Assessment of the prognostic and predictive utility of the breast cancer index (BCI): An NCIC CTG MA.14 study.
Bibliographic record
Abstract
561 Background: Breast Cancer Index (BCI), a continuous risk index, combines the ratio of HOXB13 to IL17BR (H/I) and the molecular grade index (MGI) (Jerevall et al., British J Cancer, 2011). Here, the prognostic and predictive performance of BCI for BC relapse in MA.14 trial was examined. Methods: MA.14 randomly assigned 667 hormone receptor positive (HR+) women to 5 years of tamoxifen (TAM) +/- 2 years of octreotide LAR (TAM-OCT). A representative subgroup of 299 patients was profiled by RT-PCR for BCI. The primary objective was to determine the prognostic performance of BCI based on relapse-free survival (RFS) with median 9.8 years follow-up. Association of BCI was assessed with step-wise forward stratified Cox regression. Pre-defined categories of low (L), intermediate (I) and high (H) BCI risk groups were used to provide adjusted 5- and 10-year RFS. Results: 292 of 299 patient samples passed internal analytical quality control. The 292 patients contained 49% LN+ patients and had 19.9% BC relapses. Both continuous and pre-specified BCI risk groups were significant multivariate factors (p<0.0001; p=0.007) with higher BCI associated with shorter RFS. Adjusted univariate hazard ratios and 95% CI were 2.53 (1.36 – 4.71) for BCI-H vs -L and 1.28 (0.65 – 2.52) for BCI-I vs -L. With both LN- and LN+ included, BCI-L had 5- and 10-year RFS of 94.0% and 87.5%; -I, 91.8% and 83.9%; and -H, 81.5% and 74.7%. No significant difference in BCI’s ability to stratify patients into 3 risk groups was observed between LN-/no chemotherapy subgroup vs balance of MA.14 patients (p=0.26). Higher pathologic T-stage was multivariately associated with shorter RFS (p=0.01). Interactions between trial therapy and BCI was not significant (p=0.40). Conclusions: This study confirmed the strong prognostic effect of BCI on breast cancer recurrence. BCI was prognostic in both LN- and LN+ patients. The lower 10-year RFS in the BCI-L group than in our previous studies reflected the mixed LN-/LN+ population examined. Like the parent MA.14 trial, BCI did not predict benefit of adding OCT to TAM therapy (Pritchard et al., J Clin Oncol, 2011). This retrospective study is an independent validation of the prognostic performance of BCI within a prospective trial.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".