Cost-effectiveness analysis of prognostic gene expression signature-based stratification of early breast cancer patients

Estimation of the stage-wise costs of breast cancer in Germany using a modeling approach

Breast cancer (BC) is a heterogeneous disease representing a substantial economic burden. In order to develop policies that successfully decrease this burden, the factors affecting costs need to be fully understood. Evidence suggests that early-stage BC has a lower cost than a late stage BC. We aim to provide conservative estimates of BC's stage-wise medical costs from German healthcare and the payer's perspective. To this end, we conducted a literature review of articles evaluating stage-wise costs of BC in Germany through PubMed, Web of Science, and Econ Lit databases supplemented by Google Scholar. We developed a decision tree model to estimate BC-related medical costs in Germany using available treatment and cost information. The review generated seven studies; none estimated the stage-wise costs of BC. The studies were classified into two groups: case scenarios (five studies) and two studies based on administrative data. The first sickness funds data study (Gruber et al., 2012) used information from the year 1999 to approach BC attributable cost; their results suggest a range between €3,929 and €11,787 depending on age. The second study (Kreis, Plöthner et al., 2020) used 2011-2014 data and suggested an initial phase incremental cost of €21,499, an intermediate phase cost of €2,620, and a terminal phase cost of €34,513 per incident case. Our decision tree model-based BC stage-wise cost estimates were €21,523 for stage I, €25,679 for stage II, €30,156 for stage III, and €42,086 for stage IV. Alternatively, the modeled cost estimates are €20,284 for the initial phase of care, €851 for the intermediate phase of care, and €34,963 for the terminal phase of care. Our estimates for phases of care are consistent with recent German estimates provided by Kreis et al. Furthermore, the data collected by sickness funds are collected primarily for reimbursement purposes, where the German ICD-10 classification system defines a cancer diagnosis. As a result, claims data lack the clinical information necessary to understand stage-wise BC costs. Our model-based estimates fill the gap and inform future economic evaluations of BC interventions.

Impact of the EndoPredict genomic assay on treatment decisions for oestrogen receptor-positive early breast cancer patients: benefits of physician selective testing

PURPOSE

Genomic tests improve accuracy of risk prediction for early breast cancers but these are expensive. This study evaluated the clinical utility of EndoPredict®, in terms of impact on adjuvant therapy recommendations and identification of parameters to guide selective application.

METHODS

Patients with ER-positive, HER2-negative, and early-stage invasive breast cancer were tested with EndoPredict®. Two cohorts were recruited: one consecutively and another at clinical team discretion. Systemic treatment recommendations were recorded before and after EndoPredict® results were revealed to the multidisciplinary team.

RESULTS

233 patients were recruited across five sites: 123 consecutive and 110 at clinical team discretion. In the consecutive cohort 50.6% (62/123) cases were classified high risk of recurrence by EndoPredict®, compared with 62.7% (69/110) in the selective cohort. A change in treatment recommendation was significantly more likely (p < 0.0001) in the selective cohort (43/110, 39.1%) compared to the consecutive group (11/123, 8.9%). The strongest driver of selective recruitment was intermediate grade histology, whilst logistic regression modelling demonstrated that nodal status (p < 0.001), proliferative rate (p = 0.001), and progesterone receptor positivity (p < 0.001) were the strongest discriminators of risk.

CONCLUSION

Whilst molecular risk can be predicted by traditional variables in a high proportion of cases, EndoPredict® had a greater impact on treatment decisions in those cases selected for testing at team discretion. This is indicative of the robust ability of the clinical team to identify cases most likely to benefit from testing, underscoring the value of genomic tests in the oncologists' tool kit.

Comparison of risk assessment in 1652 early ER positive, HER2 negative breast cancer in a real-world data set: classical pathological parameters vs. 12-gene molecular assay (EndoPredict)

BACKGROUND

Risk assessment on the molecular level is important in predictive pathology to determine the risk of metastatic disease for ERpos, HER2neg breast cancer. The gene expression test EndoPredict (EP) was trained and validated for prediction of a 10-year risk of distant recurrence to support therapy decisions regarding endocrine therapy alone or in combination with chemotherapy. The EP test provides the 12-gene Molecular Score (MS) and the EPclin-Score (EPclin), which combines the molecular score with tumor size and nodal status. In this project we investigated the correlation of 12-gene MS and EPclin scores with classical pathological markers.

METHODS

EndoPredict-based gene expression profiling was performed prospectively in a total of 1652 patients between 2017 and 2020. We investigated tumor grading and Ki67 cut-offs of 20% for binary classification as well as 10% and 30% for three classes (low, intermediate, high), based on national and international guidelines.

RESULTS

410 (24.8%) of 1652 patients were classified as 12-gene MS low risk and 626 (37.9%) as EPclin low risk. We found significant positive associations between 12-gene MS and grading (p < 0.001), EPclin and grading (p = 0.001), 12-gene MS and Ki67 (p < 0.001), and EPclin and Ki67 (p < 0.001). However, clinically relevant differences between EP test results, Ki67 and tumor grading were observed. For example, 118 (26.3%) of 449 patients with Ki67 > 20% were classified as low risk by EPclin. Same differences were seen comparing EP test results and tumor grading.

CONCLUSION

In this study we could show that EP risk scores are distributed differentially among Ki67 expression groups, especially in Ki67 low and high tumors with a substantial proportion of patients with EPclin high risk results in Ki67 low tumors and vice versa. This suggests that classical pathological parameters and gene expression parameters are not interchangeable, but should be used in combination for risk assessment.

A Meal Replacement Program for the Treatment of Obesity: A Cost-Effectiveness Analysis from the Swiss Payer's Perspective

BACKGROUND

Obesity is a disease associated with high direct medical costs and high indirect costs resulting from productivity loss. The high prevalence of obesity generates the need for payers to identify cost-effective weight loss approaches. Among various weight management techniques, the OPTI (Optifast®) program is a clinically recognised total meal replacement diet that can lead to significant weight loss and reduction in complications. This study's objective is to assess OPTI program's cost-effectiveness in Switzerland in comparison to "no intervention" and pharmacotherapy.

METHODS

An event-driven decision-analytic model was used to estimate the payer's cost savings through the reimbursement of OPTI program over a 1-year period as well as a lifetime in Switzerland. The analysis was performed on a broad population of people with obesity with a body mass index (BMI) higher than 30 kg/m2 following the OPTI program vs two comparators (liraglutide and "no intervention"). The model incorporated a higher risk of complications due to an increased BMI and their related healthcare costs. Data sources included published literature, clinical trials, official Swiss price/tariff lists and national population statistics. The primary perspective was that of a Swiss payer. Scenario analyses - for example, for patients with existing complications (such as myocardial infarction, stroke, type 2 diabetes mellitus) or severe obesity - were conducted to test the robustness of the results.

RESULTS

The OPTI program results in cost savings of CHF 20,886 (€ 18,724) and CHF 15,382 (€ 13,790) per person compared with "no intervention" and liraglutide 3 mg, respectively. In addition, OPTI program led to 1.133 and 0.734 quality-adjusted life years (QALYs) gained respectively against its comparators. Scenario analyses showed similar outcomes with cost savings and QALYs gained.

CONCLUSION

OPTI program is a dominant strategy compared to "no intervention" and liraglutide 3 mg as it leads to both cost savings and QALY gain. Therefore, reimbursing the OPTI program for patients with obesity would be cost-effective for Swiss payers.

Tumour profiling tests to guide adjuvant chemotherapy decisions in early breast cancer: a systematic review and economic analysis

BACKGROUND

Breast cancer and its treatment can have an impact on health-related quality of life and survival. Tumour profiling tests aim to identify whether or not women need chemotherapy owing to their risk of relapse.

OBJECTIVES

To conduct a systematic review of the effectiveness and cost-effectiveness of the tumour profiling tests oncotype DX^® (Genomic Health, Inc., Redwood City, CA, USA), MammaPrint^® (Agendia, Inc., Amsterdam, the Netherlands), Prosigna^® (NanoString Technologies, Inc., Seattle, WA, USA), EndoPredict^® (Myriad Genetics Ltd, London, UK) and immunohistochemistry 4 (IHC4). To develop a health economic model to assess the cost-effectiveness of these tests compared with clinical tools to guide the use of adjuvant chemotherapy in early-stage breast cancer from the perspective of the NHS and Personal Social Services.

DESIGN

A systematic review and health economic analysis were conducted.

REVIEW METHODS

The systematic review was partially an update of a 2013 review. Nine databases were searched in February 2017. The review included studies assessing clinical effectiveness in people with oestrogen receptor-positive, human epidermal growth factor receptor 2-negative, stage I or II cancer with zero to three positive lymph nodes. The economic analysis included a review of existing analyses and the development of a de novo model.

RESULTS

A total of 153 studies were identified. Only one completed randomised controlled trial (RCT) using a tumour profiling test in clinical practice was identified: Microarray In Node-negative Disease may Avoid ChemoTherapy (MINDACT) for MammaPrint. Other studies suggest that all the tests can provide information on the risk of relapse; however, results were more varied in lymph node-positive (LN+) patients than in lymph node-negative (LN0) patients. There is limited and varying evidence that oncotype DX and MammaPrint can predict benefit from chemotherapy. The net change in the percentage of patients with a chemotherapy recommendation or decision pre/post test ranged from an increase of 1% to a decrease of 23% among UK studies and a decrease of 0% to 64% across European studies. The health economic analysis suggests that the incremental cost-effectiveness ratios for the tests versus current practice are broadly favourable for the following scenarios: (1) oncotype DX, for the LN0 subgroup with a Nottingham Prognostic Index (NPI) of > 3.4 and the one to three positive lymph nodes (LN1-3) subgroup (if a predictive benefit is assumed); (2) IHC4 plus clinical factors (IHC4+C), for all patient subgroups; (3) Prosigna, for the LN0 subgroup with a NPI of > 3.4 and the LN1-3 subgroup; (4) EndoPredict Clinical, for the LN1-3 subgroup only; and (5) MammaPrint, for no subgroups.

LIMITATIONS

There was only one completed RCT using a tumour profiling test in clinical practice. Except for oncotype DX in the LN0 group with a NPI score of > 3.4 (clinical intermediate risk), evidence surrounding pre- and post-test chemotherapy probabilities is subject to considerable uncertainty. There is uncertainty regarding whether or not oncotype DX and MammaPrint are predictive of chemotherapy benefit. The MammaPrint analysis uses a different data source to the other four tests. The Translational substudy of the Arimidex, Tamoxifen, Alone or in Combination (TransATAC) study (used in the economic modelling) has a number of limitations.

CONCLUSIONS

The review suggests that all the tests can provide prognostic information on the risk of relapse; results were more varied in LN+ patients than in LN0 patients. There is limited and varying evidence that oncotype DX and MammaPrint are predictive of chemotherapy benefit. Health economic analyses indicate that some tests may have a favourable cost-effectiveness profile for certain patient subgroups; all estimates are subject to uncertainty. More evidence is needed on the prediction of chemotherapy benefit, long-term impacts and changes in UK pre-/post-chemotherapy decisions.

STUDY REGISTRATION

This study is registered as PROSPERO CRD42017059561.

FUNDING

The National Institute for Health Research Health Technology Assessment programme.

Gene Expression Profiling Tests for Early-Stage Invasive Breast Cancer: A Health Technology Assessment

BACKGROUND

Breast cancer is a disease in which cells in the breast grow out of control. They often form a tumour that may be seen on an x-ray or felt as a lump.Gene expression profiling (GEP) tests are intended to help predict the risk of metastasis (spread of the cancer to other parts of the body) and to identify people who will most likely benefit from chemotherapy. We conducted a health technology assessment of four GEP tests (EndoPredict, MammaPrint, Oncotype DX, and Prosigna) for people with early-stage invasive breast cancer, which included an evaluation of effectiveness, safety, cost effectiveness, the budget impact of publicly funding GEP tests, and patient preferences and values.

METHODS

We performed a systematic literature search of the clinical evidence. We assessed the risk of bias of each included study using either the Cochrane Risk of Bias tool, Prediction model Risk Of Bias ASsessment Tool (PROBAST), or Risk of Bias Assessment tool for Non-randomized Studies (RoBANS), depending on the type of study and outcome of interest, and the quality of the body of evidence according to the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) Working Group criteria. We also performed a literature survey of the quantitative evidence of preferences and values of patients and providers for GEP tests.We performed an economic evidence review to identify published studies assessing the cost-effectiveness of each of the four GEP tests compared with usual care or with one another for people with early-stage invasive breast cancer. We adapted a decision-analytic model to compare the costs and outcomes of care that includes a GEP test with usual care without a GEP test over a lifetime horizon. We also estimated the budget impact of publicly funding GEP tests to be conducted in Ontario, compared with funding tests conducted through the out-of-country program and compared with no funding of tests in any location.To contextualize the potential value of GEP tests, we spoke with people who have been diagnosed with early-stage invasive breast cancer.

RESULTS

We included 68 studies in the clinical evidence review. Within the lymph-node-negative (LN-) population, GEP tests can prognosticate the risk of distant recurrence (GRADE: Moderate) and may predict chemotherapy benefit (GRADE: Low). The evidence for prognostic and predictive ability (ability to indicate the risk of an outcome and ability to predict who will benefit from chemotherapy, respectively) was lower for the lymph-node-positive (LN+) population (GRADE: Very Low to Low). GEP tests may also lead to changes in treatment (GRADE: Low) and generally may increase physician confidence in treatment recommendations (GRADE: Low).Our economic evidence review showed that GEP tests are generally cost-effective compared with usual care.Our primary economic evaluation showed that all GEP test strategies were more effective (led to more quality-adjusted life-years [QALYs]) than usual care and can be considered cost-effective below a willingness-to-pay of $20,000 per QALY gained. There was some uncertainty in our results. At a willingness-to-pay of $50,000 per QALY gained, the probability of each test being cost-effective compared to usual care was 63.0%, 89.2%, 89.2%, and 100% for EndoPredict, MammaPrint, Oncotype DX, and Prosigna, respectively.Sensitivity analyses showed our results were robust to variation in subgroups considered (i.e., LN+ and premenopausal), discount rates, age, and utilities. However, cost parameter assumptions did influence our results. Our scenario analysis comparing tests showed Oncotype DX was likely cost-effective compared with MammaPrint, and Prosigna was likely cost-effective compared with EndoPredict. When the GEP tests were compared with a clinical tool, the cost-effectiveness of the tests varied. Assuming a higher uptake of GEP tests, we estimated the budget impact to publicly fund GEP tests in Ontario would be between $1.29 million (Year 1) and $2.22 million (Year 5) compared to the current scenario of publicly funded GEP tests through the out-of-country program.Gene expression profiling tests are valued by patients and physicians for the additional information they provide for treatment decision-making. Patients are satisfied with what they learn from GEP tests and feel GEP tests can help reduce decisional uncertainty and anxiety.

CONCLUSIONS

Gene expression profiling tests can likely prognosticate the risk of distant recurrence and some tests may also predict chemotherapy benefit. In people with breast cancer that is ER+, LN-, and human epidermal growth factor receptor 2 (HER2)-negative, GEP tests are likely cost-effective compared with no testing. The GEP tests are also likely cost-effective in LN+ and premenopausal people. Compared with funding GEP tests through the out-of-country program, publicly funding GEP tests in Ontario would cost an additional $1 million to $2 million annually, assuming a higher uptake of tests. GEP tests are valued by both patients and physicians for chemotherapy treatment decision-making.

A molecular multi-gene classifier for disease diagnostics

Despite its early promise as a diagnostic and prognostic tool, gene expression profiling remains cost-prohibitive and challenging to implement in a clinical setting. Here, we introduce a molecular computation strategy for analysing the information contained in complex gene expression signatures without the need for costly instrumentation. Our workflow begins by training a computational classifier on labelled gene expression data. This in silico classifier is then realized at the molecular level to enable expression analysis and classification of previously uncharacterized samples. Classification occurs through a series of molecular interactions between RNA inputs and engineered DNA probes designed to differentially weigh each input according to its importance. We validate our technology with two applications: a classifier for early cancer diagnostics and a classifier for differentiating viral and bacterial respiratory infections based on host gene expression. Together, our results demonstrate a general and modular framework for low-cost gene expression analysis.

Systematic review of the clinical and economic value of gene expression profiles for invasive early breast cancer available in Europe

Gene expression profiles with prognostic capacities have shown good performance in multiple clinical trials. However, with multiple assays available and numerous types of validation studies performed, the added value for daily clinical practice is still unclear. In Europe, the MammaPrint, OncotypeDX, PAM50/Prosigna and Endopredict assays are commercially available. In this systematic review, we aim to assess these assays on four important criteria: Assay development and methodology, clinical validation, clinical utility and economic value. We performed a literature search covering PubMed, Embase, Web of Science and Cochrane, for studies related to one or more of the four selected assays. We identified 147 papers for inclusion in this review. MammaPrint and OncotypeDX both have evidence available, including level IA clinical trial results for both assays. Both assays provide prognostic information. Predictive value has only been shown for OncotypeDX. In the clinical utility studies, a higher reduction in chemotherapy was achieved by OncotypeDX, although the number of available studies differ considerably between tests. On average, economic evaluations estimate that genomic testing results in a moderate increase in total costs, but that these costs are acceptable in relation to the expected improved patient outcome. PAM50/prosigna and EndoPredict showed comparable prognostic capacities, but with less economical and clinical utility studies. Furthermore, for these assays no level IA trial data are available yet. In summary, all assays have shown excellent prognostic capacities. The differences in the quantity and quality of evidence are discussed. Future studies shall focus on the selection of appropriate subgroups for testing and long-term outcome of validation trials, in order to determine the place of these assays in daily clinical practice.

Decision impact and feasibility of different ASCO-recommended biomarkers in early breast cancer: Prospective comparison of molecular marker EndoPredict and protein marker uPA/PAI-1

Background

Adjuvant therapy decisions in early breast cancer are based on accurate risk assessment. Urokinase plasminogen activator (uPA) and plaminogen activator inhibitor-1 (PAI-1) have been the first biomarkers in hormone receptor (HR) positive breast cancer to reach highest level of evidence. The EndoPredict test (EPclin) combines gene expression information with nodal status and tumor size. The aim of this prospective study was to compare uPA/PAI-1 and EPclin as prognostic biomarkers with regard to feasibility, risk stratification and impact on adjuvant therapy recommendation.

Materials and method

395 patients with HR positive, HER2 negative, intermediate risk breast cancer were enrolled. Relations and concordance of histologic grading as well as EPclin and uPA/PAI-1 values were assessed by Spearman’s rank correlation coefficient and Cohen’s Kappa. To compare decision impact of EPclin and uPA/PAI-1 three independent case discussions were held: One with known uPA/PAI-1 and EPclin results, one blinded to EPclin alone and another one blinded to both EPclin and uPA/PAI-1.

Results

EPclin could be determined in all 395 (100%), uPA/PAI-1 in 190 (48%) of the tumor samples. EPclin allocated 250 patients (63%) to the low-risk group and 145 patients (37%) to the high-risk group, whereas uPA/PAI-1 allocated 88 patients (46%) to the low-risk group and 102 patients (54%) to the high-risk group. In 59% of cases, both tests showed concordant results. EPclin resulted more frequently in a change of therapy recommendation than the uPA/PAI-1 test (46% vs 24%). Recommendation of adjuvant chemotherapy (CTX) was abandoned twice as often by EPclin (45%) compared to uPA/PAI-1 (22%).

Conclusion

In this first prospective comparison of EPclin and uPA/PAI-1 we found, that EPclin is superior to uPA/PAI-1 with respect to feasibility and decision impact. This leads to substantial avoidance of adjuvant CTX in endocrine-sensitive, HER2-negative breast cancer. Data collection for patients´ clinical outcome is ongoing.

'Omics Approaches in Breast Cancer Research and Clinical Practice

Our understanding of the natural history of breast cancer has evolved alongside technologies to study its genomic, transcriptomic, proteomic, and metabolomics landscapes. These technologies have helped decipher multiple molecular pathways dysregulated in breast cancer. First-generation 'omics analyses considered each of these dimensions individually, but it is becoming increasingly clear that more holistic, integrative approaches are required to fully understand complex biological systems. The 'omics represent an exciting era of discovery in breast cancer research, although important issues need to be addressed to realize the clinical utility of these data through precision cancer care. How can the data be applied to predict response to molecular-targeted therapies? When should treatment decisions be based on tumor genetics rather than histology? And with the sudden explosion of "big data" from large 'omics consortia and new precision clinical trials, how do we now negotiate evidence-based pathways to clinical translation through this apparent sea of opportunity? The aim of this review is to provide a broad overview of 'omics technologies used in breast cancer research today, the current state-of-play in terms of applying this new knowledge in the clinic, and the practical and ethical issues that will be central to the public discussion on the future of precision cancer care.