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Cancer patients show heterogeneous phenotypes and very different outcomes and responses even to common treatments, such as standard chemotherapy. This state-of-affairs has motivated the need for the comprehensive characterization of cancer phenotypes and fueled the generation of large omics datasets, comprising multiple omics data reported for the same patients, which might now allow us to start deciphering cancer heterogeneity and implement personalized therapeutic strategies. In this work, we performed the analysis of four cancer types obtained from the latest efforts by The Cancer Genome Atlas, for which seven distinct omics data were available for each patient, in addition to curated clinical outcomes. We performed a uniform pipeline for raw data preprocessing and adopted the Cancer Integration via MultIkernel LeaRning (CIMLR) integrative clustering method to extract cancer subtypes. We then systematically review the discovered clusters for the considered cancer types, highlighting novel associations between the different omics and prognosis.

Highlights • A major obstacle for the effective treatment of PDAC is its molecular heterogeneity. • Stratification of PDAC using markers highly specific, reproducible, sensitive, easily measurable and inexpensive is necessary. • At the early stages, clinician’s priority lies in rapid diagnosis, so that the patient receives surgery without delay. • At advanced disease stages, priority is to determine the tumor subtype and select a suitable effective treatment., A major obstacle for the effective treatment of pancreatic ductal adenocarcinoma (PDAC) is its molecular heterogeneity, reflected by the diverse clinical outcomes and responses to therapies that occur. The tumors of patients with PDAC must therefore be closely examined and classified before treatment initiation in order to predict the natural evolution of the disease and the response to therapy. To stratify patients, it is absolutely necessary to identify biological markers that are highly specific and reproducible, and easily measurable by inexpensive sensitive techniques. Several promising strategies to find biomarkers are already available or under development, such as the use of liquid biopsies to detect circulating tumor cells, circulating free DNA, methylated DNA, circulating RNA, and exosomes and extracellular vesicles, as well as immunological markers and molecular markers. Such biomarkers are capable of classifying patients with PDAC and predicting their therapeutic sensitivity. Interestingly, developing chemograms using primary cell lines or organoids and analyzing the resulting high-throughput data via artificial intelligence would be highly beneficial to patients. How can exploiting these biomarkers benefit patients with resectable, borderline resectable, locally advanced, and metastatic PDAC? In fact, the utility of these biomarkers depends on the patient's clinical situation. At the early stages of the disease, the clinician's priority lies in rapid diagnosis, so that the patient receives surgery without delay; at advanced disease stages, where therapeutic possibilities are severely limited, the priority is to determine the PDAC tumor subtype so as to estimate the clinical outcome and select a suitable effective treatment., Graphical abstract Image, graphical abstract

Aside from PD-L1 expression, biomarkers of response to immune checkpoint inhibitors (ICIs) in non-small-cell lung cancer (NSCLC) are needed. In a previous retrospective analysis, we documented that fecal Akkermansia muciniphila (Akk) was associated with clinical benefit of ICI in patients with NSCLC or kidney cancer. In the current study, we performed shotgun-metagenomics-based microbiome profiling in a large cohort of patients with advanced NSCLC (n = 338) treated with first- or second-line ICIs to prospectively validate the predictive value of fecal Akk. Baseline stool Akk was associated with increased objective response rates and overall survival in multivariate analyses, independent of PD-L1 expression, antibiotics, and performance status. Intestinal Akk was accompanied by a richer commensalism, including Eubacterium hallii and Bifidobacterium adolescentis, and a more inflamed tumor microenvironment in a subset of patients. However, antibiotic use (20% of cases) coincided with a relative dominance of Akk above 4.8% accompanied with the genus Clostridium, both associated with resistance to ICI. Our study shows significant differences in relative abundance of Akk that may represent potential biomarkers to refine patient stratification in future studies.

Introduction Autism Spectrum Disorder (ASD) is a neurodevelopmental condition characterized by limited social interaction, communication impairments, restricted interests and repetitive behaviors. It occurs in pediatric age, within the first 3 years of life, and lasts for a lifetime with severe consequences for the individual and his/her family and with very high social costs. In the last decades, ASD has increased dramatically reaching the prevalence of 1:68. Although genetics play a key role in ASD, its etiology is complex and recent studies hypothesize a gene-environment interaction. Indeed, causative or predisposing genetic variants have been detected only in 30% of patients and thousands of genes are involved. Here, for the first time, linked-read whole genome sequencing of ASD patients is used to access disease associated regions unmappable by short-reads NGS. Materials and Methods Ten children suffering from ASD (diagnosed by ADOS 2 and ADI-R), including 3 couples of affected siblings, were enrolled, peripheral blood collected and HMW DNA isolated. All the procedures had been approved by Ethical Committee as well as clinical data collected according to current privacy laws. HMW DNA, 50 kb in size or greater, was submitted to 10Xgenomics microfluidics partitioning and barcoding and then to Illumina library preparation and whole genome-next generation sequencing. Data were analyzed through 10x Long Ranger pipelines to find SNVs, in/del and larger structural variants in comparison to 1,000 genomes, genome aggregation database and NHLBI-ESP populations. Genes affected by variants were compared with those already associated with ASD: SFARI database, large-scale sequencing studies, bioinformatics predictions. Results The linked-read sequencing approach successfully produced sequences up to 9mln bps in length haplo-blocks. This technique allowed to detect variants in whole genome of ASD patients. We found genes affected by mutations already listed in SFARI and previously predicted by bioinformatics analysis and discovered hundreds of new variants. Among these latter, we focused on those homozygous in the 3 couples of siblings and found that some of them are related to neuro-development and -physiology or to xenobiotic metabolism. Conclusion For the first time, we applied the linked-read sequencing technology for studying ASD genomics. This powerful approach allowed us deciphering the genomics heterogeneity in ADS and highlighting variations otherwise undetectable by classic NGS. Indeed, we identified homozygous variants within genes or regulatory regions common to 3 couples of affected siblings. Further studies will be performed to validate the new ASD variations by PCR amplification and Sanger sequencing in a large size of ASD samples and in public data; then, the new biomarkers will be used to stratify ASD patients population. Data integration will be also performed to identify pathways and gene networks involved in the disorder to understand disease mechanisms and design target-driven personalized treatment. Acknowledgements: EU project GEMMA (grant agreement No 825033), EPTRI and CNRBiOmics.

Amyotrophic lateral sclerosis (ALS), also known as motor neurone disease, is an incurable neurodegenerative condition, characterized by the loss of upper and lower motor neurons. It affects 1-1.8/100,000 individuals worldwide, and the number of cases is projected to increase as the population ages. Thus, there is an urgent need to identify both therapeutic targets and disease-specific biomarkers - biomarkers that would be useful to diagnose and stratify patients into different sub-groups for therapeutic strategies, as well as biomarkers to follow the efficacy of any treatment tested during clinical trials. There is a lack of knowledge about pathogenesis and many hypotheses. Numerous ‘omics’ studies have been conducted on ALS in the past decade to identify a disease-signature in tissues and circulating biomarkers. The first goal of the present review was to group the molecular pathways that have been implicated in monogenic forms of ALS, to enable the description of patient strata corresponding to each pathway grouping. This strategy allowed us to suggest 14 strata, each potentially targetable by different pharmacological strategies. The second goal of this review was to identify diagnostic/prognostic biomarker candidates consistently observed across the literature. For this purpose, we explore previous biomarker-relevant ‘omics’ studies of ALS and summarize their findings, focusing on potential circulating biomarker candidates. We systematically review 118 papers on biomarkers published during the last decade. Several candidate markers were consistently shared across the results of different studies in either cerebrospinal fluid (CSF) or blood (leukocyte or serum/plasma). Although these candidates still need to be validated in a systematic manner, we suggest the use of combinations of biomarkers that would likely reflect the “health status” of different tissues, including motor neuron health (e.g. pNFH and NF-L, cystatin C, Transthyretin), inflammation status (e.g. MCP-1, miR451), muscle health (miR-338-3p, miR-206) and metabolism (homocysteine, glutamate, cholesterol). In light of these studies and because ALS is increasingly perceived as a multi-system disease, the identification of a panel of biomarkers that accurately reflect features of pathology is a priority, not only for diagnostic purposes but also for prognostic or predictive applications.