Abstract LB-274: Whole-exome sequencing reveals tyrosine kinase-resistant mutations in pretreatment EGFR-mutant lung adenocarcinomas
Bibliographic record
Abstract
Abstract Lung cancer is the most lethal malignancy around the world. In Hong Kong, chronic tobacco use is prevalent in male but <20% of female patients smoke. Epidermal growth factor receptor (EGFR) mutations affect 30% and 67% of male and female lung adenocarcinomas, respectively. Treatment responses to tyrosine kinase inhibitors (TKI) are not uniform and all patients eventually develop drug resistance. Mutation screening of immediate pre-treatment tumors is the most ideal for assessing drug targets but in practice, excision specimens of primary or metastatic sites are more efficient in terms of tumor quantity, timeliness and patient acceptance. However, information on the applicability of this approach and correlation with the clinical outcome is limited. For this purpose, we compared the whole exome mutation profiles of 39 excised EGFR mutant cancers treated with first line TKI with the clinical response type, including 15 stage I/II and 24 stage III/IV tumors. They included 16 non-responders (NR) defined clinically as those with stable or progressive disease, and 23 responders (R) defined by partial or complete tumor shrinkage. Considering only the non-synonymous SNV and INDEL mutations of coding regions of known actionable targets and genes listed in both the CGC and COSMIC cancer databases, the tumors harbored 54 mutated genes including 26 recurrently mutated and 28 genes involving only 1 tumor. Excluding EGFR, TP53 was the most commonly mutated gene occurring in 25/39 (64.1%) cases. Most known resistant genes involved in the EGFR and bypass signaling network showed mutations only in the NR group including recurrent mutations of PTEN (3/16), PIK3CA (2/16) and NF1 (2/16), and single case mutations of AKT1, ALK, RAF1 and KDR. Two EGFR network candidates showed mutations in both the NR and R groups, including HGF (2/16 NR, 1/23 R) and ROS1 (1 case in either group). Four post-treatment (post-TKI) tumors of acquired resistance were also analyzed, all of which showed EGFR T790M while no pre-treatment NR or R tumor harbored this mutation. Notably, mutations in the β-catenin pathway were prominent in the NR and post-TKI tumors, including APC (2/16 NR), CTNNB1 (1/16 NR, 1/4 post-TKI) and c-MYC (1/16 NR, 1/4 post-TKI) while they were not detected in the R tumors. Also, nonsense mutations of ARID1A was observed in 2/16 NR but none of the R tumors. In summary, our findings revealed candidate TKI resistant mutations involving the EGFR and bypass signaling networks in pre-treatment excision specimens particularly in non-responding patients. The EGFR T790M was not detectable in pre-treatment samples but was prevalent in post-TKI treated cancers. While this study is limited by its small cohort size, the findings indicate deep sequencing analysis of pre-treatment excision specimens of EGFR-mutant lung adenocarcinomas is warranted for detection of resistant mutations and predicting treatment response. Note: This abstract was not presented at the meeting. Citation Format: Xu-yuan Gao, Hang Xu, James CM Ho, Oscar SH Chan, Feng Xu, Junwen Wang, Victor HF Lee, Vicky PC Tin, Zhijie Xiao, Siqi Wang, Judy WP Yam, Maria P. Wong. Whole-exome sequencing reveals tyrosine kinase-resistant mutations in pretreatment EGFR-mutant lung adenocarcinomas [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr LB-274. doi:10.1158/1538-7445.AM2017-LB-274
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 | 0.001 |
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".