Addressing the Challenges of High-Throughput Cancer Tissue Proteomics for Clinical Application: ProCan.

Area of Science:

• Oncology
• Proteomics
• Biomarker Discovery

Background:

• The cancer tissue proteome holds potential for novel predictive biomarkers.
• Advancements in mass spectrometry (MS)-based tissue proteomics enable comprehensive molecular profiling.
• Integrating 'omics' research strategies and big-data analytics is crucial for cancer research.

Purpose of the Study:

• To establish ProCan, a program generating high-quality tissue proteomic data across diverse cancer types.
• To exploit MS-based tissue proteomics for biomarker discovery in oncology.
• To create a large proteomics knowledge-base for accelerating cancer research.

Main Methods:

• Utilizing data-independent acquisition-MS for proteomic analysis.
• Employing annotated tissue samples sourced through clinical and research collaborations.
• Addressing challenges in study design, sample preparation, data acquisition, and analysis for high-throughput research.

Main Results:

• The ProCan program is actively generating extensive tissue proteomic datasets.
• The methodology is designed to handle the complexities of high-throughput translational research.
• A robust approach to data acquisition and analysis is being implemented.

Conclusions:

• The ProCan initiative is poised to significantly contribute to cancer biomarker discovery.
• The developed proteomics knowledge-base will integrate with other cancer 'omics' data.
• This program aims to accelerate the translation of proteomic insights into clinical applications for cancer patients.