Biology subjects

Thomas, D. M.

Publications and source records attributed to Thomas, D. M..

10 recordsLinked to original sources

bioRxiv2024

Engineered paediatric tumours retain patient tumour genotype and phenotype for precision medicine.

Precision medicine for paediatric and adult cancers that includes drug sensitivity profiling, can identify effective therapies for individual patients. However, obtaining adequate biopsy samples for high-throughput (HTP) screening remains challenging, with tumours needing to be expanded in culture or patient-derived xenografts - this is time-consuming and often unsuccessful. Herein, we have developed paediatric patient-derived tumour models using an engineered extracellular matrix (ECM) tissue mimic hydrogel system and HTP 3D bioprinting. Gene expression analysis from neuroblastoma and sarcoma patients identified key components of the ECM in these tumour types. Engineered hydrogels with ECM-mimic peptides were used to create patient-specific tumour organoids, modelling tumour growth conditions. Expanded tumour organoids recapitulated the genetic and phenotypic characteristics of the original tumours and retained tumourgenicity. Screening of these models identified individualised drug sensitivities. Our approach offers a timely and clinically relevant technology platform for precision medicine in paediatric cancers, potentially transforming preclinical testing across cancer types.

cancer biology↗

bioRxiv2024

Family-Wide Photoproximity Profiling of Integrin Protein Social Networks in Cancer

Integrin family transmembrane receptors mediate dynamic interactions between cells and their extracellular microenvironment. The heterogeneous interaction partners of integrins directly regulate cell adhesion, motility, proliferation, and intracellular signaling. Despite the recognized importance of protein-protein interactions and the formation of signaling hubs around integrins, the ability to detect and quantify these dynamic binding partners with high spatial and temporal resolution remains challenging. Here, we developed an integrin-family-directed quantitative photoproximity protein interaction (PhotoPPI) profiling method to detect and quantify native integrin-centered protein social networks on live cells and tissues without the need for genetic manipulation, antibodies, or non-physiologic cell culture conditions. We drafted quantitative maps of integrin-centered protein social networks, highlighting conserved and unique binding partners between different cell types and cellular microenvironments. Comparison of integrin social networks in cancer cell lines of diverse tissue of origin and disease state identified specific AND-gate binding partners involved cell migration, microenvironmental interactions and proliferation that serve as markers of tumor cell metastatic state. Finally, we identified unique combinations - or barcodes - of integrin-proximal proteins on the surface of pre- and post-metastatic triple negative breast cancer (TNBC) cells whose expression strongly correlate with both positive and negative disease progression and outcomes in TNBC patients. Taken together, these data provide the first family-wide high-resolution maps of native protein interactors on live cells and identify dynamic integrin-centered social networks as potential AND-gate markers of cell identity, microenvironmental context and disease state.

cancer biology↗

bioRxiv2023

Computational repurposing of oncology drugs through off-target drug binding interactions from pharmacological databases

PURPOSESystematic repurposing of approved medicine for another indication represents an attractive strategy to accelerating drug development in oncology. Herein we present a strategy of combining biomarker testing with drug repurposing to identify new treatments for patients with advanced cancer. METHODSTumours were sequenced with Illumina TruSight Oncology 500 (TSO-500) platform or the FoundationOne(R) CDx panel. Mutations were manually screened by two medical oncology clinicians and pathogenic mutations were categorised with reference to the literature. Variants of unknown significance were classified as potentially pathogenic if a plausible mechanism and computational prediction of pathogenicity existed. Gain of function mutations were evaluated through the repurposing databases Probe Miner, the Broad Institute Drug Repurposing Hub (Broad Institute DRH) and TOPOGRAPH. Gain of function mutations were classified as repurposing events if they were identified in Probe Miner, were not indexed in TOPOGRAPH which captures active clinical trial biomarkers and excluding mutations for which a known FDA-approved biomarker label exists. The performance of the computational repurposing approach was validated by evaluating its ability to identify known FDA-approved biomarkers. Exploratory functional analyses were performed with gene expression data and CRISPR-dependency data sourced from the DepMap portal. The total repurposable genome was identified by evaluating all possible gene-FDA drug approved combinations in the Probe Miner dataset. RESULTSThe computational repurposing approach was highly accurate at identifying FDA therapies with known biomarkers (94%). Using a real-world dataset of next-generation sequencing molecular reports (n = 94) and excluding the identification of mutations that would render patients eligible for FDA-licensed therapies or local clinical trials, it was found that a meaningful percentage of patients (14%) would have an off-label therapeutic identified through this approach. Exploratory analyses were performed, including the identification of drug-target interactions that have been previously described in the medicinal chemistry literature but are not well known, and the evaluation of the frequency of theoretical drug repurposing events in the TCGA pan-cancer dataset (73% of samples in the cohort). CONCLUSIONOverall, a computational drug repurposing approach may assist in identifying novel repurposing events in cancer patients with advanced tumours and no access to standard therapies. Further validation is needed to confirm the utility of a precision oncology approach using drug repurposing.

biochemistry↗

bioRxiv2021

Streamlined use of protein structures in variant analysis

MotivationVariant analysis is a core task in bioinformatics that requires integrating data from many sources. This process can be helped by using 3D structures of proteins, which can provide a spatial context that can provide insight into how variants affect function. Many available tools can help with mapping variants onto structures; but each has specific restrictions, with the result that many researchers fail to benefit from valuable insights that could be gained from structural data. ResultsTo address this, we have created a streamlined system for incorporating 3D structures into variant analysis. Variants can be easily specified via URLs that are easily readable and writable, and use the notation recommended by the Human Genome Variation Society (HGVS). For example, https://aquaria.app/SARS-CoV-2/S/?N501Y specifies the N501Y variant of SARS-CoV-2 S protein. In addition to mapping variants onto structures, our system provides summary information from multiple external resources, including COSMIC, CATH-FunVar, and PredictProtein. Furthermore, our system identifies and summarizes structures containing the variant, as well as the variant-position. Our system supports essentially any mutation for any well-studied protein, and uses all available structural data -- including models inferred via very remote homology -- integrated into a system that is fast and simple to use. By giving researchers easy, streamlined access to a wealth of structural information during variant analysis, our system will help in revealing novel insights into the molecular mechanisms underlying protein function in health and disease. AvailabilityOur resource is freely available at the project home page (https://aquaria.app). After peer review, the code will be openly available via a GPL version 2 license at https://github.com/ODonoghueLab/Aquaria. PSSH2, the database of sequence-to-structure alignments, is also freely available for download at https://zenodo.org/record/4279164. Contactsean@odonoghuelab.org Supplementary informationNone.

bioinformatics↗

bioRxiv2019

mity: A highly sensitive mitochondrial variant analysis pipeline for whole genome sequencing data

MotivationMitochondrial diseases (MDs) are the most common group of inherited metabolic disorders and are often challenging to diagnose due to extensive genotype-phenotype heterogeneity. MDs are caused by mutations in the nuclear or mitochondrial genome, where pathogenic mitochondrial variants are usually heteroplasmic and typically at much lower allelic fraction in the blood than affected tissues. Both genomes can now be readily analysed using unbiased whole genome sequencing (WGS), but most nuclear variant detection methods fail to detect low heteroplasmy variants in the mitochondrial genome. ResultsWe present mity, a bioinformatics pipeline for detecting and interpreting heteroplasmic SNVs and INDELs in the mitochondrial genome using WGS data. In 2,980 healthy controls, we observed on average 3,166x coverage in the mitochondrial genome using WGS from blood. mity utilises this high depth to detect pathogenic mitochondrial variants, even at low heteroplasmy. mity enables easy interpretation of mitochondrial variants and can be incorporated into existing diagnostic WGS pipelines. This could simplify the diagnostic pathway, avoid invasive tissue biopsies and increase the diagnostic rate for MDs and other conditions caused by impaired mitochondrial function. Availabilitymity is available from https://github.com/KCCG/mityunder an MIT license. Contactclare.puttick@crick.ac.uk, carolyn.sue@sydney.edu.au, MCowley@ccia.org.au

bioinformatics↗

bioRxiv2019

Risk of differential cancer types over age in families with Li-Fraumeni syndrome: a validation study using multi-center cohorts

Li-Fraumeni syndrome (LFS) is a rare hereditary cancer syndrome associated with an autosomal dominant mutation inheritance in the TP53 tumor suppressor gene and a wide spectrum of cancer diagnoses. An accurate estimation of the penetrance of different cancer types in LFS is crucial to improve the clinical characterization and management of high-risk individuals of LFS. Our competing risk-based statistical model incorporates the pedigree structure efficiently into the penetrance estimation and corrects for the ascertainment bias. A set of TP53 penetrance for three cancer types (breast, BR/sarcoma, SA/others, OT) involved in LFS is then estimated from 186 pediatric-sarcoma families collected at MD Anderson Cancer Center. The penetrance was then validated on a mixed cohort of clinically ascertained families with cancer (total number of families=668). The age-dependent onset probability distributions of specific cancer types are different. For breast cancer, the TP53 penetrance goes up at an earlier age than the reported BRCA1/2 penetrance. We validated the prediction performance of the penetrance estimates via two independent cohorts combined (BR=85, SA=540, OT=158). We obtained areas under the ROC curves (AUCs) of 0.92 (BR), 0.75 (SA), and 0.81 (OT). Our R package, LFSPRO, provides risk estimates for the diagnosis of breast cancer, sarcoma, other cancers or death before cancer diagnosis in future.\n\nSignificanceCancer-specific penetrance can facilitate clinical characterization of LFS and will contribute to the management of families at high risk of LFS.

genetics↗

bioRxiv2018

The Medical Genome Reference Bank: Whole genomes and phenotype of 2,570 healthy elderly

Population health research is increasingly focused on the genetic determinants of healthy ageing, but there is no public resource of whole genome sequences and phenotype data from healthy elderly individuals. Here we describe the Medical Genome Reference Bank (MGRB), comprising whole genome sequence and phenotype of 2,570 elderly Australians depleted for cancer, cardiovascular disease, and dementia. We analysed the MGRB for single-nucleotide, indel and structural variation in the nuclear and mitochondrial genomes. Individuals in the MGRB had fewer disease-associated common and rare germline variants, relative to both cancer cases and the gnomAD and UK BioBank cohorts, consistent with risk depletion. Pervasive age-related somatic changes were correlated with grip strength in men, suggesting blood-derived whole genomes may also provide a biologic measure of age-related functional deterioration. The MGRB provides a broadly applicable reference cohort for clinical genetics and genomic association studies, and for understanding the genetics of healthy ageing. This research has been conducted using the UK Biobank Resource under Application Number 17984.

genomics↗

bioRxiv2018

Network-aware mutation clustering of cancer

The grouping of cancers across tissue boundaries is central to precision oncology, but remains a difficult problem. Here we present EPICC (Experimental Protein Interaction Clustering of Cancer), a novel technique to cluster cancer patients based on DNA mutation profile, that leverages knowledge of protein-protein interactions to reduce noise and amplify biological signal. We applied EPICC to data from The Cancer Genome Atlas (TCGA), and both recapitulated known cancer clusterings, and identified new cross-tissue cancer groups that may indicate novel cancer molecular subtypes. Investigation of EPICC clusters revealed new protein modules which were recurrently mutated across cancers, and indicate new avenues for research into cancer biology. EPICC leveraged the Vodafone DreamLab citizen science platform, and we provide our results as a resource for researchers to investigate the role of protein modules in cancer.

bioinformatics↗

bioRxiv2018

The Medical Genome Reference Bank: a whole-genome data resource of 4,000 healthy elderly individuals. Rationale and cohort design

Allele frequency data from human reference populations is of increasing value for filtering and assignment of pathogenicity to genetic variants. Aged and healthy populations are more likely to be selectively depleted of pathogenic alleles, and therefore particularly suitable as a reference populations for the major diseases of clinical and public health importance. However, reference studies of the healthy elderly have remained under-represented in human genetics. We have developed the Medical Genome Reference Bank (MGRB), a large-scale comprehensive whole-genome dataset of confirmed healthy elderly individuals, to provide a publicly accessible resource for health-related research, and for clinical genetics. It also represents a useful resource for studying the genetics of healthy aging. The MGRB comprises 4,000 healthy, older individuals with no reported history of cancer, cardiovascular disease or dementia, recruited from two Australian community-based cohorts. DNA derived from blood samples will be subject to whole genome sequencing. The MGRB will measure genome-wide genetic variation in 4,000 individuals, mostly of European decent, aged 60-95 years (mean age [≥] 75 years). The MGRB has committed to a policy of data sharing, employing a hierarchical data management system to maintain participant privacy and confidentiality, whilst maximizing research and clinical usage of the database. The MGRB will represent a dataset of international significance, broadly accessible to the clinical and genetic research community.

genomics↗

bioRxiv2017

Whole genome optical mapping reveals multiple fusion events chained by large novel sequences in cancer

Genomic rearrangements are common in cancer, with demonstrated links to disease progression and treatment response. These rearrangements can be complex, resulting in fusions of multiple chromosomal fragments and generation of derivative chromosomes. While methods exist for detecting individual fusions, they are generally unable to reconstruct complex chained events. To overcome these limitations, we adopted a new optical mapping approach, allowing for megabase length DNA to be captured, and in turn rearranged genomes to be visualized without loss of integrity. Whole genome mapping (Bionano Genomics) of a well-studied highly rearranged liposarcoma cell line, resulted in 3,338 assembled haploid genome maps, including 101 fusion maps. These fusion maps represent 175 Mb of highly rearranged genomic regions, illuminating the complex architecture of chained fusions, including content, order, orientation, and size. Spanning the junction of 151 chromosomal translocations, we found a total of 32 Mb of novel interspersed sequences that were not detected from short-read sequencing. We demonstrate that optical mapping provides a powerful new approach for capturing a higher level of complex genomic architecture, creating a scaffold for renewed interpretation of sequencing data of particular relevance to human cancer.

genomics↗