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Background: There is a lack of information as to which molecular processes, present at diagnosis, favor tumour escape from standard-of-care treatments in cervical cancer (CC). RAIDs consortium (www.raids-fp7.eu), conducted a prospectively monitored trial, [BioRAIDs (NCT02428842)] with the objectives to generate high quality samples and molecular assessments to stratify patient populations and to identify molecular patterns associated with poor outcome. Methods: Between 2013 and 2017, RAIDs collected a prospective CC sample and clinical dataset involving 419 participant patients from 18 centers in seven EU countries. Next Generation Sequencing has so far been carried out on a total of 182 samples from 377 evaluable (48%) patients, allowing to define dominant genetic alterations. Reverse phase protein expression arrays (RPPA) was applied to group patients into clusters. Activation of key genetic pathways and protein expression signatures were tested for associations with outcome. Findings: At a median follow up (FU) of 22 months, progression-free survival rates of this FIGO stage IB1-IV population, treated predominantly (87%) by chemoradiation, were65•4% [CI95%: 60•2-71.1]. Dominant oncogenic alterations were seen in PIK3CA (40%), while dominant suppressor gene alterations were seen in KMT2D (15%) and KMT2C (16%). Cumulative frequency of loss-of-function (LOF) mutations in any epigenetic modulator gene alteration was 47% and it was associated with PIK3CA gene alterations in 32%. Patients with tumours harboring alterations in both pathways had a significantly poorer PFS. A new finding was the detection of a high frequency of gains of TLR4 gene amplifications (10%), as well as amplifications, mutations, and non-frame-shift deletions of Androgen receptor (AR) gene in 7% of patients. Finally, RPPA protein expression analysis defined three expression clusters. Interpretation: Our data suggests that patient population may be stratified into four different treatment strategies based on molecular markers at the outset. Fund: European Union's Seventh Program grant agreement No 304810. Keywords: Bioraids study, Prospective database, Cervical cancers, Whole exome sequencing, Reverse phase protein array, Epigenetics pathways, PI3KCA, Patient stratification

Uveal melanoma is the most common primary intraocular malignancy in adults. Up to 50% of UM patients develop metastatic disease, usually in the liver. When metastatic, the prognosis is poor, and few treatment options exist. Here, we investigated the feasibility of establishing patient-derived xenografts (PDXs) from a patient’s tumor in order to screen for therapies that the patient could benefit from. Samples obtained from 29 primary tumors and liver metastases of uveal melanoma were grafted into SCID mice. PDX models were successfully established for 35% of primary patient tumors and 67% of liver metastases. The tumor take rate was proportional to the risk of metastases. PDXs showed the same morphology, the same GNAQ/11, BAP1, and SF3B1 mutations, and the same chromosome 3 and 8q status as the corresponding patient samples. Six PDX models were challenged with two compounds for 4 weeks. We show that, for 31% of patients with high or intermediate risk of metastasis, the timing to obtain efficacy results on PDX models derived from their primary tumors was compatible with the selection of the therapy to treat the patient after relapse. PDXs could thus be a valid tool (“avatar”) to select the best personalized therapy for one third of patients that are most at risk of relapse.

With the advent of high-throughput biotechnological platforms and their ever-growing capacity, life science has turned into a digitized, computational and data-intensive discipline. As a consequence, standard analysis with a bioinformatics pipeline in the context of routine production has become a challenge such that the data can be processed in real-time and delivered to the end-users as fast as possible. The usage of workflow management systems along with packaging systems and containerization technologies offer an opportunity to tackle this challenge. While very powerful, they can be used and combined in many multiple ways which may differ from one developer to another. Therefore, promoting the homogeneity of the workflow implementation requires guidelines and protocols which detail how the source code of the bioinformatics pipeline should be written and organized to ensure its usability, maintainability, interoperability, sustainability, portability, reproducibility, scalability and efficiency. Capitalizing on Nextflow, Conda, Docker, Singularity and the nf-core initiative, we propose a set of best practices along the development life cycle of the bioinformatics pipeline and deployment for production operations which target different expert communities including i) the bioinformaticians and statisticians ii) the software engineers and iii) the data managers and core facility engineers. We implemented Geniac (Automatic Configuration GENerator and Installer for nextflow pipelines) which consists of a toolbox with three components: i) a technical documentation available at https://geniac.readthedocs.io to detail coding guidelines for the bioinformatics pipeline with Nextflow, ii) a command line interface with a linter to check that the code respects the guidelines, and iii) an add-on to generate configuration files, build the containers and deploy the pipeline. The Geniac toolbox aims at the harmonization of development practices across developers and automation of the generation of configuration files and containers by parsing the source code of the Nextflow pipeline.

Atopic dermatitis (AD) and psoriasis (PSO) are associated with dysbiosis. Here, by analyses of skin microbiome and host transcriptome of AD and PSO patients, the authors find distinct microbial and disease-related gene transcriptomic signatures that differentiate both diseases.

The use of a bioinformatics pipeline as a tool to support diagnostic and theranostic decisions in the healthcare process requires the definition of detailed development workflow guidelines. Therefore, we implemented protocols that describe step-by-step all the command lines and actions that the developers have to follow. Our protocols capitalized on the two powerful and widely used tools git and GitLab, and are based on gitflow, a well-established workflow in the software engineering community. They address two use cases: a nominal mode to develop a new feature in the bioinformatics pipeline and a hotfix mode to correct a bug that occurred in the production environment. The protocols are available as a comprehensive documentation at https://biogitflow.readthedocs.io and the main concepts, steps and principles are presented in this report.

Life science has entered the so-called 'big data era' where biologists, clinicians and bioinformaticians are overwhelmed with high-throughput sequencing data. While they offer new insights to decipher the genome structure they also raise major challenges to use them for daily clinical practice care and diagnosis purposes as they are bigger and bigger. Therefore, we implemented a software to reduce the time to delivery for the alignment and the sorting of high-throughput sequencing data. Our solution is implemented using Message Passing Interface and is intended for high-performance computing architecture. The software scales linearly with respect to the size of the data and ensures a total reproducibility with the traditional tools. For example, a 300X whole genome can be aligned and sorted within less than 9 hours with 128 cores. The software offers significant speed-up using multi-cores and multi-nodes parallelization.

Life science has entered the so-called 'big data era' where biologists, clinicians and bioinformaticians are overwhelmed with high-throughput sequencing data. While they offer new insights to decipher the genome structure they also raise major challenges to use them for daily clinical practice care and diagnosis purposes as they are bigger and bigger. Therefore, we implemented a software to reduce the time to delivery for the alignment and the sorting of high-throughput sequencing data. Our solution is implemented using Message Passing Interface and is intended for high-performance computing architecture. The software scales linearly with respect to the size of the data and ensures a total reproducibility with the traditional tools. For example, a 300X whole genome can be aligned and sorted within less than 9 hours with 128 cores. The software offers significant speed-up using multi-cores and multi-nodes parallelization.

Endocytosis of the type I interferon receptor (IFNAR) is required for JAK/STAT signalling. Here the authors show that the internalized IFNAR1 and IFNAR2 subunits are differentially sorted by the retromer complex at the early endosome and this controls JAK/STAT signalling and gene transcription.

Patients with non-small cell lung cancer (NSCLC) are routinely treated with cytotoxic agents such as cisplatin. Through a genome-wide siRNA-based screen, we identified vitamin B6 metabolism as a central regulator of cisplatin responses in vitro and in vivo. By aggravating a bioenergetic catastrophe that involves the depletion of intracellular glutathione, vitamin B6 exacerbates cisplatin-mediated DNA damage, thus sensitizing a large panel of cancer cell lines to apoptosis. Moreover, vitamin B6 sensitizes cancer cells to apoptosis induction by distinct types of physical and chemical stress, including multiple chemotherapeutics. This effect requires pyridoxal kinase (PDXK), the enzyme that generates the bioactive form of vitamin B6. In line with a general role of vitamin B6 in stress responses, low PDXK expression levels were found to be associated with poor disease outcome in two independent cohorts of patients with NSCLC. These results indicate that PDXK expression levels constitute a biomarker for risk stratification among patients with NSCLC.

MotivationArray-CGH can be used to determine DNA copy number, imbalances in which are a fundamental factor in the genesis and progression of tumors. The discovery of classes with similar patterns of array-CGH profiles therefore adds to our understanding of cancer and the treatment of patients. Various input data representations for array-CGH, dissimilarity measures between tumor samples and clustering algorithms may be used for this purpose. The choice between procedures is often difficult. An evaluation procedure is therefore required to select the best class discovery method (combination of one input data representation, one dissimilarity measure and one clustering algorithm) for array-CGH. Robustness of the resulting classes is a common requirement, but no stability-based comparison of class discovery methods for array-CGH profiles has ever been reported.ResultsWe applied several class discovery methods and evaluated the stability of their solutions, with a modified version of Bertoni's [Formula: see text]-based test [1]. Our version relaxes the assumption of independency required by original Bertoni's [Formula: see text]-based test. We conclude that Minimal Regions of alteration (a concept introduced by [2]) for input data representation, sim [3] or agree [4] for dissimilarity measure and the use of average group distance in the clustering algorithm produce the most robust classes of array-CGH profiles.AvailabilityThe software is available from http://bioinfo.curie.fr/projects/cgh-clustering. It has also been partly integrated into "Visualization and analysis of array-CGH"(VAMP)[5]. The data sets used are publicly available from ACTuDB [6].

MotivationReverse phase protein array (RPPA) is a powerful dot-blot technology that allows studying protein expression levels as well as post-translational modifications in a large number of samples simultaneously. Yet, correct interpretation of RPPA data has remained a major challenge for its broad-scale application and its translation into clinical research. Satisfying quantification tools are available to assess a relative protein expression level from a serial dilution curve. However, appropriate tools allowing the normalization of the data for external sources of variation are currently missing.ResultsHere we propose a new method, called NormaCurve, that allows simultaneous quantification and normalization of RPPA data. For this, we modified the quantification method SuperCurve in order to include normalization for (i) background fluorescence, (ii) variation in the total amount of spotted protein and (iii) spatial bias on the arrays. Using a spike-in design with a purified protein, we test the capacity of different models to properly estimate normalized relative expression levels. The best performing model, NormaCurve, takes into account a negative control array without primary antibody, an array stained with a total protein stain and spatial covariates. We show that this normalization is reproducible and we discuss the number of serial dilutions and the number of replicates that are required to obtain robust data. We thus provide a ready-to-use method for reliable and reproducible normalization of RPPA data, which should facilitate the interpretation and the development of this promising technology.AvailabilityThe raw data, the scripts and the normacurve package are available at the following web site: http://microarrays.curie.fr.