Biological-based models of carcinogenesis in the lung from radiation and smoking

Lung adenocarcinoma and squamous cell carcinoma are the deadliest cancers worldwide. Smoking and ionizing radiation are potent carcinogens affecting strongly both lung cancer subtypes. Several biological analyses have been performed to characterise the genetic mutations leading to adenocarcinoma and squamous cell carcinoma, and different genomic spectra have been observed. Biological markers of smoking related damage could be found, leading to a deep knowledge of cellular smoking effects. Less is known about the biological effects of radiation in human carcinogenesis. Risks have been quantified with epidemiological studies of these carcinogens. Based on the biologically substantiated assumption that the number of mutations is linearly related to the dose, in radiation epidemiology it is standard to model effects linearly. These models do however not have a biological interpretation and are disconnected from general statistical methods. Here we fill both gaps. First we apply statistical generalised additive models to examine the functional relation between risk and smoking and radiation effects. Secondly, with mechanistic multi-scale models we integrate molecular biology and epidemiology to describe the carcinogenesis of lung adenocarcinoma and squamous cell carcinoma. To investigate the incidence of lung adenocarcinoma and lung squamous cell carcinoma we analysed two cohorts: first the Life Span Study cohort of atomic bomb survivors of Hiroshima and Nagasaki, and second the Eldorado cohort of Canadian Uranium miners. Exposures differed strongly between cohorts. Residents of Hiroshima and Nagasaki were exposed to a relative high dose of gamma radiation for a short time, while the miners were exposed to a protracted and lower exposure to alpha and gamma radiation. Information about smoking habits is available only for the former cohort. Three types of models were applied to analyse the effects of radiation and smoking: state-of-the-art statistical risk models of radiation protection, statistical generalized additive models and mechanistic risk models. Although there were quantitative differences in effect size and significance, each result is presented below only for a single model. For lung adenocarcinoma the best mechanistic model was a two pathway model. Smoking and radiation effects showed markedly different patterns: both acted on the apoptosis rate of precancerous cells but on different pathways without any interaction. A linear radiation effect was found in one pathway and a linear-exponential smoking effect in the other pathway. Independently of these results we analysed genomic data of American patients. It is known that the genetic damage of people with adenocarcinoma can be grouped into three pathways: the receptor mutant (RMUT ) pathway, the transducer mutant pathway (TMUT ), and other signatures (OWT ). We could show that signatures of TMUT and the OWT pathways do differ much less from each other than both differed to the RMUT pathway. Therefore, there is also genetic evidence that adenocarcinoma fall into two main classes. The two pathways of the mechanistic model could be associated to the RMUT and RMUT+OWT pathways by their risk patterns in age and smoking. On the other hand, for squamous cell carcinoma one pathway was sufficient to describe the incidence data. Although effects of radiation appeared to be highly significant, they could be traced back to arise only from the first five years of follow up (33 cases therein). When the first five years were excluded, no significant radiation effect could be found. Interestingly, for lung squamous cell carcinoma the mechanistic models could fit the effects of cigarette smoking in initiation and promotion. This was different for lung adenocarcinoma, where the main effect of smoking was a promotion of already existing pre-cancerous clones. For both, lung adenocarcinoma and squamous cell carcinoma, no interaction between radiation and smoking could be fitted for the Life Span Study cohort. Results from analysis of the Eldorado cohort were in line with the results presented above. For lung adenocarcinoma both, the state-of-the-art statistical risk models and the generalised additive models, could find only a significant effect of radiation exposure. For lung squamous cell carcinoma, vice versa, both models could find only a significant effect of gamma radiation exposure. Concluding, we showed that lung cancer cannot be investigated as a single endpoint but the different subtypes have to be analysed separately. Different radiation qualities act differently to the different subtypes, indicating different biological processes. Analogously, although smoking is an important risk factor for all subtypes, its effects were different and with different magnitudes

Biological-based models of carcinogenesis in the lung from radiation and smoking

Lung adenocarcinoma and squamous cell carcinoma are the deadliest cancers worldwide. Smoking and ionizing radiation are potent carcinogens affecting strongly both lung cancer subtypes. Several biological analyses have been performed to characterise the genetic mutations leading to adenocarcinoma and squamous cell carcinoma, and different genomic spectra have been observed. Biological markers of smoking related damage could be found, leading to a deep knowledge of cellular smoking effects. Less is known about the biological effects of radiation in human carcinogenesis. Risks have been quantified with epidemiological studies of these carcinogens. Based on the biologically substantiated assumption that the number of mutations is linearly related to the dose, in radiation epidemiology it is standard to model effects linearly. These models do however not have a biological interpretation and are disconnected from general statistical methods. Here we fill both gaps. First we apply statistical generalised additive models to examine the functional relation between risk and smoking and radiation effects. Secondly, with mechanistic multi-scale models we integrate molecular biology and epidemiology to describe the carcinogenesis of lung adenocarcinoma and squamous cell carcinoma. To investigate the incidence of lung adenocarcinoma and lung squamous cell carcinoma we analysed two cohorts: first the Life Span Study cohort of atomic bomb survivors of Hiroshima and Nagasaki, and second the Eldorado cohort of Canadian Uranium miners. Exposures differed strongly between cohorts. Residents of Hiroshima and Nagasaki were exposed to a relative high dose of gamma radiation for a short time, while the miners were exposed to a protracted and lower exposure to alpha and gamma radiation. Information about smoking habits is available only for the former cohort. Three types of models were applied to analyse the effects of radiation and smoking: state-of-the-art statistical risk models of radiation protection, statistical generalized additive models and mechanistic risk models. Although there were quantitative differences in effect size and significance, each result is presented below only for a single model. For lung adenocarcinoma the best mechanistic model was a two pathway model. Smoking and radiation effects showed markedly different patterns: both acted on the apoptosis rate of precancerous cells but on different pathways without any interaction. A linear radiation effect was found in one pathway and a linear-exponential smoking effect in the other pathway. Independently of these results we analysed genomic data of American patients. It is known that the genetic damage of people with adenocarcinoma can be grouped into three pathways: the receptor mutant (RMUT ) pathway, the transducer mutant pathway (TMUT ), and other signatures (OWT ). We could show that signatures of TMUT and the OWT pathways do differ much less from each other than both differed to the RMUT pathway. Therefore, there is also genetic evidence that adenocarcinoma fall into two main classes. The two pathways of the mechanistic model could be associated to the RMUT and RMUT+OWT pathways by their risk patterns in age and smoking. On the other hand, for squamous cell carcinoma one pathway was sufficient to describe the incidence data. Although effects of radiation appeared to be highly significant, they could be traced back to arise only from the first five years of follow up (33 cases therein). When the first five years were excluded, no significant radiation effect could be found. Interestingly, for lung squamous cell carcinoma the mechanistic models could fit the effects of cigarette smoking in initiation and promotion. This was different for lung adenocarcinoma, where the main effect of smoking was a promotion of already existing pre-cancerous clones. For both, lung adenocarcinoma and squamous cell carcinoma, no interaction between radiation and smoking could be fitted for the Life Span Study cohort. Results from analysis of the Eldorado cohort were in line with the results presented above. For lung adenocarcinoma both, the state-of-the-art statistical risk models and the generalised additive models, could find only a significant effect of radiation exposure. For lung squamous cell carcinoma, vice versa, both models could find only a significant effect of gamma radiation exposure. Concluding, we showed that lung cancer cannot be investigated as a single endpoint but the different subtypes have to be analysed separately. Different radiation qualities act differently to the different subtypes, indicating different biological processes. Analogously, although smoking is an important risk factor for all subtypes, its effects were different and with different magnitudes

Enhanced Spike-specific, but attenuated Nucleocapsid-specific T cell responses upon SARS-CoV-2 breakthrough versus non-breakthrough infections

SARS-CoV-2 vaccine breakthrough infections frequently occurred even before the emergence of Omicron variants. Yet, relatively little is known about the impact of vaccination on SARS-CoV-2-specific T cell and antibody response dynamics upon breakthrough infection. We have therefore studied the dynamics of CD4 and CD8 T cells targeting the vaccine-encoded Spike and the non-encoded Nucleocapsid antigens during breakthrough infections (BTI, n=24) and in unvaccinated control infections (non-BTI, n=30). Subjects with vaccine breakthrough infection had significantly higher CD4 and CD8 T cell responses targeting the vaccine-encoded Spike during the first and third/fourth week after PCR diagnosis compared to non-vaccinated controls, respectively. In contrast, CD4 T cells targeting the non-vaccine encoded Nucleocapsid antigen were of significantly lower magnitude in BTI as compared to non-BTI. Hence, previous vaccination was linked to enhanced T cell responses targeting the vaccine-encoded Spike antigen, while responses against the non-vaccine encoded Nucleocapsid antigen were significantly attenuated

Nucleocapsid-specific T cell responses associate with control of SARS-CoV-2 in the upper airways before seroconversion

Despite intensive research since the emergence of SARS-CoV-2, it has remained unclear precisely which components of the early immune response protect against the development of severe COVID-19. Here, we perform a comprehensive immunogenetic and virologic analysis of nasopharyngeal and peripheral blood samples obtained during the acute phase of infection with SARS-CoV-2. We find that soluble and transcriptional markers of systemic inflammation peak during the first week after symptom onset and correlate directly with upper airways viral loads (UA-VLs), whereas the contemporaneous frequencies of circulating viral nucleocapsid (NC)-specific CD4+ and CD8+ T cells correlate inversely with various inflammatory markers and UA-VLs. In addition, we show that high frequencies of activated CD4+ and CD8+ T cells are present in acutely infected nasopharyngeal tissue, many of which express genes encoding various effector molecules, such as cytotoxic proteins and IFN-γ. The presence of IFNG mRNA-expressing CD4+ and CD8+ T cells in the infected epithelium is further linked with common patterns of gene expression among virus-susceptible target cells and better local control of SARS-CoV-2. Collectively, these results identify an immune correlate of protection against SARS-CoV-2, which could inform the development of more effective vaccines to combat the acute and chronic illnesses attributable to COVID-19

Enhanced Spike-specific, but attenuated Nucleocapsid-specific T cell responses upon SARS-CoV-2 breakthrough versus non-breakthrough infections

SARS-CoV-2 vaccine breakthrough infections frequently occurred even before the emergence of Omicron variants. Yet, relatively little is known about the impact of vaccination on SARS-CoV-2-specific T cell and antibody response dynamics upon breakthrough infection. We have therefore studied the dynamics of CD4 and CD8 T cells targeting the vaccine-encoded Spike and the non-encoded Nucleocapsid antigens during breakthrough infections (BTI, n=24) and in unvaccinated control infections (non-BTI, n=30). Subjects with vaccine breakthrough infection had significantly higher CD4 and CD8 T cell responses targeting the vaccine-encoded Spike during the first and third/fourth week after PCR diagnosis compared to non-vaccinated controls, respectively. In contrast, CD4 T cells targeting the non-vaccine encoded Nucleocapsid antigen were of significantly lower magnitude in BTI as compared to non-BTI. Hence, previous vaccination was linked to enhanced T cell responses targeting the vaccine-encoded Spike antigen, while responses against the non-vaccine encoded Nucleocapsid antigen were significantly attenuated

Modeling the spread of COVID-19 in Germany: Early assessment and possible scenarios

The novel coronavirus (SARS-CoV-2), identified in China at the end of December 2019 and causing the disease COVID-19, has meanwhile led to outbreaks all over the globe with about 2.2 million confirmed cases and more than 150,000 deaths as of April 17, 2020. In this work, mathematical models are used to reproduce data of the early evolution of the COVID-19 outbreak in Germany, taking into account the effect of actual and hypothetical non-pharmaceutical interventions. Systems of differential equations of SEIR type are extended to account for undetected infections, stages of infection, and age groups. The models are calibrated on data until April 5. Data from April 6 to 14 are used for model validation. We simulate different possible strategies for the mitigation of the current outbreak, slowing down the spread of the virus and thus reducing the peak in daily diagnosed cases, the demand for hospitalization or intensive care units admissions, and eventually the number of fatalities. Our results suggest that a partial (and gradual) lifting of introduced control measures could soon be possible if accompanied by further increased testing activity, strict isolation of detected cases, and reduced contact to risk groups

The impact of current and future control measures on the spread of COVID-19 in Germany

The novel coronavirus (SARS-CoV-2), identified in China at the end of December 2019 and causing the disease COVID-19, has meanwhile led to outbreaks all over the globe with about 2.2 million confirmed cases and more than 150,000 deaths as of April 17, 2020 [37]. In view of most recent information on testing activity [32], we present here an update of our initial work [4]. In this work, mathematical models have been developed to study the spread of COVID-19 among the population in Germany and to asses the impact of non-pharmaceutical interventions. Systems of differential equations of SEIR type are extended here to account for undetected infections, as well as for stages of infections and age groups. The models are calibrated on data until April 5, data from April 6 to 14 are used for model validation. We simulate different possible strategies for the mitigation of the current outbreak, slowing down the spread of the virus and thus reducing the peak in daily diagnosed cases, the demand for hospitalization or intensive care units admissions, and eventually the number of fatalities. Our results suggest that a partial (and gradual) lifting of introduced control measures could soon be possible if accompanied by further increased testing activity, strict isolation of detected cases and reduced contact to risk groups

A first study on the impact of current and future control measures on the spread of COVID-19 in Germany

The novel coronavirus (SARS-CoV-2), identified in China at the end of December 2019 and causing the disease COVID-19, has meanwhile led to outbreaks all over the globe, with about 571,700 confirmed cases and about 26,500 deaths as of March 28th, 2020. We present here the preliminary results of a mathematical study directed at informing on the possible application or lifting of control measures in Germany. The developed mathematical models allow to study the spread of COVID-19 among the population in Germany and to asses the impact of non-pharmaceutical interventions

Integrative modelling of reported case numbers and seroprevalence reveals time-dependent test efficiency and infection rates

Contento L, Castelletti N, Raimúndez E, et al. Integrative modelling of reported case numbers and seroprevalence reveals time-dependent test efficiency and infection rates. medRxiv. 2021