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.

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.

Integrating deep learning CT-scan model, biological and clinical variables to predict severity of COVID-19 patients.

The SARS-COV-2 pandemic has put pressure on intensive care units, so that identifying predictors of disease severity is a priority. We collect 58 clinical and biological variables, and chest CT scan data, from 1003 coronavirus-infected patients from two French hospitals. We train a deep learning model based on CT scans to predict severity. We then construct the multimodal AI-severity score that includes 5 clinical and biological variables (age, sex, oxygenation, urea, platelet) in addition to the deep learning model. We show that neural network analysis of CT-scans brings unique prognosis information, although it is correlated with other markers of severity (oxygenation, LDH, and CRP) explaining the measurable but limited 0.03 increase of AUC obtained when adding CT-scan information to clinical variables. Here, we show that when comparing AI-severity with 11 existing severity scores, we find significantly improved prognosis performance; AI-severity can therefore rapidly become a reference scoring approach.

Nurse navigators' telemonitoring for cancer patients with COVID-19: a French case study.

PURPOSE: The Gustave Roussy Cancer Institute implemented a patient-reported outcome platform (CAPRI-COVID) for cancer patients with coronavirus disease 2019 (COVID-19) to quarantine patients at home while ensuring monitoring of COVID-related symptoms and securing the care pathway. In this study, we described the CAPRI-COVID intervention, evaluated its use, and presented results of the tracking indicators with a focus on the nurse navigators' (NNs) activities and the experience of patients. METHODS: Data of 130 cancer patients with COVID-19 diagnosed from March 23 to June 5, 2020, were collected. Six COVID-related symptoms were monitored daily, either by the patient via the CAPRI mobile application (CAPRI App) or by NNs via telemonitoring. In the cases of worsening or new-onset symptoms, an automated alert was sent to the platform, and NNs could immediately consult an emergency physician for future course of action. RESULTS: All 130 patients (median age: 59 years; 59.2% female) were monitored during the study period. There were no deaths or admissions to the intensive care unit attributable to COVID-19; 7.8% of patients were hospitalized (excluding scheduled hospitalization), and 17.1% were admitted to the emergency department at least once during the monitoring period. NNs carried out 1412 regular monitoring calls (average of 10.9 calls per patient), while 55% of the patients downloaded the CAPRI App. CONCLUSIONS: Most patients monitored with CAPRI-COVID were quarantined during the first wave of the pandemic. In addition to the CAPRI App, which helped limit phone calls, NNs played an essential role in patient management.

Determinants of the outcomes of patients with cancer infected with SARS-CoV-2: results from the Gustave Roussy cohort.

Patients with cancer are presumed to be at increased risk of severe COVID-19 outcomes due to underlying malignancy and treatment-induced immunosuppression. Of the first 178 patients managed for COVID-19 at the Gustave Roussy Cancer Centre, 125 (70.2%) were hospitalized, 47 (26.4%) developed clinical worsening and 31 (17.4%) died. An age of over 70 years, smoking status, metastatic disease, cytotoxic chemotherapy and an Eastern Cooperative Oncology Group score of ≥2 at the last visit were the strongest determinants of increased risk of death. In multivariable analysis, the Eastern Cooperative Oncology Group score remained the only predictor of death. In contrast, immunotherapy, hormone therapy and targeted therapy did not increase clinical worsening or death risk. Biomarker studies found that C-reactive protein and lactate dehydrogenase levels were significantly associated with an increased risk of clinical worsening, while C-reactive protein and D-dimer levels were associated with an increased risk of death. COVID-19 management impacted the oncological treatment strategy, inducing a median 20 d delay in 41% of patients and adaptation of the therapeutic strategy in 30% of patients.

Immune responses during COVID-19 infection.

Over the past 16 years, three coronaviruses (CoVs), severe acute respiratory syndrome CoV (SARS-CoV) in 2002, Middle East respiratory syndrome CoV (MERS-CoV) in 2012 and 2015, and SARS-CoV-2 in 2020, have been causing severe and fatal human epidemics. The unpredictability of coronavirus disease-19 (COVID-19) poses a major burden on health care and economic systems across the world. This is caused by the paucity of in-depth knowledge of the risk factors for severe COVID-19, insufficient diagnostic tools for the detection of SARS-CoV-2, as well as the absence of specific and effective drug treatments. While protective humoral and cellular immune responses are usually mounted against these betacoronaviruses, immune responses to SARS-CoV2 sometimes derail towards inflammatory tissue damage, leading to rapid admissions to intensive care units. The lack of knowledge on mechanisms that tilt the balance between these two opposite outcomes poses major threats to many ongoing clinical trials dealing with immunostimulatory or immunoregulatory therapeutics. This review will discuss innate and cognate immune responses underlying protective or deleterious immune reactions against these pathogenic coronaviruses.