Abstract 5548: Clinical management and decision making in early ER-positive breast cancers through improved prognosis and pathway directed molecular profiling
Bibliographic record
Abstract
Abstract Hormone receptor positive (HR+ve) breast cancer (BCa) comprises over 80% of all newly diagnosed BCas. While there is an initial good response to anti-hormone therapies, many patients will experience a recurrence. Validated prognostic tests are used to guide chemotherapy decisions, but the goal of precision medicine has yet to be achieved. We developed and validated a 95-gene prognostic signature (Bayani et al 2017) from the TEAM trial (van de Velde et al, 2011), demonstrating this risk classifier performed as well as the 21-gene, 50-gene and 70-gene tests and can be used in HER2+/-ve cases including only nodal status. RNA profiling has improved decisions regarding adjuvant chemotherapy but is insufficient for stratification to targeted therapies increasingly available in the early setting. The genomic landscape of BCas has identified recurrent patterns of mutation and copy-number changes. Except for HER2, there are few genes for whom mutational or gene dosage are reliable for stratification to targeted therapies. It is increasingly evident that a multi-omic approach to precision medicine is needed to encompass the biological complexity of cancer. Here we present the findings from the RNA profiling of patients from the TEAM trial using a custom diagnostic-grade NGS panel of the 95-gene risk classifier and DNA sequencing using a large (500 gene) comprehensive genomic profiling panel (OCAPlus, Thermo Fisher Scientific). 95-gene prognostic results of 1,182 patients showed prognostic utility using the custom panel with 265 (22%) low-risk patients experiencing >90% relapse free survival (DRFS)(HR=4.47 (95% CI 2.46-8.02, p=5.54e-07)) at 10 years. In 857 cases profiled with OCAPlus, the genes most frequently mutated included PIK3CA (53%), MAPK31 (25%), TP53(17%), CDH1 (17%) and GATA3 (10%). Frequent copy number changes were identified in CCND1 (18%), FGFR1 (12%), and MDM2 (5%). To investigate the potential for stratification to targeted therapies, a pathway approach was taken to identify aberrations in targetable signaling pathways. Among the 788 cases with both OCAPlus and the 95-gene results, the most frequently impacted pathways were PI3K/AKT (67%), HHR Pathway (55%), Chromatin regulation (50%), RAS/RAF/MEK/ERK (40%) and Cell Cycle (37%). To address the clinical need for those patients deemed at risk for recurrence, the consequence of aberrations in those pathways were investigated. Among 95-gene high-risk patients (n=604), those with mutations in genes of the Cell Cycle pathway experienced poorer DRFS (HR=1.95 (95%, CI 1.29-2.95, p= 0.0159), suggesting these patients might benefit cell cycle-targeting therapies. With no reliable biomarkers to predict response, and with associated side effects/toxicities of these agents, this offers a rational pathway-directed method of decision making for those identified as high-risk of recurrence. Citation Format: John M. Bartlett, Cheryl Crozier, Vinay K. Mittal, Dan Dion, Angela De Luca, Adam E. Sundby, Elizabeth Woroszchuk, Bradley d’Souza, Louis Gasparini, Mary Anne Quintayo, Mehar Chahal, Anna Y. Lee, Mathieu Larivière, Kyusung S. Park, Anupma Sharma, Jeffrey M. Smith, Seth Sadis, Daniel W. Rea, Melanie Spears, Jane Bayani. Clinical management and decision making in early ER-positive breast cancers through improved prognosis and pathway directed molecular profiling. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5548.
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".