Andrew Brown’s research while affiliated with Sun Yat-Sen University and other places

Background Abdominal aortic aneurysms (AAAs) are focal dilatations of the abdominal aorta that expand progressively, increasing their risk of rupture. Rupture of an AAA is associated with high mortality rates, but the mechanisms underlying the initiation, expansion, and rupture of AAAs are not yet fully understood. We aimed to characterize the pathophysiology of AAAs and identify new genes associated with AAA initiation and progression. Methods and Results This study used RNA sequencing data on 140 samples, becoming the largest RNA sequencing data set for differential expression studies of AAAs. We performed differential expression analyses and analyses of differential splicing between dilated and nondilated aortic tissue samples, and between AAAs of different diameters. We identified 3002 differentially expressed genes between AAAs and controls that were independent of ischemic time, 1425 of which were new. Additionally, 8 genes ( EXTL3 , ZFR , DUSP8 , DISP1 , USP33 , VPS37C , ZNF784 , RFX1 ) were differentially expressed between AAAs of varying diameters and between AAAs and control samples. Finally, 7 genes ( SPP1 , FHL1 , GNAS , MORF4L2 , HMGN1 , ARL1 , RNASE4 ) with differential splicing patterns were also differentially expressed genes between AAAs and controls, suggesting that splicing differences in these genes may contribute to the observed expression changes and disease development. Conclusions This study identifies new genes and splicing patterns associated with AAAs and validates previous relevant pathways on AAAs. These findings contribute to the understanding of the complex mechanisms underlying AAAs and may provide potential targets to limit AAA progression and mortality risk.

Identification of genes and proteins mediating the activity of GWAS variants requires molecular data from disease relevant tissues, but these may be difficult to collect. Using multiple gene expression reference datasets and GWAS summary statistics for T2D we identified 1,818 unique genes associated with T2D. Comparing the performance of different reference datasets, we found that sample size, and not the relevance of the tissue to the disease, was the critical factor in identifying relevant genes. Genes implicated using a well powered expression dataset were also more likely to have multiple lines of genetic evidence. A targeted proteomics reference dataset from plasma samples showed similar power to identify T2D related proteins as gene expression with the same sample size. Accounting for BMI reduces power across all tissues and phenotypes by ~30%, suggesting that many GWAS links to T2D are mediated by BMI, potentially implicating insulin resistance related effects. Finally, using data from smaller GWAS studies with precisely defined T2D subtypes uncovers genes directly relevant to that subtype, such as LST1, an immune response gene for Severe Autoimmune Diabetes and TRMT2A, involved in beta-cell apoptosis, for Severe Insulin Deficient Diabetes. Our work demonstrates the benefits of well powered reference datasets in accessible tissues and well-defined disease subtypes when studying complex diseases involving multiple tissues.

The production of multiple transcripts per gene is a process regulated by inherited genetic variants and epitranscriptomic modifications, and plays a prominent role in modulating complex traits and diseases. To simultaneously characterize the effect of genetic variants on transcript abundance and N6-methyladenosine (m6A) modifications, we produced long-read native poly(A) RNA-seq data for 60 genetically different lymphoblastoid cell lines (LCLs) from the 1000 Genomes/Geuvadis project. We identified a high diversity of both annotated (31%) and unannotated (61%) transcripts, with only a small proportion expressed across individuals (35% and 7%, respectively). In a genome-wide genetic analysis on transcripts, we identified 105 trQTLs, of which 76 were not detected as eQTLs using a larger published short-read RNAseq dataset (317 samples). A population wide characterization of m6A methylation DRACH motifs identified an average of 40.1 m6A modifications on 6,222 genes. Genetic association analysis of highly variable modifications from 1,155 genes identified m6A modification quantitative trait loci (m6A-QTLs) for 16 transcripts. Colocalization analysis of trQTL and m6A-QTLs, identified 33 candidate transcripts mediating GWAS traits, with 46.4% of the colocalized trQTLs implicating novel risk transcripts. Overall, the simultaneous characterization of transcripts and post-transcriptional modifications identified genetic effects on transcription often missed when using other sequencing technologies.

The phenomenon of mixed/heterogenous treatment responses to cancer therapies within an individual patient presents a challenging clinical scenario. Furthermore, the molecular basis of mixed intra-patient tumor responses remains unclear. Here, we show that patients with metastatic lung adenocarcinoma harbouring co-mutations of EGFR and TP53 , are more likely to have mixed intra-patient tumor responses to EGFR tyrosine kinase inhibition (TKI), compared to those with an EGFR mutation alone. The combined presence of whole genome doubling (WGD) and TP53 co-mutations leads to increased genome instability and genomic copy number aberrations in genes implicated in EGFR TKI resistance. Using mouse models and an in vitro isogenic p53 -mutant model system, we provide evidence that WGD provides diverse routes to drug resistance by increasing the probability of acquiring copy-number gains or losses relative to non-WGD cells. These data provide a molecular basis for mixed tumor responses to targeted therapy, within an individual patient, with implications for therapeutic strategies.

Background: Abdominal aortic aneurysms (AAA) are focal dilatations of the abdominal aorta. They are normally asymptomatic and progressively expand, increasing their risk of rupture. Rupture of an AAA is associated with high mortality rates, but the mechanisms underlying the initiation, expansion and rupture of AAA are not yet fully understood. This study aims to characterize and identify new genes associated with the pathophysiology of AAA through differential expression analyses between dilated and non-dilated aortic tissue samples, and between AAA of different diameters. Our study used RNA-seq data on 140 samples, becoming the largest RNA-seq dataset for differential expression studies of AAA. Results: We identified 7,454 differentially expressed genes (DEGs) between AAA and controls, 2,851 of which were new compared to previous microarray studies. Notably, a novel cluster on adenosine triphosphate synthesis regulation emerged as strongly associated with AAA. Additionally, exploring AAA of different diameters identified eight genes (EXTL3, ZFR, DUSP8, DISP1, USP33, VPS37C, ZNF784, RFX1) that overlapped with the DEGs between AAA and controls, implying roles in both disease onset and progression. Seven genes (SPP1, FHL1, GNAS, MORF4L2, HMGN1, ARL1, RNASE4) with differential splicing patterns were also DEGs between AAA and controls, suggesting that splicing differences contribute to the observed expression changes and the disease development. Conclusions: This study identified new genes and pathways associated with AAA onset and progression and validated previous relevant roles of inflammation and intracellular calcium regulation. These findings provide insights into the complex mechanisms underlying AAA and indicate potential targets to limit AAA progression and mortality risk.

Aims: Most patients with advanced malignancy suffer bone metastases, which pose a significant challenge to orthopaedic services and burden to the health economy. This study aimed to assess adherence to the British Orthopaedic Oncology Society (BOOS)/British Orthopaedic Association (BOA) guidelines on patients with metastatic bone disease (MBD) in the UK. Methods: A prospective, multicentre, national collaborative audit was designed and delivered by a trainee-led collaborative group. Data were collected over three months (1 April 2021 to 30 June 2021) for all patients presenting with MBD. A data collection tool allowed investigators at each hospital to compare practice against guidelines. Data were collated and analyzed centrally to quantify compliance from 84 hospitals in the UK for a total of 1,137 patients who were eligible for inclusion. Results: A total of 846 patients with pelvic and appendicular MBD were analyzed, after excluding those with only spinal metastatic disease. A designated MBD lead was not present in 39% of centres (33/84). Adequate radiographs were not performed in 19% of patients (160/846), and 29% (247/846) did not have an up-to-date CT of thorax, abdomen, and pelvis to stage their disease. Compliance was low obtaining an oncological opinion (69%; 584/846) and prognosis estimations (38%; 223/846). Surgery was performed in 38% of patients (319/846), with the rates of up-to-date radiological investigations and oncology input with prognosis below the expected standard. Of the 25% (215/846) presenting with a solitary metastasis, a tertiary opinion from a MBD centre and biopsy was sought in 60% (130/215). Conclusion: Current practice in the UK does not comply with national guidelines, especially regarding investigations prior to surgery and for patients with solitary metastases. This study highlights the need for investment and improvement in care. The recent publication of the British Orthopaedic Association Standards for Trauma (BOAST) defines auditable standards to drive these improvements for this vulnerable patient group.

In 2008 the role of clinical imaging in oncology drug development was reviewed. The review outlined where imaging was being applied and considered the diverse demands across the phases of drug development. A limited set of imaging techniques was being used, largely based on structural measures of disease evaluated using established response criteria such as response evaluation criteria in solid tumours (RECIST). Beyond structure, functional tissue imaging such as dynamic contrast enhanced (DCE) magnetic resonance imaging (MRI) and metabolic measures using [18F]fluorodeoxyglucose ([18F]FDG) positron emission tomography (PET) were being increasingly incorporated. Specific challenges related to the implementation of imaging were outlined including standardisation of scanning across study centres and consistency of analysis and reporting. More than a decade on the needs of modern drug development are reviewed, how imaging has evolved to support new drug development demands, the potential to translate state-of-the-art methods into routine tools and what is needed to enable the effective use of this broadening clinical trial toolset. In this review we challenge the clinical and scientific imaging community to help refine existing clinical trial methods and innovate to deliver the next generation of techniques. Strong industry-academic partnerships and pre-competitive opportunities to co-ordinate efforts will ensure imaging technologies maintain a crucial role delivering innovative medicines to treat cancer.

The application of multiple omics technologies in biomedical cohorts has the potential to reveal patient-level disease characteristics and individualized response to treatment. However, the scale and heterogeneous nature of multi-modal data makes integration and inference a non-trivial task. We developed a deep-learning-based framework, multi-omics variational autoencoders (MOVE), to integrate such data and applied it to a cohort of 789 people with newly diagnosed type 2 diabetes with deep multi-omics phenotyping from the DIRECT consortium. Using in silico perturbations, we identified drug–omics associations across the multi-modal datasets for the 20 most prevalent drugs given to people with type 2 diabetes with substantially higher sensitivity than univariate statistical tests. From these, we among others, identified novel associations between metformin and the gut microbiota as well as opposite molecular responses for the two statins, simvastatin and atorvastatin. We used the associations to quantify drug–drug similarities, assess the degree of polypharmacy and conclude that drug effects are distributed across the multi-omics modalities. Clinical multi-omics data are integrated and analyzed using a generative deep-learning model.