{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,6,13]],"date-time":"2024-06-13T11:51:46Z","timestamp":1718279506900},"reference-count":31,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,7,22]],"date-time":"2023-07-22T00:00:00Z","timestamp":1689984000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,7,22]],"date-time":"2023-07-22T00:00:00Z","timestamp":1689984000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Bioinformatics"],"abstract":"Abstract<\/jats:title>\n Background<\/jats:title>\n 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.<\/jats:p>\n <\/jats:sec>\n Results<\/jats:title>\n 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\u00a0dichotomizing cell signal intensity distribution quantiles as predictors of\u00a0continuous, categorical or survival outcomes. The proposed algorithm was used to select optimal quantile biomarkers of breast cancer progression based on cancer cells\u2019 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.<\/jats:p>\n <\/jats:sec>\n Conclusion<\/jats:title>\n 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.<\/jats:p>\n <\/jats:sec>","DOI":"10.1186\/s12859-023-05408-8","type":"journal-article","created":{"date-parts":[[2023,7,22]],"date-time":"2023-07-22T17:01:36Z","timestamp":1690045296000},"update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Selection of optimal quantile protein biomarkers based on cell-level immunohistochemistry data"],"prefix":"10.1186","volume":"24","author":[{"given":"Misung","family":"Yi","sequence":"first","affiliation":[]},{"given":"Tingting","family":"Zhan","sequence":"additional","affiliation":[]},{"given":"Amy R.","family":"Peck","sequence":"additional","affiliation":[]},{"given":"Jeffrey A.","family":"Hooke","sequence":"additional","affiliation":[]},{"given":"Albert J.","family":"Kovatich","sequence":"additional","affiliation":[]},{"given":"Craig D.","family":"Shriver","sequence":"additional","affiliation":[]},{"given":"Hai","family":"Hu","sequence":"additional","affiliation":[]},{"given":"Yunguang","family":"Sun","sequence":"additional","affiliation":[]},{"given":"Hallgeir","family":"Rui","sequence":"additional","affiliation":[]},{"given":"Inna","family":"Chervoneva","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,7,22]]},"reference":[{"issue":"2","key":"5408_CR1","doi-asserted-by":"publisher","first-page":"140","DOI":"10.1016\/j.molonc.2012.01.010","volume":"6","author":"NL Henry","year":"2012","unstructured":"Henry NL, Hayes DF. 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Informed consent does not apply since only archival and deidentified human tumor specimens and associated data were made available for these analyses, hence the need for informed consent was waived by the Institutional Review Board of Thomas Jefferson University (primary study center) and the studies were approved as non-human subject research by the Institutional Review Boards of Thomas Jefferson University, Philadelphia, PA, Walter Reed National Military Medical Center, Bethesda, MD, and Medical College of Wisconsin, Milwaukee, WI.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"not applicable","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no competing conflict ofinterests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"298"}}