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Tumor mutational burden (TMB), the total number of somatic coding mutations in a tumor, is emerging as a promising biomarker for immunotherapy response in cancer patients. TMB can be quantitated by a number of NGS-based sequencing technologies. Whole Exome Sequencing (WES) allows comprehensive measurement of TMB and is considered the gold standard. However, to date WES remains confined to research settings, due to high cost of the large genomic space sequenced. In the clinical setting, instead, targeted enrichment panels (gene panels) of various genomic sizes are emerging as the routine technology for TMB assessment. This stimulated the development of various methods for panel-based TMB quantification, and prompted the multiplication of studies assessing whether TMB can be confidently estimated from the smaller genomic space sampled by gene panels. In this review, we inventory the collection of available gene panels tested for this purpose, illustrating their technical specifications and describing their accuracy and clinical value in TMB assessment. Moreover, we highlight how various experimental, platform-related or methodological variables, as well as bioinformatic pipelines, influence panel-based TMB quantification. The lack of harmonization in panel-based TMB quantification, of adequate methods to convert TMB estimates across different panels and of robust predictive cutoffs, currently represents one of the main limitations to adopt TMB as a biomarker in clinical practice. This overview on the heterogeneous landscape of panel-based TMB quantification aims at providing a context to discuss common standards and illustrates the strong need of further validation and consolidation studies for the clinical interpretation of panel-based TMB values.

Global-scale quantum communication links will form the backbone of the quantum internet. However, exponential loss in optical fibres precludes any realistic application beyond few hundred kilometres. Quantum repeaters and space-based systems offer solutions to overcome this limitation. Here, we analyse the use of quantum memory (QM)-equipped satellites for quantum communication focussing on global range repeaters and memory-assisted (MA-) QKD, where QMs help increase the key rate by synchronising otherwise probabilistic detection events. We demonstrate that satellites equipped with QMs provide three orders of magnitude faster entanglement distribution rates than existing protocols based on fibre-based repeaters or space systems without QMs. We analyse how entanglement distribution performance depends on memory characteristics, determine benchmarks to assess the performance of different tasks and propose various architectures for light-matter interfaces. Our work provides a roadmap to realise unconditionally secure quantum communications over global distances with near-term technologies.

Background Interpreting the clinical consequences of genetic variants is the central problem in modern clinical genomics, for both hereditary diseases and oncology. However, clinical validation lags behind the pace of discovery, leading to distressing uncertainty for patients, physicians and researchers. This “interpretation gap” changes over time as evidence accumulates, and variants initially deemed of uncertain (VUS) significance may be subsequently reclassified in pathogenic/benign. We previously developed RENOVO, a random forest-based tool able to predict variant pathogenicity based on publicly available information from GnomAD and dbNFSP, and tested on variants that have changed their classification status over time. Here, we comprehensively evaluated the accuracy of RENOVO predictions on variants that have been reclassified over the last four years. Methods we retrieved 16 retrospective instances of the ClinVar database, every 3 months since March 2020 to March 2024, and analyzed time trends of variant classifications. We identified variants that changed their status over time and compared RENOVO predictions generated in 2020 with the actual reclassifications. Results VUS have become the most represented class in ClinVar (44.97% vs. 9.75% (likely) pathogenic and 40,33% (likely) benign). The rate of VUS reclassification is linear and slow compared to the rate of VUS reporting, exponential and currently ~ 30x faster, creating a growing divide between what can be sequenced vs. what can be interpreted. Out of 10,196 VUS variants in January 2020 that have undergone a clinically meaningful reclassification to march 2024, RENOVO correctly classified 82.6% in 2020. In addition, RENOVO correctly identified the majority of the few variants that switched clinically meaningful classes (e.g., from benign to pathogenic and vice versa). We highlight variant classes and clinically relevant genes for which RENOVO provides particularly accurate estimates. In particularly, genes characterized by large prevalence of high- or low-impact variants (e.g., POLE, NOTCH1, FANCM etc.). Suboptimal RENOVO predictions mostly concern genes validated through dedicated consortia (e.g., BRCA1/2), in which RENOVO would anyway have a limited impact. Conclusions Time trend analysis demonstrates that the current model of variant interpretation cannot keep up with variant discovery. Machine learning-based tools like RENOVO confirm high accuracy that can aid in clinical practice and research.

Concerted efforts are underway to establish an infrastructure for a global quantum Internet to realise a spectrum of quantum technologies. This will enable more precise sensors, secure communications, and faster data processing. Quantum communications are a front‐runner with quantum networks already implemented in several metropolitan areas. A number of recent proposals have modelled the use of space segments to overcome range limitations of purely terrestrial networks. Rapid progress in the design of quantum devices have enabled their deployment in space for in‐orbit demonstrations. We review developments in this emerging area of space‐based quantum technologies and provide a roadmap of key milestones towards a complete, global quantum networked landscape. Small satellites hold increasing promise to provide a cost effective coverage required to realise the quantum Internet. The state of art in small satellite missions is reviewed and the most current in‐field demonstrations of quantum cryptography are collated. The important challenges in space quantum technologies that must be overcome and recent efforts to mitigate their effects are summarised. A perspective on future developments that would improve the performance of space quantum communications is included. The authors conclude with a discussion on fundamental physics experiments that could take advantage of a global, space‐based quantum network.

Circulating tumour cell (CTC) clusters have been proposed to be major players in the metastatic spread of breast cancer, particularly during advanced disease stages. Yet, it is unclear whether or not they manifest in early breast cancer, as their occurrence in patients with metastasis-free primary disease has not been thoroughly evaluated. In this study, exploiting nanostructured titanium oxide-coated slides for shear-free CTC identification, we detect clustered CTCs in the curative setting of multiple patients with early breast cancer prior to surgical treatment, highlighting their presence already at early disease stages. These results spotlight an important aspect of metastasis biology and the possibility to intervene with anti-cluster therapeutics already during the early manifestation of breast cancer.

On 8 and 9 February 2018, the IFOM-IEO campus in Milan hosted the Milan summit on Precision Medicine, which gathered clinical and translational research experts from academia, industry and regulatory bodies to discuss the state of the art of precision medicine in Europe. The meeting was pervaded by a generalised feeling of excitement for a field that is perceived to be technologically mature for the transition into clinical routine but still hampered by numerous obstacles of a methodological, ethical, regulatory and possibly cultural nature. Through lively discussions, the attendees tried to identify realistic ways to implement a technology-rich precision approach to cancer patients.

Identification of a pathogenic variant in NF1 is diagnostic for neurofibromatosis, but is often impossible at the moment of variant detection due to many factors including allelic heterogeneity, sequence homology, and the lack of functional assays. Computational tools may aid in interpretation but are not established for NF1. Here, we optimized our random forest-based predictor RENOVO for NF1 variant interpretation. RENOVO was developed using an approach of “database archaeology”: by comparing versions of ClinVar over the years, we defined “stable” variants that maintained the same pathogenic/likely pathogenic/benign/likely benign (P/LP/B/LB) classification over time (n = 3579, the training set), and “unstable” variants that were initially classified as Variants of Unknown Significance (VUS) but were subsequently reclassified as P/LP/B/LB (n = 57, the test set). This approach allows to retrospectively measure accuracy on prediction with insufficient information, reproducing the scenario of maximal clinical utility. We further validated performance on: (i) validation set 1: 100 NF1 variants classified as VUS at the time of RENOVO development and subsequently reclassified as P/LP/B/LB in ClinVar; (ii) validation set 2: 15 de novo variants discovered in a prospective clinical cohort and subsequently reclassified per ACMG criteria. RENOVO obtained consistently high accuracy on all datasets: 98.6% on the training test, 96.5% in the test set, 82% in validation set 1 (but 96.2% for missense variants) and 93.7% on validation set 2. In conclusion, RENOVO-NF1 accurately interprets NF1 variants for which information at the time of detection is insufficient for ACMG classification and may overcome diagnostic challenges in neurofibromatosis.

The recent 58th Annual American Society of Haematology (ASH) meeting held in San Diego shed light on the usual mixture of groundbreaking basic and translational science and the recent practice-changing clinical trials. Recurrent themes this year were the use of recent next-generation sequencing (NGS) techniques to perfect prognostic stratification and disease monitoring. Newer prospects on the role of metabolism in normal and malignant haemopoiesis and mature data on long-awaited trials on immunotherapy and CAR-T cells in lymphoid neoplasms were also discussed.

The 57th American Society of Haematology (ASH) meeting held in Orlando, FL was certainly the year when myeloma management changed for good, with a plethora of newly Food and Drug Administration (FDA)-approved drugs showing impressive outcome improvements and the introduction of new techniques for disease monitoring. Also, chimeric antigen receptor (CAR) T cells continued their triumphal march, consolidating their success in lymphoma and chronic lymhocytic leukaemia (CLL) and venturing into new fields such as again multiple myeloma. Some experimental drugs showed long-awaited results (midostaurin in FLT3-mutated acute myeloid leukaemia (AML)) and some brand new drugs showed promising results in the clinic after extensive preclinical studies, such as those targeting new epigenetic factors (histone methyltransferases) and apoptosis.

The COVID-19 pandemic has had a devastating impact worldwide and has been a great challenge for the scientific community. Vaccines against SARS-CoV-2 are now efficiently lessening COVID-19 mortality, although finding a cure for this infection is still a priority. An unbalanced immune response and the uncontrolled release of proinflammatory cytokines are features of COVID-19 pathophysiology and contribute to disease progression and worsening. Histone deacetylases (HDACs) have gained interest in immunology, as they regulate the innate and adaptative immune response at different levels. Inhibitors of these enzymes have already proven therapeutic potential in cancer and are currently being investigated for the treatment of autoimmune diseases. We thus tested the effects of different HDAC inhibitors, with a focus on a selective HDAC6 inhibitor, on immune and epithelial cells in in vitro models that mimic cells activation after viral infection. Our data indicate that HDAC inhibitors reduce cytokines release by airway epithelial cells, monocytes and macrophages. This anti-inflammatory effect occurs together with the reduction of monocytes activation and T cell exhaustion and with an increase of T cell differentiation towards a T central memory phenotype. Moreover, HDAC inhibitors hinder IFN-I expression and downstream effects in both airway epithelial cells and immune cells, thus potentially counteracting the negative effects promoted in critical COVID-19 patients by the late or persistent IFN-I pathway activation. All these data suggest that an epigenetic therapeutic approach based on HDAC inhibitors represents a promising pharmacological treatment for severe COVID-19 patients.