Paving the path for implementation of clinical genomic sequencing globally: Are we ready?

Despite the emerging evidence in recent years, successful implementation of clinical genomic sequencing (CGS) remains limited and is challenged by a range of barriers. These include a lack of standardized practices, limited economic assessments for specific indications, limited meaningful patient engagement in health policy decision-making, and the associated costs and resource demand for implementation. Although CGS is gradually becoming more available and accessible worldwide, large variations and disparities remain, and reflections on the lessons learned for successful implementation are sparse. In this commentary, members of the Global Economics and Evaluation of Clinical Genomics Sequencing Working Group (GEECS) describe the global landscape of CGS in the context of health economics and policy and propose evidence-based solutions to address existing and future barriers to CGS implementation. The topics discussed are reflected as two overarching themes: (1) system readiness for CGS and (2) evidence, assessments, and approval processes. These themes highlight the need for health economics, public health, and infrastructure and operational considerations; a robust patient- and family-centered evidence base on CGS outcomes; and a comprehensive, collaborative, interdisciplinary approach.

Impact on costs and outcomes of multi-gene panel testing for advanced solid malignancies: a cost-consequence analysis using linked administrative data.

Background: To date, economic analyses of tissue-based next generation sequencing genomic profiling (NGS) for advanced solid tumors have typically required models with assumptions, with little real-world evidence on overall survival (OS), clinical trial enrollment or end-of-life quality of care. Methods: Cost consequence analysis of NGS testing (555 or 161-gene panels) for advanced solid tumors through the OCTANE clinical trial (NCT02906943). This is a longitudinal, propensity score-matched retrospective cohort study in Ontario, Canada using linked administrative data. Patients enrolled in OCTANE at Princess Margaret Cancer Centre from August 2016 until March 2019 were matched with contemporary patients without large gene panel testing from across Ontario not enrolled in OCTANE. Patients were matched according to 19 patient, disease and treatment variables. Full 2-year follow-up data was available. Sensitivity analyses considered alternative matched cohorts. Main Outcomes were mean per capita costs (2019 Canadian dollars) from a public payer's perspective, OS, clinical trial enrollment and end-of-life quality metrics. Findings: There were 782 OCTANE patients with 782 matched controls. Variables were balanced after matching (standardized difference <0.10). There were higher mean health-care costs with OCTANE ($79,702 vs. $59,550), mainly due to outpatient and specialist visits. Publicly funded drug costs were less with OCTANE ($20,015 vs. $24,465). OCTANE enrollment was not associated with improved OS (restricted mean survival time [standard error]: 1.50 (±0.03) vs. 1.44 (±0.03) years, log-rank p = 0.153), varying by tumor type. In five tumor types with ≥35 OCTANE patients, OS was similar in three (breast, colon, uterus, all p > 0.40), and greater in two (ovary, biliary, both p < 0.05). OCTANE was associated with greater clinical trial enrollment (25.4% vs. 9.5%, p < 0.001) and better end-of-life quality due to less death in hospital (10.2% vs. 16.4%, p = 0.003). Results were robust in sensitivity analysis. Interpretation: We found an increase in healthcare costs associated with multi-gene panel testing for advanced cancer treatment. The impact on OS was not significant, but varied across tumor types. OCTANE was associated with greater trial enrollment, lower publicly funded drug costs and fewer in-hospital deaths suggesting important considerations in determining the value of NGS panel testing for advanced cancers. Funding: T.P H holds a research grant provided by the Ontario Institute for Cancer Research through funding provided by the Government of Ontario (#IA-035 and P.HSR.158) and through funding of the Canadian Network for Learning Healthcare Systems and Cost-Effective 'Omics Innovation (CLEO) via Genome Canada (G05CHS).

Real-world diagnostic outcomes and cost-effectiveness of genome-wide sequencing for developmental and seizure disorders: Evidence from Canada.

PURPOSE: To determine real-world diagnostic rates, cost trajectories, and cost-effectiveness of exome sequencing (ES) and genome sequencing (GS) for children with developmental and/or seizure disorders in British Columbia, Canada. METHODS: Based on medical records review, we estimated real-world costs and outcomes for 491 patients who underwent standard of care (SOC) diagnostic testing at British Columbia Children's Hospital. Results informed a state-transition Markov model examining cost-effectiveness of 3 competing diagnostic strategies: (1) SOC with last-tier access to ES, (2) streamlined ES access, and (3) first-tier GS. RESULTS: Through SOC, 49.4% (95% CI: 40.6, 58.2) of patients were diagnosed at an average cost of C$11,683 per patient (95% CI: 9200, 14,166). Compared with SOC, earlier ES or GS access yielded similar or improved diagnostic rates and shorter times to genetic diagnosis, with 94% of simulations demonstrating cost savings for streamlined ES and 60% for first-tier GS. Net benefit from the perspective of the health care system was C$2956 (95% CI: -608, 6519) for streamlined ES compared with SOC. CONCLUSION: Using real-world data, we found earlier access to ES may yield more rapid genetic diagnosis of childhood developmental and seizure disorders and cost savings compared with current practice in a Canadian health care system.

Toward Best Practices for Economic Evaluations of Tumor-Agnostic Therapies: A Review of Current Barriers and Solutions.

OBJECTIVES: Cancer therapies targeting tumor-agnostic biomarkers are challenging traditional health technology assessment (HTA) frameworks. The high prevalence of nonrandomized single-arm trials, heterogeneity, and small benefiting populations are driving outcomes uncertainty, challenging healthcare decision making. We conducted a structured literature review to identify barriers and prioritize solutions to generating economic evidence for tumor-agnostic therapies. METHODS: We searched MEDLINE and Embase for English-language studies conducting economic evaluations of tumor-agnostic treatments or exploring related challenges and solutions. We included studies published by December 2022 and supplemented our review with Canadian Agency for Drugs and Technologies in Health and National Institute for Health and Care Excellence technical reports for approved tumor-agnostic therapies. Three reviewers abstracted and summarized key methodological and empirical study characteristics. Challenges and solutions were identified through authors' statements and categorized using directed content analysis. RESULTS: Twenty-six studies met our inclusion criteria. Studies spanned economic evaluations (n = 5), reimbursement reviews (n = 4), qualitative research (n = 1), methods validations (n = 3), and commentaries or literature reviews (n = 13). Challenges encountered related to (1) the treatment setting and clinical trial designs, (2) a lack of data or low-quality data on clinical and cost parameters, and (3) an inability to produce evidence that meets HTA guidelines. Although attempted solutions centered on analytic approaches for managing missing data, proposed solutions highlighted the need for real-world evidence combined with life-cycle HTA to reduce future evidentiary uncertainty. CONCLUSIONS: Therapeutic innovation outpaces HTA evidence generation and the methods that support it. Existing HTA frameworks must be adapted for tumor-agnostic treatments to support future economic evaluations enabling timely patient access.

Genetic matching for time-dependent treatments: a longitudinal extension and simulation study.

BACKGROUND: Longitudinal matching can mitigate confounding in observational, real-world studies of time-dependent treatments. To date, these methods have required iterative, manual re-specifications to achieve covariate balance. We propose a longitudinal extension of genetic matching, a machine learning approach that automates balancing of covariate histories. We examine performance by comparing the proposed extension against baseline propensity score matching and time-dependent propensity score matching. METHODS: To evaluate comparative performance, we developed a Monte Carlo simulation framework that reflects a static treatment assigned at multiple time points. Data generation considers a treatment assignment model, a continuous outcome model, and underlying covariates. In simulation, we generated 1,000 datasets, each consisting of 1,000 subjects, and applied: (1) nearest neighbour matching on time-invariant, baseline propensity scores; (2) sequential risk set matching on time-dependent propensity scores; and (3) longitudinal genetic matching on time-dependent covariates. To measure comparative performance, we estimated covariate balance, efficiency, bias, and root mean squared error (RMSE) of treatment effect estimates. In scenario analysis, we varied underlying assumptions for assumed covariate distributions, correlations, treatment assignment models, and outcome models. RESULTS: In all scenarios, baseline propensity score matching resulted in biased effect estimation in the presence of time-dependent confounding, with mean bias ranging from 29.7% to 37.2%. In contrast, time-dependent propensity score matching and longitudinal genetic matching achieved stronger covariate balance and yielded less biased estimation, with mean bias ranging from 0.7% to 13.7%. Across scenarios, longitudinal genetic matching achieved similar or better performance than time-dependent propensity score matching without requiring manual re-specifications or normality of covariates. CONCLUSIONS: While the most appropriate longitudinal method will depend on research questions and underlying data patterns, our study can help guide these decisions. Simulation results demonstrate the validity of our longitudinal genetic matching approach for supporting future real-world assessments of treatments accessible at multiple time points.

Development and early-stage evaluation of a patient portal to enhance familial communication about hereditary cancer susceptibility testing: A patient-driven approach.

INTRODUCTION: Genetic testing for hereditary cancer syndromes (HCSs) can improve health outcomes through cancer risk mitigation strategies. Effective communication between tested individuals and their family members is key to reducing the hereditary cancer burden. Our objective was to develop a patient portal to improve familial communication for patients undergoing HCS genetic testing, followed by an early-phase evaluation. METHODS: The portal was developed following the completion of 25 semistructured interviews with individuals having undergone HCS susceptibility testing at BC Cancer. Following initial development, we recruited patients and healthcare providers to provide critical feedback informing portal refinement. Quantitative feedback was summarized using descriptive statistics, and qualitative feedback was synthesized by two reviewers who engaged in iterative discussion within the research team to prioritize recommendations for integration. RESULTS: The patient portal includes four key components consisting of (a) targeted educational information about hereditary cancer and HBOC syndrome associated risks and testing process overview, (b) a general frequently asked questions 'FAQ' page informed by the qualitative interviews, patient partner feedback, and consultation with the HCP, (c) guidance to support familial communication including a video developed with a patient partner describing their lived experience navigating the communication process and (d) a series of lay summaries of genetic test findings to support information transfer among family members. Thirteen healthcare providers and seven patients participated in user testing. Domains within which participant recommendations were provided included presentation, educational content and process clarification. CONCLUSIONS: This investigation demonstrates the value of continual integration of patient and provider preferences through the development of tools endeavouring to assist with complex genomics-informed decision-making. Our work aims to broaden the population-wide impact of HCS testing programs by improving communication processes between probands and their potentially affected family members. PATIENT OR PUBLIC CONTRIBUTION: This work involved a patient partner who was actively engaged in all aspects of the research investigation including protocol development, review and editing of all study documentation (including that of the previously published qualitative investigation), interpretation of results, as well as reviewing and editing the manuscript. Patient partners and healthcare professionals were recruited as research participants to provide critical feedback on the patient portal.

A perspective on life-cycle health technology assessment and real-world evidence for precision oncology in Canada.

Health technology assessment (HTA) can be used to make healthcare systems more equitable and efficient. Advances in precision oncology are challenging conventional thinking about HTA. Precision oncology advances are rapid, involve small patient groups, and are frequently evaluated without a randomized comparison group. In light of these challenges, mechanisms to manage precision oncology uncertainties are critical. We propose a life-cycle HTA framework and outline supporting criteria to manage uncertainties based on real world data collected from learning healthcare systems. If appropriately designed, we argue that life-cycle HTA is the driver of real world evidence generation and furthers our understanding of comparative effectiveness and value. We conclude that life-cycle HTA deliberation processes must be embedded into healthcare systems for an agile response to the constantly changing landscape of precision oncology innovation. We encourage further research outlining the core requirements, infrastructure, and checklists needed to achieve the goal of learning healthcare supporting life-cycle HTA.

Defining a Core Data Set for the Economic Evaluation of Precision Oncology.

OBJECTIVES: Precision oncology is generating vast amounts of multiomic data to improve human health and accelerate research. Existing clinical study designs and attendant data are unable to provide comparative evidence for economic evaluations. This lack of evidence can cause inconsistent and inappropriate reimbursement. Our study defines a core data set to facilitate economic evaluations of precision oncology. METHODS: We conducted a literature review of economic evaluations of next-generation sequencing technologies, a common application of precision oncology, published between 2005 and 2018 and indexed in PubMed (MEDLINE). Based on this review, we developed a preliminary core data set for informal expert feedback. We then used a modified-Delphi approach with individuals involved in implementation and evaluation of precision medicine, including 2 survey rounds followed by a final voting conference to refine the data set. RESULTS: Two authors determined that variation in published data elements was reached after abstraction of 20 economic evaluations. Expert consultation refined the data set to 83 unique data elements, and a multidisciplinary sample of 46 experts participated in the modified-Delphi process. A total of 68 elements (81%) were selected as required, spanning demographics and clinical characteristics, genomic data, cancer treatment, health and quality of life outcomes, and resource use. CONCLUSIONS: Cost-effectiveness analyses will fail to reflect the real-world impacts of precision oncology without data to accurately characterize patient care trajectories and outcomes. Data collection in accordance with the proposed core data set will promote standardization and enable the generation of decision-grade evidence to inform reimbursement.

Early-stage economic analysis of research-based comprehensive genomic sequencing for advanced cancer care.

Genomic research is driving discovery for future population benefit. Limited evidence exists on immediate patient and health system impacts of research participation. This study uses real-world data and quasi-experimental matching to examine early-stage cost and health impacts of research-based genomic sequencing. British Columbia's Personalized OncoGenomics (POG) single-arm program applies whole genome and transcriptome analysis (WGTA) to characterize genomic landscapes in advanced cancers. Our cohort includes POG patients enrolled between 2014 and 2015 and 1:1 genetic algorithm-matched usual care controls. We undertake a cost consequence analysis and estimate 1-year effects of WGTA on patient management, patient survival, and health system costs reported in 2015 Canadian dollars. WGTA costs are imputed and forecast using system of equations modeling. We use Kaplan-Meier survival analysis to explore survival differences and inverse probability of censoring weighted linear regression to estimate mean 1-year survival times and costs. Non-parametric bootstrapping simulates sampling distributions and enables scenario analysis, revealing drivers of incremental costs, survival, and net monetary benefit for assumed willingness to pay thresholds. We identified 230 POG patients and 230 matched controls for cohort inclusion. The mean period cost of research-funded WGTA was $26,211 (SD: $14,191). Sequencing costs declined rapidly, with WGTA forecasts hitting $13,741 in 2021. The incremental healthcare system effect (non-research expenditures) was $5203 (95% CI: 75, 10,424) compared to usual care. No overall survival differences were observed, but outcome heterogeneity was present. POG patients receiving WGTA-informed treatment experienced incremental survival gains of 2.49 months (95% CI: 1.32, 3.64). Future cost consequences became favorable as WGTA cost drivers declined and WGTA-informed treatment rates improved to 60%. Our study demonstrates the ability of real-world data to support evaluations of only-in-research health technologies. We identify situations where precision oncology research initiatives may produce survival benefit at a cost that is within healthcare systems' willingness to pay. This economic evidence informs the early-stage healthcare impacts of precision oncology research.

Allocating healthcare resources to genomic testing in Canada: latest evidence and current challenges.

Precision medicine (PM) informed by next-generation sequencing (NGS) poses challenges for health technology assessment (HTA). To date, there has been limited reimbursement of genomic testing with NGS in Canada, particularly for whole-genome and whole-exome sequencing (WGS/WES). Through a structured literature review, we examine Canadian economic evidence and evidentiary challenges for the adoption of genomic testing. We searched Medline (PubMed) for published Canadian studies generating economic evidence for PM informed by NGS. Our search focused on studies examining the costs and/or value of NGS. We reviewed included studies and summarized results according to evaluation type, clinical context, NGS technology, and test strategy. We then grouped HTA challenges encountered by authors when evaluating NGS. Our review included twenty-five studies. To determine the economic impacts of NGS-informed PM in Canada, studies applied cost-effectiveness analysis (52%, n = 13), stated preference analysis (20%, n = 5), cost-consequence analysis (16%, n = 4), and healthcare resource utilization or costing analysis (12%, n = 3). NGS panels were the most common technology evaluated (n = 13), followed by WGS and/or WES (n = 8). The included studies highlighted multiple challenges when generating economic evidence, many of which remain unaddressed. Challenges were broadly related to (1) accounting for all NGS outcomes; (2) addressing uncertainty; and (3) improving consistency of economic approaches. Canadian studies are beginning to produce estimates of the economic impacts of NGS-informed PM, yet challenges for HTA remain. While solutions and real-world evidence are generated, lifecycle health technology management methods can be designed to better support resource allocation decisions for genomic testing in Canada.

Clinical and cost outcomes following genomics-informed treatment for advanced cancers.

BACKGROUND: Single-arm trials are common in precision oncology. Owing to the lack of randomized counterfactual, resultant data are not amenable to comparative outcomes analyses. Difference-in-difference (DID) methods present an opportunity to generate causal estimates of time-varying treatment outcomes. Using DID, our study estimates within-cohort effects of genomics-informed treatment versus standard care on clinical and cost outcomes. METHODS: We focus on adults with advanced cancers enrolled in the single-arm BC Cancer Personalized OncoGenomics program between 2012 and 2017. All individuals had a minimum of 1-year follow up. Logistic regression explored baseline differences across patients who received a genomics-informed treatment versus a standard care treatment after genomic sequencing. DID estimated the incremental effects of genomics-informed treatment on time to treatment discontinuation (TTD), time to next treatment (TTNT), and costs. TTD and TTNT correlate with improved response and survival. RESULTS: Our study cohort included 346 patients, of whom 140 (40%) received genomics-informed treatment after sequencing and 206 (60%) received standard care treatment. No significant differences in baseline characteristics were detected across treatment groups. DID estimated that the incremental effect of genomics-informed versus standard care treatment was 102 days (95% CI: 35, 167) on TTD, 91 days (95% CI: -9, 175) on TTNT, and CAD$91,098 (95% CI: $46,848, $176,598) on costs. Effects were most pronounced in gastrointestinal cancer patients. CONCLUSIONS: Genomics-informed treatment had a statistically significant effect on TTD compared to standard care treatment, but at increased treatment costs. Within-cohort evidence generated through this single-arm study informs the early-stage comparative effectiveness of precision oncology.

Toward the diagnosis of rare childhood genetic diseases: what do parents value most?

Genomic testing is becoming routine for diagnosing rare childhood genetic disease. Evidence underlying sustainable implementation is limited, focusing on short-term endpoints such as diagnostic yield, unable to fully characterize patient and family valued outcomes. Although genomic testing is becoming widely available, evidentiary and outcomes uncertainty persist as key challenges for implementation. We examine whether the current evidence base reflects public tolerance for uncertainty for genomics to diagnose rare childhood genetic disease. We conducted focus groups with general population parents in Vancouver, Canada, and Oxford, United Kingdom, to discuss expectations and concerns related to genomic testing to diagnose rare childhood genetic disease. Applying a purposive sampling technique, recruitment continued until thematic saturation was reached. Transcripts were analysed using thematic analysis. Thirty-three parents participated across four focus groups. Participants valued causal diagnoses alongside management strategies to improve patient health and wellbeing. Further, participants valued expanding the evidence base to reduce evidentiary uncertainty while ensuring security of information. Willingness to pay out of pocket for testing reflected perceived familial health benefit. Diagnostic yield fails to fully capture valued outcomes, and efforts to resolve uncertainty better reflect public priorities. Evaluations of genomic testing that fully integrate valued endpoints are necessary to ensure consistency with best practices and public willingness to accept the uncertain familial benefit.

Matching methods in precision oncology: An introduction and illustrative example.

BACKGROUND: Randomized controlled trials (RCTs) are uncommon in precision oncology. We provide an introduction and illustrative example of matching methods for evaluating precision oncology in the absence of RCTs. We focus on British Columbia's Personalized OncoGenomics (POG) program, which applies whole-genome and transcriptome analysis (WGTA) to inform advanced cancer care. METHODS: Our cohort comprises 230 POG patients enrolled between 2014 and 2015 and matched POG-naive controls. We generated our matched cohort using 1:1 propensity score matching (PSM) and genetic matching prior to exploring survival differences. RESULTS: We find that genetic matching outperformed PSM when balancing covariates. In all cohorts, overall survival did not significantly differ across POG and POG-naive patients (p > 0.05). Stratification by WGTA-informed treatment indicated unmatched survival differences. Patients whose WGTA information led to treatment change were at a reduced hazard of death compared to POG-naive controls in all cohorts, with estimated hazard ratios ranging from 0.33 (95% CI: 0.13, 0.81) to 0.41 (95% CI: 0.17, 0.98). CONCLUSION: These results signal that clinical effectiveness of precision oncology approaches will depend on rates of genomics-informed treatment change. Our study will guide future evaluations of precision oncology and support reliable effect estimation when RCT data are unavailable.

Genetic testing for hereditary cancer syndromes: patient recommendations for improved risk communication.

BACKGROUND: Multi-gene panel testing is replacing single-gene testing for patients with suspected hereditary cancer syndromes. The detection of a hereditary cancer syndrome allows tested individuals to initiate enhanced primary and secondary prevention efforts-where available-with a view to reduce disease burden. Current policy prevents testing programmes from communicating genetic test results with potentially affected family members, yet it is well documented that tested individuals face multiple challenges in initiating such discussions with relatives. OBJECTIVE: In response to this challenge, we sought patient recommendations about how to improve genetic risk communication to enhance interfamilial discussions about primary and secondary disease prevention. DESIGN: We conducted 25 semi-structured interviews with individuals who received genetic testing through British Columbia's Hereditary Cancer Program between 2017 and 2018. Interviews were professionally transcribed and analysed using a constant comparative approach. RESULTS: Participants described difficulty engaging in conversations with relatives who were resistant to receiving genetic risk information, when communicating with younger relatives and where participants reported strained familial relationships. Participants recommended that testing facilities provide a summary of results and implications and that resources be made available to prepare patients for challenging discussions with family members. DISCUSSION: Our study demonstrates that individuals undergoing genetic testing for suspected hereditary cancer syndromes would benefit from additional supportive resources alongside genetic counselling. Providing this on-going support will enhance the accurate and transparent communication of risk to facilitate the uptake of cascade testing and enhanced prevention strategies.

Validation of a Cyclic Algorithm to Proxy Number of Lines of Systemic Cancer Therapy Using Administrative Data.

PURPOSE: Researchers are automating the process for identifying the number of lines of systemic cancer therapy received by patients. To date, algorithm development has involved manual modifications to predefined classification rules. In this study, we propose a supervised learning algorithm for determining the best-performing proxy for number of lines of therapy and validate this approach in four patient groups. MATERIALS AND METHODS: We retrospectively analyzed BC Cancer pharmacy records from patients' cancer diagnosis until end of follow-up (cohort-specific, 2014/2015). We created and validated a cyclic algorithm in patients with advanced cancers of varying histologies, diffuse large B-cell lymphoma, follicular lymphoma, and chronic lymphocytic leukemia. To assess internal and external validity, we used a split-sample approach for all analyses and considered lines of therapy identified through manual review as our criterion standard. We measured agreement using correlation coefficients, mean squared error, nonparametric hypothesis testing, and quantile-quantile plots. RESULTS: Cohorts comprised 91 patients with advanced cancers, 121 with chronic lymphocytic leukemia, 440 with follicular lymphoma, and 679 with diffuse large B-cell lymphoma. Number of lines of therapy received and patients' treatment period length varied substantially across cohorts. Despite these differences, our algorithm successfully identified a best-performing proxy for number of lines of therapy for each cohort, which was moderate to highly correlated with (within-sample: 0.73 ≤ Pearson correlation ≤ 0.84; out-of-sample: 0.52 ≤ Pearson correlation ≤ 0.76) and whose distribution did not significantly differ from the criterion standard within or out of sample (P > .10). CONCLUSION: Supervised learning is an ideal tool for generating a best-performing proxy that recognizes prescription drug patterns and approximates number of lines of therapy. Our cyclic approach can be used in jurisdictions with access to administrative pharmacy data.

Valuation of Health and Nonhealth Outcomes from Next-Generation Sequencing: Approaches, Challenges, and Solutions.

BACKGROUND: Next-generation sequencing (NGS) technologies have seen variable adoption in the clinic. This is partly due to a lack of clinical and economic studies, with the latter increasingly challenged to examine patient preferences for health and nonhealth outcomes (e.g., false-positive rate). OBJECTIVES: To conduct a structured review of studies valuing patients' preference-based utility for NGS outcomes, to highlight identified methodological challenges, and to consider how studies addressed identified challenges. METHODS: We searched MEDLINE (PubMed), Embase (Ovid), and Web of Science for published studies examining outcomes from health care decisions informed by NGS. We focused our search on direct elicitations of preference-based utility. We reviewed included studies and qualitatively grouped and summarized stated challenges and solutions by theme. RESULTS: Eleven studies were included. Most of them (n = 6) used discrete choice experiments to value utility. We categorized challenges into four themes: 1) valuing the full range of NGS outcomes, 2) accounting for accuracy and uncertainty surrounding effectiveness, 3) allowing for simultaneous multiple and cascading risks, and 4) incorporating downstream consequences. Studies found strong evidence of utility for NGS information, regardless of health improvement. Investigators addressed challenges by simplifying complex choices, by including health outcomes alongside nonhealth outcomes, and by using multiple elicitation techniques. CONCLUSIONS: The breadth and complexity of NGS-derived information makes the technology a unique and challenging application for utility valuation. Failing to account for the utility or disutility of NGS-related nonhealth outcomes may lead to overinvestment or underinvestment in NGS, and so there is a need for research addressing unresolved challenges.

Patient preferences for massively parallel sequencing genetic testing of colorectal cancer risk: a discrete choice experiment.

This study enumerated patients' preference-based personal utility and willingness-to-pay for massively parallel sequencing (MPS) genetic testing of colorectal cancer (CRC) risk. Our setting was the New Exome Technology in (NEXT) Medicine Study, a randomized control trial of usual care genetic testing vs. exome sequencing. Using a discrete choice experiment (DCE), we elicited patient preferences for information on genetic causes of CRC. We estimated personal utility for the following four attributes: proportion of individuals with a genetic cause of CRC who receive a diagnosis, number of tests used, wait time for results, and cost. A total of 122 patients completed our DCE (66% response rate). On average, patients preferred genetic tests identifying more individuals with a diagnosis and involving a shorter wait time. Assuming MPS identifies more individuals with a Mendelian form of CRC risk, involves fewer tests, and results in a shorter wait than traditional diagnostic testing, average willingness-to-pay (WTP) for MPS ranged from US$400 (95% CI: $300, $500) to US$1541 (95% CI: $1224, $1859). These results indicate that patients value information on genetic causes of CRC and replacing traditional diagnostic testing with MPS testing will increase patients' utility. Future research exploring the costs and benefits of MPS for CRC risk is warranted.

Economic Evaluations of Next-Generation Precision Oncology: A Critical Review.

PURPOSE: Precision oncology has the potential to improve patient health and reduce treatment costs. Yet the up-front cost of genomic testing with next-generation sequencing (NGS) technologies can be prohibitive. Our study is a structured review of economic evaluations of precision oncology informed by NGS. The aim is to characterize the availability and scope of economic evidence. MATERIALS AND METHODS: We searched Medline (PubMed), Embase (Ovid), and Web of Science databases for English-language full-text peer-reviewed articles published between 2000 and 2016. We focused our search on articles that estimated the benefit of precision oncology in relation to its costs. We excluded studies that did not undertake full economic evaluations or did not focus on NGS technologies. We reviewed all included studies and summarized key methodological and empirical study characteristics. RESULTS: Fifty-five economic evaluations met our inclusion criteria. The number of published studies increased steadily, from three studies between 2005 and 2007 to 26 between 2014 and 2016. Most studies evaluated multiplex panels (86%). We found testing was frequently used to predict prognosis (67%), to diagnose patients (24%), or to identify targeted therapeutic options (7%). Methods and cost effectiveness differed according to NGS technology, test strategy, and cancer type. Deterministic and probabilistic analyses were typically used to characterize parameter and decision uncertainty (91% and 75%). CONCLUSION: Although the availability of economic evidence examining precision oncology increased over time, methods used often did not align with current guidelines. Future evaluations should undertake extensive sensitivity analysis to address all sources of uncertainty associated with rapidly changing NGS technologies. Furthermore, additional research is needed evaluating the cost effectiveness of more comprehensive next-generation technologies before implementing these on a wider scale.

The cost and cost trajectory of whole-genome analysis guiding treatment of patients with advanced cancers.

BACKGROUND: Limited data exist on the real-world costs of applying whole-genome analysis (WGA) in a clinical setting. We estimated the costs of applying WGA to guide treatments for patients with advanced cancers and characterized how costs evolve over time. METHODS: The setting is the British Columbia Cancer Agency Personalized OncoGenomics (POG) program in British Columbia, Canada. Cost data were obtained for patients who enrolled in the program from 2012 to 2015. We estimated mean WGA costs using bootstrapping. We applied time series analysis and produced 10-year forecasts to determine when costs are expected to reach critical thresholds. RESULTS: The mean cost of WGA over the study period was CDN$34,886 per patient (95% CI: $34,051, $35,721). Over time, WGA costs decreased, driven by a reduction in costs of sequencing. Yet, costs of other components of WGA increased. Forecasting showed WGA costs may not reach critical thresholds within the next 10 years. CONCLUSION: WGA costs decreased over the studied time horizon, but expenditures needed to realize WGA remain significant. Future research exploring costs and benefits of WGA-guided cancer care are crucial to guide health policy.