Abstract P4-11-06: TLE3 is not a predictive biomarker for taxane sensitivity in the NCIC CTG MA.21 clinical trial
Bibliographic record
Abstract
Abstract Background: TLE3, a nuclear transcriptional repressor downstream of the WNT signaling pathway, has been identified as a candidate predictive biomarker of taxane benefit in early breast cancer. However, robust clinical evidence is required before implementing novel diagnostic biomarkers. We tested the hypothesis that TLE3 predicts for benefit from taxane containing polychemotherapy in the NCIC CTG MA.21 clinical trial. Methods: MA.21 accrued 2104 patients [701 each to cyclophosphamide, epirubicin, and 5 fluorouracil (CEF) and epirubicin and cyclophosphamide with filgrastim and epoetin alfa followed by paclitaxel (EC/T), 702 to doxorubicin and cyclophosphamide followed by paclitaxel (AC/T)] who were followed median 8 years by the final analysis. EC/T and CEF were not significantly different (p= 0.69) while AC/T was inferior to both EC/T and CEF (respectively, p=0.001 and p=0004). Tissue microarrays were constructed from 1097 of the 2104 patients. Patient characteristics were well balanced between those included in the TLE3 analysis and the full trial population. Up to four 0.6 mm tumor cores were stained for TLE3 expression by immunohistochemistry using a previously validated methodology. Continuous visual TLE3 score was the average % positive stain across all cores, while continuous automated score was sum of cells with positive stain/ total cells assessed in all cores. The primary objective used the EC/T (taxane-containing) and CEF (non-taxane; similar dose-density) arms for a test of predictive effect of TLE3 on relapse free survival. TLE3 was positive if >30% of cells stained, the established cut-point, with data available from at least 1 core/tumor. We also examined quartile cut-points, multivariate effects of TLE3, and compared AC/T and CEF. Results: MA.21 patients had 83.2% TLE3+ tumors by visual score and 80.6% TLE3+ by automated image analysis greater than the predicted rate of TLE3 positivity (58.6%) based on prior series and adjusting for clinicopathological features. TLE3 expression was significantly positively associated with ER expression (91.2% of ER+ were TLE3+; p<0.0001). There was no evidence of a predictive effect of TLE3 expression with respect to taxane benefit using the established 30% cut-point, nor quartile cut-points. The treatment and TLE3 interaction term for the EC/T by CEF comparison was not significant (stratified p=0.68, for manual TLE3; p=0.44, for automated TLE3). Conclusions: MA.21 patients had a much higher proportion of TLE3+ tumors than anticipated. Multiple assessments of TLE3 cut-points yielded no evidence that it was predictive of taxane benefit. In our trial TLE3 expression was not a biomarker for taxane benefit (EC/T vs CEF) when using either previously established or common quartile cut-offs for expression in breast cancer. Citation Format: John MS Bartlett, Torsten O Nielsen, Dongxia Gao, Karen A Gelmon, Mary Anne Quintayo, Jane Starczynski, Lei Han, Margot J Burnell, Mark N Levine, Lois E Shepherd, Judy-Anne W Chapman. TLE3 is not a predictive biomarker for taxane sensitivity in the NCIC CTG MA.21 clinical trial [abstract]. In: Proceedings of the Thirty-Seventh Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2014 Dec 9-13; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2015;75(9 Suppl):Abstract nr P4-11-06.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".