Abstract
The National Cancer Institute’s Clinical Proteomic Tumor Analysis Consortium (CPTAC) recently generated harmonized genomic, transcriptomic, proteomic, and clinical data for over 1,000 tumors across 10 cohorts to facilitate pan-cancer discovery research. However, protein expression comparison across CPTAC cohorts remains challenging due to non-uniform missing data and varying protein expression distribution patterns across tumor types. To enable the cancer research community to conduct robust cross-cohort protein expression analysis, we present a curated and normalized pan-cancer protein expression dataset derived from the CPTAC pan-cancer study. Our workflow integrates systematic filtering, various missing data handling and normalization strategies. We developed a novel algorithm to select robustly expressed proteins in tumors within any CPTAC cohort; applied a cohort hybrid imputation approach to protein abundance values from FragPipe within each cohort, based on protein expression distribution patterns; and calculated intensity-based absolute quantification using protein abundance values and applied both global and smooth quantile normalization methods. Our analysis demonstrates that global quantile normalization surpasses both smooth quantile normalization and no normalization, as evidenced by its higher rank correlation across cancer cohorts between CPTAC and TCGA for selected proteins. The findings suggest that combining cohort hybrid imputation with global quantile normalization is an effective method for creating a normalized CPTAC pan-cancer protein dataset, which can facilitate the study of protein expression across different cancer types and accelerate cancer research.
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Data availability
The search output and reports from the FragPipe, Protein‑level data without normalization and without imputation, Protein‑level data after imputation (no normalization) and protein‑level data for each normalization method after imputation of protein abundance estimation for the 10 CPTAC indications used in this work and scripts can be downloaded from https://doi.org/10.5281/zenodo.17203216.
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Acknowledgements
We gratefully acknowledge the CPTAC for providing open-source proteomics data and Deborah Shuman of AstraZeneca for editing the manuscript and formatting the figures. We also acknowledge the presentation of a portion of this work at the AACR Annual Meeting 2024 (April 5–10, 2024, San Diego; https://aacrjournals.org/cancerres/issue/84/7_Supplement).
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This study was funded by AstraZeneca.
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JW and WZ conceived and designed the study. JW, XT, YW, BSP, JB, EH, and WZ analyzed and interpreted the data. All authors contributed to the writing, review, and/or revision of the manuscript, have approved the final version of the manuscript, and agree to be accountable for all aspects of the work.
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Wang, J., Tian, X., Yu, W. et al. Enabling cross-indication protein expression analysis using a curated pan-cancer dataset and a tailored workflow. Sci Rep (2026). https://doi.org/10.1038/s41598-026-44872-z
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DOI: https://doi.org/10.1038/s41598-026-44872-z
