37. IN VIVO FUNCTIONAL GENOMIC SCREEN TO IDENTIFY NOVEL DRIVERS OF LUNG-TO-BRAIN METASTASIS
Bibliographic record
Abstract
Abstract Brain metastasis, the most common tumour of the central nervous system, occurs in 20–36% of primary cancers. In particular, 40% of patients with non-small cell lung cancer (NSCLC) develop brain metastases, with a dismal survival of approximately 4–11 weeks without treatment, and 16 months with treatment. This highlights a large unmet need to develop novel targeted therapies for the treatment of lung-to-brain metastases (LBM). Genomic interrogation of LBM using CRISPR technology can inform preventative therapies targeting genetic vulnerabilities in both primary and metastatic tumours. Loss-of-function studies present limitations in metastasis research, as knocking out genes essential for survival in the primary tumour cells can thwart the metastatic cascade prematurely. However, gene overexpression using CRISPR activation (CRISPRa) has the potential for overcoming dependencies of gene essentiality. We theorize that an in vivo genome-wide CRISPRa screen will identify novel genes that, when overexpressed, drive LBM. We have developed a patient-derived orthotopic murine xenograft model of LBM using primary patient-derived NSCLC cell lines (termed LTX cells) from the Swanton Lab TRACERx study. We are now poised to transduce LTX cells with a human genome-wide CRISPRa single guide RNA (sgRNA) library, and to subsequently inject the cells into the lungs of immunocompromised mice. We will then track the process of LBM using bioluminescent and MRI imaging until mice reach endpoint. Sequencing of primary lung tumours and subsequent brain metastases promises to uncover enriched sgRNAs, which may represent novel drivers of primary lung tumour formation and LBM. To the best of our knowledge, this study is the first in vivo genome-wide CRISPRa screen focused on identifying novel drivers of LBM, and can inform future preventative therapies to improve survival outcomes for NSCLC patients.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".