Establishment of CORONET, COVID-19 Risk in Oncology Evaluation Tool, to Identify Patients With Cancer at Low Versus High Risk of Severe Complications of COVID-19 Disease On Presentation to Hospital.

PURPOSE: Patients with cancer are at increased risk of severe COVID-19 disease, but have heterogeneous presentations and outcomes. Decision-making tools for hospital admission, severity prediction, and increased monitoring for early intervention are critical. We sought to identify features of COVID-19 disease in patients with cancer predicting severe disease and build a decision support online tool, COVID-19 Risk in Oncology Evaluation Tool (CORONET). METHODS: Patients with active cancer (stage I-IV) and laboratory-confirmed COVID-19 disease presenting to hospitals worldwide were included. Discharge (within 24 hours), admission (≥ 24 hours inpatient), oxygen (O2) requirement, and death were combined in a 0-3 point severity scale. Association of features with outcomes were investigated using Lasso regression and Random Forest combined with Shapley Additive Explanations. The CORONET model was then examined in the entire cohort to build an online CORONET decision support tool. Admission and severe disease thresholds were established through pragmatically defined cost functions. Finally, the CORONET model was validated on an external cohort. RESULTS: The model development data set comprised 920 patients, with median age 70 (range 5-99) years, 56% males, 44% females, and 81% solid versus 19% hematologic cancers. In derivation, Random Forest demonstrated superior performance over Lasso with lower mean squared error (0.801 v 0.807) and was selected for development. During validation (n = 282 patients), the performance of CORONET varied depending on the country cohort. CORONET cutoffs for admission and mortality of 1.0 and 2.3 were established. The CORONET decision support tool recommended admission for 95% of patients eventually requiring oxygen and 97% of those who died (94% and 98% in validation, respectively). The specificity for mortality prediction was 92% and 83% in derivation and validation, respectively. Shapley Additive Explanations revealed that National Early Warning Score 2, C-reactive protein, and albumin were the most important features contributing to COVID-19 severity prediction in patients with cancer at time of hospital presentation. CONCLUSION: CORONET, a decision support tool validated in health care systems worldwide, can aid admission decisions and predict COVID-19 severity in patients with cancer.

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.

An International Comparison of Presentation, Outcomes and CORONET Predictive Score Performance in Patients with Cancer Presenting with COVID-19 across Different Pandemic Waves.

Patients with cancer have been shown to have increased risk of COVID-19 severity. We previously built and validated the COVID-19 Risk in Oncology Evaluation Tool (CORONET) to predict the likely severity of COVID-19 in patients with active cancer who present to hospital. We assessed the differences in presentation and outcomes of patients with cancer and COVID-19, depending on the wave of the pandemic. We examined differences in features at presentation and outcomes in patients worldwide, depending on the waves of the pandemic: wave 1 D614G (n = 1430), wave 2 Alpha (n = 475), and wave 4 Omicron variant (n = 63, UK and Spain only). The performance of CORONET was evaluated on 258, 48, and 54 patients for each wave, respectively. We found that mortality rates were reduced in subsequent waves. The majority of patients were vaccinated in wave 4, and 94% were treated with steroids if they required oxygen. The stages of cancer and the median ages of patients significantly differed, but features associated with worse COVID-19 outcomes remained predictive and did not differ between waves. The CORONET tool performed well in all waves, with scores in an area under the curve (AUC) of >0.72. We concluded that patients with cancer who present to hospital with COVID-19 have similar features of severity, which remain discriminatory despite differences in variants and vaccination status. Survival improved following the first wave of the pandemic, which may be associated with vaccination and the increased steroid use in those patients requiring oxygen. The CORONET model demonstrated good performance, independent of the SARS-CoV-2 variants.

International Association for the Study of Lung Cancer Study of the Impact of Coronavirus Disease 2019 on International Lung Cancer Clinical Trials.

INTRODUCTION: To evaluate the effects of the global coronavirus disease 2019 (COVID-19) pandemic on lung cancer trials, we surveyed investigators and collected aggregate enrollment data for lung cancer trials across the world before and during the pandemic. METHODS: A Data Collection Survey collected aggregate monthly enrollment numbers from 294 global lung cancer trials for 2019 to 2020. A 64-question Action Survey evaluated the impact of COVID-19 on clinical trials and identified mitigation strategies implemented. RESULTS: Clinical trial enrollment declined from 2019 to 2020 by 14% globally. Most reductions in enrollment occurred in April to June where we found significant decreases in individual site enrollment (p = 0.0309). Enrollment was not significantly different in October 2019 to December of 2019 versus 2020 (p = 0.25). The most frequent challenges identified by the Action Survey (N = 172) were fewer eligible patients (63%), decrease in protocol compliance (56%), and suspension of trials (54%). Patient-specific challenges included access to trial site (49%), ability to travel (54%), and willingness to visit the site (59%). The most frequent mitigation strategies included modified monitoring requirements (47%), telehealth visits (45%), modified required visits (25%), mail-order medications (25%), and laboratory (27%) and radiology (21%) tests at nonstudy facilities. Sites that felt the most effective mitigation strategies were telehealth visits (85%), remote patient-reported symptom collection (85%), off-site procedures (85%), and remote consenting (89%). CONCLUSIONS: The COVID-19 pandemic created many challenges for lung cancer clinical trials conduct and enrollment. Mitigation strategies were used and, although the pandemic worsened, trial enrollment improved. A more flexible approach may improve enrollment and access to clinical trials, even beyond the pandemic.

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.

Impact of COVID-19 on healthcare organisation and cancer outcomes.

BACKGROUND: Changes in the management of patients with cancer and delays in treatment delivery during the COVID-19 pandemic may impact the use of hospital resources and cancer mortality. PATIENTS AND METHODS: Patient flows, patient pathways and use of hospital resources during the pandemic were simulated using a discrete event simulation model and patient-level data from a large French comprehensive cancer centre's discharge database, considering two scenarios of delays: massive return of patients from November 2020 (early-return) or March 2021 (late-return). Expected additional cancer deaths at 5 years and mortality rate were estimated using individual hazard ratios based on literature. RESULTS: The number of patients requiring hospital care during the simulation period was 13,000. In both scenarios, 6-8% of patients were estimated to present a delay of >2 months. The overall additional cancer deaths at 5 years were estimated at 88 in early-return and 145 in late-return scenario, with increased additional deaths estimated for sarcomas, gynaecological, liver, head and neck, breast cancer and acute leukaemia. This represents a relative additional cancer mortality rate at 5 years of 4.4 and 6.8% for patients expected in year 2020, 0.5 and 1.3% in 2021 and 0.5 and 0.5% in 2022 for each scenario, respectively. CONCLUSIONS: Pandemic-related diagnostic and treatment delays in patients with cancer are expected to impact patient survival. In the perspective of recurrent pandemics or alternative events requiring an intensive use of limited hospital resources, patients should be informed not to postpone care, and medical resources for patients with cancer should be sanctuarised.

Author Correction: Caring for patients with cancer in the COVID-19 era.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

OncoAlert Round Table Discussions: The Global COVID-19 Experience.

The speed and spread of the COVID-19 pandemic has been affecting the entire world for the past several months. OncoAlert is a social media network made up of more than 140 oncology stakeholders: oncologists (medical, radiation, and surgical), oncology nurses, and patient advocates who share the mission of fighting cancer by means of education and dissemination of information. As a response to the COVID-19 pandemic, OncoAlert hosted The Round Table Discussions. We have documented this effort along with further discussion about the COVID-19 pandemic and the consequences on patients living with cancer to disseminate this information to our colleagues worldwide.

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.

Metabolomic analyses of COVID-19 patients unravel stage-dependent and prognostic biomarkers.

The circulating metabolome provides a snapshot of the physiological state of the organism responding to pathogenic challenges. Here we report alterations in the plasma metabolome reflecting the clinical presentation of COVID-19 patients with mild (ambulatory) diseases, moderate disease (radiologically confirmed pneumonitis, hospitalization and oxygen therapy), and critical disease (in intensive care). This analysis revealed major disease- and stage-associated shifts in the metabolome, meaning that at least 77 metabolites including amino acids, lipids, polyamines and sugars, as well as their derivatives, were altered in critical COVID-19 patient's plasma as compared to mild COVID-19 patients. Among a uniformly moderate cohort of patients who received tocilizumab, only 10 metabolites were different among individuals with a favorable evolution as compared to those who required transfer into the intensive care unit. The elevation of one single metabolite, anthranilic acid, had a poor prognostic value, correlating with the maintenance of high interleukin-10 and -18 levels. Given that products of the kynurenine pathway including anthranilic acid have immunosuppressive properties, we speculate on the therapeutic utility to inhibit the rate-limiting enzymes of this pathway including indoleamine 2,3-dioxygenase and tryptophan 2,3-dioxygenase.

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.

Supporting Clinical Decision-Making during the SARS-CoV-2 Pandemic through a Global Research Commitment: The TERAVOLT Experience.

To understand the real impact of COVID-19 on cancer patients, an entirely new data collection effort was initiated within the Thoracic Cancers International COVID-19 Collaboration (TERAVOLT). TERAVOLT reported high mortality related to COVID-19 infection in thoracic cancer patients and identified several negative prognostic factors. In this commentary, we discuss the importance and limits of patient registries to support decision-making in thoracic cancer during the SARS-CoV-2 pandemic.

Impact of COVID-19 Pandemic on Cancer Research.

The COVID-19 pandemic is profoundly changing cancer researchers and cancer research. Leaders from different fields and at different career stages share their perspectives.

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.

Elevated Calprotectin and Abnormal Myeloid Cell Subsets Discriminate Severe from Mild COVID-19.

Blood myeloid cells are known to be dysregulated in coronavirus disease 2019 (COVID-19), caused by SARS-CoV-2. It is unknown whether the innate myeloid response differs with disease severity and whether markers of innate immunity discriminate high-risk patients. Thus, we performed high-dimensional flow cytometry and single-cell RNA sequencing of COVID-19 patient peripheral blood cells and detected disappearance of non-classical CD14LowCD16High monocytes, accumulation of HLA-DRLow classical monocytes (Human Leukocyte Antigen - DR isotype), and release of massive amounts of calprotectin (S100A8/S100A9) in severe cases. Immature CD10LowCD101-CXCR4+/- neutrophils with an immunosuppressive profile accumulated in the blood and lungs, suggesting emergency myelopoiesis. Finally, we show that calprotectin plasma level and a routine flow cytometry assay detecting decreased frequencies of non-classical monocytes could discriminate patients who develop a severe form of COVID-19, suggesting a predictive value that deserves prospective evaluation.

COVID-19 management in a cancer center: the ICU storm.

A novel coronavirus, SARS-CoV-2, was first reported as a respiratory illness in December 2019 in Wuhan, China. Since then, the World Health Organization (WHO) Emergency Committee declared a global health. COVID-19 has now spread worldwide and is responsible of more than 472,216 persons, out of 9,100,090 officially diagnosed worldwide since 23 of June. In the context of cancer patients, COVID-19 has a severe impact, regarding pulmonary infection but also cancer treatments in this fragile and immunocompromised population, and ICU admission for cancer patients in the context of COVID-19 requires ethical and clinical consideration. In our cancer center, intensivists, oncologists, pharmacists, and hospital administrators had to prepare for a substantial increase in critical care bed capacity (from 10 ICU beds, 6 medical intensive care beds, and 12 surgical intensive care beds, bed capacity was increased to 28 medical intensive care beds with ventilating capacity) and to adapt infrastructure (i.e., ICU beds), supplies (i.e., drugs, ventilators, protective materials), and staff (i.e., nurses and medical staff). Overall, thirty-three COVID-19 patients were admitted in our ICU, 17 cancer-free and 16 with cancer, and 23 required mechanical ventilation, resulting in 4 deaths (of them two patients with cancer). We report here management of a dedicated intensive care unit of a cancer center during the COVID-19 infection pandemic, considering resource allocation and redistribution of healthcare workers.

TERAVOLT: Thoracic Cancers International COVID-19 Collaboration

Prior publications on small subsets of cancer patients infected with SARS CoV-2 have shown an increased risk of mortality compared to the general population. Furthermore, patients with thoracic malignancies are thought to be at particularly high risk given their older age, smoking habits, and pre-existing cardio-pulmonary comorbidities. For this reason, physicians around the world have formed TERAVOLT, a global consortium dedicated to understanding the impact of COVID-19 on patients with thoracic malignancies.