University of Lethbridge, Lethbridge, Alberta, Canada;
University of Calgary, Calgary, Alberta, Canada.
Cancer Genomics Proteomics. 2023 Sep-Oct;20(5):417-432. doi: 10.21873/cgp.20394.
BACKGROUND/AIM: Lung cancer remains the main culprit in cancer-related mortality worldwide. Transcript fusions play a critical role in the initiation and progression of multiple cancers. Treatment approaches based on specific targeting of discovered driver events, such as mutations in EGFR, and fusions in NTRK, ROS1, and ALK genes led to profound improvements in clinical outcomes. The formation of chimeric proteins due to genomic rearrangements or at the post-transcriptional level is widespread and plays a critical role in tumor initiation and progression. Yet, the fusion landscape of lung cancer remains underexplored.
We used the JAFFA pipeline to discover transcript fusions in early-stage non-small cell lung cancer (NSCLC). The set of detected fusions was further analyzed to identify recurrent events, genes with multiple partners and fusions with high predicted oncogenic potential. Finally, we used a generalized linear model (GLM) to establish statistical associations between fusion occurrences and clinicopathological variables. RNA sequencing was used to discover and characterize transcript fusions in 270 NSCLC samples selected from the Glans-Look specimen repository. The samples were obtained during the early stages of disease prior to the initiation of chemo- or radiotherapy.
We identified a set of 792 fusions where 751 were novel, and 33 were recurrent. Four of the 33 recurrent fusions were significantly associated with clinicopathological variables. Several of the fusion partners were represented by well-established oncogenes ERBB4, BRAF, FGFR2, and MET.
The data presented in this study allow researchers to identify, select, and validate promising candidates for targeted clinical interventions.
背景/目的:肺癌仍然是全球癌症相关死亡的主要原因。转录融合在多种癌症的发生和发展中起着关键作用。基于特定靶向发现的驱动事件的治疗方法,如 EGFR 突变和 NTRK、ROS1 和 ALK 基因融合,导致临床结局的显著改善。由于基因组重排或转录后水平形成的嵌合蛋白广泛存在,并在肿瘤的发生和发展中起着关键作用。然而,肺癌的融合景观仍未得到充分探索。
我们使用 JAFFA 管道在早期非小细胞肺癌(NSCLC)中发现转录融合。进一步分析检测到的融合以识别反复出现的事件、具有多个伙伴的基因和具有高预测致癌潜力的融合。最后,我们使用广义线性模型(GLM)来建立融合发生与临床病理变量之间的统计关联。使用 RNA 测序从 Glans-Look 标本库中选择的 270 个 NSCLC 样本中发现和表征转录融合。这些样本是在疾病早期、化疗或放疗开始之前获得的。
我们确定了一组 792 个融合,其中 751 个是新的,33 个是反复出现的。33 个反复出现的融合中有 4 个与临床病理变量显著相关。几个融合伙伴由公认的致癌基因 ERBB4、BRAF、FGFR2 和 MET 代表。
本研究提供的数据使研究人员能够识别、选择和验证有希望用于靶向临床干预的候选药物。