Tumour break load is a biologically relevant feature of genomic instability with prognostic value in colorectal cancer

BACKGROUND: Clinically implemented prognostic biomarkers are lacking for the 80% of colorectal cancers (CRCs) that exhibit chromosomal instability (CIN). CIN is characterised by chromosome segregation errors and double-strand break repair defects that lead to somatic copy number aberrations (SCNAs) and chromosomal rearrangement-associated structural variants (SVs), respectively. We hypothesise that the number of SVs is a distinct feature of genomic instability and defined a new measure to quantify SVs: the tumour break load (TBL). The present study aimed to characterise the biological impact and clinical relevance of TBL in CRC. METHODS: Disease-free survival and SCNA data were obtained from The Cancer Genome Atlas and two independent CRC studies. TBL was defined as the sum of SCNA-associated SVs. RNA gene expression data of microsatellite stable (MSS) CRC samples were used to train an RNA-based TBL classifier. Dichotomised DNA-based TBL data were used for survival analysis. RESULTS: TBL shows large variation in CRC with poor correlation to tumour mutational burden and fraction of genome altered. TBL impact on tumour biology was illustrated by the high accuracy of classifying cancers in TBL-high and TBL-low (area under the receiver operating characteristic curve [AUC]: 0.88; p < 0.01). High TBL was associated with disease recurrence in 85 stages II-III MSS CRCs from The Cancer Genome Atlas (hazard ratio [HR]: 6.1; p = 0.007) and in two independent validation series of 57 untreated stages II-III (HR: 4.1; p = 0.012) and 74 untreated stage II MSS CRCs (HR: 2.4; p = 0.01). CONCLUSION: TBL is a prognostic biomarker in patients with non-metastatic MSS CRC with great potential to be implemented in routine molecular diagnostics

A Micro-Costing Framework for Circulating Tumor DNA Testing in Dutch Clinical Practice

Circulating tumor DNA (ctDNA) is a promising new biomarker with multiple potential applications in cancer care. Estimating total cost of ctDNA testing is necessary for reimbursement and implementation, but challenging because of variations in workflow. We aimed to develop a micro-costing framework for consistent cost calculation of ctDNA testing. First, the foundation of the framework was built, based on the complete step-wise diagnostic workflow of ctDNA testing. Second, the costing method was set up, including costs for personnel, materials, equipment, overhead, and failures. Third, the framework was evaluated by experts and applied to six case studies, including PCR-, mass spectrometry–, and next-generation sequencing–based platforms, from three Dutch hospitals. The developed ctDNA micro-costing framework includes the diagnostic workflow from blood sample collection to diagnostic test result. The framework was developed from a Dutch perspective and takes testing volume into account. An open access tool is provided to allow for laboratory-specific calculations to explore the total costs of ctDNA testing specific workflow parameters matching the setting of interest. It also allows to straightforwardly assess the impact of alternative prices or assumptions on the cost per sample by simply varying the input parameters. The case studies showed a wide range of costs, from €168 to €7638 ($199 to$9124) per sample, and generated information. These costs are sensitive to the (coverage of) platform, setting, and testing volume

A Balkán és az Oszmán Birodalom III. : Társadalmi és gazdasági átalakulások a 18. század végétől a 20. század közepéig : Szerbia, Macedónia, Bosznia

High-throughput molecular profiling techniques are routinely generating vast amounts of data for translational medicine studies. Secure access controlled systems are needed to manage, store, transfer and distribute these data due to its personally identifiable nature. The European Genome-phenome Archive (EGA) was created to facilitate access and management to long-term archival of bio-molecular data. Each data provider is responsible for ensuring a Data Access Committee is in place to grant access to data stored in the EGA. Moreover, the transfer of data during upload and download is encrypted. ELIXIR, a European research infrastructure for life-science data, initiated a project (2016 Human Data Implementation Study) to understand and document the ELIXIR requirements for secure management of controlled-access data. As part of this project, a full ecosystem was designed to connect archived raw experimental molecular profiling data with interpreted data and the computational workflows, using the CTMM Translational Research IT (CTMM-TraIT) infrastructure http://www.ctmm-trait.nl as an example. Here we present the first outcomes of this project, a framework to enable the download of EGA data to a Galaxy server in a secure way. Galaxy provides an intuitive user interface for molecular biologists and bioinformaticians to run and design data analysis workflows. More specifically, we developed a tool -- ega_download_streamer - that can download data securely from EGA into a Galaxy server, which can subsequently be further processed. This tool will allow a user within the browser to run an entire analysis containing sensitive data from EGA, and to make this analysis available for other researchers in a reproducible manner, as shown with a proof of concept study. The tool ega_download_streamer is available in the Galaxy tool shed: https://toolshed.g2.bx.psu.edu/view/yhoogstrate/ega_download_streamer

Fusion transcripts and their genomic breakpoints in polyadenylated and ribosomal RNA-minus RNA sequencing data

BACKGROUND: Fusion genes are typically identified by RNA sequencing (RNA-seq) without elucidating the causal genomic breakpoints. However, non–poly(A)-enriched RNA-seq contains large proportions of intronic reads that also span genomic breakpoints. RESULTS: We have developed an algorithm, Dr. Disco, that searches for fusion transcripts by taking an entire reference genome into account as search space. This includes exons but also introns, intergenic regions, and sequences that do not meet splice junction motifs. Using 1,275 RNA-seq samples, we investigated to what extent genomic breakpoints can be extracted from RNA-seq data and their implications regarding poly(A)-enriched and ribosomal RNA–minus RNA-seq data. Comparison with whole-genome sequencing data revealed that most genomic breakpoints are not, or minimally, transcribed while, in contrast, the genomic breakpoints of all 32 TMPRSS2-ERG–positive tumours were present at RNA level. We also revealed tumours in which the ERG breakpoint was located before ERG, which co-existed with additional deletions and messenger RNA that incorporated intergenic cryptic exons. In breast cancer we identified rearrangement hot spots near CCND1 and in glioma near CDK4 and MDM2 and could directly associate this with increased expression. Furthermore, in all datasets we find fusions to intergenic regions, often spanning multiple cryptic exons that potentially encode neo-antigens. Thus, fusion transcripts other than classical gene-to-gene fusions are prominently present and can be identified using RNA-seq. CONCLUSION: By using the full potential of non–poly(A)-enriched RNA-seq data, sophisticated analysis can reliably identify expressed genomic breakpoints and their transcriptional effects

High Frequency of Interactions between Lung Cancer Susceptibility Genes in the Mouse: Mapping of Sluc5 to Sluc14

Although several genes that cause monogenic familial cancer syndromes have been identified, susceptibility to sporadic cancer remains unresolved. Animal experiments have demonstrated multigenic control of tumor susceptibility. Recently, we described four mouse lung cancer susceptibility (Sluc) loci, the main effects of which are masked by their mutual interactions. Because such interactions can considerably affect the strategies for identification of cancer susceptibility genes in humans, it is necessary to establish whether they are common or rare. Here, we report the mapping of 10 additional Sluc loci and show that 13 of the 14 Sluc loci are involved in one or more interactions, demonstrating that interactions of tumor susceptibility genes are frequent and that they probably form complex networks.

Silencing core spliceosome sm gene expression induces a cytotoxic splicing switch in the proteasome subunit beta 3 mRNA in non-small cell lung cancer cells

The core spliceosomal Sm proteins were recently proposed as cancer-selective lethal targets in non-small cell lung cancer (NSCLC). In contrast, the loss of the commonly mutated cancer target SF3B1 appeared to be toxic to non-malignant cells as well. In the current study, the transcriptomes of A549 NSCLC cells, in which SF3B1 or SNRPD3 was silenced, were compared using RNA sequencing. The skipping of exon 4 of the proteasomal subunit beta type-3 (PSMB3) mRNA, resulting in a shorter PSMB3-S variant, occurred only after silencing SNRPD3. This bservation was extended to the other six Sm genes. Remarkably, the alternative splicing of PSMB3 mRNA upon Sm gene silencing was not observed in non-malignant IMR-90 lung fibroblasts. Furthermore, PSMB3 was found to be overexpressed in NSCLC clinical samples and PSMB3 expression correlated with Sm gene expression. Moreover, a high PSMB3 expression corresponds to worse survival in patients with lung adenocarcinomas. Finally, silencing the canonical full-length PSMB3-L, but not the shorter PSMB3-S variant, was cytotoxic and was accompanied by a decrease in proteasomal activity. Together, silencing Sm genes, but not SF3B1, causes a cytotoxic alternative splicing switch in the PSMB3 mRNA in NSCLC cells only

Tumour break load is a biologically relevant feature of genomic instability with prognostic value in colorectal cancer

BACKGROUND: Clinically implemented prognostic biomarkers are lacking for the 80% of colorectal cancers (CRCs) that exhibit chromosomal instability (CIN). CIN is characterised by chromosome segregation errors and double-strand break repair defects that lead to somatic copy number aberrations (SCNAs) and chromosomal rearrangement-associated structural variants (SVs), respectively. We hypothesise that the number of SVs is a distinct feature of genomic instability and defined a new measure to quantify SVs: the tumour break load (TBL). The present study aimed to characterise the biological impact and clinical relevance of TBL in CRC. METHODS: Disease-free survival and SCNA data were obtained from The Cancer Genome Atlas and two independent CRC studies. TBL was defined as the sum of SCNA-associated SVs. RNA gene expression data of microsatellite stable (MSS) CRC samples were used to train an RNA-based TBL classifier. Dichotomised DNA-based TBL data were used for survival analysis. RESULTS: TBL shows large variation in CRC with poor correlation to tumour mutational burden and fraction of genome altered. TBL impact on tumour biology was illustrated by the high accuracy of classifying cancers in TBL-high and TBL-low (area under the receiver operating characteristic curve [AUC]: 0.88; p < 0.01). High TBL was associated with disease recurrence in 85 stages II-III MSS CRCs from The Cancer Genome Atlas (hazard ratio [HR]: 6.1; p = 0.007) and in two independent validation series of 57 untreated stages II-III (HR: 4.1; p = 0.012) and 74 untreated stage II MSS CRCs (HR: 2.4; p = 0.01). CONCLUSION: TBL is a prognostic biomarker in patients with non-metastatic MSS CRC with great potential to be implemented in routine molecular diagnostics

Tumour break load is a biologically relevant feature of genomic instability with prognostic value in colorectal cancer

BACKGROUND: Clinically implemented prognostic biomarkers are lacking for the 80% of colorectal cancers (CRCs) that exhibit chromosomal instability (CIN). CIN is characterised by chromosome segregation errors and double-strand break repair defects that lead to somatic copy number aberrations (SCNAs) and chromosomal rearrangement-associated structural variants (SVs), respectively. We hypothesise that the number of SVs is a distinct feature of genomic instability and defined a new measure to quantify SVs: the tumour break load (TBL). The present study aimed to characterise the biological impact and clinical relevance of TBL in CRC. METHODS: Disease-free survival and SCNA data were obtained from The Cancer Genome Atlas and two independent CRC studies. TBL was defined as the sum of SCNA-associated SVs. RNA gene expression data of microsatellite stable (MSS) CRC samples were used to train an RNA-based TBL classifier. Dichotomised DNA-based TBL data were used for survival analysis. RESULTS: TBL shows large variation in CRC with poor correlation to tumour mutational burden and fraction of genome altered. TBL impact on tumour biology was illustrated by the high accuracy of classifying cancers in TBL-high and TBL-low (area under the receiver operating characteristic curve [AUC]: 0.88; p < 0.01). High TBL was associated with disease recurrence in 85 stages II-III MSS CRCs from The Cancer Genome Atlas (hazard ratio [HR]: 6.1; p = 0.007) and in two independent validation series of 57 untreated stages II-III (HR: 4.1; p = 0.012) and 74 untreated stage II MSS CRCs (HR: 2.4; p = 0.01). CONCLUSION: TBL is a prognostic biomarker in patients with non-metastatic MSS CRC with great potential to be implemented in routine molecular diagnostics

GeneBreak: detection of recurrent DNA copy number aberration-associated chromosomal breakpoints within genes

Development of cancer is driven by somatic alterations, including numerical and structural chromosomal aberrations. Currently, several computational methods are available and are widely applied to detect numerical copy number aberrations (CNAs) of chromosomal segments in tumor genomes. However, there is lack of computational methods that systematically detect structural chromosomal aberrations by virtue of the genomic location of CNA-associated chromosomal breaks and identify genes that appear non-randomly affected by chromosomal breakpoints across (large) series of tumor samples. ‘GeneBreak’ is developed to systematically identify genes recurrently affected by the genomic location of chromosomal CNA-associated breaks by a genome-wide approach, which can be applied to DNA copy number data obtained by array-Comparative Genomic Hybridization (CGH) or by (low-pass) whole genome sequencing (WGS). First, ‘GeneBreak’ collects the genomic locations of chromosomal CNA-associated breaks that were previously pinpointed by the segmentation algorithm that was applied to obtain CNA profiles. Next, a tailored annotation approach for breakpoint-to-gene mapping is implemented. Finally, dedicated cohort-based statistics is incorporated with correction for covariates that influence the probability to be a breakpoint gene. In addition, multiple testing correction is integrated to reveal recurrent breakpoint events. This easy-to-use algorithm, ‘GeneBreak’, is implemented in R (www.cran.r-project.org) and is available from Bioconductor (www.bioconductor.org/packages/release/bioc/html/GeneBreak.html)