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Background: Melanoma is a heterogeneous cancer influenced by the plasticity of melanoma cells and their dynamic adaptations to microenvironmental cues. Melanoma cells transition between well-defined transcriptional cell states that impact treatment response and resistance., Methods: In this study, we applied single-cell RNA sequencing to interrogate the molecular features of immunotherapy-naive and immunotherapy-resistant melanoma tumours in response to ex vivo BRAF/MEK inhibitor treatment., Findings: We confirm the presence of four distinct melanoma cell states - melanocytic, transitory, neural-crest like and undifferentiated, and identify enrichment of neural crest-like and undifferentiated melanoma cells in immunotherapy-resistant tumours. Furthermore, we introduce an integrated computational approach to identify subsets of responding and nonresponding melanoma cells within the transcriptional cell states., Interpretation: Nonresponding melanoma cells are identified in all transcriptional cell states and are predisposed to BRAF/MEK inhibitor resistance due to pro-inflammatory IL6 and TNFɑ signalling. Our study provides a framework to study treatment response within distinct melanoma cell states and indicate that tumour-intrinsic pro-inflammatory signalling contributes to BRAF/MEK inhibitor resistance., Funding: This work was supported by Macquarie University, Melanoma Institute Australia, and the National Health and Medical Research Council of Australia (NHMRC; grant 2012860, 2028055)., Competing Interests: Declaration of interests JHL has received honorarium from Merck, Sharp & Dohme and AstraZeneca, received conference support from Pfizer and Merck, Sharp & Dohme, and consulting fees from Boxer Capitol. JHL is a consultant for Merck, Sharp & Dohme, Sanofi, Cancer Council and EviO. RAS has received fees for professional services from MetaOptima Technology Inc., F. Hoffmann-La Roche Ltd, Evaxion, Provectus Biopharmaceuticals Australia, Qbiotics, Novartis, Merck Sharp & Dohme, NeraCare, AMGEN Inc., Bristol-Myers Squibb, Myriad Genetics, GlaxoSmithKline, IO Biotech ApS and SkylineDX B.V. GVL is consultant advisor for Agenus, Amgen, Array Biopharma, AstraZeneca, Bayer Health Care, BioNTech, Boehringer Ingelheim, Bristol Myers Squibb, Evaxion, Hexal AG (Sandoz Company), Highlight Therapeutics S.L., IO Biotech, Immunocore, Innovent Biologics USA, Merck Sharpe & Dohme, Novartis, PHMR Ltd, Pierre Fabre, Qbiotics, Regeneron, Scancell Limited and SkylineDX B.V. GVL has received conference support from Bristol Myers Squibb and Pierre Fabre. All other authors declare no conflicts of interest., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Transcriptomic signatures designed to predict melanoma patient responses to PD-1 blockade have been reported but rarely validated. We now show that intra-patient heterogeneity of tumor responses to PD-1 inhibition limit the predictive performance of these signatures. We reasoned that resistance mechanisms will reflect the tumor microenvironment, and thus we examined PD-1 inhibitor resistance relative to T-cell activity in 94 melanoma tumors collected at baseline and at time of PD-1 inhibitor progression. Tumors were analyzed using RNA sequencing and flow cytometry, and validated functionally. These analyses confirm that major histocompatibility complex (MHC) class I downregulation is a hallmark of resistance to PD-1 inhibitors and is associated with the MITF low /AXL high de-differentiated phenotype and cancer-associated fibroblast signatures. We demonstrate that TGFß drives the treatment resistant phenotype (MITF low /AXL high ) and contributes to MHC class I downregulation in melanoma. Combinations of anti-PD-1 with drugs that target the TGFß signaling pathway and/or which reverse melanoma de-differentiation may be effective future therapeutic strategies.

Validating newly discovered biomarkers in large, publicly available data sets is often difficult and requires specialized computer programming skills. Melanoma Explorer is a web application that enables easy interrogation of melanoma omics data sets that are freely available in online data repositories with a point-and-click interface. Two use cases are demonstrated. First, the relationship of lysozyme mRNA expression is shown to be prognostic in two independent gene expression microarray data sets. Second, a figure from a journal article showing the relationship of tumour thickness and miR-382 abundance is reproduced. Melanoma Explorer is demonstrated to be a useful tool for reproducing results of published studies and providing additional evidence for biomarkers in independent data sets.

Purpose: Combination PD-1 and CTLA-4 inhibitor therapy has dramatically improved the survival of patients with advanced melanoma but is also associated with significant immune-related toxicities. This study sought to identify circulating cytokine biomarkers of treatment response and immune-related toxicity., Experimental Design: The expression of 65 cytokines was profiled longitudinally in 98 patients with melanoma treated with PD-1 inhibitors, alone or in combination with anti-CTLA-4, and in an independent validation cohort of 49 patients treated with combination anti-PD-1 and anti-CTLA-4. Cytokine expression was correlated with RECIST response and immune-related toxicity, defined as toxicity that warranted permanent discontinuation of treatment and administration of high-dose steroids., Results: Eleven cytokines were significantly upregulated in patients with severe immune-related toxicities at baseline (PRE) and early during treatment (EDT). The expression of these 11 cytokines was integrated into a single toxicity score, the CYTOX (cytokine toxicity) score, and the predictive utility of this score was confirmed in the discovery and validation cohorts. The AUC for the CYTOX score in the validation cohort was 0.68 at PRE [95% confidence interval (CI), 0.51-0.84; P = 0.037] and 0.70 at EDT (95% CI, 0.55-0.85; P = 0.017) using ROC analysis., Conclusions: The CYTOX score is predictive of severe immune-related toxicity in patients with melanoma treated with combination anti-CTLA-4 and anti-PD-1 immunotherapy. This score, which includes proinflammatory cytokines such as IL1a, IL2, and IFNα2, may help in the early management of severe, potentially life-threatening immune-related toxicity. See related commentary by Johnson and Balko, p. 1452 ., (©2018 American Association for Cancer Research.)

Purpose: BRAF V600E and V600K melanomas have distinct clinicopathologic features, and V600K appear to be less responsive to BRAFi ± MEKi . We investigated mechanisms for this and explored whether genotype affects response to immunotherapy., Experimental Design: Pretreatment formalin-fixed paraffin-embedded tumors from patients treated with BRAFi ± MEKi underwent gene expression profiling and DNA sequencing. Molecular results were validated using The Cancer Genome Atlas (TCGA) data. An independent cohort of V600E/K patients treated with anti-PD-1 immunotherapy was examined., Results: Baseline tissue and clinical outcome with BRAFi ± MEKi were studied in 93 patients (78 V600E, 15 V600K). V600K patients had numerically less tumor regression (median, -31% vs. -52%, P = 0.154) and shorter progression-free survival (PFS; median, 5.7 vs. 7.1 months, P = 0.15) compared with V600E. V600K melanomas had lower expression of the ERK pathway feedback regulator dual-specificity phosphatase 6, confirmed with TCGA data (116 V600E, 17 V600K). Pathway analysis showed V600K had lower expression of ERK and higher expression of PI3K-AKT genes than V600E. Higher mutational load was observed in V600K, with a higher proportion of mutations in PIK3R1 and tumor-suppressor genes. In patients treated with anti-PD-1, V600K ( n = 19) had superior outcomes than V600E ( n = 84), including response rate (53% vs. 29%, P = 0.059), PFS (median, 19 vs. 2.7 months, P = 0.049), and overall survival (20.4 vs. 11.7 months, P = 0.081)., Conclusions: BRAF V600K melanomas appear to benefit less from BRAFi ± MEKi than V600E, potentially due to less reliance on ERK pathway activation and greater use of alternative pathways. In contrast, these melanomas have higher mutational load and respond better to immunotherapy., (©2019 American Association for Cancer Research.)

Purpose: To examine the relationship between immune activity, PD-L1 expression, and tumor cell signaling, in metastatic melanomas prior to and during treatment with targeted MAPK inhibitors. Experimental Design: Thirty-eight tumors from 17 patients treated with BRAF inhibitor ( n = 12) or combination BRAF/MEK inhibitors ( n = 5) with known PD-L1 expression were analyzed. RNA expression arrays were performed on all pretreatment (PRE, n = 17), early during treatment (EDT, n = 8), and progression (PROG, n = 13) biopsies. HLA-A/HLA-DPB1 expression was assessed by IHC. Results: Gene set enrichment analysis (GSEA) of PRE, EDT, and PROG melanomas revealed that transcriptome signatures indicative of immune cell activation were strongly positively correlated with PD-L1 staining. In contrast, MAPK signaling and canonical Wnt/-β-catenin activity was negatively associated with PD-L1 melanoma expression. The expression of PD-L1 and immune activation signatures did not simply reflect the degree or type of immune cell infiltration, and was not sufficient for tumor response to MAPK inhibition. Conclusions: PD-L1 expression correlates with immune cells and immune activity signatures in melanoma, but is not sufficient for tumor response to MAPK inhibition, as many PRE and PROG melanomas displayed both PD-L1 positivity and immune activation signatures. This confirms that immune escape is common in MAPK inhibitor-treated tumors. This has important implications for the selection of second-line immunotherapy because analysis of mechanisms of immune escape will likely be required to identify patients likely to respond to such therapies. Clin Cancer Res; 23(20); 6054-61. ©2017 AACR ., (©2017 American Association for Cancer Research.)

A consistent difference in average expression level, often referred to as differential expression (DE), has long been used to identify genes useful for classification. However, recent cancer studies have shown that when transcription factors or epigenetic signals become deregulated, a change in expression variability (DV) of target genes is frequently observed. This suggests that assessing the importance of genes by either differential expression or variability alone potentially misses sets of important biomarkers that could lead to improved predictions and treatments. Here, we describe a new approach for assessing the importance of genes based on differential distribution (DD), which combines information from differential expression and differential variability into a unified metric. We show that feature ranking and selection stability based on DD can perform two to three times better than DE or DV alone, and that DD yields equivalent error rates to DE and DV. Finally, assessing genes via differential distribution produces a complementary set of selected genes to DE and DV, potentially opening up new categories of biomarkers., (© The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.)

In this modern era of precision medicine, molecular signatures identified from advanced omics technologies hold great promise to better guide clinical decisions. However, current approaches are often location-specific due to the inherent differences between platforms and across multiple centres, thus limiting the transferability of molecular signatures. We present Cross-Platform Omics Prediction (CPOP), a penalised regression model that can use omics data to predict patient outcomes in a platform-independent manner and across time and experiments. CPOP improves on the traditional prediction framework of using gene-based features by selecting ratio-based features with similar estimated effect sizes. These components gave CPOP the ability to have a stable performance across datasets of similar biology, minimising the effect of technical noise often generated by omics platforms. We present a comprehensive evaluation using melanoma transcriptomics data to demonstrate its potential to be used as a critical part of a clinical screening framework for precision medicine. Additional assessment of generalisation was demonstrated with ovarian cancer and inflammatory bowel disease studies. [ABSTRACT FROM AUTHOR]

Motivation Gene annotation and pathway databases such as Gene Ontology and Kyoto Encyclopaedia of Genes and Genomes are important tools in Gene-Set Test (GST) that describe gene biological functions and associated pathways. GST aims to establish an association relationship between a gene-set of interest and an annotation. Importantly, GST tests for over-representation of genes in an annotation term. One implicit assumption of GST is that the gene expression platform captures the complete or a very large proportion of the genome. However, this assumption is neither satisfied for the increasingly popular boutique array nor the custom designed gene expression profiling platform. Specifically, conventional GST is no longer appropriate due to the gene-set selection bias induced during the construction of these platforms. Results We propose bcGST, a bias-corrected GST by introducing bias-correction terms in the contingency table needed for calculating the Fisher's Exact Test. The adjustment method works by estimating the proportion of genes captured on the array with respect to the genome in order to assist filtration of annotation terms that would otherwise be falsely included or excluded. We illustrate the practicality of bcGST and its stability through multiple differential gene expression analyses in melanoma and the Cancer Genome Atlas cancer studies. Availability and implementation The bcGST method is made available as a Shiny web application at http://shiny.maths.usyd.edu.au/bcGST/. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

The use of circulating tumor DNA (ctDNA) to monitor cancer progression and response to therapy has significant potential but there is only limited data on whether this technique can detect the presence of low frequency subclones that may ultimately confer therapy resistance. In this study, we sought to evaluate whether whole-exome sequencing (WES) of ctDNA could accurately profile the mutation landscape of metastatic melanoma. We used WES to identify variants in matched, tumor-derived genomic DNA (gDNA) and plasma-derived ctDNA isolated from a cohort of 10 metastatic cutaneous melanoma patients. WES parameters such as sequencing coverage and total sequencing reads were comparable between gDNA and ctDNA. The mutant allele frequency of common single nucleotide variants was lower in ctDNA, reflecting the lower read depth and minor fraction of ctDNA within the total circulating free DNA pool. There was also variable concordance between gDNA and ctDNA based on the total number and identity of detected variants and this was independent of the tumor biopsy site. Nevertheless, established melanoma driver mutations and several other melanoma-associated mutations were concordant between matched gDNA and ctDNA. This study highlights that WES of ctDNA could capture clinically relevant mutations present in melanoma metastases and that enhanced sequencing sensitivity will be required to identify low frequency mutations. [ABSTRACT FROM AUTHOR]