Abstract C053: BACE1 cleaves EGFR to drive lung adenocarcinoma brain metastasis
Bibliographic record
Abstract
Abstract Brain metastasis occurs in up to 40% of patients with non-small cell lung cancer (NSCLC). Considerable genomic heterogeneity exists between the primary lung tumour and respective brain metastasis; however, the identity of the genes capable of driving brain metastasis is incompletely understood. Here, we carried out an in vivo genome-wide CRISPR activation (CRISPRa) screen to identify molecular drivers of brain metastasis from a NSCLC patient-derived xenograft model. We discovered activation of the Alzheimer’s disease associated b-site amyloid precursor protein cleaving enzyme 1 (BACE1) led to a significant increase in brain metastasis. Furthermore, BACE1 knockout or inhibition with Verubecestat treatment blocked NSCLC brain metastasis. Mechanistically, we identified BACE1 cleaves an N-terminal fragment of EGFR to activate downstream signalling through MEK and ERK and drive this metastatic phenotype. Together our data highlights the power of in vivo CRISPR screening to unveil novel molecular drivers and potential therapeutic targets of NSCLC brain metastasis. Citation Format: Shawn C Chafe, Kui Zhai, Sheila K Singh. BACE1 cleaves EGFR to drive lung adenocarcinoma brain metastasis [abstract]. In: Proceedings of the AACR-NCI-EORTC Virtual International Conference on Molecular Targets and Cancer Therapeutics; 2023 Oct 11-15; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2023;22(12 Suppl):Abstract nr C053.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".