Summary: We present SVDetect, a program designed to identify genomic structural variations from paired-end and mate-pair next-generation sequencing data produced by the Illumina GA and ABI SOLiD platforms. Applying both sliding-window and clustering strategies, we use anomalously mapped read pairs provided by current short read aligners to localize genomic rearrangements and classify them according to their type, e.g. large insertions–deletions, inversions, duplications and balanced or unbalanced inter-chromosomal translocations. SVDetect outputs predicted structural variants in various file formats for appropriate graphical visualization.Availability: Source code and sample data are available at http://svdetect.sourceforge.net/Contact: svdetect@curie.frSupplementary information: Supplementary data are available at Bioinformatics online.
Drosophila provides a powerful system for defining the complex genetic programs that drive organogenesis. Under control of the steroid hormone ecdysone, the adult heart in Drosophila forms during metamorphosis by a remodelling of the larval cardiac organ. Here, we evaluated the extent to which transcriptional signatures revealed by genomic approaches can provide new insights into the molecular pathways that underlie heart organogenesis. Whole-genome expression profiling at eight successive time-points covering adult heart formation revealed a highly dynamic temporal map of gene expression through 13 transcript clusters with distinct expression kinetics. A functional atlas of the transcriptome profile strikingly points to the genomic transcriptional response of the ecdysone cascade, and a sharp regulation of key components belonging to a few evolutionarily conserved signalling pathways. A reverse genetic analysis provided evidence that these specific signalling pathways are involved in discrete steps of adult heart formation. In particular, the Wnt signalling pathway is shown to participate in inflow tract and cardiomyocyte differentiation, while activation of the PDGF-VEGF pathway is required for cardiac valve formation. Thus, a detailed temporal map of gene expression can reveal signalling pathways responsible for specific developmental programs and provides here substantial grasp into heart formation.
Alterations of chromatin modifiers are frequent in cancer, but their functional consequences often remain unclear. Focusing on the Polycomb protein EZH2 that deposits the H3K27me3 (trimethylation of Lys27 of histone H3) mark, we showed that its high expression in solid tumors is a consequence, not a cause, of tumorigenesis. In mouse and human models, EZH2 is dispensable for prostate cancer development and restrains breast tumorigenesis. High EZH2 expression in tumors results from a tight coupling to proliferation to ensure H3K27me3 homeostasis. However, this process malfunctions in breast cancer. Low EZH2 expression relative to proliferation and mutations in Polycomb genes actually indicate poor prognosis and occur in metastases. We show that while altered EZH2 activity consistently modulates a subset of its target genes, it promotes a wider transcriptional instability. Importantly, transcriptional changes that are consequences of EZH2 loss are predominantly irreversible. Our study provides an unexpected understanding of EZH2's contribution to solid tumors with important therapeutic implications.
Precision medicine (PM) requires the delivery of individually adapted medical care based on the genetic characteristics of each patient and his/her tumor. The last decade witnessed the development of high-throughput technologies such as microarrays and next-generation sequencing which paved the way to PM in the field of oncology. While the cost of these technologies decreases, we are facing an exponential increase in the amount of data produced. Our ability to use this information in daily practice relies strongly on the availability of an efficient bioinformatics system that assists in the translation of knowledge from the bench towards molecular targeting and diagnosis. Clinical trials and routine diagnoses constitute different approaches, both requiring a strong bioinformatics environment capable of (i) warranting the integration and the traceability of data, (ii) ensuring the correct processing and analyses of genomic data, and (iii) applying well-defined and reproducible procedures for workflow management and decision-making. To address the issues, a seamless information system was developed at Institut Curie which facilitates the data integration and tracks in real-time the processing of individual samples. Moreover, computational pipelines were developed to identify reliably genomic alterations and mutations from the molecular profiles of each patient. After a rigorous quality control, a meaningful report is delivered to the clinicians and biologists for the therapeutic decision. The complete bioinformatics environment and the key points of its implementation are presented in the context of the SHIVA clinical trial, a multicentric randomized phase II trial comparing targeted therapy based on tumor molecular profiling versus conventional therapy in patients with refractory cancer. The numerous challenges faced in practice during the setting up and the conduct of this trial are discussed as an illustration of PM application.
To meet challenges in terms of throughput and turnaround time, many diagnostic laboratories are shifting from Sanger sequencing to higher throughput next-generation sequencing (NGS) platforms. Bearing in mind that the performance and quality criteria expected from NGS in diagnostic or research settings are strikingly different, we have developed an Ion Torrent's PGM-based routine diagnostic procedure for BRCA1/2 sequencing. The procedure was first tested on a training set of 62 control samples, and then blindly validated on 77 samples in parallel with our routine technique. The training set was composed of difficult cases, for example, insertions and/or deletions of various sizes, large-scale rearrangements and, obviously, mutations occurring in homopolymer regions. We also compared two bioinformatic solutions in this diagnostic context, an in-house academic pipeline and the commercially available NextGene software (Softgenetics). NextGene analysis provided higher sensitivity, as four previously undetected single-nucleotide variations were found. Regarding specificity, an average of 1.5 confirmatory Sanger sequencings per patient was needed for complete BRCA1/2 screening. Large-scale rearrangements were identified by two distinct analyses, that is, bioinformatics and fragment analysis with electrophoresis profile comparison. Turnaround time was enhanced, as a series of 30 patients were sequenced by one technician, making the results available for the clinician in 10 working days following blood sampling. BRCA1/2 genes are a good model, representative of the difficulties commonly encountered in diagnostic settings, which is why we believe our findings are of interest for the whole community, and the pipeline described can be adapted by any user of PGM for diagnostic purposes.
Experimental tumors raised in rodents represent an important preclinical tool to develop innovative anticancer compounds before clinical testing. Amongst others such models include solid tumors raised in syngeneic fully immunocompetent hosts and tumors spontaneously growing in genetically engineered mice (GEM) and derivate thereof. These model platforms have gained additional value since the manipulation of the immune system to fight cancer has led to tangible benefits for cancer patients. In the current study, we analyzed somatic mutation profiles from whole-exome sequencing (WES) data in a panel of 14 different mouse models covering 6 major cancer types. 4 models were GEM-derived, all other lines were developed by injection of established cell lines into the corresponding mouse strain. In parallel, these models were evaluated for their sensitivity towards checkpoint inhibitors (α-CTLA-4, α-PD-1 or α-PDL-1) in mono- or combined therapy with cytostatic and/or targeted agents.WES achieved an average-of-coverage of 165X in tumor models and normal DNA. A median mutation rate of 34 somatic mutations (m)/MB was detected, ranging from 7 m/MB (GEM derived NSCLC model KP) to 328 m/MB (syngeneic NSCLC line Lewis Lung) in exons. Mutation rates were markedly lower in GEM-derived models as in syngeneic lines (median of 9 vs 43 m/MB). This reflects very well the different underlying carcinogenic mechanism of these two types of models. The cross-comparison of tissue-transplants vs cell lines from GEM-derived model KP revealed that 75% of the mutations found in the primary KP could also be detected in the corresponding cell lines KP1 and KP4. Of note, the mutation count increased 1.3- (KP4) and 2.9-fold (KP1) during cell line establishment. Every model depicted a distinct profile against modulators of the immune system dividing the panel in responders and non-responders. In our hands no significant correlation could be determined between mutational load and sensitivity towards checkpoint inhibition in vivo. This might be related to the fact that the dataset was not broad enough and the number of models per entity was too small, rendering the subtype analysis within the panel not feasible. However, a strong tendency was observed when investigating the colon lines Colon26, CT26 and MC38 showing best response to the combination of PD-1+CTLA-4 inhibitors and in parallel the highest mutation rates (52, 64 and 59 m/MB, respectively) compared to non-responders B16-F10, CloudmanS91, 4T1 and KP1 (23 m/MB on average). Mouse models of cancer are a relevant tool for preclinical studies specifically for immuno-oncology. The molecular characterization of these models will help to optimize their use in drug discovery. They will support the development of innovative drugs and indentification of biomarkers to classify the patient cohort profiting the most from these new compounds. Citation Format: Bruno Zeitouni, Cordula Tschuch, Jason M. Davis, Anne-Lise Peille, Yana Raeva, Manuel Landesfeind, Sheri Barnes, Julia B. Schüler. Whole-exome somatic mutation analysis of mouse cancer models and implications for preclinical immunomodulatory drug development [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 1840. doi:10.1158/1538-7445.AM2017-1840
Patient-derived xenografts (PDX) have emerged as an important translational research tool for understanding tumor biology and enabling drug efficacy testing. They are established by transfer of patient tumor into immune compromised mice with the intent of using them as Avatars; operating under the assumption that they closely resemble patient tumors. In this study, we established 27 PDX from 100 resected gastric cancers and studied their fidelity in histological and molecular subtypes. We show that the established PDX preserved histology and molecular subtypes of parental tumors. However, in depth investigation of the entire cohort revealed that not all histological and molecular subtypes are established. Also, for the established PDX models, genetic changes are selected at early passages and rare subclones can emerge in PDX. This study highlights the importance of considering the molecular and evolutionary characteristics of PDX for a proper use of such models, particularly for Avatar trials.
Patient-derived xenograft tumor models (PDX) are of increasing interest for anti-cancer agent testing due to their close resemblance to patient tumors. An accurate molecular characterization of the models is essential 1) to select the PDX that best fit the genetic requirements for a successful cancer therapy investigation and 2) to identify potential predictive biomarkers of response. In this study, we evaluated the quality of mutation profiles from whole-exome sequencing (WES) in terms of concordance with previously acquired mutation data in a large collection of PDX. Further, we analyzed the persistence of disclosed mutations at the transcript level with RNA-Seq. From 339 PDX, DNA was extracted and enriched in exonic regions with Agilent SureSelect kits before Illumina HiSeq 2000 sequencing with a minimum expected average-of-coverage of 100X. Raw paired-end reads were analyzed by a PDX-specific bioinformatics pipeline to identify the human mutation profile. Sequenom Oncocarta and Sanger sequencing data acquired for 29 cancer genes in 272 PDX was used to evaluate the WES mutation profiles. In parallel, 92 PDX were profiled with RNA-Seq (100M sequencing reads required) and we investigated the expressed mutation profiles by comparing with mutations from WES data. Among 502 point mutations found with classical methods, 95% were retrieved by WES analyses, revealing the very high sensitivity of the PDX-specific bioinformatics pipeline. 5% of mutations were missed because of a low coverage, particularly in the STK11 gene and in the KRAS gene of pancreatic models, possibly due to poor gene enrichment and high mouse stroma content, respectively. Deeper sequencing could potentially overcome this lack of coverage. Additionally, the WES analysis pipeline displays a high specificity, reporting only 1 additional mutation at gene positions covered with the classical methods. Finally, 507 mutations were detected by WES at positions not interrogated by classical methods emphasizing the necessity for next-generation sequencing (NGS) to obtain a comprehensive mutational spectrum. The number of mutations found using RNA-Seq data was on average two times lower and covered 15% of the mutations detected in WES. This was mainly due to the non-expression of genes or isoforms (40%), the mono-allelic expression of genes (30%), and low coverage data (15%). RNA-Seq analysis restricted to expressed genes represents a substantial complement to WES mutation data and enhances understanding of actual gene alterations in cancer cells. This study demonstrated the high quality of mutation profiles obtained by WES and highlights the importance of integrating expression data to accurately predict the impact of a mutation at the protein level. An accurate molecular characterization of models is crucial for the selection of PDX with a specific genetic background for the evaluation of anticancer agents. Citation Format: Manuel Landesfeind, Bruno Zeitouni, Anne-Lise Peille, Vincent Vuaroqueaux. Combining whole-exome and RNA-Seq data improves the quality of PDX mutation profiles. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 2701.
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