Biology subjects

Lavikka, K.

Publications and source records attributed to Lavikka, K..

4 recordsLinked to original sources

bioRxiv2023

Deciphering Cancer Genomes with GenomeSpy: A Grammar-Based Visualization Toolkit

BackgroundVisualization is an indispensable facet of genomic data analysis. Despite the abundance of specialized visualization tools, there remains a distinct need for tailored solutions. However, their implementation typically requires extensive programming expertise from bioinformaticians and software developers, especially when building interactive applications. Toolkits based on visualization grammars offer a more accessible, declarative way to author new visualizations. Nevertheless, current grammar-based solutions fall short in adequately supporting the interactive analysis of large data sets with extensive sample collections, a pivotal task often encountered in cancer research. ResultsWe present GenomeSpy, a grammar-based toolkit for authoring tailored, interactive visualizations for genomic data analysis. Users can implement new visualization designs with little effort by using combinatorial building blocks that are put together with a declarative language. These fully customizable visualizations can be embedded in web pages or end-user-oriented applications. The toolkit also includes a fully customizable but user-friendly application for analyzing sample collections, which may comprise genomic and clinical data. Findings can be bookmarked and shared as links that incorporate provenance information. A distinctive element of GenomeSpys architecture is its effective use of the graphics processing unit (GPU) in all rendering. GPU usage enables a high frame rate and smoothly animated interactions, such as navigation within a genome. We demonstrate the utility of GenomeSpy by characterizing the genomic landscape of 753 ovarian cancer samples from patients in the DECIDER clinical trial. Our results expand the understanding of the genomic architecture in ovarian cancer, particularly the diversity of chromosomal instability. We also show how GenomeSpy enabled the discovery of clinically actionable genomic aberrations. ConclusionsGenomeSpy is a visualization toolkit applicable to a wide range of tasks pertinent to genome analysis. It offers high flexibility and exceptional performance in interactive analysis. The toolkit is open source with an MIT license, implemented in JavaScript, and available at https://genomespy.app/.

bioinformatics↗

bioRxiv2022

L1 retrotransposition is regulated post-transcriptionally In High-Grade Serous Ovarian Cancer

L1 retrotransposons are the only protein-coding active transposable elements in the human genome. Although silenced during normal conditions, they are highly expressed in human epithelial cancers including high-grade serous ovarian cancer (HGSC), where they transcribe to form L1 mRNA and subsequently integrate into the genome by a process called retrotransposition. Despite of high L1 protein expression in the earliest phases of HGSC, these tumors do not accrue many somatic L1 insertions. To understand this unexplained disconnect, we monitored the transcription and retrotransposition activity of two frequently expressed retrotransposition-competent (RC)-L1 (RC-L1) in 64 clinical tumor specimens from 34 HGSC patients and found that despite the presence of RC-L1 mRNA, a third of samples did not acquire somatic L1 insertions. In addition to high inter-patient variability in retrotransposition frequency, there was remarkable intra-patient heterogeneity in L1 insertion patterns between tumor sites, indicating that L1 retrotransposition is highly dynamic in vivo. Comparison of genomic and transcriptomic features of L1-null tumors with L1-high tumors (those with [≥]5 somatic L1 insertions) showed that retrotransposition was favored by increased rate of cell proliferation.

cancer biology↗

bioRxiv2022

Evolutionary states and trajectories characterized by distinct pathways stratify ovarian high-grade serous carcinoma patients

Ovarian high-grade serous carcinoma (HGSC) is typically diagnosed at an advanced stage, with multiple genetically heterogeneous clones existing in the tumors long before therapeutic intervention. Herein we characterized HGSC evolutionary states using whole-genome sequencing data from 214 samples of 55 HGSC patients in the prospective, longitudinal, multiregion DECIDER study. Comparison of the tissues revealed that site-of-origin samples have 70% more unique clones than the metastatic tumors or ascites. By integrating clonal composition and topology of HGSC tumors we discovered three evolutionary states that represent a continuum from genomically highly variable to stable tumors with significant association to treatment response. The states and their evolutionary trajectories were characterized by unique, targetable pathways, which were validated with RNA-seq data. Our study reveals that genomic heterogeneity is unaffected by the current standard-of-care and pinpoints effective treatment targets for each group. All genomics data are available via an interactive visualization platform for rapid exploration.

cancer biology↗

bioRxiv2022

A platform for efficient establishment, expansion and drug response profiling of high-grade serous ovarian cancer organoids

The broad research use of organoids from high-grade serous ovarian carcinoma (HGSC) has been hampered by low culture success rates and limited availability of fresh tumor material. Here we describe a method for generation and long-term expansion of HGSC organoids with efficacy markedly improved over previous reports (55% vs. 23-38%). We established organoids from cryopreserved material, demonstrating the feasibility of using viably biobanked tissue for HGSC organoid derivation. Genomic, histologic and single-cell transcriptomic analyses revealed that organoids recapitulated genetic and phenotypic features of original tumors. Organoid drug responses correlated with clinical treatment outcomes, although in culture conditions-dependent manner and only in organoids maintained in human plasma-like medium (HPLM). Organoids from consenting patients are available to the research community through a public biobank and organoid genomic data explorable through an interactive online tool. Taken together, this resource facilitates the application of HGSC organoids in basic and translational ovarian cancer research.

cancer biology↗