Acute Immune Signatures and Their Legacies in Severe Acute Respiratory Syndrome Coronavirus-2 Infected Cancer Patients

Abstract

Given the immune system's importance for cancer surveillance and treatment, we have investigated how it may be affected by SARS-CoV-2 infection of cancer patients. Across some heterogeneity in tumor type, stage, and treatment, virus-exposed solid cancer patients display a dominant impact of SARS-CoV-2, apparent from the resemblance of their immune signatures to those for COVID-19⁺ non-cancer patients. This is not the case for hematological malignancies, with virus-exposed patients collectively displaying heterogeneous humoral responses, an exhausted T cell phenotype and a high prevalence of prolonged virus shedding. Furthermore, while recovered solid cancer patients' immunophenotypes resemble those of non-virus-exposed cancer patients, recovered hematological cancer patients display distinct, lingering immunological legacies. Thus, while solid cancer patients, including those with advanced disease, seem no more at risk of SARS-CoV-2-associated immune dysregulation than the general population, hematological cancer patients show complex immunological consequences of SARS-CoV-2 exposure that might usefully inform their care.

Keywords: COVID-19; SARS-CoV-2; antibodies; cancer; hemato-oncological; immune; seroconversion; vaccine; virus shedding.

Graphical abstract

Figure 1

Cancer Progression and Prolonged Viral Persistence Among COVID-19⁺ Cancer Patients (A) Stratification of COVID-19 severity groups by cancer type. (B) Timeline of illness of 41 COVID-19⁺ cancer patients by tumor type. (C) Stacked bar graph shows the disease status of cancer in patients grouped according to COVID-19 severity. Association between categorical variables was assessed by chi-squared test (p < 0.001). (D) Timeline of detection of SARS-CoV-2 on nasopharyngeal swabs. Day 1 indicates collection date of the earliest positive sample. Blue dots mark the date of the earliest negative rRT-PCR test; red dots the latest sample tested positive. The shaded area indicates the reported median duration of virus shedding. (E) Correlation multivariate regression analysis of the clinical parameters within the cancer cohort (red = statistically significant correlations). (F) i–ii, Quantification of significant parameters captured on clinical blood tests in COVID-19⁺ cancer patients; healthy ranges are indicated in purple. One-way ANOVA was used to compare continuous variable among three groups of severity, while independent samples t test was used between two groups (COVID-19⁺ versus non-COVID-19). p < 0.05 was considered statistically significant. See also Figure S1 and Table S1.

Figure 2

Longitudinal Profiling of Actively Infected and Recovered SARS-CoV-2⁺ Patients (A) Schematic of time points for blood results from 41 patients in the COVID-19⁺ cancer cohorts. (B and C) Time course of ( i) lymphocytes, (ii) neutrophils, (iii) neutrophil-to-lymphocyte ratio in active COVID-19 disease for solid (B) and hematological (C) cancer patients with mild World Health Organization (WHO) score (0–3) and moderate/severe illness, (WHO score 4–10). Points represent the median of each patient's measurements in the corresponding time bin, thin lines connect measurements on the same patient, thick lines show the mean of all patients at each time point stratified by severity. Healthy ranges are highlighted in purple. (D) Fold change of blood parameters for each solid (i) or hematological (ii) cancer patient between the time of worst abnormality and the pre-COVID blood tests 4–6 weeks prior to COVID-19 presentation. (E) Fold change of blood parameters for solid (i); hematological (ii) cancer patients between results after recovery and the last pre-COVID-19 results. Shaded area denotes measurements within 10% of pre-COVID levels. See also Figure S2 and Tables S2 and S3.

Figure 3

Distinct COVID-19 Immune Signatures in Solid and Hematological Cancer Patients (A) Overview of the nine patient cohorts grouped according to cancer type and COVID-19 status. (B) PCA analysis of 153 phenotypes in 31 COVID-19⁺ cancer patients. PC-1 and PC-2 explain 17.2% and 11.9% of the variance. (C–F) Volcano plot of 246 non-redundant immune parameters analyzed in active COVID-19⁺ (C) solid; (D) hematological cancer; recovered COVID-19⁺ (E) solid; (F) hematological cancers versus their respective non-COVID-19 cancer controls. Red circles = significantly altered parameters in COVID-19⁺ cancer patients (fold change >1.5, false discovery rate-adjusted p < 0.05). See also Figure S3.

Figure 4

Altered Immune Cell Populations in Actively Infected SARS-CoV-2⁺ Cancer Patients (A) i–vi, Quantification (cell counts/mL) of whole blood major innate and adaptive immune populations. (B) Correlation between (i) T cells; (ii) Basophil cell counts and COVID-19 severity in actively infected solid cancer patients (Kendall's tau for semi-partial correlation; adjusted for age and sex). (C) Quantile scaled heatmap depicting hierarchical clustering of relative cytokine levels (quantile scaled pg/mL) 22 unique cytokines measured in six cohorts. (D) Cytokine concentrations of (i) IL-6, (ii) IL-10, (iii) IP-10, (iv) IL-8. Boxplots show median, lower, and upper quartiles (box) and 1.5 times the interquartile range (whiskers). Each circle represents a single patient. Statistical significance highlighted in red (cell counts: t test with robust standard errors on estimated marginal means from linear regression, adjusted for age and sex, p < 0.05; cytokine concentrations: t test with robust standard errors on estimated marginal means from tobit regression, adjusted for age and sex, p < 0.05). See also Figure S4.

Figure 5

T cell Dysfunction in Active SARS-CoV-2-Infected Cancer Patients (A and B) Quantification (cell count/mL) of whole blood (A) (i) CD8 T cells, (ii) naive CD8 T cells, and (B) CD4 T cells. (C) Log₂ fold change in total counts of major CD4 T cell subpopulations in active COVID-19⁺ solid cancer patients relative to solid cancer non-COVID-19 patients. (D) Quantified cell counts (cell/mL) of differentiated CD4⁺ T cell (i–iii) and (iv) naive subtypes. (E) Correlation between naive CD4 T cells and COVID-19 severity in solid cancer patients with active COVID-19 infection (Kendall's tau for semi-partial correlation, adjusted for age and sex, threshold p < 0.01). (F) Log₂ fold change in the frequencies of activation and exhaustion surface marker expression on CD4⁺ and CD8⁺ T cell in solid cancer (dark blue) and hematological cancer (yellow) COVID-19⁺ patients relative to non-COVID-19 cancer patients. (G and H) Representative flow cytometry plots and frequency analysis of (G) PD-1⁺TIM3⁺/CD45RA⁻Th1 cell and (H) PD-1⁺TIM3⁺/CD45RA⁺CD4⁻ cell. Boxplots show the median, lower and upper quartiles (box), and 1.5 times the interquartile range (whiskers). Each circle represents a single patient. Statistical significance highlighted in red (cell counts: t test with robust standard errors on estimated marginal means from linear regression, adjusted for age and sex, p < 0.05; frequencies: Wald test with robust standard errors on estimated marginal means from beta regression, adjusted for age and sex, p < 0.05). See also Figure S5.

Figure 6

Heterogeneous Humoral Responses to SARS-CoV-2 in Cancer Patients (A) Log₂ fold changes of B cell subtype frequencies and total counts in active COVID-19⁺ infections in solid (dark blue) and hematological (yellow) cancer patients, relative to their respective cancer non-COVID-19 controls. (B) Antibody titers of COVID-19⁺ cancer patients versus time since first positive rRT-PCR test. Titer measurements for (i) IgG RBD, (ii) IgM RBD, (iii) IgG spike, (iv) IgM spike. Each point represents a single sample, with the status of COVID-19 infection denoted as active (circle) or recovered (triangle) in solid (dark blue) and hematological cancer (yellow) patients. Longitudinal samples from the same patient are linked. Dotted horizontal line indicates the cutoff for sero-positivity (>0.15). (C and D) Peak antibody titers of IgG and IgM against SARS-CoV-2 spike and RBD in solid, hematological and non-cancer COVID-19⁺ patients, who are either (C) actively infected or (D) recovered. Boxplots show the median, lower and upper quartiles (box), and 1.5 times the interquartile range (whiskers). Shape denotes disease status, active infection (circle), and recovered (triangle). Statistical significance highlighted in red (cell counts: t test with robust standard errors on estimated marginal means from linear regression, adjusted for age and sex, p < 0.05; frequencies and serology: Wald test with robust standard errors on estimated marginal means from beta regression, adjusted for age and sex, p < 0.05). See also Figure S6 and Table S4.

Figure 7

Immune Legacies in Recovered COVID-19⁺ Hematological Cancer Patients (A) Log₂ fold changes in frequency and total counts of innate and adaptive immune parameters in recovered COVID-19 solid (dark blue) and hematological (yellow) cancer patients, relative to their respective cancer non-COVID-19 patients. (B and C) Quantified cell counts of B (i) CD8⁺ and (ii) CD4⁺ T cells; and differentiated CD8⁺ C (i) EM and (ii) CM T cells in whole blood. (D) (i) Frequency of exhausted T cells via the double positive expression of TIM3⁺PD1⁺ in CD45RA⁺CD4⁻ T cells and (ii) quantified cell count of activated CD8 T cell in whole blood. (E) Changes in the composition of granulocytic cells, presented through the frequencies of (i) Basophils, (ii) Eosinophils, and (iii) CD56^bright NK cells. Boxplots show the median, lower and upper quartiles (box), and 1.5 times the interquartile range (whiskers). Each point represents a single patient. Statistical significance, highlighted in red (cell counts: t test with robust standard errors on estimated marginal means from linear regression, adjusted for age and sex, p < 0.05; frequencies: Wald test with robust standard errors on estimated marginal means from beta regression, adjusted for age and sex, p < 0.05). See also Figure S7.