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A gene expression-based single sample predictor of lung adenocarcinoma molecular subtype and prognosis

Liljedahl, Helena LU ; Karlsson, Anna LU ; Oskarsdottir, Gudrun N. LU ; Salomonsson, Annette LU ; Brunnström, Hans LU orcid ; Erlingsdottir, Gigja ; Jönsson, Mats LU ; Isaksson, Sofi LU ; Arbajian, Elsa LU and Ortiz-Villalón, Cristian , et al. (2021) In International Journal of Cancer 148(1). p.238-251
Abstract

Disease recurrence in surgically treated lung adenocarcinoma (AC) remains high. New approaches for risk stratification beyond tumor stage are needed. Gene expression-based AC subtypes such as the Cancer Genome Atlas Network (TCGA) terminal-respiratory unit (TRU), proximal-inflammatory (PI) and proximal-proliferative (PP) subtypes have been associated with prognosis, but show methodological limitations for robust clinical use. We aimed to derive a platform independent single sample predictor (SSP) for molecular subtype assignment and risk stratification that could function in a clinical setting. Two-class (TRU/nonTRU=SSP2) and three-class (TRU/PP/PI=SSP3) SSPs using the AIMS algorithm were trained in 1655 ACs (n = 9659 genes) from public... (More)

Disease recurrence in surgically treated lung adenocarcinoma (AC) remains high. New approaches for risk stratification beyond tumor stage are needed. Gene expression-based AC subtypes such as the Cancer Genome Atlas Network (TCGA) terminal-respiratory unit (TRU), proximal-inflammatory (PI) and proximal-proliferative (PP) subtypes have been associated with prognosis, but show methodological limitations for robust clinical use. We aimed to derive a platform independent single sample predictor (SSP) for molecular subtype assignment and risk stratification that could function in a clinical setting. Two-class (TRU/nonTRU=SSP2) and three-class (TRU/PP/PI=SSP3) SSPs using the AIMS algorithm were trained in 1655 ACs (n = 9659 genes) from public repositories vs TCGA centroid subtypes. Validation and survival analysis were performed in 977 patients using overall survival (OS) and distant metastasis-free survival (DMFS) as endpoints. In the validation cohort, SSP2 and SSP3 showed accuracies of 0.85 and 0.81, respectively. SSPs captured relevant biology previously associated with the TCGA subtypes and were associated with prognosis. In survival analysis, OS and DMFS for cases discordantly classified between TCGA and SSP2 favored the SSP2 classification. In resected Stage I patients, SSP2 identified TRU-cases with better OS (hazard ratio [HR] = 0.30; 95% confidence interval [CI] = 0.18-0.49) and DMFS (TRU HR = 0.52; 95% CI = 0.33-0.83) independent of age, Stage IA/IB and gender. SSP2 was transformed into a NanoString nCounter assay and tested in 44 Stage I patients using RNA from formalin-fixed tissue, providing prognostic stratification (relapse-free interval, HR = 3.2; 95% CI = 1.2-8.8). In conclusion, gene expression-based SSPs can provide molecular subtype and independent prognostic information in early-stage lung ACs. SSPs may overcome critical limitations in the applicability of gene signatures in lung cancer.

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organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
gene expression, lung adenocarcinoma, molecular subtypes, prognosis, single sample predictor
in
International Journal of Cancer
volume
148
issue
1
pages
14 pages
publisher
John Wiley & Sons Inc.
external identifiers
  • pmid:32745259
  • scopus:85089293201
ISSN
0020-7136
DOI
10.1002/ijc.33242
language
English
LU publication?
yes
id
c5d2e359-5e35-4c36-abcb-e2a6c41c61e9
date added to LUP
2020-08-19 14:24:06
date last changed
2022-08-11 08:21:42
@article{c5d2e359-5e35-4c36-abcb-e2a6c41c61e9,
  abstract     = {{<p>Disease recurrence in surgically treated lung adenocarcinoma (AC) remains high. New approaches for risk stratification beyond tumor stage are needed. Gene expression-based AC subtypes such as the Cancer Genome Atlas Network (TCGA) terminal-respiratory unit (TRU), proximal-inflammatory (PI) and proximal-proliferative (PP) subtypes have been associated with prognosis, but show methodological limitations for robust clinical use. We aimed to derive a platform independent single sample predictor (SSP) for molecular subtype assignment and risk stratification that could function in a clinical setting. Two-class (TRU/nonTRU=SSP2) and three-class (TRU/PP/PI=SSP3) SSPs using the AIMS algorithm were trained in 1655 ACs (n = 9659 genes) from public repositories vs TCGA centroid subtypes. Validation and survival analysis were performed in 977 patients using overall survival (OS) and distant metastasis-free survival (DMFS) as endpoints. In the validation cohort, SSP2 and SSP3 showed accuracies of 0.85 and 0.81, respectively. SSPs captured relevant biology previously associated with the TCGA subtypes and were associated with prognosis. In survival analysis, OS and DMFS for cases discordantly classified between TCGA and SSP2 favored the SSP2 classification. In resected Stage I patients, SSP2 identified TRU-cases with better OS (hazard ratio [HR] = 0.30; 95% confidence interval [CI] = 0.18-0.49) and DMFS (TRU HR = 0.52; 95% CI = 0.33-0.83) independent of age, Stage IA/IB and gender. SSP2 was transformed into a NanoString nCounter assay and tested in 44 Stage I patients using RNA from formalin-fixed tissue, providing prognostic stratification (relapse-free interval, HR = 3.2; 95% CI = 1.2-8.8). In conclusion, gene expression-based SSPs can provide molecular subtype and independent prognostic information in early-stage lung ACs. SSPs may overcome critical limitations in the applicability of gene signatures in lung cancer.</p>}},
  author       = {{Liljedahl, Helena and Karlsson, Anna and Oskarsdottir, Gudrun N. and Salomonsson, Annette and Brunnström, Hans and Erlingsdottir, Gigja and Jönsson, Mats and Isaksson, Sofi and Arbajian, Elsa and Ortiz-Villalón, Cristian and Hussein, Aziz and Bergman, Bengt and Vikström, Anders and Monsef, Nastaran and Branden, Eva and Koyi, Hirsh and de Petris, Luigi and Patthey, Annika and Behndig, Annelie F. and Johansson, Mikael and Planck, Maria and Staaf, Johan}},
  issn         = {{0020-7136}},
  keywords     = {{gene expression; lung adenocarcinoma; molecular subtypes; prognosis; single sample predictor}},
  language     = {{eng}},
  month        = {{01}},
  number       = {{1}},
  pages        = {{238--251}},
  publisher    = {{John Wiley & Sons Inc.}},
  series       = {{International Journal of Cancer}},
  title        = {{A gene expression-based single sample predictor of lung adenocarcinoma molecular subtype and prognosis}},
  url          = {{http://dx.doi.org/10.1002/ijc.33242}},
  doi          = {{10.1002/ijc.33242}},
  volume       = {{148}},
  year         = {{2021}},
}