Benefits of Adding Paclitaxel to Adjuvant Doxorubicin/Cyclophosphamide Depending on HER2 & ER Status: Analysis of Tumor Tissue Microarrays and Immunohistochemistry in CALGB 9344 (Intergroup 0148).
Bibliographic record
Abstract
Abstract Background: CALGB 9344/INT 0148 demonstrated that, in women with node-positive breast cancer who received standard local control and hormonal therapies, adding paclitaxel (P) to adjuvant doxorubicin/cyclophosphamide (AC) confers significant benefit. Analyses of 1322 of 3121 patients in the study suggested that HER2-pos or ER-neg tumors benefited from P, but the most common tumor subset, HER2-neg & ER-pos, appeared to derive no additional relapse-free (RFS) or overall (OS) survival benefit. Although that study had a built-in confirmation, it was limited by having HER2 data on only 42% of the patients and ER was not centrally assessed.Methods: We constructed tissue microarrays (TMAs) from 2039 (65%) of the pathology blocks from the study, including 957 that were part of the previous HER2 study and 1054 that had not been assessed previously. A total of 2376 (76%) tumors had at least one assessment of both markers. HER2 & ER were determined by central pathology using validated protocols for immunohistochemical staining and scoring. Hypotheses addressing treatment effect by HER2 & ER categories were prespecified. Endpoints were RFS & OS. Analysis within each of the 4 subsets of ER/HER2 combinations was by Kaplan-Meier, logrank tests, and proportional hazards modeling.Results: TMA results agreed with previous whole section methods for HER2 (N = 957; concordance 0.93, intra-class correlation 0.81, kappa 0.76) and with local clinical assessment of ER (N = 1938; concordance 0.87, kappa 0.73). In multivariate Cox models of RFS, HER2 had a significant interaction with paclitaxel on both the entire TMA cohort (p=0.001) and on the newly analyzed set of patients (p=0.04). For the entire set of 2376 patients, taking marker positive to be when either whole section analysis or TMA was positive, the RFS hazard ratios of P vs not P with 95% confidence intervals are shown in Table 1. The table is not qualitatively different if PgR-pos/ER-neg tumors are included with ER-pos or if the endpoint is OS.RFS Hazard Ratios of P vs Not P with 95% Confidence Intervals ER-negER-posHER2-neg0.89 (0.79-0.99); p=0.027, N=6811.01 (0.92-1.10);p=0.95, N=1342HER2-pos0.73 (0.59-0.89); p=0.0018, N=1920.77 (0.65-0.92);p=0.028, N=277 Conclusion: TMA-based marker studies are concordant with whole section analyses. Adjuvant P following AC in node-positive breast cancer improves outcome for HER2-pos tumors regardless of ER status and also for triple- or double-negative tumors, but it does not benefit the majority of patients: women with ER-pos & HER2-neg tumors. This observation is consistent with those from other trials that have investigated adjuvant P. CALGB 9344 provides 3 independent subsets, each showing a statistically significant benefit of paclitaxel. This demonstrates that smaller adjuvant phase III trials are highly feasible if non-responding patients are excluded from the patient mix. Citation Information: Cancer Res 2009;69(24 Suppl):Abstract nr 606.
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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 | 0.000 | 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) | 0.001 | 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".