Distinct systemic immune networks define severe vs. mild COVID-19 in hematologic and solid cancer patients.

Introduction: The COVID-19 pandemic, caused by the coronavirus SARS-CoV-2, has impacted health across all sectors of society. A cytokine-release syndrome, combined with an inefficient response of innate immune cells to directly combat the virus, characterizes the severe form of COVID-19. While immune factors involved in the development of severe COVID-19 in the general population are becoming clearer, identification of the immune mechanisms behind severe disease in oncologic patients remains uncertain. Methods: Here we evaluated the systemic immune response through the analysis of soluble blood immune factors and anti-SARS-CoV-2 antibodies within the early days of a positive SARS-CoV-2 diagnostic in oncologic patients. Results: Individuals with hematologic malignancies that went on to die from COVID-19 displayed at diagnosis severe leukopenia, low antibody production against SARS-CoV-2 proteins, and elevated production of innate immune cell recruitment and activation factors. These patients also displayed correlation networks in which IL-2, IL-13, TNF-alpha, IFN-gamma, and FGF2 were the focal points. Hematologic cancer patients that showed highly networked and coordinated anti-SARS-CoV-2 antibody production, with central importance of IL-4, IL-5, IL-12A, IL-15, and IL-17A, presented only mild COVID-19. Conversely, solid tumor patients that had elevated levels of inflammatory cytokines IL-6, CXCL8, and lost the coordinate production of anti-virus antibodies developed severe COVID-19 and died. Patients that displayed positive correlation networks between anti-virus antibodies, and a regulatory axis involving IL-10 and inflammatory cytokines recovered from the disease. We also provided evidence that CXCL8 is a strong predictor of death for oncologic patients and could be an indicator of poor prognosis within days of the positive diagnostic of SARS-CoV-2 infection. Conclusion: Our findings defined distinct systemic immune profiles associated with COVID-19 clinical outcome of patients with cancer and COVID-19. These systemic immune networks shed light on potential immune mechanisms involved in disease outcome, as well as identify potential clinically useful biomarkers.

Distinct systemic immune networks define severe vs. mild COVID-19 in hematologic and solid cancer patients

Introduction The COVID-19 pandemic, caused by the coronavirus SARS-CoV-2, has impacted health across all sectors of society. A cytokine-release syndrome, combined with an inefficient response of innate immune cells to directly combat the virus, characterizes the severe form of COVID-19. While immune factors involved in the development of severe COVID-19 in the general population are becoming clearer, identification of the immune mechanisms behind severe disease in oncologic patients remains uncertain. Methods Here we evaluated the systemic immune response through the analysis of soluble blood immune factors and anti-SARS-CoV-2 antibodies within the early days of a positive SARS-CoV-2 diagnostic in oncologic patients. Results Individuals with hematologic malignancies that went on to die from COVID-19 displayed at diagnosis severe leukopenia, low antibody production against SARS-CoV-2 proteins, and elevated production of innate immune cell recruitment and activation factors. These patients also displayed correlation networks in which IL-2, IL-13, TNF-alpha, IFN-gamma, and FGF2 were the focal points. Hematologic cancer patients that showed highly networked and coordinated anti-SARS-CoV-2 antibody production, with central importance of IL-4, IL-5, IL-12A, IL-15, and IL-17A, presented only mild COVID-19. Conversely, solid tumor patients that had elevated levels of inflammatory cytokines IL-6, CXCL8, and lost the coordinate production of anti-virus antibodies developed severe COVID-19 and died. Patients that displayed positive correlation networks between anti-virus antibodies, and a regulatory axis involving IL-10 and inflammatory cytokines recovered from the disease. We also provided evidence that CXCL8 is a strong predictor of death for oncologic patients and could be an indicator of poor prognosis within days of the positive diagnostic of SARS-CoV-2 infection. Conclusion Our findings defined distinct systemic immune profiles associated with COVID-19 clinical outcome of patients with cancer and COVID-19. These systemic immune networks shed light on potential immune mechanisms involved in disease outcome, as well as identify potential clinically useful biomarkers.

Excess mortality by specific causes of deaths in the city of São Paulo, Brazil, during the COVID-19 pandemic.

BACKGROUND: To investigate the excess of deaths by specific causes, in the first half of 2020 in the city of São Paulo-Brazil, during the COVID-19 pandemic. METHODS: Ecological study conducted from 01/01 to 06/30 of 2019 and 2020. Population and mortality data were obtained from DATASUS. The standardized mortality ratio (SMR) by age was calculated by comparing the standardized mortality rate in 2020 to that of 2019, for overall and specific mortality. The ratio between the standardized mortality rate due to COVID-19 in men as compared to women was calculated for 2020. Crude mortality rates were standardized using the direct method. RESULTS: COVID-19 was responsible for 94.4% of the excess deaths in São Paulo. In 2020 there was an increase in overall mortality observed among both men (SMR 1.3, 95% CI 1.17-1.42) and women (SMR 1.2, 95% CI 1.06-1.36) as well as a towards reduced mortality for all cancers. Mortality due to COVID-19 was twice as high for men as for women (SMR 2.1, 95% CI 1.67-2.59). There was an excess of deaths observed in men above 45 years of age, and in women from the age group of 60 to 79 years. CONCLUSION: There was an increase in overall mortality during the first six months of 2020 in São Paulo, which seems to be related to the COVID-19 pandemic. Chronic health conditions, such as cancer and other non-communicable diseases, should not be disregarded.

Differences in mortality of cancer patients with COVID-19 in a Brazilian cancer center.

INTRODUCTION: To analyze COVID-19 mortality in cancer patients and associated factors such as age, sex, type of insurance, situation at COVID-19 diagnosis, and cancer histology during the pandemic at a cancer center in Brazil. METHODS: Cross-sectional study carried out from April 02, 2020 to August 31, 2020 at A.C. Camargo Cancer Center (ACCCC), in São Paulo, Brazil. Cases were extracted from the Hospital Cancer Registry. COVID-19 lethality rates by histology were calculated; multiple logistic regression was used to identify factors associated with COVID-19 mortality. The log-rank test was applied to compare the survival curves for each variable. RESULTS: Of the 411 patients analyzed, 51 (12.4%) died due to COVID-19. Death occurred at an average age of 63 years. The fatality rate was higher for lung (0.333) and hematological (0.213) cancers and was associated with age over 60 years. The greatest chances of death from COVID-19 were in cases of lung (odds ratio, OR, 4.05, 95% confidence interval, CI 1.33-12.34) and hematological (OR 2.17, 95% CI 0.96-4.90) cancers, and in patients currently undergoing cancer treatment (OR 2.77, 95% CI 1.25-6.13). There were no statistical differences in survival by sex, age group, type of insurance, situation at the diagnosis of COVID-19, and histology of cancer for COVID-19. CONCLUSIONS: Mortality due to COVID-19 in cancer patients is heterogeneous. These findings reinforce the need for individualized strategies for the management of different types of cancer that reduce the risk of death from COVID-19.