Exploring targeted therapy of osteosarcoma using proteomics data.

Area of Science:

• Oncology
• Proteomics
• Drug Discovery

Background:

• Osteosarcoma (OS) treatment faces challenges due to drug resistance and disease heterogeneity.
• Recurrent and metastatic outcomes persist despite multimodal therapies.
• Novel therapeutic strategies are urgently needed for improved patient outcomes.

Purpose of the Study:

• To identify potential targeted therapy candidates for osteosarcoma by analyzing protein expression profiles.
• To explore the repurposing of FDA-approved drugs for osteosarcoma treatment.
• To leverage proteomics data for discovering new therapeutic avenues.

Main Methods:

• A systematic review of proteomics studies from PubMed and an in-house repository.
• Data categorized into OS vs. osteoblastic cells, metastasis, and chemoresistance.
• Differentially expressed proteins (DEPs) cross-referenced with FDA-approved drug targets.

Main Results:

• Many targets of FDA-approved antineoplastic agents (epigenetic regulators, kinases, proteasomes) are highly expressed in OS.
• Overexpressed proteins in OS are also targets of non-cancer FDA-approved drugs (immunosuppressants, antiarrhythmics).
• Identified potential anticancer activity of transferase enzyme inhibitors and common targets like PPAT, LDHB, PKM2, and CTSD.

Conclusions:

• Text mining of proteomics data is beneficial for identifying therapeutic targets in OS.
• Repurposing existing FDA-approved drugs offers a promising strategy for osteosarcoma treatment.
• Chemical inhibitors show potential for drug screening and development in OS therapy.