The rapidly spreading outbreak of COVID-19 disease is caused by the SARS-CoV-2 virus, first reported in December 2019 in Wuhan, China. As of June 17, 2020, this virus has infected over 8.2 million people but ranges in symptom severity, making it difficult to assess its overall infection rate. There is a need for rapid and accurate diagnostics to better monitor and prevent the spread of COVID-19. In this review, we present and evaluate two main types of diagnostics with FDA-EUA status for COVID-19: nucleic acid testing for detection of SARS-CoV-2 RNA, and serological assays for detection of SARS-CoV-2 specific IgG and IgM patient antibodies, along with the necessary sample preparation for accurate diagnoses. In particular, we cover and compare tests such as the CDC 2019-nCoV RT-PCR Diagnostic Panel, Cellex's qSARS-CoV-2 IgG/IgM Rapid Test, and point-of-care tests such as Abbott's ID NOW COVID-19 Test. Antibody testing is especially important in understanding the prevalence of the virus in the community and to identify those who have gained immunity. We conclude by highlighting the future of COVID-19 diagnostics, which include the need for quantitative testing and the development of emerging biosensors as point-of-care tests.

Gene expression analysis at the point-of-care is important for rapid disease diagnosis, but traditional techniques are limited by multiplexing capabilities, bulky equipment, and cost. We present a gene expression analysis platform using a giant magnetoresistive (GMR) biosensor array, which allows multiplexed transcript detection and quantification through cost-effective magnetic detection. In this work, we have characterized the sensitivity, dynamic range, and quantification accuracy of Polymerase chain reaction (PCR)-amplified complementary DNA (cDNA) on the GMR for the reference gene GAPDH. A synthetic GAPDH single-stranded DNA (ssDNA) standard was used to calibrate the detection, and ssDNA dilutions were qPCR-amplified to obtain a standard curve. We demonstrate that the GMR platform provides a dynamic range of 4 orders of magnitude and a limit of detection of 1 pM and 0.1 pM respectively for 15 and 18-cycle amplified synthetic GAPDH PCR products. The quantitative results of GMR analysis of cell-line RNA were confirmed by qPCR.

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Using microarray technology for genetic analysis in biological experiments requires computationally intensive tools to interpret results. The main objective here is to develop a "meta-analysis" tool that enables researchers to "spray" microarray data over a network of relevant gene regulation relationships, extracted from a database of published gene regulatory pathway models. The consistency of the data from a microarray experiment is evaluated to determine if it agrees or contradicts with previous findings. The database is limited to "activate" and "inhibit" gene regulatory relationships at this point and a heuristic graph based approach is developed for consistency checking. Predictions are made for the regulation of genes that were not a part of the microarray experiment, but are related to the experiment through regulatory relationships. This meta-analysis will not only highlight consistent findings but also pinpoint genes that were missed in earlier experiments and should be considered in subsequent analysis.

Neoantigen-based biomarkers are a promising approach for stratifying patient response to immunotherapy; however, current neoantigen prediction methods are not accurate enough to optimize these biomarkers. Sequence variability in the major histocompatibility complex (MHC) leads to the presentation of diverse neoantigens to T cells, and accurately representing this diversity in neoantigen prediction is critical for improvement. Previously, we published data from 25 mono-allelic cell lines and built an associated MHC class I, pan-allelic neoantigen prediction algorithm (SHERPATM). Here, we profile an additional 84 MHC alleles including 37 that have never previously been profiled with mono-allelic immunopeptidomics, explore the impact of MHC variability on peptide binding and improve neoantigen prediction of the SHERPA algorithm. To generate the data, we stably and transiently transfected 109 different MHC alleles (43 HLA-A, 56 -B and 10 -C alleles) into independent K562 HLA-null cell lines, immunoprecipitated intact MHC complexes using a W6/32 antibody and profiled the bound peptides using LC/MS-MS. We recovered a median of 1430 peptides per allele, with yields from the transient transfections being consistently higher than the stable transfections. Nearly all alleles have a strong anchor residue in the ninth position, but the positions of the secondary anchor residue vary by gene. HLA-B showed a stronger preference for the second position while HLA-A exhibited more variability across the first, second and third positions. In addition to the 109 mono-allelic cell lines, SHERPA increases generalizability by systematically integrating an additional 104 mono-allelic and 384 multi-allelic samples with publicly available immunopeptidomics data. The 186 alleles in the resulting training dataset have an average allelic coverage of 98% across 18 different US-based ethnicities. We evaluated our updated performance on 10% held-out mono-allelic test data from multiple cell line sources. The positive predictive value (PPV) of SHERPA was markedly higher than either NetMHCPan 4.1 or MHCFlurry-2.0 (1.45 and 1.58-fold increase, respectively), with further gains when only the 37 previously unprofiled alleles were considered (1.51 and 1.79-fold increase, respectively). Furthermore, the SHERPA model was able to detect 1.38-fold more immunogenic epitopes than either other method. Finally, we performed predictions with SHERPA across millions of synthetic binding pockets and peptides to elucidate the impact of MHC variability on peptide diversity. We found a strong correlation between binding pocket positions that highly influence peptide binding and those that are evolutionarily divergent. In conclusion, we profiled 109 mono-allelic cell lines, showed key trends in MHC-associated peptides and improved the SHERPA neoantigen prediction model. Citation Format: Rachel Marty Pyke, Steven Dea, Hima Anbunathan, Charles W. Abbott, Neeraja Ravi, Jason Harris, Gabor Bartha, Sejal Desai, Rena McClory, John West, Michael P. Snyder, Richard O. Chen, Sean Michael Boyle. Mono-allelic immunopeptidomics data from 109 MHC-I alleles reveals variability in binding preferences and improves neoantigen prediction algorithm [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5640.