Abstract P2-08-29: Defining a signature of residual risk following endocrine treatment in the tamoxifen and exemestane adjuvant multinational (TEAM) trial
Bibliographic record
Abstract
Abstract Introduction: There are a number of commercially-available tests to stratify risk for women diagnosed with early breast cancer. While such "Generation I" tests are increasingly being used, a consensus is growing that these tests are moderately accurate in assessing risk. Moreover, Generation I tests fail to direct more personalized treatment. Therefore, there is a clear need for more informative "Generation II" tests that better assess risk, also on the long term, and provide theranostic targets. To this end, we have performed an mRNA abundance-based analysis trained in the 790 patients of the UK TEAM cohort to identify a signature of residual risk , to be validated in the remaining 3000 patients from the TEAM pathology study. Methods: RNA extracted from the tumors of respective TEAM pathology study patients were profiled using a 165-gene NanoString code set. The gene list was compiled from targets that comprise many of the existing risk assessment tests, in addition to genes known to be of importance for breast cancer pathogenesis. Signal intensities were normalized using the R statistical environment; 336 different combinations of preprocessing methods were assessed and the most optimal method selected using unbiased criteria. A10-fold cross-validation approach, in combination with a network-based patient risk score calculation formula, was used to derive a 95-gene signature. Briefly, genes were first filtered based on a Cox regression p-value threshold of 0.25; the sum of the weighted mRNA abundance levels of the result genes was calculated for each patient as the risk score. Patient-wise risk scores were then used in a multivariate Cox proportional hazards model along with clinical covariates such as age, grade, HER2 status and nodal status, using DRFS truncated to 10 years as an end-point. Results: Univariate survival analysis revealed a number of significantly prognostic candidates. The resulting 95-gene signature identified in the training set, stratified patients into high and low risk with an HRhigh of 2.74 (p<2.06 x10-4) when adjusted for age, grade, HER2 status and nodal status; resulting in an AUC of 0.73. Modular analyses of the genes comprising the 95-gene signature identified pathways associated with receptor tyrosine kinase signalling, regulation of cell cycle, and the spindle assembly checkpoint. Additionally, the composition of the gene-list made it possible to characterize the patients into their intrinsic subtypes and to determine their relative risk for recurrence relative to assessment tools available today . The validation of the 95-gene signature will be conducted in the remaining samples in the TEAM pathology study using the bioinformatics strategy described above. Conclusions: The impact of test-guided therapy using multi-parametric tests is increasingly being felt in the clinic, and is reshaping modern health-care economics. A successful Generation II multi-parametric test will better discriminate those that are truly at high risk for recurrence following endocrine therapy and indicate potential therapeutic options for intervention for those who would not benefit from current modalities. Citation Format: Bayani J, Yao CQ, Quintayo MA, Haider S, Brookes CL, Yan F, van de Velde CJH, Hasenburg A, Kieback DG, Markopoulos C, Dirix L, Seynaeve C, Boutros PC, Rea DW, Bartlett JMS. Defining a signature of residual risk following endocrine treatment in the tamoxifen and exemestane adjuvant multinational (TEAM) trial. [abstract]. In: Proceedings of the Thirty-Eighth Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2015 Dec 8-12; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2016;76(4 Suppl):Abstract nr P2-08-29.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".