Predicting tumour content of liquid biopsies from cell-free DNA.

BACKGROUND: Liquid biopsy is a minimally-invasive method of sampling bodily fluids, capable of revealing evidence of cancer. The distribution of cell-free DNA (cfDNA) fragment lengths has been shown to differ between healthy subjects and cancer patients, whereby the distributional shift correlates with the sample's tumour content. These fragmentomic data have not yet been utilised to directly quantify the proportion of tumour-derived cfDNA in a liquid biopsy. RESULTS: We used statistical learning to predict tumour content from Fourier and wavelet transforms of cfDNA length distributions in samples from 118 cancer patients. The model was validated on an independent dilution series of patient plasma. CONCLUSIONS: This proof of concept suggests that our fragmentomic methodology could be useful for predicting tumour content in liquid biopsies.

Analysis of serum proteomics data identifies a quantitative association between beta-defensin 2 at baseline and clinical response to IL-17 blockade in psoriatic arthritis.

OBJECTIVES: Despite several effective targeted therapies, biomarkers that predict whether a patient with psoriatic arthritis (PsA) will respond to a particular treatment are currently lacking. METHODS: We analysed proteomics data from serum samples of nearly 2000 patients with PsA in placebo-controlled phase-III clinical trials of the interleukin-17 inhibitor secukinumab. To discover predictive biomarkers of clinical response, we used statistical learning with controlled feature selection. The top candidate was validated using an ELISA and was separately assessed in a trial of almost 800 patients with PsA treated with secukinumab or the tumour necrosis factor inhibitor adalimumab. RESULTS: Serum levels of beta-defensin 2 (BD-2) at baseline were found to be robustly associated with subsequent clinical response (eg, American College of Rheumatology definition of 20%, 50% and 70% improvement) to secukinumab, but not to placebo. This finding was validated in two independent clinical studies not used for discovery. Although BD-2 is known to be associated with psoriasis severity, the predictivity of BD-2 was independent of baseline Psoriasis Area and Severity Index. The association between BD-2 and response to secukinumab was observed as early as 4 weeks and maintained up to 52 weeks. BD-2 was also found to predict response to treatment with adalimumab. Unlike in PsA, BD-2 was not predictive of response to secukinumab in rheumatoid arthritis. CONCLUSIONS: In PsA, BD-2 at baseline is quantitatively associated with clinical response to secukinumab. Patients with high levels of BD-2 at baseline reach and sustain higher rates of clinical response after treatment with secukinumab.

Discovery of antimicrobials by massively parallelized growth assays (Mex).

The number of newly approved antimicrobial compounds has been steadily decreasing over the past 50 years emphasizing the need for novel antimicrobial substances. Here we present Mex, a method for the high-throughput discovery of novel antimicrobials, that relies on E. coli self-screening to determine the bioactivity of more than ten thousand naturally occurring peptides. Analysis of thousands of E. coli growth curves using next-generation sequencing enables the identification of more than 1000 previously unknown antimicrobial peptides. Additionally, by incorporating the kinetics of growth inhibition, a first indication of the mode of action is obtained, which has implications for the ultimate usefulness of the peptides in question. The most promising peptides of the screen are chemically synthesized and their activity is determined in standardized susceptibility assays. Ten out of 15 investigated peptides efficiently eradicate bacteria at a minimal inhibitory concentration in the lower µM or upper nM range. This work represents a step-change in the high-throughput discovery of functionally diverse antimicrobials.

Structure-function relationships of HDL in diabetes and coronary heart disease.

High-density lipoproteins (HDL) contain hundreds of lipid species and proteins and exert many potentially vasoprotective and antidiabetogenic activities on cells. To resolve structure-function-disease relationships of HDL, we characterized HDL of 51 healthy subjects and 98 patients with diabetes (T2DM), coronary heart disease (CHD), or both for protein and lipid composition, as well as functionality in 5 cell types. The integration of 40 clinical characteristics, 34 nuclear magnetic resonance (NMR) features, 182 proteins, 227 lipid species, and 12 functional read-outs by high-dimensional statistical modeling revealed, first, that CHD and T2DM are associated with different changes of HDL in size distribution, protein and lipid composition, and function. Second, different cellular functions of HDL are weakly correlated with each other and determined by different structural components. Cholesterol efflux capacity (CEC) was no proxy of other functions. Third, 3 potentially novel determinants of HDL function were identified and validated by the use of artificially reconstituted HDL, namely the sphingadienine-based sphingomyelin SM 42:3 and glycosylphosphatidylinositol-phospholipase D1 for the ability of HDL to inhibit starvation-induced apoptosis of human aortic endothelial cells and apolipoprotein F for the ability of HDL to promote maximal respiration of brown adipocytes.

Inferring signalling dynamics by integrating interventional with observational data.

MOTIVATION: In order to infer a cell signalling network, we generally need interventional data from perturbation experiments. If the perturbation experiments are time-resolved, then signal progression through the network can be inferred. However, such designs are infeasible for large signalling networks, where it is more common to have steady-state perturbation data on the one hand, and a non-interventional time series on the other. Such was the design in a recent experiment investigating the coordination of epithelial-mesenchymal transition (EMT) in murine mammary gland cells. We aimed to infer the underlying signalling network of transcription factors and microRNAs coordinating EMT, as well as the signal progression during EMT. RESULTS: In the context of nested effects models, we developed a method for integrating perturbation data with a non-interventional time series. We applied the model to RNA sequencing data obtained from an EMT experiment. Part of the network inferred from RNA interference was validated experimentally using luciferase reporter assays. Our model extension is formulated as an integer linear programme, which can be solved efficiently using heuristic algorithms. This extension allowed us to infer the signal progression through the network during an EMT time course, and thereby assess when each regulator is necessary for EMT to advance. AVAILABILITY AND IMPLEMENTATION: R package at https://github.com/cbg-ethz/timeseriesNEM. The RNA sequencing data and microscopy images can be explored through a Shiny app at https://emt.bsse.ethz.ch. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A Hierarchical Regulatory Landscape during the Multiple Stages of EMT.

Epithelial-mesenchymal transition (EMT) enables cells to gain migratory and invasive features underlined by major transcriptional and epigenetic reprogramming. However, most studies have focused on the endpoints of the EMT process, and the epistatic hierarchy of the transcriptional networks underlying EMT has remained elusive. We have used a siRNA-based, functional high-content microscopy screen to identify 46 (co)transcription factors ((co)TFs) and 13 miRNAs critically required for EMT in normal murine mammary gland (NMuMG) cells. We compared dynamic gene expression during EMT kinetics and used functional perturbation of critical (co)TFs and miRNAs to identify groups and networks of EMT genes. Computational analysis as well as functional validation experiments revealed interaction networks between TFs and miRNAs and delineated the hierarchical and functional interactions of multiple EMT regulatory networks in NMuMG cells.