Abstract PO3-15-04: Oncotype DX Recurrence Score as an Informative Tool to Optimize Neoadjuvant Therapy in HR-positive, HER2-negative Breast Cancers
Bibliographic record
Abstract
Abstract Oncotype DX Recurrence Score as an Informative Tool to Optimize Neoadjuvant Therapy in HR-positive, HER2-negative Breast Cancers Background: Neoadjuvant therapy (NAT) in HR+/HER2- breast cancers rarely leads to pathological complete response (pCR) and often doesn’t achieve substantial tumor shrinkage. Therefore, neoadjuvant therapy in HR+/HER2- breast cancers has the most value in the assessment of in vivo treatment response. For this purpose, biomarkers that can predict clinical and pathological response to systemic therapy could help inform treatment. The role of Oncotype DX testing in the management of early-stage HR+/HER2- breast cancer has been widely recognized in the adjuvant setting. However, no large prospective studies evaluating the Oncotype DX test in the neoadjuvant setting have clearly shown its predictive or prognostic utility to date. In this study, we evaluated the effect of the recurrence score (RS) on systemic treatment recommendations and its correlation to NAT response. Methods: Patients with clinically selected T2-T4 and/or clinically node positive HR+/HER2- breast cancer who were referred to BC Cancer Vancouver for consideration of NAT between September 2021 – April 2023 were screened for study eligibility. Suitable candidates who agreed to participate in the study had Oncotype DX testing on core biopsy specimens and Ki-67 testing on the core biopsy and final resection specimen. Clinico-pathological information, treatment regimens, operative details, and clinical, radiologic, and pathological responses were recorded. Results: Of the 66 patients who met eligibility criteria, 40 enrolled in the study comprising the accrual rate of 60.6 %. There were multiple factors that influenced patients and/or Oncologists participation in the study highlighting the low uptake of NAT in HR+/HER2- tumors. Technical feasibility of obtaining Oncotype DX from core biopsy samples was 95% which correlates with our previously reported data on a smaller cohort. The mean turnaround time from patient consent to RS report was 18.5 calendar days. The mean time from the initial consult to the start of NAT was 18.8 calendar days. Amongst all tumors tested, 28 % had RS equal or greater than 26 while only 2 % of participants had a score less than 10. Overall, 32 % of treatment recommendations were changed based on RS. Approximately 31% of patients who were initially recommended to receive neoadjuvant chemotherapy were spared from this treatment based on low RS. Conversely, 23.8% of patients who were recommended to pursue upfront surgery were escalated to chemotherapy due to a high RS. Approximately 88 % of the participants who were treated with neoadjuvant chemotherapy had a favourable clinical response and 38.5% had a complete clinical response. There were no pCR observed amongst patients who completed surgery after neoadjuvant chemotherapy, but half of them were rendered eligible for less surgery (either spared a mastectomy and/or an axillary dissection). Conclusions: Oncotype DX testing changed systemic treatment recommendations in one third of neoadjuvant patients and presents a unique opportunity for personalized medicine. Pre-operative testing thus influenced clinical decision-making, allowing treatment de-escalation and omission of chemotherapy in those with a low RS and conversely an earlier escalation of systemic therapy for those with a high RS. Turnaround time of the Oncotype DX assay on a core biopsy sample was acceptable and did not significantly delay treatment initiation. The benefit of the Oncotype DX assay prior to NAT was valuable for predicting in vivo response although pCR was not observed in any of the patients enrolled. Analyses are ongoing to further correlate RS to neoadjuvant response and to dynamic Ki-67 and MRI changes. Citation Format: Lidiya Luzhna, Stephen Chia, Mehrnoosh Pauls, Nathalie Levasseur. Oncotype DX Recurrence Score as an Informative Tool to Optimize Neoadjuvant Therapy in HR-positive, HER2-negative Breast Cancers [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO3-15-04.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| 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 | 0.000 |
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".