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.

Identification of MicroRNA-mRNA Networks in Melanoma and Their Association with PD-1 Checkpoint Blockade Outcomes.

Metastatic melanoma is a deadly malignancy with poor outcomes historically. Immuno-oncology (IO) agents, targeting immune checkpoint molecules such as cytotoxic T-lymphocyte associated protein-4 (CTLA-4) and programmed cell death-1 (PD-1), have revolutionized melanoma treatment and outcomes, achieving significant response rates and remarkable long-term survival. Despite these vast improvements, roughly half of melanoma patients do not achieve long-term clinical benefit from IO therapies and there is an urgent need to understand and mitigate mechanisms of resistance. MicroRNAs are key post-transcriptional regulators of gene expression that regulate many aspects of cancer biology, including immune evasion. We used network analysis to define two core microRNA-mRNA networks in melanoma tissues and cell lines corresponding to 'MITF-low' and 'Keratin' transcriptomic subsets of melanoma. We then evaluated expression of these core microRNAs in pre-PD-1-inhibitor-treated melanoma patients and observed that higher expression of miR-100-5p and miR-125b-5p were associated with significantly improved overall survival. These findings suggest that miR-100-5p and 125b-5p are potential markers of response to PD-1 inhibitors, and further evaluation of these microRNA-mRNA interactions may yield further insight into melanoma resistance to PD-1 inhibitors.

Gut microbiota signatures are associated with toxicity to combined CTLA-4 and PD-1 blockade.

Treatment with combined immune checkpoint blockade (CICB) targeting CTLA-4 and PD-1 is associated with clinical benefit across tumor types, but also a high rate of immune-related adverse events. Insights into biomarkers and mechanisms of response and toxicity to CICB are needed. To address this, we profiled the blood, tumor and gut microbiome of 77 patients with advanced melanoma treated with CICB, with a high rate of any ≥grade 3 immune-related adverse events (49%) with parallel studies in pre-clinical models. Tumor-associated immune and genomic biomarkers of response to CICB were similar to those identified for ICB monotherapy, and toxicity from CICB was associated with a more diverse peripheral T-cell repertoire. Profiling of gut microbiota demonstrated a significantly higher abundance of Bacteroides intestinalis in patients with toxicity, with upregulation of mucosal IL-1ß in patient samples of colitis and in pre-clinical models. Together, these data offer potential new therapeutic angles for targeting toxicity to CICB.

Validation of a Novel Cutaneous Neoplasm Diagnostic Self-Efficacy Instrument (CNDSEI) for Evaluating User-Perceived Confidence With Dermoscopy.

BACKGROUND: Accurate medical image interpretation is an essential proficiency for multiple medical specialties, including dermatologists and primary care providers. A dermatoscope, a ×10-×20 magnifying lens paired with a light source, enables enhanced visualization of skin cancer structures beyond standard visual inspection. Skilled interpretation of dermoscopic images improves diagnostic accuracy for skin cancer. OBJECTIVE: Design and validation of Cutaneous Neoplasm Diagnostic Self-Efficacy Instrument (CNDSEI)-a new tool to assess dermatology residents' confidence in dermoscopic diagnosis of skin tumors. METHODS: In the 2018-2019 academic year, the authors administered the CNDSEI and the Long Dermoscopy Assessment (LDA), to measure dermoscopic image interpretation accuracy, to residents in 9 dermatology residency programs prior to dermoscopy educational intervention exposure. The authors conducted CNDSEI item analysis with inspection of response distribution histograms, assessed internal reliability using Cronbach's coefficient alpha (α) and construct validity by comparing baseline CNDSEI and LDA results for corresponding lesions with one-way analysis of variance (ANOVA). RESULTS: At baseline, residents respectively demonstrated significantly higher and lower CNDSEI scores for correctly and incorrectly diagnosed lesions on the LDA (P = 0.001). The internal consistency reliability of CNDSEI responses for the majority (13/15) of the lesion types was excellent (α ≥ 0.9) or good (0.8≥ α <0.9). CONCLUSIONS: The CNDSEI pilot established that the tool reliably measures user dermoscopic image interpretation confidence and that self-efficacy correlates with diagnostic accuracy. Precise alignment of medical image diagnostic performance and the self-efficacy instrument content offers opportunity for construct validation of novel medical image interpretation self-efficacy instruments.

Calcium-sensing receptor autoantibody-mediated hypoparathyroidism associated with immune checkpoint inhibitor therapy: diagnosis and long-term follow-up.

BACKGROUND: Immune checkpoint inhibitors (ICIs) have produced significant survival benefit across many tumor types. However, immune-related adverse events are common including autoimmune responses against different endocrine organs. Here, a case of ICI-mediated hypoparathyroidism focusing on long-term follow-up and insights into its etiology is presented. CASE AND METHODS: A 73-year-old man developed severe symptomatic hypocalcemia after the initiation of ipilimumab and nivolumab for the treatment of metastatic melanoma. Hypoparathyroidism was diagnosed with undetectable intact parathyroid hormone (PTH). Immunoprecipitation assays, ELISAs, and cell-based functional assays were used to test the patient for antibodies against the calcium-sensing receptor (CaSR). NACHT leucine-rich repeat protein 5 (NALP5) and cytokine antibodies were measured in radioligand binding assays and ELISAs, respectively. RESULTS: The patient's symptoms improved with aggressive calcium and vitamin D supplementation. At 3 years and 3 months since the diagnosis of hypoparathyroidism, PTH was still inappropriately low at 7.6 pg/mL, and attempted discontinuation of calcium and calcitriol resulted in recurrent symptomatic hypocalcemia. Analysis for an autoimmune etiology of the patient's hypoparathyroidism indicated that CaSR antibodies were negative before treatment and detected at multiple time points afterwards, and corresponded to the patient's clinical course of hypoparathyroidism. CaSR antibodies purified from the patient's serum activated the human CaSR. The patient was seronegative for NALP5 and cytokine antibodies, indicating that their hypoparathyroidism was not a manifestation of autoimmune polyendocrine syndrome type 1. CONCLUSION: The etiology of hypocalcemia is likely autoimmune hypoparathyroidism caused by the development of CaSR-activating antibodies that might prevent PTH release from the parathyroid.

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.