Proteomic-based stemness score measures oncogenic dedifferentiation and enables the identification of druggable targets

Abstract

Cancer progression and therapeutic resistance are closely linked to a stemness phenotype. Here, we introduce a protein-expression-based stemness index (PROTsi) to evaluate oncogenic dedifferentiation in relation to histopathology, molecular features, and clinical outcomes. Utilizing datasets from the Clinical Proteomic Tumor Analysis Consortium across 11 tumor types, we validate PROTsi's effectiveness in accurately quantifying stem-like features. Through integration of PROTsi with multi-omics, including protein post-translational modifications, we identify molecular features associated with stemness and proteins that act as active nodes within transcriptional networks, driving tumor aggressiveness. Proteins highly correlated with stemness were identified as potential drug targets, both shared and tumor specific. These stemness-associated proteins demonstrate predictive value for clinical outcomes, as confirmed by immunohistochemistry in multiple samples. The findings emphasize PROTsi's efficacy as a valuable tool for selecting predictive protein targets, a crucial step in customizing anti-cancer therapy and advancing the clinical development of cures for cancer patients.

Keywords: biomarkers; cancer; drug targets; kinase activity; machine learning; mass spectrometry; multiomics; proteomics; stemness; tumor plasticity.

Graphical abstract

Figure 1

Stratification of CPTAC tumor types with newly developed protein-expression-based stemness index (PROTsi) (A) Key steps of the study: data collection, PROTsi calculation, data integration with PROTsi, proteogenomic analysis, drug targets and predictive markers identification, and IHC validation. (B) Collected data obtained from 11 tumor types. The outer circle layer includes 11 CPTAC tumor types (BR, breast cancer; CCRCC, clear cell renal cell carcinoma; CO, colon cancer; GBM, glioblastoma; PBT, pediatric brain tumors; HNSCC, head and neck squamous cell carcinoma; LSCC, lung squamous cell carcinoma; LUAD, lung adenocarcinoma; OV, ovarian cancer; PDA, pancreatic ductal adenocarcinoma; and UCEC, uterine corpus endometrial carcinoma), with the total number of samples that served for proteogenomic and clinical data collection used for downstream analysis. (C) Tumor types stratified by PROTsi (right) and previously generated mRNAsi.2018 (left). (D) Correlation between mRNAsi.2018 and PROTsi across tumor types. p < 0.05 is considered statistically significant. See also Figure S1.

Figure 2

PROTsi integration with data on copy-number variation, mRNA, miRNA and protein expression, and clinical outcomes across tumors Shown are 12 proteins positively and 12 proteins negatively correlated with stemness, along with their molecular mechanisms in cancer stemness across tumors. The top bar represents PROTsi values from low (left) to high (right), with samples as vertical bars. Left columns detail copy-number variation (CNV) and miRNA and PROTsi correlation, while right columns show protein functional annotation, including therapeutic use, tier classification, and functional family. Overall survival and progression-free survival (PFS) columns indicate predictive value with hazard ratio (HR) and p value, adjusted for age at diagnosis. Statistical significance (∗p < 0.05) is marked by asterisks. See also Figures S2 and S3.

Figure 3

Protein PTMs associated with cancer stemness Top: correlation of stemness and PTMs at site level. Shared acetylation (A), glycosylation (B), and phosphorylation (C) sites across tumors. UpSet plots display positively (red) and negatively (blue) correlated sites for each modification type. Bottom: correlation of stemness and PTMs at protein level. Protein acetylation, glycosylation, and phosphorylation correlated with stemness in shared tumors. Chord diagrams display the top 20 modified proteins with positive (D) and negative (E) correlations.

Figure 4

Activity score of kinases associated with stemness Activity distributions of kinases, with synchronized association with PROTsi stratified by tumor types. Kinases of consistent correlation direction in all tumor types and of significance in at least eight tumor types were selected. Kinases with positive correlations or negative correlations were grouped separately in the plot, with the kinase group identity annotated on the left. Samples of each tumor type were ordered by the PROTsi scores with lowest to the left. See also Figure S4.

Figure 5

Protein-protein interactions and key biological pathways associated with stemness identified in tumors (A) Protein-protein interactions among 100 stemness-associated proteins, with red indicating upregulation and blue indicating downregulation. (B) Top 10 enriched Reactome pathways associated with stemness, ranked by statistical significance (p < 0.05). Red dots represent p values, and maroon dots indicate false discovery rate values. (C) Stemness-associated pathways shared across tumor types. Maroon rectangles highlight significant involvement of stemness-related proteins in Reactome pathways present in at least two tumor types (p < 0.05). See also Figure S5.

Figure 6

Candidate inhibitors targeting stemness-associated pathways (A) Candidate compounds targeting stemness-associated pathways. Left: identified candidate compounds that may inhibit cancer stemness based on CMap analysis. Right: number of tumor types in which the compounds were identified. (B) Top drug targets being targeted by at least two candidate compounds inhibiting stemness-associated pathways. Columns show specific drug targets targeted by candidate inhibitors of stemness, which are presented in rows. (C) Mechanisms of action (MoAs) inhibiting stemness. Left: columns represent identified inhibitors of stemness, and rows show MoAs. Right: MoAs active in tumors. See also Figure S6.

Figure 7

Selected protein targets identified by PROTsi have prognostic value of cancer progression (A) Patterns of stemness-associated proteins measured by IHC in CCRCC primary tumors. Representative images show levels (low and high) of selected proteins, along with a four-tier semi-quantitative pathology evaluation scale based on H-score methodology. (B) Prognostic value of the validated stemness-associated proteins. Kaplan-Meier curves show the correlation between IHC positivity scores and PFS. Log rank p < 0.05 is considered statistically significant. (C) Risk prediction for the stemness-associated proteins influencing PFS. Hazard ratio and 95% confidence interval were calculated for PFS analysis of validated stemness-related proteins using the log rank test.