Faculty Bibliography

There is currently no targeted therapy to treat NF1-mutant melanomas. Herein, we compared the genomic and transcriptomic signatures of NF1-mutant and NF1-WT melanoma to reveal potential treatment targets for this subset of patients. Genomic alterations were verified using qPCR, and differentially expressed genes were independently validated using TCGA data, and immunohistochemistry (IHC). Digital spatial profiling (DSP) with multiplex IHC and immunofluorescence (IF) were used to validate the signatures. The efficacy of combinational regimens driven by these signatures was tested through in vitro assays using low-passage cell lines. Pathogenic NF1 mutations were identified in 27% cases. NF1-mutant melanoma expressed higher proliferative markers MK167 and CDC20 compared to NF1-WT (P=0.008), which was independently validated both in the TCGA dataset (P=0.01, P=0.03) and with IHC (P=0.013, P=0.036), respectively. DSP analysis showed upregulation of LY6E within the tumor cells [FDR<0.01, lg2FC>1], confirmed with multiplex IF showing co-localization of LY6E in melanoma cells. The combination of MEK and CDC20 co-inhibition induced both cytotoxic and cytostatic effects, decreasing CDC20 expression in multiple NF1-MUT cell lines. In conclusion, NF1-mutant melanoma is associated with a distinct genomic and transcriptomic profile. Our data support investigating CDC20 inhibition with MAPK pathway inhibitors as a targeted regimen in this melanoma subtype.

Background: The high mortality of cutaneous melanoma (CM) is partly due to unpredictable patterns of disease progression in patients with early-stage lesions. The reliable prediction of advanced disease risk from early-stage CM, is an urgent clinical need, especially given the recent expansion of immune checkpoint inhibitor therapy to the adjuvant setting. In our study, we comprehensively investigated the role of germline variants as CM prognostic markers. Methods: We performed a genome-wide association analysis in two independent cohorts of N=551 (discovery), and N=550 (validation) early-stage immunotherapy-naïve melanoma patients. A multivariable Cox proportional hazard regression model was used to identify associations with overall survival in the discovery group, followed by a validation analysis. Transcriptomic profiling and survival analysis were used to elucidate the biological relevance of candidate genes associated with CM progression. Results: We found two independent associations of germline variants with melanoma prognosis. The alternate alleles of these two SNPs were both associated with an increased risk of death [rs60970102 in MELK: HR=3.14 (2.05"“4.81), p=1.48×10^-7; and rs77480547 in SH3BP4: HR=3.02 (2.02"“4.52), p=7.58×10^-8, both in the pooled cohort]. The addition of the combined risk alleles (CRA) of the identified variants into the prognostic model improved the predictive power, as opposed to a model of clinical covariates alone. Conclusions: Our study provides suggestive evidence of novel melanoma germline prognostic markers, implicating two candidate genes: an oncogene MELK and a tumor suppressor SH3BP4, both previously suggested to affect CM progression. Pending further validation, these findings suggest that the genetic factors may improve the prognostic stratification of high-risk early-stage CM patients, and propose putative biological insights for potential therapeutic investigation of these targets to prevent aggressive outcome from early-stage melanoma.

INTRODUCTION:We are conducting a multicenter study to identify classifiers predictive of disease-specific survival in patients with primary melanomas. Here we delineate the unique aspects, challenges, and best practices for optimizing a study of generally small-sized pigmented tumor samples including primary melanomas of at least 1.05mm from AJTCC TNM stage IIA-IIID patients. We also evaluated tissue-derived predictors of extracted nucleic acids' quality and success in downstream testing. This ongoing study will target 1,000 melanomas within the international InterMEL consortium. METHODS:Following a pre-established protocol, participating centers ship formalin-fixed paraffin embedded (FFPE) tissue sections to Memorial Sloan Kettering Cancer Center for the centralized handling, dermatopathology review and histology-guided coextraction of RNA and DNA. Samples are distributed for evaluation of somatic mutations using next gen sequencing (NGS) with the MSK-IMPACTTM assay, methylation-profiling (Infinium MethylationEPIC arrays), and miRNA expression (Nanostring nCounter Human v3 miRNA Expression Assay). RESULTS:Sufficient material was obtained for screening of miRNA expression in 683/685 (99%) eligible melanomas, methylation in 467 (68%), and somatic mutations in 560 (82%). In 446/685 (65%) cases, aliquots of RNA/DNA were sufficient for testing with all three platforms. Among samples evaluated by the time of this analysis, the mean NGS coverage was 249x, 59 (18.6%) samples had coverage below 100x, and 41/414 (10%) failed methylation QC due to low intensity probes or insufficient Meta-Mixed Interquartile (BMIQ)- and single sample (ss)- Noob normalizations. Six of 683 RNAs (1%) failed Nanostring QC due to the low proportion of probes above the minimum threshold. Age of the FFPE tissue blocks (p<0.001) and time elapsed from sectioning to co-extraction (p = 0.002) were associated with methylation screening failures. Melanin reduced the ability to amplify fragments of 200bp or greater (absent/lightly pigmented vs heavily pigmented, p<0.003). Conversely, heavily pigmented tumors rendered greater amounts of RNA (p<0.001), and of RNA above 200 nucleotides (p<0.001). CONCLUSION:Our experience with many archival tissues demonstrates that with careful management of tissue processing and quality control it is possible to conduct multi-omic studies in a complex multi-institutional setting for investigations involving minute quantities of FFPE tumors, as in studies of early-stage melanoma. The study describes, for the first time, the optimal strategy for obtaining archival and limited tumor tissue, the characteristics of the nucleic acids co-extracted from a unique cell lysate, and success rate in downstream applications. In addition, our findings provide an estimate of the anticipated attrition that will guide other large multicenter research and consortia.

BACKGROUND:Recent data suggest that patients with stage III melanoma are at high risk for developing central nervous system (CNS) metastases. Because a subset of patients with stage II melanoma experiences worse survival outcomes than some patients with stage III disease, the authors investigated the risk of CNS metastasis in stage II melanoma to inform surveillance guidelines for this population. METHODS:test, the cumulative incidence, and Cox multivariable regression analyses were performed to evaluate the association between baseline characteristics and the development of CNS metastases. RESULTS:Patients with stage III melanoma had a higher rate of developing brain metastases than those with stage II melanoma (100 of 468 patients [21.4%] vs. 82 of 586 patients [14.0%], respectively; p = .002). However, patients who had stage IIC melanoma had a significantly higher rate of isolated first recurrences in the CNS compared with those who had stage III disease (12.1% vs. 3.6%; p = .002). The risk of ever developing brain metastases was similarly elevated for patients who had stage IIC disease (hazard ratio [HR], 3.16; 95% CI, 1.77-5.66), stage IIIB disease (HR, 2.83; 95% CI, 1.63-4.91), and stage IIIC disease (HR, 2.93; 95% CI, 1.81-4.74), and the risk was highest in patients who had stage IIID disease (HR, 8.59; 95% CI: 4.11-17.97). CONCLUSIONS:Patients with stage IIC melanoma are at elevated risk for first recurrence in the CNS. Surveillance strategies that incorporate serial neuroimaging should be considered for these individuals until more accurate predictive markers can be identified.

Survival outcomes in melanoma, and their association with mutations in the telomerase reverse transcriptase (TERT) promoter, remain uncertain. In addition, few studies have examined whether these associations are affected by a nearby common germline polymorphism, or vary based on melanoma histopathological subtype. We analyzed 408 primary tumors from a prospective melanoma cohort for somatic TERT^-124[C>T] and TERT^-146[C>T] mutations, the germline polymorphism rs2853669, and BRAF^V600 and NRAS^Q61 mutations. We tested the associations between these variants and clinicopathologic factors and survival outcomes. TERT^-124[C>T] was associated with thicker tumors, ulceration, mitoses (>0/mm²), nodular histotype and CNS involvement. In a multivariable model controlling for AJCC stage, TERT^-124[C>T] was an independent predictor of shorter recurrence-free survival (RFS) (HR=2.58, p=0.001), and overall survival (HR=2.47, p=0.029). Patients with the germline variant and TERT^-124[C>T] mutant melanomas had significantly shorter RFS than those patients lacking either or both sequence variants (p<0.04). The impact of the germline variant appeared to be more pronounced in superficial spreading compared to nodular melanoma. No associations were found between survival and TERT^-146[C>T], BRAF or NRAS mutations. These findings strongly suggest that TERT^-124[C>T] mutation is a biomarker of aggressive primary melanomas, an effect that may be modulated by rs2853669.

PURPOSE:Adjuvant immunotherapy produces durable benefit for patients with resected melanoma, but many develop recurrence and/or immune-related adverse events (irAE). We investigated whether baseline serum autoantibody (autoAb) signatures predicted recurrence and severe toxicity in patients treated with adjuvant nivolumab, ipilimumab, or ipilimumab plus nivolumab. EXPERIMENTAL DESIGN:This study included 950 patients: 565 from CheckMate 238 (408 ipilimumab versus 157 nivolumab) and 385 from CheckMate 915 (190 nivolumab versus 195 ipilimumab plus nivolumab). Serum autoAbs were profiled using the HuProt Human Proteome Microarray v4.0 (CDI Laboratories, Mayaguez, PR). Analysis of baseline differentially expressed autoAbs was followed by recurrence and severe toxicity signature building for each regimen, testing of the signatures, and additional independent validation for nivolumab using patients from CheckMate 915. RESULTS:In the nivolumab independent validation cohort, high recurrence score predicted significantly worse recurrence-free survival [RFS; adjusted HR (aHR), 3.60; 95% confidence interval (CI), 1.98-6.55], and outperformed a model composed of clinical variables including PD-L1 expression (P < 0.001). Severe toxicity score was a significant predictor of severe irAEs (aHR, 13.53; 95% CI, 2.59-86.65). In the ipilimumab test cohort, high recurrence score was associated with significantly worse RFS (aHR, 3.21; 95% CI, 1.38-7.45) and severe toxicity score significantly predicted severe irAEs (aHR, 11.04; 95% CI, 3.84-37.25). In the ipilimumab plus nivolumab test cohort, high autoAb recurrence score was associated with significantly worse RFS (aHR, 6.45; 95% CI, 1.48-28.02), and high severe toxicity score was significantly associated with severe irAEs (aHR, 23.44; 95% CI, 4.10-212.50). CONCLUSIONS:Baseline serum autoAb signatures predicted recurrence and severe toxicity in patients treated with adjuvant immunotherapy. Prospective testing of the signatures that include datasets with longer follow-up and rare but more severe toxicities will help determine their generalizability and potential clinical utility. See related commentary by Hassel and Luke, p. 3914.

Advances in immune checkpoint and combination therapy have led to improvement in overall survival for patients with advanced melanoma. Improved understanding of the tumor, tumor microenvironment and tumor immune-evasion mechanisms has resulted in new approaches to targeting and harnessing the host immune response. Combination modalities with other immunotherapy agents, chemotherapy, radiotherapy, electrochemotherapy are also being explored to overcome resistance and to potentiate the immune response. In addition, novel approaches such as adoptive cell therapy, oncogenic viruses, vaccines and different strategies of drug administration including sequential, or combination treatment are being tested. Despite the progress in diagnosis of melanocytic lesions, correct classification of patients, selection of appropriate adjuvant and systemic theràapies, and prediction of response to therapy remain real challenges in melanoma. Improved understanding of the tumor microenvironment, tumor immunity and response to therapy has prompted extensive translational and clinical research in melanoma. There is a growing evidence that genomic and immune features of pre-treatment tumor biopsies may correlate with response in patients with melanoma and other cancers, but they have yet to be fully characterized and implemented clinically. Development of novel biomarker platforms may help to improve diagnostics and predictive accuracy for selection of patients for specific treatment. Overall, the future research efforts in melanoma therapeutics and translational research should focus on several aspects including: (a) developing robust biomarkers to predict efficacy of therapeutic modalities to guide clinical decision-making and optimize treatment regimens, (b) identifying mechanisms of therapeutic resistance to immune checkpoint inhibitors that are potentially actionable, (c) identifying biomarkers to predict therapy-induced adverse events, and (d) studying mechanism of actions of therapeutic agents and developing algorithms to optimize combination treatments. During the Melanoma Bridge meeting (December 2nd-4th, 2021, Naples, Italy) discussions focused on the currently approved systemic and local therapies for advanced melanoma and discussed novel biomarker strategies and advances in precision medicine as well as the impact of COVID-19 pandemic on management of melanoma patients.

Transcriptional heterogeneity among malignant cells of a tumor has been studied in individual cancer types and shown to be organized into cancer cell states; however, it remains unclear to what extent these states span tumor types, constituting general features of cancer. Here, we perform a pan-cancer single-cell RNA-sequencing analysis across 15 cancer types and identify a catalog of gene modules whose expression defines recurrent cancer cell states including 'stress', 'interferon response', 'epithelial-mesenchymal transition', 'metal response', 'basal' and 'ciliated'. Spatial transcriptomic analysis linked the interferon response in cancer cells to T cells and macrophages in the tumor microenvironment. Using mouse models, we further found that induction of the interferon response module varies by tumor location and is diminished upon elimination of lymphocytes. Our work provides a framework for studying how cancer cell states interact with the tumor microenvironment to form organized systems capable of immune evasion, drug resistance and metastasis.

BACKGROUND:The prognostic value of tumor-infiltrating lymphocytes (TILs) assessed by machine learning algorithms in melanoma patients has been previously demonstrated but has not been widely adopted in the clinic. We evaluated the prognostic value of objective automated electronic TILs (eTILs) quantification to define a subset of melanoma patients with a low risk of relapse after surgical treatment. METHODS:We analyzed data for 785 patients from 5 independent cohorts from multiple institutions to validate our previous finding that automated TIL score is prognostic in clinically-localized primary melanoma patients. Using serial tissue sections of the Yale TMA-76 melanoma cohort, both immunofluorescence and Hematoxylin-and-Eosin (H&E) staining were performed to understand the molecular characteristics of each TIL phenotype and their associations with survival outcomes. FINDINGS/RESULTS:Five previously-described TIL variables were each significantly associated with overall survival (p<0.0001). Assessing the receiver operating characteristic (ROC) curves by comparing the clinical impact of two models suggests that etTILs (electronic total TILs) (AUC: 0.793, specificity: 0.627, sensitivity: 0.938) outperformed eTILs (AUC: 0.77, specificity: 0.51, sensitivity: 0.938). We also found that the specific molecular subtype of cells representing TILs includes predominantly cells that are CD3+ and CD8+ or CD4+ T cells. INTERPRETATION/CONCLUSIONS:eTIL% and etTILs scores are robust prognostic markers in patients with primary melanoma and may identify a subgroup of stage II patients at high risk of recurrence who may benefit from adjuvant therapy. We also show the molecular correlates behind these scores. Our data support the need for prospective testing of this algorithm in a clinical trial. FUNDING/BACKGROUND:This work was also supported by a sponsored research agreements from Navigate Biopharma and NextCure and by grants from the NIH including the Yale SPORE in in Skin Cancer, P50 CA121974, the Yale SPORE in Lung Cancer, P50 CA196530, NYU SPORE in Skin Cancer P50CA225450 and the Yale Cancer Center Support Grant, P30CA016359.

Image-based analysis as a method for mutation detection can be advantageous in settings when tumor tissue is limited or unavailable for direct testing. Here, we utilize two distinct and complementary machine learning methods of analyzing whole slide images (WSI) for predicting mutated BRAF. In the first method, WSI of melanomas from 256 patients were used to train a deep convolutional neural network (CNN) in order to develop a fully automated model that first selects for tumor-rich areas (Area Under the Curve AUC=0.96) then predicts for mutated BRAF (AUC=0.71). Saliency mapping was performed and revealed that pixels corresponding to nuclei were the most relevant to network learning. In the second method, WSI were analyzed using a pathomics pipeline that first annotates nuclei and then quantifies nuclear features, demonstrating that mutated BRAF nuclei were significantly larger and rounder nuclei compared to BRAF WT nuclei. Lastly, we developed a model that combines clinical information, deep learning, and pathomics that improves the predictive performance for mutated BRAF to AUC=0.89. Not only does this provide additional insights on how BRAF mutations affect tumor structural characteristics, machine learning-based analysis of WSI has the potential to be integrated into higher order models for understanding tumor biology.