Abstract B082: Precision medicine guided by next-generation sequencing: Slow recognition of emerging technologies leads to crucial coverage gaps in health insurance

While the medical/research community has made great strides in the fight against cancer, providing new and innovative methods and technologies that improve the diagnosis and treatment of the disease, health insurers are slow to recognize the significance of these developments. Numerous biomarkers have been identified for various types of cancers and a variety of therapies have been developed to target these specific genetic abnormalities. However, just as the various types of cancers have varying biomarkers, each patient's tumor bears its own specific genetic signature, causing each patient to respond to prescribed therapies in a different way. Next-generation sequencing (NGS) of a large test panel allows physicians a much broader knowledge of tumor-specific mutations in order to provide the best possible therapy tailored to the patient's specific needs. This individualized treatment is the patient's best hope for managing and defeating the disease. NGS identifies genetic abnormalities present in the patient's tumor. Testing a wider range of genes provides a more specific genetic profile of the patient's tumor, offering a broader range of treatment options and predictive indicators of a patient's response to therapy. The Feist Weiller Cancer Center Genomics Core sequences solid tumor tissue using a 435-gene panel. We provide a comprehensive report identifying genetic variants that are currently targeted by FDA-approved therapies. For each actionable variant, the report lists targeted therapies currently in use for the patient's disease and for other cancers, therapies associated with resistance when the variant is present, and new therapies in clinical trial for the patient's disease. Our panel sequences whole coding regions and is able to detect less common variants in therapy-targeted pathways, dramatically increasing the chance of matching the patient to available treatment. Our panel also measures microsatellite instability and mutation burden, identifying patients as candidates for immuno-oncology therapy. At the Cancer Center, we have tested approximately 200 patients using this panel. In the state of Louisiana, our payer breakdown consists of 44% Medicare, 31% state Medicaid, and 20% private insurance. The federally funded Medicare program recognizes the benefits of genetic testing in cancer treatment and reimburses for the test. However, many private insurers consider broad panel testing to be investigational and many providers of state Medicaid deny coverage of numerous Current Procedural Terminology (CPT) codes covering molecular diagnostic testing. This coverage gap leaves a large portion of our state's population without the necessary access to crucial information needed to make an informed decision concerning cancer treatment.

Citation Format: Phoebe A. Rollyson, Camille Abshire, Adam Greer, Ellen Friday, Catherine Chaudoir, Glenn Mills. Precision medicine guided by next-generation sequencing: Slow recognition of emerging technologies leads to crucial coverage gaps in health insurance [abstract]. In: Proceedings of the Eleventh AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2018 Nov 2-5; New Orleans, LA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2020;29(6 Suppl):Abstract nr B082.

Bioinformatics multivariate analysis determined a set of phase-specific biomarker candidates in a novel mouse model for viral myocarditis

BACKGROUND

Myocarditis is an inflammatory disease of the cardiac muscle and is mainly caused by viral infections. Viral myocarditis has been proposed to be divided into 3 phases: the acute viral phase, the subacute immune phase, and the chronic cardiac remodeling phase. Although individualized therapy should be applied depending on the phase, no clinical or experimental studies have found biomarkers that distinguish between the 3 phases. Theiler's murine encephalomyelitis virus belongs to the genus Cardiovirus and can cause myocarditis in susceptible mouse strains.

METHODS AND RESULTS

Using this novel model for viral myocarditis induced with Theiler's murine encephalomyelitis virus, we conducted multivariate analysis including echocardiography, serum troponin and viral RNA titration, and microarray to identify the biomarker candidates that can discriminate the 3 phases. Using C3H mice infected with Theiler's murine encephalomyelitis virus on 4, 7, and 60 days post infection, we conducted bioinformatics analyses, including principal component analysis and k-means clustering of microarray data, because our traditional cardiac and serum assays, including 2-way comparison of microarray data, did not lead to the identification of a single biomarker. Principal component analysis separated heart samples clearly between the groups of 4, 7, and 60 days post infection. Representative genes contributing to the separation were as follows: 4 and 7 days post infection, innate immunity-related genes, such as Irf7 and Cxcl9; 7 and 60 days post infection, acquired immunity-related genes, such as Cd3g and H2-Aa; and cardiac remodeling-related genes, such as Mmp12 and Gpnmb.

CONCLUSIONS

Sets of molecules, not single molecules, identified by unsupervised principal component analysis, were found to be useful as phase-specific biomarkers.