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

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 noncancer patients for RT-qPCR cycle thresholds measuring acute respiratory syndrome coronavirus-2 (SARS-CoV-2) in 1063 patients (58% with cancer), 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. Cancer patients with a prolonged SARS-CoV-2 RNA detection exhibited the typical immunopathology of severe COVID-19 at the early phase of infection including circulation of immature neutrophils, depletion of nonconventional 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+, EomeshighTCF-1high, 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.

Cancer surgery during the COVID-19 pandemic: The experience of a comprehensive cancer center performing preoperative screening by RT-PCR and chest CT scan.

BACKGROUND AND OBJECTIVES: During the worldwide pandemic of coronavirus disease 2019 (COVID-19), oncological procedures considered to be urgent could not be delayed, and a specific procedure was required to continue surgical activity. The objective was to assess the efficacy of our preoperative screening algorithm. METHODS: This observational retrospective study was performed between the 25th of March and the 12th of May 2020 in a comprehensive cancer center in France. Patients undergoing elective oncologic surgery were tested by preoperative nasopharyngeal reverse-transcription polymerase chain reaction (RT-PCR) that could be associated with a chest computerized tomography (CT) scan. RESULTS: Of the 510 screening tests (in 477 patients), only 5% (15/477) were positive for COVID-19 in 24 patients (18 RT-PCR+ and 7 CT scan+/RT-PCR-). Four patients were ultimately false positives based on the CT scan. In total, only 4.2% (20/477) of the patients were COVID-19+. The positivity rate decreased with time after the containment measures were implemented (from 7.4% to 0.8%). In the COVID-19+ group, 20% of the patients had postoperative pulmonary complications, whereas this was the case for 5% of the patients in the COVID-19 group. CONCLUSIONS: Maintaining secure surgical activity is achievable and paramount in oncology care, even during the COVID-19 pandemic, with appropriate screening based on preoperative RT-PCR.

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.

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.