Hypertension and beta-blocker use as prognostic and predictive factors in metastatic colorectal cancer: A retrospective analysis of NCIC CTG CO.17.
Bibliographic record
Abstract
e15025 Background: Adrenergic receptor stimulation is involved in the development of hypertension (HTN), and has been implicated in progression and dissemination of metastases in various tumours including colon cancer. Furthermore adrenergic antagonists, such as beta-blockers (BB), demonstrate inhibition of invasion and migration in colon cancer cell lines and have been associated with decreased mortality in colorectal cancer. We examined the association of baseline HTN (bHTN) and use of BB on overall (OS) or progression-free survival (PFS) of pts with pre-treated, chemotherapy refractory, metastatic colorectal cancer (mCRC). We also examined bHTN as a predictor of Cetuximab (CET) efficacy. Methods: Using data from NCIC CO.17 (CET vs. BSC), we coded bHTN and use of anti-HTN medications (Rx), including BB, for 572 pts. Chi-square test was used to assess association between these variables and baseline characteristics. Univariate and multivariate analyses of OS and PFS by bHTN and BB use were performed using Cox regression models. Results: Pts with bHTN (149/572) and those using BB (60/572) were older, had diminished performance status, and higher creatinine levels. bHTN, BB use and anti-HTN Rx use were not found to be prognostic for improved OS and PFS, though a trend towards significance was noted between BB use and improved PFS (HR 1.38 [0.97- 1.96], p= 0.077). bHTN and BB use were not significant predictors of CET benefit. However, pts with bHTN tended to have a stronger treatment effect in PFS from CET (interaction p = 0.074). Conclusions: In chemo-refractory mCRC, neither bHTN nor BB use is a significant prognostic factor. bHTN and BB use are not predictive factors for CET benefit, though pts with bHTN may benefit from a stronger treatment effect with CET. Patients who are chemo-refractory may be biologically selected and the impact of BB use in earlier lines may thus still warrant investigation. Predictive effects of bHTN. Interaction HR (CET vs BSC) (95% CI) [p-value] HR (95% CI) [p-value] OS bHTN 0.67 (0.45-0.98) [0.038] 1.20 (0.77-1.86) [0.418] No-bHTN 0.77 (0.62 -0.95) [0.015] PFS bHTN 0.49 (0.34-0.69) [<0.0001] 1.43 (0.97-2.11) [0.074] No-bHTN 0.75 (0.62-0.92) [0.005]
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".