Clinical impact of <i>TP53</i> functional mutations in patients with metastatic colorectal cancer treated with bevacizumab and chemotherapy
Bibliographic record
Abstract
BACKGROUND: Clinical and experimental studies indicate that the tumor protein p53 (TP53) gene loss of function due to missense mutations (MMs) may confer sensitivity to anti-angiogenics. This effect seems to be linked to cross-talk mechanisms among TP53, vascular endothelial growth factor (VEGF), and VEGF receptors. We investigated whether specific TP53 MMs are associated with clinical outcomes of patients with metastatic colorectal cancer (mCRC) treated with first-line chemotherapy plus Bevacizumab. The study focused on KRAS-mutated, liver-only mCRC cases as a homogeneous subgroup that may represent a relevant setting for exploring this association. MATERIALS AND METHODS: MMs were identified on primary tumors. MMs were classified by mutant-specific residual transcriptional activity scores (TP53RTAS) as transcriptionally inactive (TP53inactive = TP53RTAS 0%) or active (TP53active = TP53RTAS ≥ 1%) and used for stratifying patients in progression-free survival (PFS), response rate, and overall survival (OS) analyses. RESULTS: The study population consisted of 62 patients. MMs were found in 39 cases (62%) with 16 having TP53inactive and 23 TP53active MMs. Patients with TP53inactive MMs showed better PFS in comparison with the remaining groups (wild-type and TP53active). This effect was retained in the multivariate model. A similar clinical impact was observed in the OS analysis. There was a significant difference in the overall response rate and rate of post-treatment resection of liver metastases between the TP53inactive and the wild-type or TP53active MMs cases. CONCLUSIONS: Specific TP53 MMs may identify sub-groups of patients who benefit from Bevacizumab-based systemic therapy and these findings could lead to novel tailored treatment strategies in this setting.
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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 | 0.000 | 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) | 0.000 | 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".