Consensus gene modules strategy identifies candidate blood-based biomarkers for primary Sjögren's disease.

Primary Sjögren disease (pSD) is an autoimmune disease characterized by lymphoid infiltration of exocrine glands leading to dryness of the mucosal surfaces and by the production of autoantibodies. The pathophysiology of pSD remains elusive and no treatment with demonstrated efficacy is available yet. To better understand the biology underlying pSD heterogeneity, we aimed at identifying Consensus gene Modules (CMs) that summarize the high-dimensional transcriptomic data of whole blood samples in pSD patients. We performed unsupervised gene classification on four data sets and identified thirteen CMs. We annotated and interpreted each of these CMs as corresponding to cell type abundances or biological functions by using gene set enrichment analyses and transcriptomic profiles of sorted blood cell subsets. Correlation with independently measured cell type abundances by flow cytometry confirmed these annotations. We used these CMs to reconcile previously proposed patient stratifications of pSD. Importantly, we showed that the expression of modules representing lymphocytes and erythrocytes before treatment initiation is associated with response to hydroxychloroquine and leflunomide combination therapy in a clinical trial. These consensus modules will help the identification and translation of blood-based predictive biomarkers for the treatment of pSD.

Genes involved in the WNT and vesicular trafficking pathways are associated with melanoma predisposition.

Multifactorial predisposition to melanoma includes genes involved in pigmentation, immunity and DNA repair. Nonetheless, missing heritability in melanoma is still important. We studied the role of 335 candidate SNPs in melanoma susceptibility by using a dedicated chip and investigating 110 genes involved in different pathways. A discovery set was comprised of 1069 melanoma patients and 925 controls from France. Data were replicated using validation phases II (1085 cases and 801 controls from Spain) and III (1808 cases and 1894 controls from Germany and a second set of Spanish samples). In addition, an exome sequencing study was performed in three high-risk French melanoma families. Nineteen SNPs in 17 genes were initially associated with melanoma in the French population. Six SNPs were replicated in phase II, including two new SNPs in the WNT3 (rs199524) and VPS41 (rs11773094) genes. The role of VPS41 and WNT3 was confirmed in a meta-analysis (3940 melanoma cases and 3620 controls) with two-side p values of 0.002, (OR = 0.86) and 4.07 × 10(-10) (OR = 0.80), respectively. Exome sequencing revealed a non-synonymous VPS41 variant in one family that was shown to be strongly associated with familial melanoma (OR = 4.46, p = 0.001) in an independent sample of 178 melanoma families. WNT3 belongs to WNT pathway known to play a crucial role in melanoma, whereas VPS41 regulates vesicular trafficking and is thought to play a role in pigmentation. Our work identified two new pathways involved in melanoma predisposition. These results may be useful in the future for identifying individuals highly predisposed to melanoma.

A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis.

During the last 3 years, a number of approaches for the normalization of RNA sequencing data have emerged in the literature, differing both in the type of bias adjustment and in the statistical strategy adopted. However, as data continue to accumulate, there has been no clear consensus on the appropriate normalization method to be used or the impact of a chosen method on the downstream analysis. In this work, we focus on a comprehensive comparison of seven recently proposed normalization methods for the differential analysis of RNA-seq data, with an emphasis on the use of varied real and simulated datasets involving different species and experimental designs to represent data characteristics commonly observed in practice. Based on this comparison study, we propose practical recommendations on the appropriate normalization method to be used and its impact on the differential analysis of RNA-seq data.

The FOXE1 locus is a major genetic determinant for familial nonmedullary thyroid carcinoma.

Thyroid cancer is the most common endocrine malignancy and nonmedullary thyroid carcinoma (NMTC) represents 90% of all cases. NMTC risk in first-degree relatives of affected cases is elevated fivefold to ninefold. Familial NMTC (FNMTC) accounts for about 3-7% of all thyroid tumors and is a more aggressive clinical entity than its sporadic counterparts. Linkage analysis on high-risk families performed a decade ago mapped several susceptibility loci, but did not lead to the identification of high-penetrance causal germline mutations. More recently, a genome-wide association study (GWAS) identified common single nucleotide polymorphisms (SNPs) affecting the risk of sporadic NMTC. We sought to verify if the newly identified genetic risk factors for NMTC are relevant for FNMTC as well. We genotyped 23 SNPs at 11 candidate loci in 672 subjects belonging to 133 pedigrees with at least two NMTC cases. Statistical analysis was performed using family-based association tests, modified quasi-likelihood score and logistic-normal models. SNPs at 9q22.33 near FOXE1 showed convincing evidence of association with NMTC risk in these high-risk families. The other tested loci resulted negative. These findings confirm the importance of the SNPs identified by recent GWAS on sporadic NMTC on FNMTC as well. However, the proposed FOXE1 causal variants do not show the strongest association signal. Moreover, mutation screening of the FOXE1 coding sequence in the FNMTC cases did not identify rarer causal variants, suggesting that other yet unidentified variants at this locus are involved in FNMTC etiology.

Radiotherapy-activated NBTXR3 nanoparticles promote ferroptosis through induction of lysosomal membrane permeabilization.

PURPOSE: Radiotherapy-activated NBTXR3 (NBTXR3 + RT) has demonstrated superior efficacy in cancer cell destruction and tumor growth control, compared to radiotherapy (RT), in preclinical and clinical settings. Previous studies highlighted the immunomodulatory properties of NBTXR3 + RT, such as modification of tumor cell immunogenicity/adjuvanticity, producing an effective local tumor control and abscopal effect, related to an enhanced antitumor immune response. Furthermore, NBTXR3 + RT has shown potential in restoring anti-PD1 efficacy in a refractory tumor model. However, the early events leading to these results, such as NBTXR3 endocytosis, intracellular trafficking and primary biological responses induced by NBTXR3 + RT remain poorly understood. METHODS: We analyzed by transmission electron microscopy endocytosis and intracellular localization of NBTXR3 nanoparticles after endocytosis in various cell lines, in vitro and in vivo. A kinetic of NBTXR3 endocytosis and its impact on lysosomes was conducted using LysoTracker staining, and a RNAseq analysis was performed. We investigated the ability of NBTXR3 + RT to induce lysosomal membrane permeabilization (LMP) and ferroptosis by analyzing lipid peroxidation. Additionally, we evaluated the recapture by cancer cells of NBTXR3 released from dead cells. RESULTS: NBTXR3 nanoparticles were rapidly internalized by cells mainly through macropinocytosis and in a less extend by clathrin-dependent endocytosis. NBTXR3-containing endosomes were then fused with lysosomes. The day following NBTXR3 addition, we measured a significant increase in LysoTracker lysosome labeling intensity, in vitro as in vivo. Following RT, a significant lysosomal membrane permeabilization (LMP) was measured exclusively in cells treated with NBTXR3 + RT, while RT had no effect. The day post-irradiation, a significant increase in lipid peroxidation, a biomarker of ferroptosis, was measured with NBTXR3 + RT compared to RT. Moreover, we demonstrated that NBTXR3 nanoparticles released from dead cells can be recaptured by cancer cells. CONCLUSIONS: Our findings provide novel insights into the early and specific biological effects induced by NBTXR3 + RT, especially LMP, not induced by RT in our models. The subsequent significant increase in lipid peroxidation partially explains the enhanced cancer cell killing capacity of NBTXR3 + RT compared to RT, potentially by promoting ferroptosis. This study improves our understanding of the cellular mechanisms underlying NBTXR3 + RT and highlights its potential as an agnostic therapeutic strategy for solid cancers treatment.

Virtual patients, digital twins and causal disease models: Paving the ground for in silico clinical trials.

Computational models are being explored to simulate in silico the efficacy and safety of drug candidates and medical devices. Disease models that are based on patients' profiling data are being produced to represent interactomes of genes or proteins and to infer causality in the pathophysiology, which makes it possible to mimic the impact of drugs on relevant targets. Virtual patients designed from medical records as well as digital twins are generated to simulate specific organs and to predict treatment efficacy at the individual patient level. As the acceptance of digital evidence by regulators grows, predictive artificial intelligence (AI)-based models will support the design of confirmatory trials in humans and will accelerate the development of efficient drugs and medical devices.

Unraveling the relationships between alpha- and beta-adrenergic modulation and the risk of heart failure.

Background: The effects of α and ß adrenergic receptor modulation on the risk of developing heart failure (HF) remains uncertain due to a lack of randomized controlled trials. This study aimed to estimate the effects of α and ß adrenergic receptors modulation on the risk of HF and to provide proof of principle for genetic target validation studies in HF. Methods: Genetic variants within the cis regions encoding the adrenergic receptors α1A, α2B, ß1, and ß2 associated with blood pressure in a 757,601-participant genome-wide association study (GWAS) were selected as instruments to perform a drug target Mendelian randomization study. Effects of these variants on HF risk were derived from the HERMES GWAS (542,362 controls; 40,805 HF cases). Results: Lower α1A or ß1 activity was associated with reduced HF risk: odds ratio (OR) 0.83 (95% CI 0.74-0.93, P = 0.001) and 0.95 (95% CI 0.93-0.97, P = 8 × 10-6). Conversely, lower α2B activity was associated with increased HF risk: OR 1.09 (95% CI 1.05-1.12, P = 3 × 10-7). No evidence of an effect of lower ß2 activity on HF risk was found: OR 0.99 (95% CI 0.92-1.07, P = 0.95). Complementary analyses showed that these effects were consistent with those on left ventricular dimensions and acted independently of any potential effect on coronary artery disease. Conclusions: This study provides genetic evidence that α1A or ß1 receptor inhibition will likely decrease HF risk, while lower α2B activity may increase this risk. Genetic variant analysis can assist with drug development for HF prevention.

TGFß receptor gene variants in systemic sclerosis-related pulmonary arterial hypertension: results from a multicentre EUSTAR study of European Caucasian patients.

INTRODUCTION: Systemic sclerosis (SSc)-related pulmonary arterial hypertension (PAH) has emerged as a major mortality prognostic factor. Mutations of transforming growth factor beta (TGFß) receptor genes strongly contribute to idiopathic and familial PAH. OBJECTIVE: To explore the genetic bases of SSc-PAH, we combined direct sequencing and genotyping of candidate genes encoding TGFß receptor family members. MATERIALS AND METHODS: TGFß receptor genes, BMPR2, ALK1, TGFR2 and ENG, were sequenced in 10 SSc-PAH patients, nine SSc and seven controls. In addition, 22 single-nucleotide polymorphisms (SNP) of these four candidate genes were tested for association in a first set of 824 French Caucasian SSc patients (including 54 SSc-PAH) and 939 controls. The replication set consisted of 1516 European SSc (including 219 SSc-PAH) and 3129 controls from the European League Against Rheumatism Scleroderma Trials and Research group network. RESULTS: No mutation was identified by direct sequencing. However, two repertoried SNP, ENG rs35400405 and ALK1 rs2277382, were found in SSc-PAH patients only. The genotyping of 22 SNP including the latter showed that only rs2277382 was associated with SSc-PAH (p=0.0066, OR 2.13, 95% CI 1.24 to 3.65). Nevertheless, this was not replicated with the following result in combined analysis: p=0.123, OR 0.79, 95% CI 0.59 to 1.07. CONCLUSIONS: This study demonstrates the lack of association between these TGFß receptor gene polymorphisms and SSc-PAH using both sequencing and genotyping methods.

C8orf13-BLK is a genetic risk locus for systemic sclerosis and has additive effects with BANK1: results from a large french cohort and meta-analysis.

OBJECTIVE: Accumulating evidence suggests that B cells are involved in systemic sclerosis (SSc). BANK1 has been reproducibly reported to be associated with diffuse cutaneous SSc (dcSSc). BLK encodes another B cell signal transducer, and a functional variant at the C8orf13-BLK locus has been found to be associated with SSc in Caucasians. However, no independent replication has been reported, and there are discrepancies in the genotype-phenotype correlation between these studies in Caucasians and another study performed in the Japanese population. Therefore, in a large cohort of French Caucasians and using a meta-analysis of the available data, this study was undertaken to determine whether the C8orf13-BLK locus is associated with SSc, and to assess the possibility of interaction between BLK and BANK1 in SSc. METHODS: The C8orf13-BLK rs13277113 genotype was determined in 1,031 patients with SSc and 1,014 control subjects for whom BANK1 genotypes were available. Meta-analysis of the 3 available data sets (6,078 individuals) was also performed. RESULTS: Minor allele frequencies for rs13277113 revealed an association restricted to the dcSSc subtype (P = 0.012, odds ratio [OR] 1.29) in the French sample. Meta-analysis of the combined Caucasian populations showed an association of this genotype with both SSc (P = 0.0013, OR 1.16, 95% confidence interval [95% CI] 1.06-1.26) and dcSSc (P = 0.0012, OR 1.23, 95% CI 1.08-1.39). Inclusion of the Japanese population confirmed the overall association with the disease, with the strongest association observed with dcSSc (P = 3.27 × 10⁻5, OR 1.27). Secondary analysis in the French sample revealed additive effects between C8orf13-BLK and BANK1, mainly in the dcSSc subset. CONCLUSION: These results confirm C8orf13-BLK as an SSc risk locus. The strongest effects, and particularly additive effects, were observed in the interaction between C8orf13-BLK and BANK1 in the dcSSc subset.

Artificial intelligence-enhanced drug design and development: Toward a computational precision medicine.

Artificial Intelligence (AI) relies upon a convergence of technologies with further synergies with life science technologies to capture the value of massive multi-modal data in the form of predictive models supporting decision-making. AI and machine learning (ML) enhance drug design and development by improving our understanding of disease heterogeneity, identifying dysregulated molecular pathways and therapeutic targets, designing and optimizing drug candidates, as well as evaluating in silico clinical efficacy. By providing an unprecedented level of knowledge on both patient specificities and drug candidate properties, AI is fostering the emergence of a computational precision medicine allowing the design of therapies or preventive measures tailored to the singularities of individual patients in terms of their physiology, disease features, and exposure to environmental risks.

Combinational Drug Repurposing from Genetic Networks Applied to Alzheimer's Disease.

BACKGROUND: Human diseases are multi-factorial biological phenomena resulting from perturbations of numerous functional networks. The complex nature of human diseases explains frequently observed marginal or transitory efficacy of mono-therapeutic interventions. For this reason, combination therapy is being increasingly evaluated as a biologically plausible strategy for reversing disease state, fostering the development of dedicated methodological and experimental approaches. In parallel, genome-wide association studies (GWAS) provide a prominent opportunity for disclosing human-specific therapeutic targets and rational drug repurposing. OBJECTIVE: In this context, our objective was to elaborate an integrated computational platform to accelerate discovery and experimental validation of synergistic combinations of repurposed drugs for treatment of common human diseases. METHODS: The proposed approach combines adapted statistical analysis of GWAS data, pathway-based functional annotation of genetic findings using gene set enrichment technique, computational reconstruction of signaling networks enriched in disease-associated genes, selection of candidate repurposed drugs and proof-of-concept combinational experimental screening. RESULTS: It enables robust identification of signaling pathways enriched in disease susceptibility loci. Therapeutic targeting of the disease-associated signaling networks provides a reliable way for rational drug repurposing and rapid development of synergistic drug combinations for common human diseases. CONCLUSION: Here we demonstrate the feasibility and efficacy of the proposed approach with an experiment application to Alzheimer's disease.

Model-based computational precision medicine to develop combination therapies for autoimmune diseases.

INTRODUCTION: The complex pathophysiology of autoimmune diseases (AIDs) is being progressively deciphered, providing evidence for a multiplicity of pro-inflammatory pathways underlying heterogeneous clinical phenotypes and disease evolution. AREAS COVERED: Treatment strategies involving drug combinations are emerging as a preferred option to achieve remission in a vast majority of patients affected by systemic AIDs. The design of appropriate drug combinations can benefit from AID modeling following a comprehensive multi-omics molecular profiling of patients combined with Artificial Intelligence (AI)-powered computational analyses. Such disease models support patient stratification in homogeneous subgroups, shed light on dysregulated pro-inflammatory pathways and yield hypotheses regarding potential therapeutic targets and candidate biomarkers to stratify and monitor patients during treatment. AID models inform the rational design of combination therapies interfering with independent pro-inflammatory pathways related to either one of five prominent immune compartments contributing to the pathophysiology of AIDs, i.e. pro-inflammatory signals originating from tissues, innate immune mechanisms, T lymphocyte activation, autoantibodies and B cell activation, as well as soluble mediators involved in immune cross-talk. EXPERT OPINION: The optimal management of AIDs in the future will rely upon rationally designed combination therapies, as a modality of a model-based Computational Precision Medicine taking into account the patients' biological and clinical specificities.

Industrializing AI-powered drug discovery: lessons learned from the Patrimony computing platform.

INTRODUCTION: As a mid-size international pharmaceutical company, we initiated 4 years ago the launch of a dedicated high-throughput computing platform supporting drug discovery. The platform named 'Patrimony' was built up on the initial predicate to capitalize on our proprietary data while leveraging public data sources in order to foster a Computational Precision Medicine approach with the power of artificial intelligence. AREAS COVERED: Specifically, Patrimony is designed to identify novel therapeutic target candidates. With several successful use cases in immuno-inflammatory diseases, and current ongoing extension to applications to oncology and neurology, we document how this industrial computational platform has had a transformational impact on our R&D, making it more competitive, as well time and cost effective through a model-based educated selection of therapeutic targets and drug candidates. EXPERT OPINION: We report our achievements, but also our challenges in implementing data access and governance processes, building up hardware and user interfaces, and acculturing scientists to use predictive models to inform decisions.

The interferon regulatory factor 5 gene confers susceptibility to rheumatoid arthritis and influences its erosive phenotype.

BACKGROUND: Increased expression of type I IFN genes, also referred to as an IFN signature, has been detected in various autoimmune diseases including rheumatoid arthritis (RA). Interferon regulatory factors, such as IRF5, coordinate type I IFN expression. Multiple IRF5 variants were suggested as autoimmunity susceptibility factors. OBJECTIVE: As the linkage proof remains important to establish fully any genetic RA susceptibility factor, the authors took advantage of the largest reported European trio family resource dedicated to RA to test for linkage IRF5 and performed a genotype-phenotype analysis. METHODS: 1140 European Caucasian individuals from 380 RA trio families were genotyped for IRF5 rs3757385, rs2004640 and rs10954213 single nucleotide polymorphisms (SNP). RESULTS: Single marker analysis provided linkage evidence for each IRF5 SNP investigated. IRF5 linked to RA with two haplotypes: the CTA risk haplotype 'R' (transmission (T)=60.6%, p=23.1×10(-5)) and the AGG protective haplotype 'P' (T=39.6%, p=0.0015). Linkage was significantly stronger in non-erosive disease for both IRF5 R and P haplotypes (T=73.9%, p=4.20×10(-5) and T=19.6%, p=3.66×10(-5), respectively). Multivariate logistic regression analysis found IRF5 linked to RA independently of the rheumatoid factor status. IRF5 RR and PP haplotypic genotypes were associated with RA, restricted to the non-erosive phenotype: p=1.68×10(-4), OR 4.80, 95% CI 2.06 to 11.19; p=0.003, OR 0.17, 95% CI 0.05 to 0.57, respectively. CONCLUSION: This study provides the 'association and linkage proof' establishing IRF5 as a RA susceptibility gene and the identification of a genetic factor that seems to contribute to the modulation of the erosive phenotype. Further studies are warranted to clarify the role of IRF5 in RA and its subphenotypes.

PTPN22 R620W genotype-phenotype correlation analysis and gene-environment interaction study in early rheumatoid arthritis: results from the ESPOIR cohort.

OBJECTIVES: To investigate genotype-phenotype correlation and gene-environment interaction between PTPN22 R620W environmental factors such as tobacco/hormonal treatments in an inception cohort of RA patients. METHODS: An intra-cohort study including 532 Caucasian RA patients genotyped for the PTPN22 rs2476601 polymorphism was performed. Anti-CCP and RF status at baseline, presence of bone erosions at 1 year, HLADR1 and/or DR4 status, demography, comorbidities, exposure to tobacco with the cumulative dose in pack-years, hormonal treatments and treatments received for RA were collected. Logistic regression was performed to estimate the ORs and multiplicative interaction with adjustment for confounding factors. Gene-environment interaction was estimated by the relative excess risk due to interaction (RERI), attributable proportion (AP) and synergy index (SI). RESULTS: PTPN22 620W risk allele was associated with ACPA production [odds ratio (OR) = 2.21, 95% CI 1.4, 3.4, P < 0.0001]. Hormonal treatment exposition and smoking were found to act with a protective effect against ACPA production (OR = 0.44, 95% CI 0.3, 0.7, P = 0.001) and early bone erosion (OR = 0.56, 95% CI 0.4-0.8, P = 0.003), respectively, and independently of HLADR and PTPN22 status. No evidence for a gene-environment interaction was detected. CONCLUSION: These data provide new insights into the pathogenesis of RA, underlying the pivotal key role of environmental factors in the typical heterogeneity of RA.

A simple inclusion criteria combination increases the rate of cartilage loss in patients with knee osteoarthritis.

Objectives: Selection of patients with KL radiographic grade 2 and 3 is widely used in clinical trials, but this approach could have some limitations. The purpose of this study performed on OsteoArthritis Initiative (OAI) data is to assess whether adding OARSI-JSN to KL grading could select a population with increased rate of cartilage loss. Indeed, KL is not compartment-specific and not uniformly graded amongst expert readers. OARSI-JSN is another established, compartment-specific grading scale that specifically captures the joint space narrowing from radiographs. Design: 1019 knee radiographs data from the progression cohort of the OAI public database were used. Cartilage loss measured with magnetic resonance imaging was evaluated using change over 1 year from baseline in cartilage thickness in the central Medial Tibio-Femoral Compartment (cMTFC) in the KL2-3 and KL2-3+JSN1-2 populations. Results: The mean cMTFC cartilage loss over one year was -0.135 â€‹± â€‹0.29 â€‹mm (median â€‹= â€‹-0.095 â€‹mm) in the KL2-3 population and -0.176 â€‹± â€‹0.29 â€‹mm (median â€‹= â€‹-0.140 â€‹mm) in the KL2-3 +JSN1-2 population. Conclusions: OARSI-JSN appears to be an effective inclusion criterion to be considered in combination with the KL grade in future clinical trials testing the structural efficacy of DMOADs in a time window of 1-year as it contributes to identify knees in whom the disease progresses rapidly.

Network-based repurposing identifies anti-alarmins as drug candidates to control severe lung inflammation in COVID-19.

While establishing worldwide collective immunity with anti SARS-CoV-2 vaccines, COVID-19 remains a major health issue with dramatic ensuing economic consequences. In the transition, repurposing existing drugs remains the fastest cost-effective approach to alleviate the burden on health services, most particularly by reducing the incidence of the acute respiratory distress syndrome associated with severe COVID-19. We undertook a computational repurposing approach to identify candidate therapeutic drugs to control progression towards severe airways inflammation during COVID-19. Molecular profiling data were obtained from public sources regarding SARS-CoV-2 infected epithelial or endothelial cells, immune dysregulations associated with severe COVID-19 and lung inflammation induced by other respiratory viruses. From these data, we generated a protein-protein interactome modeling the evolution of lung inflammation during COVID-19 from inception to an established cytokine release syndrome. This predictive model assembling severe COVID-19-related proteins supports a role for known contributors to the cytokine storm such as IL1ß, IL6, TNFα, JAK2, but also less prominent actors such as IL17, IL23 and C5a. Importantly our analysis points out to alarmins such as TSLP, IL33, members of the S100 family and their receptors (ST2, RAGE) as targets of major therapeutic interest. By evaluating the network-based distances between severe COVID-19-related proteins and known drug targets, network computing identified drugs which could be repurposed to prevent or slow down progression towards severe airways inflammation. This analysis confirmed the interest of dexamethasone, JAK2 inhibitors, estrogens and further identified various drugs either available or in development interacting with the aforementioned targets. We most particularly recommend considering various inhibitors of alarmins or their receptors, currently receiving little attention in this indication, as candidate treatments for severe COVID-19.

A new molecular classification to drive precision treatment strategies in primary Sjögren's syndrome.

There is currently no approved treatment for primary Sjögren's syndrome, a disease that primarily affects adult women. The difficulty in developing effective therapies is -in part- because of the heterogeneity in the clinical manifestation and pathophysiology of the disease. Finding common molecular signatures among patient subgroups could improve our understanding of disease etiology, and facilitate the development of targeted therapeutics. Here, we report, in a cross-sectional cohort, a molecular classification scheme for Sjögren's syndrome patients based on the multi-omic profiling of whole blood samples from a European cohort of over 300 patients, and a similar number of age and gender-matched healthy volunteers. Using transcriptomic, genomic, epigenetic, cytokine expression and flow cytometry data, combined with clinical parameters, we identify four groups of patients with distinct patterns of immune dysregulation. The biomarkers we identify can be used by machine learning classifiers to sort future patients into subgroups, allowing the re-evaluation of response to treatments in clinical trials.

Molecular apocrine differentiation is a common feature of breast cancer in patients with germline PTEN mutations.

INTRODUCTION: Breast carcinoma is the main malignant tumor occurring in patients with Cowden disease, a cancer-prone syndrome caused by germline mutation of the tumor suppressor gene PTEN characterized by the occurrence throughout life of hyperplastic, hamartomatous and malignant growths affecting various organs. The absence of known histological features for breast cancer arising in a PTEN-mutant background prompted us to explore them for potential new markers. METHODS: We first performed a microarray study of three tumors from patients with Cowden disease in the context of a transcriptomic study of 74 familial breast cancers. A subsequent histological and immunohistochemical study including 12 additional cases of Cowden disease breast carcinomas was performed to confirm the microarray data. RESULTS: Unsupervised clustering of the 74 familial tumors followed the intrinsic gene classification of breast cancer except for a group of five tumors that included the three Cowden tumors. The gene expression profile of the Cowden tumors shows considerable overlap with that of a breast cancer subgroup known as molecular apocrine breast carcinoma, which is suspected to have increased androgenic signaling and shows frequent ERBB2 amplification in sporadic tumors. The histological and immunohistochemical study showed that several cases had apocrine histological features and expressed GGT1, which is a potential new marker for apocrine breast carcinoma. CONCLUSIONS: These data suggest that activation of the ERBB2-PI3K-AKT pathway by loss of PTEN at early stages of tumorigenesis promotes the formation of breast tumors with apocrine features.

ChEBI: a database and ontology for chemical entities of biological interest.

Chemical Entities of Biological Interest (ChEBI) is a freely available dictionary of molecular entities focused on 'small' chemical compounds. The molecular entities in question are either natural products or synthetic products used to intervene in the processes of living organisms. Genome-encoded macromolecules (nucleic acids, proteins and peptides derived from proteins by cleavage) are not as a rule included in ChEBI. In addition to molecular entities, ChEBI contains groups (parts of molecular entities) and classes of entities. ChEBI includes an ontological classification, whereby the relationships between molecular entities or classes of entities and their parents and/or children are specified. ChEBI is available online at http://www.ebi.ac.uk/chebi/