Prediction and discrimination of osteoporotic hip fracture in postmenopausal women.

Osteoporotic hip fractures increase dramatically with age and are responsible for considerable morbidity and mortality. Several treatments to prevent the occurrence of hip fracture have been validated in large randomized trials and the current challenge is to improve the identification of individuals at high risk of fracture who would benefit from therapeutic or preventive intervention. We have performed an exhaustive literature review on hip fracture predictors, focusing primarily on clinical risk factors, dual X-ray absorptiometry (DXA), quantitative ultrasound, and bone markers. This review is based on original articles and meta-analyses. We have selected studies that aim both to predict the risk of hip fracture and to discriminate individuals with or without fracture. We have included only postmenopausal women in our review. For studies involving both men and women, only results concerning women have been considered. Regarding clinical factors, only prospective studies have been taken into account. Predictive factors have been used as stand-alone tools to predict hip fracture or sequentially through successive selection processes or by combination into risk scores. There is still much debate as to whether or not the combination of these various parameters, as risk scores or as sequential or concurrent combinations, could help to better predict hip fracture. There are conflicting results on whether or not such combinations provide improvement over each method alone. Sequential combination of bone mineral density and ultrasound parameters might be cost-effective compared with DXA alone, because of fewer bone mineral density measurements. However, use of multiple techniques may increase costs. One problem that precludes comparison of most published studies is that they use either relative risk, or absolute risk, or sensitivity and specificity. The absolute risk of individuals given their risk factors and bone assessment results would be a more appropriate model for decision-making than relative risk. Currently, a group appointed by the World Health Organization and lead by Professor John Kanis is working on such a model. It will therefore be possible to further assess the best choice of threshold to optimize the number of women needed to screen for each country and each treatment.

Interpretation and use of FRAX in clinical practice.

The introduction of the WHO FRAX® algorithms has facilitated the assessment of fracture risk on the basis of fracture probability. Its use in fracture risk prediction has strengths, but also limitations of which the clinician should be aware and are the focus of this review INTRODUCTION: The International Osteoporosis Foundation (IOF) and the International Society for Clinical Densitometry (ISCD) appointed a joint Task Force to develop resource documents in order to make recommendations on how to improve FRAX and better inform clinicians who use FRAX. The Task Force met in November 2010 for 3 days to discuss these topics which form the focus of this review. METHODS: This study reviews the resource documents and joint position statements of ISCD and IOF. RESULTS: Details on the clinical risk factors currently used in FRAX are provided, and the reasons for the exclusion of others are provided. Recommendations are made for the development of surrogate models where country-specific FRAX models are not available. CONCLUSIONS: The wish list of clinicians for the modulation of FRAX is large, but in many instances, these wishes cannot presently be fulfilled; however, an explanation and understanding of the reasons may be helpful in translating the information provided by FRAX into clinical practice.

Personalized modeling for drug concentration prediction using Support Vector Machine

Building a personalized model to describe the drug concentration inside the human body for each patient is highly important to the clinical practice and demanding to the modeling tools. Instead of using traditional explicit methods, in this paper we propose a machine learning approach to describe the relation between the drug concentration and patients' features. Machine learning has been largely applied to analyze data in various domains, but it is still new to personalized medicine, especially dose individualization. We focus mainly on the prediction of the drug concentrations as well as the analysis of different features' influence. Models are built based on Support Vector Machine and the prediction results are compared with the traditional analytical models.

Prediction of infant drug exposure through breastfeeding: population PK modeling and simulation of fluoxetine

BACKGROUND: Risks of significant infant drug exposurethrough breastmilk are poorly defined for many drugs, and largescalepopulation data are lacking. We used population pharmacokinetics(PK) modeling to predict fluoxetine exposure levels ofinfants via mother's milk in a simulated population of 1000 motherinfantpairs.METHODS: Using our original data on fluoxetine PK of 25breastfeeding women, a population PK model was developed withNONMEM and parameters, including milk concentrations, wereestimated. An exponential distribution model was used to account forindividual variation. Simulation random and distribution-constrainedassignment of doses, dosing time, feeding intervals and milk volumewas conducted to generate 1000 mother-infant pairs with characteristicssuch as the steady-state serum concentrations (Css) and infantdose relative to the maternal weight-adjusted dose (relative infantdose: RID). Full bioavailability and a conservative point estimate of1-month-old infant CYP2D6 activity to be 20% of the adult value(adjusted by weigth) according to a recent study, were assumed forinfant Css calculations.RESULTS: A linear 2-compartment model was selected as thebest model. Derived parameters, including milk-to-plasma ratios(mean: 0.66; SD: 0.34; range, 0 - 1.1) were consistent with the valuesreported in the literature. The estimated RID was below 10% in >95%of infants. The model predicted median infant-mother Css ratio was0.096 (range 0.035 - 0.25); literature reported mean was 0.07 (range0-0.59). Moreover, the predicted incidence of infant-mother Css ratioof >0.2 was less than 1%.CONCLUSION: Our in silico model prediction is consistent withclinical observations, suggesting that substantial systemic fluoxetineexposure in infants through human milk is rare, but further analysisshould include active metabolites. Our approach may be valid forother drugs. [supported by CIHR and Swiss National Science Foundation(SNSF)]

IL28B expression depends on a novel TT/-G polymorphism which improves HCV clearance prediction.

Approximately 3% of the world population is chronically infected with the hepatitis C virus (HCV), with potential development of cirrhosis and hepatocellular carcinoma. Despite the availability of new antiviral agents, treatment remains suboptimal. Genome-wide association studies (GWAS) identified rs12979860, a polymorphism nearby IL28B, as an important predictor of HCV clearance. We report the identification of a novel TT/-G polymorphism in the CpG region upstream of IL28B, which is a better predictor of HCV clearance than rs12979860. By using peripheral blood mononuclear cells (PBMCs) from individuals carrying different allelic combinations of the TT/-G and rs12979860 polymorphisms, we show that induction of IL28B and IFN-γ-inducible protein 10 (IP-10) mRNA relies on TT/-G, but not rs12979860, making TT/-G the only functional variant identified so far. This novel step in understanding the genetic regulation of IL28B may have important implications for clinical practice, as the use of TT/G genotyping instead of rs12979860 would improve patient management.

Integrated Analysis of Molecular and Clinical Prognostic Factors in Stage II/III Colon Cancer.

Background The prognostic potential of individual clinical and molecular parameters in stage II/III colon cancer has been investigated, but a thorough multivariable assessment of their relative impact is missing. Methods Tumors from patients (N = 1404) in the PETACC3 adjuvant chemotherapy trial were examined for BRAF and KRAS mutations, microsatellite instability (MSI), chromosome 18q loss of heterozygosity (18qLOH), and SMAD4 expression. Their importance in predicting relapse-free survival (RFS) and overall survival (OS) was assessed by Kaplan-Meier analyses, Cox regression models, and recursive partitioning trees. All statistical tests were two-sided. Results MSI-high status and SMAD4 focal loss of expression were identified as independent prognostic factors with better RFS (hazard ratio [HR] of recurrence = 0.54, 95% CI = 0.37 to 0.81, P = .003) and OS (HR of death = 0.43, 95% CI = 0.27 to 0.70, P = .001) for MSI-high status and worse RFS (HR = 1.47, 95% CI = 1.19 to 1.81, P < .001) and OS (HR = 1.58, 95% CI = 1.23 to 2.01, P < .001) for SMAD4 loss. 18qLOH did not have any prognostic value in RFS or OS. Recursive partitioning identified refinements of TNM into new clinically interesting prognostic subgroups. Notably, T3N1 tumors with MSI-high status and retained SMAD4 expression had outcomes similar to stage II disease. Conclusions Concomitant assessment of molecular and clinical markers in multivariable analysis is essential to confirm or refute their independent prognostic value. Including molecular markers with independent prognostic value might allow more accurate prediction of prognosis than TNM staging alone.

Prediction of myocardial infarction size using the SYNTAX score in patients treated with primary percutaneous coronary intervention for acute ST- segment elevation myocardial infarction

Objectives The relevance of the SYNTAX score for the particular case of patients with acute ST- segment elevation myocardial infarction (STEMI) undergoing primary percutaneous coronary intervention (PPCI)  has previously only been studied in the setting of post hoc analysis of large prospective randomized clinical trials. A "real-life" population approach has never been explored before. The aim of this study was to evaluate the impact of the SYNTAX score for the prediction of the myocardial infarction size, estimated by the creatin-kinase (CK) peak value, using the SYNTAX score in patients treated with primary coronary intervention for acute ST-segment elevation myocardial infarction. Methods The primary endpoint of the study was myocardial infarction size as measured by the CK peak value. The SYNTAX score was calculated retrospectively in 253 consecutive patients with acute ST-segment elevation myocardial infarction (STEMI) undergoing primary percutaneous coronary intervention (PPCI) in a large tertiary referral center in Switzerland, between January 2009 and June 2010. Linear regression analysis was performed to compare myocardial infarction size with the SYNTAX score. This same endpoint was then stratified according to SYNTAX score tertiles: low <22 (n=178), intermediate [22-32] (n=60), and high >=33 (n=15). Results There were no significant differences in terms of clinical characteristics between the three groups. When stratified according to the SYNTAX score tertiles, average CK peak values of 1985 (low<22), 3336 (intermediate [22-32]) and 3684 (high>=33) were obtained with a p-value <0.0001. Bartlett's test for equal variances between the three groups was 9.999 (p-value <0.0067). A moderate Pearson product-moment correlation coefficient (r=0.4074) with a high statistical significance level (p-value <0.0001) was found. The coefficient of determination (R^2=0.1660) showed that approximately 17% of the variation of CK peak value (myocardial infarction size) could be explained by the SYNTAX score, i.e. by the coronary disease complexity. Conclusion In an all-comers population, the SYNTAX score is an additional tool in predicting myocardial infarction size in patients treated with primary percutaneous coronary intervention (PPCI). The stratification of patients in different risk groups according to SYNTAX enables to identify a high-risk population that may warrant particular patient care.

Prediction of postnatal outcome in fetuses with pulmonary hypoplasia by fetal-MRI: Comparison between lung volumetry and pulmonary signal intensity values

Purpose: Pulmonary hypoplasia is a determinant parameter for extra-uterine life. In the last years, MRI appears as a complement to US in order to evaluate the degree of pulmonary hypoplasia in foetuses with congenital anomalies, by using different methods - fetal lung volumetry (FLV), lung-to-liver signal intensity ratio (LLSIR)-. But until now, information about the correlation between the MRI prediction and the real postnatal outcome is limited. Methods and materials: We retrospectively reviewed the fetal MRI performed at our Institution in the last 8 years and selected the cases with suspicion of fetal pulmonary hypoplasia (n = 30). The pulmonary volumetry data of these foetuses were collected and the lung-to-liver signal intensity ratio (LLSIR) measures performed. These data were compared with those obtained from a control group of 25 foetuses considered as normal at MRI. The data of the study group were also correlated with the autopsy records or the post-natal clinical information of the patients. Results: As expected, the control group showed higher FLV and LLSIR values than the problem group at all gestational ages. Higher values of FLV and LLSIR were associated with a better post-natal outcome. Sensitivity, specificity, positive and negative predictive values and accuracy for the relative LLSIR and the relative FLV showed no significant differences. Conclusion: Our data show that not only the FLV but also the relative LLSIR inform about the degree of fetal lung development. This information may help to predict the fetal outcome and to evaluate the need for neonatal intensive care.

Development and validation of a clinical decision rule for the diagnosis of influenza.

INTRODUCTION: A clinical decision rule to improve the accuracy of a diagnosis of influenza could help clinicians avoid unnecessary use of diagnostic tests and treatments. Our objective was to develop and validate a simple clinical decision rule for diagnosis of influenza. METHODS: We combined data from 2 studies of influenza diagnosis in adult outpatients with suspected influenza: one set in California and one in Switzerland. Patients in both studies underwent a structured history and physical examination and had a reference standard test for influenza (polymerase chain reaction or culture). We randomly divided the dataset into derivation and validation groups and then evaluated simple heuristics and decision rules from previous studies and 3 rules based on our own multivariate analysis. Cutpoints for stratification of risk groups in each model were determined using the derivation group before evaluating them in the validation group. For each decision rule, the positive predictive value and likelihood ratio for influenza in low-, moderate-, and high-risk groups, and the percentage of patients allocated to each risk group, were reported. RESULTS: The simple heuristics (fever and cough; fever, cough, and acute onset) were helpful when positive but not when negative. The most useful and accurate clinical rule assigned 2 points for fever plus cough, 2 points for myalgias, and 1 point each for duration <48 hours and chills or sweats. The risk of influenza was 8% for 0 to 2 points, 30% for 3 points, and 59% for 4 to 6 points; the rule performed similarly in derivation and validation groups. Approximately two-thirds of patients fell into the low- or high-risk group and would not require further diagnostic testing. CONCLUSION: A simple, valid clinical rule can be used to guide point-of-care testing and empiric therapy for patients with suspected influenza.

Prediction of functional outcome 18 months after a first psychotic episode: a proton magnetic resonance spectroscopy study.

CONTEXT: Recent magnetic resonance imaging studies have attempted to relate volumetric brain measurements in early schizophrenia to clinical and functional outcome some years later. These studies have generally been negative, perhaps because gray and white matter volumes inaccurately assess the underlying dysfunction that might be predictive of outcome. OBJECTIVE: To investigate the predictive value of frontal and temporal spectroscopy measures for outcome in patients with first-episode psychoses. DESIGN: Left prefrontal cortex and left mediotemporal lobe voxels were assessed using proton magnetic resonance spectroscopy to provide the ratio of N-acetylaspartate (NAA) and choline-containing compounds to creatine and phosphocreatine (Cr) (NAA/Cr ratio). These data were used to predict outcome at 18 months after admission, as assessed by a systematic medical record audit. SETTING: Early psychosis clinic. PARTICIPANTS: Forty-six patients with first-episode psychosis. MAIN OUTCOME MEASURES: We used regression models that included age at imaging and duration of untreated psychosis to predict outcome scores on the Global Assessment of Functioning Scale, Clinical Global Impression scales, and Social and Occupational Functional Assessment Scale, as well as the number of admissions during the treatment period. We then further considered the contributions of premorbid function and baseline level of negative symptoms. RESULTS: The only spectroscopic predictor of outcome was the NAA/Cr ratio in the prefrontal cortex. Low scores on this variable were related to poorer outcome on all measures. In addition, the frontal NAA/Cr ratio explained 17% to 30% of the variance in outcome. CONCLUSIONS: Prefrontal neuronal dysfunction is an inconsistent feature of early psychosis; rather, it is an early marker of poor prognosis across the first years of illness. The extent to which this can be used to guide treatment and whether it predicts outcome some years after first presentation are questions for further research.

Early multimodal outcome prediction after cardiac arrest in patients treated with hypothermia.

OBJECTIVES: Therapeutic hypothermia and pharmacological sedation may influence outcome prediction after cardiac arrest. The use of a multimodal approach, including clinical examination, electroencephalography, somatosensory-evoked potentials, and serum neuron-specific enolase, is recommended; however, no study examined the comparative performance of these predictors or addressed their optimal combination. DESIGN: Prospective cohort study. SETTING: Adult ICU of an academic hospital. PATIENTS: One hundred thirty-four consecutive adults treated with therapeutic hypothermia after cardiac arrest. MEASUREMENTS AND MAIN RESULTS: Variables related to the cardiac arrest (cardiac rhythm, time to return of spontaneous circulation), clinical examination (brainstem reflexes and myoclonus), electroencephalography reactivity during therapeutic hypothermia, somatosensory-evoked potentials, and serum neuron-specific enolase. Models to predict clinical outcome at 3 months (assessed using the Cerebral Performance Categories: 5 = death; 3-5 = poor recovery) were evaluated using ordinal logistic regressions and receiving operator characteristic curves. Seventy-two patients (54%) had a poor outcome (of whom, 62 died), and 62 had a good outcome. Multivariable ordinal logistic regression identified absence of electroencephalography reactivity (p < 0.001), incomplete recovery of brainstem reflexes in normothermia (p = 0.013), and neuron-specific enolase higher than 33 μg/L (p = 0.029), but not somatosensory-evoked potentials, as independent predictors of poor outcome. The combination of clinical examination, electroencephalography reactivity, and neuron-specific enolase yielded the best predictive performance (receiving operator characteristic areas: 0.89 for mortality and 0.88 for poor outcome), with 100% positive predictive value. Addition of somatosensory-evoked potentials to this model did not improve prognostic accuracy. CONCLUSIONS: Combination of clinical examination, electroencephalography reactivity, and serum neuron-specific enolase offers the best outcome predictive performance for prognostication of early postanoxic coma, whereas somatosensory-evoked potentials do not add any complementary information. Although prognostication of poor outcome seems excellent, future studies are needed to further improve prediction of good prognosis, which still remains inaccurate.

Validation of a clinical algorithm to identify low-risk patients with pulmonary embolism.

Summary Background: We previously derived a clinical prognostic algorithm to identify patients with pulmonary embolism (PE) who are at low-risk of short-term mortality who could be safely discharged early or treated entirely in an outpatient setting. Objectives: To externally validate the clinical prognostic algorithm in an independent patient sample. Methods: We validated the algorithm in 983 consecutive patients prospectively diagnosed with PE at an emergency department of a university hospital. Patients with none of the algorithm's 10 prognostic variables (age >/= 70 years, cancer, heart failure, chronic lung disease, chronic renal disease, cerebrovascular disease, pulse >/= 110/min., systolic blood pressure < 100 mm Hg, oxygen saturation < 90%, and altered mental status) at baseline were defined as low-risk. We compared 30-day overall mortality among low-risk patients based on the algorithm between the validation and the original derivation sample. We also assessed the rate of PE-related and bleeding-related mortality among low-risk patients. Results: Overall, the algorithm classified 16.3% of patients with PE as low-risk. Mortality at 30 days was 1.9% among low-risk patients and did not differ between the validation and the original derivation sample. Among low-risk patients, only 0.6% died from definite or possible PE, and 0% died from bleeding. Conclusions: This study validates an easy-to-use, clinical prognostic algorithm for PE that accurately identifies patients with PE who are at low-risk of short-term mortality. Low-risk patients based on our algorithm are potential candidates for less costly outpatient treatment.

Correlates of frailty, prediction of functional decline and preventative approaches - selected results from the lausanne cohort 65+ (lc65+) study (Switzerland) (Lausanne) and the longitudinal urban cohort ageing study (Lucas) (Germany)

Overall introduction.- Longitudinal studies have been designed to investigate prospectively, from their beginning, the pathway leading from health to frailty and to disability. Knowledge about determinants of healthy ageing and health behaviour (resources) as well as risks of functional decline is required to propose appropriate preventative interventions. The functional status in older people is important considering clinical outcome in general, healthcare need and mortality. Part I.- Results and interventions from lucas (longitudinal urban cohort ageing study). Authors.- J. Anders, U. Dapp, L. Neumann, F. Pröfener, C. Minder, S. Golgert, A. Daubmann, K. Wegscheider,. W. von Renteln-Kruse Methods.- The LUCAS core project is a longitudinal cohort of urban community-dwelling people 60 years and older, recruited in 2000/2001. Further LUCAS projects are cross-sectional comparative and interventional studies (RCT). Results.- The emphasis will be on geriatric medical care in a population-based approach, discussing different forms of access, too. (Dapp et al. BMC Geriatrics 2012, 12:35; http://www.biomedcentral.com/1471-2318/12/35): - longitudinal data from the LUCAS urban cohort (n = 3.326) will be presented covering 10 years of observation, including the prediction of functional decline, need of nursing care, and mortality by using a self-filling screening tool; - interventions to prevent functional decline do focus on first (pre-clinical) signs of pre-frailty before entering the frailty-cascade ("Active Health Promotion in Old Age", "geriatric mobility centre") or disability ("home visits"). Conclusions.- The LUCAS research consortium was established to study particular aspects of functional competence, its changes with ageing, to detect pre-clinical signs of functional decline, and to address questions on how to maintain functional competence and to prevent adverse outcome in different settings. The multidimensional data base allows the exploration of several further questions. Gait performance was exmined by GAITRite®-System. Supported by the Federal Ministry for Education and Research (BMBF Funding No. 01ET1002A). Part II.- Selected results from the lausanne cohort 65+ (Lc65 + ) Study (Switzerland). Authors.- Prof Santos-Eggimann Brigitte, Dr Seematter-Bagnoud Laurence, Prof Büla Christophe, Dr Rochat Stéphane. Methods.- The Lc65+ cohort was launched in 2004 with the random selection of 3054 eligible individuals aged 65 to 70 (birth year 1934-1938) in the non-institutionalized population of Lausanne (Switzerland). Results.- Information is collected about life course social and health-related events, socio-economics, medical and psychosocial dimensions, lifestyle habits, limitations in activities of daily living, mobility impairments, and falls. Gait performance are objectively measured using body-fixed sensors. Frailty is assessed using Fried's frailty phenotype. Follow-up consists in annual self-completed questionnaires, as well as physical examination and physical and mental performance tests every three years. - Lausanne cohort 65+ (Lc65 + ): design and longitudinal outcomes. The baseline data collection was completed among 1422 participants in 2004-2005 through self-completed questionnaires, face-to-face interviews, physical examination and tests of mental and physical performances. Information about institutionalization, self-reported health services utilization, and death is also assessed. An additional random sample (n = 1525) of 65-70 years old subjects was recruited in 2009 (birth year 1939-1943). - lecture no 4: alcohol intake and gait parameters: prevalent and longitudinal association in the Lc65+ study. The association between alcohol intake and gait performance was investigated.

Integrated Analysis of High-Resolution Gene Expression and Copy Number Profiling Identified Biallelic Deletion of CDKN2A/2B Tumor Suppressor Locus As the Most Frequent and Unique Genomic Abnormality in Diffuse Large B-Cell Lymphoma (DLBCL) with Strong Prognostic Value in Both GCB and ABC Subtypes and Not Overcome by a Dose-Intensive Immunochemotherapy Regimen Plus Rituximab. Results of a Prospective GELA Clinical Trial Program

Background and aim of the study: Genomic gains and losses play a crucial role in the development and progression of DLBCL and are closely related to gene expression profiles (GEP), including the germinal center B-cell like (GCB) and activated B-cell like (ABC) cell of origin (COO) molecular signatures. To identify new oncogenes or tumor suppressor genes (TSG) involved in DLBCL pathogenesis and to determine their prognostic values, an integrated analysis of high-resolution gene expression and copy number profiling was performed. Patients and methods: Two hundred and eight adult patients with de novo CD20+ DLBCL enrolled in the prospective multicentric randomized LNH-03 GELA trials (LNH03-1B, -2B, -3B, 39B, -5B, -6B, -7B) with available frozen tumour samples, centralized reviewing and adequate DNA/RNA quality were selected. 116 patients were treated by Rituximab(R)-CHOP/R-miniCHOP and 92 patients were treated by the high dose (R)-ACVBP regimen dedicated to patients younger than 60 years (y) in frontline. Tumour samples were simultaneously analysed by high resolution comparative genomic hybridization (CGH, Agilent, 144K) and gene expression arrays (Affymetrix, U133+2). Minimal common regions (MCR), as defined by segments that affect the same chromosomal region in different cases, were delineated. Gene expression and MCR data sets were merged using Gene expression and dosage integrator algorithm (GEDI, Lenz et al. PNAS 2008) to identify new potential driver genes. Results: A total of 1363 recurrent (defined by a penetrance > 5%) MCRs within the DLBCL data set, ranging in size from 386 bp, affecting a single gene, to more than 24 Mb were identified by CGH. Of these MCRs, 756 (55%) showed a significant association with gene expression: 396 (59%) gains, 354 (52%) single-copy deletions, and 6 (67%) homozygous deletions. By this integrated approach, in addition to previously reported genes (CDKN2A/2B, PTEN, DLEU2, TNFAIP3, B2M, CD58, TNFRSF14, FOXP1, REL...), several genes targeted by gene copy abnormalities with a dosage effect and potential physiopathological impact were identified, including genes with TSG activity involved in cell cycle (HACE1, CDKN2C) immune response (CD68, CD177, CD70, TNFSF9, IRAK2), DNA integrity (XRCC2, BRCA1, NCOR1, NF1, FHIT) or oncogenic functions (CD79b, PTPRT, MALT1, AUTS2, MCL1, PTTG1...) with distinct distribution according to COO signature. The CDKN2A/2B tumor suppressor locus (9p21) was deleted homozygously in 27% of cases and hemizygously in 9% of cases. Biallelic loss was observed in 49% of ABC DLBCL and in 10% of GCB DLBCL. This deletion was strongly correlated to age and associated to a limited number of additional genetic abnormalities including trisomy 3, 18 and short gains/losses of Chr. 1, 2, 19 regions (FDR < 0.01), allowing to identify genes that may have synergistic effects with CDKN2A/2B inactivation. With a median follow-up of 42.9 months, only CDKN2A/2B biallelic deletion strongly correlates (FDR p.value < 0.01) to a poor outcome in the entire cohort (4y PFS = 44% [32-61] respectively vs. 74% [66-82] for patients in germline configuration; 4y OS = 53% [39-72] vs 83% [76-90]). In a Cox proportional hazard prediction of the PFS, CDKN2A/2B deletion remains predictive (HR = 1.9 [1.1-3.2], p = 0.02) when combined with IPI (HR = 2.4 [1.4-4.1], p = 0.001) and GCB status (HR = 1.3 [0.8-2.3], p = 0.31). This difference remains predictive in the subgroup of patients treated by R-CHOP (4y PFS = 43% [29-63] vs. 66% [55-78], p=0.02), in patients treated by R-ACVBP (4y PFS = 49% [28-84] vs. 83% [74-92], p=0.003), and in GCB (4y PFS = 50% [27-93] vs. 81% [73-90], p=0.02), or ABC/unclassified (5y PFS = 42% [28-61] vs. 67% [55-82] p = 0.009) molecular subtypes (Figure 1). Conclusion: We report for the first time an integrated genetic analysis of a large cohort of DLBCL patients included in a prospective multicentric clinical trial program allowing identifying new potential driver genes with pathogenic impact. However CDKN2A/2B deletion constitutes the strongest and unique prognostic factor of chemoresistance to R-CHOP, regardless the COO signature, which is not overcome by a more intensified immunochemotherapy. Patients displaying this frequent genomic abnormality warrant new and dedicated therapeutic approaches.

Social Cognition is not Reducible to Theory of Mind. When Children use Deontic Rules to Predict Others' Behaviors

The objective of this paper is to discuss whether children have a capacity for deonticreasoning that is irreducible to mentalizing. The results of two experiments point tothe existence of such non-mentalistic understanding and prediction of the behaviourof others. In Study 1, young children (3- and 4-year-olds) were told different versionsof classic false-belief tasks, some of which were modified by the introduction of a ruleor a regularity. When the task (a standard change of location task) included a rule, theperformance of 3-year-olds, who fail traditional false-belief tasks, significantly improved.In Study 2, 3-year-olds proved to be able to infer a rule from a social situation and touse it in order to predict the behaviour of a character involved in a modified versionof the false-belief task. These studies suggest that rules play a central role in the socialcognition of young children and that deontic reasoning might not necessarily involvemind reading.