BSBM-09 IN VIVO CRISPR ACTIVATION SCREEN IDENTIFIES ß-SITE AMYLOID PRECURSOR PROTEIN CLEAVING ENZYME 1 (BACE1) AS A DRIVER OF NON-SMALL CELL LUNG CANCER 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 identified activation of the Alzheimer’s disease associated ß-site amyloid precursor protein cleaving enzyme 1 (BACE1) led to a significant increase in brain metastasis. BACE1 is highly expressed in NSCLC and patients with BACE1 expressed in their metastatic brain tumour survived for shorter periods. Genetic loss with CRISPR-Cas9 or pharmacological inhibition of BACE1 with MK-8931 decreased cell proliferation and sphere forming capacity in vitro. Furthermore, BACE1 knockout and MK-8931 treatment blocked brain metastasis in vivo. Mechanistically, we identified BACE1 activation lead to downstream signalling through MEK and ERK. Together our data highlights the power of in vivo CRISPR screening to identify novel molecular drivers and potential therapeutic targets of NSCLC brain metastasis.
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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 | 0.001 | 0.001 |
| 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".