Hughes, Alysia

Abstract Description Evidence consistent with immunoediting in human cancer has been identified through associative studies. To provide direct evidence that differences in the neoantigen profile are immune-mediated, we compared the neoantigen profiles of cutaneous squamous cell carcinoma (cSCC) from immunocompetent and immunosuppressed patients. Despite consistency in the overall mutational burden and signature, immunocompetent patients have a lower clonal mutational burden compared to immunosuppressed patients. Furthermore, clonal mutations in cSCC from immunocompetent patients have a lower proportion of neoantigens predicted to bind MHC class I, supporting the role of the immune system in shaping the neoantigen profile. In single-cell RNA sequencing of intratumoral CD8+ T cells from a separate cohort of cSCC from immunocompetent and immunosuppressed patients, we observe an increase in exhausted CD8+ T cells in tumors from immunocompetent patients, suggesting an explanation for the persistence of binding neoantigens subclonally. Finally, we compare clonal and subclonal neoantigens as an unbiased screen for characteristics associated with neoantigen immunogenicity. Neoantigens with a large increase in predicted MHC class I binding relative to the normal peptide are enriched in subclonal neoantigens in immunocompetent patients. Overall, this work supports the immune system’s role in sculpting the neoantigen profile of cSCC. Funding Sources Supported by American Cancer Society - Simone Charitable Foundation - Discovery Boost Grant, AZ-DBG-23-1151311-01-IBCD; NIH F30CA281056; R35GM149235 Topic Categories Tumor Immunology: Cellular Responses and Tumor Microevironment (TIME)

Background Challenges in predicting which neoantigens mediate tumor rejection limit the efficacy of neoantigen vaccines to treat cancers, especially for cancers with a high mutational burden like cutaneous squamous cell carcinoma (cSCC). Only a small portion of neoantigens prioritized by current methods elicit effective T cell responses, demonstrating the critical need for improved criteria for the prediction of tumor-rejecting neoantigens. Methods Publicly available human cSCC datasets were used to assess the frequency of shared mutations between patients. A transplantable ultraviolet light-induced mouse model of cSCC was generated. The mutational signature and driver mutations in the mouse model were compared with human tumors. Neoantigens were prioritized in the mouse model, and tumor-rejecting neoantigens were identified through (enzyme-linked immunosorbent spot (ELISpot) and in vivo prophylactic vaccination. Binding of the neoantigens and corresponding wild-type peptides to major histocompatibility complex (MHC) class I was determined. Structural modeling of peptide:MHC complexes was performed to assess for changes in structural characteristics of the neoantigens relative to the wild-type peptides. Results A minority of human cSCC tumors shared neoantigens. The mouse cSCC model recapitulated the mutational signature and driver mutations found in human disease and was constrained by CD8 T cells. Two MHC class I neoantigens were identified in the mouse model that constrained cSCC growth. One tumor-rejecting neoantigen exhibited improved MHC binding, and the other had increased solvent accessibility of the mutated residue, compared with wild-type. Across known neoantigens that do not impact MHC binding, increased exposure of the mutated residue distinguished tumor-rejecting from non-immunogenic neoantigens. Conclusions Given the paucity of shared mutations, this work supports the need for personalized neoantigen vaccines in cSCC. To facilitate further discovery, we provide a clinically relevant mouse cSCC model with two defined neoantigens that mediate tumor rejection. Structural changes in the exposure of features that promote T cell receptor recognition defined tumor-rejecting neoantigens. Incorporation of structural modeling to predict changes in T cell receptor accessibility is anticipated to improve the selection of neoantigens for inclusion in personalized cancer vaccines.

BACKGROUND There is a scarcity of artificial intelligence models trained on frozen pathology. One way to expand the clinical utility of models trained on permanent pathology is by applying them to frozen sections and fine-tune based on weaknesses. OBJECTIVE To qualitatively evaluate a deep learning model trained on permanent pathology to classify squamous cell carcinoma on Mohs surgery frozen sections to learn model shortcomings and inform retraining and fine-tuning. MATERIALS AND METHODS The authors trained a model for classification of tumor on 746 skin biopsy slides and tested it on 15 Mohs surgery frozen sections. The authors estimated performance metrics and compared the regions of interest generated by the model with the original H&E slides. RESULTS The model achieved an AUC-ROC of 0.985 and 0.796 for tumor classification in permanent pathology and in frozen sections, respectively. Regions of interest for frozen sections with scarce tumor areas were inaccurate, focusing on normal tissue for slides classified as false negative, or highlighting structures different from tumor (e.g., inflammation, muscle, and nerves) for slides classified as true positive. CONCLUSION Deep anatomical structures more commonly present in Mohs frozen pathology might represent data out-of-distribution for models trained on permanent pathology, potentially leading to inadequate model outputs.

Background: Subcutaneous lymphomas are a heterogeneous group of diseases with variable clinical behavior that ranges from a lymphoproliferative disorder to an aggressive cytotoxic lymphoma. Subcutaneous panniculitis-like T-cell lymphoma (SPTCL) is an uncommon, often indolent cutaneous lymphoma that localizes to the subcutaneous adipose tissue. These tumors exhibit an alpha/beta phenotype (abSPTCL). Primary cutaneous gamma-delta T-cell lymphoma (PCgdTCL) is a rare, highly aggressive lymphoma that frequently involves the subcutaneous adipose tissue. The goal of this study is to identify key inflammatory and oncogenic pathways in SPTCL and PCgdTCL to determine which mutational or expression patterns differentiate abSPTCL and PCgdTCL. A comprehensive catalog of cancer gene expression profiles generated via RNA sequencing on patient samples is essential to understand tumorigenesis and to develop effective therapies. Methods: We performed transcriptomic profiling analysis using RNA sequencing in lesional skin biopsies from patients of abSPTCL (n=10), PCgdTCL (n=9), and controls (n=5). Differential analyses were performed on the entire transcriptomic profiles among the 3 groups. Results: Although the transcriptomic profiles of abSPTCL and PCgdTCL samples had similarities, there were notable differences between these groups that may elucidate unique pathways that propagate each disease. In contrast to abSPTCL, PCgdTCL samples demonstrated upregulation of cell motility and proliferation, and downregulation of pathways involving apoptosis and tumor necrosis factor production. The most significantly upregulated gene pathways in both abSPTCL and PCgdTCL samples were those involving immune and inflammatory response, viral defense, type I interferon signaling, interferon-gamma-mediated signaling, and positive regulation of T cell proliferation. The most significantly downregulated gene pathways in the two groups were those involving cell adhesion, rRNA processing and translation, and extracellular matrix organization. The top 5 upregulated immune response and downregulated cell adhesion hub genes for abSPTCL highlighted by gene interaction network analysis included: TNF, CTLA4, CD86, IL10, CCR5 & ITGB5, ITGA8, VWF, APP, CDH5, respectively. Conclusion: Our transcriptomic profiling pinpoints disease-related enrichment pathways and gene functions in abSPTCL and PCgdTCL patients, which may shed light on molecular mechanisms that drive disease activity. Hub genes in abSPTCL represent potential drug targets for further development of novel therapies. Disclosures Mangold: Kirin:Membership on an entity's Board of Directors or advisory committees;Elorac:Research Funding;Sun Pharma:Research Funding;MiRagen:Research Funding;Solagenix:Research Funding.