EPIKOL, a chromatin-focused CRISPR/Cas9-based screening platform, to identify cancer-specific epigenetic vulnerabilities
Bibliographic record
Abstract
Dysregulation of the epigenome due to alterations in chromatin modifier proteins commonly contribute to malignant transformation. To interrogate the roles of epigenetic modifiers in cancer cells, we generated an epigenome-wide CRISPR-Cas9 knockout library (EPIKOL) that targets a wide-range of epigenetic modifiers and their cofactors. We conducted eight screens in two different cancer types and showed that EPIKOL performs with high efficiency in terms of sgRNA distribution and depletion of essential genes. We discovered novel epigenetic modifiers that regulate triple-negative breast cancer (TNBC) and prostate cancer cell fitness. We confirmed the growth-regulatory functions of individual candidates, including SS18L2 and members of the NSL complex (KANSL2, KANSL3, KAT8) in TNBC cells. Overall, we show that EPIKOL, a focused sgRNA library targeting ~800 genes, can reveal epigenetic modifiers that are essential for cancer cell fitness under in vitro and in vivo conditions and enable the identification of novel anti-cancer targets. Due to its comprehensive epigenome-wide targets and relatively high number of sgRNAs per gene, EPIKOL will facilitate studies examining functional roles of epigenetic modifiers in a wide range of contexts, such as screens in primary cells, patient-derived xenografts as well as in vivo models.
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".