More Than Results: The Clinical and Research Relationship in the Evolving Detection and Surveillance of SARS-CoV-2.

INTRODUCTION: During the coronavirus disease 2019 (COVID-19) pandemic, real-time reverse transcription polymerase chain reaction (RT-PCR) became an essential tool for laboratories to provide high-sensitivity qualitative diagnostic testing for patients and real-time data to public health officials. Here we explore the predictive value of quantitative data from RT-PCR cycle threshold (Ct) values in epidemiological measures, symptom presentation, and variant transition. METHODS: To examine the association with hospitalizations and deaths, data from 74,479 patients referred to the Avera Institute for Human Genetics (AIHG) for COVID-19 testing in 2020 were matched by calendar week to epidemiological data reported by the South Dakota Department of Health. We explored the association between symptom data, patient age, and Ct values for 101 patients. We also explored changes in Ct values during variant transition detected by genomic surveillance sequencing of the AIHG testing population during 2021. RESULTS: Measures from AIHG diagnostic testing strongly explain variance in the South Dakota state positivity percentage (R2 = 0.758), a two-week delay in hospitalizations (R2 = 0.856), and a four-week delay in deaths (R2 = 0.854). Based on factor analysis of patient symptoms, three groups could be distinguished which had different presentations of age, Ct value, and time from collection. Additionally, conflicting Ct value results among SARSCoV- 2 variants during variant transition may reflect the community transmission dynamics. CONCLUSIONS: Measures of Ct value in RT-PCR diagnostic assays combined with routine screening have valuable applications in monitoring the dynamics of SARS-CoV-2 within communities.

RNA based individualized drug selection in breast cancer patients without patient-matched normal tissue.

BACKGROUND: While standard RNA expression tests stratify patients into risk groups, RNA-Seq can guide personalized drug selection based on expressed mutations, fusion genes, and differential expression (DE) between tumor and normal tissue. However, patient-matched normal tissue may be unavailable. Additionally, biological variability in normal tissue and technological biases may confound results. Therefore, we present normal expression reference data for two sequencing methods that are suitable for breast biopsies. RESULTS: We identified breast cancer related and drug related genes that are expressed uniformly across our normal samples. Large subsets of these genes are identical for formalin fixed paraffin embedded samples and fresh frozen samples. Adipocyte signatures were detected in frozen compared to formalin samples, prepared by surgeons and pathologists, respectively. Gene expression confounded by adipocytes was identified using fat tissue samples. Finally, immune repertoire statistics were obtained for healthy breast, tumor and fat tissues. CONCLUSIONS: Our reference data can be used with patient tumor samples that are asservated and sequenced with a matching aforementioned method. Coefficients of variation are given for normal gene expression. Thus, potential drug selection can be based on confidently overexpressed genes and immune repertoire statistics. MATERIALS AND METHODS: Normal expression from formalin and frozen healthy breast tissue samples using Roche Kapa RiboErase (total RNA) (19 formalin, 9 frozen) and Illumina TruSeq RNA Access (targeted RNA-Seq, aka TruSeq RNA Exome) (11 formalin, 1 frozen), and fat tissue (6 frozen Access). Tumor DE using 10 formalin total RNA tumor samples and 1 frozen targeted RNA tumor sample.