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Enabling cross-indication protein expression analysis using a curated pan-cancer dataset and a tailored workflow.

Jixin Wang¹, Xiaowen Tian², Wen Yu³

¹Oncology Data Science & AI, AstraZeneca, Gaithersburg, MD, USA.

Scientific Reports

|March 24, 2026

Summary

We developed a robust method to normalize proteomic data from the National Cancer Institute's Clinical Proteomic Tumor Analysis Consortium (CPTAC) pan-cancer study. This curated dataset enables reliable cross-cohort protein expression analysis for cancer research.

Keywords:

CPTAC Differential analysis Normalization Proteomics TCGA iBAQ

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Area of Science:

Proteomics
Cancer Biology
Bioinformatics

Background:

The National Cancer Institute's Clinical Proteomic Tumor Analysis Consortium (CPTAC) generated comprehensive multi-omics data for over 1,000 tumors.
Comparing protein expression across CPTAC cohorts is difficult due to missing data and varied expression patterns.

Purpose of the Study:

To create a curated and normalized pan-cancer protein expression dataset from CPTAC data.
To enable robust cross-cohort protein expression analysis for the cancer research community.

Main Methods:

Developed a novel algorithm for selecting robustly expressed proteins within CPTAC cohorts.
Applied a cohort hybrid imputation approach for protein abundance values.
Utilized intensity-based absolute quantification and compared global vs. smooth quantile normalization.

Main Results:

Global quantile normalization showed superior performance compared to smooth quantile normalization and no normalization.
Higher rank correlation across cancer cohorts was observed between CPTAC and TCGA using global quantile normalization.
The proposed workflow effectively addresses missing data and normalizes protein expression patterns.

Conclusions:

Combining cohort hybrid imputation with global quantile normalization creates an effective normalized CPTAC pan-cancer protein dataset.
This normalized dataset will facilitate the study of protein expression across diverse cancer types.
The findings accelerate pan-cancer discovery research by enabling reliable proteomic data comparison.