Abstract P6-07-06: Effect of serum biomarkers (activin A, CAIX, HER2, TIMP-1, and uPA) on outcome in HER2+ metastatic breast cancer patients treated in first line with lapatinib or trastuzumab combined with taxane: CCTG MA.31
Bibliographic record
Abstract
Abstract Background: In MA.31, the lapatinib-taxane combination led to shorter PFS than trastuzumab-taxane in HER2+ metastatic breast cancer. We investigated the prognostic and predictive effects of pretreatment serum biomarkers. Methods: MA.31 accrued 652 patients; 537 (82%) were centrally-confirmed HER2+. Biomarkers were categorized for univariate and multivariate predictive investigations with a median cut-point, ULN cut-points (15 ng/ml- HER2; 506 pg/ml- CAIX; 454 pg/ml- TIMP-1; 1940 pg/ml– uPA; 600 pg/ml- activin A), and custom cut-points (30 and 100 ng/ml for HER2). Stratified step-wise forward Cox multivariate analysis used continuous and categorical biomarkers for PFS in the ITT and central HER2+ populations; central HER2+ biomarker results are shown. Results: Serum was banked for 472 (72%) of 652 patients. Higher serum activin A (>median; >ULN; p<0.0001); higher CAIX (>median; >ULN; p=0.02; p=0.001); higher HER2 (>median; >15; >30; or >100 ng/ml; p=0.05-0.002) and higher TIMP-1 (>median; >ULN; p=0.001; p=0.02) had shorter univariate PFS. In multivariate analysis for PFS: higher continuous activin A (HR=6.75 with Box-Cox transformation, P<0.0001) was associated with significantly shorter PFS, along with treatment arm, prior adjuvant anthracyclines, and higher central EGFR status. In multivariate analysis for OS: higher continuous activin A (HR=85.9, with Box-Cox transformation, P<0.0001) was associated with significantly shorter OS, along with treatment arm and higher central EGFR status. The interaction terms of serum biomarkers with treatment were not significant. Elevated serum activin A was also significant at the median cutpoint for PFS (HR 1.79, p=0.0002) and OS (HR 2.39, p=0.006) in multivariate analysis. Conclusions: Higher serum activin A was a significant independent prognostic biomarker of shorter progression-free and overall survival. No serum biomarker was predictive of differential response to lapatinib vs. trastuzumab. Evaluation of activin A and CAIX-targeted therapy in addition to HER2-targeted therapy may be warranted in patients with elevated serum levels of these biomarkers. *AK, MH, DH, & JH contributed equally Grant: PA Breast Cancer Coalition. Citation Format: Kang A, Hupp M, Ho D, Huang J, Leitzel K, Ali S, Shepherd L, Parulekar WR, Ellis CE, Rocco CJ, Zhu L, Virk S, Nomikos D, Aparicio S, Gelmon KA, Truica C, Al-Marrawi Y, Rizvi S, Vasekar M, Nagabhairu V, Polimera H, Marks E, Richardson A, Ali AS, Krecko L, Carney WP, Downs S, Chen BE, Lipton A. Effect of serum biomarkers (activin A, CAIX, HER2, TIMP-1, and uPA) on outcome in HER2+ metastatic breast cancer patients treated in first line with lapatinib or trastuzumab combined with taxane: CCTG MA.31 [abstract]. In: Proceedings of the 2016 San Antonio Breast Cancer Symposium; 2016 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2017;77(4 Suppl):Abstract nr P6-07-06.
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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) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".