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.

Gastric cancer is the fourth most common cancer diagnosed and the second most frequent cause of cancer-related death worldwide. Multiple factors can contribute to the development of gastric cancer, including H. pylori infection, dietary behaviour and life style, possibly resulting in distinct cancer subtypes with different drug sensitivity profiles. In the present study we searched for gastric cancer mutation patterns in the dataset of the “The Cancer Genome Atlas” (TCGA) and in our collection of patient derived xenografts (PDX). In a second part, we evaluated gene alteration patterns for their implications for drug sensitivity. In both TCGA and our PDX datasets, Whole Exome Sequencing analyses revealed two subsets of gastric tumors characterized by specific mutation signatures, with different types and numbers of genomic alterations. The first subset (60% and 75% of samples) contained lower levels of mutations and was characterized by increased numbers of large chromosomal rearrangements resulting in gene loss or amplifications. The second subset of tumors (25%-40% of samples) revealed higher levels of mutations that were predominantly nucleic acid substitutions and small indels linked to mismatch repair genes including MLH1 or MSH3 and to high microsatellite instability. In both subsets, the mutation spectrum was dominated by C>T transitions with an increase of small indels in the subset of highly-mutated tumors. At the gene level, the genes which were mutated in our gastric PDX collection overlapped to great extent with the mutations found in TCGA tumors, especially regarding the most frequently mutated genes. In the first subset, high levels of gene amplifications and deletions were found, including growth factor receptor amplifications in EGFR and HER2. Furthermore, the mutation frequency in genes associated with drug resistance such as KRAS was decreased. The tumors with growth factor receptor amplification responded consistently to therapies such as Cetuximab or Trastuzumab. In contrast, an increased frequency of mutations in oncogenes and tumor suppressors, including KRAS (n=5/10), PIK3CA (n=5/10) and PTEN (n=7/10), was found in the second subset. The mutational profile of these tumors suggest the use of compounds targeting downstream molecules, such as PIK3CA, or targeting effectors of DNA repair, such as PARP, for anti-cancer therapy. Of note, no association was found between the mutation groups and sensitivity to chemotherapeutic agents such as 5FU, Cisplatin or Paclitaxel. In conclusion, we identified two subsets of gastric tumors both in the TCGA dataset and in our collection of PDX models, characterized by distinct genomic alteration profiles suggesting different therapeutic approaches. Currently, we are assessing drug sensitivity profiles within the two subsets in our PDX models. Citation Format: Anne-Lise Peille, Swee-Seong Wong, Florian Kiefer, Bruno Zeitouni, Armin Maier, Frederic Foucault, Tim Kees, Vincent Vuaroqueaux, Amit Aggarwal, Christoph Reinhard, Heinz Herbert Fiebig. Whole exome sequencing analyses of gastric cancers reveal two distinct genomic alteration patterns with implications in drug sensitivity. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr LB-314. doi:10.1158/1538-7445.AM2014-LB-314