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dc.contributor.authorLiljedahl, Helena
dc.contributor.authorKarlsson, Anna
dc.contributor.authorOskarsdottir, Gudrun N
dc.contributor.authorSalomonsson, Annette
dc.contributor.authorBrunnström, Hans
dc.contributor.authorErlingsdottir, Gigja
dc.contributor.authorJönsson, Mats
dc.contributor.authorIsaksson, Sofi
dc.contributor.authorArbajian, Elsa
dc.contributor.authorOrtiz-Villalón, Cristian
dc.contributor.authorHussein, Aziz
dc.contributor.authorBergman, Bengt
dc.contributor.authorVikström, Anders
dc.contributor.authorMonsef, Nastaran
dc.contributor.authorBranden, Eva
dc.contributor.authorKoyi, Hirsh
dc.contributor.authorde Petris, Luigi
dc.contributor.authorPatthey, Annika
dc.contributor.authorBehndig, Annelie F
dc.contributor.authorJohansson, Mikael
dc.contributor.authorPlanck, Maria
dc.contributor.authorStaaf, Johan
dc.date.accessioned2020-09-09T15:13:23Z
dc.date.available2020-09-09T15:13:23Z
dc.date.issued2020-08-03
dc.date.submitted2020-09
dc.identifier.citationLiljedahl H, Karlsson A, Oskarsdottir GN, et al. A gene expression-based single sample predictor of lung adenocarcinoma molecular subtype and prognosis [published online ahead of print, 2020 Aug 3]. Int J Cancer. 2020;10.1002/ijc.33242. doi:10.1002/ijc.33242en_US
dc.identifier.pmid32745259
dc.identifier.doi10.1002/ijc.33242
dc.identifier.urihttp://hdl.handle.net/2336/621523
dc.descriptionTo access publisher's full text version of this article, please click on the hyperlink in Additional Links field or click on the hyperlink at the top of the page marked Downloaden_US
dc.description.abstractDisease 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. Keywords: gene expression; lung adenocarcinoma; molecular subtypes; prognosis; single sample predictor.en_US
dc.description.sponsorshipBioCare Swedish Cancer Society Fru Berta Kamprads Stiftelse Sjoberg Foundation Stiftelsen Jubileumsklinikens Forskningsfond mot Cancer National Health Services (Region Skane/ALF)en_US
dc.language.isoenen_US
dc.publisherWileyen_US
dc.relation.urlhttps://onlinelibrary.wiley.com/doi/full/10.1002/ijc.33242en_US
dc.rights© 2020 The Authors. International Journal of Cancer published by John Wiley & Sons Ltd on behalf of Union for International Cancer Control.
dc.subjectgene expressionen_US
dc.subjectlung adenocarcinomaen_US
dc.subjectmolecular subtypesen_US
dc.subjectprognosisen_US
dc.subjectsingle sample predictoren_US
dc.subjectLungnakrabbameinen_US
dc.subject.meshLung Neoplasmsen_US
dc.titleA gene expression-based single sample predictor of lung adenocarcinoma molecular subtype and prognosis.en_US
dc.typeArticleen_US
dc.identifier.eissn1097-0215
dc.contributor.department1Division of Oncology and Pathology, Department of Clinical Sciences Lund, Lund University, Medicon Village, Lund, Sweden. 2Department of Respiratory Medicine and Allergology, Skåne University Hospital, Lund, Sweden. 3Department of Pathology, Laboratory Medicine Region Skåne, Lund, Sweden. 4Department of Pathology, Landspitali University Hospital, Reykjavik, Iceland. 5Department of Laboratory Medicine, Department of Pathology, Skåne University Hospital, Malmö, Sweden. 6Department of Pathology, Karolinska University Hospital, Stockholm, Sweden. 7Department of Pathology and Cytology, Sahlgrenska University Hospital, Gothenburg, Sweden. 8Department of Respiratory Medicine, Sahlgrenska University Hospital, Gothenburg, Sweden. 9Department of Pulmonary Medicine, University Hospital Linköping, Linköping, Sweden. 10Department of Pathology and Department of Clinical and Experimental Medicine, Linköping University, Linköping, Sweden. 11Respiratory Medicine Unit, Department of Medicine Solna and CMM, Karolinska Institute and Karolinska University Hospital Solna, Stockholm, Sweden. 12Centre for Research and Development, Uppsala University/Region Gävleborg, Gävle, Sweden. 13Thoracic Oncology Unit, Karolinska University Hospital and Department Oncology-Pathology, Karolinska Institute, Stockholm, Sweden. 14Department of Medical Biosciences, Pathology, Umeå University, Umeå, Sweden. 15Department of Public Health and Clinical Medicine, Division of Medicine, Umeå University, Umeå, Sweden. 16Department of Radiation Sciences, Oncology, Umeå University, Umeå, Sweden.en_US
dc.identifier.journalInternational journal of canceren_US
dc.rights.accessOpen Access - Opinn aðganguren_US
dc.departmentcodePTT12
dc.source.journaltitleInternational journal of cancer
refterms.dateFOA2020-09-09T15:13:24Z
dc.source.countryUnited States


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