bioRxivCancer BiologyPreprint
Prospective pan-cancer phosphoproteomics at clinical scale extends therapeutic options in precision oncology
Annika SchneideriD, Julia Wortmann, Cecilia Bang JenseniD, Leonardo Estrada Duenas, Maria‐Veronica TeleanuiD, Amirhossein SakhtemaniD, Firas HamoodiD, Florian P BayeriD, Christoph StangeiD, Jeremiah Bintang Santoso, Jennifer HüelleiniD, Nindo B. PunturiiD, Lee DolatiD, Peter HorakiD, Resch MoritziD, Nicole KabellaiD, Stefanie HöferiD, Simon KreutzfeldtiD, Christoph E. HeiligiD, Maximilian WerneriD, Chen HongiD, Barbara HutteriD, Katja Beck, Eva Reisinger, Katrin Pfütze, , Bernhard KüsteriD
Abstract
Genomics-guided precision oncology has improved survival in cancer entities with actionable mutations but cannot capture oncogenic signaling that manifests at the protein level. Here, we report a prospective, real-world pan-cancer study profiling proteomes and phosphoproteomes of 1,998 tumor samples from adults and children with rare or advanced cancers enrolled in the German precision oncology programs DKFZ/NCT/DKTK MASTER, CATCH and INFORM and their molecular tumor boards (MTBs). We developed tumor proteome activity status (TOPAS) scores for 46 clinically relevant kinases, an immune activity score capturing antigen presentation and T-cell activation and identified therapeutically targetable cell-surface proteins for 94% of patients. These readouts enhance MTB recommendations by exposing actionable non-genomic kinase activity, refining interpretation of oncogenic genome alterations, and highlighting cell-surface treatment options. Three proof-of-concept analyses indicate clinical utility including kinase activity-stratified pazopanib response in sarcoma, immune activity score-tracked checkpoint-inhibitor outcomes pan-cancer, and a phosphoproteomic biomarker distinguishing EGFR-inhibitor response in chordoma.
- organism: human
- assay: proteomics
- research focus: drug response / drug discovery
- research focus: cell type annotation
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: experimental assay
- approach: statistical model
- Citations
- 0
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