Biology subjects

Sargeant, T.

Publications and source records attributed to Sargeant, T..

3 recordsLinked to original sources

bioRxiv2018

SIS-seq, a molecular ‘time machine’, connects single cell fate with gene programs

Conventional single cell RNA-seq methods are destructive, such that a given cell cannot also then be tested for fate and function, without a time machine. Here, we develop a clonal method SIS-seq, whereby single cells are allowed to divide, and progeny cells are assayed separately in SISter conditions; some for fate, others by RNA-seq. By cross-correlating progenitor gene expression with mature cell fate within a clone, and doing this for many clones, we can identify the earliest gene expression signatures of dendritic cell subset development. SIS-seq could be used to study other populations harboring clonal heterogeneity, including stem, reprogrammed and cancer cells to reveal the transcriptional origins of fate decisions.

systems biology↗

bioRxiv2018

SuperFreq: Integrated mutation detection and clonal tracking in cancer

Analysing multiple cancer samples from an individual patient can provide insight into the way the disease evolves. Monitoring the expansion and contraction of distinct clones helps to reveal the mutations that initiate the disease and those that drive progression. Existing approaches for clonal tracking from sequencing data typically require the user to combine multiple tools that are not purpose-built for this task. Furthermore, most methods require a matched normal (non-tumour) sample, which limits the scope of application. We developed SuperFreq, a cancer exome sequencing analysis pipeline that integrates identification of somatic single nucleotide variants (SNVs) and copy number alterations (CNAs) and clonal tracking for both. SuperFreq does not require a matched normal and instead relies on unrelated controls. When analysing multiple samples from a single patient, SuperFreq cross checks variant calls to improve clonal tracking, which helps to separate somatic from germline variants, and to resolve overlapping CNA calls. To demonstrate our software we analysed 304 cancer-normal exome samples across 33 cancer types in The Cancer Genome Atlas (TCGA) and evaluated the quality of the SNV and CNA calls. We simulated clonal evolution through in silico mixing of cancer and normal samples in known proportion. We found that SuperFreq identified 93% of clones with a cellular fraction of at least 50% and mutations were assigned to the correct clone with high recall and precision. In addition, SuperFreq maintained a similar level of performance for most aspects of the analysis when run without a matched normal. SuperFreq is highly versatile and can be applied in many different experimental settings for the analysis of exomes and other capture libraries. We demonstrate an application of SuperFreq to leukaemia patients with diagnosis and relapse samples. SuperFreq is implemented in R and available on github at https://github.com/ChristofferFlensburg/SuperFreq.

bioinformatics↗

bioRxiv2017

Dynamic changes in clonal architecture during disease progression in follicular lymphoma

Follicular lymphoma (FL) is typically a slow growing cancer that can be effectively treated. Some patients undergo transformation to diffuse large B cell lymphoma (DLBCL), which is frequently resistant to chemotherapy and is generally fatal. Targeted sequencing of DNA and RNA was applied to identify mutations and transcriptional changes that accompanied transformation in a cohort of 16 patients, including 14 with paired samples. In most cases we found mutations that were specific to the FL clone dominant at diagnosis, supporting the view that DLBCL does not develop directly from FL, but from an ancestral progenitor. We identified frequent mutations in TP53, cell cycle regulators (cyclins and cyclin-dependent kinases) and the PI3K pathway, as well as recurrent somatic copy number variants (SCNVs) on chromosome 3, 7 and 17p associated with transformation. An integrated analysis of RNA and DNA identified allele specific expression changes in oncogenes, including MYC, that could be attributed to structural rearrangements. By focusing on serial samples taken from two patients, we identified evidence of convergent tumour evolution, where clonal expansion was repeatedly associated with mutations targeting the same genes or pathways. Analysis of serial samples is a powerful way to identify core dependencies that support lymphoma growth.

cancer biology↗