GEMiCCL: mining genotype and expression data of cancer cell lines with elaborate visualization

KoNA: Korean Nucleotide Archive as A New Data Repository for Nucleotide Sequence Data

During the last decade, the generation and accumulation of petabase-scale high-throughput sequencing data have resulted in great challenges, including access to human data, as well as transfer, storage, and sharing of enormous amounts of data. To promote data-driven biological research, the Korean government announced that all biological data generated from government-funded research projects should be deposited at the Korea BioData Station (K-BDS), which consists of multiple databases for individual data types. Here, we introduce the Korean Nucleotide Archive (KoNA), a repository of nucleotide sequence data. As of July 2022, the Korean Read Archive in KoNA has collected over 477 TB of raw next-generation sequencing data from national genome projects. To ensure data quality and prepare for international alignment, a standard operating procedure was adopted, which is similar to that of the International Nucleotide Sequence Database Collaboration. The standard operating procedure includes quality control processes for submitted data and metadata using an automated pipeline, followed by manual examination. To ensure fast and stable data transfer, a high-speed transmission system called GBox is used in KoNA. Furthermore, the data uploaded to or downloaded from KoNA through GBox can be readily processed using a cloud computing service called Bio-Express. This seamless coupling of KoNA, GBox, and Bio-Express enhances the data experience, including submission, access, and analysis of raw nucleotide sequences. KoNA not only satisfies the unmet needs for a national sequence repository in Korea but also provides datasets to researchers globally and contributes to advances in genomics. The KoNA is available at https://www.kobic.re.kr/kona/.

DSMZCellDive: Diving into high-throughput cell line data

Human and animal cell lines serve as model systems in a wide range of life sciences such as cancer and infection research or drug screening. Reproducible data are highly dependent on authenticated, contaminant-free cell lines, no better delivered than by the official and certified biorepositories. Offering a web portal to high-throughput information on these model systems will facilitate working with and comparing to these references by data otherwise dispersed at different sources. We here provide DSMZCellDive to access a comprehensive data source on human and animal cell lines, freely available at celldive.dsmz.de. A wide variety of data sources are generated such as RNA-seq transcriptome data and STR (short tandem repeats) profiles. Several starting points ease entering the database via browsing, searching or visualising. This web tool is designed for further expansion on meta and high-throughput data to be generated in future. Explicated examples for the power of this novel tool include analysis of B-cell differentiation markers, homeo-oncogene expression, and measurement of genomic loss of heterozygosities by an enlarged STR panel of 17 loci. Sharing the data on cell lines by the biorepository itself will be of benefit to the scientific community  since it (1) supports the selection of appropriate model cell lines, (2) ensures reliability, (3) avoids misleading data, (4) saves on additional experimentals, and (5) serves as reference for genomic and gene expression data.

A unified platform enabling biomarker ranking and validation for 1,562 drugs using transcriptomic data of 1,250 cancer cell lines

Intro

In vitro cell line models provide a valuable resource to investigate compounds useful in the systemic chemotherapy of cancer. However, the due to the dispersal of the data into several different databases, the utilization of these resources is limited. Here, our aim was to establish a platform enabling the validation of chemoresistance-associated genes and the ranking of available cell line models.

Methods

We processed four independent databases, DepMap, GDSC1, GDSC2, and CTRP. The gene expression data was quantile normalized and HUGO gene names were assigned to have unambiguous identification of the genes. Resistance values were exported for all agents. The correlation between gene expression and therapy resistance is computed using ROC test.

Results

We combined four datasets with chemosensitivity data of 1562 agents and transcriptome-level gene expression of 1250 cancer cell lines. We have set up an online tool utilizing this database to correlate available cell line sensitivity data and treatment response in a uniform analysis pipeline (www.rocplot.com/cells). We employed the established pipeline to by rank genes related to resistance against afatinib and lapatinib, two inhibitors of the tyrosine-kinase domain of ERBB2.

Discussion

The computational tool is useful 1) to correlate gene expression with resistance, 2) to identify and rank resistant and sensitive cell lines, and 3) to rank resistance associated genes, cancer hallmarks, and gene ontology pathways. The platform will be an invaluable support to speed up cancer research by validating gene-resistance correlations and by selecting the best cell line models for new experiments.

FAK inhibition radiosensitizes pancreatic ductal adenocarcinoma cells in vitro

Introduction

Focal adhesion kinase (FAK) is a nonreceptor tyrosine kinase protein frequently overexpressed in cancer and has been linked to an increase in the stem cell population of tumors, resistance to therapy, and metastatic spread. Pharmacological FAK inhibition in pancreatic cancer has received increased attention over the last few years, either alone or in combination with other therapeutics including chemotherapy and immunotherapy. However, its prognostic value and its role in radioresistance of pancreatic ducal adenocarcinoma (PDAC) is unknown.

Methods and materials

Using the TCGA and GTEx databases, we investigated the genetic alterations and mRNA expression levels of PTK2 (the encoding-gene for FAK) in normal pancreatic tissue and pancreatic cancer and its impact on patient survival. Furthermore, we evaluated the expression of FAK and its tyrosine domain Ty-397 in three pancreatic cancer cell lines. We went further and evaluated the role of a commercial FAK tyrosine kinase inhibitor VS-4718 on the viability and radiosensitization of the pancreatic cell lines as well as its effect on the extracellular matrix (ECM) production from the pancreatic stellate cells. Furthermore, we tested the effect of combining radiation with VS-4718 in a three-dimensional (3D) multicellular pancreatic tumor spheroid model.

Results

A database analysis revealed a relevant increase in genetic alterations and mRNA expression of the PTK2 in PDAC, which were associated with lower progression-free survival. In vitro, there was only variation in the basal phosphorylation level of FAK in cell lines. VS-4718 radiosensitized pancreatic cell lines only in the presence of ECM-producing pancreatic stellate cells and markedly reduced the ECM production in the stromal cells. Finally, using a 3D multicellular tumor model, the combination of VS-4718 and radiotherapy significantly reduced the growth of tumor aggregates.

Conclusion

Pharmacological inhibition of FAK in pancreatic cancer could be a novel therapeutic strategy as our results show a radiosensitization effect of VS-4718 in vitro in a multicellular 2D- and in a 3D-model of pancreatic cancer.

Electronic supplementary material

The online version of this article (10.1007/s00066-020-01666-0) contains supplementary material, which is available to authorized users.

Bioinformatics services for analyzing massive genomic datasets

The explosive growth of next-generation sequencing data has resulted in ultra-large-scale datasets and ensuing computational problems. In Korea, the amount of genomic data has been increasing rapidly in the recent years. Leveraging these big data requires researchers to use large-scale computational resources and analysis pipelines. A promising solution for addressing this computational challenge is cloud computing, where CPUs, memory, storage, and programs are accessible in the form of virtual machines. Here, we present a cloud computing-based system, Bio-Express, that provides user-friendly, cost-effective analysis of massive genomic datasets. Bio-Express is loaded with predefined multi-omics data analysis pipelines, which are divided into genome, transcriptome, epigenome, and metagenome pipelines. Users can employ predefined pipelines or create a new pipeline for analyzing their own omics data. We also developed several web-based services for facilitating downstream analysis of genome data. Bio-Express web service is freely available at https://www.bioexpress.re.kr/.