Specific synthetic lethal killing of RAD54B-deficient human colorectal cancer cells by FEN1 silencing
Bibliographic record
Abstract
Mutations that cause chromosome instability (CIN) in cancer cells produce "sublethal" deficiencies in an essential process (chromosome segregation) and, therefore, may represent a major untapped resource that could be exploited for therapeutic benefit in the treatment of cancer. If second-site unlinked genes can be identified, that when knocked down, cause a synthetic lethal (SL) phenotype in combination with a somatic mutation in a CIN gene, novel candidate therapeutic targets will be identified. To test this idea, we took a cross species SL candidate gene approach by recapitulating a SL interaction observed between rad54 and rad27 mutations in yeast, via knockdown of the highly sequence- and functionally-related proteins RAD54B and FEN1 in a cancer cell line. We show that knockdown of RAD54B, a gene known to be somatically mutated in cancer, causes CIN in mammalian cells. Using high-content microscopy techniques, we demonstrate that RAD54B-deficient human colorectal cancer cells are sensitive to SL killing by reduced FEN1 expression, while isogenic RAD54B proficient cells are not. This conserved SL interaction suggests that extrapolating SL interactions observed in model organisms for homologous genes mutated in human cancers will aid in the identification of novel therapeutic targets for specific killing of cancerous cells exhibiting CIN.
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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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".