<scp><i>TP53</i></scp>‐Mutated Myeloid Neoplasms: 2024 Update on Diagnosis, Risk‐Stratification, and Management
Bibliographic record
Abstract
ABSTRACT Alterations in the tumor suppressor gene TP53 are common in human cancers and are associated with an aggressive nature. Approximately 8%–12% of myelodysplastic syndrome (MDS) and acute myeloid leukemia (AML) harbor TP53 mutations ( TP53 mut ) and present immense challenges due to inherent chemoresistance and poor outcomes. As TP53 mut are more common in older individuals and those with secondary/therapy‐related myeloid neoplasms (MN), their incidence is expected to increase with an aging population and rising proportion of cancer survivors. Treatments used for other MN—intensive chemotherapy, hypomethylating agents, and the BCL‐2 inhibitor venetoclax—do not improve the survival of TP53 mut MN patients meaningfully. Additionally, further development of many promising agents has been discontinued, highlighting the challenges. Widespread acknowledgment of these problems led to the recognition of TP53 mut MN as a distinct entity in the 5th edition of the World Health Organization and International Consensus Classifications. However, critical discrepancies between the two classifications may lead to under‐ or overestimation of the prognostic risk. Here, we review recent advances in the biology, diagnosis, and treatment of TP53 mut MN. The development of TP53 mut MN is positioned at the intersection of age, hereditary predisposition, and anti‐cancer therapies. Precursor TP53 mut clones can be detected years prior to the eventual leukemic transformation—raising the possibility of early intervention. We discuss the two classification systems and the bearing of the discrepancies between the two on timely and effective management. We provide novel evidence in the areas of discrepancies. Finally, we review the current therapeutic landscape and the obvious limitations of the currently used therapies.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".