Faculty Bibliography

PURPOSE/UNASSIGNED:Cancer treatment has been revolutionized by immune checkpoint inhibitors (ICI). However, a subset of patients do not respond and/or they experience significant adverse events. Attempts to integrate reliable biomarkers of ICI response as part of standard care have been hampered by limited generalizability. We previously reported our supervised machine learning (ML) model in a retrospective cohort of metastatic melanoma. EXPERIMENTAL DESIGN/UNASSIGNED:In this study, we expanded our testing to include larger cohorts of patients with melanoma accrued at several sites, including patients enrolled in clinical trials in both adjuvant and metastatic settings. We examined pretreatment hematoxylin and eosin slides from 639 patients with stage III/IV melanoma treated with ICIs [anti-cytotoxic T-lymphocyte-associated protein 4 (n = 212), anti-programmed death 1 (n = 271), or the combination (n = 156)]. We tested the generalizability of our supervised ML algorithm to predict response to ICIs in the metastatic melanoma cohort and then developed a self-supervised ML model to identify the histologic morphologies associated with patients' survival following ICI use in adjuvant and metastatic melanoma cohorts. RESULTS/UNASSIGNED:We predicted the response to ICI treatment with an AUC of 0.72. The deep convolutional neural network classified patients into high and low risk based on their likelihood of progression-free survival (P < 0.0001). We uncovered a novel association of specific histomorphologic tumor features-epithelioid histology and a low tumor-stroma ratio-with survival following ICI treatment. CONCLUSIONS/UNASSIGNED:Our data support the generalizability of our developed ML algorithm in predicting response to ICI treatment in patients with metastatic unresectable melanoma. We also showed, for the first time, tumor features associated with patients' overall survival.

Aberrant glycosylation is a hallmark of cancer biology, and altered glycosylation influences multiple facets of melanoma progression. To identify glycosyltransferases, glycans, and glycoproteins essential for melanoma maintenance, we conducted an in vivo growth screen with a pooled short hairpin RNA library of glycosyltransferases, lectin microarray profiling of benign nevus and melanoma samples, and mass spectrometry-based glycoproteomics. We found that α-2,3-sialyltransferases ST3GAL1 and ST3GAL2 and corresponding α-2,3-linked sialosides are up-regulated in melanoma compared to nevi and are essential for melanoma growth. Glycoproteomics revealed that glycoprotein targets of ST3GAL1 and ST3GAL2 are enriched in transmembrane proteins involved in growth signaling, including the amino acid transporter SLC3A2/CD98hc. CD98hc suppression mimicked the effect of ST3GAL1 and ST3GAL2 silencing, inhibiting melanoma cell proliferation. We found that both CD98hc protein stability and its prosurvival effect on melanoma are dependent upon α-2,3-sialylation mediated by ST3GAL1 and ST3GAL2. Our studies reveal α-2,3-sialosides functionally contributing to melanoma maintenance, supporting ST3GAL1 and ST3GAL2 as therapeutic targets in melanoma.

UNLABELLED:Melanoma being one of the most common and deadliest skin cancers has been increasing since the past decade. Patients at advanced stages of the disease have very poor prognoses, as opposed to at the earlier stages. Nowadays, the standard of care of advanced melanoma is resection, followed by immune checkpoint inhibition-based immunotherapy. However, a substantial proportion of patients either do not respond or develop resistance. This underscores a need for novel approaches and therapeutic targets as well as a better understanding of the mechanisms of melanoma pathogenesis. Long noncoding RNAs (lncRNA) comprise a poorly characterized class of functional players and promising targets in promoting malignancy. Certain lncRNAs have been identified to play integral roles in melanoma progression and drug resistance; however, systematic screens to uncover novel functional lncRNAs are scarce. In this study, we profile differentially expressed lncRNAs in patient-derived short-term metastatic cultures and BRAF-MEK inhibition-resistant cells. We conduct a focused growth-related CRISPR inhibition screen of overexpressed lncRNAs, validate, and functionally characterize lncRNA hits with respect to cellular growth, invasive capacities, and apoptosis in vitro as well as the transcriptomic impact of our lead candidate the novel lncRNA XLOC_030781. In sum, we extend the current knowledge of ncRNAs and their potential relevance in melanoma. SIGNIFICANCE/UNASSIGNED:LncRNAs have emerged as novel players in regulating many cellular aspects also in melanoma. The number of functional significances of most lncRNAs remains elusive. We provide a comprehensive strategy to identify functionally relevant lncRNAs in melanoma by combining expression profiling with CRISPR inhibition growths screens. Our results broaden the characterized lncRNAs as potential targets for future therapeutic applications.

IMPORTANCE/UNASSIGNED:Tumor-infiltrating lymphocytes (TILs) are a provocative biomarker in melanoma, influencing diagnosis, prognosis, and immunotherapy outcomes; however, traditional pathologist-read TIL assessment on hematoxylin and eosin-stained slides is prone to interobserver variability, leading to inconsistent clinical decisions. Therefore, development of newer TIL scoring approaches that produce more reliable and consistent readouts is important. OBJECTIVE/UNASSIGNED:To evaluate the analytical and clinical validity of a machine learning algorithm for TIL quantification in melanoma compared with traditional pathologist-read methods. DESIGN, SETTING, AND PARTICIPANTS/UNASSIGNED:This multioperator, global, multi-institutional prognostic study compared TIL scoring reproducibility between traditional pathologist-read methods and an artificial intelligence (AI)-driven approach. The study was conducted using retrospective cohorts of patients with melanoma between January 2022 and June 2023 across 45 institutions, with tissue evaluated by participants from academic, clinical, and research institutions. Participants were selected to ensure diverse expertise and professional backgrounds. MAIN OUTCOMES AND MEASURES/UNASSIGNED:Intraclass correlation coefficient (ICC) values were calculated for the manual and AI-assisted arms using log-transformed data. Kendall W values were calculated for Clark scores (brisk = 3, nonbrisk = 2, and sparse = 1). Reliabilities of ICC and W values were classified as moderate (0.40-0.60), good (0.61-0.80), or excellent (>0.80). AI TIL measurements were dichotomized using the 16.6 and median cutoffs. Univariable and multivariable Cox regression analyses assessed the prognostic value of TIL scores adjusted for clinicopathologic variables. RESULTS/UNASSIGNED:There were 111 patients with melanoma in the independent testing cohort (median [range] age at diagnosis, 61.0 [25.0-87.0] years; 56 [50.5%] male) who contributed melanoma whole tissue sections. A total of 98 participants evaluated TILs on 60 hematoxylin and eosin-stained melanoma tissue sections. All 40 participants in the manual arm were pathologists, while the AI-assisted arm included 11 pathologists and 47 nonpathologists (scientists). The AI algorithm demonstrated superior reproducibility, with ICCs higher than 0.90 for all machine learning TIL variables, significantly outperforming manual assessments (ICC, 0.61 for AI-derived stromal TILs vs Kendall W, 0.44 for manual Clark TIL scoring). AI-based TIL scores showed prognostic associations with patient outcomes (n = 111) using the median cutoff approach with a hazard ratio (HR) of 0.45 (95% CI, 0.26-0.80; P = .005), and using the cutoff of 16.6, with an HR of 0.56 (95% CI, 0.32-0.98; P = .04). CONCLUSIONS AND RELEVANCE/UNASSIGNED:In this prognostic study of TIL quantification in melanoma, the AI algorithm demonstrated superior reproducibility and prognostic associations compared with traditional methods. Although the retrospective nature of the cohorts limits demonstration of clinical utility, the publicly available dataset and open-source AI tool offer a foundation for future validation and integration into melanoma management.

Targeted therapies and immunotherapy have improved treatment outcomes for many melanoma patients. However, patients whose melanomas harbor driver mutations in the neurofibromin 1 (NF1) tumor suppressor gene often lack effective targeted treatment options when their tumors do not respond to immunotherapy. In this study, we utilized patient-derived short-term cultures (STCs) and multiomics approaches to identify molecular features that could inform the development of therapies for patients with NF1 mutant melanoma. Differential gene expression analysis revealed that the epidermal growth factor receptor (EGFR) is highly expressed and active in NF1 mutant melanoma cells, where it hyper-activates ERK and AKT, leading to increased tumor cell proliferation, survival, and growth. In contrast, genetic or pharmacological inhibition of EGFR hindered cell proliferation and survival and suppressed tumor growth in patient-derived NF1 mutant melanoma models but not in NF1 wild-type models. These results reveal a connection between NF1 loss and increased EGFR expression that is critical for the survival and growth of NF1 mutant melanoma cells in patient-derived culture and xenograft models, irrespective of their BRAF and NRAS mutation status.

Response to immune checkpoint inhibitors (ICIs) in metastatic melanoma (MM) varies among patients, and current baseline biomarkers predicting treatment outcomes are limited. As mitochondrial (MT) metabolism has emerged as an important regulator of host immune function, we explored the association of host MT genetics (MT haplogroups) with ICI efficacy in 1,225 ICI-treated patients with MM from the clinical trial CheckMate-067 and the International Germline Immuno-Oncology Melanoma Consortium. We discovered and validated significant associations of MT haplogroup T (HG-T) with resistance to anti-programmed cell death protein-1-based ICI (both single-agent and combination) and have shown that HG-T is independent from established tumor predictors. We also found that patients belonging to HG-T exhibit a unique nivolumab-resistant baseline peripheral CD8⁺ T cell repertoire compared to other MT haplogroups, providing, to our knowledge, the first link between MT inheritance, host immunity and ICI resistance. The study proposes a host blood-based biomarker with stand-alone clinical value predicting ICI efficacy and points to an ICI-resistance mechanism associated with MT metabolism, with clinical relevance in immuno-oncology.

PURPOSE/OBJECTIVE:Patients with stage II and III cutaneous primary melanoma vary considerably in their risk of melanoma-related death. We explore the ability of methylation profiling to distinguish primary melanoma methylation classes and their associations with clinicopathologic characteristics and survival. MATERIALS AND METHODS/METHODS:InterMEL is a retrospective case-control study that assembled primary cutaneous melanomas from American Joint Committee on Cancer (AJCC) 8th edition stage II and III patients diagnosed between 1998 and 2015 in the United States and Australia. Cases are patients who died of melanoma within 5 years from original diagnosis. Controls survived longer than 5 years without evidence of melanoma recurrence or relapse. Methylation classes, distinguished by consensus clustering of 850K methylation data, were evaluated for their clinicopathologic characteristics, 5-year survival status, and differentially methylated gene sets. RESULTS:= .007). CIMP and IM had a 2-fold higher likelihood of 5-year death from melanoma than LM (CIMP odds ratio [OR], 2.16 [95% CI, 1.18 to 3.96]; IM OR, 2.00 [95% CI, 1.12 to 3.58]) in a multivariable model adjusted for age, sex, log Breslow thickness, ulceration, mitotic index, and N stage. Despite more extensive CpG island hypermethylation in CIMP, CIMP and IM shared similar patterns of differential methylation and gene set enrichment compared with LM. CONCLUSION/CONCLUSIONS:Melanoma MethylClasses may provide clinical value in predicting 5-year death from melanoma among patients with primary melanoma independent of other clinicopathologic factors.

PURPOSE/UNASSIGNED:Immune checkpoint inhibition (ICI) shows benefits in adjuvant (AT) and neoadjuvant melanoma treatments. However, ICI frequently induces severe immune-related adverse events (irAE). Unlike metastatic disease, in which irAEs are a clinical trade-off for treatment that improves survival, the toxicity burden from ICI in the AT setting is a substantial clinical problem urging for irAE-predictive biomarkers. EXPERIMENTAL DESIGN/UNASSIGNED:We assessed postsurgical, pre-ICI treatment peripheral CD4+ and CD8+ T cells from clinical trial patients (CheckMate 915) treated with AT nivolumab (n = 130) or ipilimumab/nivolumab (COMBO, n = 82). Performing RNA sequencing differential gene expression analysis, we tested baseline differences associated with severe (grades 3-5) irAEs and constructed an irAE-predictive model using least absolute shrinkage and selection operator-regularized logistic regression. RESULTS/UNASSIGNED:The analysis of predicted protein-protein interactions among differentially expressed genes in peripheral CD4+ cells revealed significant enrichment of the spleen tyrosine kinase (SYK) pathway, associated with severe irAEs in COMBO-treated patients. This gene expression signature predicted severe-irAE COMBO patients (χ2P value = 0.001) with 73% accuracy and was independent of disease recurrence (P = 0.79). The irAE-predictive model incorporating this gene expression signature demonstrated 82% accuracy (χ2P value = 8.91E-06). CONCLUSIONS/UNASSIGNED:We identified baseline gene expression differences in key immune pathways of peripheral blood T cells from COMBO-treated patients with grades 3 to 5 irAEs and defined a SYK-related gene signature correctly identifying ∼60% of COMBO-treated patients with grades 3 to 5 irAEs. This finding aligns with our previous work linking anti-CTLA4 irAEs with a germline variant associated with high SYK expression. This gene signature may serve as a baseline biomarker of severe grade 3 to 5 irAE risk, which is especially important in AT treatment.

Diverse cellular insults converge on activation of the heat shock factor 1 (HSF1), which regulates the proteotoxic stress response to maintain protein homoeostasis. HSF1 regulates numerous gene programmes beyond the proteotoxic stress response in a cell-type- and context-specific manner to promote malignancy. However, the role(s) of HSF1 in immune populations of the tumour microenvironment remain elusive. Here, we leverage an in vivo model of HSF1 activation and single-cell transcriptomic tumour profiling to show that augmented HSF1 activity in natural killer (NK) cells impairs cytotoxicity, cytokine production and subsequent anti-tumour immunity. Mechanistically, HSF1 directly binds and regulates the expression of key mediators of NK cell effector function. This work demonstrates that HSF1 regulates the immune response under the stress conditions of the tumour microenvironment. These findings have important implications for enhancing the efficacy of adoptive NK cell therapies and for designing combinatorial strategies including modulators of NK cell-mediated tumour killing.