HSP27 Expression as a Novel Predictive Biomarker for Bevacizumab: is it Cost Effective?

A Practical Guide to Modeling and Conducting a Cost-Effectiveness Analysis of Companion Biomarker Tests for Targeted Therapies Using R: Tutorial Paper

Despite the increasing number of potential biomarkers identified in laboratories and reported in much literature, the adoption of biomarkers routinely available in clinical practice to inform treatment decisions is very limited. Reimbursement decisions for new health technologies are often informed by economic evaluations; however, economic evaluations of diagnostics/testing technologies, such as companion biomarker tests, are far less frequently reported than drugs. Furthermore, few countries provide the health economic evaluation methods guide specific to co-dependent technologies such as companion diagnostics or precision medicines. Therefore, this paper aims to guide the process of the development of cost-effectiveness models of cancer biomarkers for targeted therapies, focusing on companion diagnostics. This tutorial paper provides practical guidance on how to conduct economic evaluations of cancer biomarkers and how to model the characteristics of the biomarker tests as part of the value for money of corresponding targeted therapies. This paper presents a brief introduction to the methods and data requirements, a step-by-step guide to constructing a health economic model of companion cancer biomarkers, and a discussion of issues that arise in their application to healthcare decision making. This practical guidance is provided in R, and worked examples are provided in this paper with R codes in the accompanying electronic supplementary material.

How are we evaluating the cost-effectiveness of companion biomarkers for targeted cancer therapies? A systematic review

Background

Despite the increasing economic assessment of biomarker-guided therapies, no clear agreement exists whether existing methods are sufficient or whether different methods might produce different cost-effectiveness results. This study aims to examine current practices of modeling companion biomarkers when assessing the cost-effectiveness of targeted cancer therapies. It investigates the current methods in modeling the characteristics of companion diagnostics based on existing economic evaluations of biomarker-guided therapies in cancer.

Methods

A literature search was performed using Medline, Embase, EconLit, Cochrane library for economic evaluations of biomarker-guided therapies with companion diagnostics in cancer. Preferred Reporting Items of Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed. Studies were selected using pre-specified eligibility criteria based on the PICO framework. To make the included studies more comparable, we qualitatively synthesized the data under nine domains of methods where consensus was deemed lacking.

Results

Only four of the twenty-two studies included in this review were found to be of good quality with respect to incorporating the characteristics of companion biomarkers in economic evaluations. However, many evaluations focused on a pre-selected patient group rather than including all patients regardless of their biomarker status. Companion biomarker characteristics captured in evaluations were often limited to the cost or the accuracy of the test. Often, only the costs of biomarker testing were modelled. Clinical outcomes and health state utilities were often not included due to the limited data generated by clinical trials. Methods of economic evaluation were not applied consistently in assessments of companion cancer biomarkers for targeted therapies. It was also shown that conflicting cost-effectiveness results were likely depending on what comparator arm was chosen and what comparison structure was designed in the model.

Conclusion

We found no consistent approach applied in assessing the value of companion biomarker tests and including the characteristics of biomarkers in an economic evaluation of targeted oncology therapies. Currently, many economic evaluations fail to capture the full value of companion biomarkers beyond sensitivity/specificity and cost related to biomarker testing.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08725-4.

General Structural and Functional Features of Molecular Chaperones

Molecular chaperones are a group of structurally diverse and highly conserved ubiquitous proteins. They play crucial roles in facilitating the correct folding of proteins in vivo by preventing protein aggregation or facilitating the appropriate folding and assembly of proteins. Heat shock proteins form the major class of molecular chaperones that are responsible for protein folding events in the cell. This is achieved by ATP-dependent (folding machines) or ATP-independent mechanisms (holders). Heat shock proteins are induced by a variety of stresses, besides heat shock. The large and varied heat shock protein class is categorised into several subfamilies based on their sizes in kDa namely, small Hsps (HSPB), J domain proteins (Hsp40/DNAJ), Hsp60 (HSPD/E; Chaperonins), Hsp70 (HSPA), Hsp90 (HSPC), and Hsp100. Heat shock proteins are localised to different compartments in the cell to carry out tasks specific to their environment. Most heat shock proteins form large oligomeric structures, and their functions are usually regulated by a variety of cochaperones and cofactors. Heat shock proteins do not function in isolation but are rather part of the chaperone network in the cell. The general structural and functional features of the major heat shock protein families are discussed, including their roles in human disease. Their function is particularly important in disease due to increased stress in the cell. Vector-borne parasites affecting human health encounter stress during transmission between invertebrate vectors and mammalian hosts. Members of the main classes of heat shock proteins are all represented in Plasmodium falciparum, the causative agent of cerebral malaria, and they play specific functions in differentiation, cytoprotection, signal transduction, and virulence.