Variable genomic breakpoints have been identified through the application of target-capture next-generation DNA sequencing (DNA NGS) for ALK/ROS1/RET fusion detection in non-small cell lung cancer (NSCLC). We investigated whether ALK/ROS1/RET genomic breakpoint location can predict matched targeted therapy efficacy.
NSCLCs were analyzed by DNA NGS, target-specific next-generation RNA sequencing (RNA NGS), whole-transcriptome sequencing (WTS) and immunohistochemistry (IHC).
In total, 3787 NSCLC samples were analyzed. DNA NGS detected ALK, ROS1 and RET fusions in 241, 59 and 76 cases, respectively. These fusions were divided into canonical (single EML4-ALK, CD74/EZR/TPM3/SDC4-ROS1 and KIF5B/CCDC6-RET fusions), non-canonical (single non-EML4-ALK, non-CD74/EZR/TPM3/SDC4-ROS1 and non-KIF5B/CCDC6-RET fusions) and primary/reciprocal (both primary and reciprocal rearrangements were detected) subtypes based on genomic breakpoint position, and non-canonical and primary/reciprocal subtypes were defined as uncommon fusions. Further RNA sequencing/IHC showed that 6 of 47 (12.8%) uncommon fusions actually were non-productive rearrangements that generated no aberrant transcripts/proteins. Moreover, genomic breakpoints of canonical ALK/RET, but not ROS1, fusions always predicted breakpoints at the transcript level, whereas 85.4% (35/41) of uncommon fusions actually produced canonical fusion transcripts. Patients with uncommon ALK fusion (n=31) who received first-line crizotinib exhibited shorter median progression-free survival (PFS) than patients with canonical ALK fusion (n=53, 8.4 months versus 12.0 months; P=0.004). However, no difference in PFS was observed when only ALK RNA/protein-positive cases were analyzed (P=0.185).
Uncommon ALK/ROS1/RET genomic breakpoint is an unreliable predictor of matched targeted therapy efficacy. Functional validation by RNA or protein assay may add value for accurate detection and interpretation of rare fusions.
Copyright © 2020 International Association for the Study of Lung Cancer. Published by Elsevier Inc. All rights reserved.
About The Expert
Weihua Li
Lei Guo
Yutao Liu
Lin Dong
Lin Yang
Li Chen
Kaihua Liu
Yang Shao
Jianming Ying
References
PubMed