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Quantile Index Biomarkers Based on Single-Cell Expression Data.

ABSTRACT: Current histocytometry methods enable single-cell quantification of biomolecules in tumor tissue sections by multiple detection technologies, including multiplex fluorescence-based immunohistochemistry or in situ hybridization. Quantitative pathology platforms can provide distributions of cellular signal intensity (CSI) levels of biomolecules across the entire cell populations of interest within the sampled tumor tissue. However, the heterogeneity of CSI levels is usually ignored, and the simple mean signal intensity value is considered a cancer biomarker. Here we consider the entire distribution of CSI expression levels of a given biomolecule in the cancer cell population as a predictor of clinical outcome. The proposed quantile index (QI) biomarker is defined as the weighted average of CSI distribution quantiles in individual tumors. The weight for each quantile is determined by fitting a functional regression model for a clinical outcome. That is, the weights are optimized so that the resulting QI has the highest power to predict a relevant clinical outcome. The proposed QI biomarkers were derived for proteins expressed in cancer cells of malignant breast tumors and demonstrated improved prognostic value compared with the standard mean signal intensity predictors. The R package Qindex implementing QI biomarkers has been developed. The proposed approach is not limited to immunohistochemistry data and can be based on any cell-level expressions of proteins or nucleic acids.

SUBMITTER: Yi M

PROVIDER: S-EPMC10524910 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature

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Quantile Index Biomarkers Based on Single-Cell Expression Data.

Yi Misung M Zhan Tingting T Peck Amy R AR Hooke Jeffrey A JA Kovatich Albert J AJ Shriver Craig D CD Hu Hai H Sun Yunguang Y Rui Hallgeir H Chervoneva Inna I

Laboratory investigation; a journal of technical methods and pathology 20230422 8

Current histocytometry methods enable single-cell quantification of biomolecules in tumor tissue sections by multiple detection technologies, including multiplex fluorescence-based immunohistochemistry or in situ hybridization. Quantitative pathology platforms can provide distributions of cellular signal intensity (CSI) levels of biomolecules across the entire cell populations of interest within the sampled tumor tissue. However, the heterogeneity of CSI levels is usually ignored, and the simple ...[more]

PMID: 37088463

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Project description: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.

Project description:BackgroundPrevious studies have reported a negative relationship between thyroid-stimulating hormone (TSH) and renal function in euthyroid individuals, but others have found that higher free thyroxine (FT4) was associated with an increased risk of chronic kidney disease. This study was designed to analyze the relationship between thyroid and renal function from a new perspective of sensitivity to thyroid hormone.MethodsThis retrospective study included 2831 euthyroid individuals who underwent a health examination at the First Hospital of China Medical University between January 2017 and December 2018. Parametric Thyroid Feedback Quantile-based Index (PTFQIFT4), TSH index (TSHI), thyrotroph T4 resistance index (TT4RI), free triiodothyronine to FT4 ratio (FT3/FT4), the secretory capacity of the thyroid gland (SPINA-GT) and the sum activity of peripheral deiodinases (SPINA-GD) were calculated. We also innovated the TT3RI and PTFQIFT3 indices based on FT3 and TSH. Renal function was assessed by estimated glomerular filtration rate (eGFR) CKD-EPI and creatinine-cystatin C-KDIGO equations.ResultsAfter adjustment of basic characteristics and comorbidities, linear regression showed that eGFR CKD-EPI was positively associated with FT3/FT4 (β = 23.31), and inversely correlated to PTFQI FT4 (β= -2.69) (both p < .001). When comparing the fourth versus the first quartile of PTFQI FT4, the odds ratio (OR) for a reduced renal function was 1.89 (95% CI 1.28-2.80), and the OR was 0.64 (95% CI 0.43-0.95) when comparing quartiles of FT3/FT4 (both pfor trend< .05). In addition, for every 1SD increase in PTFQI FT4, the OR for a reduced renal function was 1.27 (95%CI 1.10-1.47). TSHI, TT4RI and TT3RI also showed a negative correlation to renal function. Similar results were obtained in SPINA-GD as in FT3/FT4.ConclusionsIn euthyroid individuals, decreased sensitivity to thyroid hormone is associated with reduced renal function. The composite PTFQIFT4 index correlates more strongly to renal function than TSH or T4 alone.KEY MESSAGESDecreased sensitivity to thyroid hormone is associated with reduced renal function in the euthyroid population.The recently developed composite index PTFQIFT4 seems to correlate more strongly to renal function than individual TSH or FT4 parameters.Innovative indices TT3RI and PTFQIFT3 based on the interaction between T3 and TSH may also reflect sensitivity to thyroid hormone.

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Quantile Index Biomarkers Based on Single-Cell Expression Data.

Publications

Quantile Index Biomarkers Based on Single-Cell Expression Data.

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