Selective killing of RAD54B-deficient colorectal cancer cells through SOD1 inhibition
Bibliographic record
Abstract
C cancer (CRC) affects both males and females, and is the second leading cause of cancer-related deaths in North America. Novel insight into the disease is highly warranted so that new therapeutic approaches can be developed. Synthetic lethality refers to the lethal combination of 2 independently viable mutations and has been extensively studied in model organisms such as yeast. Recently, synthetic lethality has been applied in cancer contexts and is beginning to show potential as a new therapeutic modality. RAD54B is an excellent candidate for therapeutic targeting as it is mutated in CRC and numerous other tumor types. RAD54B is an evolutionarily conserved protein that functions in DNA repair and is essential for chromosome stability. RAD54B deficiencies cause chromosome instability, which we believe can be targeted through a synthetic lethal approach. Utilizing cross-species approaches, 80 candidate synthetic lethal interactors of RAD54B were subjected to an RNAi-based highcontent screen in human cells. Subsequent direct tests validated an interaction between RAD54B and superoxide dismutase 1 (SOD1) in both CRC cells and immortalized fibroblasts. Real-time cell analyses showed that chemical inhibitors are able to selectively kill RAD54B-deficient cells and demonstrated the decreases in cell numbers observed is due to cellular cytotoxicity rather than cell cycle arrest. Thus, we have identified and validated SOD1 as a novel candidate therapeutic target for the treatment of RAD54B-deficient CRCs cells. These data have far reaching implications as RAD54B defects have been identified in numerous tumor types including, prostate, ovarian, bladder and breast. Kirk J. McManus et al., J Stem Cell Res Ther 2013, 3:5 http://dx.doi.org/10.4172/2157-7633.S1.010
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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".