Genome‐Wide Screening in Haploid Stem Cells Reveals Synthetic Lethality Targeting <scp> <i>MLH1</i> </scp> and <scp> <i>TP53</i> </scp> Deficient Tumours
Bibliographic record
Abstract
Synthetic lethality is defined as a type of genetic interaction where the combination of two genetic events results in cell death, whereas each of them separately does not. Synthetic lethality can be a useful tool in personalised oncology. MLH1 is a cancer-related gene that has a central role in DNA mismatch-repair and TP53 is the most frequently mutated gene in cancer. To identify genetic events that can lead to tumour death once either MLH1 or TP53 is mutated, a genome-wide genetic screening was performed. Thus, mutations in all protein-coding genes were introduced into haploid human embryonic stem cells (hESCs) with and without loss-of-function mutations in the MLH1 or TP53 genes. These experiments uncovered a list of putative hits with EXO1, NR5A2, and PLK2 genes for MLH1, and MYH10 gene for TP53 emerging as the most promising candidates. Synthetic lethal interactions of these genes were validated genetically or chemically using small molecules that inhibit these genes. The specific effects of SR1848, which inhibits NR5A2, ON1231320 or BI2536, which inhibits PLK2, and blebbistatin, which inhibits MYH10, were further validated in cancer cell lines. Finally, animal studies with CCL xenografts showed the selective effect of the small molecule BI2536 on MLH1-null tumours and of blebbistatin on TP53-mutated tumours. Thus, demonstrating their potential for personalised medicine, and the robustness of genetic screening in haploid hESCs in the context of cancer therapeutics.
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 | 0.000 |
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".