ABSTRACT
Introduction: Current SARS-CoV-2 vaccines are effective at preventing COVID-19 or limiting disease severity in healthy individuals, but effectiveness is lower among patients with cancer or immunosuppression. Vaccine effectiveness wanes with time and varies by vaccine type. Moreover, current vaccines are based on the ancestral SARS-CoV-2 spike protein sequence, and emerging viral variants evade vaccine induced immunity. Booster doses partially overcome these issues, but there are limited clinical data on the durability of protection afforded by boosters - especially against SARS-CoV-2 variants. Methods: Here we describe a mechanistic mathematical model for vaccination-induced immunity in patients with cancer and use it to predict vaccine effectiveness taking into account current and possible future viral, host and vaccine characteristics. Crucially, this allows predictions over time frames currently not reported in the clinical literature. The model incorporates the infection of lung epithelium by SARS-CoV-2, the response of innate and adaptive immune cells to infection, the production of pro-and anti-inflammatory cytokines, the activation of the coagulation cascade. The model further accounts for the interactions between the virus, immune cells and tumor cells as well as for vaccination-induced immunity and anti-cancer therapies. Results: Model predictions were validated with available clinical data. The model predicts that for healthy individuals vaccinated and boosted with mRNA-1273, BNT-162b2a, and Ad26.COV2.S, robust immunogenicity against the ancestral and delta variant extends beyond a year. Immunogenicity is enhanced following booster vaccination in patients with cancer on various anti-cancer therapies and for patients without cancer on immunosuppressive agents. However, our model predicts that more than one booster dose will be required for patients with cancer, or on immunosuppression, to maintain protective immunity against current and hypothetical future variants. SARS-CoV2 variants with enhanced binding to target cells, reduced affinity for vaccine-generated antibodies or reduced immunogenicity resulted in lower antibody levels and more severe disease compared with variants with enhanced viral replication or internalization rates. Conclusion: For patients with cancer and immunosuppressed individuals, SARS-CoV2 variants with enhanced ability to bind to target cells, altered antibody affinity or reduced immunogenicity could lead to breakthrough infections even after a single booster dose. Our mathematical model is useful for anticipating and planning future vaccinations in patients with cancer.
ABSTRACT
RationaleAcute respiratory distress syndrome (ARDS) secondary to Coronavirus Disease-2019 (COVID-19) is characterized by substantial heterogeneity in clinical, biochemical, and physiological characteristics. However, the pathophysiology of severe COVID-19 infection is poorly understood. Among “classical' ARDS cohorts, previous studies established two predominant biological phenotypes - patients with and without evidence of a hyperinflammatory response - with important prognostic and therapeutic implications. The phenotypic profile of COVID-19 associated ARDS remains unknown. Methods We used latent class modeling via a multivariate mixture model to identify phenotypes from clinical and biochemical data collected from 263 patients admitted to Massachusetts General Hospital intensive care unit with COVID-19-associated ARDS between March 13 and August 2, 2020. Classdefining variables included demographic features, respiratory parameters, hematologic and inflammatory biomarkers, and markers of end-organ function. Interleukin-6 (IL-6) and fibrinogen levels, which were available for n = 53 and n = 189 patients, respectively, were incorporated post-hoc. Results We identified two distinct latent classes representing 74.4% (Class 1, n = 193) and 26.6% (Class 2, n = 70) of the cohort, respectively. Posterior probability of class assignment was high (median 98.2%, IQR [98.0%, 100%]). To understand each class's distinguishing biological features, we compared the standardized mean of the continuous class-defining variables (Fig. 1A). The minority phenotype (class 2, n = 70, 26.6%) demonstrated increased markers of vascular dysregulation, with mild relative hyper-inflammation and dramatically increased markers of end-organ dysfunction (e.g., creatinine, troponin). There was little distinction according to respiratory parameters. The class 2 phenotype was characterized by significantly decreased fibrinogen and increased IL- 6 compared to Class 1 (Fig. 1B), even though these variables were not used in the statistical inference. Furthermore, the 28-day mortality among the class 2 phenotype was more than double that of the class 1 phenotype (40.0% vs. 23.3%, OR 2.3, 95% CI [1.3, 4.1]). Conclusion We identified distinct phenotypic profiles in COVID-19 associated ARDS, with little variation according to respiratory physiology but with important variation according to systemic and extra-pulmonary markers. Phenotypic identity was highly associated with shortterm mortality risk. The class 2 phenotype exhibited prominent signatures of vascular dysregulation, suggesting that vascular dysfunction may play an important role in the clinical progression of severe COVID-19-related disease.