Tumor cell-intrinsic PD-1 promotes Merkel cell carcinoma growth by activating downstream mTOR-mitochondrial ROS signaling.

Merkel cell carcinoma (MCC) is a rare and aggressive skin cancer. Inhibitors targeting the programmed cell death 1 (PD-1) immune checkpoint have improved MCC patient outcomes by boosting antitumor T cell immunity. Here, we identify PD-1 as a growth-promoting receptor intrinsic to MCC cells. In human MCC lines and clinical tumors, RT-PCR-based sequencing, immunoblotting, flow cytometry, and immunofluorescence analyses demonstrated PD-1 gene and protein expression by MCC cells. MCC-PD-1 ligation enhanced, and its inhibition or silencing suppressed, in vitro proliferation and in vivo tumor xenograft growth. Consistently, MCC-PD-1 binding to PD-L1 or PD-L2 induced, while antibody-mediated PD-1 blockade inhibited, protumorigenic mTOR signaling, mitochondrial (mt) respiration, and ROS generation. Last, pharmacologic inhibition of mTOR or mtROS reversed MCC-PD-1:PD-L1-dependent proliferation and synergized with PD-1 checkpoint blockade in suppressing tumorigenesis. Our results identify an MCC-PD-1-mTOR-mtROS axis as a tumor growth-accelerating mechanism, the blockade of which might contribute to clinical response in patients with MCC.

Inhibition of Melanoma Cell-Intrinsic Tim-3 Stimulates MAPK-Dependent Tumorigenesis.

T-cell immunoglobulin mucin family member 3 (Tim-3) is an immune checkpoint receptor that dampens effector functions and causes terminal exhaustion of cytotoxic T cells. Tim-3 inhibitors are under investigation in immuno-oncology (IO) trials, because blockade of T-cell-Tim-3 enhances antitumor immunity. Here, we identify an additional role for Tim-3 as a growth-suppressive receptor intrinsic to melanoma cells. Inhibition of melanoma cell-Tim-3 promoted tumor growth in both immunocompetent and immunocompromised mice, while melanoma-specific Tim-3 overexpression attenuated tumorigenesis. Ab-mediated Tim-3 blockade inhibited growth of immunogenic murine melanomas in T-cell-competent hosts, consistent with established antitumor effects of T-cell-Tim-3 inhibition. In contrast, Tim-3 Ab administration stimulated tumorigenesis of both highly and lesser immunogenic murine and human melanomas in T-cell-deficient mice, confirming growth-promoting effects of melanoma-Tim-3 antagonism. Melanoma-Tim-3 activation suppressed, while its blockade enhanced, phosphorylation of pro-proliferative downstream MAPK signaling mediators. Finally, pharmacologic MAPK inhibition reversed unwanted Tim-3 Ab-mediated tumorigenesis in T-cell-deficient mice and enhanced desired antitumor activity of Tim-3 interference in T-cell-competent hosts. These results identify melanoma-Tim-3 blockade as a mechanism that antagonizes T-cell-Tim-3-directed IO therapeutic efficacy. They further reveal MAPK targeting as a combination strategy for circumventing adverse consequences of unintended melanoma-Tim-3 inhibition. SIGNIFICANCE: Tim-3 is a growth-suppressive receptor intrinsic to melanoma cells, the blockade of which promotes MAPK-dependent tumorigenesis and thus counteracts antitumor activity of T-cell-directed Tim-3 inhibition.

Distinct antibody clones detect PD-1 checkpoint expression and block PD-L1 interactions on live murine melanoma cells.

Monoclonal antibodies (abs) targeting the programmed cell death 1 (PD-1) immune checkpoint pathway have revolutionized tumor therapy. Because T-cell-directed PD-1 blockade boosts tumor immunity, anti-PD-1 abs have been developed for examining T-cell-PD-1 functions. More recently, PD-1 expression has also been reported directly on cancer cells of various etiology, including in melanoma. Nevertheless, there is a paucity of studies validating anti-PD-1 ab clone utility in specific assay types for characterizing tumor cell-intrinsic PD-1. Here, we demonstrate reactivity of several anti-murine PD-1 ab clones and recombinant PD-L1 with live B16-F10 melanoma cells and YUMM lines using multiple independent methodologies, positive and negative PD-1-specific controls, including PD-1-overexpressing and PD-1 knockout cells. Flow cytometric analyses with two separate anti-PD-1 ab clones, 29F.1A12 and RMP1-30, revealed PD-1 surface protein expression on live murine melanoma cells, which was corroborated by marked enrichment in PD-1 gene (Pdcd1) expression. Immunoblotting, immunoprecipitation, and mass spectrometric sequencing confirmed PD-1 protein expression by B16-F10 cells. Recombinant PD-L1 also recognized melanoma cell-expressed PD-1, the blockade of which by 29F.1A12 fully abrogated PD-1:PD-L1 binding. Together, our data provides multiple lines of evidence establishing PD-1 expression by live murine melanoma cells and validates ab clones and assay systems for tumor cell-directed PD-1 pathway investigations.

Joint reconstruction and classification of tumor cells and cell interactions in melanoma tissue sections with synthesized training data.

PURPOSE: Cancers are almost always diagnosed by morphologic features in tissue sections. In this context, machine learning tools provide new opportunities to describe tumor immune cell interactions within the tumor microenvironment and thus provide phenotypic information that might be predictive for the response to immunotherapy. METHODS: We develop a machine learning approach using variational networks for joint image denoising and classification of tissue sections for melanoma, which is an established model tumor for immuno-oncology research. The manual annotation of real training data would require substantial user interaction of experienced pathologists for each single training image, and the training of larger networks would rely on a very large number of such data sets with ground truth annotation. To overcome this bottleneck, we synthesize training data together with a proper tissue structure classification. To this end, a stochastic data generation process is used to mimic cell morphology, cell distribution and tissue architecture in the tumor microenvironment. Particular components of this tool are random placement and rotation of a large number of patches for presegmented cell nuclei, a stochastic fast marching approach to mimic the geometry of cells and texture generation based on a color covariance analysis of real data. Here, the generated training data reflect a large range of interaction patterns. RESULTS: In several applications to histological tissue sections, we analyze the efficiency and accuracy of the proposed approach. As a result, depending on the scenario considered, almost all cells and nuclei which ought to be detected are actually marked as classified and hardly any misclassifications occur. CONCLUSIONS: The proposed method allows for a computer-aided screening of histological tissue sections utilizing variational networks with a particular emphasis on tumor immune cell interactions and on the robust cell nuclei classification.