Different Phenotyping Approaches Lead to Dissimilar Biologic Profiles in Men With Chronic Fatigue After Radiation Therapy

Li Rebekah Feng, Kristin A Dickinson, Neila Kline, Leorey N. Saligan

Research output: Contribution to journalArticle

7 Citations (Scopus)

Abstract

Context Cancer-related fatigue (CRF) persists months after treatment completion. Although a CRF biomarker has not yet been identified, validated self-report questionnaires are used to define and phenotype CRF in the discovery of potential biomarkers. Objectives The purposes of this study are to identify CRF subjects using three well-known CRF phenotyping approaches using validated self-report questionnaires and to compare the biologic profiles that are associated with each CRF phenotype. Methods Fatigue in men with nonmetastatic prostate cancer receiving external beam radiation therapy was measured at baseline (T1), midpoint (T2), end point (T3), and one-year post–external beam radiation therapy (T4) using the Functional Assessment of Cancer Therapy–Fatigue (FACT-F) and Patient Reported Outcomes Measurement Information System–Fatigue. Chronic fatigue (CF) and nonfatigue subjects were grouped based on three commonly used phenotyping approaches: 1) T4 FACT-F <43; 2) T1–T4 decline in FACT-F score ≥3 points; 3) T4 Patient Reported Outcomes Measurement Information System–Fatigue T-score >50. Differential gene expressions using whole-genome microarray analysis were compared in each of the phenotyping criterion. Results The study enrolled 43 men, where 34%–38% had CF based on the three phenotyping approaches. Distinct gene expression patterns were observed between CF and nonfatigue subjects in each of the three CRF phenotyping approaches: 1) Approach 1 had the largest number of differentially expressed genes and 2) Approaches 2 and 3 had 40 and 21 differentially expressed genes between the fatigue groups, respectively. Conclusion The variation in genetic profiles for CRF suggests that phenotypic profiling for CRF should be carefully considered because it directly influences biomarker discovery investigations.

Original languageEnglish (US)
Pages (from-to)832-840
Number of pages9
JournalJournal of Pain and Symptom Management
Volume52
Issue number6
DOIs
StatePublished - Dec 1 2016
Externally publishedYes

Fingerprint

Fatigue
Radiotherapy
Neoplasms
Biomarkers
Self Report
Phenotype
Gene Expression
Microarray Analysis
Genes
Prostatic Neoplasms
Genome

Keywords

  • Cancer-related fatigue
  • fatigue phenotypes
  • prostate cancer
  • radiation therapy
  • transcriptome profiles

ASJC Scopus subject areas

  • Nursing(all)
  • Clinical Neurology
  • Anesthesiology and Pain Medicine

Cite this

Different Phenotyping Approaches Lead to Dissimilar Biologic Profiles in Men With Chronic Fatigue After Radiation Therapy. / Feng, Li Rebekah; Dickinson, Kristin A; Kline, Neila; Saligan, Leorey N.

In: Journal of Pain and Symptom Management, Vol. 52, No. 6, 01.12.2016, p. 832-840.

Research output: Contribution to journalArticle

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abstract = "Context Cancer-related fatigue (CRF) persists months after treatment completion. Although a CRF biomarker has not yet been identified, validated self-report questionnaires are used to define and phenotype CRF in the discovery of potential biomarkers. Objectives The purposes of this study are to identify CRF subjects using three well-known CRF phenotyping approaches using validated self-report questionnaires and to compare the biologic profiles that are associated with each CRF phenotype. Methods Fatigue in men with nonmetastatic prostate cancer receiving external beam radiation therapy was measured at baseline (T1), midpoint (T2), end point (T3), and one-year post–external beam radiation therapy (T4) using the Functional Assessment of Cancer Therapy–Fatigue (FACT-F) and Patient Reported Outcomes Measurement Information System–Fatigue. Chronic fatigue (CF) and nonfatigue subjects were grouped based on three commonly used phenotyping approaches: 1) T4 FACT-F <43; 2) T1–T4 decline in FACT-F score ≥3 points; 3) T4 Patient Reported Outcomes Measurement Information System–Fatigue T-score >50. Differential gene expressions using whole-genome microarray analysis were compared in each of the phenotyping criterion. Results The study enrolled 43 men, where 34{\%}–38{\%} had CF based on the three phenotyping approaches. Distinct gene expression patterns were observed between CF and nonfatigue subjects in each of the three CRF phenotyping approaches: 1) Approach 1 had the largest number of differentially expressed genes and 2) Approaches 2 and 3 had 40 and 21 differentially expressed genes between the fatigue groups, respectively. Conclusion The variation in genetic profiles for CRF suggests that phenotypic profiling for CRF should be carefully considered because it directly influences biomarker discovery investigations.",
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N2 - Context Cancer-related fatigue (CRF) persists months after treatment completion. Although a CRF biomarker has not yet been identified, validated self-report questionnaires are used to define and phenotype CRF in the discovery of potential biomarkers. Objectives The purposes of this study are to identify CRF subjects using three well-known CRF phenotyping approaches using validated self-report questionnaires and to compare the biologic profiles that are associated with each CRF phenotype. Methods Fatigue in men with nonmetastatic prostate cancer receiving external beam radiation therapy was measured at baseline (T1), midpoint (T2), end point (T3), and one-year post–external beam radiation therapy (T4) using the Functional Assessment of Cancer Therapy–Fatigue (FACT-F) and Patient Reported Outcomes Measurement Information System–Fatigue. Chronic fatigue (CF) and nonfatigue subjects were grouped based on three commonly used phenotyping approaches: 1) T4 FACT-F <43; 2) T1–T4 decline in FACT-F score ≥3 points; 3) T4 Patient Reported Outcomes Measurement Information System–Fatigue T-score >50. Differential gene expressions using whole-genome microarray analysis were compared in each of the phenotyping criterion. Results The study enrolled 43 men, where 34%–38% had CF based on the three phenotyping approaches. Distinct gene expression patterns were observed between CF and nonfatigue subjects in each of the three CRF phenotyping approaches: 1) Approach 1 had the largest number of differentially expressed genes and 2) Approaches 2 and 3 had 40 and 21 differentially expressed genes between the fatigue groups, respectively. Conclusion The variation in genetic profiles for CRF suggests that phenotypic profiling for CRF should be carefully considered because it directly influences biomarker discovery investigations.

AB - Context Cancer-related fatigue (CRF) persists months after treatment completion. Although a CRF biomarker has not yet been identified, validated self-report questionnaires are used to define and phenotype CRF in the discovery of potential biomarkers. Objectives The purposes of this study are to identify CRF subjects using three well-known CRF phenotyping approaches using validated self-report questionnaires and to compare the biologic profiles that are associated with each CRF phenotype. Methods Fatigue in men with nonmetastatic prostate cancer receiving external beam radiation therapy was measured at baseline (T1), midpoint (T2), end point (T3), and one-year post–external beam radiation therapy (T4) using the Functional Assessment of Cancer Therapy–Fatigue (FACT-F) and Patient Reported Outcomes Measurement Information System–Fatigue. Chronic fatigue (CF) and nonfatigue subjects were grouped based on three commonly used phenotyping approaches: 1) T4 FACT-F <43; 2) T1–T4 decline in FACT-F score ≥3 points; 3) T4 Patient Reported Outcomes Measurement Information System–Fatigue T-score >50. Differential gene expressions using whole-genome microarray analysis were compared in each of the phenotyping criterion. Results The study enrolled 43 men, where 34%–38% had CF based on the three phenotyping approaches. Distinct gene expression patterns were observed between CF and nonfatigue subjects in each of the three CRF phenotyping approaches: 1) Approach 1 had the largest number of differentially expressed genes and 2) Approaches 2 and 3 had 40 and 21 differentially expressed genes between the fatigue groups, respectively. Conclusion The variation in genetic profiles for CRF suggests that phenotypic profiling for CRF should be carefully considered because it directly influences biomarker discovery investigations.

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