Functional diversity of the <i>TP53</i> mutome revealed by saturating CRISPR mutagenesis
Bibliographic record
Abstract
Abstract The tumor suppressor gene TP53 is the most frequently mutated gene in various cancers. Unlike other tumor suppressors, TP53 is mostly hit by missense mutations, of which more than 2,000 have been described in cancer patients. To take advantage of TP53 mutation status for personalized therapy, a deeper knowledge of the functional ramifications of specific mutations is required as evidence of the functional heterogeneity of mutant p53 proteins mounts. Here, we report on a CRISPR-based saturation mutagenesis screen of 9,225 variants expressed from the endogenous TP53 gene locus of a cancer cell. By tracking changes in the abundance of individual variants in response to specific p53-pathway stimulation, we were able to construct high-resolution functional activity maps of the TP53 mutome, covering ∼94.5% of all cancer-associated missense mutations. The results demonstrate the impact of individual mutations on tumor cell fitness with unprecedented precision and coverage, even revealing underlying mechanisms such as apoptosis. The high discriminatory power also resolves subtle loss-of-function phenotypes and highlights a subset of mutants as particularly promising targets for pharmacological reactivation. Moreover, the data offer intriguing insight into the role of aberrant splicing and nonsense-mediated mRNA decay in clearing truncated proteins due to not only nonsense, frameshift, and splice-site mutations but also missense and synonymous mutations. Surprisingly, no missense mutation provided an immediate proliferative advantage over a null mutation. Nonetheless, cells with a missense, but not null mutations, acquired pro-metastatic properties after prolonged growth in mice, emphasizing the significance of mutant p53-directed clonal evolution in the progression of tumors towards metastasis.
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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".