Abstract 741: Molecular and clinicopathological characteristics of Chinese non-small cell lung cancers with <i>ROS1</i> gene fusions identified by next-generation sequencing
Bibliographic record
Abstract
Abstract Background ROS1 gene rearrangements are reported at a prevalence of 1-2% in unselected cancer populations including non-small cell lung cancer (NSCLC), and notably higher in lung adenocarcinoma. More than twenty ROS1 fusion partners have been identified, which continues to increase due to the adoption of next-generation sequencing (NGS) for genetic testing. The tyrosine kinase inhibitors (TKIs) including crizotinib, lorlatinib, and entrectinib, have demonstrated favorable efficacy in the treatment of ROS1-rearranged NSCLCs. Methods A total of 17,158 Chinese non-small cell lung cancer (NSCLC) patients whose tumor specimen and/or circulating cell-free DNA (cfDNA) underwent genomic profiling by hybridization capture-based targeted NGS of exons and introns of cancer related genes were retrospectively reviewed. Clinical characteristics, treatment history, and progression-free survival (PFS) for crizotinib of a subset of 37 patients were further evaluated. Results A total of 288 ROS1-positive NSCLC patients were identified in the cohort at a frequency of 1.7% (288/17158). ROS1 fusions retaining the intact ROS1 kinase domain were confirmed in a subset of 258 patients (1.5%) with the most common fusion partner being CD74 (104/258, 40%), followed by EZR (34/258, 13%), SDC4 (28/258, 11%), SLC34A2 (27/258, 11%), and other recurrent ones at low frequency including TPM3, GOPC, MYH9, and CCDC6. Most frequent breakpoints on ROS1 are spreading in introns 33 (37%), 31 (25%), 32 (17%) and 34 (11%) with no obvious hotspots. 5.4% of cases (14/258) were fused to intergenic regions (IGR), mainly on the same chromosome of ROS1, with unknown fusion products. Notably, CD74 (63%) and EZR (50%) are more frequently fused with ROS1 intron 33, while ROS1 intron 31 was the most common breakpoint region of fusion events involving SDC4 (79%) and SLC34A2 (81%). Median PFS (mPFS) was not significantly different between fusions involving breakpoints in ROS1 introns 31-34 for crizotinib treatment, whereas non-CD74 patients (n=23) trended to demonstrate longer mPFS than that of CD74-ROS1 cases (n=14) (12.0 versus 9.9 months). Previously reported acquired resistance to crizotinib in ROS1-rearranged patients, including ROS1 G2032R and S1986F mutations, were observed in 11 out of 37 patients upon disease progression. In addition, two out of three patients who had uncharacterized fusion partners (IGR-ROS1) achieved durable clinical benefit on crizotinib. Conclusion We hereby report the prevalence of ROS1 fusions of 1.7% in a large Chinses NSCLC population detected by NGS testing and the most frequent fusion partners including CD74, EZR, SDC4, and SLC34A2. Crizotinib has demonstrated robust response in treating ROS1-rearranged NSCLC, particularly in non-CD74 ROS1-positive patients. Keywords ROS1, gene fusion, non-small cell lung cancer, crizotinib, breakpoint, next-generation sequencing Citation Format: Dongling Gao, Yuchen Han, Zhihua Zhao, Qiuxiang Ou, Xiaoling Tong, Ruiying Zhao, Nan Dong, Xue Wu, Wencai Li, Guozhong Jiang. Molecular and clinicopathological characteristics of Chinese non-small cell lung cancers with ROS1 gene fusions identified by next-generation sequencing [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 741.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".