Selection of optimal quantile protein biomarkers based on cell-level immunohistochemistry data

BMC Bioinformatics

Research

Hai Hu¹  Amy R. Peck²  Yunguang Sun²  Hallgeir Rui²  Misung Yi³  Inna Chervoneva³  Tingting Zhan³  Albert J. Kovatich⁴  Craig D. Shriver⁴  Jeffrey A. Hooke⁴ 

[1] Chan Soon-Shiong Institute of Molecular Medicine at Windber, Windber, PA, USA;Department of Pathology, Medical College of Wisconsin, 53226, Milwaukee, WI, USA;Division of Biostatistics, Department of Pharmacology and Experimental Therapeutics, Sidney Kimmel Medical College, Thomas Jefferson University, 19107, Philadelphia, PA, USA;John P. Murtha Cancer Center, Uniformed Services University and Walter Reed National Military Medical Center, Bethesda, MD, USA;

关键词: Cellular protein expression;    Distribution quantiles;    Cancer biomarkers;    Tissue microarrays;    Breast cancer;   

DOI  :  10.1186/s12859-023-05408-8

 received in 2022-09-21, accepted in 2023-07-10,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundProtein biomarkers of cancer progression and response to therapy are increasingly important for improving personalized medicine. Advanced quantitative pathology platforms enable measurement of protein expression in tissues at the single-cell level. However, this rich quantitative cell-by-cell biomarker information is most often not exploited. Instead, it is reduced to a single mean across the cells of interest or converted into a simple proportion of binary biomarker-positive or -negative cells.ResultsWe investigated the utility of retaining all quantitative information at the single-cell level by considering the values of the quantile function (inverse of the cumulative distribution function) estimated from a sample of cell signal intensity levels in a tumor tissue. An algorithm was developed for selecting optimal cutoffs for dichotomizing cell signal intensity distribution quantiles as predictors of continuous, categorical or survival outcomes. The proposed algorithm was used to select optimal quantile biomarkers of breast cancer progression based on cancer cells’ cell signal intensity levels of nuclear protein Ki-67, Proliferating cell nuclear antigen, Programmed cell death 1 ligand 2, and Progesterone receptor. The performance of the resulting optimal quantile biomarkers was validated and compared to the standard cancer compartment mean signal intensity markers using an independent external validation cohort. For Ki-67, the optimal quantile biomarker was also compared to established biomarkers based on percentages of Ki67-positive cells. For proteins significantly associated with PFS in the external validation cohort, the optimal quantile biomarkers yielded either larger or similar effect size (hazard ratio for progression-free survival) as compared to cancer compartment mean signal intensity biomarkers.ConclusionThe optimal quantile protein biomarkers yield generally improved prognostic value as compared to the standard protein expression markers. The proposed methodology has a broad application to single-cell data from genomics, transcriptomics, proteomics, or metabolomics studies at the single cell level.

【 授权许可】

CC BY   
© The Author(s) 2023

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