Abstract 3484: Mechanism of BRAF V600E resistance revealed by cell panel screening and bioinformatics
Bibliographic record
Abstract
Abstract The BRAF V600E mutation is pivotal target in the treatment of melanoma. Dabrafenib, one of the most potent inhibitors approved by the FDA, exhibits differential sensitivity across different cancer types. To explore the underlying mechanisms responsible for the resistance of certain cancers to Dabrafenib, we tested a panel of 20 BRAF V600E positive cancer cell lines. Our results showed that 15 of these cell lines demonstrated sensitivity to Dabrafenib, whereas the remaining 5 exhibited resistance. We then investigated the correlation between Dabrafenib potency and gene expression patterns based on public high throughput sequencing data via bioinformatics methods. The results uncovered that the cell lines resistant to Dabrafenib showed low expression of MAP3K1, alongside with elevated expression of G0S2 and PIK3CG. These alterations are known to promote tumor cell proliferation, migration, and invasion, thereby contributing to a reduced sensitivity to BRAF inhibitors. Subsequent in vitro experiments validated the findings derived from our analysis. This unbiased, bioinformatics-driven approach provides valuable insight into the reasons behind treatment resistance and aids in the development of next-generation treatment approaches for inhibitor-resistant cancers. Citation Format: Kaiqiang Hu, Qiuyuan Yang, Pengwei Pan, Fang He. Mechanism of BRAF V600E resistance revealed by cell panel screening and bioinformatics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3484.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".