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
Emerging retrospective analyses show that cancer patients are more likely to develop severe COVID-19. Thecauses for these worse outcomes are unclear, but data suggest that cancer therapies, which can suppress theimmune system, are not responsible for increased COVID-19 severity. An alternative hypothesis is that commonmolecular pathways are altered in cancer and COVID-19, resulting in worsened disease outcomes. Our previouswork demonstrated that activated renin angiotensin signaling (RAS) modulates the tumor microenvironment, resulting in worse outcomes and therapy resistance. Inhibition of this pathway using angiotensin receptor blockers(ARBs) or angiotensin converting enzyme inhibitors (ACEIs) can improve the outcomes of cancer therapies.Similarly, there is great interest in understanding the implications of RAS in COVID-19 progression because a keycomponent of this system, ACE2, is also the docking site for the SARS-CoV-2 virus. Indeed, multiple clinical trialsare currently evaluating whether ARBs/ACEIs benefit or harm COVID-19 patients. To help guide administration ofthese drugs, we adapted our existing computational modeling framework of the cancer microenvironment usingavailable data to simulate COVID-19 progression in patients. Using a systems biology approach, we mechanisticallymodeled the interaction of the RAS and coagulation pathways with COVID-19 infection. We further explored theefficacy of various antiviral, antithrombotic, and RAS-targeted treatment regimens to identify synergisticcombinations as well as optimal schedules for therapy. The system is complex, given that viral binding of ACE2interferes with its antiinflammatory signaling. When ACE2 is bound by the virus, its local activity decreases, leadingto immune dysregulation and risk of coagulopathy, predictors of COVID-19 severity and mortality. To optimizecombination treatments for cancer patients who contract COVID-19, multiple simulations were run by combiningdifferent therapeutics currently in clinical trials to predict their effects on viral load, thrombosis, oxygen saturation, and cytokine levels. These include ARBs, ACEIs, antiviral drugs, antithrombotic agents, and anti-inflammatory drugs(e.g., anti-IL6/6R). Our simulations predict that i) there is an optimal timing for treatment with antiviral drugs such asremdesivir, related to immune activation;ii) combinations of antiviral and antithrombotic drugs are able to preventlung damage, increase blood oxygen levels, and inhibit thromboembolic events;and iii) RAS modulators can have apositive effect when added to the treatment regimen. Effective strategies for COVID-19 treatment identified by this insilico analysis will be further analyzed in combination with cancer therapeutics (e.g., immune checkpoint blockers, chemotherapy) to provide guidelines for optimal clinical management of both cancer and COVID-19.