The polarity and specificity of SARS-CoV2 -specific T lymphocyte responses determine disease susceptibility (preprint)

Optimal vaccination and immunotherapy against coronavirus disease COVID-19 relies on the in-depth comprehension of immune responses determining the individual susceptibility to be infected by SARS-CoV-2 and to develop severe disease. We characterized the polarity and specificity of circulating SARS-CoV-2-specific T cell responses against whole virus lysates or 186 unique peptides derived from the SARS-CoV-2 or SARS-CoV-1 ORFeome on 296 cancer-bearing and 86 cancer-free individuals who were either from the pre-COVID-19 era (67 individuals) or contemporary COVID-19-free (237 individuals) or who developed COVID-19 (78 individuals) in 2020/21. The ratio between the prototypic T helper 1 (TH1) cytokine, interleukin-2, and the prototypic T helper 2 (TH2) cytokine, interleukin-5 (IL-5), released from SARS-CoV-2-specific memory T cells measured in early 2020, among SARS-CoV-2-negative persons, was associated with the susceptibility of these individuals to develop PCR-detectable SARS-CoV-2 infection in late 2020 or 2021. Of note, T cells from individuals who recovered after SARS-CoV-2 re-infection spontaneously produced elevated levels of IL-5 and secreted the immunosuppressive TH2 cytokine interleukin-10 in response to SARS-CoV-2 lysate, suggesting that TH2 responses to SARS-CoV-2 are inadequate. Moreover, individuals susceptible to SARS-CoV-2 infection exhibited a deficit in the TH1 peptide repertoire affecting the highly mutated receptor binding domain (RBD) amino acids (331-525) of the spike protein. Finally, current vaccines successfully triggered anti-RBD specific TH1 responses in 88% healthy subjects that were negative prior to immunization. These findings indicate that COVID-19 protection relies on TH1 cell immunity against SARS-CoV-2 S1-RBD which in turn likely drives the phylogenetic escape of the virus. The next generation of COVID-19 vaccines should elicit high-avidity TH1 (rather than TH2)-like T cell responses against the RBD domain of current and emerging viral variants.

Prolonged SARS-CoV-2 RNA virus shedding and lymphopenia are hallmarks of COVID-19 in cancer patients with poor prognosis (preprint)

Patients with cancer are at higher risk of severe coronavirus infectious disease 2019 (COVID-19), but the mechanisms underlying virus-host interactions during cancer therapies remain elusive. When comparing nasopharyngeal swabs from cancer and non-cancer patients for RT-qPCR cycle thresholds measuring acute respiratory syndrome coronavirus-2 (SARS-CoV-2) in 1063 patients (58% with cancer, 89% COVID-19+), we found that malignant disease favors the magnitude and duration of viral RNA shedding concomitant with prolonged serum elevations of type 1 IFN that anticorrelated with anti-RBD IgG antibodies. Chronic viral RNA carriers exhibited the typical immunopathology of severe COVID-19 at the early phase of infection including circulation of immature neutrophils, depletion of non-conventional monocytes and a general lymphopenia that, however, was accompanied by a rise in plasmablasts, activated follicular T helper cells, and non-naive Granzyme B+FasL+, EomeshighTCF7high, PD-1+CD8+ Tc1 cells. Virus-induced lymphopenia worsened cancer-associated lymphocyte loss, and low lymphocyte counts correlated with chronic SARS-CoV-2 RNA shedding, COVID-19 severity and a higher risk of cancer-related death in the first and second surge of the pandemic. Lymphocyte loss correlated with significant changes in metabolites from the polyamine and biliary salt pathways as well as increased blood DNA from Enterobacteriaceae and Micrococcaceae gut family members in long term viral carriers. We surmise that cancer therapies may exacerbate the paradoxical association between lymphopenia and COVID-19-related immunopathology, and that the prevention of COVID-19-induced lymphocyte loss may reduce cancer-associated death.

AI-Driven CT-based quantification, staging and short-term outcome prediction of COVID-19 pneumonia (preprint)

Improving screening, discovering therapies, developing a vaccine and performing staging and prognosis are decisive steps in addressing the COVID-19 pandemic. Staging and prognosis are especially crucial for organizational anticipation (intensive-care bed availability, patient management planning) and accelerating drug development; through rapid, reproducible and quantified response-to-treatment assessment. In this letter, we report on an artificial intelligence solution for performing automatic staging and prognosis based on imaging, clinical, comorbidities and biological data. This approach relies on automatic computed tomography (CT)-based disease quantification using deep learning, robust data-driven identification of physiologically-inspired COVID-19 holistic patient profiling, and strong, reproducible staging/outcome prediction with good generalization properties using an ensemble of consensus methods. Highly promising results on multiple independent external evaluation cohorts along with comparisons with expert human readers demonstrate the potentials of our approach. The developed solution offers perspectives for optimal patient management, given the shortage of intensive care beds and ventilators1, 2, along with means to assess patient response to treatment.

AI-Driven CT-based quantification, staging and short-term outcome prediction of COVID-19 pneumonia (preprint)

Chest computed tomography (CT) is widely used for the management of Coronavirus disease 2019 (COVID-19) pneumonia because of its availability and rapidity. The standard of reference for confirming COVID-19 relies on microbiological tests but these tests might not be available in an emergency setting and their results are not immediately available, contrary to CT. In addition to its role for early diagnosis, CT has a prognostic role by allowing visually evaluating the extent of COVID-19 lung abnormalities. The objective of this study is to address prediction of short-term outcomes, especially need for mechanical ventilation. In this multi-centric study, we propose an end-to-end artificial intelligence solution for automatic quantification and prognosis assessment by combining automatic CT delineation of lung disease meeting performance of experts and data-driven identification of biomarkers for its prognosis. AI-driven combination of variables with CT-based biomarkers offers perspectives for optimal patient management given the shortage of intensive care beds and ventilators.