From the end of 2020, different vaccines against COVID-19 have been approved, offering a glimmer of hope and relief worldwide. However, in late 2020, new cases of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) started to re-surge, worsened by the emergence of highly infectious variants. To study this scenario, we extend the Susceptible-Exposed-Infectious-Removed model with lockdown measures used in our previous work with the inclusion of new lineages and mass vaccination campaign. We estimate model parameters using the Bayesian method Conditional Robust Calibration in two case studies: Italy and the Umbria region, the Italian region being worse affected by the emergence of variants. We then use the model to explore the dynamics of COVID-19, given different vaccination paces and a policy of gradual reopening. Our findings confirm the higher reproduction number of Umbria and the increase of transmission parameters due to the presence of new variants. The results illustrate the importance of preserving population-wide interventions, especially during the beginning of vaccination. Finally, under the hypothesis of waning immunity, the predictions show that a seasonal vaccination with a constant rate would probably be necessary to control the epidemic.

This study started from the request of providing predictions on hospitalization and Intensive Care Unit (ICU) rates that are caused by COVID-19 for the Umbria region in Italy. To this purpose, we propose the application of a computational framework to a SEIR-type (Susceptible, Exposed, Infected, Removed) epidemiological model describing the different stages of COVID-19 infection. The model discriminates between asymptomatic and symptomatic cases and it takes into account possible intervention measures in order to reduce the probability of transmission. As case studies, we analyze not only the epidemic situation in Umbria but also in Italy, in order to capture the evolution of the pandemic at a national level. First of all, we estimate model parameters through a Bayesian calibration method, called Conditional Robust Calibration (CRC), while using the official COVID-19 data of the Italian Civil Protection. Subsequently, Conditional Robustness Analysis (CRA) on the calibrated model is carried out in order to quantify the influence of epidemiological and intervention parameters on the hospitalization rates. The proposed pipeline properly describes the COVID-19 spread during the lock-down phase. It also reveals the underestimation of new positive cases and the need of promptly isolating asymptomatic and presymptomatic cases. The results emphasize the importance of the lock-down timeliness and provide accurate predictions on the current evolution of the pandemic.

Background In Italy, the beginning of 2021 was characterized by the emergence of new variants of SARS-CoV-2 and by the availability of effective vaccines that contributed to the mitigation of non-pharmaceutical interventions and to the avoidance of hospital collapse. Methods We analyzed the COVID-19 propagation in Italy starting from September 2021 with a Susceptible-Exposed-Infected-Recovered (SEIR) model that takes into account SARS-CoV-2 lineages, intervention measures and efficacious vaccines. The model was calibrated with the Bayesian method Conditional Robust Calibration (CRC) using COVID-19 data from September 2020 to May 2021. Here, we apply the Conditional Robustness Analysis (CRA) algorithm to the calibrated model in order to identify model parameters that most affect the epidemic diffusion in the long-term scenario. We focus our attention on vaccination and intervention parameters, which are the key parameters for long-term solutions for epidemic control. Results Our model successfully describes the presence of new variants and the impact of vaccinations and non-pharmaceutical interventions in the Italian scenario. The CRA analysis reveals that vaccine efficacy and waning immunity play a crucial role for pandemic control, together with asymptomatic transmission. Moreover, even though the presence of variants may impair vaccine effectiveness, virus transmission can be kept low with a constant vaccination rate and low restriction levels. Conclusions In the long term, a policy of booster vaccinations together with contact tracing and testing will be key strategies for the containment of SARS-CoV-2 spread.

Non-small cell lung cancer (NSCLC) is the most prevalent type of lung cancer, which is the leading cause of cancer death worldwide. The KRAS oncogene is one of the most common driver mutations in NSCLC patients but, despite that, there are no approved targeted therapies for tumors harboring this mutation. The MEK inhibitor Selumetinib (SE) turned out to be an ineffective drug against KRAS mutated lung cancer. Since combination therapy has shown to be beneficial in many tumors, we wanted to investigate the combination of MEK inhibition with other targeted therapies. Thus, we measured the proteomic response of KRAS mutant (MUT) and wild-type (WT) NSCLC cell lines (CLs) to the combination of SE with Everolimus (EV), an mTOR inhibitor, and Tamoxifen (TA), an Estrogen inhibitor. The expression level of 183 proteins was measured through Reverse Phase Protein Array (RPPA) in eight NSCLC CLs treated with SE, SE plus EV and SE plus TA at 6 time points: 5 minutes (min), 30 min, 1, 2, 6 and 24 hours (h). As control, measures were taken in baseline CLs and in CLs with only dimethyl sulfoxide. To study the drug combination effects, we applied a computational workflow centered on two algorithms initially developed for gene expression data. We analyzed the CLs with the Algorithm for the Reconstruction of Accurate Cellular Networks (ARACNe) to reconstruct a context specific signaling network of 2635 interactions between 73 kinases and 6 phosphatases and their substrates. Then, we analyzed the obtained network with the Virtual Inference of Protein activity by Enriched Regulon analysis (VIPER) algorithm to assess the mechanism of action of the drug compounds and how they dysregulate the activity of kinases and phosphatases over time. With respect to SE alone and apart from the drug targets, VIPER predicted the following proteins as highly inhibited over time across all CLs : 1) Aurora and LKB1 in SE+EV, 2) AMPK, c-RAF and ATR in SE+TA, 3) mTOR, p90, p70S6K, c-MET and GSK-3 in both combinations. On the other hand, Akt, ETK, Axl and FAK are all activated in SE+EV as well as PP2A, SGK1 and IGF1R in SE+TA, after 1h from the treatment. By running Gene Set Enrichment Analysis on the VIPER output against the primary drug target pathways, we found that mTOR signaling pathway exhibits a quite different evolution between KRAS MUT and WT CLs. On the other hand, other pathways revealed mechanisms of response that are CL specific. For instance, the main MEK pathways in H1734 CL (KRAS MUT) treated with SE+TA are all insensitive to the drug compounds. In conclusion, this computational approach successfully predicted protein-protein interactions and elucidated both proteomic mechanisms of drug combinations and CL specific dependencies. It allows to develop effective predictive and quantitative models reproducing each CL behavior, in order to realize the fullest potential of a targeted therapeutic approach. Citation Format: Chiara Antonini, George Rosenberger, Fortunato Bianconi, Lorenzo Tomassoni, Vienna Ludovini, Sara Baglivo, Sara Calandrini, Elisa Baldelli, Mariaelena Pierobon, Emanuel F. Petricoin, Andrea Califano. Quantitative assessment of drug combination effects in NSCLC cell lines through network-based analysis of functional protein activity [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 4384.

Italy was the first European country severely hit by the COVID-19 pandemic. In late February and March 2020, the number of people requiring hospitalization and mechanical ventilation has soared, putting a strain on the Italian health system. In the absence of pharmaceuticals therapies, the government implemented a set of mobility restrictions for transmission containment. Starting from the need of predicting hospitalization and ICU rates for the Umbria region in Italy, we propose the application of a computational framework to model the epidemic and analyze the effects of the imposed lock-down. We calibrate a compartmental model of COVID-19 clinical progression using a Bayesian method called Conditional Robust Calibration (CRC) against the daily epidemiological data. Then, we perform a robustness analysis on the calibrated model, in order to quantify the influence of model parameters on the hospital capacity and to draw possible scenarios of different containment measures. CRC confirms the hypothesis of underestimation of new positive cases and highlights how identifying presymptomatic transmission is crucial for reducing the contagion. Moreover, our results show the central importance of the lock-down timeliness and intensity, in order to curb the contagion and avoid a relapse.