Evaluation of a Zoonotic Orthopoxvirus PCR Assay for the Detection of Mpox Virus Infection.

An epidemic caused by an outbreak of mpox (formerly monkeypox) in May 2022 rapidly spread internationally, requiring an urgent response from the clinical diagnostics community. A detailed description of the clinical validation and implementation of a laboratory-developed real-time PCR test for detecting nonvariola Orthopoxvirus-specific DNA based on the newly designed RealStar Zoonotic Orthopoxvirus assay is presented. The validation was performed using an accuracy panel (n = 97) comprising skin lesion swabs in universal transport media and from mpox virus genomic DNA spiked into pooled mpox virus-negative remnant universal transport media of lesion specimens submitted for routine clinical testing in the NewYork-Presbyterian Hospital clinical laboratory system. Accuracy testing demonstrated excellent assay agreement between expected and observed results and comparable diagnostic performance to three different reference tests. Analytical sensitivity with 95% detection probability was 126 copies/mL, and analytical specificity, clinical sensitivity, and clinical specificity were 100%. In summary, the RealStar Zoonotic Orthopoxvirus assay provides a sensitive and reliable method for routine diagnosis of mpox infections.

Deep computational image analysis of immune cell niches reveals treatment-specific outcome associations in lung cancer.

The tumor immune composition influences prognosis and treatment sensitivity in lung cancer. The presence of effective adaptive immune responses is associated with increased clinical benefit after immune checkpoint blockers. Conversely, immunotherapy resistance can occur as a consequence of local T-cell exhaustion/dysfunction and upregulation of immunosuppressive signals and regulatory cells. Consequently, merely measuring the amount of tumor-infiltrating lymphocytes (TILs) may not accurately reflect the complexity of tumor-immune interactions and T-cell functional states and may not be valuable as a treatment-specific biomarker. In this work, we investigate an immune-related biomarker (PhenoTIL) and its value in associating with treatment-specific outcomes in non-small cell lung cancer (NSCLC). PhenoTIL is a novel computational pathology approach that uses machine learning to capture spatial interplay and infer functional features of immune cell niches associated with tumor rejection and patient outcomes. PhenoTIL's advantage is the computational characterization of the tumor immune microenvironment extracted from H&E-stained preparations. Association with clinical outcome and major non-small cell lung cancer (NSCLC) histology variants was studied in baseline tumor specimens from 1,774 lung cancer patients treated with immunotherapy and/or chemotherapy, including the clinical trial Checkmate 057 (NCT01673867).

A tumor vasculature-based imaging biomarker for predicting response and survival in patients with lung cancer treated with checkpoint inhibitors.

Tumor vasculature is a key component of the tumor microenvironment that can influence tumor behavior and therapeutic resistance. We present a new imaging biomarker, quantitative vessel tortuosity (QVT), and evaluate its association with response and survival in patients with non-small cell lung cancer (NSCLC) treated with immune checkpoint inhibitor (ICI) therapies. A total of 507 cases were used to evaluate different aspects of the QVT biomarkers. QVT features were extracted from computed tomography imaging of patients before and after ICI therapy to capture the tortuosity, curvature, density, and branching statistics of the nodule vasculature. Our results showed that QVT features were prognostic of OS (HR = 3.14, 0.95% CI = 1.2 to 9.68, P = 0.0006, C-index = 0.61) and could predict ICI response with AUCs of 0.66, 0.61, and 0.67 on three validation sets. Our study shows that QVT imaging biomarker could potentially aid in predicting and monitoring response to ICI in patients with NSCLC.

Rapid detection of SARS-CoV-2 variants of concern by single nucleotide polymorphism genotyping using TaqMan assays.

We evaluated the performance of SARS-CoV-2 TaqMan real-time reverse-transcription PCR (RT-qPCR) assays (ThermoFisher) for detecting 2 nonsynonymous spike protein mutations, E484K and N501Y. Assay accuracy was evaluated by whole genome sequencing (WGS). Residual nasopharyngeal SARS-CoV-2 positive samples (N = 510) from a diverse patient population in New York City submitted for routine SARS-CoV-2 testing during January-April 2020 were used. We detected 91 (18%) N501Y and 101 (20%) E484K variants. Four samples (0.8%) were positive for both variants. The assay had nearly perfect concordance with WGS in the validation subset, detecting B.1.1.7 and B.1.526 variants among others. Sensitivity and specificity ranged from 0.95 to 1.00. Positive and negative predictive values were 0.98-1.00. TaqMan genotyping successfully predicted the presence of B.1.1.7, but had significantly lower sensitivity, 62% (95% CI, 0.53, 0.71), for predicting B.1.526 sub-lineages lacking E484K. This approach is rapid and accurate for detecting SARS-CoV-2 variants and can be rapidly implemented in routine clinical setting.

Spatial interplay patterns of cancer nuclei and tumor-infiltrating lymphocytes (TILs) predict clinical benefit for immune checkpoint inhibitors.

Immune checkpoint inhibitors (ICIs) show prominent clinical activity across multiple advanced tumors. However, less than half of patients respond even after molecule-based selection. Thus, improved biomarkers are required. In this study, we use an image analysis to capture morphologic attributes relating to the spatial interaction and architecture of tumor cells and tumor-infiltrating lymphocytes (TILs) from digitized H&E images. We evaluate the association of image features with progression-free (PFS) and overall survival in non-small cell lung cancer (NSCLC) (N = 187) and gynecological cancer (N = 39) patients treated with ICIs. We demonstrated that the classifier trained with NSCLC alone was associated with PFS in independent NSCLC cohorts and also in gynecological cancer. The classifier was also associated with clinical outcome independent of clinical factors. Moreover, the classifier was associated with PFS even with low PD-L1 expression. These findings suggest that image analysis can be used to predict clinical end points in patients receiving ICI.

Image analysis reveals molecularly distinct patterns of TILs in NSCLC associated with treatment outcome.

Despite known histological, biological, and clinical differences between lung adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC), relatively little is known about the spatial differences in their corresponding immune contextures. Our study of over 1000 LUAD and LUSC tumors revealed that computationally derived patterns of tumor-infiltrating lymphocytes (TILs) on H&E images were different between LUAD (N = 421) and LUSC (N = 438), with TIL density being prognostic of overall survival in LUAD and spatial arrangement being more prognostically relevant in LUSC. In addition, the LUAD-specific TIL signature was associated with OS in an external validation set of 100 NSCLC treated with more than six different neoadjuvant chemotherapy regimens, and predictive of response to therapy in the clinical trial CA209-057 (n = 303). In LUAD, the prognostic TIL signature was primarily comprised of CD4+ T and CD8+ T cells, whereas in LUSC, the immune patterns were comprised of CD4+ T, CD8+ T, and CD20+ B cells. In both subtypes, prognostic TIL features were associated with transcriptomics-derived immune scores and biological pathways implicated in immune recognition, response, and evasion. Our results suggest the need for histologic subtype-specific TIL-based models for stratifying survival risk and predicting response to therapy. Our findings suggest that predictive models for response to therapy will need to account for the unique morphologic and molecular immune patterns as a function of histologic subtype of NSCLC.

Alignment of Fellowship Training with Practice Patterns for Molecular Pathologists: A Report of the Association for Molecular Pathology Training and Education Committee.

In the two decades since Accreditation Council for Graduate Medical Education-accredited Molecular Genetic Pathology fellowships began, the field of clinical molecular pathology has evolved considerably. The American Board of Pathology gathered data from board-certified molecular genetic pathologists assessing the alignment of skills and knowledge gained during fellowship with current needs on the job. The Association of Molecular Pathology conducted a parallel survey of program directors, and included questions on how various topics were taught during fellowship, as well as ranking their importance. Both surveys showed that most training aligned well with the practice needs of former trainees. Genomic profiling of tumors by next-generation sequencing, bioinformatics, laboratory management, and regulatory issues were topics thought to require increased emphasis in training. Topics related to clinical genetics and microbiology were deemed less important by those in practice, perhaps reflecting the increasing subspecialization of molecular pathologists. Program directors still viewed these topics as important to provide foundational knowledge. Parentage, identity, and human leukocyte antigen testing were less important to both survey audiences. These data may be helpful in guiding future adjustments to the Molecular Genetic Pathology curriculum and Accreditation Council for Graduate Medical Education program requirements.

System-wide transcriptome damage and tissue identity loss in COVID-19 patients.

The molecular mechanisms underlying the clinical manifestations of coronavirus disease 2019 (COVID-19), and what distinguishes them from common seasonal influenza virus and other lung injury states such as acute respiratory distress syndrome, remain poorly understood. To address these challenges, we combine transcriptional profiling of 646 clinical nasopharyngeal swabs and 39 patient autopsy tissues to define body-wide transcriptome changes in response to COVID-19. We then match these data with spatial protein and expression profiling across 357 tissue sections from 16 representative patient lung samples and identify tissue-compartment-specific damage wrought by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, evident as a function of varying viral loads during the clinical course of infection and tissue-type-specific expression states. Overall, our findings reveal a systemic disruption of canonical cellular and transcriptional pathways across all tissues, which can inform subsequent studies to combat the mortality of COVID-19 and to better understand the molecular dynamics of lethal SARS-CoV-2 and other respiratory infections.

Next-Generation Sequencing of Cell-Free DNA Extracted From Pleural Effusion Supernatant: Applications and Challenges.

Cell-free DNA (cfDNA) extracted from diverse specimen types has emerged as a high quality substrate for molecular tumor profiling. Analytical and pre-analytical challenges in the utilization of cfDNA extracted from pleural effusion supernatant (PES) are herein characterized in patients with metastatic non-small cell lung carcinoma (NSCLC). Pleural effusion specimens containing metastatic NSCLC were collected prospectively. After ThinPrep® (TP) and cell block (CB) preparation, DNA was extracted from residual PES and analyzed by gel electrophoresis for quality and quantity. Libraries were prepared and sequenced with a targeted next-generation sequencing (NGS) platform and panel clinically validated for plasma specimens. Results were compared with DNA extracted from corresponding FFPE samples that were sequenced using institutional targeted NGS assays clinically validated for solid tumor FFPE samples. Tumor (TC) and overall cellularity (OC) were evaluated. Fourteen specimens were collected from 13 patients. Median specimen volume was 180 mL (range, 35-1,400 mL). Median TC and OC on TP slides and CB sections were comparable. Median extracted DNA concentration was 7.4 ng/µL (range, 0.1-58.0 ng/µL), with >5 ng/µL DNA extracted from 10/14 specimens (71%). Mutations were identified in 10/14 specimens, including 1/3 specimens with median molecular coverage <1,000 reads. The minimal detected allelic fraction was 0.6%. NGS was falsely negative for the presence of one driver mutation. No correlation was identified between sample volume or OC, quality or quantity of extracted DNA, or mutation detection. Despite analytical and pre-analytical challenges, PES represents a robust source of DNA for NGS.

Interpretative differences of combined cytogenetic and molecular profiling highlights differences between MRC and ELN classifications of AML.

Acute myeloid leukemia (AML) is typically characterized clinically for prognostic purposes using both cytogenetic and molecular characteristics. However, both cytogenetic and molecular risk stratification schemas are varied and few reports have studied correlations between these schemas. We have performed a single institution retrospective review of cytogenetic and molecular classifications of AMLs seen at Penn Medicine between 2013 and 2018. One-hundred fourty-four cases were characterized according to European Leukemia Net (ELN) or Medical Research Council (MRC) criteria for cytogenetics and results compared to molecular profiling. When we analyzed the most common sequencing study results within the risk groupings, negative sequencing studies and FLT3 mutations were common in favorable AMLs, intermediate AMLs had mutations in FLT3, NPM1, DNMT3A and IDH2, while adverse AMLs had a high prevalence of TP53 mutations. We next grouped the genes on the panel by their proteins' functions and found mutations in signaling pathway genes to be common in favorable AMLs while tumor suppressors were commonly mutated in adverse AMLs. AMLs grouped by the type of chromosomal abnormality present showed that FLT3 mutations were common in AMLs with a trisomy while TP53 mutations were common in AMLs with a monosomy or a deletion. TP53 mutations are especially common in AMLs with a monosomal karyotype and often overlap with 17p loss. Interestingly, although all AMLs with TP53 mutations have a defect in the response to DNA damage, expression of P53 protein before and after irradiation is not consistently predicted by phenotype. Overall, these studies confirm the genetic complexity of AML which does not fall into simple patterns of cooperating mutations.

Early introductions and transmission of SARS-CoV-2 variant B.1.1.7 in the United States.

The emergence and spread of SARS-CoV-2 lineage B.1.1.7, first detected in the United Kingdom, has become a global public health concern because of its increased transmissibility. Over 2,500 COVID-19 cases associated with this variant have been detected in the United States (US) since December 2020, but the extent of establishment is relatively unknown. Using travel, genomic, and diagnostic data, we highlight that the primary ports of entry for B.1.1.7 in the US were in New York, California, and Florida. Furthermore, we found evidence for many independent B.1.1.7 establishments starting in early December 2020, followed by interstate spread by the end of the month. Finally, we project that B.1.1.7 will be the dominant lineage in many states by mid- to late March. Thus, genomic surveillance for B.1.1.7 and other variants urgently needs to be enhanced to better inform the public health response.

Systemic Tissue and Cellular Disruption from SARS-CoV-2 Infection revealed in COVID-19 Autopsies and Spatial Omics Tissue Maps.

The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) virus has infected over 115 million people and caused over 2.5 million deaths worldwide. Yet, the molecular mechanisms underlying the clinical manifestations of COVID-19, as well as what distinguishes them from common seasonal influenza virus and other lung injury states such as Acute Respiratory Distress Syndrome (ARDS), remains poorly understood. To address these challenges, we combined transcriptional profiling of 646 clinical nasopharyngeal swabs and 39 patient autopsy tissues, matched with spatial protein and expression profiling (GeoMx) across 357 tissue sections. These results define both body-wide and tissue-specific (heart, liver, lung, kidney, and lymph nodes) damage wrought by the SARS-CoV-2 infection, evident as a function of varying viral load (high vs. low) during the course of infection and specific, transcriptional dysregulation in splicing isoforms, T cell receptor expression, and cellular expression states. In particular, cardiac and lung tissues revealed the largest degree of splicing isoform switching and cell expression state loss. Overall, these findings reveal a systemic disruption of cellular and transcriptional pathways from COVID-19 across all tissues, which can inform subsequent studies to combat the mortality of COVID-19, as well to better understand the molecular dynamics of lethal SARS-CoV-2 infection and other viruses.

Early introductions and community transmission of SARS-CoV-2 variant B.1.1.7 in the United States.

The emergence and spread of SARS-CoV-2 lineage B.1.1.7, first detected in the United Kingdom, has become a global public health concern because of its increased transmissibility. Over 2500 COVID-19 cases associated with this variant have been detected in the US since December 2020, but the extent of establishment is relatively unknown. Using travel, genomic, and diagnostic data, we highlight the primary ports of entry for B.1.1.7 in the US and locations of possible underreporting of B.1.1.7 cases. Furthermore, we found evidence for many independent B.1.1.7 establishments starting in early December 2020, followed by interstate spread by the end of the month. Finally, we project that B.1.1.7 will be the dominant lineage in many states by mid to late March. Thus, genomic surveillance for B.1.1.7 and other variants urgently needs to be enhanced to better inform the public health response.

Machine Learning Highlights Downtrending of COVID-19 Patients with a Distinct Laboratory Profile.

Background: New York City (NYC) experienced an initial surge and gradual decline in the number of SARS-CoV-2-confirmed cases in 2020. A change in the pattern of laboratory test results in COVID-19 patients over this time has not been reported or correlated with patient outcome. Methods: We performed a retrospective study of routine laboratory and SARS-CoV-2 RT-PCR test results from 5,785 patients evaluated in a NYC hospital emergency department from March to June employing machine learning analysis. Results: A COVID-19 high-risk laboratory test result profile (COVID19-HRP), consisting of 21 routine blood tests, was identified to characterize the SARS-CoV-2 patients. Approximately half of the SARS-CoV-2 positive patients had the distinct COVID19-HRP that separated them from SARS-CoV-2 negative patients. SARS-CoV-2 patients with the COVID19-HRP had higher SARS-CoV-2 viral loads, determined by cycle threshold values from the RT-PCR, and poorer clinical outcome compared to other positive patients without the COVID12-HRP. Furthermore, the percentage of SARS-CoV-2 patients with the COVID19-HRP has significantly decreased from March/April to May/June. Notably, viral load in the SARS-CoV-2 patients declined, and their laboratory profile became less distinguishable from SARS-CoV-2 negative patients in the later phase. Conclusions: Our longitudinal analysis illustrates the temporal change of laboratory test result profile in SARS-CoV-2 patients and the COVID-19 evolvement in a US epicenter. This analysis could become an important tool in COVID-19 population disease severity tracking and prediction. In addition, this analysis may play an important role in prioritizing high-risk patients, assisting in patient triaging and optimizing the usage of resources.

Rapid Implementation of Severe Acute Respiratory Syndrome Coronavirus 2 Emergency Use Authorization RT-PCR Testing and Experience at an Academic Medical Institution.

An epidemic caused by an outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in China in December 2019 has since rapidly spread internationally, requiring urgent response from the clinical diagnostics community. We present a detailed overview of the clinical validation and implementation of the first laboratory-developed real-time RT-PCR test offered in the NewYork-Presbyterian Hospital system following the Emergency Use Authorization issued by the US Food and Drug Administration. Nasopharyngeal and sputum specimens (n = 174) were validated using newly designed dual-target real-time RT-PCR (altona RealStar SARS-CoV-2 Reagent) for detecting SARS-CoV-2 in upper respiratory tract and lower respiratory tract specimens. Accuracy testing demonstrated excellent assay agreement between expected and observed values and comparable diagnostic performance to reference tests. The limit of detection was 2.7 and 23.0 gene copies per reaction for nasopharyngeal and sputum specimens, respectively. Retrospective analysis of 1694 upper respiratory tract specimens from 1571 patients revealed increased positivity in older patients and males compared with females, and an increasing positivity rate from approximately 20% at the start of testing to 50% at the end of testing 3 weeks later. Herein, we demonstrate that the assay accurately and sensitively identifies SARS-CoV-2 in multiple specimen types in the clinical setting and summarize clinical data from early in the epidemic in New York City.

CT derived radiomic score for predicting the added benefit of adjuvant chemotherapy following surgery in stage I, II resectable non-small cell lung cancer: a retrospective multicohort study for outcome prediction.

BACKGROUND: Use of adjuvant chemotherapy in patients with early-stage lung cancer is controversial because no definite biomarker exists to identify patients who would receive added benefit from it. We aimed to develop and validate a quantitative radiomic risk score (QuRiS) and associated nomogram (QuRNom) for early-stage non-small cell lung cancer (NSCLC) that is prognostic of disease-free survival and predictive of the added benefit of adjuvant chemotherapy following surgery. METHODS: We did a retrospective multicohort study of individuals with early-stage NSCLC (stage I and II) who either received surgery alone or surgery plus adjuvant chemotherapy. We selected patients for whom we had available pre-treatment diagnostic CT scans and corresponding survival information. We used radiomic texture features derived from within and outside the primary lung nodule on chest CT scans of patients from the Cleveland Clinic Foundation (Cleveland, OH, USA; cohort D1) to develop QuRiS. A least absolute shrinkage and selection operator-Cox regularisation model was used for data dimension reduction, feature selection, and QuRiS construction. QuRiS was independently validated on a cohort of patients from the University of Pennsylvania (Philadephia, PA, USA; cohort D2) and a cohort of patients whose CT scans were derived from The Cancer Imaging Archive (cohort D3). QuRNom was constructed by integrating QuRiS with tumour and node descriptors (according to the tumour, node, metastasis staging system) and lymphovascular invasion. The primary endpoint of the study was the assessment of the performance of QuRiS and QuRNom in predicting disease-free survival. The added benefit of adjuvant chemotherapy estimated using QuRiS and QuRNom was validated by comparing patients who received adjuvant chemotherapy versus patients who underwent surgery alone in cohorts D1-D3. FINDINGS: We included: 329 patients in cohort D1 (73 [22%] had surgery plus adjuvant chemotherapy and 256 (78%) had surgery alone); 114 patients in cohort D2 (33 [29%] had surgery plus adjuvant chemotherapy and 81 (71%) had surgery alone); and 82 patients in cohort D3 (24 [29%] had surgery plus adjuvant chemotherapy and 58 (71%) had surgery alone). QuRiS comprised three intratumoral and 10 peritumoral CT-radiomic features and was found to be significantly associated with disease-free survival (ie, prognostic validation of QuRiS; hazard ratio for predicted high-risk vs predicted low-risk groups 1·56, 95% CI 1·08-2·23, p=0·016 for cohort D1; 2·66, 1·24-5·68, p=0·011 for cohort D2; and 2·67, 1·39-5·11, p=0·0029 for cohort D3). To validate the predictive performance of QuRiS, patients were partitioned into three risk groups (high, intermediate, and low risk) on the basis of their corresponding QuRiS. Patients in the high-risk group were observed to have significantly longer survival with adjuvant chemotherapy than patients who underwent surgery alone (0·27, 0·08-0·95, p=0·042, for cohort D1; 0·08, 0·01-0·42, p=0·0029, for cohorts D2 and D3 combined). As concerns QuRNom, the nomogram-estimated survival benefit was predictive of the actual efficacy of adjuvant chemotherapy (0·25, 0·12-0·55, p<0·0001, for cohort D1; 0·13, <0·01-0·99, p=0·0019 for cohort D3). INTERPRETATION: QuRiS and QuRNom were validated as being prognostic of disease-free survival and predictive of the added benefit of adjuvant chemotherapy, especially in clinically defined low-risk groups. Since QuRiS is based on routine chest CT imaging, with additional multisite independent validation it could potentially be employed for decision management in non-invasive treatment of resectable lung cancer. FUNDING: National Cancer Institute of the US National Institutes of Health, National Center for Research Resources, US Department of Veterans Affairs Biomedical Laboratory Research and Development Service, Department of Defence, National Institute of Diabetes and Digestive and Kidney Diseases, Wallace H Coulter Foundation, Case Western Reserve University, and Dana Foundation.

Routine Laboratory Blood Tests Predict SARS-CoV-2 Infection Using Machine Learning.

BACKGROUND: Accurate diagnostic strategies to identify SARS-CoV-2 positive individuals rapidly for management of patient care and protection of health care personnel are urgently needed. The predominant diagnostic test is viral RNA detection by RT-PCR from nasopharyngeal swabs specimens, however the results are not promptly obtainable in all patient care locations. Routine laboratory testing, in contrast, is readily available with a turn-around time (TAT) usually within 1-2 hours. METHOD: We developed a machine learning model incorporating patient demographic features (age, sex, race) with 27 routine laboratory tests to predict an individual's SARS-CoV-2 infection status. Laboratory testing results obtained within 2 days before the release of SARS-CoV-2 RT-PCR result were used to train a gradient boosting decision tree (GBDT) model from 3,356 SARS-CoV-2 RT-PCR tested patients (1,402 positive and 1,954 negative) evaluated at a metropolitan hospital. RESULTS: The model achieved an area under the receiver operating characteristic curve (AUC) of 0.854 (95% CI: 0.829-0.878). Application of this model to an independent patient dataset from a separate hospital resulted in a comparable AUC (0.838), validating the generalization of its use. Moreover, our model predicted initial SARS-CoV-2 RT-PCR positivity in 66% individuals whose RT-PCR result changed from negative to positive within 2 days. CONCLUSION: This model employing routine laboratory test results offers opportunities for early and rapid identification of high-risk SARS-CoV-2 infected patients before their RT-PCR results are available. It may play an important role in assisting the identification of SARS-CoV-2 infected patients in areas where RT-PCR testing is not accessible due to financial or supply constraints.

Clinical Performance of SARS-CoV-2 Molecular Tests.

Molecular testing for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the gold standard for diagnosis of coronavirus disease 2019 (COVID-19), but the clinical performance of these tests is still poorly understood, particularly with regard to disease course, patient-specific factors, and viral shedding. From 10 March to 1 May 2020, NewYork-Presbyterian laboratories performed 27,377 SARS-CoV-2 molecular assays from 22,338 patients. Repeat testing was performed for 3,432 patients, of which 2,413 had initial negative and 802 had initial positive results. Repeat-tested patients were more likely to have severe disease and low viral loads. The negative predictive value of the first-day result among repeat-tested patients was 81.3% The clinical sensitivity of SARS-CoV-2 molecular assays was estimated between 58% and 96%, depending on the unknown number of false-negative results in single-tested patients. Conversion to negative was unlikely to occur before 15 to 20 days after initial testing or 20 to 30 days after the onset of symptoms, with 50% conversion occurring at 28 days after initial testing. Conversion from first-day negative to positive results increased linearly with each day of testing, reaching 25% probability in 20 days. Sixty patients fluctuated between positive and negative results over several weeks, suggesting that caution is needed when single-test results are acted upon. In summary, our study provides estimates of the clinical performance of SARS-CoV-2 molecular assays and suggests time frames for appropriate repeat testing, namely, 15 to 20 days after a positive test and the same day or next 2 days after a negative test for patients with high suspicion for COVID-19.

Shotgun Transcriptome and Isothermal Profiling of SARS-CoV-2 Infection Reveals Unique Host Responses, Viral Diversification, and Drug Interactions.

The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has caused thousands of deaths worldwide, including >18,000 in New York City (NYC) alone. The sudden emergence of this pandemic has highlighted a pressing clinical need for rapid, scalable diagnostics that can detect infection, interrogate strain evolution, and identify novel patient biomarkers. To address these challenges, we designed a fast (30-minute) colorimetric test (LAMP) for SARS-CoV-2 infection from naso/oropharyngeal swabs, plus a large-scale shotgun metatranscriptomics platform (total-RNA-seq) for host, bacterial, and viral profiling. We applied both technologies across 857 SARS-CoV-2 clinical specimens and 86 NYC subway samples, providing a broad molecular portrait of the COVID-19 NYC outbreak. Our results define new features of SARS-CoV-2 evolution, nominate a novel, NYC-enriched viral subclade, reveal specific host responses in interferon, ACE, hematological, and olfaction pathways, and examine risks associated with use of ACE inhibitors and angiotensin receptor blockers. Together, these findings have immediate applications to SARS-CoV-2 diagnostics, public health, and new therapeutic targets.

Comparison of Two High-Throughput Reverse Transcription-PCR Systems for the Detection of Severe Acute Respiratory Syndrome Coronavirus 2.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has emerged as the cause of a worldwide pandemic. Many commercial SARS-CoV-2 reverse transcription-PCR (RT-PCR) assays have received Emergency Use Authorization from the U.S. Food and Drug Administration. However, there are limited data describing their performance, in particular the performance of high-throughput SARS-CoV-2 RT-PCR systems. We analyzed the diagnostic performance of two high-throughput systems: cobas 6800 and Panther Fusion, and their associated RT-PCR assays, with a collection of 389 nasopharyngeal specimens. The overall agreement between the platforms was 96.4% (375/389). Cohen's kappa analysis rated the strength of agreement between the two platforms as "almost perfect" (κ = 0.922; standard error, 0.051). Furthermore, there was no significant difference between corresponding cycle threshold values generated on the two systems (P value = 0.88; Student's t test). Taken together, these data imply that the two platforms can be considered comparable in terms of their clinical performance. We believe that this information will be useful for those who have already adopted these platforms or are seeking to implement high-throughput RT-PCR testing to stem the SARS-CoV-2 pandemic.