BACKGROUND: tRNA-derived small RNAs (tDRs), which are widely distributed in human tissues including blood and urine, play an important role in the progression of cancer. However, the expression of tDRs in colorectal cancer (CRC) plasma and their potential diagnostic values have not been systematically explored. METHODS: The expression profiles of tDRs in plasma of CRC and health controls (HCs) are investigated by small RNA sequencing. The level and diagnostic value of 5'-tRF-GlyGCC are evaluated by quantitative PCR in plasma samples from 105 CRC patients and 90 HCs. The mechanisms responsible for biogenesis of 5'-tRF-GlyGCC are checked by in vitro and in vivo models. RESULTS: 5'-tRF-GlyGCC is dramatically increased in plasma of CRC patients compared to that of HCs. The area under curve (AUC) for 5'-tRF-GlyGCC in CRC group is 0.882. The combination of carcinoembryonic antigen (CEA) and carbohydrate antigen 199 (CA199) with 5'-tRF-GlyGCC improves the AUC to 0.926. Consistently, the expression levels of 5'-tRF-GlyGCC in CRC cells and xenograft tissues are significantly greater than that in their corresponding controls. Blood cells co-cultured with CRC cells or mice xenografted with CRC tumors show increased levels of 5'-tRF-GlyGCC. In addition, we find that the increased expression of 5'-tRF-GlyGCC is dependent on the upregulation of AlkB homolog 3 (ALKBH3), a tRNA demethylase which can promote tRNA cleaving to generate tDRs. CONCLUSIONS: The level of 5'-tRF-GlyGCC in plasma is a promising diagnostic biomarker for CRC diagnosis.

We present CoronaHiT, a platform and throughput flexible method for sequencing SARS-CoV-2 genomes (≤ 96 on MinION or > 96 on Illumina NextSeq) depending on changing requirements experienced during the pandemic. CoronaHiT uses transposase-based library preparation of ARTIC PCR products. Method performance was demonstrated by sequencing 2 plates containing 95 and 59 SARS-CoV-2 genomes on nanopore and Illumina platforms and comparing to the ARTIC LoCost nanopore method. Of the 154 samples sequenced using all 3 methods, ≥ 90% genome coverage was obtained for 64.3% using ARTIC LoCost, 71.4% using CoronaHiT-ONT and 76.6% using CoronaHiT-Illumina, with almost identical clustering on a maximum likelihood tree. This protocol will aid the rapid expansion of SARS-CoV-2 genome sequencing globally.

To demonstrate the causative role of gut microbiome in human health and diseases, we first need to identify, via next-generation sequencing, potentially important functional members associated with specific health outcomes and disease phenotypes. However, due to the strain-level genetic complexity of the gut microbiota, microbiome datasets are highly dimensional and highly sparse in nature, making it challenging to identify putative causative agents of a particular disease phenotype. Members of an ecosystem seldomly live independently from each other. Instead, they develop local interactions and form inter-member organizations to influence the ecosystem's higher-level patterns and functions. In the ecological study of macro-organisms, members are defined as belonging to the same "guild" if they exploit the same class of resources in a similar way or work together as a coherent functional group. Translating the concept of "guild" to the study of gut microbiota, we redefine guild as a group of bacteria that show consistent co-abundant behavior and likely to work together to contribute to the same ecological function. In this opinion article, we discuss how to use guilds as the aggregation unit to reduce dimensionality and sparsity in microbiome-wide association studies for identifying candidate gut bacteria that may causatively contribute to human health and diseases.

BACKGROUND: Basal cell carcinoma (BCC) of the skin is the most common form of human cancer, with more than 90% of tumours presenting with clear genetic activation of the Hedgehog pathway. However, polygenic risk factors affecting mechanisms such as DNA repair and cell cycle checkpoints or which modulate the tumour microenvironment or host immune system play significant roles in determining whether genetic mutations culminate in BCC development. We set out to define background genetic factors that play a role in influencing BCC susceptibility via promoting or suppressing the effects of oncogenic drivers of BCC. METHODS: We performed genome-wide association studies (GWAS) on 17,416 cases and 375,455 controls. We subsequently performed statistical analysis by integrating data from population-based genetic studies of multi-omics data, including blood- and skin-specific expression quantitative trait loci and methylation quantitative trait loci, thereby defining a list of functionally relevant candidate BCC susceptibility genes from our GWAS loci. We also constructed a local GWAS functional interaction network (consisting of GWAS nearest genes) and another functional interaction network, consisting specifically of candidate BCC susceptibility genes. RESULTS: A total of 71 GWAS loci and 46 functional candidate BCC susceptibility genes were identified. Increased risk of BCC was associated with the decreased expression of 26 susceptibility genes and increased expression of 20 susceptibility genes. Pathway analysis of the functional candidate gene regulatory network revealed strong enrichment for cell cycle, cell death, and immune regulation processes, with a global enrichment of genes and proteins linked to TReg cell biology. CONCLUSIONS: Our genome-wide association analyses and functional interaction network analysis reveal an enrichment of risk variants that function in an immunosuppressive regulatory network, likely hindering cancer immune surveillance and effective antitumour immunity.

BACKGROUND: Population-based genomic screening has the predicted ability to reduce morbidity and mortality associated with medically actionable conditions. However, much research is needed to develop standards for genomic screening and to understand the perspectives of people offered this new testing modality. This is particularly true for non-European ancestry populations who are vastly underrepresented in genomic medicine research. Therefore, we implemented a pilot genomic screening program in the BioMe Biobank in New York City, where the majority of participants are of non-European ancestry. METHODS: We initiated genomic screening for well-established genes associated with hereditary breast and ovarian cancer syndrome (HBOC), Lynch syndrome (LS), and familial hypercholesterolemia (FH). We evaluated and included an additional gene (TTR) associated with hereditary transthyretin amyloidosis (hATTR), which has a common founder variant in African ancestry populations. We evaluated the characteristics of 74 participants who received results associated with these conditions. We also assessed the preferences of 7461 newly enrolled BioMe participants to receive genomic results. RESULTS: In the pilot genomic screening program, 74 consented participants received results related to HBOC (N = 26), LS (N = 6), FH (N = 8), and hATTR (N = 34). Thirty-three of 34 (97.1%) participants who received a result related to hATTR were self-reported African American/African (AA) or Hispanic/Latinx (HL), compared to 14 of 40 (35.0%) participants who received a result related to HBOC, LS, or FH. Among the 7461 participants enrolled after the BioMe protocol modification to allow the return of genomic results, 93.4% indicated that they would want to receive results. Younger participants, women, and HL participants were more likely to opt to receive results. CONCLUSIONS: The addition of TTR to a pilot genomic screening program meant that we returned results to a higher proportion of AA and HL participants, in comparison with genes traditionally included in genomic screening programs in the USA. We found that the majority of participants in a multi-ethnic biobank are interested in receiving genomic results for medically actionable conditions. These findings increase knowledge about the perspectives of diverse research participants on receiving genomic results and inform the broader implementation of genomic medicine in underrepresented patient populations.

BACKGROUND: Noninvasive prenatal testing (NIPT) of recessive monogenic diseases depends heavily on knowing the correct parental haplotypes. However, the currently used family-based haplotyping method requires pedigrees, and molecular haplotyping is highly challenging due to its high cost, long turnaround time, and complexity. Here, we proposed a new two-step approach, population-based haplotyping-NIPT (PBH-NIPT), using α-thalassemia and β-thalassemia as prototypes. METHODS: First, we deduced parental haplotypes with Beagle 4.0 with training on a large retrospective carrier screening dataset (4356 thalassemia carrier screening-positive cases). Second, we inferred fetal haplotypes using a parental haplotype-assisted hidden Markov model (HMM) and the Viterbi algorithm. RESULTS: With this approach, we enrolled 59 couples at risk of having a fetus with thalassemia and successfully inferred 94.1% (111/118) of fetal alleles. We confirmed these alleles by invasive prenatal diagnosis, with 99.1% (110/111) accuracy (95% CI, 95.1-100%). CONCLUSIONS: These results demonstrate that PBH-NIPT is a sensitive, fast, and inexpensive strategy for NIPT of thalassemia.

BACKGROUND: Accurately quantifying the risk of osteoporotic fracture is important for directing appropriate clinical interventions. While skeletal measures such as heel quantitative speed of sound (SOS) and dual-energy X-ray absorptiometry bone mineral density are able to predict the risk of osteoporotic fracture, the utility of such measurements is subject to the availability of equipment and human resources. Using data from 341,449 individuals of white British ancestry, we previously developed a genome-wide polygenic risk score (PRS), called gSOS, that captured 25.0% of the total variance in SOS. Here, we test whether gSOS can improve fracture risk prediction. METHODS: We examined the predictive power of gSOS in five genome-wide genotyped cohorts, including 90,172 individuals of European ancestry and 25,034 individuals of Asian ancestry. We calculated gSOS for each individual and tested for the association between gSOS and incident major osteoporotic fracture and hip fracture. We tested whether adding gSOS to the risk prediction models had added value over models using other commonly used clinical risk factors. RESULTS: A standard deviation decrease in gSOS was associated with an increased odds of incident major osteoporotic fracture in populations of European ancestry, with odds ratios ranging from 1.35 to 1.46 in four cohorts. It was also associated with a 1.26-fold (95% confidence interval (CI) 1.13-1.41) increased odds of incident major osteoporotic fracture in the Asian population. We demonstrated that gSOS was more predictive of incident major osteoporotic fracture (area under the receiver operating characteristic curve (AUROC) = 0.734; 95% CI 0.727-0.740) and incident hip fracture (AUROC = 0.798; 95% CI 0.791-0.805) than most traditional clinical risk factors, including prior fracture, use of corticosteroids, rheumatoid arthritis, and smoking. We also showed that adding gSOS to the Fracture Risk Assessment Tool (FRAX) could refine the risk prediction with a positive net reclassification index ranging from 0.024 to 0.072. CONCLUSIONS: We generated and validated a PRS for SOS which was associated with the risk of fracture. This score was more strongly associated with the risk of fracture than many clinical risk factors and provided an improvement in risk prediction. gSOS should be explored as a tool to improve risk stratification to identify individuals at high risk of fracture.

BACKGROUND: Immunoglobulin G4-related disease (IgG4-RD) and systemic sclerosis (SSc) are rare autoimmune diseases characterized by the presence of CD4+ cytotoxic T cells in the blood as well as inflammation and fibrosis in various organs, but they have no established etiologies. Similar to other autoimmune diseases, the gut microbiome might encode disease-triggering or disease-sustaining factors. METHODS: The gut microbiomes from IgG4-RD and SSc patients as well as healthy individuals with no recent antibiotic treatment were studied by metagenomic sequencing of stool DNA. De novo assembly-based taxonomic and functional characterization, followed by association and accessory gene set enrichment analysis, were applied to describe microbiome changes associated with both diseases. RESULTS: Microbiomes of IgG4-RD and SSc patients distinctly separated from those of healthy controls: numerous opportunistic pathogenic Clostridium and typically oral Streptococcus species were significantly overabundant, while Alistipes, Bacteroides, and butyrate-producing species were depleted in the two diseases compared to healthy controls. Accessory gene content analysis in these species revealed an enrichment of Th17-activating Eggerthella lenta strains in IgG4-RD and SSc and a preferential colonization of a homocysteine-producing strain of Clostridium bolteae in SSc. Overabundance of the classical mevalonate pathway, hydroxyproline dehydratase, and fibronectin-binding protein in disease microbiomes reflects potential functional differences in host immune recognition and extracellular matrix utilization associated with fibrosis. Strikingly, the majority of species that were differentially abundant in IgG4-RD and SSc compared to controls showed the same directionality in both diseases. Compared with multiple sclerosis and rheumatoid arthritis, the gut microbiomes of IgG4-RD and SSc showed similar signatures; in contrast, the most differentially abundant taxa were not the facultative anaerobes consistently identified in inflammatory bowel diseases, suggesting the microbial signatures of IgG4-RD and SSc do not result from mucosal inflammation and decreased anaerobism. CONCLUSIONS: These results provide an initial characterization of gut microbiome ecology in fibrosis-prone IgG4-RD and SSc and reveal microbial functions that offer insights into the pathophysiology of these rare diseases.

BACKGROUND: Clinical-grade whole-genome sequencing (cWGS) has the potential to become the standard of care within the clinic because of its breadth of coverage and lack of bias towards certain regions of the genome. Colorectal cancer presents a difficult treatment paradigm, with over 40% of patients presenting at diagnosis with metastatic disease. We hypothesised that cWGS coupled with 3' transcriptome analysis would give new insights into colorectal cancer. METHODS: Patients underwent PCR-free whole-genome sequencing and alignment and variant calling using a standardised pipeline to output SNVs, indels, SVs and CNAs. Additional insights into the mutational signatures and tumour biology were gained by the use of 3' RNA-seq. RESULTS: Fifty-four patients were studied in total. Driver analysis identified the Wnt pathway gene APC as the only consistently mutated driver in colorectal cancer. Alterations in the PI3K/mTOR pathways were seen as previously observed in CRC. Multiple private CNAs, SVs and gene fusions were unique to individual tumours. Approximately 30% of patients had a tumour mutational burden of > 10 mutations/Mb of DNA, suggesting suitability for immunotherapy. CONCLUSIONS: Clinical whole-genome sequencing offers a potential avenue for the identification of private genomic variation that may confer sensitivity to targeted agents and offer patients new options for targeted therapies.

Whole genome sequencing (WGS) has the potential to outperform clinical microarrays for the detection of structural variants (SV) including copy number variants (CNVs), but has been challenged by high false positive rates. Here we present ClinSV, a WGS based SV integration, annotation, prioritization, and visualization framework, which identified 99.8% of simulated pathogenic ClinVar CNVs > 10 kb and 11/11 pathogenic variants from matched microarrays. The false positive rate was low (1.5-4.5%) and reproducibility high (95-99%). In clinical practice, ClinSV identified reportable variants in 22 of 485 patients (4.7%) of which 35-63% were not detectable by current clinical microarray designs. ClinSV is available at https://github.com/KCCG/ClinSV .

BACKGROUND: Coat protein complex 1 (COPI) is integral in the sorting and retrograde trafficking of proteins and lipids from the Golgi apparatus to the endoplasmic reticulum (ER). In recent years, coat proteins have been implicated in human diseases known collectively as "coatopathies". METHODS: Whole exome or genome sequencing of two families with a neuro-developmental syndrome, variable microcephaly and cataracts revealed biallelic variants in COPB1, which encodes the beta-subunit of COPI (β-COP). To investigate Family 1's splice donor site variant, we undertook patient blood RNA studies and CRISPR/Cas9 modelling of this variant in a homologous region of the Xenopus tropicalis genome. To investigate Family 2's missense variant, we studied cellular phenotypes of human retinal epithelium and embryonic kidney cell lines transfected with a COPB1 expression vector into which we had introduced Family 2's mutation. RESULTS: We present a new recessive coatopathy typified by severe developmental delay and cataracts and variable microcephaly. A homozygous splice donor site variant in Family 1 results in two aberrant transcripts, one of which causes skipping of exon 8 in COPB1 pre-mRNA, and a 36 amino acid in-frame deletion, resulting in the loss of a motif at a small interaction interface between β-COP and β'-COP. Xenopus tropicalis animals with a homologous mutation, introduced by CRISPR/Cas9 genome editing, recapitulate features of the human syndrome including microcephaly and cataracts. In vitro modelling of the COPB1 c.1651T>G p.Phe551Val variant in Family 2 identifies defective Golgi to ER recycling of this mutant β-COP, with the mutant protein being retarded in the Golgi. CONCLUSIONS: This adds to the growing body of evidence that COPI subunits are essential in brain development and human health and underlines the utility of exome and genome sequencing coupled with Xenopus tropicalis CRISPR/Cas modelling for the identification and characterisation of novel rare disease genes.

BACKGROUND: Splicing of genomic exons into mRNAs is a critical prerequisite for the accurate synthesis of human proteins. Genetic variants impacting splicing underlie a substantial proportion of genetic disease, but are challenging to identify beyond those occurring at donor and acceptor dinucleotides. To address this, various methods aim to predict variant effects on splicing. Recently, deep neural networks (DNNs) have been shown to achieve better results in predicting splice variants than other strategies. METHODS: It has been unclear how best to integrate such process-specific scores into genome-wide variant effect predictors. Here, we use a recently published experimental data set to compare several machine learning methods that score variant effects on splicing. We integrate the best of those approaches into general variant effect prediction models and observe the effect on classification of known pathogenic variants. RESULTS: We integrate two specialized splicing scores into CADD (Combined Annotation Dependent Depletion; cadd.gs.washington.edu ), a widely used tool for genome-wide variant effect prediction that we previously developed to weight and integrate diverse collections of genomic annotations. With this new model, CADD-Splice, we show that inclusion of splicing DNN effect scores substantially improves predictions across multiple variant categories, without compromising overall performance. CONCLUSIONS: While splice effect scores show superior performance on splice variants, specialized predictors cannot compete with other variant scores in general variant interpretation, as the latter account for nonsense and missense effects that do not alter splicing. Although only shown here for splice scores, we believe that the applied approach will generalize to other specific molecular processes, providing a path for the further improvement of genome-wide variant effect prediction.

BACKGROUND: Since early February 2021, the causative agent of COVID-19, SARS-CoV-2, has infected over 104 million people with more than 2 million deaths according to official reports. The key to understanding the biology and virus-host interactions of SARS-CoV-2 requires the knowledge of mutation and evolution of this virus at both inter- and intra-host levels. However, despite quite a few polymorphic sites identified among SARS-CoV-2 populations, intra-host variant spectra and their evolutionary dynamics remain mostly unknown. METHODS: Using high-throughput sequencing of metatranscriptomic and hybrid captured libraries, we characterized consensus genomes and intra-host single nucleotide variations (iSNVs) of serial samples collected from eight patients with COVID-19. The distribution of iSNVs along the SARS-CoV-2 genome was analyzed and co-occurring iSNVs among COVID-19 patients were identified. We also compared the evolutionary dynamics of SARS-CoV-2 population in the respiratory tract (RT) and gastrointestinal tract (GIT). RESULTS: The 32 consensus genomes revealed the co-existence of different genotypes within the same patient. We further identified 40 intra-host single nucleotide variants (iSNVs). Most (30/40) iSNVs presented in a single patient, while ten iSNVs were found in at least two patients or identical to consensus variants. Comparing allele frequencies of the iSNVs revealed a clear genetic differentiation between intra-host populations from the respiratory tract (RT) and gastrointestinal tract (GIT), mostly driven by bottleneck events during intra-host migrations. Compared to RT populations, the GIT populations showed a better maintenance and rapid development of viral genetic diversity following the suspected intra-host bottlenecks. CONCLUSIONS: Our findings here illustrate the intra-host bottlenecks and evolutionary dynamics of SARS-CoV-2 in different anatomic sites and may provide new insights to understand the virus-host interactions of coronaviruses and other RNA viruses.

BACKGROUND: The clinical utility of personal genomic information in identifying individuals at increased risks for dyslipidemia and cardiovascular diseases remains unclear. METHODS: We used data from Biobank Japan (n = 70,657-128,305) and developed novel East Asian-specific genome-wide polygenic risk scores (PRSs) for four lipid traits. We validated (n = 4271) and subsequently tested associations of these scores with 3-year lipid changes in adolescents (n = 620), carotid intima-media thickness (cIMT) in adult women (n = 781), dyslipidemia (n = 7723), and coronary heart disease (CHD) (n = 2374 cases and 6246 controls) in type 2 diabetes (T2D) patients. RESULTS: Our PRSs aggregating 84-549 genetic variants (0.251 < correlation coefficients (r) < 0.272) had comparably stronger association with lipid variations than the typical PRSs derived based on the genome-wide significant variants (0.089 < r < 0.240). Our PRSs were robustly associated with their corresponding lipid levels (7.5 × 10- 103 < P < 1.3 × 10- 75) and 3-year lipid changes (1.4 × 10- 6 < P < 0.0130) which started to emerge in childhood and adolescence. With the adjustments for principal components (PCs), sex, age, and body mass index, there was an elevation of 5.3% in TC (β ± SE = 0.052 ± 0.002), 11.7% in TG (β ± SE = 0.111 ± 0.006), 5.8% in HDL-C (β ± SE = 0.057 ± 0.003), and 8.4% in LDL-C (β ± SE = 0.081 ± 0.004) per one standard deviation increase in the corresponding PRS. However, their predictive power was attenuated in T2D patients (0.183 < r < 0.231). When we included each PRS (for TC, TG, and LDL-C) in addition to the clinical factors and PCs, the AUC for dyslipidemia was significantly increased by 0.032-0.057 in the general population (7.5 × 10- 3 < P < 0.0400) and 0.029-0.069 in T2D patients (2.1 × 10- 10 < P < 0.0428). Moreover, the quintile of TC-related PRS was moderately associated with cIMT in adult women (β ± SE = 0.011 ± 0.005, Ptrend = 0.0182). Independent of conventional risk factors, the quintile of PRSs for TC [OR (95% CI) = 1.07 (1.03-1.11)], TG [OR (95% CI) = 1.05 (1.01-1.09)], and LDL-C [OR (95% CI) = 1.05 (1.01-1.09)] were significantly associated with increased risk of CHD in T2D patients (4.8 × 10- 4 < P < 0.0197). Further adjustment for baseline lipid drug use notably attenuated the CHD association. CONCLUSIONS: The PRSs derived and validated here highlight the potential for early genomic screening and personalized risk assessment for cardiovascular disease.

BACKGROUND: Once antibiotic-resistant bacteria become established within the gut microbiota, they can cause infections in the host and be transmitted to other people and the environment. Currently, there are no effective modalities for decreasing or preventing colonization by antibiotic-resistant bacteria. Intestinal microbiota restoration can prevent Clostridioides difficile infection (CDI) recurrences. Another potential application of microbiota restoration is suppression of non-C. difficile multidrug-resistant bacteria and overall decrease in the abundance of antibiotic resistance genes (the resistome) within the gut microbiota. This study characterizes the effects of RBX2660, a microbiota-based investigational therapeutic, on the composition and abundance of the gut microbiota and resistome, as well as multidrug-resistant organism carriage, after delivery to patients suffering from recurrent CDI. METHODS: An open-label, multi-center clinical trial in 11 centers in the USA for the safety and efficacy of RBX2660 on recurrent CDI was conducted. Fecal specimens from 29 of these subjects with recurrent CDI who received either one (N = 16) or two doses of RBX2660 (N = 13) were analyzed secondarily. Stool samples were collected prior to and at intervals up to 6 months post-therapy and analyzed in three ways: (1) 16S rRNA gene sequencing for microbiota taxonomic composition, (2) whole metagenome shotgun sequencing for functional pathways and antibiotic resistome content, and (3) selective and differential bacterial culturing followed by isolate genome sequencing to longitudinally track multidrug-resistant organisms. RESULTS: Successful prevention of CDI recurrence with RBX2660 correlated with taxonomic convergence of patient microbiota to the donor microbiota as measured by weighted UniFrac distance. RBX2660 dramatically reduced the abundance of antibiotic-resistant Enterobacteriaceae in the 2 months after administration. Fecal antibiotic resistance gene carriage decreased in direct relationship to the degree to which donor microbiota engrafted. CONCLUSIONS: Microbiota-based therapeutics reduce resistance gene abundance and resistant organisms in the recipient gut microbiome. This approach could potentially reduce the risk of infections caused by resistant organisms within the patient and the transfer of resistance genes or pathogens to others. TRIAL REGISTRATION: ClinicalTrials.gov, NCT01925417 ; registered on August 19, 2013.

BACKGROUND: The incidence of colorectal cancer (CRC) is increasing in developing countries, yet limited research on the CRC- associated microbiota has been conducted in these areas, in part due to scarce resources, facilities, and the difficulty of fresh or frozen stool storage/transport. Here, we aimed (1) to establish a broad representation of diverse developing countries (Argentina, Chile, India, and Vietnam); (2) to validate a 'resource-light' sample-collection protocol translatable in these settings using guaiac faecal occult blood test (gFOBT) cards stored and, importantly, shipped internationally at room temperature; (3) to perform initial profiling of the collective CRC-associated microbiome of these developing countries; and (4) to compare this quantitatively with established CRC biomarkers from developed countries. METHODS: We assessed the effect of international storage and transport at room temperature by replicating gFOBT from five UK volunteers, storing two in the UK, and sending replicates to institutes in the four countries. Next, to determine the effect of prolonged UK storage, DNA extraction replicates for a subset of samples were performed up to 252 days apart. To profile the CRC-associated microbiome of developing countries, gFOBT were collected from 41 treatment-naïve CRC patients and 40 non-CRC controls from across the four institutes, and V4 16S rRNA gene sequencing was performed. Finally, we constructed a random forest (RF) model that was trained and tested against existing datasets from developed countries. RESULTS: The microbiome was stably assayed when samples were stored/transported at room temperature and after prolonged UK storage. Large-scale microbiome structure was separated by country and continent, with a smaller effect from CRC. Importantly, the RF model performed similarly to models trained using external datasets and identified similar taxa of importance (Parvimonas, Peptostreptococcus, Fusobacterium, Alistipes, and Escherichia). CONCLUSIONS: This study demonstrates that gFOBT, stored and transported at room temperature, represents a suitable method of faecal sample collection for amplicon-based microbiome biomarkers in developing countries and suggests a CRC-faecal microbiome association that is consistent between developed and developing countries.

Epigenetic alterations are associated with normal biological processes such as aging or differentiation. Changes in global epigenetic signatures, together with genetic alterations, are driving events in several diseases including cancer. Comparative studies of cancer and healthy tissues found alterations in patterns of DNA methylation, histone posttranslational modifications, and changes in chromatin accessibility. Driven by sophisticated, next-generation sequencing-based technologies, recent studies discovered cancer epigenomes to be dominated by epigenetic patterns already present in the cell-of-origin, which transformed into a neoplastic cell. Tumor-specific epigenetic changes therefore need to be redefined and factors influencing epigenetic patterns need to be studied to unmask truly disease-specific alterations. The underlying mechanisms inducing cancer-associated epigenetic alterations are poorly understood. Studies of mutated epigenetic modifiers, enzymes that write, read, or edit epigenetic patterns, or mutated chromatin components, for example oncohistones, help to provide functional insights on how cancer epigenomes arise. In this review, we highlight the importance and define challenges of proper control tissues and cell populations to exploit cancer epigenomes. We summarize recent advances describing mechanisms leading to epigenetic changes in tumorigenesis and briefly discuss advances in investigating their translational potential.

BACKGROUND: Identifying the complete repertoire of genes that drive cancer in individual patients is crucial for precision oncology. Most established methods identify driver genes that are recurrently altered across patient cohorts. However, mapping these genes back to patients leaves a sizeable fraction with few or no drivers, hindering our understanding of cancer mechanisms and limiting the choice of therapeutic interventions. RESULTS: We present sysSVM2, a machine learning software that integrates cancer genetic alterations with gene systems-level properties to predict drivers in individual patients. Using simulated pan-cancer data, we optimise sysSVM2 for application to any cancer type. We benchmark its performance on real cancer data and validate its applicability to a rare cancer type with few known driver genes. We show that drivers predicted by sysSVM2 have a low false-positive rate, are stable and disrupt well-known cancer-related pathways. CONCLUSIONS: sysSVM2 can be used to identify driver alterations in patients lacking sufficient canonical drivers or belonging to rare cancer types for which assembling a large enough cohort is challenging, furthering the goals of precision oncology. As resources for the community, we provide the code to implement sysSVM2 and the pre-trained models in all TCGA cancer types ( https://github.com/ciccalab/sysSVM2 ).

BACKGROUND: Pancreatic cancer (PC) is a complex disease in which both non-genetic and genetic factors interplay. To date, 40 GWAS hits have been associated with PC risk in individuals of European descent, explaining 4.1% of the phenotypic variance. METHODS: We complemented a new conventional PC GWAS (1D) with genome spatial autocorrelation analysis (2D) permitting to prioritize low frequency variants not detected by GWAS. These were further expanded via Hi-C map (3D) interactions to gain additional insight into the inherited basis of PC. In silico functional analysis of public genomic information allowed prioritization of potentially relevant candidate variants. RESULTS: We identified several new variants located in genes for which there is experimental evidence of their implication in the biology and function of pancreatic acinar cells. Among them is a novel independent variant in NR5A2 (rs3790840) with a meta-analysis p value = 5.91E-06 in 1D approach and a Local Moran's Index (LMI) = 7.76 in 2D approach. We also identified a multi-hit region in CASC8-a lncRNA associated with pancreatic carcinogenesis-with a lowest p value = 6.91E-05. Importantly, two new PC loci were identified both by 2D and 3D approaches: SIAH3 (LMI = 18.24), CTRB2/BCAR1 (LMI = 6.03), in addition to a chromatin interacting region in XBP1-a major regulator of the ER stress and unfolded protein responses in acinar cells-identified by 3D; all of them with a strong in silico functional support. CONCLUSIONS: This multi-step strategy, combined with an in-depth in silico functional analysis, offers a comprehensive approach to advance the study of PC genetic susceptibility and could be applied to other diseases.