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. 2021 Jul;28(7):3983-3993.
doi: 10.1245/s10434-020-09276-x. Epub 2020 Nov 2.

Targeted Next-Generation Sequencing Analysis for Recurrence in Early-Stage Lung Adenocarcinoma

In Ae Kim  1   2 , Jae Young Hur  1   3 , Hee Joung Kim  1   2 , Jung Hoon Park  4 , Jae Joon Hwang  1   5 , Song Am Lee  1   5 , Seung Eun Lee  1   3 , Wan Seop Kim #  6   7 , Kye Young Lee #  8   9
Affiliations

Targeted Next-Generation Sequencing Analysis for Recurrence in Early-Stage Lung Adenocarcinoma

In Ae Kim et al. Ann Surg Oncol. 2021 Jul.

Abstract

Background: Despite surgical resection, early lung adenocarcinoma has a recurrence rate of 20-50%. No clear predictive markers for recurrence of early lung adenocarcinoma are available. Targeted next-generation sequencing (NGS) is rarely used to identify recurrence-related genes. We aimed to identify genetic alterations that can predict recurrence, by comparing the molecular profiles of patient groups with and without recurrence.

Methods: Tissues from 230 patients with resected stage I-II lung adenocarcinoma (median follow-up: 49 months) were analyzed via targeted NGS for 207 cancer-related genes. The recurrence-free survival according to the number and type of mutation was estimated using the Kaplan-Meier method. Independent predictive biomarkers related to recurrence were identified using the Cox proportional hazards model.

Results: Recurrence was observed in 64 patients (27.8%). In multivariate analysis adjusted for age, sex, smoking history, stage, surgical mode, and visceral pleural invasion, the CTNNB1 mutation and fusion genes (ALK, ROS1, RET) were negative prognostic factors for recurrence in early-stage lung adenocarcinoma (HR 4.47, p = 0.001; HR 2.73, p = 0.009). EGFR mutation was a favorable factor (HR 0.51, p = 0.016), but the CTNNB1/EGFR co-mutations were negative predictors (HR 19.2, p < 0.001). TP53 mutation was a negative predictor compared with EGFR mutation for recurrence (HR 5.24, p = 0.02).

Conclusions: Targeted NGS can provide valuable information to predict recurrence and identify patients at high recurrence risk, facilitating selection of the treatment strategy among close monitoring and adjuvant-targeted therapy. Larger datasets are required to validate these findings.

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Conflict of interest statement

The authors declare no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Figures

Fig. 1
Fig. 1
The genetic landscape in 230 early stage lung adenocarcinomas. a Comparison of the frequency of genetic alterations according to recurrence status. b Genetic landscape according to recurrence status. The asterisk indicates a statistical difference (*p < 0.05) of the genetic frequency in patients with recurrence and without recurrence. Alteration types are represented by the colors indicated. The frequency of patients with CTNNB1 mutation and fusion gene were statistically different between recurrence and no recurrence groups
Fig. 2
Fig. 2
Recurrence rate according to the number of mutations. Distribution of number of mutations in 230 stage I–II lung adenocarcinomas
Fig. 3
Fig. 3
Effect of clinical-pathologic factor and genetic alterations on recurrence in early stage lung adenocarcinoma. a Kaplan–Meier curve comparing the RFS according to stage. b Kaplan–Meier curve comparing the RFS according to cell differentiation. RFS was related to histologic differentiation. We classified cancer tissues into 3 grades according to cell differentiation. Well differentiated (WD) cancers were lepidic type, moderate differentiated (MD) tissues were acinar type and papillary type, and the poorly differentiated (PD) group was micropapillary type and solid type. c Kaplan–Meier curve according to the CTNNB1 mutation status, d fusion genes (ALK, ROS1, and RET), and e combination subgroups of major driver mutation. This graph enabled us to estimate the possibility of recurrence according to co-occurring genetic alteration
See this image and copyright information in PMC

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