1352. A Precision Medicine Approach to Predicting “Long COVID” through Machine Learning Analysis of Whole Blood Transcriptome Data
Authors: Epsi Nusrat J , Richard Stephanie A , Chenoweth Josh , Lindholm David , Mende Katrin , Ganesan Anuradha , Huprikar Nikhil , Lalani Tahaniyat , Smith Alfred , Mody Rupal , Schofield Christina , Colombo Rhonda E , Flanagan Ryan , Ewers Evan , Berjohn Catherine , Saunders David , Jones Milissa , Edwards Margaret , Simons Mark P , O’Connell Robert , Tribble David R , Tribble David R , Agan Brian , Burgess Timothy , Dalgard Clifton , Pollett Simon 
Published: November 2023

Abstract

Background
Predicting, preventing, and treating post-COVID condition (PCC; “Long COVID) is challenging due to a limited understanding of PCC mechanisms. To address this, we analyzed whole blood transcriptomic data using machine learning to identify candidate gene and gene expression pathways associated with the development of PCC.
Figure 1
(A) Clinical and demographic characteristics of 222 military health system beneficiaries (COVI D, no PCC: SARS-CoV-2 positive subjects reported no or mild symptoms on 6-month survey; COVID, PCC: SARS-CoV-2 positive subjects reported moderate to severe symptoms on 6-month survey; and Control: SARS-CoV-2 negative subjects), (B) AUROC indicates the ability of leading genes (measured early after infection, TSSK4, TUFT1, and XBP1; which have known association with pulmonary fibrosis, lung cancer, and lymphoma, respectively) which can predict which participants develop PCC versus those who do not, and (C) Identification of biomarker candidates for PCC based on machine learning algorithms. Statistical significance was determined by Mann-Whitney U test. Asterisks indicate statistical significance: ns: p &gt; 0.05, *: p ≤ 0.05

Methods
The Epidemiology, Immunology, and Clinical Characteristics of Emerging Infectious Diseases with Pandemic Potential (EPICC) study is a longitudinal cohort study exploring the impact of SARS-CoV-2 infection in military health system beneficiaries. We collected demographic and clinical data through surveys, interviews and medical record reviews. We conducted transcriptome profiling (bulk RNA-seq) on early post-infection whole blood samples from unvaccinated individuals with and without PCC at 6-months, as well as uninfected, unvaccinated controls. These three groups were matched on sex, age, and comorbidities. We identified transcriptomic signatures associated with group classes using t-tests with correction for multiple comparisons. Molecular signatures and candidate markers for PCC were developed using gene set enrichment analysis (GSEA) and machine learning, including random forest (RF) and support vector machine (SVM) methods.

Results
Out of the 5289 SARS-CoV-2 positive Military Health System beneficiaries enrolled in EPICC, 814 participants had at least one sample with blood transcriptomic profiling data. Among these, 171 unvaccinated, SARS-CoV-2 positive participants completed a 6-month follow up survey and were included in the analysis. Ninety-one percent did not report any chronic symptoms, while 8% reported moderate to severe symptoms at six months and were classified as PCC group. A control group of 51 participants was also identified. Machine learning approaches (RF: AUROC = 0.90, CI = 0.79 to 1; and SVM: AUROC = 0.89, CI = 0.76 to 1) identified three candidate biomarkers (Figure 1), including TSSK4, TUFT1, and XBP1 genes, which could be a predictive or mechanistic marker for PCC with further validation.

Conclusion
Our study demonstrates the potential of using machine learning to translate transcriptomic data into precision medicine applications, specifically for predicting the development of PCC after COVID-19.

Disclosures
Mark P. Simons, PhD, AstraZeneca: The IDCRP and HJF were funded to conduct an unrelated phase III COVID-19 monoclonal antibody immunoprophylaxis trial as part of US Govt COVID Response Timothy Burgess, MD, MPH, AstraZeneca: The IDCRP and the Henry M. Jackson Foundation (HJF) were funded to conduct an unrelated phase III COVID-19 monoclonal antibody immunoprophylaxis trial Simon Pollett, MBBS, AstraZeneca: The IDCRP and the Henry M. Jackson Foundation (HJF) were funded to conduct an unrelated phase III COVID-19 monoclonal antibody immunoprophylaxis trial