875P - Proteomic and phosphoproteomic profiling of HNSCC and the role of CDKs as potential drug targets

Background

There is an unmet need for novel targets and predictive biomarkers for head and neck squamous cell carcinoma (HNSCC) in the recurrent metastatic setting. Molecular subtypes assessed by transcriptomic data have been defined and correlations between basal subtype and response to EGFR blockage have been shown. To improve tumor subtyping, and define potential drug targets and predictive biomarkers, we performed proteomic and phosphoproteomic profiling of an extended set of HNSCC patient derived tumor models (PDX) that have been characterized for their response to multiple drug compounds and further evaluated treatment combinations.

Methods

64 HNSCC PDX tumors were analyzed by established Tandem Mass Tag (TMT)-based mass spectrometry workflow. Bioinformatic analysis was performed by integrating proteomics and phosphoproteomic with clinical annotation, drug response data, gene expression and mutation profiles of these tumors. More than 8,500 human proteins and 15,000 phosphosites were quantified across the 64 PDXs.

Results

NMF clustering on proteome and phosphoproteome levels resulted in three distinct clusters overlapping partly with RNA-based classification termed classical, mesenchymal, and basal subtypes. Differential expression analysis validated clinical biomarkers for human papillomavirus (HPV) status and highlighted additional proteins so far not connected to HNSCC. Interestingly, CDK4 expression was associated with HPV positive tumors, whereas the homolog kinase CDK6 was found to be upregulated in HPV-negative tumors belonging to the basal-like proteomic subtype. Dependency Map (DEPMAP) analysis validated that CDK6 has strong codependency with EGFR in cell lines derived from HNSCC. All evaluated models were treated with cetuximab and 25 PDX models with CDK4/6 inhibitor palbociclib as single agent treatment. Combined treatment of cetuximab and palbociclib was evaluated in EGFR resistant, CDK6 high expressing tumor models.

Conclusions

Integrative proteogenomic tumor analysis is a powerful tool for cancer classification, may predict the efficacy of targeted therapies more accurately and displays new subtype-specific druggable protein targets.

Clinical trial identification

Editorial acknowledgement

Legal entity responsible for the study

The authors.

Funding

BMBF.

Disclosure

K. Klinghammer: Financial Interests, Personal, Advisory Board: BMS, MSD; Financial Interests, Personal, Invited Speaker: Merck Sanofi, onkowissen, Biontech; Non-Financial Interests, Principal Investigator: AstraZeneca, gsk, Kura Oncol., MSD, Biontech; Non-Financial Interests, Advisory Board: DGHO, DKG, AIO. All other authors have declared no conflicts of interest.