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. What's new? New tools are needed in order to improve risk stratification and therapy selection in early‐stage lung adenocarcinoma. Inherent differences in gene expression between adenocarcinoma subtypes could facilitate the development of such tools. The authors of this study derived platform‐independent, single‐sample predictors (SSP) of adenocarcinoma subtypes, based on gene expression. Derived SSPs successfully provided prognostic information in surgically treated stage I lung adenocarcinoma patients. The single‐sample classifier was readily translated into assays applicable to archival tissue, indicating clinical utility. The findings highlight the clinical relevance of transcriptional signatures and gene expression predictors in lung adenocarcinoma, warranting their further investigation and development.