COVID-19 in patients with lung cancer.

BACKGROUND: Patients with lung cancers may have disproportionately severe coronavirus disease 2019 (COVID-19) outcomes. Understanding the patient-specific and cancer-specific features that impact the severity of COVID-19 may inform optimal cancer care during this pandemic. PATIENTS AND METHODS: We examined consecutive patients with lung cancer and confirmed diagnosis of COVID-19 (n = 102) at a single center from 12 March 2020 to 6 May 2020. Thresholds of severity were defined a priori as hospitalization, intensive care unit/intubation/do not intubate ([ICU/intubation/DNI] a composite metric of severe disease), or death. Recovery was defined as >14 days from COVID-19 test and >3 days since symptom resolution. Human leukocyte antigen (HLA) alleles were inferred from MSK-IMPACT (n = 46) and compared with controls with lung cancer and no known non-COVID-19 (n = 5166). RESULTS: COVID-19 was severe in patients with lung cancer (62% hospitalized, 25% died). Although severe, COVID-19 accounted for a minority of overall lung cancer deaths during the pandemic (11% overall). Determinants of COVID-19 severity were largely patient-specific features, including smoking status and chronic obstructive pulmonary disease [odds ratio for severe COVID-19 2.9, 95% confidence interval 1.07-9.44 comparing the median (23.5 pack-years) to never-smoker and 3.87, 95% confidence interval 1.35-9.68, respectively]. Cancer-specific features, including prior thoracic surgery/radiation and recent systemic therapies did not impact severity. Human leukocyte antigen supertypes were generally similar in mild or severe cases of COVID-19 compared with non-COVID-19 controls. Most patients recovered from COVID-19, including 25% patients initially requiring intubation. Among hospitalized patients, hydroxychloroquine did not improve COVID-19 outcomes. CONCLUSION: COVID-19 is associated with high burden of severity in patients with lung cancer. Patient-specific features, rather than cancer-specific features or treatments, are the greatest determinants of severity.

Optimized quantification of intra-host viral diversity in SARS-CoV-2 and influenza virus sequence data.

High error rates of viral RNA-dependent RNA polymerases lead to diverse intra-host viral populations during infection. Errors made during replication that are not strongly deleterious to the virus can lead to the generation of minority variants. However, accurate detection of minority variants in viral sequence data is complicated by errors introduced during sample preparation and data analysis. We used synthetic RNA controls and simulated data to test seven variant-calling tools across a range of allele frequencies and simulated coverages. We show that choice of variant caller and use of replicate sequencing have the most significant impact on single-nucleotide variant (SNV) discovery and demonstrate how both allele frequency and coverage thresholds impact both false discovery and false-negative rates. When replicates are not available, using a combination of multiple callers with more stringent cutoffs is recommended. We use these parameters to find minority variants in sequencing data from SARS-CoV-2 clinical specimens and provide guidance for studies of intra-host viral diversity using either single replicate data or data from technical replicates. Our study provides a framework for rigorous assessment of technical factors that impact SNV identification in viral samples and establishes heuristics that will inform and improve future studies of intra-host variation, viral diversity, and viral evolution. IMPORTANCE When viruses replicate inside a host cell, the virus replication machinery makes mistakes. Over time, these mistakes create mutations that result in a diverse population of viruses inside the host. Mutations that are neither lethal to the virus nor strongly beneficial can lead to minority variants that are minor members of the virus population. However, preparing samples for sequencing can also introduce errors that resemble minority variants, resulting in the inclusion of false-positive data if not filtered correctly. In this study, we aimed to determine the best methods for identification and quantification of these minority variants by testing the performance of seven commonly used variant-calling tools. We used simulated and synthetic data to test their performance against a true set of variants and then used these studies to inform variant identification in data from SARS-CoV-2 clinical specimens. Together, analyses of our data provide extensive guidance for future studies of viral diversity and evolution.