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

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A gene expression-based single sample predictor of lung adenocarcinoma molecular subtype and prognosis. / Liljedahl, Helena; Karlsson, Anna; Oskarsdottir, Gudrun N.; Salomonsson, Annette; Brunnström, Hans; Erlingsdottir, Gigja; Jönsson, Mats; Isaksson, Sofi; Arbajian, Elsa; Ortiz-Villalón, Cristian; Hussein, Aziz; Bergman, Bengt; Vikström, Anders; Monsef, Nastaran; Branden, Eva; Koyi, Hirsh; de Petris, Luigi; Patthey, Annika; Behndig, Annelie F.; Johansson, Mikael; Planck, Maria; Staaf, Johan.

In: International Journal of Cancer, Vol. 148, No. 1, 01.01.2021, p. 238-251.

Research output: Contribution to journalArticle

Harvard

Liljedahl, H, Karlsson, A, Oskarsdottir, GN, Salomonsson, A, Brunnström, H, Erlingsdottir, G, Jönsson, M, Isaksson, S, Arbajian, E, Ortiz-Villalón, C, Hussein, A, Bergman, B, Vikström, A, Monsef, N, Branden, E, Koyi, H, de Petris, L, Patthey, A, Behndig, AF, Johansson, M, Planck, M & Staaf, J 2021, 'A gene expression-based single sample predictor of lung adenocarcinoma molecular subtype and prognosis', International Journal of Cancer, vol. 148, no. 1, pp. 238-251. https://doi.org/10.1002/ijc.33242

APA

Liljedahl, H., Karlsson, A., Oskarsdottir, G. N., Salomonsson, A., Brunnström, H., Erlingsdottir, G., Jönsson, M., Isaksson, S., Arbajian, E., Ortiz-Villalón, C., Hussein, A., Bergman, B., Vikström, A., Monsef, N., Branden, E., Koyi, H., de Petris, L., Patthey, A., Behndig, A. F., ... Staaf, J. (2021). A gene expression-based single sample predictor of lung adenocarcinoma molecular subtype and prognosis. International Journal of Cancer, 148(1), 238-251. https://doi.org/10.1002/ijc.33242

CBE

Liljedahl H, Karlsson A, Oskarsdottir GN, Salomonsson A, Brunnström H, Erlingsdottir G, Jönsson M, Isaksson S, Arbajian E, Ortiz-Villalón C, Hussein A, Bergman B, Vikström A, Monsef N, Branden E, Koyi H, de Petris L, Patthey A, Behndig AF, Johansson M, Planck M, Staaf J. 2021. A gene expression-based single sample predictor of lung adenocarcinoma molecular subtype and prognosis. International Journal of Cancer. 148(1):238-251. https://doi.org/10.1002/ijc.33242

MLA

Vancouver

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Liljedahl, Helena ; Karlsson, Anna ; Oskarsdottir, Gudrun N. ; Salomonsson, Annette ; Brunnström, Hans ; Erlingsdottir, Gigja ; Jönsson, Mats ; Isaksson, Sofi ; Arbajian, Elsa ; Ortiz-Villalón, Cristian ; Hussein, Aziz ; Bergman, Bengt ; Vikström, Anders ; Monsef, Nastaran ; Branden, Eva ; Koyi, Hirsh ; de Petris, Luigi ; Patthey, Annika ; Behndig, Annelie F. ; Johansson, Mikael ; Planck, Maria ; Staaf, Johan. / A gene expression-based single sample predictor of lung adenocarcinoma molecular subtype and prognosis. In: International Journal of Cancer. 2021 ; Vol. 148, No. 1. pp. 238-251.

RIS

TY - JOUR

T1 - A gene expression-based single sample predictor of lung adenocarcinoma molecular subtype and prognosis

AU - Liljedahl, Helena

AU - Karlsson, Anna

AU - Oskarsdottir, Gudrun N.

AU - Salomonsson, Annette

AU - Brunnström, Hans

AU - Erlingsdottir, Gigja

AU - Jönsson, Mats

AU - Isaksson, Sofi

AU - Arbajian, Elsa

AU - Ortiz-Villalón, Cristian

AU - Hussein, Aziz

AU - Bergman, Bengt

AU - Vikström, Anders

AU - Monsef, Nastaran

AU - Branden, Eva

AU - Koyi, Hirsh

AU - de Petris, Luigi

AU - Patthey, Annika

AU - Behndig, Annelie F.

AU - Johansson, Mikael

AU - Planck, Maria

AU - Staaf, Johan

PY - 2021/1/1

Y1 - 2021/1/1

N2 - 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.

AB - 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.

KW - gene expression

KW - lung adenocarcinoma

KW - molecular subtypes

KW - prognosis

KW - single sample predictor

U2 - 10.1002/ijc.33242

DO - 10.1002/ijc.33242

M3 - Article

C2 - 32745259

AN - SCOPUS:85089293201

VL - 148

SP - 238

EP - 251

JO - International Journal of Cancer

JF - International Journal of Cancer

SN - 0020-7136

IS - 1

ER -