Dataset Information

A population-based gene expression signature of molecular clock phase from a single epidermal sample.

ABSTRACT: BACKGROUND:For circadian medicine to influence health, such as when to take a drug or undergo a procedure, a biomarker of molecular clock phase is required--one that is easily measured and generalizable across a broad population. It is not clear that any circadian biomarker yet satisfies these criteria. METHODS:We analyzed 24-h molecular rhythms in human dermis and epidermis at three distinct body sites, leveraging both longitudinal (n?=?20) and population (n?=?154) data. We applied cyclic ordering by periodic structure (CYCLOPS) to order the population samples where biopsy time was not recorded. With CYCLOPS-predicted phases, we used ZeitZeiger to discover potential biomarkers of clock phase. RESULTS:Circadian clock function was strongest in the epidermis, regardless of body site. We identified a 12-gene expression signature that reported molecular clock phase to within 3?h (mean error?=?2.5?h) from a single sample of epidermis--the skin's most superficial layer. This set performed well across body sites, ages, sexes, and detection platforms. CONCLUSIONS:This research shows that the clock in epidermis is more robust than dermis regardless of body site. To encourage ongoing validation of this putative biomarker in diverse populations, diseases, and experimental designs, we developed SkinPhaser--a user-friendly app to test biomarker performance in datasets ( https://github.com/gangwug/SkinPhaser ).

SUBMITTER: Wu G

PROVIDER: S-EPMC7441562 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

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A population-based gene expression signature of molecular clock phase from a single epidermal sample.

Wu Gang G Ruben Marc D MD Francey Lauren J LJ Smith David F DF Sherrill Joseph D JD Oblong John E JE Mills Kevin J KJ Hogenesch John B JB

Genome medicine 20200821 1

<h4>Background</h4>For circadian medicine to influence health, such as when to take a drug or undergo a procedure, a biomarker of molecular clock phase is required--one that is easily measured and generalizable across a broad population. It is not clear that any circadian biomarker yet satisfies these criteria.<h4>Methods</h4>We analyzed 24-h molecular rhythms in human dermis and epidermis at three distinct body sites, leveraging both longitudinal (n = 20) and population (n = 154) data. We appli ...[more]

PMID: 32825850

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Project description:BACKGROUND:Gene set scoring provides a useful approach for quantifying concordance between sample transcriptomes and selected molecular signatures. Most methods use information from all samples to score an individual sample, leading to unstable scores in small data sets and introducing biases from sample composition (e.g. varying numbers of samples for different cancer subtypes). To address these issues, we have developed a truly single sample scoring method, and associated R/Bioconductor package singscore ( https://bioconductor.org/packages/singscore ). RESULTS:We use multiple cancer data sets to compare singscore against widely-used methods, including GSVA, z-score, PLAGE, and ssGSEA. Our approach does not depend upon background samples and scores are thus stable regardless of the composition and number of samples being scored. In contrast, scores obtained by GSVA, z-score, PLAGE and ssGSEA can be unstable when less data are available (NS?<?25). The singscore method performs as well as the best performing methods in terms of power, recall, false positive rate and computational time, and provides consistently high and balanced performance across all these criteria. To enhance the impact and utility of our method, we have also included a set of functions implementing visual analysis and diagnostics to support the exploration of molecular phenotypes in single samples and across populations of data. CONCLUSIONS:The singscore method described here functions independent of sample composition in gene expression data and thus it provides stable scores, which are particularly useful for small data sets or data integration. Singscore performs well across all performance criteria, and includes a suite of powerful visualization functions to assist in the interpretation of results. This method performs as well as or better than other scoring approaches in terms of its power to distinguish samples with distinct biology and its ability to call true differential gene sets between two conditions. These scores can be used for dimensional reduction of transcriptomic data and the phenotypic landscapes obtained by scoring samples against multiple molecular signatures may provide insights for sample stratification.

Project description:BackgroundAdjuvant radiotherapy is the standard of care after breast-conserving surgery for primary breast cancer, despite a majority of patients being over- or under-treated. In contrast to adjuvant endocrine therapy and chemotherapy, no diagnostic tests are in clinical use that can stratify patients for adjuvant radiotherapy. This study presents the development and validation of a targeted gene expression assay to predict the risk of ipsilateral breast tumor recurrence and response to adjuvant radiotherapy after breast-conserving surgery in primary breast cancer.MethodsFresh-frozen primary tumors from 336 patients radically (clear margins) operated on with breast-conserving surgery with or without radiotherapy were collected. Patients were split into a discovery cohort (N = 172) and a validation cohort (N = 164). Genes predicting ipsilateral breast tumor recurrence in an Illumina HT12 v4 whole transcriptome analysis were combined with genes identified in the literature (248 genes in total) to develop a targeted radiosensitivity assay on the Nanostring nCounter platform. Single-sample predictors for ipsilateral breast tumor recurrence based on a k-top scoring pairs algorithm were trained, stratified for estrogen receptor (ER) status and radiotherapy. Two previously published profiles, the radiosensitivity signature of Speers et al., and the 10-gene signature of Eschrich et al., were also included in the targeted panel.ResultsDerived single-sample predictors were prognostic for ipsilateral breast tumor recurrence in radiotherapy-treated ER+ patients (AUC 0.67, p = 0.01), ER+ patients without radiotherapy (AUC = 0.89, p = 0.02), and radiotherapy-treated ER- patients (AUC = 0.78, p < 0.001). Among ER+ patients, radiotherapy had an excellent effect on tumors classified as radiosensitive (p < 0.001), while radiotherapy had no effect on tumors classified as radioresistant (p = 0.36) and there was a high risk of ipsilateral breast tumor recurrence (55% at 10 years). Our single-sample predictors developed in ER+ tumors and the radiosensitivity signature correlated with proliferation, while single-sample predictors developed in ER- tumors correlated with immune response. The 10-gene signature negatively correlated with both proliferation and immune response.ConclusionsOur targeted single-sample predictors were prognostic for ipsilateral breast tumor recurrence and have the potential to stratify patients for adjuvant radiotherapy. The correlation of models with biology may explain the different performance in subgroups of breast cancer.

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A population-based gene expression signature of molecular clock phase from a single epidermal sample.

Publications

A population-based gene expression signature of molecular clock phase from a single epidermal sample.

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