Identification of a set of genes potentially responsible for resistance to ferroptosis in lung adenocarcinoma cancer stem cells.

Scientific literature supports the evidence that cancer stem cells (CSCs) retain inside low reactive oxygen species (ROS) levels and are, therefore, less susceptible to cell death, including ferroptosis, a type of cell death dependent on iron-driven lipid peroxidation. A collection of lung adenocarcinoma (LUAD) primary cell lines derived from malignant pleural effusions (MPEs) of patients was used to obtain 3D spheroids enriched for stem-like properties. We observed that the ferroptosis inducer RSL3 triggered lipid peroxidation and cell death in LUAD cells when grown in 2D conditions; however, when grown in 3D conditions, all cell lines underwent a phenotypic switch, exhibiting substantial resistance to RSL3 and, therefore, protection against ferroptotic cell death. Interestingly, this phenomenon was reversed by disrupting 3D cells and growing them back in adherence, supporting the idea of CSCs plasticity, which holds that cancer cells have the dynamic ability to transition between a CSC state and a non-CSC state. Molecular analyses showed that ferroptosis resistance in 3D spheroids correlated with an increased expression of antioxidant genes and high levels of proteins involved in iron storage and export, indicating protection against oxidative stress and low availability of iron for the initiation of ferroptosis. Moreover, transcriptomic analyses highlighted a novel subset of genes commonly modulated in 3D spheroids and potentially capable of driving ferroptosis protection in LUAD-CSCs, thus allowing to better understand the mechanisms of CSC-mediated drug resistance in tumors.

Machine learning endometrial cancer risk prediction model: integrating guidelines of European Society for Medical Oncology with the tumor immune framework.

OBJECTIVE: Current prognostic factors for endometrial cancer are not sufficient to predict recurrence in early stages. Treatment choices are based on the prognostic factors included in the risk classes defined by the ESMO-ESGO-ESTRO (European Society for Medical Oncology-European Society of Gynaecological Oncology-European Society for Radiotherapy and Oncology) consensus conference with the new biomolecular classification based on POLE, TP53, and microsatellite instability status. However, a minority of early stage cases relapse regardless of their low risk profiles. Integration of the immune context status to existing molecular based models has not been fully evaluated. This study aims to investigate whether the integration of the immune landscape in the tumor microenvironment could improve clinical risk prediction models and allow better profiling of early stages. METHODS: Leveraging the potential of in silico deconvolution tools, we estimated the relative abundances of immune populations in public data and then applied feature selection methods to generate a machine learning based model for disease free survival probability prediction. RESULTS: We included information on International Federation of Gynecology and Obstetrics (FIGO) stage, tumor mutational burden, microsatellite instability, POLEmut status, interferon γ signature, and relative abundances of monocytes, natural killer cells, and CD4+T cells to build a relapse prediction model and obtained a balanced accuracy of 69%. We further identified two novel early stage profiles that undergo different pathways of recurrence. CONCLUSION: This study presents an extension of current prognostic factors for endometrial cancer by exploiting machine learning models and deconvolution techniques on available public biomolecular data. Prospective clinical trials are advisable to validate the early stage stratification.

Development of a somatic variant registry in a National Cancer Center: towards Molecular Real World Data preparedness.

The Biomedical Research field is currently advancing to develop Clinical Trials and translational projects based on Real World Evidence. To make this transition feasible, clinical centers need to work toward Data Accessibility and Interoperability. This task is particularly challenging when applied to Genomics, that entered in routinary screening in the last years via mostly amplicon-based Next-Generation Sequencing panels. Said experiments produce up to hundreds of features per patient, and their summarized results are often stored in static clinical reports, making critical information inaccessible to automated access and Federated Search consortia. In this study, we present a reanalysis of 4620 solid tumor sequencing samples in five different histology settings. Furthermore, we describe all the Bioinformatics and Data Engineering processes that were put in place in order to create a Somatic Variant Registry able to deal with the large biotechnological variability of routinary Genomics Profiling.

ALDOC- and ENO2- driven glucose metabolism sustains 3D tumor spheroids growth regardless of nutrient environmental conditions: a multi-omics analysis.

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 2 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 2 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, viability and size of 3D tumor spheroids produced by H460, HCC827, MCF7, and T47D cell lines. 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 for all lung and breast cancer cell lines we have analyzed in different nutrient environmental conditions. broader Validation of this mechanism in other cancer cells of different origin will be necessary to broaden the role of ALDOC and ENO2 to other tumor types. Future in vivo studies will be necessary to assess the role of ALDOC and ENO2 in cancer metastasis.

Dynamics of Viral Infection and Evolution of SARS-CoV-2 Variants in the Calabria Area of Southern Italy.

In this study, we report on the results of SARS-CoV-2 surveillance performed in an area of Southern Italy for 12 months (from March 2021 to February 2022). To this study, we have sequenced RNA from 609 isolates. We have identified circulating VOCs by Sanger sequencing of the S gene and defined their genotypes by whole-genome NGS sequencing of 157 representative isolates. Our results indicated that B.1 and Alpha were the only circulating lineages in Calabria in March 2021; while Alpha remained the most common variant between April 2021 and May 2021 (90 and 73%, respectively), we observed a concomitant decrease in B.1 cases and appearance of Gamma cases (6 and 21%, respectively); C.36.3 and Delta appeared in June 2021 (6 and 3%, respectively); Delta became dominant in July 2021 while Alpha continued to reduce (46 and 48%, respectively). In August 2021, Delta became the only circulating variant until the end of December 2021. As of January 2022, Omicron emerged and took over Delta (72 and 28%, respectively). No patient carrying Beta, Iota, Mu, or Eta variants was identified in this survey. Among the genomes identified in this study, some were distributed all over Europe (B1_S477N, Alpha_L5F, Delta_T95, Delta_G181V, and Delta_A222V), some were distributed in the majority of Italian regions (B1_S477N, B1_Q675H, Delta_T95I and Delta_A222V), and some were present mainly in Calabria (B1_S477N_T29I, B1_S477N_T29I_E484Q, Alpha_A67S, Alpha_A701S, and Alpha_T724I). Prediction analysis of the effects of mutations on the immune response (i.e., binding to class I MHC and/or recognition of T cells) indicated that T29I in B.1 variant; A701S in Alpha variant; and T19R in Delta variant were predicted to impair binding to class I MHC whereas the mutations A67S identified in Alpha; E484K identified in Gamma; and E156G and ΔF157/R158 identified in Delta were predicted to impair recognition by T cells. In conclusion, we report on the results of SARS-CoV-2 surveillance in Regione Calabria in the period between March 2021 and February 2022, identified variants that were enriched mainly in Calabria, and predicted the effects of identified mutations on host immune response.

Molecular dissection of a hyper-aggressive CBFB-MYH11/FLT3-ITD-positive acute myeloid leukemia.

Acute Myeloid Leukaemia (AML) is a haematological malignancy showing a hypervariable landscape of clinical outcomes and phenotypic differences, explainable by heterogeneity at the cellular and molecular level. Among the most common genomic alterations, CBFB-MYH11 rearrangement and FLT3-ITD gene mutations, have opposite clinical significance and are unfrequently associated. We present here a Molecular Case Report in which these two events co-exist an ultra-aggressive phenotype resulting in death in 4 days from hospital admittance. Somatic and germline Whole Exome Sequencing analysis was performed to uncover other putative driver mutations, de-novo genomic structural events or germline clusters increasing cancer insurgence. Only three mutations in LTK, BCAS2 and LGAS9 were found, unlikely causative of the exhibited phenotype, prompting to additional investigation of the rare CBFB-MYH11/ FLT3-ITD scenario.

Optimizing the Illumina COVIDSeq laboratorial and bioinformatics pipeline on thousands of samples for SARS-CoV-2 Variants of Concern tracking.

The SARS-CoV-2 Variants of Concern tracking via Whole Genome Sequencing represents a pillar of public health measures for the containment of the pandemic. The ability to track down the lineage distribution on a local and global scale leads to a better understanding of immune escape and to adopting interventions to contain novel outbreaks. This scenario poses a challenge for NGS laboratories worldwide that are pressed to have both a faster turnaround time and a high-throughput processing of swabs for sequencing and analysis. In this study, we present an optimization of the Illumina COVID-seq protocol carried out on thousands of SARS-CoV-2 samples at the wet and dry level. We discuss the unique challenges related to processing hundreds of swabs per week such as the tradeoff between ultra-high sensitivity and negative contamination levels, cost efficiency and bioinformatics quality metrics.

Antitumor efficacy of a neoantigen cancer vaccine delivered by electroporation is influenced by microbiota composition.

Cancer is a heterogeneous disease and its treatment remains unsatisfactory with inconstant therapeutic responses. This variability could be related, at least in part, to different and highly personalized gut microbiota compositions. Different studies have shown an impact of microbiota on antitumor therapy. It has been demonstrated that some gut bacteria influences the development and differentiation of immune cells, suggesting that different microbiota compositions could affect the efficacy of the antitumor vaccine. Emerging data suggest that recognition of neoantigens for the generation of neoantigen cancer vaccines (NCVs) could have a key role in the activity of clinical immunotherapies. However, it is still unknown whether there is a crosstalk between microbiota and NCV. This study aimed to understand the possible mechanisms of interaction between gut microbiota and NCV delivered by DNA-electroporation (DNA-EP). We found that decreased microbiota diversity induced by prolonged antibiotic (ATB) treatment is associated with higher intratumor specific immune responses and consequently to a better antitumor effect induced by NCV delivered by DNA-EP.

Design of a companion bioinformatic tool to detect the emergence and geographical distribution of SARS-CoV-2 Spike protein genetic variants.

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.