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IntroductionThe SARS-CoV-2 pandemic represented a formidable scientific and technological challenge to public health due to its rapid spread and evolution. To meet these challenges and to characterize the virus over time, the State of California established the California SARS-CoV-2 Whole Genome Sequencing (WGS) Initiative, or “California COVIDNet”. This initiative constituted an unprecedented multi-sector collaborative effort to achieve large-scale genomic surveillance of SARS-CoV-2 across California to monitor the spread of variants within the state, to detect new and emerging variants, and to characterize outbreaks in congregate, workplace, and other settings.MethodsCalifornia COVIDNet consists of 50 laboratory partners that include public health laboratories, private clinical diagnostic laboratories, and academic sequencing facilities as well as expert advisors, scientists, consultants, and contractors. Data management, sample sourcing and processing, and computational infrastructure were major challenges that had to be resolved in the midst of the pandemic chaos in order to conduct SARS-CoV-2 genomic surveillance. Data management, storage, and analytics needs were addressed with both conventional database applications and newer cloud-based data solutions, which also fulfilled computational requirements.ResultsRepresentative and randomly selected samples were sourced from state-sponsored community testing sites. Since March of 2021, California COVIDNet partners have contributed more than 450,000 SARS-CoV-2 genomes sequenced from remnant samples from both molecular and antigen tests. Combined with genomes from CDC-contracted WGS labs, there are currently nearly 800,000 genomes from all 61 local health jurisdictions (LHJs) in California in the COVIDNet sequence database. More than 5% of all reported positive tests in the state have been sequenced, with similar rates of sequencing across 5 major geographic regions in the state.DiscussionImplementation of California COVIDNet revealed challenges and limitations in the public health system. These were overcome by engaging in novel partnerships that established a successful genomic surveillance program which provided valuable data to inform the COVID-19 public health response in California. Significantly, California COVIDNet has provided a foundational data framework and computational infrastructure needed to respond to future public health crises.

The tumor suppressor gene TP53 is the most frequently mutated gene in human cancer. It encodes p53, a DNA-binding transcription factor that regulates multiple genes involved in DNA repair, metabolism, cell cycle arrest, apoptosis, and senescence. TP53 is associated with human cancer by mutations that lead to a loss of wild-type p53 function as well as mutations that confer alternate oncogenic functions that enable them to promote invasion, metastasis, proliferation, and cell survival. Identifying the discrete TP53 mutations in tumor cells may help direct therapies that are more effective. In this study, we identified the frequency of individual TP53 mutations in patients with colon adenocarcinoma (48%), non–small cell lung carcinoma (NSCLC) (36%), and glioma/glioblastoma (28%) at our institution using next-generation sequencing. We also identified the occurrence of somatic mutations in numerous actionable genes including BRAF, EGFR, KRAS, IDH1, and PIK3CA that occurred concurrently with these TP53 mutations. Of the 480 tumors examined that contained one or more mutations in the TP53 gene, 219 were colon adenocarcinomas, 215 were NSCLCs, and 46 were gliomas/glioblastomas. Among the patients positive for TP53 mutations diagnosed with colon adenocarcinoma, 50% also showed at least one mutation in pathogenic genes of which 14% were BRAF, 33% were KRAS, and 3% were NRAS. Forty-seven percent of NSCLC patients harboring TP53 mutations also had a mutation in at least one actionable pathogenic variant with the following frequencies: BRAF: 4%, EGFR: 10%, KRAS: 28%, and PIK3CA: 4%. Fifty-two percent of patients diagnosed with glioma/glioblastoma with a positive TP53 mutation had at least one concurrent mutation in a known pathogenic gene of which 9% were CDKN2A, 41% were IDH1, and 11% were PIK3CA.

ABSTRACT: Objective: Hemoglobin A1c (HbA1c) is a useful tool for the diagnosis and management of diabetes mellitus and generally an excellent marker of overall glycemic control for the preceding 8 to 12 weeks; however, the test is not without its pitfalls. A suspicion of falsely high or low HbA1c should prompt clinicians to evaluate for possible causes. Here, we present the novel use of continuous glucose monitoring (CGM) in a case of a 49-year-old African American woman with discrepant HbA1c and self-monitored blood glucose (SMBG). CGM data suggested that the HbA1c values were falsely elevated, and subsequent evaluation led to a diagnosis of hemoglobin C (HbC) trait.Methods: Case identified is described. Point-of-care (POC) and central laboratory HbA1c were measured on the Bayer DCA 2000 and Bio-Rad Variant II Turbo ion-exchange high-performance liquid chromatography platforms, respectively.Results: The patient had an elevated POC HbA1c of 10.8% (95 mmol/mol) and a central laboratory HbA1c of 9.2% (77 mmol/mol). The patient's glucose meter measured an average glucose of 138 mg/dL. The Freestyle LibrePro professional CGM was used to investigate the reliability of the patient's SMBG and showed an average glucose of 165 mg/dL. An investigation into potential causes for falsely elevated HbA1c revealed a previously undiagnosed HbC trait.Conclusion: CGM is a valuable tool to assess HbA1c and SMBG discordance and to guide subsequent diabetes management. Our results suggest that the HbA1c may have been overestimated in our patient due to HbC trait; therefore, results must be interpreted with caution.Abbreviations: CGM = continuous glucose monitoring; HbA1c = hemoglobin A1c; HbC = hemoglobin C; HPLC = high-performance liquid chromatography; POC = point-of-care; SMBG = self-monitored blood glucose