COVIDOUTCOME – Estimating COVID Severity Based on Mutation Signatures in the SARS-CoV-2 Genome

  1. Ádám Nagy
  2. Balázs Ligeti
  3. János Szebeni
  4. Sándor Pongor
  5. Balázs Győrffy

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Abstract

Introduction

Numerous studies demonstrate frequent mutations in the genome of SARS-CoV-2. Our goal was to statistically link mutations to severe disease outcome.

Methods

We used an automated machine learning approach where 1,594 viral genomes with available clinical follow-up data were used as the training set (797 “severe” and 797 “mild”). The best algorithm, based on random forest classification combined with the LASSO feature selection algorithm was employed to the training set to link mutation signatures and outcome. The performance of the final model was estimated by repeated, stratified, 10-fold cross validation (CV), then adjusted for multiple testing with Bootstrap Bias Corrected CV.

Results

We identified 26 protein and UTR mutations significantly linked to severe outcome. The best classification algorithm uses a mutation signature of 22 mutations as well as the patient’s age as the input and shows high classification efficiency with an AUC of 0.94 (CI: [0.912, 0.962]) and a prediction accuracy of 87% (CI: [0.830, 0.903]). Finally, we established an online platform ( https://covidoutcome.com/ ) which is capable to use a viral sequence and the patient’s age as the input and provides a percentage estimation of disease severity.

Discussion

We demonstrate a statistical association between mutation signatures of SARS-CoV-2 and severe outcome of COVID-19. The established analysis platform enables a real-time analysis of new viral genomes.

KEY MESSAGES

  • A statistical link between SARS-Cov-2 mutation status and severe COVID outcome was established using automated machine learning techniques based on random forest and logistic regression combined with feature selection algorithms.

  • A mutation signature based on 3,779 protein coding and 36 UTR mutations capable to identify severe outcome cases was established.

  • The trained model showed high classification performance (AUC=0.94 (CI: [0.912, 0.962]), accuracy=0.87 (CI: [0.830, 0.903])).

  • A registration-free web-server for automated classification of new samples was set up and is accessible at http://www.covidoutcome.com .

  • The established pipeline provides a quick assessment of future patients warranting a prospective clinical validation.

    • Article activity feed

    1. ScreenIT

      SciScore for 10.1101/2021.04.01.438063: (What is this?)

      Please note, not all rigor criteria are appropriate for all manuscripts.

      Table 1: Rigor

      NIH rigor criteria are not applicable to paper type.

      Table 2: Resources

      Software and Algorithms
      SentencesResources
      The CoVsurver analysis tool (https://corona.bii.a-star.edu.sg) was used to extract the mutations.
      CoVsurver
      suggested: None
      The online analysis platform (https://www.covidoutcome.com) is written in R using the R Shiny package (https://CRAN.R-project.org/package=shiny) and is running under Linux Debian 64-bit (x86_64).
      Shiny
      suggested: (Shiny, RRID:SCR_001626)
      The server fist performs a global pairwise sequence alignment between the input sequence and the reference genome of the “Wuhan strain” (hCoV-19/Wuhan/WIV04/2019), using the program of the “Biostrings” R Bioconductor package (https://bioconductor.org/packages/Biostrings/), and outputs the protein as well as UTR mutations.
      Bioconductor
      suggested: (Bioconductor, RRID:SCR_006442)

      Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

      Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

      Results from TrialIdentifier: No clinical trial numbers were referenced.

      Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

      Results from JetFighter: We did not find any issues relating to colormaps.

      Results from rtransparent:
      • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
      • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
      • No protocol registration statement was detected.

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    2. Version published to 10.1101/2021.04.01.438063 on bioRxiv