Biology subjects

Gale, D.

Publications and source records attributed to Gale, D..

8 recordsLinked to original sources

bioRxiv2023

A large-scale retrospective study in metastatic breast cancer patients using circulating tumor DNA and machine learning to predict treatment outcome and progression-free survival

PurposeMonitoring levels of circulating tumor-derived DNA (ctDNA) represents a non-invasive snapshot of tumor burden and potentially clonal evolution. Here we describe how a novel statistical model that uses serial ctDNA measurements from shallow whole genome sequencing (sWGS) in metastatic breast cancer patients produces a rapid and inexpensive assessment that is predictive of treatment response and progression-free survival. Patients and MethodsA cohort of 188 metastatic breast cancer patients had DNA extracted from serial plasma samples (total 1098, median=4, mean=5.87). Plasma DNA was assessed using sWGS and the tumor fraction in total cell free DNA estimated using ichorCNA. This approach was compared with ctDNA targeted sequencing and serial CA 15-3 measurements. The longitudinal ichorCNA values were used to develop a Bayesian learning model to predict subsequent treatment response. ResultsWe identified a transition point of 7% estimated tumor fraction to stratify patients into different categories of progression risk using ichorCNA estimates and a time-dependent Cox model, validated across different breast cancer subtypes and treatments, outperforming the alternative methods. We then developed a Bayesian learning model to predict subsequent treatment response with a sensitivity of 0.75 and a specificity of 0.66. ConclusionIn patients with metastatic breast cancer, sWGS of ctDNA and ichorCNA provide prognostic and predictive real-time valuable information on treatment response across subtypes and therapies. A prospective large-scale clinical trial to evaluate clinical benefit of early treatment changes based on ctDNA levels is now warranted.

genomics↗

bioRxiv2021

Kidney organoids: A system to study human basement membrane assembly in health and disease

Basement membranes (BMs) are complex macromolecular networks underlying all continuous layers of cells. Essential components include type IV collagen and laminins, which are affected by human genetic defects leading to a range of debilitating conditions including kidney, muscle, and cerebrovascular phenotypes. We investigated the dynamics of BM assembly in human pluripotent stem cell-derived kidney organoids. We resolved their global BM composition and discovered a conserved temporal sequence in BM assembly that paralleled mammalian fetal kidneys. We identified the emergence of key BM isoforms, which were altered by a pathogenic variant in COL4A5. Integrating organoid, fetal and adult kidney proteomes we found dynamic regulation of BM composition through development to adulthood, and with single-cell transcriptomic analysis we mapped the cellular origins of BM components. Overall, we define the complex and dynamic nature of vertebrate BM assembly and provide a platform for understanding its wider relevance in human development and disease.

cell biology↗

bioRxiv2019

Comprehensive characterisation of cell-free tumour DNA in plasma and urine of patients with renal tumours

Cell-free tumour-derived DNA (ctDNA) allows non-invasive monitoring of cancers but its utility in renal cell cancer (RCC) has not been established. Here, untargeted and targeted sequencing methods, applied to two independent cohorts of renal tumour patients (n=90), were used to determine ctDNA content in plasma and urine. Our data revealed lower plasma ctDNA levels in RCC relative to other cancers, with untargeted detection of [~]33%. A sensitive personalised approach, applied to plasma and urine from select patients improved detection to [~]50%, including in patients with early-stage and even benign lesions.\n\nA machine-learning based model predicted detection, potentially offering a means of triaging samples for personalised analysis. In addition, with limited data we observed that plasma, and for the first time, urine ctDNA may better represent tumour heterogeneity than tissue biopsy. Furthermore, longitudinal sampling of >200 plasma samples revealed that ctDNA can track disease course. Additional datasets will be required to validate these findings.\n\nOverall, our data highlight RCC as a ctDNA-low malignancy, but indicate potential clinical utility provided improvement in detection approaches.\n\nOne sentence summaryComplementary sequencing methods show that cell-free tumour DNA levels are low in renal cancer though, via various strategies, may still be informative.

cancer biology↗

bioRxiv2019

Integrated clonal analysis reveals circulating tumor DNA in urine and plasma of glioma patients

Glioma-derived cell-free tumor DNA is challenging to detect using standard liquid biopsy techniques as its levels in body fluids are very low, similar to those in patients with early stage carcinomas. By sequencing cell-free DNA across thousands of clonal and private mutations identified individually in each patients tumor we detected tumor-derived DNA in plasma (10/12, 83%) and urine samples (8/11, 72%) from the majority (7/8, 87.5%) of glioma patients tested.\n\nOne Sentence SummaryCirculating tumor DNA can be detected in the majority of plasma and urine samples from primary brain tumor patients using sequencing guided by mutations detected in multi-region tumor biopsies.

genomics↗

bioRxiv2019

High-sensitivity monitoring of ctDNA by patient-specific sequencing panels and integration of variant reads

Circulating tumor-derived DNA (ctDNA) can be used to monitor cancer dynamics noninvasively. Patients with small tumors have few copies of ctDNA in plasma, resulting in limited sensitivity to detect low-volume or residual disease. We show that sampling limitations can be overcome and sensitivity for ctDNA detection can be improved by massively parallel sequencing when hundreds to thousands of mutations are identified by tumor genotyping. We describe the INtegration of VAriant Reads (INVAR) analysis pipeline, which combines patient-specific mutation lists with both custom error-suppression methods and signal enrichment based on biological features of ctDNA. In this framework, the sensitivity can be estimated independently for each sample based on the number of informative reads, which is the product of the number of mutations analyzed and the average depth of unique sequencing reads. We applied INVAR to deep sequencing data generated by custom hybrid-capture panels, and showed that when ~106 informative reads were obtained INVAR allowed detection of tumor-derived DNA fractions to parts per million (ppm). In serial samples from patients with advanced melanoma on treatment, we detected ctDNA when imaging confirmed tumor volume of ~1cm3. In patients with resected early-stage melanoma, ctDNA was detected in 40% of patients who later relapsed, with higher rates of detection when more informative reads were obtained. We further demonstrated that INVAR can be generalized and allows improved detection of ctDNA from whole-exome and low-depth whole-genome sequencing data.

cancer biology↗

bioRxiv2019

Detection of ctDNA from dried blood spots after DNA size selection

Recent advances in the research and clinical applications of circulating tumour DNA (ctDNA) is limited by practical considerations of sample collection. Whole genome sequencing (WGS) is increasingly used for analysis of ctDNA, identifying copy-number alterations, fragment size patterns, and other genomic features. We hypothesised that low-depth WGS data may be generated from minute amounts of cell-free DNA, and that fragment-size selection may be effective to remove contaminating genomic DNA (gDNA) from small volumes of blood. There are practical advantages to using dried blood spots as these are easier to collect, facilitate serial sampling, and support novel study designs in prospective human studies, animal models and expand the utilisation of archival samples by the removal of gDNA in small volumes. We therefore developed a protocol for the isolation and analysis of cell-free DNA from dried blood spots. Analysing a dried blood spot of 50L frozen whole blood from a patient with melanoma, we identified ctDNA based on tumour-specific somatic copy-number alterations, and found a fragment size profile similar to that observed in plasma DNA processed by traditional methods. We extended this approach to detect tumour-derived cell-free DNA in a dried blood spot from a mouse xenograft model and were able to identify ctDNA from the originally grafted ascites. Together, our data suggests that ctDNA can be detected and monitored in dried blood spots. This will enable new approaches for sample collection from patients and in vivo models.

cancer biology↗

bioRxiv2017

SemEHR: A General-purpose Semantic Search System to Surface Semantic Data from Clinical Notes for Tailored Care, Trial Recruitment and Clinical Research

ObjectiveUnlocking the data contained within both structured and unstructured components of Electronic Health Records (EHRs) has the potential to provide a step change in data available forsecondary research use, generation of actionable medical insights, hospital management and trial recruitment. To achieve this, we implemented SemEHR - a semantic search and analytics, open source tool for EHRs.\n\nMethodsSemEHR implements a generic information extraction (IE) and retrieval infrastructure by identifying contextualised mentions of a wide range of biomedical concepts within EHRs. Natural Language Processing (NLP) annotations are further assembled at patient level and extended with EHR-specific knowledge to generate a timeline for each patient. The semantic data is serviced via ontology-based search and analytics interfaces.\n\nResultsSemEHR has been deployed to a number of UK hospitals including the Clinical Record Interactive Search (CRIS), an anonymised replica of the EHR of the UK South London and Maudsley (SLaM) NHS Foundation Trust, one of Europes largest providers of mental health services. In two CRIS-based studies, SemEHR achieved 93% (Hepatitis C case) and 99% (HIV case) F-Measure results in identifying true positive patients. At Kings College Hospital in London, as part of the CogStack programme (github.com/cogstack), SemEHR is being used to recruit patients into the UK Dept of Health 100k Genome Project (genomicsengland.co.uk). The validation study suggests that the tool can validate previously recruited cases and is very fast in searching phenotypes - time for recruitment criteria checking reduced from days to minutes. Validated on an open intensive care EHR data - MIMICIII, the vital signs extracted by SemEHR can achieve around 97% accuracy.\n\nConclusionResults from the multiple case studies demonstrate SemEHRs efficiency - weeks or months of work can be done within hours or minutes in some cases. SemEHR provides a more comprehensive view of a patient, bringing in more and unexpected insight compared to study-oriented bespoke information extraction systems.\n\nSemEHR is open source available at https://github.com/CogStack/SemEHR.

bioinformatics↗

bioRxiv2017

Selecting Short DNA Fragments In Plasma Improves Detection Of Circulating Tumour DNA

Introductory paragraphNon-invasive analysis of cancer genomes using cell-free circulating tumour DNA (ctDNA) is being widely implemented for clinical indications. The sensitivity for detecting the presence of ctDNA and genomic changes in ctDNA is limited by its low concentration compared to cell-free DNA of non-tumour origin. We studied the feasibility for enrichment of ctDNA by size selection, in plasma samples collected before and during chemotherapy treatment in 13 patients with recurrent high-grade serous ovarian cancer. We evaluated the effects using targeted and whole genome sequencing. Selecting DNA fragments between 90-150 bp before analysis yielded enrichment of mutated DNA fraction of up to 11-fold. This allowed identification of adverse copy number alterations, including MYC amplification, otherwise not observed. Size selection allows detection of tumour alterations masked by non-tumour DNA in plasma and could help overcome sensitivity limitations of liquid biopsy for applications in early diagnosis, detection of minimal residual disease, and genomic profiling.

genomics↗