Selection of optimal quantile protein biomarkers based on cell-level immunohistochemistry data
Abstract Background Protein 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...
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| Format: | Article |
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BMC
2023-07-01
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| Series: | BMC Bioinformatics |
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| Online Access: | https://doi.org/10.1186/s12859-023-05408-8 |
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| author | Misung Yi Tingting Zhan Amy R. Peck Jeffrey A. Hooke Albert J. Kovatich Craig D. Shriver Hai Hu Yunguang Sun Hallgeir Rui Inna Chervoneva |
| author_facet | Misung Yi Tingting Zhan Amy R. Peck Jeffrey A. Hooke Albert J. Kovatich Craig D. Shriver Hai Hu Yunguang Sun Hallgeir Rui Inna Chervoneva |
| author_sort | Misung Yi |
| collection | DOAJ |
| description | Abstract Background Protein 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. Results We 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. Conclusion The 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. |
| first_indexed | 2024-03-12T22:12:55Z |
| format | Article |
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| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-03-12T22:12:55Z |
| publishDate | 2023-07-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-557a8ed992154aa281c4dfc3aec8358d2023-07-23T11:28:10ZengBMCBMC Bioinformatics1471-21052023-07-0124111510.1186/s12859-023-05408-8Selection of optimal quantile protein biomarkers based on cell-level immunohistochemistry dataMisung Yi0Tingting Zhan1Amy R. Peck2Jeffrey A. Hooke3Albert J. Kovatich4Craig D. Shriver5Hai Hu6Yunguang Sun7Hallgeir Rui8Inna Chervoneva9Division of Biostatistics, Department of Pharmacology and Experimental Therapeutics, Sidney Kimmel Medical College, Thomas Jefferson UniversityDivision of Biostatistics, Department of Pharmacology and Experimental Therapeutics, Sidney Kimmel Medical College, Thomas Jefferson UniversityDepartment of Pathology, Medical College of WisconsinJohn P. Murtha Cancer Center, Uniformed Services University and Walter Reed National Military Medical CenterJohn P. Murtha Cancer Center, Uniformed Services University and Walter Reed National Military Medical CenterJohn P. Murtha Cancer Center, Uniformed Services University and Walter Reed National Military Medical CenterChan Soon-Shiong Institute of Molecular Medicine at WindberDepartment of Pathology, Medical College of WisconsinDepartment of Pathology, Medical College of WisconsinDivision of Biostatistics, Department of Pharmacology and Experimental Therapeutics, Sidney Kimmel Medical College, Thomas Jefferson UniversityAbstract Background Protein 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. Results We 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. Conclusion The 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.https://doi.org/10.1186/s12859-023-05408-8Cellular protein expressionDistribution quantilesCancer biomarkersTissue microarraysBreast cancer |
| spellingShingle | Misung Yi Tingting Zhan Amy R. Peck Jeffrey A. Hooke Albert J. Kovatich Craig D. Shriver Hai Hu Yunguang Sun Hallgeir Rui Inna Chervoneva Selection of optimal quantile protein biomarkers based on cell-level immunohistochemistry data BMC Bioinformatics Cellular protein expression Distribution quantiles Cancer biomarkers Tissue microarrays Breast cancer |
| title | Selection of optimal quantile protein biomarkers based on cell-level immunohistochemistry data |
| title_full | Selection of optimal quantile protein biomarkers based on cell-level immunohistochemistry data |
| title_fullStr | Selection of optimal quantile protein biomarkers based on cell-level immunohistochemistry data |
| title_full_unstemmed | Selection of optimal quantile protein biomarkers based on cell-level immunohistochemistry data |
| title_short | Selection of optimal quantile protein biomarkers based on cell-level immunohistochemistry data |
| title_sort | selection of optimal quantile protein biomarkers based on cell level immunohistochemistry data |
| topic | Cellular protein expression Distribution quantiles Cancer biomarkers Tissue microarrays Breast cancer |
| url | https://doi.org/10.1186/s12859-023-05408-8 |
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