OBJECTIVES : The current landscape of genomic medicine is undergoing rapid-innovation, and genome-based interventions are being increasingly used in the screening, diagnosis and treatment decisions in clinical practice. Our objective was to estimate the costs and outcomes of using different genome-based interventions (Multi-targeted stool DNA test and 12-gene assay) as compared with non-genomic interventions (Fecal Immunochemical Test (FIT) and a clinical guideline-based approach) from pre-clinical to post-diagnostic treatment pathways in colorectal cancer patients.

METHODS : As a part of a larger study, we modeled the entire clinical treatment pathway of colorectal cancer disease to better understand the disease and treatment systems. The costs of screening tests, genomic tests, adjuvant chemotherapy, adverse events, and administration were considered. The costs were discounted annually at 3%. We investigated outcomes in three stages including no recurrence, recurrence (local or distant), and death. The model parameters were derived from published meta-analyses studies, randomized controlled trial studies, observational studies, health utility studies, and economic evaluation studies.

RESULTS : We conducted a retrospective data analysis of 1,127 colorectal cancer patients and developed an interactive model. The sensitivity of Multi-targeted stool DNA testing to detect colorectal cancer was 92.3%, as compared to FIT (73.8%). The12-gene assay was used to group 711 stage II colorectal cancer patients into high risk (n=182, 25.60%) and low risk (n=529, 74.40%) strata based upon recurrence scores. To compare the use of the 12-gene assay with a clinical guideline-based approach, 416 stage II colorectal patients were categorized into high risk (n= 180, 43.30%) and low-risk (n=236, 56.70%) groups using well-established clinicopathological parameters.

CONCLUSIONS : This study demonstrates that the genome-based interventions involved in the clinical treatment pathway of colorectal cancer may provide more precise information for the risk stratification of patients and can be used to compute the cost or disease burden.