Analysis of RBP expression and binding sites identifies PTBP1 as a regulator of CD19 expression in B-ALL.

Despite massive improvements in the treatment of B-ALL through CART-19 immunotherapy, a large number of patients suffer a relapse due to loss of the targeted epitope. Mutations in the CD19 locus and aberrant splicing events are known to account for the absence of surface antigen. However, early molecular determinants suggesting therapy resistance as well as the time point when first signs of epitope loss appear to be detectable are not enlightened so far. By deep sequencing of the CD19 locus, we identified a blast-specific 2-nucleotide deletion in intron 2 that exists in 35% of B-ALL samples at initial diagnosis. This deletion overlaps with the binding site of RNA binding proteins (RBPs) including PTBP1 and might thereby affect CD19 splicing. Moreover, we could identify a number of other RBPs that are predicted to bind to the CD19 locus being deregulated in leukemic blasts, including NONO. Their expression is highly heterogeneous across B-ALL molecular subtypes as shown by analyzing 706 B-ALL samples accessed via the St. Jude Cloud. Mechanistically, we show that downregulation of PTBP1, but not of NONO, in 697 cells reduces CD19 total protein by increasing intron 2 retention. Isoform analysis in patient samples revealed that blasts, at diagnosis, express increased amounts of CD19 intron 2 retention compared to normal B cells. Our data suggest that loss of RBP functionality by mutations altering their binding motifs or by deregulated expression might harbor the potential for the disease-associated accumulation of therapy-resistant CD19 isoforms.

Analysis of bacterial pangenomes reduces CRISPR dark matter and reveals strong association between membranome and CRISPR-Cas systems.

CRISPR-Cas systems are prokaryotic acquired immunity mechanisms, which are found in 40% of bacterial genomes. They prevent viral infections through small DNA fragments called spacers. However, the vast majority of these spacers have not yet been associated with the virus they recognize, and it has been named CRISPR dark matter. By analyzing the spacers of tens of thousands of genomes from six bacterial species, we have been able to reduce the CRISPR dark matter from 80% to as low as 15% in some of the species. In addition, we have observed that, when a genome presents CRISPR-Cas systems, this is accompanied by particular sets of membrane proteins. Our results suggest that when bacteria present membrane proteins that make it compete better in its environment and these proteins are, in turn, receptors for specific phages, they would be forced to acquire CRISPR-Cas.

Batch effect detection and correction in RNA-seq data using machine-learning-based automated assessment of quality.

BACKGROUND: The constant evolving and development of next-generation sequencing techniques lead to high throughput data composed of datasets that include a large number of biological samples. Although a large number of samples are usually experimentally processed by batches, scientific publications are often elusive about this information, which can greatly impact the quality of the samples and confound further statistical analyzes. Because dedicated bioinformatics methods developed to detect unwanted sources of variance in the data can wrongly detect real biological signals, such methods could benefit from using a quality-aware approach. RESULTS: We recently developed statistical guidelines and a machine learning tool to automatically evaluate the quality of a next-generation-sequencing sample. We leveraged this quality assessment to detect and correct batch effects in 12 publicly available RNA-seq datasets with available batch information. We were able to distinguish batches by our quality score and used it to correct for some batch effects in sample clustering. Overall, the correction was evaluated as comparable to or better than the reference method that uses a priori knowledge of the batches (in 10 and 1 datasets of 12, respectively; total = 92%). When coupled to outlier removal, the correction was more often evaluated as better than the reference (comparable or better in 5 and 6 datasets of 12, respectively; total = 92%). CONCLUSIONS: In this work, we show the capabilities of our software to detect batches in public RNA-seq datasets from differences in the predicted quality of their samples. We also use these insights to correct the batch effect and observe the relation of sample quality and batch effect. These observations reinforce our expectation that while batch effects do correlate with differences in quality, batch effects also arise from other artifacts and are more suitably  corrected statistically in well-designed experiments.

Identification of an Immunogenic Medulloblastoma-Specific Fusion Involving EPC2 and GULP1.

Medulloblastoma is the most common malignant brain tumor in children. Immunotherapy is yet to demonstrate dramatic results in medulloblastoma, one reason being the low rate of mutations creating new antigens in this entity. In tumors with low mutational burden, gene fusions may represent a source of tumor-specific neoantigens. Here, we reviewed the landscape of fusions in medulloblastoma and analyzed their predicted immunogenicity. Furthermore, we described a new in-frame fusion protein identified by RNA-Seq. The fusion involved two genes on chromosome 2 coding for the enhancer of polycomb homolog 2 (EPC2) and GULP PTB domain containing engulfment adaptor 1 (GULP1) respectively. By qRT-PCR analysis, the fusion was detected in 3 out of 11 medulloblastoma samples, whereby 2 samples were from the same patients obtained at 2 different time points (initial diagnosis and relapse), but not in other pediatric brain tumor entities. Cloning of the full-length sequence indicated that the fusion protein contains the N-terminal enhancer of polycomb-like domain A (EPcA) of EPC2 and the coiled-coil domain of GULP1. In silico analyses predicted binding of the neoantigen-derived peptide to HLA-A*0201. A total of 50% of the fusions described in the literature were also predicted to produce an immunogenic peptide. The EPC2-GULP1 fusion peptide was able to induce a de novo T cell response characterized by interferon gamma release of CD8+ cytotoxic T cells in vitro. While the functional relevance of this fusion in medulloblastoma biology remains to be clarified, our data support an immunotherapeutic approach for pediatric medulloblastoma patients carrying the EPC2-GULP1 fusion and other immunogenic fusions.

Statistical guidelines for quality control of next-generation sequencing techniques.

More and more next-generation sequencing (NGS) data are made available every day. However, the quality of this data is not always guaranteed. Available quality control tools require profound knowledge to correctly interpret the multiplicity of quality features. Moreover, it is usually difficult to know if quality features are relevant in all experimental conditions. Therefore, the NGS community would highly benefit from condition-specific data-driven guidelines derived from many publicly available experiments, which reflect routinely generated NGS data. In this work, we have characterized well-known quality guidelines and related features in big datasets and concluded that they are too limited for assessing the quality of a given NGS file accurately. Therefore, we present new data-driven guidelines derived from the statistical analysis of many public datasets using quality features calculated by common bioinformatics tools. Thanks to this approach, we confirm the high relevance of genome mapping statistics to assess the quality of the data, and we demonstrate the limited scope of some quality features that are not relevant in all conditions. Our guidelines are available at https://cbdm.uni-mainz.de/ngs-guidelines.

seqQscorer: automated quality control of next-generation sequencing data using machine learning.

Controlling quality of next-generation sequencing (NGS) data files is a necessary but complex task. To address this problem, we statistically characterize common NGS quality features and develop a novel quality control procedure involving tree-based and deep learning classification algorithms. Predictive models, validated on internal and external functional genomics datasets, are to some extent generalizable to data from unseen species. The derived statistical guidelines and predictive models represent a valuable resource for users of NGS data to better understand quality issues and perform automatic quality control. Our guidelines and software are available at https://github.com/salbrec/seqQscorer .

CpG-Islands as Markers for Liquid Biopsies of Cancer Patients.

The analysis of tumours using biomarkers in blood is transforming cancer diagnosis and therapy. Cancers are characterised by evolving genetic alterations, making it difficult to develop reliable and broadly applicable DNA-based biomarkers for liquid biopsy. In contrast to the variability in gene mutations, the methylation pattern remains generally constant during carcinogenesis. Thus, methylation more than mutation analysis may be exploited to recognise tumour features in the blood of patients. In this work, we investigated the possibility of using global CpG (CpG means a CG motif in the context of methylation. The p represents the phosphate. This is used to distinguish CG sites meant for methylation from other CG motifs or from mentions of CG content) island methylation profiles as a basis for the prediction of cancer state of patients utilising liquid biopsy samples. We retrieved existing GEO methylation datasets on hepatocellular carcinoma (HCC) and cell-free DNA (cfDNA) from HCC patients and healthy donors, as well as healthy whole blood and purified peripheral blood mononuclear cell (PBMC) samples, and used a random forest classifier as a predictor. Additionally, we tested three different feature selection techniques in combination. When using cfDNA samples together with solid tumour samples and healthy blood samples of different origin, we could achieve an average accuracy of 0.98 in a 10-fold cross-validation. In this setting, all the feature selection methods we tested in this work showed promising results. We could also show that it is possible to use solid tumour samples and purified PBMCs as a training set and correctly predict a cfDNA sample as cancerous or healthy. In contrast to the complete set of samples, the feature selections led to varying results of the respective random forests. ANOVA feature selection worked well with this training set, and the selected features allowed the random forest to predict all cfDNA samples correctly. Feature selection based on mutual information could also lead to better than random results, but LASSO feature selection would not lead to a confident prediction. Our results show the relevance of CpG islands as tumour markers in blood.

Safety and Activity of the Combination of Ceritinib and Dasatinib in Osteosarcoma.

Osteosarcoma (OS) is the second most common cause of cancer-related death in pediatric patients. The insulin-like growth factor (IGF) pathway plays a relevant role in the biology of OS but no IGF targeted therapies have been successful as monotherapy so far. Here, we tested the effect of three IGF specific inhibitors and tested ceritinib as an off-target inhibitor, alone or in combination with dasatinib, on the proliferation of seven primary OS cells. Picropodophyllin, particularly in combination with dasatinib and the combination ceritinib/dasatinib were effective in abrogating the proliferation. The ceritinib/dasatinib combination was applied to the primary cells of a 16-year-old girl with a long history of lung metastases, and was more effective than cabozantinib and olaparib. Therefore, the combination was used to treat the patient. The treatment was well tolerated, with toxicity limited to skin rush and diarrhea. A histopathological evaluation of the tumor after three months of therapy indicated regions of high necrosis and extensive infiltration of macrophages. The extension of the necrosis was proportional to the concentration of dasatinib and ceritinib in the area, as analysed by an ultra performance liquid chromatography-tandem mass spectrometer (UPLC-MS/MS). After the cessation of the therapy, radiological analysis indicated a massive growth of the patient's liver metastases. In conclusion, these data indicate that the combination of ceritinib/dasatinib is safe and may be used to develop new therapy protocols.