SUMMARY We describe the landscape of genomic alterations in cutaneous melanomas through DNA, RNA, and protein-based analysis of 333 primary and/or metastatic melanomas from 331 patients. We establish a framework for genomic classification into one of four subtypes based on the pattern of the most prevalent significantly mutated genes: mutant BRAF, mutant RAS, mutant NF1, and Triple-WT (wild-type). Integrative analysis reveals enrichment of KIT mutations and focal amplifications and complex structural rearrangements as a feature of the Triple-WT subtype. We found no significant outcome correlation with genomic classification, but samples assigned a transcriptomic subclass enriched for immune gene expression associated with lymphocyte infiltrate on pathology review and high LCK protein expression, a T cell marker, were associated with improved patient survival. This clinicopathological and multidimensional analysis suggests that the prognosis of melanoma patients with regional metastases is influenced by tumor stroma immunobiology, offering insights to further personalize therapeutic decision-making.

Another benefit of dietary fiber The gut microbiome can modulate the immune system and influence the therapeutic response of cancer patients, yet the mechanisms underlying the effects of microbiota are presently unclear. Spencer et al . add to our understanding of how dietary habits affect microbiota and clinical outcomes to immunotherapy. In an observational study, the researchers found that melanoma patients reporting high fiber (prebiotic) consumption had a better response to checkpoint inhibitor immunotherapy compared with those patients reporting a low-fiber diet. The most marked benefit was observed for those patients reporting a combination of high fiber consumption and no use of over-the-counter probiotic supplements. These findings provide early insights as to how diet-related factors may influence the immune response. —PNK

The management of melanoma has evolved due to improved understanding of its molecular drivers. To augment the current understanding of the prevalence, patterns, and associations of mutations in this disease, the results of clinical testing of 699 advanced melanoma patients using a pan-cancer next generation sequencing (NGS) panel of hotspot regions in 46 genes were reviewed. Mutations were identified in 43 of the 46 genes on the panel. The most common mutations were BRAFV600 (36%), NRAS (21%), TP53 (16%), BRAFNon-V600 (6%), and KIT (4%). Approximately one-third of melanomas had >1 mutation detected, and the number of mutations per tumor was associated with melanoma subtype. Concurrent TP53 mutations were the most frequent event in tumors with BRAFV600 and NRAS mutations. Melanomas with BRAFNon-V600 mutations frequently harbored concurrent NRAS mutations (18%), which were rare in tumors with BRAFV600 mutations (1.6%). The prevalence of BRAFV600 and KIT mutations were significantly associated with melanoma subtypes, and BRAFV600 and TP53 mutations were significantly associated with cutaneous primary tumor location. Multiple potential therapeutic targets were identified in metastatic unknown primary and cutaneous melanomas that lacked BRAFV600 and NRAS mutations. These results enrich our understanding of the patterns and clinical associations of oncogenic mutations in melanoma.

PURPOSE Medical records contain a wealth of useful, informative data points valuable for clinical research. Most data points are stored in semistructured or unstructured legacy documents and require manual data abstraction into a structured format to render the information more readily accessible, searchable, and generally analysis ready. The substantial labor needed for this can be cost prohibitive, particularly when dealing with large patient cohorts. METHODS To establish a high-throughput approach to data abstraction, we developed a novel framework using natural language processing (NLP) and a decision-rules algorithm to extract, transform, and load (ETL) melanoma primary pathology features from pathology reports in an institutional legacy electronic medical record system into a structured database. We compared a subset of these data with a manually curated data set comprising the same patients and developed a novel scoring system to assess confidence in records generated by the algorithm, thus obviating manual review of high-confidence records while flagging specific, low-confidence records for review. RESULTS The algorithm generated 368,624 individual melanoma data points comprising 16 primary tumor prognostic factors and metadata from 23,039 patients. From these data points, a subset of 147,872 was compared with an existing, manually abstracted data set, demonstrating an exact or synonymous match between 90.4% of all data points. Additionally, the confidence-scoring algorithm demonstrated an error rate of only 3.7%. CONCLUSION Our NLP platform can identify and abstract melanoma primary prognostic factors with accuracy comparable to that of manual abstraction (< 5% error rate), with vastly greater efficiency. Principles used in the development of this algorithm could be expanded to include other melanoma-specific data points as well as disease-agnostic fields and further enhance capture of essential elements from nonstructured data.

Purpose: Overweight/obese (OW/OB) patients with metastatic melanoma unexpectedly have improved outcomes with immune checkpoint inhibitors (ICIs) and BRAF-targeted therapies. The mechanism(s) underlying this association remain unclear, thus we assessed the integrated molecular, metabolic, and immune profile of tumors, as well as gut microbiome features, for associations with patient BMI. Experimental Design: Associations between BMI [normal (NL &lt; 25) or OW/OB (BMI ≥ 25] and tumor or microbiome characteristics were examined in specimens from 782 metastatic melanoma patients across 7 cohorts. DNA associations were evaluated in the TCGA cohort. RNASeq from 4 cohorts (n=357) was batch corrected and gene set enrichment analysis (GSEA) by BMI category was performed. Metabolic profiling was conducted in a subset of patients (x=36) by LC/MS, and in flow-sorted melanoma tumor cells (x=37) and patient-derived melanoma cell lines (x=17) using the Seahorse XF assay. Gut microbiome features were examined in an independent cohort (n=371). Results: DNA mutations and copy number variations were not associated with BMI. GSEA demonstrated that tumors from OW/OB patients were metabolically quiescent, with downregulation of oxidative phosphorylation and multiple other metabolic pathways. Direct metabolite analysis and functional metabolic profiling confirmed decreased central carbon metabolism in OW/OB metastatic melanoma tumors and patient-derived cell lines. The overall structure, diversity, and taxonomy of the fecal microbiome did not differ by BMI. Conclusions: These findings suggest that the host metabolic phenotype influences melanoma metabolism and provide insight into the improved outcomes observed in OW/OB patients with metastatic melanoma treated with ICIs and targeted therapies.