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Article: Principal component analysis identifies patterns of cytokine expression in non-small cell lung cancer patients undergoing definitive radiation therapy

TitlePrincipal component analysis identifies patterns of cytokine expression in non-small cell lung cancer patients undergoing definitive radiation therapy
Authors
Issue Date2017
Citation
PLoS ONE, 2017, v. 12, n. 9, article no. e0183239 How to Cite?
Abstract© 2017 Ellsworth et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Background/Purpose: Radiation treatment (RT) stimulates the release of many immunohumoral factors, complicating the identification of clinically significant cytokine expression patterns. This study used principal component analysis (PCA) to analyze cytokines in non-small cell lung cancer (NSCLC) patients undergoing RT and explore differences in changes after hypofractionated stereotactic body radiation therapy (SBRT) and conventionally fractionated RT (CFRT) without or with chemotherapy. Methods: The dataset included 141 NSCLC patients treated on prospective clinical protocols; PCA was based on the 128 patients who had complete CK values at baseline and during treatment. Patients underwent SBRT (n = 16), CFRT (n = 18), or CFRT (n = 107) with concurrent chemotherapy (ChRT). Levels of 30 cytokines were measured from prospectively collected platelet-poor plasma samples at baseline, during RT, and after RT. PCA was used to study variations in cytokine levels in patients at each time point. Results: Median patient age was 66, and 22.7% of patients were female. PCA showed that sCD40l, fractalkine/C3, IP10, VEGF, IL-1a, IL-10, and GMCSF were responsible for most variability in baseline cytokine levels. During treatment, sCD40l, IP10, MIP-1b, fractalkine, IFN-r, and VEGF accounted for most changes in cytokine levels. In SBRT patients, the most important players were sCD40l, IP10, and MIP-1b, whereas fractalkine exhibited greater variability in CFRT alone patients. ChRT patients exhibited variability in IFN-γ and VEGF in addition to IP10, MIP-1b, and sCD40l. Conclusions: PCA can identify potentially significant patterns of cytokine expression after fractionated RT. Our PCA showed that inflammatory cytokines dominate post-treatment cytokine profiles, and the changes differ after SBRT versus CFRT, with vs without chemotherapy. Further studies are planned to validate these findings and determine the clinical significance of the cytokine profiles identified by PCA.
Persistent Identifierhttp://hdl.handle.net/10722/267078
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorEllsworth, Susannah G.-
dc.contributor.authorRabatic, Bryan M.-
dc.contributor.authorChen, Jie-
dc.contributor.authorZhao, Jing-
dc.contributor.authorCampbell, Jeffrey-
dc.contributor.authorWang, Weili-
dc.contributor.authorPi, Wenhu-
dc.contributor.authorStanton, Paul-
dc.contributor.authorMatuszak, Martha-
dc.contributor.authorJolly, Shruti-
dc.contributor.authorMiller, Amy-
dc.contributor.authorKong, Feng Ming-
dc.date.accessioned2019-01-31T07:20:27Z-
dc.date.available2019-01-31T07:20:27Z-
dc.date.issued2017-
dc.identifier.citationPLoS ONE, 2017, v. 12, n. 9, article no. e0183239-
dc.identifier.urihttp://hdl.handle.net/10722/267078-
dc.description.abstract© 2017 Ellsworth et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Background/Purpose: Radiation treatment (RT) stimulates the release of many immunohumoral factors, complicating the identification of clinically significant cytokine expression patterns. This study used principal component analysis (PCA) to analyze cytokines in non-small cell lung cancer (NSCLC) patients undergoing RT and explore differences in changes after hypofractionated stereotactic body radiation therapy (SBRT) and conventionally fractionated RT (CFRT) without or with chemotherapy. Methods: The dataset included 141 NSCLC patients treated on prospective clinical protocols; PCA was based on the 128 patients who had complete CK values at baseline and during treatment. Patients underwent SBRT (n = 16), CFRT (n = 18), or CFRT (n = 107) with concurrent chemotherapy (ChRT). Levels of 30 cytokines were measured from prospectively collected platelet-poor plasma samples at baseline, during RT, and after RT. PCA was used to study variations in cytokine levels in patients at each time point. Results: Median patient age was 66, and 22.7% of patients were female. PCA showed that sCD40l, fractalkine/C3, IP10, VEGF, IL-1a, IL-10, and GMCSF were responsible for most variability in baseline cytokine levels. During treatment, sCD40l, IP10, MIP-1b, fractalkine, IFN-r, and VEGF accounted for most changes in cytokine levels. In SBRT patients, the most important players were sCD40l, IP10, and MIP-1b, whereas fractalkine exhibited greater variability in CFRT alone patients. ChRT patients exhibited variability in IFN-γ and VEGF in addition to IP10, MIP-1b, and sCD40l. Conclusions: PCA can identify potentially significant patterns of cytokine expression after fractionated RT. Our PCA showed that inflammatory cytokines dominate post-treatment cytokine profiles, and the changes differ after SBRT versus CFRT, with vs without chemotherapy. Further studies are planned to validate these findings and determine the clinical significance of the cytokine profiles identified by PCA.-
dc.languageeng-
dc.relation.ispartofPLoS ONE-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.titlePrincipal component analysis identifies patterns of cytokine expression in non-small cell lung cancer patients undergoing definitive radiation therapy-
dc.typeArticle-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1371/journal.pone.0183239-
dc.identifier.pmid28934231-
dc.identifier.scopuseid_2-s2.0-85029672380-
dc.identifier.volume12-
dc.identifier.issue9-
dc.identifier.spagearticle no. e0183239-
dc.identifier.epagearticle no. e0183239-
dc.identifier.eissn1932-6203-
dc.identifier.isiWOS:000411339900009-

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