Phenotype-based variation as a biomarker of sensitivity to molecularly targeted therapy in melanoma

Abstract

Transcriptomic phenotypes defined for melanoma have been reported to correlate with sensitivity to various drugs. In this study, we aimed to define a minimal signature that could be used to distinguish melanoma sub-types in vitro, and to determine suitable drugs by which these sub-types can be targeted. By using primary melanoma cell lines, as well as commercially available melanoma cell lines, we find that the evaluation of MLANA and INHBA expression is as capable as one based on a combined analysis performed with genes for stemness, EMT and invasion/proliferation, in identifying melanoma subtypes that differ in their sensitivity to molecularly targeted drugs. Using this approach, we find that 75% of melanoma cell lines can be treated with either the MEK inhibitor AZD6244 or the HSP90 inhibitor 17AAG.

Fig. 1. Fibroblast/melanoma correlation plot of primary melanoma cell lines (PrMCLs), skin fibroblasts and cancer-associated fibroblasts (CAFs). C-MAP-based gene expression data for PrMCLs and publically available data from skin fibroblasts (GSE11919, GSE16715, GSE21648, GSE34309, GSE35551) and CAFs (GSE46824) were used to calculate gene expression-based correlations to a fibroblast (FIB) and a melanoma (MEL) gene list. Correlation measures (Pearson's r) for each sample are shown for FIB on the x-axis and INVMEL on the y-axis (see Materials and methods for details).

Fig. 2. Clustering melanoma cell lines based on different molecular signatures. Distribution of CMCLs (top) and PrMCLs (bottom), categorized into PRO, INV or INT groups according to an EMT score (X axis) and stemness score (Y axis). E: epithelial, M: mesenchymal phenotype. CSC: cancer stem-cell-like phenotype, non-CSC: non-cancer-stem-cell-like phenotype. Cells are shown distributed into 4 quadrants: Q1: E and CSC-like, Q2: M and CSC-like, Q3: M and non-CSC-like, and Q4: E and non-CSC-like.

Fig. 3. In silico cytotoxicity analyses of molecularly targeted drugs on CMCLs. CMCLs clustered in quadrants 2, 3, and 4 (as indicated in Fig. 2) as well as according to the PRO/INV classification were analyzed for sensitivity against the shown drugs. Cytotoxicity is shown as normalized activity area (Y axis). 1-Way ANOVA-based analysis of quadrant-based classification of cells gave p values of 0.07, 0.034 and 0.05 for 17AAG, AZD6244 and AZD0530, respectively, while the same statistical analysis for PRO, INT and INV groups generated p values of 0.025, 0.023 and 0.03 for 17AAG, AZD6244 and AZD0530, respectively. Q2: M and CSC-like, Q3: M and non-CSC-like, and Q4: E and non-CSC-like.

Fig. 4. In vitro cytotoxicity analyses of molecularly targeted drugs on melanoma cell lines. CMCLs and PrMCLs clustered in quadrants 2, 3, and 4 (Fig. 2) as well as according to the PRO/INV classification were analyzed for sensitivity against the shown drugs. Cytotoxicity is shown as log IC₅₀ (Y axis). Q2: M and CSC-like, Q3: M and non-CSC-like, and Q4: E and non-CSC-like.

Fig. 5. MLANA–INHBA-based drug sensitivity analysis of CMCLs. In silico cytotoxiciy analysis of CMCLs against 3 molecularly targeted drugs. Cells contained within quadrants identified in Fig. S3 are indicated. P values (t-test) for 17AAG, AZD0530 and AZD6244 are 0.26, 0.0002 and 0.13, respectively. Q1: E and CSC-like, Q2: M and CSC-like, Q3: M and non-CSC-like, and Q4: E and non-CSC-like.

Fig. 6. MLANA–INHBA-based drug sensitivity analysis of PrMCLs. In vitro cytotoxiciy analysis of PrMCLs against 4 molecularly targeted drugs. Cells contained within quadrants identified in Fig. S3 are indicated. P values (t-test) for 10a, 17AAG, AZD6244, and AZD0530 are 0.1, 0.43, 0.01 and 0.32, respectively. Q1: E and CSC-like, Q2: M and CSC-like, Q3: M and non-CSC-like, and Q4: E and non-CSC-like.