Cost-Effectiveness of Tumor Genomic Profiling to Guide First-Line Targeted Therapy Selection in Patients With Metastatic Lung Adenocarcinoma

Cost-Effectiveness and the Economics of Genomic Testing and Molecularly Matched Therapies

Cost-effectiveness analysis of precision oncology can help guide value-driven care. Next-generation sequencing is increasingly cost-efficient over single gene testing because diagnostic algorithms require multiple individual gene tests to determine biomarker status. Matched targeted therapy is often not cost-effective due to the high cost associated with drug treatment. However, genomic profiling can promote cost-effective care by identifying patients who are unlikely to benefit from therapy. Additional applications of genomic profiling such as universal testing for hereditary cancer syndromes and germline testing in patients with cancer may represent cost-effective approaches compared with traditional history-based diagnostic methods.

Value of next generation sequencing (NGS) testing in advanced cancer patients

OBJECTIVE

The availability of targeted therapies for oncology patients is increasing. Available genomic tests to identify treatment-eligible patients include single gene tests and gene panel tests, including the whole-exome, whole-transcriptome OncoExTra test. We assessed the costs and clinical benefits of test choice.

METHODS

A Microsoft Excel-based model was developed to evaluate test choice in patients with advanced/metastatic non-small cell lung cancer (NSCLC), breast, prostate, and colorectal cancer. Treatment pathways were based on NCCN guidelines and medical expert opinion. Inputs were derived from published literature. Annual economic results and lifetime clinical results with OncoExTra testing were projected per-tested-patient and compared with single gene testing and no testing. Separately, results were estimated for a US health plan without the OncoExTra test and with its use in 5% of patients.

RESULTS

Compared with no genomic testing, OncoExTra test use increased costs by $4,915 per patient; however, 82%-92% of individuals across tumour types were identified as eligible for targeted therapy or a clinical trial. Compared with single gene testing, OncoExTra test use decreased costs by $9,966 per-patient-tested while increasing use of approved or investigational targeted therapies by 20%. When considering a hypothetical health plan with 1 million members, 858 patients were eligible for genomic testing. Using the OncoExTra test in 5% of those eligible, per-member per-month costs decreased by $0.003, ranging from cost-savings of $0.026 in NSCLC patients to a $0.009 increase in prostate cancer patients. Cost-savings were driven by reduced treatment costs with increased clinical trial enrolment and reduced direct and indirect medical costs associated with targeted treatments.

LIMITATIONS

Limitations include the required simplifications in modelling complex conditions that may not fully reflect evolving real-world testing and treatment patterns.

CONCLUSIONS

Compared to single-gene testing, results indicate that using next generation sequencing test such as OncoExTra identified more actionable alterations, leading to improved outcomes and reduced costs.

Early prediction of response to palliative chemotherapy in patients with stage-IV gastric and esophageal cancer

BACKGROUND

The goal of therapy for many patients with advanced stage malignancies, including those with metastatic gastric and esophageal cancers, is to extend overall survival while also maintaining quality of life. After weighing the risks and benefits of treatment with palliative chemotherapy (PC) with non-curative intent, many patients decide to pursue treatment. It is known that a subset of patients who are treated with PC experience significant side effects without clinically significant survival benefits from PC.

METHODS

We use data from 150 patients with stage-IV gastric and esophageal cancers to train machine learning models that predict whether a patient with stage-IV gastric or esophageal cancers would benefit from PC, in terms of increased survival duration, at very early stages of the treatment.

RESULTS

Our findings show that machine learning can predict with high accuracy whether a patient will benefit from PC at the time of diagnosis. More accurate predictions can be obtained after only two cycles of PC (i.e., about 4 weeks after diagnosis). The results from this study are promising with regard to potential improvements in quality of life for patients near the end of life and a potential overall survival benefit by optimizing systemic therapy earlier in the treatment course of patients.

Challenges and Solutions for the Benefit Assessment of Tumor-Agnostic Therapies in Germany

OBJECTIVES

Precision medicine is increasingly important in cancer treatment. Tumor-agnostic therapies are used regardless of tumor entity because they target specific biomarkers in tumors. In Germany, the benefit assessment of oncological pharmaceuticals has traditionally been entity specific. Thus, the assessment of tumor-agnostic therapies leaves stakeholders with various challenges. Our aim was to systematically identify challenges and possible solutions for the benefit assessment of therapies in tumor-agnostic indications using a 2-step sequential qualitative approach.

METHODS

To identify relevant challenges, we conducted qualitative interviews with different stakeholders who were involved in previous benefit assessments of tumor-agnostic therapies in Germany. To identify possible solutions for these challenges, we systematically searched MEDLINE, Embase, and the websites of European health technology assessment bodies for relevant literature.

RESULTS

We identified 9 categories of challenges of which the following were deemed particularly relevant: the absence of direct comparative studies, challenges regarding the use of basket studies and indirect comparisons, challenges in determining the appropriate comparative therapy in a tumor-agnostic indication, and challenges on the system side. Seven categories of solutions were identified, including an increased use of real-world evidence, making conditional decisions in the context of systematic reassessments, splitting the field of application, and finding (new) ways to design and analyze basket studies.

CONCLUSION

A range of possible solutions, which can help to meet the identified challenges in Germany, have been found. Future research should investigate the acceptance and feasibility of these solutions.