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Background: The role of the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) neutralizing antibody response from natural infection and vaccination, and the potential determinants of this response are poorly understood. Characterizing this antibody response and the factors associated with neutralization can help inform future prevention efforts and improve clinical outcomes in those infected. Objectives: The goals of this study were to prospectively evaluate SARS-CoV-2 antibody levels and the neutralizing antibody responses among naturally infected adults and to determine demographic and behavioral factors independently associated with these responses. Methods: Serum was collected from seropositive individuals at baseline, four-weeks, and three-months following their first study visit to be evaluated for antibody levels. Detection of neutralizing antibodies was performed at baseline. Participant demographic and behavioral information was collected via web questionnaire prior to their first visit. Results: At baseline, higher antibody levels were associated with better neutralization capacity, with 83% of participants having detectable neutralizing antibodies. We found an age-dependent effect on antibody level and neutralization capacity with participants over 65 years having significantly higher levels. Ethnicity, heart disease, autoimmune disease, and COVID symptoms were associated with higher antibody levels, but not with increased neutralization capacity. Work environment during the pandemic correlated with increased neutralization capacity, while kidney or liver disease and traveling out of state after February 2020 correlated with decreased neutralization capacity, however neither correlated with antibody levels. Conclusions: Our data show that natural infection by SARS-CoV-2 can induce a humoral response reflected by high antibody levels and neutralization capacity.

Radiotherapy efficacy is the result of radiation-mediated cytotoxicity coupled with stimulation of antitumor immune responses. We develop an in silico 3-dimensional agent-based model of diverse tumor-immune ecosystems (TIES) represented as anti- or pro-tumor immune phenotypes. We validate the model in 10,469 patients across 31 tumor types by demonstrating that clinically detected tumors have pro-tumor TIES. We then quantify the likelihood radiation induces antitumor TIES shifts toward immune-mediated tumor elimination by developing the individual Radiation Immune Score (iRIS). We show iRIS distribution across 31 tumor types is consistent with the clinical effectiveness of radiotherapy, and in combination with a molecular radiosensitivity index (RSI) combines to predict pan-cancer radiocurability. We show that iRIS correlates with local control and survival in a separate cohort of 59 lung cancer patients treated with radiation. In combination, iRIS and RSI predict radiation-induced TIES shifts in individual patients and identify candidates for radiation de-escalation and treatment escalation. This is the first clinically and biologically validated computational model to simulate and predict pan-cancer response and outcomes via the perturbation of the TIES by radiotherapy.

Radiotherapy is a primary therapeutic modality widely utilized with curative intent. Traditionally tumor response was hypothesized to be due to high levels of cell death induced by irreparable DNA damage. However, the immunomodulatory aspect of radiation is now widely accepted. As such, interest into the combination of radiotherapy and immunotherapy is increasing, the synergy of which has the potential to improve tumor regression beyond that observed after either treatment alone. However, questions regarding the timing (sequential vs concurrent) and dose fractionation (hyper-, standard-, or hypo-fractionation) that result in improved anti-tumor immune responses, and thus potentially enhanced tumor inhibition, remain. Here we discuss the biological response to radiotherapy and its immunomodulatory properties before giving an overview of pre-clinical data and clinical trials concerned with answering these questions. Finally, we review published mathematical models of the impact of radiotherapy on tumor-immune interactions. Ranging from considering the impact of properties of the tumor microenvironment on the induction of anti-tumor responses, to the impact of choice of radiation site in the setting of metastatic disease, these models all have an underlying feature in common: the push towards personalized therapy.