Nanotechnology in the Regeneration of Complex Tissues.

This is the final published version. It first appeared at http://www.la-press.com/nanotechnology-in-the-regeneration-of-complex-tissues-article-a4503.Modern medicine faces a growing crisis as demand for organ transplantations continues to far outstrip supply. By stimulating the body's own repair mechanisms, regenerative medicine aims to reduce demand for organs, while the closely related field of tissue engineering promises to deliver "off-the-self" organs grown from patients' own stem cells to improve supply. To deliver on these promises, we must have reliable means of generating complex tissues. Thus far, the majority of successful tissue engineering approaches have relied on macroporous scaffolds to provide cells with both mechanical support and differentiative cues. In order to engineer complex tissues, greater attention must be paid to nanoscale cues present in a cell's microenvironment. As the extracellular matrix is capable of driving complexity during development, it must be understood and reproduced in order to recapitulate complexity in engineered tissues. This review will summarize current progress in engineering complex tissue through the integration of nanocomposites and biomimetic scaffolds.JWC was previously funded by a Scholarship from the University of Glasgow and is now in receipt of a Cancer Research UK Scholarship

Maintaining Tumor Heterogeneity in Patient-Derived Tumor Xenografts.

Preclinical models often fail to capture the diverse heterogeneity of human malignancies and as such lack clinical predictive power. Patient-derived tumor xenografts (PDX) have emerged as a powerful technology: capable of retaining the molecular heterogeneity of their originating sample. However, heterogeneity within a tumor is governed by both cell-autonomous (e.g., genetic and epigenetic heterogeneity) and non-cell-autonomous (e.g., stromal heterogeneity) drivers. Although PDXs can largely recapitulate the polygenomic architecture of human tumors, they do not fully account for heterogeneity in the tumor microenvironment. Hence, these models have substantial utility in basic and translational research in cancer biology; however, study of stromal or immune drivers of malignant progression may be limited. Similarly, PDX models offer the ability to conduct patient-specific in vivo and ex vivo drug screens, but stromal contributions to treatment responses may be under-represented. This review discusses the sources and consequences of intratumor heterogeneity and how these are recapitulated in the PDX model. Limitations of the current generation of PDXs are discussed and strategies to improve several aspects of the model with respect to preserving heterogeneity are proposed.We are grateful to Cancer Research UK for supporting all authors

Applications of Machine Learning in Drug Discovery I: Target Discovery and Small Molecule Drug Design

Drug discovery and development are long and arduous processes; recent figures point to 10 years and $2 billion USD to take a new chemical agent from discovery through to market. Moreover, though an approved blockbuster drug can be lucrative for the controlling pharmaceutical company, new therapeutic agents suffer from a 90% attrition during development, making the chances of success in the drug development process relatively low. Machine learning (ML) has re-emerged in the last several years as a powerful set of tools for unlocking value from large datasets. ML has shown great promise in improving efficiencies across numerous industries with high quality, vast, datasets. In an age of increasing access to highly curated rich sources of biological data, ML shows promise in reversing some of the negative trends shown in drug discovery and development. In this first part of our analysis of the application of ML to the drug discovery and development process, we discuss recent advances in the use of computational techniques in drug target discovery and lead molecule optimisation. We focus our analysis on oncology, though make reference to the wider field of human health and disease

Applications of Machine Learning in Drug Discovery II: Biomarker Discovery, Patient Stratification and Pharmacoeconomics

Cancer remains a leading cause of morbidity and mortality around the world. Despite significant advances in our understanding of the pathology of the disease, and the substantial public and private investment into treatment development, late-stage patients often exhaust therapeutic options. Indeed, in the US alone, there were >1.7 million new cancer diagnoses and >600,000 cancer-associated deaths in 2019. As biology in general and cancer research in particular become ever richer in data, we explore the role of machine learning (ML) in changing the cancer drug development landscape. In the first part of this analysis, we focussed on ML for target identification and drug design. We discussed the growing need for ML-based analysis as we enter an age of clinical -omic data and provided a primer to ML-based techniques for the non-statistician/mathematician. In this chapter, we will explore the problem of tumour heterogeneity together with the role of ML in the discovery and development of cancer biomarkers and for clinical trial design. We end with a brief consideration of the economics of personalised cancer treatment

Investigation of dust bands from blue ice fields in the Lewis Cliff (Beardmore) area, Antarctica: A progress report

Blue ice fields in Antarctica are well known for their high areal meteorite concentrations. The exact type of accumulation model and the age of the ice is still not well known. Dust bands on blue ice fields may help to clarify some of these problems. Dust, which has been isolated from dust band samples from blue ice areas in the Lewis Cliff/Walcott Neve area (Beardmore region), Antarctica, was studied to determine petrographic characteristics and chemical compositions. One sample has an average grain size of around 0.5mm, and is rather different from the others in its abundances of trace elements. The REE pattern and some other trace element ratios of that sample suggest it is a sediment from the local Beacon Supergroup, which has been scooped up from the ground by ice movement. The other five samples which were investigated have very small grain sizes (20μm), and abundant glass shards. Major element data on the glass shards (and some feldspar crystals, which are also present in the dust band samples) allow the conclusion that they have originated from an alkaline volcano. The chemical composition of the glasses is highly variable, some showing basanitic composition, some showing trachytic or peralkaline K-trachytic composition. The silica vs. sum of alkalis plot shows that the Lewis Cliff samples are different from dust collected at the Allan Hills, but that there is a close similarity with volcanic material from The Pleiades, Northern Victoria Land. The trace element chemistry of all volcanic samples show the characteristic volcanic trace elements, like Ta, W, Sb, Th, and the REE, enriched by a considerable factor. The REE patterns exhibit a prominent negative Eu anomaly, which may be explained by mixing basanites (no Eu anomaly, but steep REE patterns) with K-trachytes and peralkaline K-trachytes (very pronounced negative Eu anomaly). The same components are obvious in major element analyses of individual glass shards, thus each dust band is a mixture of at least three different source materials (which, however, originated from the same volcano in a single eruption). The Pleiades seem to be a likely source for the volcanic debris found in the dust bands at Lewis Cliff

Profiling lung adenocarcinoma by liquid biopsy: can one size fit all?

BACKGROUND: Cancer is first and foremost a disease of the genome. Specific genetic signatures within a tumour are prognostic of disease outcome, reflect subclonal architecture and intratumour heterogeneity, inform treatment choices and predict the emergence of resistance to targeted therapies. Minimally invasive liquid biopsies can give temporal resolution to a tumour's genetic profile and allow the monitoring of treatment response through levels of circulating tumour DNA (ctDNA). However, the detection of ctDNA in repeated liquid biopsies is currently limited by economic and time constraints associated with targeted sequencing. METHODS: Here we bioinformatically profile the mutational and copy number spectrum of The Cancer Genome Network's lung adenocarcinoma dataset to uncover recurrently mutated genomic loci. RESULTS: We build a panel of 400 hotspot mutations and show that the coverage extends to more than 80% of the dataset at a median depth of 8 mutations per patient. Additionally, we uncover several novel single-nucleotide variants present in more than 5% of patients, often in genes not commonly associated with lung adenocarcinoma. CONCLUSION: With further optimisation, this hotspot panel could allow molecular diagnostics laboratories to build curated primer banks for 'off-the-shelf' monitoring of ctDNA by droplet-based digital PCR or similar techniques, in a time- and cost-effective manner

Future potential of metagenomics in clinical laboratories

INTRODUCTION: Rapid and sensitive diagnostic strategies are necessary for patient care and public health. Most of the current conventional microbiological assays detect only a restricted panel of pathogens at a time or require a microbe to be successfully cultured from a sample. Clinical metagenomics next-generation sequencing (mNGS) has the potential to unbiasedly detect all pathogens in a sample, increasing the sensitivity for detection and enabling the discovery of unknown infectious agents. AREAS COVERED: High expectations have been built around mNGS; however, this technique is far from widely available. This review highlights the advances and currently available options in terms of costs, turnaround time, sensitivity, specificity, validation, and reproducibility of mNGS as a diagnostic tool in clinical microbiology laboratories. EXPERT OPINION: The need for a novel diagnostic tool to increase the sensitivity of microbial diagnostics is clear. mNGS has the potential to revolutionise clinical microbiology. However, its role as a diagnostic tool has yet to be widely established, which is crucial for successfully implementing the technique. A clear definition of diagnostic algorithms that include mNGS is vital to show clinical utility. Similarly to real-time PCR, mNGS will one day become a vital tool in any testing algorithm