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We previously reported on the immunogenicity and safety of BNT162b2 in a large cohort of patients with cancer after the first and second doses (Di Noia et al., 2021) [1]. Herein, we present result after six months of follow-up.This prospective study included patients affected by solid tumors and afferent to our institution who received two doses of BNT162b2 vaccine. A cohort of vaccinated healthcare workers (HCW) was used as control-group. Both cohorts were evaluated for the titer of anti-Spike (S) IgG at prefixed time-points (TPs). Time-point 4 was scheduled at 24-26 weeks after the second dose.In the current analysis, 400 patients and 232 healthcare workers were evaluated. Responders (IgG15 AU/mL) in patients group were 86.5% compared with 94.4% among healthcare workers. Also the IgG titer at TP4 was significantly inferior in patients than in healthcare workers (70.81 vs 134.64 AU/ml, p 0.001). There was a more rapid decline of the antibody level from TP3 to TP4 in patients than in healthcare workers (1.78 vs 1.3 fold). The estimated IgG half-life was significantly shorter for patients (73 days) than in healthcare workers (118 days) as well as the time to reach negative serological status (340 vs 532 days).The decline of humoral response to the vaccine observed in patients with solid cancer after six months from the first dose support the urgent need of an early additional dose.

Reproducibility in Life Sciences is challenged in the analysis of large multi-omics datasets. One of the final steps of said processes is Gene Set enrichment, where web tools represent a valuable resource but not a reliable surrogate for standardized, high-quality visualizations. The AUTO-go framework proposes standardization of the Gene Functional Enrichment process along with an R framework able to produce high-quality visualization in an automated manner, improving the reproducibility of the whole analytical process. We present three use cases in Cancer Transcriptomics and Epigenomics datasets as a proof-of-concept to visualize Multiple Differential Expression and Single Sample Gene Set Enrichment Analysis.Author SummaryBioinformatics and Data Science are routinely challenged to distill intelligible results from huge amounts of data. These results, in turn, are conveyed through plots and visualizations that should be easily reproducible for scientific soundness and ethical reasons. A specific area in which these analyses are of critical importance is Genomics, where Genes functions need to be enriched when comparing pathological states or treatments. Here we present a software framework that aims at standardizing said differential analyses and visualizations when dealing with genomics data. Finally, we show how it can be employed to shear light on publicly available datasets, even in small casuistry of Rare Cancers.

BackgroundImmune checkpoint inhibitors are still unable to provide clinical benefit to the large majority of non-small cell lung cancer (NSCLC) patients. A deeper characterization of the tumor immune microenvironment (TIME) is expected to shed light on the mechanisms of cancer immune evasion and resistance to immunotherapy. Here, we exploited malignant pleural effusions (MPEs) from lung adenocarcinoma (LUAD) patients as a model system to decipher TIME in metastatic NSCLC.MethodsMononuclear cells from MPEs (PEMC) and peripheral blood (PBMC), cell free pleural fluid and/or plasma were collected from a total of 24 LUAD patients and 12 healthy donors. Bulk-RNA sequencing was performed on total RNA extracted from PEMC and matched PBMC. The DEseq2 Bioconductor package was used to perform differential expression analysis and CIBERSORTx for the regression-based immune deconvolution of bulk gene expression data. Cytokinome analysis of cell-free pleural fluid and plasma samples was performed using a 48-Plex Assay panel. THP-1 monocytic cells were used to assess macrophage polarization. Survival analyses on NSCLC patients were performed using KM Plotter (LUAD, N=672; lung squamous cell carcinoma, N=271).ResultsTranscriptomic analysis of immune cells and cytokinome analysis of soluble factors in the pleural fluid depicted MPEs as a metastatic niche in which all the components required for an effective antitumor response are present, but conscripted in a wound-healing, proinflammatory and tumor-supportive mode. The bioinformatic deconvolution analysis revealed an immune landscape dominated by myeloid subsets with the prevalence of monocytes, protumoral macrophages and activated mast cells. Focusing on macrophages we identified an MPEs-distinctive signature associated with worse clinical outcome in LUAD patients.ConclusionsOur study reports for the first time a wide characterization of MPEs LUAD microenvironment, highlighting the importance of specific components of the myeloid compartment and opens new perspectives for the rational design of new therapies for metastatic NSCLC.

Background Tracking the genetic variability of Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2) is a crucial challenge. Mainly to identify target sequences in order to generate robust vaccines and neutralizing monoclonal antibodies, but also to track viral genetic temporal and geographic evolution and to mine for variants associated with reduced or increased disease severity. Several online tools and bioinformatic phylogenetic analyses have been released, but the main interest lies in the Spike protein, which is the pivotal element of current vaccine design, and in the Receptor Binding Domain, that accounts for most of the neutralizing the antibody activity. Methods Here, we present an open-source bioinformatic protocol, and a web portal focused on SARS-CoV-2 single mutations and minimal consensus sequence building as a companion vaccine design tool. Furthermore, we provide immunogenomic analyses to understand the impact of the most frequent RBD variations. Results Results on the whole GISAID sequence dataset at the time of the writing (October 2020) reveals an emerging mutation, S477N, located on the central part of the Spike protein Receptor Binding Domain, the Receptor Binding Motif. Immunogenomic analyses revealed some variation in mutated epitope MHC compatibility, T-cell recognition, and B-cell epitope probability for most frequent human HLAs. Conclusions This work provides a framework able to track down SARS-CoV-2 genomic variability.

Background: We present immunogenicity data 6 months after the first dose of BNT162b2 in correlation with age, gender, BMI, comorbidities and previous SARS-CoV-2 infection. Methods: An immunogenicity evaluation was carried out among health care workers (HCW) vaccinated at the Istituti Fisioterapici Ospitalieri (IFO). All HCW were asked to be vaccine by the national vaccine campaign at the beginning of 2021. Serum samples were collected on day 1 just prior to the first dose of the vaccine and on day 21 just prior to the second vaccination dose. Thereafter sera samples were collected 28, 49, 84 and 168 days after the first dose of BNT162b2. Quantitative measurement of IgG antibodies against S1/S2 antigens of SARS-CoV-2 was performed with a commercial chemiluminescent immunoassay. Results: Two hundred seventy-four HWCs were analyzed, 175 women (63.9%) and 99 men (36.1%). The maximum antibody geometric mean concentration (AbGMC) was reached at T2 (299.89 AU/mL; 95% CI: 263.53–339.52) with a significant increase compared to baseline (p < 0.0001). Thereafter, a progressive decrease was observed. At T5, a median decrease of 59.6% in COVID-19 negative, and of 67.8% in COVID-19 positive individuals were identified with respect to the highest antibody response. At T1, age and previous COVID-19 were associated with differences in antibody response, while at T2 and T3 differences in immune response were associated with age, gender and previous COVID-19. At T4 and T5, only COVID-19 positive participants demonstrated a greater antibody response, whereas no other variables seemed to influence antibody levels. Conclusions: Overall our study clearly shows antibody persistence at 6 months, albeit with a certain decline. Thus, the use of this vaccine in addressing the COVID-19 pandemic is supported by our results that in turn open debate about the need for further boosts.

Background Metastases are the major cause of cancer-related morbidity and mortality. By the time cancer cells detach from their primary site to eventually spread to distant sites, they need to acquire the ability to survive in non-adherent conditions and to proliferate within a new microenvironment in spite of stressing conditions that may severely constrain the metastatic process. In this study, we gained insight into the molecular mechanisms allowing cancer cells to survive and proliferate in an anchorage-independent manner, regardless of both tumor-intrinsic variables and nutrient culture conditions. Methods 3D spheroids derived from lung adenocarcinoma (LUAD) and breast cancer cells were cultured in either nutrient-rich or -restricted culture conditions. A multi-omics approach, including transcriptomics, proteomics, and metabolomics, was used to explore the molecular changes underlying the transition from 2D to 3D cultures. Small interfering RNA-mediated loss of function assays were used to validate the role of the identified differentially expressed genes and proteins in H460 and HCC827 LUAD as well as in MCF7 and T47D breast cancer cell lines. Results We found that the transition from 2D to 3D cultures of H460 and MCF7 cells is associated with significant changes in the expression of genes and proteins involved in metabolic reprogramming. In particular, we observed that 3D tumor spheroid growth implies the overexpression of ALDOC and ENO2 glycolytic enzymes concomitant with the enhanced consumption of glucose and fructose and the enhanced production of lactate. Transfection with siRNA against both ALDOC and ENO2 determined a significant reduction in lactate production and cell viability. Furthermore, both the number and size of spheroids produced by H460, HCC827, MCF7, and T47D cell lines were significantly reduced upon ALDOC and ENO2 knockdown. Conclusions Our results show that anchorage-independent survival and growth of cancer cells are supported by changes in genes and proteins that drive glucose metabolism towards an enhanced lactate production. Notably, this finding is valid regardless of the tumor type and nutrient environmental availability, thus suggesting the possible general involvement of this mechanism in cancer metastasis. The pan-cancer validation of this vulnerability could potentially help to slow or prevent cancer progression.