DCAF1 as a Novel Therapeutic Target for Lung Cancer
Bibliographic record
Abstract
Lung cancer remains a leading cause of mortality worldwide, ranking as the second most common cancer and deadliest form in Canada. Therefore, it is important to address the urgent unmet need for novel and effective lung cancer treatments. By mining the DepMap genome-wide shRNA dropout screen, we identified a strong dependency in non-small cell lung cancer (NSCLC) on the WD40 and E3 ligase DCAF1 protein. In-house experiments confirmed that knockdown of DCAF1 resulted in a significant suppression of growth in various tested NSCLC cell lines. Moreover, DCAF1 knockdown led to cell death in all tested cell lines, with a cell cycle defect preceding cell death specifically observed in p53 wild-type cell lines. In order to understand the underlying mechanisms of DCAF1 in NSCLC, unbiased approaches such as RNA sequencing and shotgun proteomics were utilized. Our findings revealed that DCAF1 knockdown led to downregulation of pathways associated with DNA damage, cell cycle regulation, translation, and rRNA processing. Our preliminary data indicate that defects in rRNA processing are more likely to precede changes in DNA damage and cell cycle progression and might be the primary cause of our observed growth suppression phenotype. Furthermore, as part of our efforts to develop therapeutic agents for NSCLC, a hit (Z1391232269) against the WD40 domain of DCAF1 with a Kd of 490 nM in SPR was discovered using an experimental DNA-encoded library followed by a machine learning approach. Z1391232269 was successfully co-crystalized with the WD40 domain of DCAF1. A series of structure activity relationship studies to optimize Z1391232269 identified OICR-38268 a more potent analogue with a Kd of 35 nM by SPR and an EC50 of 10 μM in cells. In conclusion, our study highlights the importance of DCAF1 for NSCLC growth and that targeting it might represent a novel therapeutic approach for lung cancer treatment. Lastly, the discovery of a DCAF1 ligand represents a significant step towards the development of targeted therapeutic agents for NSCLC.
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 | 0.000 | 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 | 0.001 |
| 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".