Abstract P4-12-05: Cost-effectiveness analysis of second-generation multi-gene expression prognostic assays compared with the standard 21-gene recurrence score assay to guide adjuvant therapy decisions in women with early stage breast cancer
Bibliographic record
Abstract
Abstract Second-generation multi-gene expression assays can generate comprehensive molecular risk scores which may inform adjuvant chemotherapy decisions in women with early breast cancer. The 12-gene EPclin score assay (EndoPredict®) and 50-gene PAM50-risk of recurrence (ROR) score assay (Prosigna®) appeared to have better prognostic value when compared to the current north American standard 21-gene recurrence score (RS) assay (Oncotype DX®). We sought to investigate the cost-effectiveness of using EPclin and ROR score assays versus RS assay in women with axillary lymph node-negative (LN−), hormone receptor–positive (HR+), and human epidermal growth factor receptor 2–negative (HER2−) early-stage operable breast cancer (ESBC) from the perspective of the Canadian public healthcare system. We developed a Markov model to project the lifetime clinical and economic consequences of operable LN- HR+ HER2− ESBC. We assumed that women within each risk category by RS assay (low, intermediate and high) would be reclassified to binary risk categories (low and high) by EPclin score assay and to three risk categories (low, intermediate and high) by ROR score assay. The decision model was parameterized using 10-year follow up data from retrospective analyses of the Arimidex, Tamoxifen, Alone or in Combination (ATAC) trial, cost data from the London Regional Cancer Program (Ontario, Canada) and secondary sources. Costs are presented in 2017 Canadian dollars. Future costs and benefits were discounted at 5%. EPclin and ROR score-based strategies led to an increase of 0.04 and 0.02 quality adjusted life years (QALY)/person and a decrease in cost of $917 and $600/person respectively, resulting in both strategies being cost-saving compared to RS-based strategy. Incorporating the EPclin and ROR score assays in place of the current standard RS-assay for operable LN- HR+ HER2− ESBC patients in Canada would result in total gains of 469 and 250 QALYs/year and total savings of $11.5 and $7.5 million/year, respectively. EPclin compared to ROR score-based strategy led to an increase of 0.02 QALY/person and a decrease in cost of $317/person, resulting in EPclin score-based strategy being dominant. Our results were most sensitive to the proportion of women classified by EPclin and ROR score assays to different risk categories and who received adjuvant chemotherapy. A value-of-information analysis revealed that the total expected value of perfect information about the EPclin and ROR score assays' clinical impact was $95 and $55 million/year, respectively. Our results indicate that the EPclin and ROR score assays are both clinically and economically attractive for patients with operable LN- HR+ HER2− ESBC in the Canadian healthcare setting. Both assays should be considered for adoption in place of the current standard RS-assay for this patient population. The EPclin compared to the ROR score assay appears to be clinically more promising and provides greater value for money in the Canadian healthcare system. Field evaluations of the EPclin and ROR score assays in real-world Canadian clinical practice are associated with a large societal benefit and warranted. Citation Format: Hannouf MB, Zaric GS, Brackstone M. Cost-effectiveness analysis of second-generation multi-gene expression prognostic assays compared with the standard 21-gene recurrence score assay to guide adjuvant therapy decisions in women with early stage breast cancer [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 P4-12-05.
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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 | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".