The Polarity and Specificity of Antiviral T Lymphocyte Responses Determine Susceptibility to SARS-CoV-2 Infection in Patients with Cancer and Healthy Individuals.

Vaccination against coronavirus disease 2019 (COVID-19) relies on the in-depth understanding of protective immune responses to severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). We characterized the polarity and specificity of memory T cells directed against SARS-CoV-2 viral lysates and peptides to determine correlates with spontaneous, virus-elicited, or vaccine-induced protection against COVID-19 in disease-free and cancer-bearing individuals. A disbalance between type 1 and 2 cytokine release was associated with high susceptibility to COVID-19. Individuals susceptible to infection exhibited a specific deficit in the T helper 1/T cytotoxic 1 (Th1/Tc1) peptide repertoire affecting the receptor binding domain of the spike protein (S1-RBD), a hotspot of viral mutations. Current vaccines triggered Th1/Tc1 responses in only a fraction of all subject categories, more effectively against the original sequence of S1-RBD than that from viral variants. We speculate that the next generation of vaccines should elicit Th1/Tc1 T-cell responses against the S1-RBD domain of emerging viral variants. SIGNIFICANCE: This study prospectively analyzed virus-specific T-cell correlates of protection against COVID-19 in healthy and cancer-bearing individuals. A disbalance between Th1/Th2 recall responses conferred susceptibility to COVID-19 in both populations, coinciding with selective defects in Th1 recognition of the receptor binding domain of spike. See related commentary by McGary and Vardhana, p. 892. This article is highlighted in the In This Issue feature, p. 873.

Progress Identifying and Analyzing the Human Proteome: 2021 Metrics from the HUPO Human Proteome Project.

The 2021 Metrics of the HUPO Human Proteome Project (HPP) show that protein expression has now been credibly detected (neXtProt PE1 level) for 18 357 (92.8%) of the 19 778 predicted proteins coded in the human genome, a gain of 483 since 2020 from reports throughout the world reanalyzed by the HPP. Conversely, the number of neXtProt PE2, PE3, and PE4 missing proteins has been reduced by 478 to 1421. This represents remarkable progress on the proteome parts list. The utilization of proteomics in a broad array of biological and clinical studies likewise continues to expand with many important findings and effective integration with other omics platforms. We present highlights from the Immunopeptidomics, Glycoproteomics, Infectious Disease, Cardiovascular, Musculo-Skeletal, Liver, and Cancers B/D-HPP teams and from the Knowledgebase, Mass Spectrometry, Antibody Profiling, and Pathology resource pillars, as well as ethical considerations important to the clinical utilization of proteomics and protein biomarkers.

Advances and Utility of the Human Plasma Proteome.

The study of proteins circulating in blood offers tremendous opportunities to diagnose, stratify, or possibly prevent diseases. With recent technological advances and the urgent need to understand the effects of COVID-19, the proteomic analysis of blood-derived serum and plasma has become even more important for studying human biology and pathophysiology. Here we provide views and perspectives about technological developments and possible clinical applications that use mass-spectrometry(MS)- or affinity-based methods. We discuss examples where plasma proteomics contributed valuable insights into SARS-CoV-2 infections, aging, and hemostasis and the opportunities offered by combining proteomics with genetic data. As a contribution to the Human Proteome Organization (HUPO) Human Plasma Proteome Project (HPPP), we present the Human Plasma PeptideAtlas build 2021-07 that comprises 4395 canonical and 1482 additional nonredundant human proteins detected in 240 MS-based experiments. In addition, we report the new Human Extracellular Vesicle PeptideAtlas 2021-06, which comprises five studies and 2757 canonical proteins detected in extracellular vesicles circulating in blood, of which 74% (2047) are in common with the plasma PeptideAtlas. Our overview summarizes the recent advances, impactful applications, and ongoing challenges for translating plasma proteomics into utility for precision medicine.

COVID-19-Associated Pneumonia: Radiobiological Insights.

The evolution of SARS-CoV-2 pneumonia to acute respiratory distress syndrome is linked to a virus-induced "cytokine storm", associated with systemic inflammation, coagulopathies, endothelial damage, thrombo-inflammation, immune system deregulation and disruption of angiotensin converting enzyme signaling pathways. To date, the most promising therapeutic approaches in COVID-19 pandemic are linked to the development of vaccines. However, the fight against COVID-19 pandemic in the short and mid-term cannot only rely on vaccines strategies, in particular given the growing proportion of more contagious and more lethal variants among exposed population (the English, South African and Brazilian variants). As long as collective immunity is still not acquired, some patients will have severe forms of the disease. Therapeutic perspectives also rely on the implementation of strategies for the prevention of secondary complications resulting from vascular endothelial damage and from immune system deregulation, which contributes to acute respiratory distress and potentially to long term irreversible tissue damage. While the anti-inflammatory effects of low dose irradiation have been exploited for a long time in the clinics, few recent physiopathological and experimental data suggested the possibility to modulate the inflammatory storm related to COVID-19 pulmonary infection by exposing patients to ionizing radiation at very low doses. Despite level of evidence is only preliminary, these preclinical findings open therapeutic perspectives and are discussed in this article.

Systematic Screening of COVID-19 Disease Based on Chest CT and RT-PCR for Cancer Patients Undergoing Radiation Therapy in a Coronavirus French Hotspot.

PURPOSE: Patients with cancer are presumed to be more vulnerable to COVID-19. We evaluated a screening strategy combining chest computed tomography (CT) and reverse-transcription polymerase chain reaction (RT-PCR) for patients treated with radiation therapy at our cancer center located in a COVID-19 French hotspot during the first wave of the pandemic. METHODS AND MATERIALS: Chest CT images were proposed during radiation therapy CT simulation. Images were reviewed by an expert radiologist according to the COVID-19 Reporting and Data System classification. Nasal swabs with RT-PCR assay were initially proposed in cases of suspicious imaging or clinical context and were eventually integrated into the systematic screening. A dedicated radiation therapy workflow was proposed for COVID-19 patients to limit the risk of contamination. RESULTS: From March 18, 2020 to May 1, 2020, 480 patients were screened by chest CT, and 313 patients had both chest CT and RT-PCR (65%). The cumulative incidence of COVID-19 was 5.4% (95% confidence interval [CI], 3.6-7.8; 26 of 480 patients). Diagnosis of COVID-19 was made before radiation therapy for 22 patients (84.6%) and during RT for 4 patients (15.3%). Chest CT directly aided the diagnosis of 7 cases in which the initial RT-PCR was negative or not feasible, out of a total of 480 patients (1.5%) and 517 chest CT acquisitions. Four patients with COVID-19 at the time of the chest CT screening had a false negative CT. Sensitivity and specificity of chest CT screening in patients with both RT-PCR and chest CT testing were estimated at 0.82 (95% CI, 0.60-0.95) and 0.98 (95% CI, 0.96-0.99), respectively. Adaptation of the radiation therapy treatment was made for all patients, with 7 postponed treatments (median: 5 days; interquartile range, 1.5-14.8). CONCLUSIONS: The benefit of systematic use of chest CT screening during CT simulation for patients undergoing radiation therapy during the COVID-19 pandemic seemed limited.

Low Doses of Radiation Increase the Immunosuppressive Profile of Lung Macrophages During Viral Infection and Pneumonia.

PURPOSE: Severe pneumonia and acute respiratory distress syndrome (ARDS) have been described in patients with severe coronavirus disease 2019 (COVID-19). Recently, early clinical data reported the feasibility of low doses of radiation therapy (RT) in the treatment of ARDS in patients with severe COVID-19. However, the involved mechanisms remained unknown. METHODS AND MATERIALS: Here, we used airways-instilled lipopolysaccharide (LPS) and influenza virus (H1N1) as murine models of pneumonia, and toll-like receptor (TLR)-3 stimulation in human lung macrophages. RESULTS: Low doses of RT (0.5-1 Gray) decreased LPS-induced pneumonia, and increased the percentage of nerve- and airway-associated macrophages producing interleukin (IL) 10. During H1N1 viral infection, we observed decreased lung tissue damage and immune cell infiltration in irradiated animals. Low doses of RT increased IL-10 production by infiltrating immune cells into the lung. Irradiation of TLR-3 ligand-stimulated human lung macrophages ex vivo increased IL-10 secretion and decreased interferon γ production in the culture supernatant. The percentage of human lung macrophages producing IL-6 was also decreased. CONCLUSIONS: Our data highlight a mechanism by which low doses of RT regulate lung inflammation and skew lung macrophages toward an anti-inflammatory profile. These data provide a preclinical mechanistic support to clinical trials evaluating low doses of RT, such as COVID-19-induced ARDS.

Research on the Human Proteome Reaches a Major Milestone: >90% of Predicted Human Proteins Now Credibly Detected, According to the HUPO Human Proteome Project.

According to the 2020 Metrics of the HUPO Human Proteome Project (HPP), expression has now been detected at the protein level for >90% of the 19 773 predicted proteins coded in the human genome. The HPP annually reports on progress made throughout the world toward credibly identifying and characterizing the complete human protein parts list and promoting proteomics as an integral part of multiomics studies in medicine and the life sciences. NeXtProt release 2020-01 classified 17 874 proteins as PE1, having strong protein-level evidence, up 180 from 17 694 one year earlier. These represent 90.4% of the 19 773 predicted coding genes (all PE1,2,3,4 proteins in neXtProt). Conversely, the number of neXtProt PE2,3,4 proteins, termed the "missing proteins" (MPs), was reduced by 230 from 2129 to 1899 since the neXtProt 2019-01 release. PeptideAtlas is the primary source of uniform reanalysis of raw mass spectrometry data for neXtProt, supplemented this year with extensive data from MassIVE. PeptideAtlas 2020-01 added 362 canonical proteins between 2019 and 2020 and MassIVE contributed 84 more, many of which converted PE1 entries based on non-MS evidence to the MS-based subgroup. The 19 Biology and Disease-driven B/D-HPP teams continue to pursue the identification of driver proteins that underlie disease states, the characterization of regulatory mechanisms controlling the functions of these proteins, their proteoforms, and their interactions, and the progression of transitions from correlation to coexpression to causal networks after system perturbations. And the Human Protein Atlas published Blood, Brain, and Metabolic Atlases.

AI-driven quantification, staging and outcome prediction of COVID-19 pneumonia.

Coronavirus disease 2019 (COVID-19) emerged in 2019 and disseminated around the world rapidly. Computed tomography (CT) imaging has been proven to be an important tool for screening, disease quantification and staging. The latter is of extreme importance for organizational anticipation (availability of intensive care unit beds, patient management planning) as well as to accelerate drug development through rapid, reproducible and quantified assessment of treatment response. Even if currently there are no specific guidelines for the staging of the patients, CT together with some clinical and biological biomarkers are used. In this study, we collected a multi-center cohort and we investigated the use of medical imaging and artificial intelligence for disease quantification, staging and outcome prediction. Our approach relies on automatic deep learning-based disease quantification using an ensemble of architectures, and a data-driven consensus for the staging and outcome prediction of the patients fusing imaging biomarkers with clinical and biological attributes. Highly promising results on multiple external/independent evaluation cohorts as well as comparisons with expert human readers demonstrate the potentials of our approach.