single-cell RNA-seq datasets of melanoma, lung, and bladder cancers along with multispectral immunofluorescent imaging of melanoma patient formalin-fixed, paraffin-embedded specimens. Suppressive function of Teee was determined by an in vitro autologous suppression assay. RESULTS: Teee had phenotypes associated with proliferation, apoptosis, exhaustion, and high expression of inhibitory molecules. Cells with a Teee gene signature were present in tumors of patients with melanoma, lung, and bladder cancers. CD4+ T cells co-expressing CD38 and CD39 in the tumor microenvironment were preferentially associated with Ki67- CD8+ T cells. Co-culture of patient Teee with autologous T cells resulted in decreased proliferation of target T cells. High baseline intratumoral frequencies of Teee were associated with checkpoint immunotherapy resistance and poor overall survival in patients with metastatic melanoma. CONCLUSIONS: These results demonstrate that a novel population of CD4+ T cells co-expressing CD38 and CD39 is found both in the peripheral blood and tumor of patients with melanoma and is associated with checkpoint immunotherapy resistance.

CD4 Phenotypes Are Associated with Reduced Expansion of Tumor-Infiltrating Lymphocytes in Melanoma Patients Treated with Adoptive Cell Therapy.

Tumor-infiltrating lymphocyte (TIL) adoptive cell therapy is effective in treating malignant melanoma, but its success relies on the adequate ex vivo expansion of TIL. To assess correlates of TIL expansion, CD4+ and CD8+ TIL were analyzed by RNA sequencing (RNA-seq) and chromatin immunoprecipitation sequencing of acetylated histone 3. Patients were grouped into "TIL high" and "TIL low" based on division at the median number of TIL infused. Greater numbers of TIL infused correlated with longer overall survival, and increased frequencies of CD4+ cells infused were negatively correlated with the number of TIL infused. RNA-seq analysis of CD4+ TIL showed increases in Th2/Th17/regulatory T cell-related transcripts and pathways in the TIL-low group. Analysis of a public single-cell RNA-seq dataset validated findings that increased frequencies of CD4+ cells were negatively correlated with the number of TIL infused. TIL-low patients had significantly increased frequencies of CD4+ cells expressing ETS2 and OSM and trended toward increased expression of TNFRSF18.

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.