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Consensus of gene expression phenotypes and prognostic risk predictors in primary lung adenocarcinoma

Ringnér, Markus LU and Staaf, Johan LU (2016) In Oncotarget 7(33). p.52957-52973
Abstract

Transcriptional profiling of lung adenocarcinomas has identified numerous gene expression phenotype (GEP) and risk prediction (RP) signatures associated with patient outcome. However, classification agreement between signatures, underlying transcriptional programs, and independent signature validation are less studied. We classified 2395 transcriptional adenocarcinoma profiles, assembled from 17 public cohorts, using 11 GEP and seven RP signatures, finding that 16 signatures were associated with patient survival in the total cohort and in multiple individual cohorts. For significant signatures, total cohort hazard ratios were ~2 in univariate analyses (mean=1.95, range=1.4-2.6). Strong classification agreement between signatures was... (More)

Transcriptional profiling of lung adenocarcinomas has identified numerous gene expression phenotype (GEP) and risk prediction (RP) signatures associated with patient outcome. However, classification agreement between signatures, underlying transcriptional programs, and independent signature validation are less studied. We classified 2395 transcriptional adenocarcinoma profiles, assembled from 17 public cohorts, using 11 GEP and seven RP signatures, finding that 16 signatures were associated with patient survival in the total cohort and in multiple individual cohorts. For significant signatures, total cohort hazard ratios were ~2 in univariate analyses (mean=1.95, range=1.4-2.6). Strong classification agreement between signatures was observed, especially for predicted low-risk patients by adenocarcinoma-derived signatures. Expression of proliferation-related genes correlated strongly with GEP subtype classifications and RP scores, driving the gene signature association with prognosis. A three-group consensus definition of samples across 10 GEP classifiers demonstrated aggregation of samples with specific smoking patterns, gender, and EGFR/KRAS mutations, while survival differences were only significant when patients were divided into low- or high-risk. In summary, our study demonstrates a consensus between GEPs and RPs in lung adenocarcinoma through a common underlying transcriptional program. This consensus generalizes reported problems with current signatures in a clinical context, stressing development of new adenocarcinoma-specific single sample predictors for clinical use.

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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
lung adenocarcinoma, molecular subtype, gene expression, prognostic, risk predictor
in
Oncotarget
volume
7
issue
33
pages
17 pages
publisher
Impact Journals, LLC
external identifiers
  • Scopus:84982298754
ISSN
1949-2553
DOI
10.18632/oncotarget.10641http://dx.doi.org/10.18632/oncotarget.10641
language
English
LU publication?
yes
id
035d7c1f-146d-47f3-ac58-b733130596f3
date added to LUP
2016-08-16 12:04:11
date last changed
2016-10-25 13:16:54
@misc{035d7c1f-146d-47f3-ac58-b733130596f3,
  abstract     = {<p>Transcriptional profiling of lung adenocarcinomas has identified numerous gene expression phenotype (GEP) and risk prediction (RP) signatures associated with patient outcome. However, classification agreement between signatures, underlying transcriptional programs, and independent signature validation are less studied. We classified 2395 transcriptional adenocarcinoma profiles, assembled from 17 public cohorts, using 11 GEP and seven RP signatures, finding that 16 signatures were associated with patient survival in the total cohort and in multiple individual cohorts. For significant signatures, total cohort hazard ratios were ~2 in univariate analyses (mean=1.95, range=1.4-2.6). Strong classification agreement between signatures was observed, especially for predicted low-risk patients by adenocarcinoma-derived signatures. Expression of proliferation-related genes correlated strongly with GEP subtype classifications and RP scores, driving the gene signature association with prognosis. A three-group consensus definition of samples across 10 GEP classifiers demonstrated aggregation of samples with specific smoking patterns, gender, and EGFR/KRAS mutations, while survival differences were only significant when patients were divided into low- or high-risk. In summary, our study demonstrates a consensus between GEPs and RPs in lung adenocarcinoma through a common underlying transcriptional program. This consensus generalizes reported problems with current signatures in a clinical context, stressing development of new adenocarcinoma-specific single sample predictors for clinical use.</p>},
  author       = {Ringnér, Markus and Staaf, Johan},
  issn         = {1949-2553},
  keyword      = {lung adenocarcinoma,molecular subtype,gene expression,prognostic,risk predictor},
  language     = {eng},
  month        = {07},
  number       = {33},
  pages        = {52957--52973},
  publisher    = {ARRAY(0x9ecb3b8)},
  series       = {Oncotarget},
  title        = {Consensus of gene expression phenotypes and prognostic risk predictors in primary lung adenocarcinoma},
  url          = {http://dx.doi.org/10.18632/oncotarget.10641http://dx.doi.org/10.18632/oncotarget.10641},
  volume       = {7},
  year         = {2016},
}