GCOD - GeneChip Oncology Database

Pyrroline-5-Carboxylate Reductase 1: a novel target for sensitizing multiple myeloma cells to bortezomib by inhibition of PRAS40-mediated protein synthesis

Background

Multiple myeloma (MM) remains an incurable cancer despite advances in therapy. Therefore, the search for new targets is still essential to uncover potential treatment strategies. Metabolic changes, induced by the hypoxic bone marrow, contribute to both MM cell survival and drug resistance. Pyrroline-5-carboxylate reductase 1 and 2 (PYCR1 and PYCR2) are two mitochondrial enzymes that facilitate the last step in the glutamine-to-proline conversion. Overexpression of PYCR1 is involved in progression of several cancers, however, its’ role in hematological cancers is unknown. In this study, we investigated whether PYCR affects MM viability, proliferation and response to bortezomib.

Methods

Correlation of PYCR1/2 with overall survival was investigated in the MMRF CoMMpass trial (653 patients). OPM-2 and RPMI-8226 MM cell lines were used to perform in vitro experiments. RPMI-8226 cells were supplemented with ¹³C-glutamine for 48 h in both normoxia and hypoxia (< 1% O₂, by chamber) to perform a tracer study. PYCR1 was inhibited by siRNA or the small molecule inhibitor pargyline. Apoptosis was measured using Annexin V and 7-AAD staining, viability by CellTiterGlo assay and proliferation by BrdU incorporation. Differential protein expression was evaluated using Western Blot. The SUnSET method was used to measure protein synthesis. All in vitro experiments were performed in hypoxic conditions.

Results

We found that PYCR1 and PYCR2 mRNA expression correlated with an inferior overall survival. MM cells from relapsed/refractory patients express significantly higher levels of PYCR1 mRNA. In line with the strong expression of PYCR1, we performed a tracer study in RPMI-8226 cells, which revealed an increased conversion of ¹³C-glutamine to proline in hypoxia. PYCR1 inhibition reduced MM viability and proliferation and increased apoptosis. Mechanistically, we found that PYCR1 silencing reduced protein levels of p-PRAS40, p-mTOR, p-p70, p-S6, p-4EBP1 and p-eIF4E levels, suggesting a decrease in protein synthesis, which we also confirmed in vitro. Pargyline and siPYCR1 increased bortezomib-mediated apoptosis. Finally, combination therapy of pargyline with bortezomib reduced viability in CD138⁺ MM cells and reduced tumor burden in the murine 5TGM1 model compared to single agents.

Conclusions

This study identifies PYCR1 as a novel target in bortezomib-based combination therapies for MM.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13046-022-02250-3.

Cancer RNA-Seq Nexus: a database of phenotype-specific transcriptome profiling in cancer cells

The genome-wide transcriptome profiling of cancerous and normal tissue samples can provide insights into the molecular mechanisms of cancer initiation and progression. RNA Sequencing (RNA-Seq) is a revolutionary tool that has been used extensively in cancer research. However, no existing RNA-Seq database provides all of the following features: (i) large-scale and comprehensive data archives and analyses, including coding-transcript profiling, long non-coding RNA (lncRNA) profiling and coexpression networks; (ii) phenotype-oriented data organization and searching and (iii) the visualization of expression profiles, differential expression and regulatory networks. We have constructed the first public database that meets these criteria, the Cancer RNA-Seq Nexus (CRN, http://syslab4.nchu.edu.tw/CRN). CRN has a user-friendly web interface designed to facilitate cancer research and personalized medicine. It is an open resource for intuitive data exploration, providing coding-transcript/lncRNA expression profiles to support researchers generating new hypotheses in cancer research and personalized medicine.

Metabolic enzyme expression highlights a key role for MTHFD2 and the mitochondrial folate pathway in cancer

Metabolic remodeling is now widely regarded as a hallmark of cancer, but it is not clear whether individual metabolic strategies are frequently exploited by many tumours. Here we compare messenger RNA profiles of 1,454 metabolic enzymes across 1,981 tumours spanning 19 cancer types to identify enzymes that are consistently differentially expressed. Our meta-analysis recovers established targets of some of the most widely used chemotherapeutics, including dihydrofolate reductase, thymidylate synthase and ribonucleotide reductase, while also spotlighting new enzymes, such as the mitochondrial proline biosynthetic enzyme PYCR1. The highest scoring pathway is mitochondrial one-carbon metabolism and is centred on MTHFD2. MTHFD2 RNA and protein are markedly elevated in many cancers and correlated with poor survival in breast cancer. MTHFD2 is expressed in the developing embryo, but is absent in most healthy adult tissues, even those that are proliferating. Our study highlights the importance of mitochondrial compartmentalization of one-carbon metabolism in cancer and raises important therapeutic hypotheses.

ProfileDB: a resource for proteomics and cross-omics biomarker discovery

The increasing size and complexity of high-throughput datasets pose a growing challenge for researchers. Often very different (cross-omics) techniques with individual data analysis pipelines are employed making a unified biomarker discovery strategy and a direct comparison of different experiments difficult and time consuming. Here we present the comprehensive web-based application ProfileDB. The application is designed to integrate data from different high-throughput 'omics' data types (Transcriptomics, Proteomics, Metabolomics) with clinical parameters and prior knowledge on pathways and ontologies. Beyond data storage, ProfileDB provides a set of dedicated tools for study inspection and data visualization. The user can gain insights into a complex experiment with just a few mouse clicks. We will demonstrate the application by presenting typical use cases for the identification of proteomics biomarkers. All presented analyses can be reproduced using the public ProfileDB web server. The ProfileDB application is available by standard browser (Firefox 18+, Internet Explorer Version 9+) technology via http://profileDB.-microdiscovery.de/ (login and pass-word: profileDB). The installation contains several public datasets including different cross-'omics' experiments. This article is part of a Special Issue entitled: Biomarkers: A Proteomic Challenge.

canEvolve: a web portal for integrative oncogenomics

Background & Objective

Genome-wide profiles of tumors obtained using functional genomics platforms are being deposited to the public repositories at an astronomical scale, as a result of focused efforts by individual laboratories and large projects such as the Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium. Consequently, there is an urgent need for reliable tools that integrate and interpret these data in light of current knowledge and disseminate results to biomedical researchers in a user-friendly manner. We have built the canEvolve web portal to meet this need.

Results

canEvolve query functionalities are designed to fulfill most frequent analysis needs of cancer researchers with a view to generate novel hypotheses. canEvolve stores gene, microRNA (miRNA) and protein expression profiles, copy number alterations for multiple cancer types, and protein-protein interaction information. canEvolve allows querying of results of primary analysis, integrative analysis and network analysis of oncogenomics data. The querying for primary analysis includes differential gene and miRNA expression as well as changes in gene copy number measured with SNP microarrays. canEvolve provides results of integrative analysis of gene expression profiles with copy number alterations and with miRNA profiles as well as generalized integrative analysis using gene set enrichment analysis. The network analysis capability includes storage and visualization of gene co-expression, inferred gene regulatory networks and protein-protein interaction information. Finally, canEvolve provides correlations between gene expression and clinical outcomes in terms of univariate survival analysis.

Conclusion

At present canEvolve provides different types of information extracted from 90 cancer genomics studies comprising of more than 10,000 patients. The presence of multiple data types, novel integrative analysis for identifying regulators of oncogenesis, network analysis and ability to query gene lists/pathways are distinctive features of canEvolve. canEvolve will facilitate integrative and meta-analysis of oncogenomics datasets.

Availability

The canEvolve web portal is available at http://www.canevolve.org/.

Reuse of public genome-wide gene expression data

Our understanding of gene expression has changed dramatically over the past decade, largely catalysed by technological developments. High-throughput experiments - microarrays and next-generation sequencing - have generated large amounts of genome-wide gene expression data that are collected in public archives. Added-value databases process, analyse and annotate these data further to make them accessible to every biologist. In this Review, we discuss the utility of the gene expression data that are in the public domain and how researchers are making use of these data. Reuse of public data can be very powerful, but there are many obstacles in data preparation and analysis and in the interpretation of the results. We will discuss these challenges and provide recommendations that we believe can improve the utility of such data.

iBBiG: iterative binary bi-clustering of gene sets

Motivation: Meta-analysis of genomics data seeks to identify genes associated with a biological phenotype across multiple datasets; however, merging data from different platforms by their features (genes) is challenging. Meta-analysis using functionally or biologically characterized gene sets simplifies data integration is biologically intuitive and is seen as having great potential, but is an emerging field with few established statistical methods.

Results: We transform gene expression profiles into binary gene set profiles by discretizing results of gene set enrichment analyses and apply a new iterative bi-clustering algorithm (iBBiG) to identify groups of gene sets that are coordinately associated with groups of phenotypes across multiple studies. iBBiG is optimized for meta-analysis of large numbers of diverse genomics data that may have unmatched samples. It does not require prior knowledge of the number or size of clusters. When applied to simulated data, it outperforms commonly used clustering methods, discovers overlapping clusters of diverse sizes and is robust in the presence of noise. We apply it to meta-analysis of breast cancer studies, where iBBiG extracted novel gene set—phenotype association that predicted tumor metastases within tumor subtypes.

Availability: Implemented in the Bioconductor package iBBiG

Contact:aedin@jimmy.harvard.edu

eXframe: reusable framework for storage, analysis and visualization of genomics experiments

Background

Genome-wide experiments are routinely conducted to measure gene expression, DNA-protein interactions and epigenetic status. Structured metadata for these experiments is imperative for a complete understanding of experimental conditions, to enable consistent data processing and to allow retrieval, comparison, and integration of experimental results. Even though several repositories have been developed for genomics data, only a few provide annotation of samples and assays using controlled vocabularies. Moreover, many of them are tailored for a single type of technology or measurement and do not support the integration of multiple data types.

Results

We have developed eXframe - a reusable web-based framework for genomics experiments that provides 1) the ability to publish structured data compliant with accepted standards 2) support for multiple data types including microarrays and next generation sequencing 3) query, analysis and visualization integration tools (enabled by consistent processing of the raw data and annotation of samples) and is available as open-source software. We present two case studies where this software is currently being used to build repositories of genomics experiments - one contains data from hematopoietic stem cells and another from Parkinson's disease patients.

Conclusion

The web-based framework eXframe offers structured annotation of experiments as well as uniform processing and storage of molecular data from microarray and next generation sequencing platforms. The framework allows users to query and integrate information across species, technologies, measurement types and experimental conditions. Our framework is reusable and freely modifiable - other groups or institutions can deploy their own custom web-based repositories based on this software. It is interoperable with the most important data formats in this domain. We hope that other groups will not only use eXframe, but also contribute their own useful modifications.