Aneuploidy in children with relapsed B-cell precursor acute lymphoblastic leukaemia: clinical importance of detecting a hypodiploid origin of relapse.

Aneuploidy is common in paediatric B-cell precursor acute lymphoblastic leukaemia (ALL). Specific subgroups, such as high hyperdiploidy (>50 chromosomes or DNA Index ≥1·16) and hypodiploidy (<45 chromosomes), predict outcome of patients after primary treatment. Whether aneuploidy has a prognostic value for relapsed disease is yet to be determined. Using DNA index and centromere screening by multiplex ligation-dependent probe amplification, we investigated aneuploidy in 413 children treated for first relapse of B-cell precursor ALL according to the ALL-REZ BFM 2002 protocol. Ten-year event-free survival of patients with high hyperdiploid relapses approached 70%, whereas it was only 40% in low hyperdiploid relapses. Three patients with apparent hyperdiploid relapse had TP53 mutations. In these cases, array-based allelotyping revealed a hypodiploid origin with absence of the hypodiploid founder clone (masked hypodiploidy). Collectively, patients with evident or masked hypodiploid relapses showed an extremely low event-free survival rate of 9%. Importantly, the current relapse risk stratification did not identify cases with masked hypodiploidy as high-risk patients, due to their favourable clinical presentation. In multivariate analysis, hypodiploidy proved to be an independent prognostic factor. This finding supports stratification of relapses with hypodiploid origin into high-risk arms in future trials or allocation of patients to alternative treatment approaches.

IntOGen-mutations identifies cancer drivers across tumor types.

The IntOGen-mutations platform (http://www.intogen.org/mutations/) summarizes somatic mutations, genes and pathways involved in tumorigenesis. It identifies and visualizes cancer drivers, analyzing 4,623 exomes from 13 cancer sites. It provides support to cancer researchers, aids the identification of drivers across tumor cohorts and helps rank mutations for better clinical decision-making.

jHeatmap: an interactive heatmap viewer for the web.

SUMMARY: The generation of large volumes of omics data to conduct exploratory studies has become feasible and is now extensively used to gain new insights in life sciences. The effective exploration of the generated data by experts is a crucial step for the successful extraction of knowledge from these datasets. This requires availability of intuitive and interactive visualization tools that can display complex data. Matrix heatmaps are graphical representations frequently used for the description of complex omics data. Here, we present jHeatmap, a web-based tool that allows interactive matrix heatmap visualization and exploration. It is an adaptable javascript library designed to be embedded by means of basic coding skills into web portals to visualize data matrices as interactive and customizable heatmaps. AVAILABILITY: jHeatmap is freely available at the GitHub code repository at https://github.com/jheatmap/jheatmap. Working examples and the documentation may be found at http://jheatmap.github.io/jheatmap.

OncodriveROLE classifies cancer driver genes in loss of function and activating mode of action.

MOTIVATION: Several computational methods have been developed to identify cancer drivers genes-genes responsible for cancer development upon specific alterations. These alterations can cause the loss of function (LoF) of the gene product, for instance, in tumor suppressors, or increase or change its activity or function, if it is an oncogene. Distinguishing between these two classes is important to understand tumorigenesis in patients and has implications for therapy decision making. Here, we assess the capacity of multiple gene features related to the pattern of genomic alterations across tumors to distinguish between activating and LoF cancer genes, and we present an automated approach to aid the classification of novel cancer drivers according to their role. RESULT: OncodriveROLE is a machine learning-based approach that classifies driver genes according to their role, using several properties related to the pattern of alterations across tumors. The method shows an accuracy of 0.93 and Matthew's correlation coefficient of 0.84 classifying genes in the Cancer Gene Census. The OncodriveROLE classifier, its results when applied to two lists of predicted cancer drivers and TCGA-derived mutation and copy number features used by the classifier are available at http://bg.upf.edu/oncodrive-role. AVAILABILITY AND IMPLEMENTATION: The R implementation of the OncodriveROLE classifier is available at http://bg.upf.edu/oncodrive-role. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SVGMap: configurable image browser for experimental data.

SUMMARY: Spatial data visualization is very useful to represent biological data and quickly interpret the results. For instance, to show the expression pattern of a gene in different tissues of a fly, an intuitive approach is to draw the fly with the corresponding tissues and color the expression of the gene in each of them. However, the creation of these visual representations may be a burdensome task. Here we present SVGMap, a java application that automatizes the generation of high-quality graphics for singular data items (e.g. genes) and biological conditions. SVGMap contains a browser that allows the user to navigate the different images created and can be used as a web-based results publishing tool. AVAILABILITY: SVGMap is freely available as precompiled java package as well as source code at http://bg.upf.edu/svgmap. It requires Java 6 and any recent web browser with JavaScript enabled. The software can be run on Linux, Mac OS X and Windows systems. CONTACT: nuria.lopez@upf.edu

FAT1 expression in T-cell acute lymphoblastic leukemia (T-ALL) modulates proliferation and WNT signaling.

FAT atypical cadherin 1 (FAT1), a transmembrane protein, is frequently mutated in various cancer types and has been described as context-dependent tumor suppressor or oncogene. The FAT1 gene is mutated in 12-16% of T-cell acute leukemia (T-ALL) and aberrantly expressed in about 54% of T-ALL cases contrasted with absent expression in normal T-cells. Here, we characterized FAT1 expression and profiled the methylation status from T-ALL patients. In our T-ALL cohort, 53% of patient samples were FAT1 positive (FAT1pos) compared to only 16% FAT1 positivity in early T-ALL patient samples. Aberrant expression of FAT1 was strongly associated with FAT1 promotor hypomethylation, yet a subset, mainly consisting of TLX1-driven T-ALL patient samples showed methylation-independent high FAT1 expression. Genes correlating with FAT1 expression revealed enrichment in WNT signaling genes representing the most enriched single pathway. FAT1 knockdown or knockout led to impaired proliferation and downregulation of WNT pathway target genes (CCND1, MYC, LEF1), while FAT1 overexpressing conveyed a proliferative advantage. To conclude, we characterized a subtype pattern of FAT1 gene expression in adult T-ALL patients correlating with promotor methylation status. FAT1 dependent proliferation and WNT signaling discloses an impact on deeper understanding of T-ALL leukemogenesis as a fundament for prospective therapeutic strategies.

An alternative CYB5A transcript is expressed in aneuploid ALL and enriched in relapse.

BACKGROUND: B-cell precursor acute lymphoblastic leukemia (BCP-ALL) is a genetically heterogenous malignancy with poor prognosis in relapsed adult patients. The genetic basis for relapse in aneuploid subtypes such as near haploid (NH) and high hyperdiploid (HeH) BCP-ALL is only poorly understood. Pathogenic genetic alterations remain to be identified. To this end, we investigated the dynamics of genetic alterations in a matched initial diagnosis-relapse (ID-REL) BCP-ALL cohort. Here, we firstly report the identification of the novel genetic alteration CYB5Aalt, an alternative transcript of CYB5A, in two independent cohorts. METHODS: We identified CYB5alt in the RNAseq-analysis of a matched ID-REL BCP-ALL cohort with 50 patients and quantified its expression in various molecular BCP-ALL subtypes. Findings were validated in an independent cohort of 140 first diagnosis samples from adult BCP-ALL patients. Derived from patient material, the alternative open reading frame of CYB5Aalt was cloned (pCYB5Aalt) and pCYB5Aalt or the empty vector were stably overexpressed in NALM-6 cells. RNA sequencing was performed of pCYB5Aalt clones and empty vector controls followed by differential expression analysis, gene set enrichment analysis and complementing cell death and viability assays to determine functional implications of CYB5Aalt. RESULTS: RNAseq data analysis revealed non-canonical exon usage of CYB5Aalt starting from a previously undescribed transcription start site. CYB5Aalt expression was increased in relapsed BCP-ALL and its occurrence was specific towards the shared gene expression cluster of NH and HeH BCP-ALL in independent cohorts. Overexpression of pCYB5Aalt in NALM-6 cells induced a distinct transcriptional program compared to empty vector controls with downregulation of pathways related to reported functions of CYB5A wildtype. Interestingly, CYB5A wildtype expression was decreased in CYB5Aalt samples in silico and in vitro. Additionally, pCYB5Aalt NALM-6 elicited a more resistant drug response. CONCLUSIONS: Across all age groups, CYB5Aalt was the most frequent secondary genetic event in relapsed NH and HeH BCP-ALL. In addition to its high subgroup specificity, CYB5Aalt is a novel candidate to be potentially implicated in therapy resistance in NH and HeH BCP-ALL. This is underlined by overexpressing CYB5Aalt providing first evidence for a functional role in BCL2-mediated apoptosis.

Long non-coding RNAs defining major subtypes of B cell precursor acute lymphoblastic leukemia.

BACKGROUND: Long non-coding RNAs (lncRNAs) have emerged as a novel class of RNA due to its diverse mechanism in cancer development and progression. However, the role and expression pattern of lncRNAs in molecular subtypes of B cell acute lymphoblastic leukemia (BCP-ALL) have not yet been investigated. Here, we assess to what extent lncRNA expression and DNA methylation is driving the progression of relapsed BCP-ALL subtypes and we determine if the expression and DNA methylation profile of lncRNAs correlates with established BCP-ALL subtypes. METHODS: We performed RNA sequencing and DNA methylation (Illumina Infinium microarray) of 40 diagnosis and 42 relapse samples from 45 BCP-ALL patients in a German cohort and quantified lncRNA expression. Unsupervised clustering was applied to ascertain and confirm that the lncRNA-based classification of the BCP-ALL molecular subtypes is present in both our cohort and an independent validation cohort of 47 patients. A differential expression and differential methylation analysis was applied to determine the subtype-specific, relapse-specific, and differentially methylated lncRNAs. Potential functions of subtype-specific lncRNAs were determined by using co-expression-based analysis on nearby (cis) and distally (trans) located protein-coding genes. RESULTS: Using an integrative Bioinformatics analysis, we developed a comprehensive catalog of 1235 aberrantly dysregulated BCP-ALL subtype-specific and 942 relapse-specific lncRNAs and the methylation profile of three subtypes of BCP-ALL. The 1235 subtype-specific lncRNA signature represented a similar classification of the molecular subtypes of BCP-ALL in the independent validation cohort. We identified a strong correlation between the DUX4-specific lncRNAs and genes involved in the activation of TGF-ß and Hippo signaling pathways. Similarly, Ph-like-specific lncRNAs were correlated with genes involved in the activation of PI3K-AKT, mTOR, and JAK-STAT signaling pathways. Interestingly, the relapse-specific lncRNAs correlated with the activation of metabolic and signaling pathways. Finally, we found 23 promoter methylated lncRNAs epigenetically facilitating their expression levels. CONCLUSION: Here, we describe a set of subtype-specific and relapse-specific lncRNAs from three major BCP-ALL subtypes and define their potential functions and epigenetic regulation. The subtype-specific lncRNAs are reproducible and can effectively stratify BCP-ALL subtypes. Our data uncover the diverse mechanism of action of lncRNAs in BCP-ALL subtypes defining which lncRNAs are involved in the pathogenesis of disease and are relevant for the stratification of BCP-ALL subtypes.

PAX5 biallelic genomic alterations define a novel subgroup of B-cell precursor acute lymphoblastic leukemia.

Chromosomal rearrangements and specific aneuploidy patterns are initiating events and define subgroups in B-cell precursor acute lymphoblastic leukemia (BCP-ALL). Here we analyzed 250 BCP-ALL cases and identified a novel subgroup ('PAX5-plus', n = 19) by distinct DNA methylation and gene expression profiles. All patients in this subgroup harbored mutations in the B-lineage transcription factor PAX5, with p.P80R as hotspot. Mutations either affected two independent codons, consistent with compound heterozygosity, or suffered LOH predominantly through chromosome 9p aberrations. These biallelic events resulted in disruption of PAX5 transcriptional programs regulating B-cell differentiation and tumor suppressor functions. Homozygous CDKN2A/B deletions and RAS-activating hotspot mutations were highly enriched as cooperating events in the genomic profile of PAX5-plus ALL. Together, this defined a specific pattern of triple alterations, exclusive to the novel subgroup. PAX5-plus ALL was observed in pediatric and adult patients. Although restricted by the limited sample size, a tendency for more favorable clinical outcome was observed, with 10 of 12 adult PAX5-plus patients achieving long-term survival. PAX5-plus represents the first BCP-ALL subgroup defined by sequence alterations in contrast to gross chromosomal events and exemplifies how deregulated differentiation (PAX5), impaired cell cycle control (CDKN2A/B) and sustained proliferative signaling (RAS) cooperatively drive leukemogenesis.

Integrated analysis of relapsed B-cell precursor Acute Lymphoblastic Leukemia identifies subtype-specific cytokine and metabolic signatures.

Recent efforts reclassified B-Cell Precursor Acute Lymphoblastic Leukemia (BCP-ALL) into more refined subtypes. Nevertheless, outcomes of relapsed BCP-ALL remain unsatisfactory, particularly in adult patients where the molecular basis of relapse is still poorly understood. To elucidate the evolution of relapse in BCP-ALL, we established a comprehensive multi-omics dataset including DNA-sequencing, RNA-sequencing, DNA methylation array and proteome MASS-spec data from matched diagnosis and relapse samples of BCP-ALL patients (n = 50) including the subtypes DUX4, Ph-like and two aneuploid subtypes. Relapse-specific alterations were enriched for chromatin modifiers, nucleotide and steroid metabolism including the novel candidates FPGS, AGBL and ZNF483. The proteome expression analysis unraveled deregulation of metabolic pathways at relapse including the key proteins G6PD, TKT, GPI and PGD. Moreover, we identified a novel relapse-specific gene signature specific for DUX4 BCP-ALL patients highlighting chemotaxis and cytokine environment as a possible driver event at relapse. This study presents novel insights at distinct molecular levels of relapsed BCP-ALL based on a comprehensive multi-omics integrated data set including a valuable proteomics data set. The relapse specific aberrations reveal metabolic signatures on genomic and proteomic levels in BCP-ALL relapse. Furthermore, the chemokine expression signature in DUX4 relapse underscores the distinct status of DUX4-fusion BCP-ALL.

Cancer Genome Interpreter annotates the biological and clinical relevance of tumor alterations.

While tumor genome sequencing has become widely available in clinical and research settings, the interpretation of tumor somatic variants remains an important bottleneck. Here we present the Cancer Genome Interpreter, a versatile platform that automates the interpretation of newly sequenced cancer genomes, annotating the potential of alterations detected in tumors to act as drivers and their possible effect on treatment response. The results are organized in different levels of evidence according to current knowledge, which we envision can support a broad range of oncology use cases. The resource is publicly available at http://www.cancergenomeinterpreter.org .

In silico prescription of anticancer drugs to cohorts of 28 tumor types reveals targeting opportunities.

Large efforts dedicated to detect somatic alterations across tumor genomes/exomes are expected to produce significant improvements in precision cancer medicine. However, high inter-tumor heterogeneity is a major obstacle to developing and applying therapeutic targeted agents to treat most cancer patients. Here, we offer a comprehensive assessment of the scope of targeted therapeutic agents in a large pan-cancer cohort. We developed an in silico prescription strategy based on identification of the driver alterations in each tumor and their druggability options. Although relatively few tumors are tractable by approved agents following clinical guidelines (5.9%), up to 40.2% could benefit from different repurposing options, and up to 73.3% considering treatments currently under clinical investigation. We also identified 80 therapeutically targetable cancer genes.

Visualizing multidimensional cancer genomics data.

Cancer genomics projects employ high-throughput technologies to identify the complete catalog of somatic alterations that characterize the genome, transcriptome and epigenome of cohorts of tumor samples. Examples include projects carried out by the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). A crucial step in the extraction of knowledge from the data is the exploration by experts of the different alterations, as well as the multiple relationships between them. To that end, the use of intuitive visualization tools that can integrate different types of alterations with clinical data is essential to the field of cancer genomics. Here, we review effective and common visualization techniques for exploring oncogenomics data and discuss a selection of tools that allow researchers to effectively visualize multidimensional oncogenomics datasets. The review covers visualization methods employed by tools such as Circos, Gitools, the Integrative Genomics Viewer, Cytoscape, Savant Genome Browser, StratomeX and platforms such as cBio Cancer Genomics Portal, IntOGen, the UCSC Cancer Genomics Browser, the Regulome Explorer and the Cancer Genome Workbench.