Umeå University's logo

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.

INTRODUCTION: Endometrioid endometrial carcinoma is a cancer type with generally excellent prognosis when diagnosed at an early stage, but there is a subset of patients with relapsing disease in spite of early diagnosis and surgical treatment. There is a need to find prognostic markers to identify these patients with increased risk of relapse. Depth of myometrial invasion, histological grade, and presence of lymphovascular invasion are known risk factors. DNA content (ploidy) and proliferation measured as S-phase fraction (SPF) have been discussed as prognostic markers but need additional evaluation.

MATERIAL AND METHODS: We evaluated relapse-free survival (RFS) with respect to ploidy and SPF, which was analyzed by flow cytometry on fresh tumor tissue, in a cohort of 1001 women treated for stage I endometrioid endometrial carcinoma in northern Sweden during the period of 1993-2010, with a median follow up time of 12.0 years. Data were obtained from historical records.

RESULTS: In simple analysis, both aneuploidy and high SPF were associated to increased risk of relapse with hazard ratios (HR) 2.37 (95% CI 1.52-3.70) and 1.94 (95% CI 1.24-3.02), respectively. Our data also confirmed stage, tumor grade, and ploidy as independent prognostic markers in an age adjusted cox regression multivariable analysis but we did not find SPF to contribute to prognosis. However, the combination of aneuploidy and high SPF identified a group of patients with increased risk of relapse, HR 2.02 (95% CI 1.19-3.44).

CONCLUSION: In this study, which is the largest study of ploidy and SPF in stage I endometrioid endometrial carcinoma using fresh frozen tissue, aneuploidy was shown to be an independent prognostic marker. Furthermore, the combination of aneuploidy and high SPF could be used to identify patients with increased risk of relapse.

Background: Smoking is by far the most important cause of lung cancer. However, lung cancer among never-smokers is common and increasing [1]. A smoking-independent subgroup of lung adenocarcinoma with certain molecular and clinical features exists [2-3]. Therefore, as 1st project within the Swedish Molecular Initiative against Lung cancer (SMIL) we aim to characterize never-smoking lung cancer for etiological, diagnostic and therapeutic purposes.

Method: Through the Swedish National Lung Cancer Registry [1], we identified all individuals who underwent surgery for lung cancer in Sweden 2005-2014 and who were registered as never-smokers (n=540). At each study site (n=6), clinical data were reviewed by a thoracic oncologist/pulmonologist through patients' medical charts and archived tumor tissues were retrieved and reviewed by a thoracic pathologist. For subsequent studies, we extracted DNA and RNA (using the Qiagen AllPrep kit for FFPE tissue) and constructed tissue microarrays. As a first pre-planned analysis, we performed fusion gene mapping using an RNA-based NanoString nCounter Elements assay, as previously described [4].

Result: In the first 212 (out of 540) analyzed samples, we detected 17 fusions involving ALK, 8 involving RET, and 2 involving NRG1. In addition, MET exon 14 skipping was found in 17 samples. In total, these findings involved 21% of analyzed cases. Additional results from further studies on the cohort will be presented.

Conclusion: SMIL is an ongoing nation-wide molecular research collaboration on lung cancer where we currently collect one of the largest never-smoking lung tumor cohorts worldwide. From the first pre-planned analyses, we conclude that, in a population-based cohort of early stage lung cancer from never-smokers, druggable oncogenic fusions are frequent.