Abstract
Background: Lung adenocarcinoma (LUAD) possesses a poor prognosis with a low 5-year survival rate even for stages I-III resected patients, it is thus critical to understand the determinants that affect the survival and discover new potentially prognostic biomarkers. Somatic copy number alterations (CNAs) are major source of genomic variations driving tumor evolution, CNAs screening may identify prognostic biomarkers.Methods: Oncoscan MIP array was used to analyze the patterns of CNAs on formalin fixed paraffin embedded(FFPE) tumor specimens from 163 consecutive stage I-III resected LUAD patients, 145 out of which received platinum-based adjuvant chemotherapy.Results: Of the 163 patients, 91(55.8%) were recurred within 3 years after surgery. The most common aberrations in our cohort were 1q, 5p, 5q, 7p, 8q, 14p, 16p, 17q, 20q for copy number gains and 8p, 9p, 13p, 16q, 18q for losses. The GISTIC2 analysis produced 45 amplification peaks and 40 deletion peaks, involving some reported genes TERT, EGFR, MYC, CCND1, CDK4, MDM2, ERBB2, NKX2-1, CCNE1, and CDKN2A, most of which were consistent with TCGA database. The amplifications of 12p12.1 (CMAS, GOLT1B, YS2, LDHB, RECQL, ETNK1, IAPP, PYROXD1, KRAS) and KDM5A were correlated with worse prognosis in our cohort, this result was further validated in 506 LUAD patients from TCGA. In addition, 163 patients could be well-classified into five groups, and the clinical outcomes were significantly different based on threshold copy number at reoccurring alteration peaks. Among the 145 patients who received adjuvant chemotherapy, focal amplification of ERBB2 and deletion of 4q34.3 were found to be specific in relapsed patients, this result was validated in an independent group of Imielinski et al., demonstrating these two CNAs may contribute to resected LUAD recurrence after adjuvant chemotherapy.Conclusion: This study suggests that CNAs profiling may be a potential prognostic classifier in resected LAUD patients. Amplifications of 12p12.1 and KDM5A might be prognostic biomarkers for LUAD, and amplification of ERBB2 and deletion of 4q34.3 predicted early relapse after adjuvant chemotherapy. These novel findings may provide implication for better implementation of precision therapy for lung cancer patients.
Highlights
About 85 % of lung cancers are non-small cell lung cancer (NSCLC), the majority of which are lung adenocarcinoma (LUAD) and squamous cell carcinoma subtypes [1]
As a fact that LUAD composed of the majority of lung cancer, and the copy number alterations (CNAs) spectrum difference do exist between LUAD and squamous cell carcinoma, it is important to explore the prognostic predictive value of CNA in LUAD subtype
The goal of this study was to evaluate the genetic CNA profiles in LUAD and to assess the predictive value of CNA for survival benefits, subsequently we investigated potential CNA associated with adjuvant chemotherapy in a group of patients who received platinum-based adjuvant chemotherapy treatment
Summary
About 85 % of lung cancers are non-small cell lung cancer (NSCLC), the majority of which are lung adenocarcinoma (LUAD) and squamous cell carcinoma subtypes [1]. The clinical outcome varies among individuals sharing the same clinical features, suggesting the existence of unknown factors that influence the disease outcomes, this necessitates the discovery of new potentially prognostic biomarkers which can identify patients with higher risk of relapse after surgical resection. As a fact that LUAD composed of the majority of lung cancer, and the CNA spectrum difference do exist between LUAD and squamous cell carcinoma, it is important to explore the prognostic predictive value of CNA in LUAD subtype. Lung adenocarcinoma (LUAD) possesses a poor prognosis with a low 5-year survival rate even for stages I-III resected patients, it is critical to understand the determinants that affect the survival and discover new potentially prognostic biomarkers. Somatic copy number alterations (CNAs) are major source of genomic variations driving tumor evolution, CNAs screening may identify prognostic biomarkers
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