Insights for precision oncology from the integration of genomic and clinical data of 13,880 tumors from the 100,000 Genomes Cancer Programme.

The Cancer Programme of the 100,000 Genomes Project was an initiative to provide whole-genome sequencing (WGS) for patients with cancer, evaluating opportunities for precision cancer care within the UK National Healthcare System (NHS). Genomics England, alongside NHS England, analyzed WGS data from 13,880 solid tumors spanning 33 cancer types, integrating genomic data with real-world treatment and outcome data, within a secure Research Environment. Incidence of somatic mutations in genes recommended for standard-of-care testing varied across cancer types. For instance, in glioblastoma multiforme, small variants were present in 94% of cases and copy number aberrations in at least one gene in 58% of cases, while sarcoma demonstrated the highest occurrence of actionable structural variants (13%). Homologous recombination deficiency was identified in 40% of high-grade serous ovarian cancer cases with 30% linked to pathogenic germline variants, highlighting the value of combined somatic and germline analysis. The linkage of WGS and longitudinal life course clinical data allowed the assessment of treatment outcomes for patients stratified according to pangenomic markers. Our findings demonstrate the utility of linking genomic and real-world clinical data to enable survival analysis to identify cancer genes that affect prognosis and advance our understanding of how cancer genomics impacts patient outcomes.

Substitution mutational signatures in whole-genome-sequenced cancers in the UK population.

Whole-genome sequencing (WGS) permits comprehensive cancer genome analyses, revealing mutational signatures, imprints of DNA damage and repair processes that have arisen in each patient's cancer. We performed mutational signature analyses on 12,222 WGS tumor-normal matched pairs, from patients recruited via the UK National Health Service. We contrasted our results to two independent cancer WGS datasets, the International Cancer Genome Consortium (ICGC) and Hartwig Foundation, involving 18,640 WGS cancers in total. Our analyses add 40 single and 18 double substitution signatures to the current mutational signature tally. Critically, we show for each organ, that cancers have a limited number of 'common' signatures and a long tail of 'rare' signatures. We provide a practical solution for utilizing this concept of common versus rare signatures in future analyses.

Urinary- and Plasma-Derived Exosomes Reveal a Distinct MicroRNA Signature Associated With Albuminuria in Hypertension.

Urinary albumin excretion (UAE) is a marker of cardiovascular risk and renal damage in hypertension. MicroRNAs (miRNAs) packaged into exosomes function as paracrine effectors in cell communication and the kidney is not exempt. This study aimed to state an exosomal miRNA profile/signature associated to hypertension with increased UAE and the impact of profibrotic TGF-ß1 (transforming growth factor ß1) on exosomes miRNA release. Therefore, exosomes samples from patients with hypertension with/without UAE were isolated and characterized. Three individual and unique small RNA libraries from each subject were prepared (total plasma, urinary, and plasma-derived exosomes) for next-generation sequencing profiling. Differentially expressed miRNAs were over-represented in Kyoto Encyclopedia of Genes and Genomes pathways, and selected miRNAs were validated by real-time quantitative polymerase chain reaction in a confirmation cohort. Thus, a signature of 29 dysregulated circulating miRNAs was identified in UAE hypertensive subjects, regulating 21 pathways. Moreover, changes in the levels of 4 exosomes-miRNAs were validated in a confirmation cohort and found associated with albuminuria. In particular miR-26a, major regulator of TGF-ß signaling, was found downregulated in both type of exosomes when compared with healthy controls and to hypertension normoalbuminurics (P<0.01). Similarly, decreased miR-26a levels were found in podocyte-derived exosomes after TGF-ß stress. Our results revealed an exosomes miRNA signature associated to albuminuria in hypertension. In particular, exosomes miR-26a seemed to play a key role in the regulation of TGF-ß, a relevant effector in podocyte damage. These findings support the use of exosomes miRNAs as biomarkers of cardiovascular risk progression and therapeutic tools in early kidney damage.

Optimization of small RNA library preparation protocol from human urinary exosomes.

BACKGROUND: Sequencing of miRNAs isolated from exosomes has great potential to identify novel disease biomarkers, but exosomes have low amount of RNA, hindering adequate analysis and quantification. Here, we have assessed several steps in developing an optimized small RNA (sRNA) library preparation protocol for next-generation sequencing (NGS) miRNA analysis from urinary exosomes. METHODS: A total of 24 urinary exosome samples from donors were included in this study. RNA was extracted by column-based methods. The quality of extracted RNA was assessed by spectrophotometric quantification and Bioanalyzer software analysis. All libraries were prepared using the CleanTag small RNA library preparation protocol and the effect of our additional modifications on adapter-dimer presence, sequencing data and tagged small RNA library population was also analyzed. RESULTS: Our results show that good quality sequencing libraries can be prepared following our optimized small RNA library preparation protocol from urinary exosomes. When the size selection by gel purification step was included within the workflow, adapter-dimer was totally removed from cDNA libraries. Furthermore, the inclusion of this modification step within small RNA library protocol augmented the small RNA mapped reads, with an especially significant 37% increase in miRNA reads, and the gel purification step made no difference to the tagged miRNA population. CONCLUSIONS: This study provides researchers with an optimized small RNA library preparation workflow for next generation sequencing based exosome-associated miRNA analysis that yields a high amount of miRNA mapped reads without skewing the tagged miRNA population significantly.

PyCellBase, an efficient python package for easy retrieval of biological data from heterogeneous sources.

BACKGROUND: Biological databases and repositories are incrementing in diversity and complexity over the years. This rapid expansion of current and new sources of biological knowledge raises serious problems of data accessibility and integration. To handle the growing necessity of unification, CellBase was created as an integrative solution. CellBase provides a centralized NoSQL database containing biological information from different and heterogeneous sources. Access to this information is done through a RESTful web service API, which provides an efficient interface to the data. RESULTS: In this work we present PyCellBase, a Python package that provides programmatic access to the rich RESTful web service API offered by CellBase. This package offers a fast and user-friendly access to biological information without the need of installing any local database. In addition, a series of command-line tools are provided to perform common bioinformatic tasks, such as variant annotation. CellBase data is always available by a high-availability cluster and queries have been tuned to ensure a real-time performance. CONCLUSION: PyCellBase is an open-source Python package that provides an efficient access to heterogeneous biological information. It allows to perform tasks that require a comprehensive set of knowledge resources, as for example variant annotation. Queries can be easily fine-tuned to retrieve the desired information of particular biological features. PyCellBase offers the convenience of an object-oriented scripting language and provides the ability to integrate the obtained results into other Python applications and pipelines.

Babelomics 5.0: functional interpretation for new generations of genomic data.

Babelomics has been running for more than one decade offering a user-friendly interface for the functional analysis of gene expression and genomic data. Here we present its fifth release, which includes support for Next Generation Sequencing data including gene expression (RNA-seq), exome or genome resequencing. Babelomics has simplified its interface, being now more intuitive. Improved visualization options, such as a genome viewer as well as an interactive network viewer, have been implemented. New technical enhancements at both, client and server sides, makes the user experience faster and more dynamic. Babelomics offers user-friendly access to a full range of methods that cover: (i) primary data analysis, (ii) a variety of tests for different experimental designs and (iii) different enrichment and network analysis algorithms for the interpretation of the results of such tests in the proper functional context. In addition to the public server, local copies of Babelomics can be downloaded and installed. Babelomics is freely available at: http://www.babelomics.org.