Unbiased Drug Target Prediction Reveals Sensitivity to Ferroptosis Inducers, HDAC and RTK Inhibitors in Melanoma Subtypes

Abstract

Background: The utilization of PD1 and CTLA4 inhibitors has revolutionized the treatment of malignant melanoma (MM). However, resistance to targeted and immune-checkpoint-based therapies still poses a significant problem.

Objective: Here, we mine large-scale MM proteogenomic data to identify druggable targets and forecast treatment efficacy and resistance.

Methods: Leveraging protein profiles from established MM subtypes and molecular structures of 82 cancer treatment drugs, we identified nine candidate hub proteins, mTOR, FYN, PIK3CB, EGFR, MAPK3, MAP4K1, MAP2K1, SRC, and AKT1, across five distinct MM subtypes. These proteins are potential drug targets applicable to one or multiple MM subtypes. Additionally, by integrating proteogenomic profiles obtained from MM subtypes with MM cell line dependency and drug sensitivity data, we identified a total of 162 potentially targetable genes. Lastly, we identified 20 compounds exhibiting potential drug impact in at least one melanoma subtype.

Results: Employing these unbiased approaches, we have uncovered compounds targeting ferroptosis demonstrating a striking 30× fold difference in sensitivity among different subtypes.

Conclusions: Our results suggest innovative and novel therapeutic strategies by stratifying melanoma samples through proteomic profiling, offering a spectrum of novel therapeutic interventions and prospects for combination therapy.

Keywords: HDAC; RTK inhibitor; drug target prediction; ferroptosis; malignant melanoma; skin cancer.

Figure 1

Flowchart of approaches for screening candidate target proteins, mapping the genetic dependency, and predicting compound sensitivity by pharmacogenomics. The canonical structure of 82 cancer drugs was collected from the PubChem database and uploaded onto the SwissTargetPrediction web service to predict possible protein targets. Putative targets previously reported as melanoma subtype‐specific proteins were subjected to pathways enrichment analysis, and nine proteins were detected as hubs proteins after performing protein–protein interaction (PPI) analysis using the STRING database. Parallelly, the transcriptomic profiles of 48 melanoma cell lines were collected from the Cancer Cell Line Encyclopedia (CCLE) and were scored by similarity to five proteomic subtypes of melanoma using single sample Gene Set Enrichment Analysis (ssGSEA). Then, Achilles and CRISPR databases were utilized to detect genetic resistance and vulnerabilities, and the CTRPv2 database was used for detecting compound sensitivity.

Figure 2

Identifying druggable targets in melanoma subtypes. (a) Drug types used to identify protein targets in melanoma subtypes. (b) Distribution of the ‘protein class’ of the predicted target proteins. (c) Functional association network of candidate target proteins upregulated in molecular‐defined subtypes of patients with malignant melanoma. Proteins encircled in red color were identified as central network ‘hubs’. (d) Cellular component enrichment analysis (FDR <5%). (e) Sankey plot indicating how many proteins upregulated (likely activated) in each MM subtype were predicted as targets of the different cancer drug types. RTKs, receptor tyrosine kinases.

Figure 3

Pathway enrichment analysis of predicted drug targets based on the KEGG database (FDR <5%). Different colors indicate the subtypes in which specific pathways were enriched. Additionally, this figure displays the proteins activated in each subtype that contribute to these biological pathways.

Figure 4

Genetic dependency map of melanoma cell lines with similarity scores to the five melanoma subtypes defined by proteomics. Predicted susceptibility (a, genes indicated with red) and resistance (b, genes indicated with blue) to genetic targeting of individual genes in different melanoma subtypes. Across melanoma cell lines, each subtype signature score was correlated to the genetic dependency score for each gene obtained from DepMap. Genetic dependencies with significant (FDR < 0.25, P < 0.05) positive (a) or negative (b) Pearson correlation with its corresponding subtype signature scores are indicated. Pathway analyses (DAVID, followed by REVIGO) of gene dependencies of MIT high and MIT low tumors are displayed in panels (c) and (d), respectively.

Figure 5

Compound sensitivity map of melanoma subtypes defined by proteogenomics. Predicted sensitivity (a) and resistance (b) to pharmacologic targeting of different melanoma subtypes. The subtype signature score for each melanoma cell line was correlated to the AUC value of each compound from CTRPv2. Pharmacologic compounds with AUC values with significant (Pearson, FDR < 0.25, P < 0.05) positive (a) or negative (b) correlation with its corresponding subtype signature score are indicated. Known targets for each compound that have demonstrated significant correlation with its subtype‐specific signature score are indicated with red, blue, or gray for positive, negative, or nonsignificant correlation, respectively. (c) Dose–response curves (raw values obtained from deposited public data) of different ferroptotic compounds demonstrate the magnitude of difference between various subtypes.