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J Pain Symptom Manage. 2016 Dec;52(6):832-840. doi: 10.1016/j.jpainsymman.2016.07.007. Epub 2016 Aug 9.

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

Author information

1
National Institute of Nursing Research, National Institutes of Health, Bethesda, Maryland, USA.
2
Vassar College, Poughkeepsie, New York, USA.
3
National Institute of Nursing Research, National Institutes of Health, Bethesda, Maryland, USA. Electronic address: saliganl@mail.nih.gov.

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.

KEYWORDS:

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

PMID:
27521284
PMCID:
PMC5154838
DOI:
10.1016/j.jpainsymman.2016.07.007
[Indexed for MEDLINE]
Free PMC Article

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