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Prognostic and chemotherapy predictive value of gene-expression phenotypes in primary lung adenocarcinoma.

Ringnér, Markus LU ; Jönsson, Göran B LU and Staaf, Johan LU (2016) In Clinical Cancer Research 22(1). p.218-229
Abstract
Purpose Primary lung adenocarcinoma remains a deadly disease. Gene expression phenotypes (GEPs) in adenocarcinoma have potential to provide clinically relevant disease stratification for improved prognosis and treatment prediction, given appropriate clinical and methodological validation. Experimental Design 2395 transcriptional adenocarcinoma profiles were assembled from 17 public cohorts and classified by a nearest centroid GEP classifier into three subtypes: terminal respiratory unit (TRU), proximal-proliferative, and proximal-inflammatory, and additionally scored by five transcriptional metagenes representing different biological processes, including proliferation. Prognostic and chemotherapy predictive associations of the subtypes... (More)
Purpose Primary lung adenocarcinoma remains a deadly disease. Gene expression phenotypes (GEPs) in adenocarcinoma have potential to provide clinically relevant disease stratification for improved prognosis and treatment prediction, given appropriate clinical and methodological validation. Experimental Design 2395 transcriptional adenocarcinoma profiles were assembled from 17 public cohorts and classified by a nearest centroid GEP classifier into three subtypes: terminal respiratory unit (TRU), proximal-proliferative, and proximal-inflammatory, and additionally scored by five transcriptional metagenes representing different biological processes, including proliferation. Prognostic and chemotherapy predictive associations of the subtypes were analyzed by univariate and multivariate analysis using overall survival or distant metastasis-free survival as endpoints. Results Overall, GEPs were associated with patient outcome in both univariate and multivariate analyses, although not in all individual cohorts. The prognostically relevant division was between TRU and non-TRU classified cases, with expression of proliferation-associated genes as a key prognostic component. In contrast, GEP classification was not predictive of adjuvant chemotherapy response. GEP classification showed stability to random perturbations of genes or samples and alterations to classification procedures (typically <10% of cases per cohort switching subtype). High classification variability (>20% of cases switching subtype) was observed when removing larger or entire fractions of a single subtype, due to gene-centering shifts not addressable by the classifier. Conclusions In a large-scale evaluation we show that GEPs add prognostic value to standard clinicopathological variables in lung adenocarcinoma. Subject to classifier refinement and confirmation in prospective cohorts, GEPs have potential to impact the prognostication of adenocarcinoma patients through a molecularly driven disease stratification. (Less)
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author
organization
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type
Contribution to journal
publication status
published
subject
in
Clinical Cancer Research
volume
22
issue
1
pages
218 - 229
publisher
American Association for Cancer Research
external identifiers
  • pmid:26265693
  • wos:000367550300025
  • scopus:84954530331
ISSN
1078-0432
DOI
10.1158/1078-0432.CCR-15-0529
language
English
LU publication?
yes
id
c243bb1f-350a-4071-a1e5-7a2b7cc503fb (old id 7844170)
alternative location
http://www.ncbi.nlm.nih.gov/pubmed/26265693?dopt=Abstract
date added to LUP
2015-09-05 17:03:20
date last changed
2017-06-04 03:20:39
@article{c243bb1f-350a-4071-a1e5-7a2b7cc503fb,
  abstract     = {Purpose Primary lung adenocarcinoma remains a deadly disease. Gene expression phenotypes (GEPs) in adenocarcinoma have potential to provide clinically relevant disease stratification for improved prognosis and treatment prediction, given appropriate clinical and methodological validation. Experimental Design 2395 transcriptional adenocarcinoma profiles were assembled from 17 public cohorts and classified by a nearest centroid GEP classifier into three subtypes: terminal respiratory unit (TRU), proximal-proliferative, and proximal-inflammatory, and additionally scored by five transcriptional metagenes representing different biological processes, including proliferation. Prognostic and chemotherapy predictive associations of the subtypes were analyzed by univariate and multivariate analysis using overall survival or distant metastasis-free survival as endpoints. Results Overall, GEPs were associated with patient outcome in both univariate and multivariate analyses, although not in all individual cohorts. The prognostically relevant division was between TRU and non-TRU classified cases, with expression of proliferation-associated genes as a key prognostic component. In contrast, GEP classification was not predictive of adjuvant chemotherapy response. GEP classification showed stability to random perturbations of genes or samples and alterations to classification procedures (typically &lt;10% of cases per cohort switching subtype). High classification variability (&gt;20% of cases switching subtype) was observed when removing larger or entire fractions of a single subtype, due to gene-centering shifts not addressable by the classifier. Conclusions In a large-scale evaluation we show that GEPs add prognostic value to standard clinicopathological variables in lung adenocarcinoma. Subject to classifier refinement and confirmation in prospective cohorts, GEPs have potential to impact the prognostication of adenocarcinoma patients through a molecularly driven disease stratification.},
  author       = {Ringnér, Markus and Jönsson, Göran B and Staaf, Johan},
  issn         = {1078-0432},
  language     = {eng},
  number       = {1},
  pages        = {218--229},
  publisher    = {American Association for Cancer Research},
  series       = {Clinical Cancer Research},
  title        = {Prognostic and chemotherapy predictive value of gene-expression phenotypes in primary lung adenocarcinoma.},
  url          = {http://dx.doi.org/10.1158/1078-0432.CCR-15-0529},
  volume       = {22},
  year         = {2016},
}