Probabilistic approaches for classifying highly variable anti-SARS-CoV-2 antibody responses

Autor: Joanna Rorbach, Mattias Forsell, Gunilla B. Karlsson Hedestam, Chris Wallace, Soo Aleman, Leo Hanke, Monika Adori, Jonathan M. Coquet, Joakim Dillner, Laura Perez Vidakovics, Ainhoa Moliner Morro, Gordana Bogdanovic, Sharesta Khoenkhoen, Marco Mandolesi, Jan Albert, Ben Murrell, Nastasiya F. Grinberg, Marcus Ahl, Daniel J. Sheward, Tobias Allander, Murray Christian, Pradeepa Pushparaj, Sandra Muschiol, Gerald M. McInerney, Xaquin Castro Dopico, Martin Corcoran, Changil Kim
Rok vydání: 2020
Předmět:
DOI: 10.1101/2020.07.17.20155937
Popis: Antibody responses vary widely between individuals1, complicating the correct classification of low-titer measurements using conventional assay cut-offs. We found all participants in a clinically diverse cohort of SARS-CoV-2 PCR+ individuals (n=105) – and n=33 PCR+ hospital staff – to have detectable IgG specific for pre-fusion-stabilized spike (S) glycoprotein trimers, while 98% of persons had IgG specific for the receptor-binding domain (RBD). However, anti-viral IgG levels differed by several orders of magnitude between individuals and were associated with disease severity, with critically ill patients displaying the highest anti-viral antibody titers and strongest in vitro neutralizing responses. Parallel analysis of random healthy blood donors and pregnant women (n=1,000) of unknown serostatus, further demonstrated highly variable IgG titers amongst seroconverters, although these were generally lower than in hospitalized patients and included several measurements that scored between the classical 3 and 6SD assay cut-offs. Since the correct classification of seropositivity is critical for individual- and population-level metrics, we compared different probabilistic algorithms for their ability to assign likelihood of past infection. To do this, we used tandem anti-S and -RBD IgG responses from our PCR+ individuals (n=138) and a large cohort of historical negative controls (n=595) as training data, and generated an equal-weighted learner from the output of support vector machines and linear discriminant analysis. Applied to test samples, this approach provided a more quantitative way to interpret anti-viral titers over a large continuum, scrutinizing measurements overlapping the negative control background more closely and offering a probability-based diagnosis with potential clinical utility. Especially as most SARS-CoV-2 infections result in asymptomatic or mild disease, these platform-independent approaches improve individual and epidemiological estimates of seropositivity, critical for effective management of the pandemic and monitoring the response to vaccination.
Databáze: OpenAIRE
načítá se...