Caris Molecular Intelligence (CMI) is a test that is used to help guide management of advanced cancer patients fit for further treatment (based on clinical ECOG-status, estimated life-expectancy and quality of life). Upon testing of formalin-fixed paraffin-embedded (FFPE) cancer tissue, Caris provides a report that describes biomarker results leading potentially to cancer treatments associated with increased or decreased likelihood of benefit, based on molecular and phenotypic characteristics of the tumour, taking the primary tumor site into consideration as well. Until now, no published evidence is available on the health economic impact of CMI. One may argue that CMI has potential for resource-allocation optimisation, since the test would prevent cancer patients from initiating treatments that offer little or no benefit. Other specialists may discuss, that the CMI test itself is rather expensive and that these costs finally have to be added to the overall economic burden of cancer treatment and that the test does not finally change the management of cancer patients.
However, the data of the present study shows that patients that have had a CMI test have a high probability to have their treatment plans changed (88%) as compared to the planned treatment before receiving the CMI report. This is a much higher percentage as observed with pure DNA-NGS only tests, where adapted, targeted treatment is performed in only 32% of tested individuals [15]. One of the major reasons for this high clinical utility is probably that CMI includes various technologies in its approach and not only NGS as profiling technique. The impact on treatment choice is directly dependent on the panel of biomarkers tested, the frequency of those biomarkers in the population and the level of evidence presented to the oncologist in support of a change in treatment decision. DNA next-generation sequencing only is mainly focused on targeted therapies, whereas CMI provides the most comprehensive information, including chemotherapies, endocrine treatments, immunotherapies as well as targeted therapies, analysing DNA, RNA and proteins with multiple technologies and continuous updates of the panel, reflecting latest evidence and scientific results. Hence, the clinical benefit of CMI test is probably the crucial variable that renders this test cost-effective.
Indeed, we could show that CMI-guided treatment is in line and within the range with previously planned treatments. To enable price comparison (cost per treatment cycle), all the drug regimens were based on 21 days cycles, which could represent a limitation of this cost comparison, because the effect of guided-therapy on duration of therapy is unknown. As all regimens were treated the same in all groups (actual, planned, CMI-guided treatments), the influence on the accuracy of the results should be minimal.
One major concern of this analysis is the great diversity of cancer patients included in studies on molecular profiling. Patients usually do not have the same stage or clinical presentation and this may hamper a proper comparison. Moreover, data regarding CMI-guided treatment in site-specific cancers are missing, which makes it difficult to compare it with approved drugs for single cancers. Furthermore, no data coming from randomized trials are available. Such data would be necessary to exclude a confounding bias such as survival outcome (observational vs prospective design).
Additionally, traditional cost-effectiveness analyses cannot be applied easily due to the fact, that tumor profiling tests report biomarkers. This information about biomarkers can then potentially influence the physician’s choice for treatment strategy but this information is only one factor driving the choice for treatment. Therefore, multi-dimensional rationales about the scope of tumor profiling, biomarker driven treatment options, cost-effectiveness on one hand and outcome results and quality of life-data on the other hand have to be implemented.