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

Research output: Contribution to journalArticle


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.


  • Gudrun N. Oskarsdottir
  • Gigja Erlingsdottir
  • Cristian Ortiz-Villalón
  • Aziz Hussein
  • Bengt Bergman
  • Anders Vikström
  • Nastaran Monsef
  • Eva Branden
  • Hirsh Koyi
  • Luigi de Petris
  • Annika Patthey
  • Annelie F. Behndig
  • Mikael Johansson
External organisations
  • Skåne University Hospital
  • Region Skåne
  • National University Hospital of Iceland
  • Karolinska University Hospital
  • Sahlgrenska University Hospital
  • Linköping University Hospital
  • Linköping University
  • Uppsala University
  • Umeå University
  • Karolinska Institutet
Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • Medical Genetics
  • Cancer and Oncology


  • gene expression, lung adenocarcinoma, molecular subtypes, prognosis, single sample predictor
Original languageEnglish
Pages (from-to)238-251
Number of pages14
JournalInternational Journal of Cancer
Issue number1
Early online date2020 Aug 3
Publication statusPublished - 2021 Jan 1
Publication categoryResearch