Preclinical mouse models suggest that the gut microbiome modulates tumor response to checkpoint blockade immunotherapy; however, this has not been well-characterized in human cancer patients. Here we examined the oral and gut microbiome of melanoma patients undergoing anti–programmed cell death 1 protein (PD-1) immunotherapy (n = 112). Significant differences were observed in the diversity and composition of the patient gut microbiome of responders versus nonresponders. Analysis of patient fecal microbiome samples (n = 43, 30 responders, 13 nonresponders) showed significantly higher alpha diversity (P < 0.01) and relative abundance of bacteria of the Ruminococcaceae family (P < 0.01) in responding patients. Metagenomic studies revealed functional differences in gut bacteria in responders, including enrichment of anabolic pathways. Immune profiling suggested enhanced systemic and antitumor immunity in responding patients with a favorable gut microbiome as well as in germ-free mice receiving fecal transplants from responding patients. Together, these data have important implications for the treatment of melanoma patients with immune checkpoint inhibitors.

SUMMARY Treatment of cancer has been revolutionized by immune checkpoint blockade therapies. Despite the high rate of response in advanced melanoma, the majority of patients succumb to disease. To identify factors associated with success or failure of checkpoint therapy, we profiled transcriptomes of 16,291 individual immune cells from 48 tumor samples of melanoma patients treated with checkpoint inhibitors. Two distinct states of CD8+ T cells were defined by clustering, and associated with patient tumor regression or progression. A single transcription factor, TCF7, was visualized within CD8+ T cells in fixed tumor samples and predicted positive clinical outcome in an independent cohort of checkpoint-treated patients. We delineated the epigenetic landscape and clonality of these T cell states, and demonstrated enhanced anti-tumor immunity by targeting novel combinations of factors in exhausted cells. Our study of immune cell transcriptomes from tumors demonstrates a strategy for identifying predictors, mechanisms and targets for enhancing checkpoint immunotherapy.

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Immune checkpoint blockade represents a major breakthrough in cancer therapy, however responses are not universal. Genomic and immune features in pre-treatment tumor biopsies have been reported to correlate with response in patients with melanoma and other cancers, but robust biomarkers have not been identified. We studied a cohort of metastatic melanoma patients initially treated with cytotoxic T-lymphocyte-associated antigen-4 (CTLA-4) blockade (n=53) followed by programmed death-1 (PD-1) blockade at progression (n=46), and analyzed immune signatures in longitudinal tissue samples collected at multiple time points during therapy. In these studies, we demonstrate that adaptive immune signatures in tumor biopsy samples obtained early during the course of treatment are highly predictive of response to immune checkpoint blockade, and also demonstrate differential effects on the tumor microenvironment induced by CTLA-4 and PD-1 blockade. Importantly, potential mechanisms of therapeutic resistance to immune checkpoint blockade were also identified. Significance These studies demonstrate that adaptive immune signatures in early on-treatment tumor biopsies are predictive of response to checkpoint blockade, and yield insight into mechanisms of therapeutic resistance. These concepts have far-reaching implications in this age of precision medicine, and should be explored in immune checkpoint blockade treatment across cancer types.

Immune checkpoint blockade produces clinical benefit in many patients. However better biomarkers of response are still needed, and mechanisms of resistance remain incompletely understood. To address this, we recently studied a cohort of melanoma patients treated with sequential checkpoint blockade against cytotoxic T lymphocyte antigen-4 (CTLA-4) followed by programmed death receptor-1 (PD-1), and identified immune markers of response and resistance. Building on these studies, we performed deep molecular profiling including T-cell receptor sequencing (TCR-seq) and whole exome sequencing (WES) within the same cohort, and demonstrated that a more clonal T cell repertoire was predictive of response to PD-1 but not CTLA-4 blockade. Analysis of copy number alterations identified a higher burden of copy number loss in non-responders to CTLA-4 and PD-1 blockade and found that it was associated with decreased expression of genes in immune-related pathways. The effect of mutational load and burden of copy number loss on response was non-redundant, suggesting the potential utility of a combinatorial biomarker to optimize patient care with checkpoint blockade therapy.

Preclinical studies suggest that treatment with neoadjuvant immune checkpoint blockade is associated with enhanced survival and antigen-specific T cell responses over adjuvant treatment 1 ; however, optimal regimens have not been defined. Herein, we report results from a randomized phase II study of neoadjuvant nivolumab versus combined ipilimumab with nivolumab in 23 patients with high-risk resectable melanoma (NCT02519322). RECIST overall response rates (ORR), pathologic complete response rates (pCR), treatment-related adverse events (trAEs), and immune correlates of response were assessed. Treatment with combined ipilimumab and nivolumab yielded high response rates (RECIST ORR 73%, pCR 45%) but substantial toxicity (73% grade 3 trAEs), whereas treatment with nivolumab monotherapy yielded modest responses (ORR 25%, pCR 25%) and low toxicity (8% grade 3 trAEs). Immune correlates of response were identified, demonstrating higher lymphoid infiltrates in responders to both therapies and a more clonal and diverse T cell infiltrate in responders to nivolumab monotherapy. These results are the first to describe the feasibility of neoadjuvant immune checkpoint blockade in melanoma and emphasize the need for additional studies to optimize treatment regimens and to validate putative biomarkers.

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