Data science applied to precision medicine

L’essor de la médecine de précision permet d’envisager une prise en charge de plus en plus personnalisée du patient afin d’adapter son diagnostic, sa thérapeutique et son suivi. Cette médecine de précision repose aujourd’hui sur la croissance rapide du volume et de la diversité de données disponibles permettant de mieux caractériser et individualiser le patient, à l’ère des « omiques ». Le développement accéléré de l’apprentissage automatique et l’avènement de l’apprentissage profond ont permis, au cours des deux dernières décennies, d’apporter de nouveaux outils afin d’analyser les données massives. Ayant déjà provoqué un changement de paradigme dans plusieurs domaines non médicaux, ces disciplines se retrouvent de plus en plus fréquemment dans la recherche biomédicale afin de répondre à la problématique des données massives en santé.Ce mémoire reviendra sur les évolutions récentes de la science des données au travers de plusieurs exemples d’applications concrètes réalisées durant ces travaux de thèse qui offrent aujourd’hui des pistes sur les voies d’intégration de l’apprentissage machine et de l’apprentissage profond au développement de la médecine de précision : la recherche de biomarqueurs pronostiques et thérapeutiques épigénétiques et transcriptomiques dans le retard de croissance in utero ; la recherche d’anomalies morphologiques des polynucléaires neutrophiles dans la maladie d’Alzheimer ; et enfin l’automatisation de l’analyse biologique des électrophorèses des protéines sériques et des immunosoustractions dans le cadre du diagnostic et du suivi des gammapathies monoclonales.The development of precision medicine now makes it possible to consider an increasingly personalized health care in order to adapt diagnosis, treatment and follow-up to each patient. This precision medicine is based on the rapid growth in the volume and diversity of available data, allowing for better characterization and individualization of patients in the era of "omics". The accelerated development of machine learning and the advent of deep learning have, over the last two decades, provided new tools to analyze Big Data. Having already triggered a paradigm shift in several non-medical fields, these disciplines are increasingly being used in biomedical research to address the issue of massive data in health.This dissertation will review recent developments in data science through several examples of concrete applications realized during the course of this work, which now offer avenues for the integration of machine learning and deep learning in the development of precision medicine. Respectively: the search for epigenetic and transcriptomic prognostic and therapeutic biomarkers in fetal growth retardation; the search for morphological abnormalities of polymorphonuclear neutrophils in Alzheimer's disease; and finally, the automation of the serum protein electrophoresis and immunotyping assays in the context of the diagnosis and monitoring of monoclonal gammapathies

Early recognition of cardiac surgery-associated acute kidney injury: lack of added value of TIMP2 IGFBP7 over short-term changes in creatinine (an observational pilot study)

International audienceBackground: For the detection of cardiac surgery-associated acute kidney injury (CS-AKI), the performance of urine tissue inhibitor of metalloproteinase 2 insulin-like growth factor-binding protein 7 (TIMP2 IGFBP7) has never been compared with that of very early changes in plasma creatinine (∆pCr). We hypothesized that, in the context of perioperative haemodilution, lack of postoperative decrease in pCr would be of honourable performance for the detection of CS-AKI. We therefore aimed at comparing these biomarkers and their kinetics (primary objective). As secondary objectives, we assessed plasma neutrophil gelatinase-associated lipocalin (pNGAL), cystatin C (pCysC) and urea (pUrea). We also determined the ability of these biomarkers to early discriminate persistent from transient CS-AKI.Methods: Patients over 75 years-old undergoing aortic valve replacement with cardiopulmonary bypass (CPB) were included in this prospective observational study. Biomarkers were measured before/after CPB and at the sixth postoperative hour (H6).Results: In 65 patients, CS-AKI occurred in 27 (42%). ∆pCr from post-CPB to H6 (∆pCrpostCPB-H6): outperformed TIMP2 IGFBP7 at H6 and its intra- or postoperative changes: area under the receiver operating characteristic curve (AUCROC) of 0.84 [95%CI:0.73-0.92] vs. ≤0.67 [95%CI:0.54-0.78], p ≤ 0.03. The AUCROC of pNGAL, pCysC and pUrea did not exceed 0.72 [95%CI:0.59-0.83]. Indexing biomarkers levels for blood or urine dilution did not improve their performance. Combining TIMP2 IGFBP7 and ∆pCrpostCPB-H6 was of no evident added value over considering ∆pCrpostCPB-H6 alone. For the early recognition of persistent CS-AKI, no biomarker outperformed ∆pCrpostCPB-H6 (AUCROC = 0.69 [95%CI:0.48-0.85]).Conclusions: In this hypothesis-generating study mostly testing early detection of mild CS-AKI, there was no evident added value of the tested modern biomarkers over early minimal postoperative changes in pCr: despite the common perioperative hemodilution in the setting of cardiac surgery, if pCr failed to decline within the 6 h after CPB, the development of CS-AKI was likely. Confirmatory studies with more severe forms of CS-AKI are required

An immuno-lipidomic signature revealed by metabolomic and machine-learning approaches in labial salivary gland to diagnose primary Sjögrens syndrome.

INTRODUCTION: Assessing labial salivary gland exocrinopathy is a cornerstone in primary Sjögrens syndrome. Currently this relies on the histopathologic diagnosis of focal lymphocytic sialadenitis and computing a focus score by counting lym=phocyte foci. However, those lesions represent advanced stages of primary Sjögrens syndrome, although earlier recognition of primary Sjögrens syndrome and its effective treatment could prevent irreversible damage to labial salivary gland. This study aimed at finding early biomarkers of primary Sjögrens syndrome in labial salivary gland combining metabolomics and machine-learning approaches. METHODS: We used a standardized targeted metabolomic approach involving high performance liquid chromatography coupled with mass spectrometry among newly diagnosed primary Sjögrens syndrome (n=40) and non- primary Sjögrens syndrome sicca (n=40) participants in a prospective cohort. A metabolic signature predictive of primary Sjögrens syndrome status was explored using linear (logistic regression with elastic-net regularization) and non-linear (random forests) machine learning architectures, after splitting the data set into training, validation, and test sets. RESULTS: Among 126 metabolites accurately measured, we identified a discriminant signature composed of six metabolites with robust performances (ROC-AUC = 0.86) for predicting primary Sjögrens syndrome status. This signature included the well-known immune-metabolite kynurenine and five phospholipids (LysoPC C28:0; PCaa C26:0; PCaaC30:2; PCae C30:1, and PCaeC30:2). It was split into two main components: the first including the phospholipids was related to the intensity of lymphocytic infiltrates in salivary glands, while the second represented by kynurenine was independently associated with the presence of anti-SSA antibodies in participant serum. CONCLUSION: Our results reveal an immuno-lipidomic signature in labial salivary gland that accurately distinguishes early primary Sjögrens syndrome from other causes of sicca symptoms

A Metabolomic Profiling of Intra-Uterine Growth Restriction in Placenta and Cord Blood Points to an Impairment of Lipid and Energetic Metabolism

(1) Background: Intrauterine growth restriction (IUGR) involves metabolic changes that may be responsible for an increased risk of metabolic and cardiovascular diseases in adulthood. Several metabolomic profiles have been reported in maternal blood and urine, amniotic fluid, cord blood and newborn urine, but the placenta has been poorly studied so far. (2) Methods: To decipher the origin of this metabolic reprogramming, we conducted a targeted metabolomics study replicated in two cohorts of placenta and one cohort of cord blood by measuring 188 metabolites by mass spectrometry. (3) Results: OPLS-DA multivariate analyses enabled clear discriminations between IUGR and controls, with good predictive capabilities and low overfitting in the two placental cohorts and in cord blood. A signature of 25 discriminating metabolites shared by both placental cohorts was identified. This signature points to sharp impairment of lipid and mitochondrial metabolism with an increased reliance on the creatine-phosphocreatine system by IUGR placentas. Increased placental insulin resistance and significant alteration of fatty acids oxidation, together with relatively higher phospholipase activity in IUGR placentas, were also highlighted. (4) Conclusions: Our results show a deep lipid and energetic remodeling in IUGR placentas that may have a lasting effect on the fetal metabolism

Metabolomics reveals highly regional specificity of cerebral sexual dimorphism in mice

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Cancer/Testis Antigen 55 is required for cancer cell proliferation and mitochondrial DNA maintenance

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Post-infarct cardiac remodeling predictions with machine learning

International audienceBackground: We sought to improve the risk prediction of 3-month left ventricular remodeling (LVR) occurrence after myocardial infarction (MI), using a machine learning approach.Methods: Patients were included from a prospective cohort study analyzing the incidence of LVR in ST-elevation MI in 443 patients that were monitored at Angers University Hospital, France. Clinical, biological and cardiac magnetic resonance (CMR) imaging data from the first week post MI were collected, and LVR was assessed with CMR at 3 month. Data were processed with a machine learning pipeline using multiple feature selection algorithms to identify the most informative variables.Results: We retrieved 133 clinical, biological and CMR imaging variables, from 379 patients with ST-elevation MI. A baseline logistic regression model using previously known variables achieved an AUC of 0.71 on the test set, with 67% sensitivity and 64% specificity. In comparison, our best predictive model was a neural network using seven variables (in order of importance): creatine kinase, mean corpuscular volume, baseline left atrial surface, history of diabetes, history of hypertension, red blood cell distribution width, and creatinine. This model achieved an AUC of 0.78 on the test set, reaching a sensitivity of 92% and a specificity of 55%, outperforming the baseline model.Conclusion: These preliminary results show the value of using an unbiased data-driven machine learning approach. We reached a higher level of sensitivity compared to traditional methods for the prediction of a 3-month post-MI LV

Enterococcus faecalis endocarditis risk assessment in patients with bacteremia: External validation of the DENOVA score

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Metabolomic Sexual Dimorphism of the Mouse Brain is Predominantly Abolished by Gonadectomy with a Higher Impact on Females

International audienceThe importance of sexual dimorphism of the mouse brain metabolome was recently highlighted, in addition to a high regional specificity found between the frontal cortex, the cerebellum, and the brain stem. To address the origin of this dimorphism, we performed gonadectomy on both sexes, followed by a metabolomic study targeting 188 metabolites in the three brain regions. While sham controls, which underwent the same surgical procedure without gonadectomy, reproduced the regional sexual dimorphism of the metabolome previously identified, no sex difference was identifiable after gonadectomy, through both univariate and multivariate analyses. These experiments also made it possible to identify which sex was responsible for the dimorphism for 35 metabolites. The female sex contributed to the difference for more than 80% of them. Our results show that gonads are the main contributors to the brain sexual dimorphism previously observed, especially in females