Transcriptomic Analysis Reveals Prognostic Molecular Signatures of Stage I Melanoma

Abstract

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.

Figure 1. Analysis workflow of the study

Figure 2

Replicating Lund and TCGA signatures using LMC dataset. Kaplan-Meier plots showing the Melanoma-specific survival (MSS) for (A) Lund 4-classes (HI- high-immune, NL- normal-like, Pigm.- pigmentation, Prolif.- proliferative), (B) Lund 2-grades (low grade and high grade) and (C) TCGA 3-classes (immune, keratin, MITF low) across the whole LMC dataset. In LMC stage I subset, Kaplan-Meier plots showing the MSS for (D) Lund 4-classes, (E) Lund 2-grades, and (F) TCGA 3-classes. Pvalues are from log-rank test. Samples which could not be classified into any of the classes were not used in survival analysis.

Figure 3. Defining LMC signature and its prognostic value.

(A) The area under the CDF and its relative change with increasing k. The delta area graph shows little variation at k=6. Heatmap of consensus matrices at k=5 and 6. The blue color indicates high consensus score and the white color indicates low consensus (B) Kaplan-Meier plot showing the MSS for the six classes in (B) the whole LMC dataset, (C) the LMC stage I, and (D) relapse-free survival in the Lund cohort (Pvalue from log-rank test, or Wald test for two-groups comparison). Seven mucosal tumors were excluded from analysis. (E) ROC curves comparing the prognostic value of the LMC signature to that of Sentinel Node Biopsy (SNB) in the whole dataset. The AUCs for LMC class+ stage pre-SNB and stage post-SNB were not significantly different (DeLong’s test P=0.7). (F) The ROC curve comparing prognostic value of LMC signature with SNB in the stage I pre-SNB group. All but one patient were stage IB pre-SNB, therefore AUC for LMC signature alone was compared to stage post-SNB and the difference was not significant (DeLong’s test P=0.7). The difference in AUCs between stage post-SNB alone and LMC class +stage post-SNB was also not significant (DeLong’s test P=0.1).

Figure 4. Biological characterization of the six LMC classes.

(A) The heatmap shows gene expression across the classes with tumor samples placed in columns and genes in rows. Blue depicts low expression and red depicts high expression. Each gene expression was standardized to mean 0 and standard deviation 1. The up- and down-regulated nodal genes identified in network analyses are shown under the heatmap. The barplot shows the overlap between the LMC classes and (B) Lund 4-classes (HI- high-immune, NL- normal-like, Pigm- pigmentation, Prolif- proliferative), and (C) TCGA 3-classes. The samples that could not be classified into the Lund 4-classes and TCGA 3-classes were labelled here as Uncls. (D) The modules (defined by a list of differentially upregulated genes) associated with melanoma-specific biological pathways as identified by the Lund group (29). Boxplots of immune and cell cycle module scores (standardized expressions) within the 6 LMC classes and correlation matrix of immune, cell cycle, MITF, stroma and interferon module scores. The module score variation across the classes was tested using the Kruskal-Wallis test.

Figure 5. Biological characterization of LMC class 6 and association with response to immunotherapy.

(A) Network of upregulated genes in the LMC class 6 with key genes (highest betweenness centrality) shown as large circles. Sub-networks are shown in different colors. (B) Expression of JUN across the six LMC classes (Pvalue from Kruskal-Wallis test). (C) JUN copy number alterations in LMC class 6 vs other classes. (D) The 6-gene based EMT score in tumors across the six LMC classes (Pvalue from Kruskal-Wallis test). (E) The gene expression of NFKB1 across the 6 LMC classes (Pvalue from Kruskal-Wallis test). (F) The LMC classes association with response to immunotherapy in three cohorts (Pvalue from Fisher’s exact test). Patients in these cohorts were classified into the 6 LMC classes by the NCC method. (G) Expression of AXL across the six LMC classes in the Hugo Cohort dataset (Pvalue from Mann–Whitney U test). (H) Kaplan-Meier plot showing survival curves of LMC class 1, class 3, and class 6 in the Riaz Cohort. Other LMC classes had <5 samples and were excluded.