Abstract 682: Associations of inflammatory biomarkers with survival among patients with stage III colon cancer (CALGB/SWOG 80702 [Alliance])
Bibliographic record
Abstract
Abstract Chronic inflammation is a well-established carcinogenic pathway for colorectal cancer (CRC), but the role of inflammation in CRC prognosis is not well understood. Since cytokines are central in extensive biological networks to regulate inflammation, it is of great importance to investigate inflammatory cytokines and CRC survival. Among an NCI-sponsored adjuvant chemotherapy trial for patients with stage III colon cancer conducted between 2010 and 2015 (CALGB/SWOG 80702), we collected serum samples from 1,498 participants at a median of 1.6 months (interquartile [IQR]: 1.3-1.9 months) after surgery. IL-6, TNFR2, and hsCRP were assayed as biomarkers of chronic inflammation and were categorized into quintiles. The primary outcome was disease-free survival, defined as time from randomization to recurrence or death from any cause. Secondary outcomes included overall survival and time to recurrence. We estimated the associations of inflammatory biomarkers with survival using multivariable Cox proportional hazards regression. Of 1,498 participants (median follow-up: 5.9 years), mean age was 61.3 years, 82.2% were White, 55.5% were male, and 387 experienced recurrence or death. Medians (IQRs) of biomarkers were 2.6mg/L (1.2-5.6mg/L) for IL-6, 2.9×103pg/mL (2.3-3.6×103pg/mL) for TNFR2, and 2.6mg/L (1.2-5.6mg/L) for hsCRP. Compared to the lowest quintile (Q1), the highest quintile (Q5) in inflammatory biomarkers was significantly associated with worse disease-free survival (hazard ratios were 1.52 [95% CI, 1.08-2.15] for IL-6, 1.76 [95% CI, 1.22-2.54] for TNFR2, and 1.66 [95% CI, 1.17-2.35] for hsCRP). Similar patterns were observed for overall survival and time to recurrence. Our findings suggest that higher levels of inflammation after diagnosis were significantly associated with worse survival among those with stage III colon cancer, and reducing inflammation may be considered as a potential intervention to improve CRC survival. Associations of Inflammatory Biomarkers with Survival after Adjustment for Clinicopathologic Factors Q1 Q2 Q3 Q4 Q5 P for Trend Disease-free survival IL-6 1.00 1.14 (0.80-1.62) 1.37 (0.97-1.94) 1.44 (1.02-2.03) 1.52 (1.08-2.15) 0.02 TNFR2 1.00 1.21 (0.84-1.74) 1.43 (0.99-2.05) 1.56 (1.09-2.25) 1.76 (1.22-2.54) 0.002 hsCRP 1.00 1.34 (0.95-1.90) 1.62 (1.15-2.29) 1.26 (0.88-1.80) 1.66 (1.17-2.35) 0.04 Overall survival IL-6 1.00 1.44 (0.91-2.29) 1.56 (0.98-2.48) 1.48 (0.93-2.34) 1.68 (1.07-2.65) 0.09 TNFR2 1.00 1.57 (0.94-2.63) 1.88 (1.13-3.13) 1.92 (1.15-3.20) 2.31 (1.39-3.84) 0.003 hsCRP 1.00 1.27 (0.81-2.00) 1.72 (1.11-2.65) 1.22 (0.77-1.92) 1.73 (1.12-2.69) 0.06 Time to recurrence IL-6 1.00 1.34 (0.92-1.95) 1.48 (1.01-2.16) 1.55 (1.06-2.27) 1.54 (1.05-2.26) 0.07 TNFR2 1.00 1.14 (0.78-1.67) 1.28 (0.87-1.87) 1.48 (1.01-2.16) 1.54 (1.04-2.27) 0.02 hsCRP 1.00 1.27 (0.87-1.83) 1.64 (1.14-2.36) 1.21 (0.82-1.77) 1.55 (1.07-2.26) 0.12 Citation Format: En Cheng, Shi Qian, Anthony F. Shields, Elizabeth M. Cespedes Feliciano, Andrew B. Nixon, Ardaman P. Shergill, Katherine A. Guthrie, Felix Couture, Philip Kuebler, Pankaj Kumar, Dequincy Lewis, Benjamin Tan, Eileen M. O'Reilly, Justin C. Brown, Philip A. Philip, Bette J. Caan, Jeffrey A. Meyerhardt. Associations of inflammatory biomarkers with survival among patients with stage III colon cancer (CALGB/SWOG 80702 [Alliance]) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 682.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".