Pharmacological activation of the p53 pathway in haematological malignancies
Bibliographic record
Abstract
p53 gene mutations are rarely detected at diagnosis in common haematological cancers such as multiple myeloma (MM), acute myeloid leukaemia (AML), chronic lymphocytic leukaemia (CLL) and Hodgkin's disease (HD), although their prevalence may increase with progression to more aggressive or advanced stages. Therapeutic induction of p53 might therefore be particularly suitable for the treatment of haematological malignancies. Some of the anti-tumour activity of current chemotherapeutics has been derived from activation of p53. However, until recently it was unknown whether p53 signalling is functional in certain haematological cancers including MM and if p53 activity is sufficient to trigger an apoptotic response. With the recent discovery of nutlins, which represent the first highly selective small molecule inhibitors of the p53-MDM2 interaction, pharmacological tools are now available to induce p53 irrespective of upstream signalling defects, and to functionally analyse the downstream p53 pathway in primary leukaemia and lymphoma cells. Combination therapy is emerging as a key factor, and development of non-genotoxic combinations seems very promising for tackling the problems of toxicity and resistance. This review will highlight recent findings in the research into molecules capable of modulating p53 protein activities and mechanisms that activate the p53 pathway, restoring response to therapy in haematological malignancies.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".