Standardization and harmonization of distributed multi-center proteotype analysis supporting precision medicine...

Area of Science:

• Proteomics
• Molecular Biology
• Bioinformatics

Background:

• Cancer research requires large, diverse datasets for robust clinical insights.
• Current multi-center data generation lacks standardization, hindering collaborative analysis.
• Distributed data generation is crucial for global cancer research initiatives.

Purpose of the Study:

• To standardize a proteotype data generation and analysis workflow for distributed molecular data acquisition.
• To evaluate the feasibility and reproducibility of quantitative proteotype data generation across multiple international sites.
• To enable the digitization of large clinical specimen cohorts for precision medicine.

Main Methods:

• Developed and standardized a proteotype data generation and analysis workflow.
• Utilized harmonized mass spectrometry (MS) instrument platforms and standardized data acquisition procedures.
• Implemented a high-resolution MS1-based quantitative data-independent acquisition (HRMS1-DIA) workflow.

Main Results:

• Demonstrated robust, sensitive, and reproducible proteotype data generation across eleven international sites.
• Achieved 24/7 operation mode over seven consecutive days, confirming workflow stability.
• Confirmed the feasibility of coordinated proteotype data acquisition from clinical specimens using standardized strategies.

Conclusions:

• The standardized workflow enables distributed proteotype data generation, crucial for large-scale cancer research.
• This approach facilitates the digitization of multi-omic data from clinical cohorts across multiple sites.
• The work is a prerequisite for realizing molecular precision medicine on a global scale.