Biology subjects

Goranova, T.

Publications and source records attributed to Goranova, T..

5 recordsLinked to original sources

bioRxiv2024

Primary culture of high grade serous ovarian cancer cells, selection and derivation of three cell lines

Cell line models for high grade serous ovarian cancer (HGSOC) are limited in number and are poorly clinically annotated. Consequently, existing models often fail to recapitulate common features of HGSOC, inhibiting mechanistic and therapeutic discovery. We generated and characterised three spontaneously immortalized continuous HGSOC cell lines named CIOV1, CIOV2, and CIOV3 and confirmed that each cell line retained the genomic and pathologic characteristics of its parental tumour. We show that subclonal cell populations present at initiation, expanded and contracted during the culturing process before converging on a stable immortalized line. These lines are new valuable models to study acquired chemoresistance of HGSOC.

cancer biology↗

bioRxiv2021

The genomic landscape of early stage ovarian high grade serous carcinoma

PurposeHigh grade serous carcinoma (HGSC) is the commonest type of ovarian cancer. Nearly all HGSC cases are diagnosed at late stage and it is not clear whether early stage HGSC has unique characteristics compared to late stage tumours. Experimental DesignWe analysed samples from 45 patients with FIGO stage I - IIA HGSC - 40 from the pathology archives of three large UK cancer centres and 5 from the BriTROC-1 study. We performed shallow whole genome sequencing (sWGS) and targeted next generation sequencing to investigate somatic mutations and copy number alterations. We compared results to 51 stage IIIC/IV HGSC patients from the BriTROC-1 study. ResultsThere was no difference in median age between the early stage (median 61.3 years, range 40-84) and late stage (median 62.3 years, range 34-76) patients at diagnosis. TP53 mutations were near-universal (92% early stage, 100% late stage samples) and there were no significant differences in the rates of other somatic mutations, including BRCA1 and BRCA2, or focal copy number alterations between early- and late-stage cohorts. There were also no unique amplifications or deletions in either cohort. However, median ploidy was greater in late stage (median 3.1) than early stage (median 2.0) samples. In addition, there were higher numbers of breakpoints per 10MB and per chromosome arm and higher absolute copy number in late stage than early stage cohorts; early stage samples had longer segment length. Overall copy number signature exposures were significantly different between early and late stage samples with greater signature 3 exposure in early stage and greater signature 4 in late stage. Both simplex plot and unsupervised hierarchical clustering suggested that a subset of late stage samples retain early stage appearances with high signature 3 and co-clustering with the early stage samples ConclusionsThese data suggest that there are no unique mutations or focal copy number alterations in early stage HGSC. However, whole genome duplication is significantly more common in late-stage disease, suggesting evolution during disease progression. However, a subset of late stage HGSC retains early-stage features, which are associated with improved overall survival.

cancer biology↗

bioRxiv2018

frenchFISH: Poisson models for quantifying DNA copy-number from fluorescence in situ hybridisation of tissue sections

Chromosomal aberration and DNA copy number change are robust hallmarks of cancer. Imaging of spots generated using fluorescence in situ hybridisation (FISH) of locus specific probes is routinely used to detect copy number changes in tumour nuclei. However, it often does not perform well on solid tumour tissue sections, where partially represented or overlapping nuclei are common. To overcome these challenges, we have developed a computational approach called FrenchFISH, which comprises a nuclear volume correction method coupled with two types of Poisson models: either a Poisson model for improved manual spot counting without the need for control probes; or a homogenous Poisson Point Process model for automated spot counting. We benchmarked the performance of FrenchFISH against previous approaches in a controlled simulation scenario and exemplify its use in 12 ovarian cancer FFPE-tissue sections, for which we assess copy number alterations in three loci (c-Myc, hTERC and SE7). We show that FrenchFISH outperforms standard spot counting approaches and that the automated spot counting is significantly faster than manual without loss of performance. FrenchFISH is a general approach that can be used to enhance clinical diagnosis on sections of any tissue.\n\nAuthor summaryCancer genomes can look very chaotic, because cancer cells are unable to fully repair errors in DNA replication during cell division. While a healthy genome has two copies of every chromosome, in a cancer genome some pieces can be lost completely and others can appear in 50 copies. To diagnose cancers and to decide on the right therapeutic strategy for a patient, it can be very important to know how many copies of a particular piece of DNA exist in a cell. The standard technique used in the clinic to assess DNA copy number is called FISH, short for fluorescence in situ hybridisation. This technique uses fluorescent probes that bind to a DNA piece of interest and show up as glowing spots in a microscopic image. Counting the spots in an image is a labour-and time-intensive process that is generally done by well-trained experts. Here we present a statistical approach to automatically count FISH spots, which outperforms previously proposed methods, and has the potential to substantially speed up clinical diagnostics.

bioinformatics↗

bioRxiv2017

Copy-number signatures and mutational processes in ovarian carcinoma

Tumours with profound copy-number aberration elude molecular stratification due to their genomic complexity. By representing this complexity as a mixture of copy-number signatures, we provide molecular explanations for differing clinical outcomes. Here we present a method for copy-number signature identification, deriving eight signatures in 117 shallow whole-genome sequenced high-grade serous ovarian cancers (HGSOC), which validated on independent cohorts of 95 deep whole-genome sequenced, and 402 SNP array-profiled cases. Three copy-number signatures predicted longer overall survival, while the others predicted poorer outcome. We found evidence for the mutational processes giving rise to copy-number change for six of the eight signatures via correlations with other genomic features. Our results provide insights into the pathogenesis of HGSOC by uncovering multiple mutational processes that shape genomes following TP53 mutation. Importantly, our work shows that most HGSOC have a mixture of mutational processes suggesting that targeting a single mutator phenotype may be therapeutically suboptimal.

cancer biology↗

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↗