Abstract 74: Heterogeneity response to afatinib in gastric cancer patient with uncommon EGFR mutations
Bibliographic record
Abstract
Abstract Introduction: Gastric cancer is the third leading cause of cancer mortality worldwide. Gatric cancer based on driver gene mutation detection may benefit patients by facilitating molecular targeted drugs development and improving survival in gastric cancer patients. On the other hand, the complex and heterogeneous molecular mechanisms of gastric cancer also plays an essential role in the drug resistance. Methods: Whole exome sequencing (WES) was performed, revealing uncommon EGFR ex21 L861Q and ex18 G719S mutation of formalin-fixed paraffin-embedded sections from primary tumor and different metastatic lesions in a gastric cancer patient. The periodic circulating tumor DNA (ctDNA) was also determined by next generation sequencing (NGS). Stable gastric cancer cell and NIH-3T3 cell lines harboring the mutations were established to investigate the effect and mechanisms. Results: Tumors with compond EGFR L861Q/G719S mutations and EGFR gene amplfication are sensitive to afatinib, which caused tumor progression in short time. However, the lung metastatic lesion, which lacked EGFR gene amplification demonstrated primary resistance to afatinib. A dramatic increase of MET gene copy number may collectively related to the patient’s rapid progression. Periodic mutation profiling of patient’s ctDNA by NGS correspondingly revealed compond EGFR L861Q/G719S mutations, and a consitent increase of MET gene amplification. In in vitro studies, afatinib treatment reduced proliferation and inhibited EGFR phosphorylation in L861Q/G719S and L861Q mutant cells. Conclusions: Afatinib may be a beneficial therapeutic option for a subset of gastric cancer patients with rare EGFR mutations in their tumors. Our results also illustrated the great potential of ctDNA profiling for treatment decision-making to patients with gastric cancer. Note: This abstract was not presented at the meeting. Citation Format: Qin Liu, Jia Wei, Yang Yang, Yue Wang, Baorui Liu, Yang Shao. Heterogeneity response to afatinib in gastric cancer patient with uncommon EGFR mutations [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 74.
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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) | 0.001 | 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 | 0.001 | 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".