Hypoxia-inducible factor (HIF1α) gene expression in human shock states.

International audienceABSTRACT: INTRODUCTION: Hypoxia-inducible factor-1 (HIF1) controls the expression of genes involved in the cellular response to hypoxia. No information is available on its expression in critically ill patients. Thus, we designed the first clinical study in order to evaluate the role of HIF1α as a prognosis marker in patients suffering from shock. METHODS: Fifty consecutive adult patients with shock and 11 healthy volunteers were prospectively enrolled in the study. RNA was extracted from whole blood samples and expression of HIF1α was assessed over the first four hours of shock. The primary objective was to assess HIF1α as a prognostic marker in shock. Secondary objectives were to evaluate the role of HIF1α as a diagnostic and follow-up marker. Patient survival was evaluated at day 28. RESULTS: The causes of shock were sepsis (78%), hemorrhage (18%), and cardiac dysfunction (4%). HIF1α expression was significantly higher in the shock patients than in the healthy volunteers (121 (range: 72-168) versus 48 (range: 38-54) normalized copies, P <0.01), whatever the measured isoforms. It was similar in non-survivors and survivors (108 (range 84-183) versus 121(range 72-185) normalized copies, P = 0.92), and did not significantly change within the study period. CONCLUSIONS: The present study is the first to demonstrate an increased expression of HIF1α in patients with shock. Further studies are needed to clarify the potential association with outcome. Our findings reinforce the value of monitoring plasma lactate levels to guide the treatment of shock

Classification of and risk factors for hematologic complications in a French national cohort of 102 patients with Shwachman-Diamond syndrome.

International audienceBACKGROUND: Patients with the Shwachman-Diamond syndrome often develop hematologic complications. No risk factors for these complications have so far been identified. The aim of this study was to classify the hematologic complications occurring in patients with Shwachman-Diamond syndrome and to investigate the risk factors for these complications. DESIGN AND METHODS: One hundred and two patients with Shwachman-Diamond syndrome, with a median follow-up of 11.6 years, were studied. Major hematologic complications were considered in the case of definitive severe cytopenia (i.e. anemia <7 g/dL or thrombocytopenia <20 × 10(9)/L), classified as malignant (myelodysplasia/leukemia) according to the 2008 World Health Organization classification or as non-malignant. RESULTS: Severe cytopenia was observed in 21 patients and classified as malignant severe cytopenia (n=9), non-malignant severe cytopenia (n=9) and malignant severe cytopenia preceded by non-malignant severe cytopenia (n=3). The 20-year cumulative risk of severe cytopenia was 24.3% (95% confidence interval: 15.3%-38.5%). Young age at first symptoms (<3 months) and low hematologic parameters both at diagnosis of the disease and during the follow-up were associated with severe hematologic complications (P<0.001). Fifteen novel SBDS mutations were identified. Genotype analysis showed no discernible prognostic value. CONCLUSIONS Patients with Shwachman-Diamond syndrome with very early symptoms or cytopenia at diagnosis (even mild anemia or thrombocytopenia) should be considered at a high risk of severe hematologic complications, malignant or non-malignant. Transient severe cytopenia or an indolent cytogenetic clone had no deleterious value

Development of a Series of Kynurenine 3-Monooxygenase Inhibitors Leading to a Clinical Candidate for the Treatment of Acute Pancreatitis

Recently, we reported a novel role for KMO in the pathogenesis of acute pancreatitis (AP). A number of inhibitors of kynurenine 3-monooxygenase (KMO) have previously been described as potential treatments for neurodegenerative conditions and particularly for Huntington’s disease. However, the inhibitors reported to date have insufficient aqueous solubility relative to their cellular potency to be compatible with the intravenous (iv) dosing route required in AP. We have identified and optimized a novel series of high affinity KMO inhibitors with favorable physicochemical properties. The leading example is exquisitely selective, has low clearance in two species, prevents lung and kidney damage in a rat model of acute pancreatitis, and is progressing into preclinical development

Hypoxia inducible factor 1α gene (HIF-1α) splice variants: potential prognostic biomarkers in breast cancer

<p>Abstract</p> <p>Background</p> <p>Hypoxia-inducible factor 1 (HIF-1) is a master transcriptional regulator of genes regulating oxygen homeostasis. The HIF-1 protein is composed of two HIF-1α and HIF-1β/aryl hydrocarbon receptor nuclear translocator (ARNT) subunits. The prognostic relevance of HIF-1α protein overexpression has been shown in breast cancer. The impact of HIF-1α alternative splice variant expression on breast cancer prognosis in terms of metastasis risk is not well known.</p> <p>Methods</p> <p>Using real-time quantitative reverse transcription PCR assays, we measured mRNA concentrations of total <it>HIF-1α </it>and 4 variants in breast tissue specimens in a series of 29 normal tissues or benign lesions (normal/benign) and 53 primary carcinomas. In breast cancers <it>HIF-1α </it>splice variant levels were compared to clinicopathological parameters including tumour microvessel density and metastasis-free survival.</p> <p>Results</p> <p><it>HIF-1α </it>isoforms containing a three base pairs TAG insertion between exon 1 and exon 2 (designated <it>HIF-1α</it>^<it>TAG</it>) and <it>HIF-1α</it>^{<it>736 </it>}mRNAs were found expressed at higher levels in oestrogen receptor (OR)-negative carcinomas compared to normal/benign tissues (<it>P </it>= 0.009 and <it>P </it>= 0.004 respectively). In breast carcinoma specimens, lymph node status was significantly associated with <it>HIF-1α</it>^{<it>TAG </it>}mRNA levels (<it>P </it>= 0.037). Significant statistical association was found between tumour grade and <it>HIF-1α</it>^{<it>TAG </it>}(<it>P </it>= 0.048), and total <it>HIF-1α </it>(<it>P </it>= 0.048) mRNA levels. <it>HIF-1α</it>^{<it>TAG </it>}mRNA levels were also inversely correlated with both oestrogen and progesterone receptor status (<it>P </it>= 0.005 and <it>P </it>= 0.033 respectively). Univariate analysis showed that high <it>HIF-1α</it>^{<it>TAG </it>}mRNA levels correlated with shortened metastasis free survival (<it>P </it>= 0.01).</p> <p>Conclusions</p> <p>Our results show for the first time that mRNA expression of a <it>HIF-1α</it>^{<it>TAG </it>}splice variant reflects a stage of breast cancer progression and is associated with a worse prognosis.</p> <p>See commentary: <url>http://www.biomedcentral.com/1741-7015/8/45</url></p

Intracranial Aneurysm Classifier Using Phenotypic Factors: An International Pooled Analysis

Intracranial aneurysms (IAs) are usually asymptomatic with a low risk of rupture, but consequences of aneurysmal subarachnoid hemorrhage (aSAH) are severe. Identifying IAs at risk of rupture has important clinical and socio-economic consequences. The goal of this study was to assess the effect of patient and IA characteristics on the likelihood of IA being diagnosed incidentally versus ruptured. Patients were recruited at 21 international centers. Seven phenotypic patient characteristics and three IA characteristics were recorded. The analyzed cohort included 7992 patients. Multivariate analysis demonstrated that: (1) IA location is the strongest factor associated with IA rupture status at diagnosis; (2) Risk factor awareness (hypertension, smoking) increases the likelihood of being diagnosed with unruptured IA; (3) Patients with ruptured IAs in high-risk locations tend to be older, and their IAs are smaller; (4) Smokers with ruptured IAs tend to be younger, and their IAs are larger; (5) Female patients with ruptured IAs tend to be older, and their IAs are smaller; (6) IA size and age at rupture correlate. The assessment of associations regarding patient and IA characteristics with IA rupture allows us to refine IA disease models and provide data to develop risk instruments for clinicians to support personalized decision-making

Detection of CALR and MPL Mutations in Low Allelic Burden JAK2 V617F Essential Thrombocythemia

International audienceMyeloproliferative neoplasms are clonal hematopoietic stem cell disorders characterized by aberrant proliferation and an increased tendency toward leukemic transformation. The genes JAK2, MPL, and CALR are frequently altered in these syndromes, and their mutations are often a strong argument for diagnosis. We analyzed the mutational profiles of these three genes in a cohort of 164 suspected myeloproliferative neoplasms. JAK2 V617F mutation was detected by real-time PCR, whereas high-resolution melting analysis followed by Sanger sequencing were used for searching for mutations in JAK2 exon 12, CALR, and MPL. JAK2 V617F mutation was associated with CALR (n Z 4) and MPL (n Z 4) mutations in 8 of 103 essential thrombocytosis patients. These cases were harboring a JAK2 V617F allelic burden of <4% and a significantly higher platelet count compared with JAK2 V617F (P < 0.001) and CALR (P Z 0.001) single-mutation patients. The findings from this study support the possibility of coexisting mutations of the JAK2, CALR, and MPL genes in myeloproliferative neoplasms and suggest that CALR and MPL should be analyzed not only in JAK2-negative patients but also in low V617F mutation patients. Follow-up of these double-mutation cases will be important for determining whether this group of patients presents particular evolution or complications

Machine learning random forest for predicting oncosomatic variant NGS analysis

International audienceSince 2017, we have used IonTorrent NGS platform in our hospital to diagnose and treat cancer. Analyzing variants at each run requires considerable time, and we are still struggling with some variants that appear correct on the metrics at first, but are found to be negative upon further investigation. Can any machine learning algorithm (ML) help us classify NGS variants? This has led us to investigate which ML can fit our NGS data and to develop a tool that can be routinely implemented to help biologists. Currently, one of the greatest challenges in medicine is processing a significant quantity of data. This is particularly true in molecular biology with the advantage of next-generation sequencing (NGS) for profiling and identifying molecular tumors and their treatment. In addition to bioinformatics pipelines, artificial intelligence (AI) can be valuable in helping to analyze mutation variants. Generating sequencing data from patient DNA samples has become easy to perform in clinical trials. However, analyzing the massive quantities of genomic or transcriptomic data and extracting the key biomarkers associated with a clinical response to a specific therapy requires a formidable combination of scientific expertise, biomolecular skills and a panel of bioinformatic and biostatistic tools, in which artificial intelligence is now successful in developing future routine diagnostics. However, cancer genome complexity and technical artifacts make identifying real variants challenging. We present a machine learning method for classifying pathogenic single nucleotide variants (SNVs), single nucleotide polymorphisms (SNPs), multiple nucleotide variants (MNVs), insertions, and deletions detected by NGS from different types of tumor specimens, such as: colorectal, melanoma, lung and glioma cancer. We compared our NGS data to different machine learning algorithms using the k-fold cross-validation method and to neural networks (deep learning) to measure the performance of the different ML algorithms and determine which one is a valid model for confirming NGS variant calls in cancer diagnosis. We trained our machine learning with 70% of our data samples, extracted from our local database (our data structure had 7 parameters: chromosome, position, exon, variant allele frequency, minor allele frequency, coverage and protein description) and validated it with the 30% remaining data. The model offering the best accuracy was chosen and implemented in the NGS analysis routine. Artificial intelligence was developed with the R script language version 3.6.0. We trained our model on 70% of 102,011 variants. Our best error rate (0.22%) was found with random forest machine learning (ntree = 500 and mtry = 4), with an AUC of 0.99. Neural networks achieved some good scores. The final trained model with the neural network achieved an accuracy of 98% and an ROC-AUC of 0.99 with validation data. We tested our RF model to interpret more than 2000 variants from our NGS database: 20 variants were misclassified (error rate < 1%). The errors were nomenclature problems and false positives. After adding false positives to our training database and implementing our RF model routinely, our error rate was always < 0.5%. The RF model shows excellent results for oncosomatic NGS interpretation and can easily be implemented in other molecular biology laboratories. AI is becoming increasingly important in molecular biomedical analysis and can be very helpful in processing medical data. Neural networks show a good capacity in variant classification, and in the future, they may be useful in predicting more complex variants

First case of B ALL with KMT2A-MAML2 rearrangement: a case report

Abstract Background A large number of chromosomal translocations of the human KMT2A gene, better known as the MLL gene, have so far been characterized. Genetic rearrangements involving KMT2A gene are frequently involved in lymphoid, myeloid and mixed lineage leukemia. One of its rare fusion partners, the mastermind like 2 (MAML2) gene has been reported in four cases of myeloid neoplasms after chemotherapy so far: two acute myeloid leukemias (AML) and two myelodysplasic syndrome (MDS), and two cases of secondary T-cell acute lymphoblastic leukemia (T-ALL). Case presentation Here we report the case of a KMT2A - MAML2 fusion discovered by Next-Generation Sequencing (NGS) analysis in front of an inv11 (q21q23) present in a 47-year-old female previously treated for a sarcoma in 2014, who had a B acute lymphoid leukemia (B ALL). Conclusion It is, to our knowledge, the first case of B acute lymphoblastic leukemia with this fusion gene. At the molecular level, two rearrangements were detected using RNA sequencing juxtaposing exon 7 to exon 2 and exon 9 to intron 1–2 of the KMT2A and MAML2 genes respectively, and one rearrangement using Sanger sequencing juxtaposing exon 8 and exon 2