Abstract PD10-07: PD10-07 Low plasma estradiol, low expression of estrogen responsive genes and TP53 mutations are associated with poor anti-proliferative response to aromatase inhibitors
Bibliographic record
Abstract
Abstract Background: Aromatase inhibitors (AIs) are highly effective at reducing recurrences and mortality in postmenopausal patients with estrogen receptor positive breast cancer (ER+ BC). Poor anti-proliferative (Ki67) response or ER+ BCs to AIs after 2 weeks is associated with worse long-term outcomes. Factors that relate to the degree of the response may identify markers and/or mechansims of resistance. Methods: The PeriOperative Endocrine Therapy for Individualizing Care (POETIC) trial randomized 4,480 with ER+ BC to 2 weeks’ AI before surgery or no presurgical treatment. All patients within the bottom 15% of Ki67 responders to AI (poor responders [PRs]; n=177 with RNA extracted) were selected from and matched to good responders (GRs) within the 50% showing the best response (n=190). Matching was based on baseline Ki67 levels as measured by immunohistochemistry (IHC). Response to AI was measured by the percentage change in Ki67 after 2 weeks’ treatment. PRs were further divided into groups expressing high ESR1 (PRs ESR1HIGH; n=119) and low ESR1 (PRs ESR1LOW; n=58) levels since there were very few GRs with low ESR1. RNAseq, targeted exome DNA sequencing of 87 BC/resistance related genes and measurement of plasma estradiol levels by mass spectrometry were performed to understand mechanisms of de novo resistance. Intrinsic subtypes were estimated from RNAseq data. Results: More than 90% of PRs ESR1LOW were non-luminal subtypes with low expression of estrogen-responsive genes. In contrast, 11% of PRs ESR1HIGH were non-luminal compared to 4% of GRs but only HER2-enriched subtypes were significantly higher in PR ESR1HIGH (p=0.05, Fisher exact). While AI treatment had limited impact on Ki67 IHC values in PRs ESR1HIGH, PGR expression was more than 2-fold lower after 2 weeks of AI. Gene-set enrichment analysis showed significantly lower expression of estrogen-response genes in PRs ESR1HIGH compared to GRs (FDR< 10-9) at baseline despite similar percentage of Luminal subtypes in PRs ESR1HIGH and GRs. Plasma estradiol levels were correlated with expression of estrogen-response genes (FDR=0.01) and levels were significantly lower in PRs ESR1HIGH compared to GRs (p=0.003, Mann Whitney). PRs ESR1HIGH had significantly more mutations in RB1, TP53, ARID1B and DNAH11 genes (p< 0.05, Fisher exact). TP53 mutations were significantly enriched in Luminal-A PRs ESR1HIGH compared to GRs (22% and 3% respectively; p=0.003, Fisher exact), but not in Luminal-B tumors (23% and 15% mutated respectively). Discussion and conclusions: In approximately 33% of PRs, de novo AI resistance was associated with and most likely due to low expression of ER/ESR1 and estrogen-responsive genes in non-luminal tumors. In the remaining tumors, AI treatment still impacted some estrogen responsive genes but had limited downstream impact on suppressing proliferation. This might be due to mutations including in TP53 that limit suppression of proliferation downstream of estrogen signaling. The proportion of Luminal tumors in GRs and PRs ESR1HIGH was similar, suggesting better outcome of Luminal-A tumors on AI is likely due to their better intrinsic prognosis rather than better response to endocrine therapy. Citation Format: Eugene F. Schuster, Elena López-Knowles, Anastasia Alataki, Lila Zabaglo, Elizabeth Folkerd, David Evans, Kally Sidhu, Holly Tovey, Perry Maxwell, Nicholas Turner, Stephen Johnston, Manuel Salto-Tellez, Maggie Chon U Cheang, John Robertson, Ian Smith, Judith Bliss, Mitch Dowsett. PD10-07 Low plasma estradiol, low expression of estrogen responsive genes and TP53 mutations are associated with poor anti-proliferative response to aromatase inhibitors [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr PD10-07.
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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 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".