The characterization of somatic genomic variation associated with the biology of tumors is fundamental for cancer research and personalized medicine, as it guides the reliability and impact of cancer studies and genomic-based decisions in clinical oncology. However, the quality and scope of tumor genome analysis across cancer research centers and hospitals are currently highly heterogeneous, limiting the consistency of tumor diagnoses across hospitals and the possibilities of data sharing and data integration across studies. With the aim of providing users with actionable and personalized recommendations for the overall enhancement and harmonization of somatic variant identification across research and clinical environments, we have developed ONCOLINER. Using specifically designed mosaic and tumorized genomes for the analysis of recall and precision across somatic SNVs, insertions or deletions (indels), and structural variants (SVs), we demonstrate that ONCOLINER is capable of improving and harmonizing genome analysis across three state-of-the-art variant discovery pipelines in genomic oncology.

Importance There is still considerable controversy in the literature regarding the capacity of intramuscular messenger RNA (mRNA) vaccination to induce a mucosal immune response. Objective To compare serum and salivary IgG and IgA levels among mRNA-vaccinated individuals with or without previous SARS-CoV-2 infection. Design, Setting, and Participants In this cohort study, SARS-CoV-2-naive participants and those with previous infection were consecutively included in the CoviCompare P and CoviCompare M mRNA vaccination trials and followed up to day 180 after vaccination with either the BNT162b2 (Pfizer-BioNTech) vaccine or the mRNA-1273 (Moderna) vaccine at the beginning of the COVID-19 vaccination campaign (from February 19 to June 8, 2021) in France. Data were analyzed from October 25, 2022, to July 13, 2023. Main Outcomes and Measures An ultrasensitive digital enzyme-linked immunosorbent assay was used for the comparison of SARS-CoV-2 spike-specific serum and salivary IgG and IgA levels. Spike-specific secretory IgA level was also quantified at selected times. Results A total of 427 individuals were included in 3 groups: participants with SARS-CoV-2 prior to vaccination who received 1 single dose of BNT162b2 (Pfizer-BioNTech) (n = 120) and SARS-CoV-2-naive individuals who received 2 doses of mRNA-1273 (Moderna) (n = 172) or 2 doses of BNT162b2 (Pfizer-BioNTech) (n = 135). The median age was 68 (IQR, 39-75) years, and 228 (53.4%) were men. SARS-CoV-2 spike-specific IgG saliva levels increased after 1 or 2 vaccine injections in individuals with previous infection and SARS-CoV-2-naive individuals. After vaccination, SARS-CoV-2-specific saliva IgA levels, normalized with respect to total IgA levels, were significantly higher in participants with previous infection, as compared with the most responsive mRNA-1273 (Moderna) recipients (median normalized levels, 155 x 10(-5) vs 37 x 10(-5) at day 29; 107 x 10(-5) vs 54 x 10(-5) at day 57; and 104 x 10(-5) vs 70 x 10(-5) at day 180 [P < .001]). In contrast, compared with day 1, spike-specific IgA levels in the BNT162b2-vaccinated SARS-CoV-2-naive group increased only at day 57 (36 x 10(-5) vs 49 x 10(-5) [P = .01]). Bona fide multimeric secretory IgA levels were significantly higher in individuals with previous infection compared with SARS-CoV-2-naive individuals after 2 antigenic stimulations (median optical density, 0.36 [IQR, 0.16-0.63] vs 0.16 [IQR, 0.10-0.22]; P < .001). Conclusions and Relevance The findings of this cohort study suggest that mRNA vaccination was associated with mucosal immunity in individuals without prior SARS-CoV-2 infection, but at much lower levels than in previously infected individuals. Further studies are needed to determine the association between specific saliva IgA levels and prevention of infection or transmission.

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.

Supplementary Movie 1 from miR-181a and miR-630 Regulate Cisplatin-Induced Cancer Cell Death

Supplementary Figure, Table and Movie Legends from miR-181a and miR-630 Regulate Cisplatin-Induced Cancer Cell Death

Supplementary Figure 1 from Spi-1/PU.1 Oncogene Accelerates DNA Replication Fork Elongation and Promotes Genetic Instability in the Absence of DNA Breakage

Abstract MicroRNAs (miRNA) are noncoding RNAs that regulate multiple cellular processes, including proliferation and apoptosis. We used microarray technology to identify miRNAs that were upregulated by non–small cell lung cancer (NSCLC) A549 cells in response to cisplatin (CDDP). The corresponding synthetic miRNA precursors (pre-miRNAs) per se were not lethal when transfected into A549 cells yet affected cell death induction by CDDP, C2-ceramide, cadmium, etoposide, and mitoxantrone in an inducer-specific fashion. Whereas synthetic miRNA inhibitors (anti-miRNAs) targeting miR-181a and miR-630 failed to modulate the response of A549 to CDDP, pre-miR-181a and pre-miR-630 enhanced and reduced CDDP-triggered cell death, respectively. Pre-miR-181a and pre-miR-630 consistently modulated mitochondrial/postmitochondrial steps of the intrinsic pathway of apoptosis, including Bax oligomerization, mitochondrial transmembrane potential dissipation, and the proteolytic maturation of caspase-9 and caspase-3. In addition, pre-miR-630 blocked early manifestations of the DNA damage response, including the phosphorylation of the ataxia-telangiectasia mutated (ATM) kinase and of two ATM substrates, histone H2AX and p53. Pharmacologic and genetic inhibition of p53 corroborated the hypothesis that pre-miR-630 (but not pre-miR-181a) blocks the upstream signaling pathways that are ignited by DNA damage and converge on p53 activation. Pre-miR-630 arrested A549 cells in the G0-G1 phase of the cell cycle, correlating with increased levels of the cell cycle inhibitor p27Kip1 as well as with reduced proliferation rates and resulting in greatly diminished sensitivity of A549 cells to the late S-G2-M cell cycle arrest mediated by CDDP. Altogether, these results identify miR-181a and miR-630 as novel modulators of the CDDP response in NSCLC. Cancer Res; 70(5); 1793–803

Supplementary Figures 1-6 from miR-181a and miR-630 Regulate Cisplatin-Induced Cancer Cell Death

Supplementary Methods, Figure Legends 1-4 from Spi-1/PU.1 Oncogene Accelerates DNA Replication Fork Elongation and Promotes Genetic Instability in the Absence of DNA Breakage

Supplementary Video 3 from High PTP4A3 Phosphatase Expression Correlates with Metastatic Risk in Uveal Melanoma Patients

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.

Background: Loss-of-function mutations in the filaggrin (FLG) gene directly alter skin barrier function and critically influence atopic inflammation. While skin barrier dysfunction, Th2-associated inflammation and bacterial dysbiosis are well-known characteristics of atopic dermatitis (AD), the mechanisms interconnecting genotype, transcriptome and microbiome remain largely elusive. Objective: In-depth analysis of FLG genotype-associated skin gene expression alterations and host-microbe interactions in AD. Methods: Multi-omics characterization of a cohort of AD patients carrying heterozygous loss-of-function mutations in the FLG gene (AD(Mut)) (n = 15), along with matched wild-type (AD(Wt)) patients and healthycontrols. Detailed clinical characterization, microarray gene expression and 16 S rRNA-based microbial marker gene data were generated and analyzed. Results: In the context of filaggrin dysfunction, the transcriptome was characterized by dysregulation of barrier function and water homeostasis, while the lesional skin of AD(Wt )demonstrated the specific upregulation of pro-inflammatory cytokines and T-cell proliferation. S. aureus dominated the microbiome in both patient groups, however, shifting microbial communities could be observed when comparing healthy with non-lesional AD(Wt) or AD(Mut) skin, offering the opportunity to identify microbe-associated transcriptomic signatures. Moreover, an AD core signature with 28 genes, including CCL13, CCL18, BTC, SCIN, RAB31 and PCLO was identified. Conclusions: Our integrative approach provides molecular insights for the concept that FLG loss-of-function mutations are a genetic shortcut to atopic inflammation and unravels the complex interplay between genotype, transcriptome and microbiome in the human holobiont. Crown Copyright (c) 2022 Published by Elsevier B.V. on behalf of Japanese Society for Investigative Dermatology. All rights reserved.

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 two powerful and widely used tools: git and GitLab. 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.

The analytic platform described in this chapter uses proteins extracted from cultured cells as an infinite source of material to set up, validate, and quality control an RPPA platform. Readout of the arrays uses near-infrared fluorescence labeling and data normalization is performed using the bioinformatics package NormaCurve.In the first part, we will describe the advantages, drawbacks, and different applications of cell line material for RPPA. In the second part, we will describe how the staining protocol, the method of readout, and the normalization method applied afterward are interconnected and should be considered together. Finally, we will describe the NormaCurve package, which is freely available, and its requirements for implementation.Four protocols are provided in this chapter: (1) Protein lysis of cell lines using a homemade Laemmli buffer, (2) RPPA staining for fluorescent readout including a signal amplification step, (3) total protein staining in the visible spectrum for normalization purposes, and (4) total protein staining in the near-infrared spectrum for normalization purposes.

Despite recent advances in understanding microbial diversity in skin homeostasis, the relevance of microbial dysbiosis in inflammatory disease is poorly understood. Here we perform a comparative analysis of skin microbial communities coupled to global patterns of cutaneous gene expression in patients with atopic dermatitis or psoriasis. The skin microbiota is analysed by 16S amplicon or whole genome sequencing and the skin transcriptome by microarrays, followed by integration of the data layers. We find that atopic dermatitis and psoriasis can be classified by distinct microbes, which differ from healthy volunteers microbiome composition. Atopic dermatitis is dominated by a single microbe ( Staphylococcus aureus ), and associated with a disease relevant host transcriptomic signature enriched for skin barrier function, tryptophan metabolism and immune activation. In contrast, psoriasis is characterized by co-occurring communities of microbes with weak associations with disease related gene expression. Our work provides a basis for biomarker discovery and targeted therapies in skin dysbiosis.