Biology subjects

Lehtonen, R.

Publications and source records attributed to Lehtonen, R..

5 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↗

bioRxiv2019

PRISM: Recovering cell type specific expression profilesfrom composite RNA-seq data

A major challenge in analyzing cancer patient transcriptomes is that the tumors are inherently heterogeneous and evolving. We analyzed 214 bulk RNA samples of a longitudinal, prospective ovarian cancer cohort and found that the sample composition changes systematically due to chemotherapy and between the anatomical sites, preventing direct comparison of treatment-naive and treated samples. To overcome this, we developed PRISM, a latent statistical framework to simultaneously extract the sample composition and cell type specific whole-transcriptome profiles adapted to each individual sample. Our results indicate that the PRISM-derived composition-free transcriptomic profiles and signatures derived from them predict the patient response better than the composite raw bulk data. We validated our findings in independent ovarian cancer and melanoma cohorts, and verified that PRISM accurately estimates the composition and cell type specific expression through whole-genome sequencing and RNA in situ hybridization experiments. PRISM is freely available with full source code and documentation.

bioinformatics↗

bioRxiv2019

Epigenetic inhibitors sensitize DLBCL cells to rituximab and doxorubicin

Primary therapy for diffuse large B-cell lymphoma (DLBCL) is an immunochemotherapy regimen comprising rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP). While R-CHOP cures 60% of the patients with DLBCL, those who do not respond or relapse have dismal prognosis, and effective treatment strategies are needed. Due to the plastic nature of the epigenome and its fundamental role in regulating cell identity, we hypothesized that reprogramming the epigenome can overcome resistance to R-CHOP. We developed a novel drug screening protocol to identify epigenetic modifiers that sensitize DLBCL cell lines to immunochemotherapy. Of the herein tested 60 epigenetic compounds, we identified several histone deacetylase (HDAC) and histone methyltransferase (HMT) inhibitors that acted synergistically with immunochemotherapy. We show that sensitization through HDAC and HMT inhibitors is achieved by dysregulating homologous recombination, a central DNA repair pathway, as well as by disrupting the cell cycle and affecting the apoptotic pathway. Epigenetic inhibitors are well-tolerated, which together with our findings support their use in combination with immunochemotherapy in patients with primary refractory and relapsed DLBCL.

cancer biology↗

bioRxiv2018

ESR1, WT1, WNT4, ATM and TERT loci are major contributors to uterine leiomyoma predisposition

Uterine leiomyomas (ULs) are benign tumors that are a major burden to womens health. A genome-wide association study on 5,417 UL cases and 331,791 controls was performed, followed by replication of the genomic risk in two cohorts. Effects of the identified risk alleles were evaluated in view of molecular and clinical features.\n\nFive loci displayed a genome-wide significant association; the previously reported TNRC6B, and four novel loci ESR1 (ER), WT1, WNT4, and ATM. The sixth hit TERT is also a conceivable target. The combined polygenic risk contributed by these loci was associated with MED12 mutation-positive tumors. The findings link genes for uterine development and genetic stability to leiomyomagenesis. While the fundamental role of sex hormones in UL aetiology has been clear, this work reveals a connection to estrogen receptor alpha on genetic level and suggests that determinants of UL growth associated with estrogen exposure have an inherited component.

genomics↗

bioRxiv2018

Identifying differentially methylated sites in samples with varying tumor purity

DNA methylation aberrations are common in many cancer types. A major challenge hindering comparison of patient-derived samples is that they comprise of heterogeneous collection of cancer and microenvironment cells. We present a computational method that allows comparing cancer methylomes in two or more heterogeneous tumor samples featuring differing, unknown fraction of cancer cells. The method is unique in that it allows comparison also in the absence of normal cell control samples and without prior tumor purity estimates, as these are often unavailable or unreliable in clinical samples. We use simulations and next-generation methylome, RNA, and whole-genome sequencing data from two cancer types to demonstrate that the method is accurate and outperforms alternatives. The results show that our method adapts well to various cancer types and to a wide range of tumor content, and works robustly without a control or with controls derived from various sources. The method is freely available at https://bitbucket.org/anthakki/dmml.

bioinformatics↗