P1-06-13: An Amplicon-Driven Aromatase Inhibitor Response (ADAIR) Signature Provides an Orthogonal Risk Classifier for ER+ Breast Cancer.
Bibliographic record
Abstract
Abstract Background: Many gene signatures have been proposed to predict outcomes for estrogen receptor positive (ER+) breast cancer; most are solely based on mRNA expression data without integration of genomic aberrations that are the primary drivers of disease. We coupled gene expression and copy number variation data to improve the current generation of prognostic algorithms. Methods: mRNA expression based discovery was conducted in 167 patients with low (<10%) or high (>10%) levels of Ki67 after neoadjuvant aromatase inhibition. Genes that were significant in SAM analysis (q ≤0.05) were used if significantly correlated (P≤0.05) with copy number gains detected by aCGH. Interrogation for association with relapse-free survival (RFS) (P≤0.05) in a large public microarray dataset produced an Amplicon-Driven Aromatase Inhibitor Response (ADAIR) signature. Each gene was subject to rigorous independent validation in public microarray datasets and by NanoString on archival tumor RNA accrued from patients treated with adjuvant tamoxifen (UBC-TAM) that were previously profiled for PAM50 subtyping and risk of relapse (ROR) analysis. To determine underlying biology, pathway and transcriptional factor (TF) network analyses were conducted. Results: A 54-gene ADAIR signature of 27 FR(favorable response) and 27 UR(unfavorable response) genes was chosen based on statistical and genomic information. 80% of the ADAIR genes were univariately prognostic for RFS in UBC-TAM. The multigene-based ADAIR risk classifier of endocrine sensitive, intermediate and insensitive categories were prognostic for relapse in the combined public data (p=2.72e-08) and UBC-TAM (p= 1.51e-08). Multivariable survival analysis showed that ADAIR was independently prognostic from standard clinical variables. The ADAIR risk classifier was highly concordant with the PAM50-gene based intrinsic subtype and ROR using subtype information (ROR-S) in all datasets in the analysis (for ROR-S, p=3.50E-44 in combined public data and p=2.16E-47 in UBC-TAM). ADAIR significantly stratified the patients in the medium ROR subtype risk group (p= 0.007 in public cohort, p=0.005 in TAM), suggesting clinical utility. Pathway analysis indicated that the FR gene signature was enriched for cell survival genes, while UR genes were largely cell cycle related. Two major TFs, E2F1 and GABPB1 were predicted to regulate 22 and 13 signature genes. Amplification/overexpression of E2F1 regulated genes characterizes the UR signature. In contrast, the FR signature downregulates the transcriptional repressor GABPB1, resulting in upregulated NFKB1 possibly mediating a survival response. Conclusions: These data suggest that current gene expression signatures can be improved upon through the inclusion of genes whose over-expression is linked to the gene copy number gains typical of the ER+ breast cancer genome. Functionally, tumors sensitive to estrogen deprivation are associated with genes that promote cell survival, whereas resistant tumors are associated with genes that drive estrogen independent cell cycle progression. This study underscores the profound differences in the transcriptome of estrogen-dependent and independent breast cancer beyond the patterns identified by the established classifiers. Citation Information: Cancer Res 2011;71(24 Suppl):Abstract nr P1-06-13.
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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 | 0.001 | 0.001 |
| 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) | 0.002 | 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".