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The immune response to melanoma improves the survival in untreated patients and predicts the response to immune checkpoint blockade. Here, we report genetic and environmental predictors of the immune response in a large primary cutaneous melanoma cohort. Bioinformatic analysis of 703 tumor transcriptomes was used to infer immune cell infiltration and to categorize tumors into immune subgroups, which were then investigated for association with biological pathways, clinicopathologic factors, and copy number alterations. Three subgroups, with “low”, “intermediate”, and “high” immune signals, were identified in primary tumors and replicated in metastatic tumors. Genes in the low subgroup were enriched for cell-cycle and metabolic pathways, whereas genes in the high subgroup were enriched for IFN and NF-κB signaling. We identified high MYC expression partially driven by amplification, HLA-B downregulation, and deletion of IFNγ and NF-κB pathway genes as the regulators of immune suppression. Furthermore, we showed that cigarette smoking, a globally detrimental environmental factor, modulates immunity, reducing the survival primarily in patients with a strong immune response. Together, these analyses identify a set of factors that can be easily assessed that may serve as predictors of response to immunotherapy in patients with melanoma.Significance:These findings identify novel genetic and environmental modulators of the immune response against primary cutaneous melanoma and predict their impact on patient survival.See related commentary by Anichini, p. 2457

Gene lists per immune cell type after successive filtration steps.

Purpose:Previously identified transcriptomic signatures have been based on primary and metastatic melanomas with relatively few American Joint Committee on Cancer (AJCC) stage I tumors, given difficulties in sampling small tumors. The advent of adjuvant therapies has highlighted the need for better prognostic and predictive biomarkers, especially for AJCC stage I and stage II disease.Experimental Design:A total of 687 primary melanoma transcriptomes were generated from the Leeds Melanoma Cohort (LMC). The prognostic value of existing signatures across all the AJCC stages was tested. Unsupervised clustering was performed, and the prognostic value of the resultant signature was compared with that of sentinel node biopsy (SNB) and tested as a biomarker in three published immunotherapy datasets.Results:Previous Lund and The Cancer Genome Atlas signatures predicted outcome in the LMC dataset (P = 10−8 to 10−4) but showed a significant interaction with AJCC stage (P = 0.04) and did not predict outcome in stage I tumors (P = 0.3–0.7). Consensus-based classification of the LMC dataset identified six classes that predicted outcome, notably in stage I disease. LMC class was a similar indicator of prognosis when compared with SNB, and it added prognostic value to the genes reported by Gerami and colleagues. One particular LMC class consistently predicted poor outcome in patients receiving immunotherapy in two of three tested datasets. Biological characterization of this class revealed high JUN and AXL expression and evidence of epithelial-to-mesenchymal transition.Conclusions:A transcriptomic signature of primary melanoma was identified with prognostic value, including in stage I melanoma and in patients undergoing immunotherapy.

Additional information on methods used and supplementary figures and tables. Figure S1: Schematic summary of consensus clustering; Figure S2: Comparison of the newly obtained immune subgroups with two published melanoma molecular signatures; Figure S3: Whole transcriptome differences among the three immune subgroups; Figure S4: Association of GPR15 gene expression, smoking and the three immune subgroups; Table S1: Survival analysis for the immune cell scores in our data (LMC) and TCGA datasets; Table S2: Association between clinico-pathological characteristics and the three immune subgroups in the LMC; Table S3: Differences in the immune cell scores between ever and never smokers in the High Immune Subgroup; Table S4: Association between smoking and tumor histological features in the High Immune Subgroup.

List of 70 genes shared between the signature used to generate the three immune subgroups in this study and the TCGA signature as well as tables of additional results from the analysis of the 3 immune subgroups: Genes and pathways upregulated in the High and Low Immune Subgroups; List of genes significantly correlating with MYC in melanoma cell lines; Results of copy number data analysis for MYC, NF-kappa B and IFN-gamma pathways; Results of the survival analyses of environmental and clinico-pathological factors with alternative definitions of smoking variable.

Supplementary Figures and tables

Melanoma is a heterogenous malignancy with an unpredictable clinical course. Most patients who present in the clinic are diagnosed with primary melanoma, yet large-scale sequencing efforts have focused primarily on metastatic disease. In this study we sequence-profiled 524 American Joint Committee on Cancer Stage I-III primary tumours. Our analysis of these data reveals recurrent driver mutations, mutually exclusive genetic interactions, where two genes were never or rarely co-mutated, and an absence of co-occurring genetic events. Further, we intersected copy number calls from our primary melanoma data with whole-genome CRISPR screening data to identify the transcription factor interferon regulatory factor 4 (IRF4) as a melanoma-associated dependency. © 2022 The Authors. The Journal of Pathology published by John WileySons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

An implementation of the windows based DLBCL automatic classification (DAC) algorithm for determining cell of origin (COO) written in the R language and designed for use with the HTG EdgeSeq Reveal Software package is described. Classifications using the new implementation (DAC-R) were compared to the those using the original version (DAC-Win), the HTG DLBCL COO classifier, the HTG EdgeSeq Reveal DLBCL algorithm. Classifications made using HTG data were tested for concordance with those made using WG-DASL data for the same samples. To analyse small numbers of samples a background dataset was developed to allow for the reliable classification of individual cases. Classification accuracy was assessed using percentage of agreement of discrete classification groups and Pearson correlation of probability scores where appropriate. In the data tested it was seen that the correlation of probability scores for DAC-R and DAC-Win was 0.9985 or higher. Agreement with the HTG DLBCL COO classifier was 85.1% and agreement with the HTG EdgeSeq Reveal DLBCL algorithm was also high (Pearson correlation of GCB probabilities = 0.91). Agreement between classifications made using HTG and WG-DASL data was also high (83.7%). In summary, the R based algorithm of DAC successfully replicates the functionality of the original routine and produces comparable COO calls in data produced from the HTG Pan B-Cell Lymphoma Panel to the other methods listed.

Microscopic ulceration is an independent predictor of melanoma death. Here, we used systems biology to query the role of host and tumour-specific processes in defining the phenotype. Albumin level as a measure of systemic inflammation was predictive of fewer tumour-infiltrating lymphocytes and poorer survival in the Leeds Melanoma Cohort. Ulcerated melanomas were thicker and more mitotically active (with corresponding transcriptomic upregulated cell cycle pathways). Sequencing identified tumoural p53 and APC mutations, and TUBB2B amplification as associated with the phenotype. Ulcerated tumours had perturbed expression of cytokine genes, consistent with protumourigenic inflammation and histological and transcriptomic evidence for reduced adaptive immune cell infiltration. Pathway/network analysis of multiomic data using neural networks highlighted a role for the β-catenin pathway in the ulceration, linking genomic changes in the tumour to immunosuppression and cell proliferation. In summary, the data suggest that ulceration is in part associated with genomic changes but that host factors also predict melanoma death with evidence of reduced immune responses to the tumour.

Purpose: Previously identified transcriptomic signatures have been based on primary and metastatic melanomas with relatively few American Joint Committee on Cancer (AJCC) stage I tumors, given difficulties in sampling small tumors. The advent of adjuvant therapies has highlighted the need for better prognostic and predictive biomarkers, especially for AJCC stage I and stage II disease. Experimental Design: A total of 687 primary melanoma transcriptomes were generated from the Leeds Melanoma Cohort (LMC). The prognostic value of existing signatures across all the AJCC stages was tested. Unsupervised clustering was performed, and the prognostic value of the resultant signature was compared with that of sentinel node biopsy (SNB) and tested as a biomarker in three published immunotherapy datasets. Results: Previous Lund and The Cancer Genome Atlas signatures predicted outcome in the LMC dataset (P = 10−8 to 10−4) but showed a significant interaction with AJCC stage (P = 0.04) and did not predict outcome in stage I tumors (P = 0.3–0.7). Consensus-based classification of the LMC dataset identified six classes that predicted outcome, notably in stage I disease. LMC class was a similar indicator of prognosis when compared with SNB, and it added prognostic value to the genes reported by Gerami and colleagues. One particular LMC class consistently predicted poor outcome in patients receiving immunotherapy in two of three tested datasets. Biological characterization of this class revealed high JUN and AXL expression and evidence of epithelial-to-mesenchymal transition. Conclusions: A transcriptomic signature of primary melanoma was identified with prognostic value, including in stage I melanoma and in patients undergoing immunotherapy.

The immune response to melanoma improves the survival in untreated patients and predicts the response to immune checkpoint blockade. Here, we report genetic and environmental predictors of the immune response in a large primary cutaneous melanoma cohort. Bioinformatic analysis of 703 tumor transcriptomes was used to infer immune cell infiltration and to categorize tumors into immune subgroups, which were then investigated for association with biological pathways, clinicopathologic factors, and copy number alterations. Three subgroups, with “low”, “intermediate”, and “high” immune signals, were identified in primary tumors and replicated in metastatic tumors. Genes in the low subgroup were enriched for cell-cycle and metabolic pathways, whereas genes in the high subgroup were enriched for IFN and NF-κB signaling. We identified high MYC expression partially driven by amplification, HLA-B downregulation, and deletion of IFNγ and NF-κB pathway genes as the regulators of immune suppression. Furthermore, we showed that cigarette smoking, a globally detrimental environmental factor, modulates immunity, reducing the survival primarily in patients with a strong immune response. Together, these analyses identify a set of factors that can be easily assessed that may serve as predictors of response to immunotherapy in patients with melanoma. Significance: These findings identify novel genetic and environmental modulators of the immune response against primary cutaneous melanoma and predict their impact on patient survival. See related commentary by Anichini, p. 2457