Obesity Is Associated with Altered Tumor Metabolism in Metastatic Melanoma.

PURPOSE: Overweight/obese (OW/OB) patients with metastatic melanoma unexpectedly have improved outcomes with immune checkpoint inhibitors (ICI) 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 body mass index (BMI). EXPERIMENTAL DESIGN: Associations between BMI [normal (NL < 25) or OW/OB (BMI ≥ 25)] and tumor or microbiome characteristics were examined in specimens from 782 patients with metastatic melanoma across 7 cohorts. DNA associations were evaluated in The Cancer Genome Atlas cohort. RNA sequencing 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. See related commentary by Smalley, p. 5.

Enhancing Case Capture, Quality, and Completeness of Primary Melanoma Pathology Records via Natural Language Processing.

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.

Beyond BRAF(V600): clinical mutation panel testing by next-generation sequencing in advanced melanoma.

The management of melanoma has evolved owing 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 events in tumors with BRAFV600 and NRAS mutations. Melanomas with BRAFNon-V600mutations 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.