Abstract PD4-11: Copy-number and targeted sequencing analyses to identify distinct prognostic groups: Implications for patient selection to targeted therapies amongst anti-endocrine therapy resistant early breast cancers
Bibliographic record
Abstract
Abstract Hormone receptor positive breast cancer remains an ongoing therapeutic challenge. Despite optimal anti-endocrine therapies, most breast cancer deaths follow a diagnosis of early luminal cancer. Data describing molecular events in breast cancer has yet to be translated into actionable information to inform medical management and benefit patients. To understand the impact of multiple aberrations in the context of current therapy, we assessed the prognostic ability of genomic signatures as a putative stratification tool to targeted therapies. The analysis was performed based on an a priori hypothesis relating to molecular pathways which might predict response to targeted therapies currently under evaluation in late-stage clinical trials. In a case-control fashion, 420 patients from the Tamoxifen vs Exemstane Adjuvant Multinational Trial (TEAM) pathology cohort, were analysed to determine the prognostic, ability for these mutational and copy-number biomarkers representing the CCND/CDK, FGFR/FGF and AKT/PIK3CA to inform potential response to therapies targeting these pathways. Copy number analysis was performed using the Affymetrix Oncoscan™ Assay. Targeted sequencing was performed in a subset of samples for genes based on signaling cassettes mined from the ICGC. Pathways were identified as aberrant if there were copy number aberrations (CNAs) and/or mutations in any of the predetermined pathway genes: 1) CCND1/CCND2/CCND3/CDK4/CDK6 2) FGFR1/FGFR2/FGFR2/FGFR4 and 3) AKT1/AKT2/PIK3CA/PTEN. Kaplan Meier and log rank analyses were used for DRFS between groups. Hazard ratios were calculated using the Cox proportional hazard models adjusted for age, tumour size, grade, lymph node and HER2 status. 390/420 samples passed informatics QC filters. For the CCND/CDK pathway, patients with no CNA changes experienced a better DRFS (HR=1.94, 95% CI 1.45-2.61, p< 0.001). For the FGFR/FGF pathway, a similar outcome is seen among patients without CNAs (HR = 1.43, 95% CI 1.07-1.92 p=0.017). For AKT/PIK3CA, a decrease in DRFS was seen in those with aberrations (H=1.34, 95% CI 1.00-1.81, p=0.053). We demonstrated that CNAs of genes within CDK4/CCND, PIK3CA/AKT and FGFR pathways are independently linked to high risk of relapse following endocrine treatment. In this way, improving the clinical management of early breast cancers could be made, firstly by identifying those patients for whom current endocrine therapies are sufficient, thus reducing unnecessary treatment; and secondly, identifying those patients who are at high-risk for recurrence despite optimal endocrine therapy and the linking molecular features driving these cancers to treatment with targeted therapies. Citation Format: Bayani J, Kornaga EN, Crozier C, Jang GH, Kalatskaya I, Trinh QM, Yao CQ, Livingstone J, Hasenburg A, Kieback DG, Markopoulos C, Dirix L, Boutros PC, Spears M, Stein LD, Rea D, Bartlett JMS. Copy-number and targeted sequencing analyses to identify distinct prognostic groups: Implications for patient selection to targeted therapies amongst anti-endocrine therapy resistant early breast cancers [abstract]. In: Proceedings of the 2017 San Antonio Breast Cancer Symposium; 2017 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2018;78(4 Suppl):Abstract nr PD4-11.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".