IMPORTANCE Performing DNA genetic testing (DGT) for hereditary cancer genes is now a wellaccepted clinical practice; however, the interpretation of DNA variation remains a challenge for laboratories and clinicians. Adding RNA genetic testing (RGT) enhances DGT by clarifying the clinical actionability of hereditary cancer gene variants, thus improving clinicians' ability to accurately apply strategies for cancer risk reduction and treatment. OBJECTIVE To evaluate whether RGT is associated with improvement in the diagnostic outcome of DGT and in the delivery of personalized cancer risk management for patients with hereditary cancer predisposition. DESIGN, SETTING, AND PARTICIPANTS Diagnostic study in which patients and/or families with inconclusive variants detected by DGT in genes associated with hereditary breast and ovarian cancer, Lynch syndrome, and hereditary diffuse gastric cancer sent blood samples for RGT from March 2016 to April 2018. Clinicians who ordered genetic testing and received a reclassification report for these variants were surveyed to assess whether RGT-related variant reclassifications changed clinical management of these patients. To quantify the potential number of tested individuals who could benefit from RGT, a cohort of 307 812 patients who underwent DGT for hereditary cancer were separately queried to identify variants predicted to affect splicing. Data analysis was conducted from March 2016 and September 2018. MAIN OUTCOMES AND MEASURES Variant reclassification outcomes following RGT, clinical management changes associated with RGT-related variant reclassifications, and the proportion of patients who would likely be affected by a concurrent DGT and RGT multigene panel testing approach. Author affiliations and article information are listed at the end of this article. Open Access. This is an open access article distributed under the terms of the CC-BY-NC-ND License.
Germline variants in tumor suppressor genes (TSGs) can result in RNA mis-splicing and predisposition to cancer. However, identification of variants that impact splicing remains a challenge, contributing to a substantial proportion of patients with suspected hereditary cancer syndromes remaining without a molecular diagnosis. To address this, we used capture RNAsequencing (RNA-seq) to generate a splicing profile of 18 TSGs (APC,
Clinical genetic testing for hereditary breast and ovarian cancer (HBOC) is becoming widespread. However, the interpretation of variants of unknown significance (VUS) in HBOC genes, such as the clinically actionable genes BRCA1 and BRCA2, remain a challenge. Among the variants that are frequently classified as VUS are those with unclear effects on splicing. In order to address this issue we developed a high-throughput RNA-massively parallel sequencing assay—CloneSeq—capable to perform quantitative and qualitative analysis of transcripts in cell lines and HBOC patients. This assay is based on cloning of RT-PCR products followed by massive parallel sequencing of the cloned transcripts. To validate this assay we compared it to the RNA splicing assays recommended by members of the ENIGMA (Evidence-based Network for the Interpretation of Germline Mutant Alleles) consortium. This comparison was performed using well-characterized lymphoblastoid cell lines (LCLs) generated from carriers of the BRCA1 or BRCA2 germline variants that have been previously described to be associated with splicing defects. CloneSeq was able to replicate the ENIGMA results, in addition to providing quantitative characterization of BRCA1 and BRCA2 germline splicing alterations in a high-throughput fashion. Furthermore, CloneSeq was used to analyze blood samples obtained from carriers of BRCA1 or BRCA2 germline sequence variants, including the novel uncharacterized alteration BRCA1 c.5152+5G>T, which was identified in a HBOC family. CloneSeq provided a high-resolution picture of all the transcripts induced by BRCA1 c.5152+5G>T, indicating it results in significant levels of exon skipping. This analysis proved to be important for the classification of BRCA1 c.5152+5G>T as a clinically actionable likely pathogenic variant. Reclassifications such as these are fundamental in order to offer preventive measures, targeted treatment, and pre-symptomatic screening to the correct individuals.
BACKGROUND: Hypoglycemia is a major limiting factor in achieving glycemic control in persons with diabetes. In some instances, recovery from a severe hypoglycemia event may require health care resource utilization (HCRU), including the use of emergency medical services (EMS), visits to the emergency department (ED), and inpatient hospitalization. OBJECTIVES: To (a) describe the profiles of patients who experience severe hypoglycemic events and (b) characterize HCRU and the associated cost related to severe hypoglycemia treatment. METHODS: This retrospective, observational cohort study used administrative claims data from IBM MarketScan Research Databases.The study examined a cohort of subjects who experienced severe hypoglycemic events that involved HCRU during the 1-year index period. Baseline patient demographic data were collected according to patient profiles, such as payer type, type of diabetes, age, and type of insulin. HCRU and the associated cost data categorized by the patient profiles and care progression scenarios were described. RESULTS: 9,563 patients from the IBM MarketScan Research Databases experienced a severe hypoglycemic event during the index period and were included in the study; approximately 75% of those patients did not experience a severe hypoglycemic event in the previous year. Of the 9,563 patients in the cohort, the largest patient profile (n = 1,767, 18.5%) consisted of those who were on Medicaid, had type 2 diabetes, and used basal/bolus or premixed-only insulins. Overall, more than 90% of the index severe hypoglycemic events involved visits to the ED. EMS claims in the 24 hours before the ED visit were found for half of the severe hypoglycemic events (51.5%). CONCLUSIONS:Differences in HCRU and the associated costs for the treatment of severe hypoglycemia were observed among patients based on insurance, diabetes, and insulin types. Clinicians need to be aware
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