E-health and the performativity of the health democracy

Since at least the 1990s, a movement quoted as the “health democracy,” has set out to establish new rights for patients, and changes current professional practices. Its dynamic can be analyzed through the lens of performativity, a whole wave of research with the aim to understand how a theory or doctrine can feasibly make real what it theorizes and encourages. “Health democracy” intends to reduce the disproportionate distribution of power in doctor/patient relationships. In parallel, different innovations related to the ir- ruption of E-health (social networks, web applications, and other devices) are currently modifying the practices, and thereby reconstructing the relationships between patients and professionals. Based on a corpus analysis, using a scoping review method, this article ex- plores the ways E-health modifies the process of performativity in the “health democracy”. Two effects are identified: a co-production introduced in the classic relationship between patients and healthcare professionals thanks to a better follow-up at distance, and a new form of expertise based on the information circulating on the internet. Each effect develops its own benefits and risks. In order to optimize this new added-value offered by E-health on patient engagement, many managerial consequences must be taken into account. Em- ploying a narrative approach to the dynamics currently at play, it establishes that E-health represents a process of performativity of health democracy by “overflowing”. It also high- lights a risk of counter-performativity: in that if the traditional patient/doctor relationship is less asymmetric, answering to the “health democracy”’s demand may pose another risk related to the use of internet-based information that threats this equilibrium

E-health and the performativity of the health democracy

Since at least the 1990s, a movement quoted as the “health democracy,” has set out to establish new rights for patients, and changes current professional practices. Its dynamic can be analyzed through the lens of performativity, a whole wave of research with the aim to understand how a theory or doctrine can feasibly make real what it theorizes and encourages. “Health democracy” intends to reduce the disproportionate distribution of power in doctor/patient relationships. In parallel, different innovations related to the ir- ruption of E-health (social networks, web applications, and other devices) are currently modifying the practices, and thereby reconstructing the relationships between patients and professionals. Based on a corpus analysis, using a scoping review method, this article ex- plores the ways E-health modifies the process of performativity in the “health democracy”. Two effects are identified: a co-production introduced in the classic relationship between patients and healthcare professionals thanks to a better follow-up at distance, and a new form of expertise based on the information circulating on the internet. Each effect develops its own benefits and risks. In order to optimize this new added-value offered by E-health on patient engagement, many managerial consequences must be taken into account. Em- ploying a narrative approach to the dynamics currently at play, it establishes that E-health represents a process of performativity of health democracy by “overflowing”. It also high- lights a risk of counter-performativity: in that if the traditional patient/doctor relationship is less asymmetric, answering to the “health democracy”’s demand may pose another risk related to the use of internet-based information that threats this equilibrium

The dChip survival analysis module for microarray data

International audienceBACKGROUND: Genome-wide expression signatures are emerging as potential marker for overall survival and disease recurrence risk as evidenced by recent commercialization of gene expression based biomarkers in breast cancer. Similar predictions have recently been carried out using genome-wide copy number alterations and microRNAs. Existing software packages for microarray data analysis provide functions to define expression-based survival gene signatures. However, there is no software that can perform survival analysis using SNP array data or draw survival curves interactively for expression-based sample clusters. RESULTS: We have developed the survival analysis module in the dChip software that performs survival analysis across the genome for gene expression and copy number microarray data. Built on the current dChip software's microarray analysis functions such as chromosome display and clustering, the new survival functions include interactive exploring of Kaplan-Meier (K-M) plots using expression or copy number data, computing survival p-values from the log-rank test and Cox models, and using permutation to identify significant chromosome regions associated with survival. CONCLUSIONS: The dChip survival module provides user-friendly way to perform survival analysis and visualize the results in the context of genes and cytobands. It requires no coding expertise and only minimal learning curve for thousands of existing dChip users. The implementation in Visual C++ also enables fast computation. The software and demonstration data are freely available at http://dchip-surv.chenglilab.org

The shaping and functional consequences of the dosage effect landscape in multiple myeloma

Background: Multiple myeloma (MM) is a malignant proliferation of plasma B cells. Based on recurrent aneuploidy such as copy number alterations (CNAs), myeloma is divided into two subtypes with different CNA patterns and patient survival outcomes. How aneuploidy events arise, and whether they contribute to cancer cell evolution are actively studied. The large amount of transcriptomic changes resultant of CNAs (dosage effect) pose big challenges for identifying functional consequences of CNAs in myeloma in terms of specific driver genes and pathways. In this study, we hypothesize that gene-wise dosage effect varies as a result from complex regulatory networks that translate the impact of CNAs to gene expression, and studying this variation can provide insights into functional effects of CNAs. Results: We propose gene-wise dosage effect score and genome-wide karyotype plot as tools to measure and visualize concordant copy number and expression changes across cancer samples. We find that dosage effect in myeloma is widespread yet variable, and it is correlated with gene expression level and CNA frequencies in different chromosomes. Our analysis suggests that despite the enrichment of differentially expressed genes between hyperdiploid MM and non-hyperdiploid MM in the trisomy chromosomes, the chromosomal proportion of dosage sensitive genes is higher in the non-trisomy chromosomes. Dosage-sensitive genes are enriched by genes with protein translation and localization functions, and dosage resistant genes are enriched by apoptosis genes. These results point to future studies on differential dosage sensitivity and resistance of pro- and anti-proliferation pathways and their variation across patients as therapeutic targets and prognosis markers. Conclusions: Our findings support the hypothesis that recurrent CNAs in myeloma are selected by their functional consequences. The novel dosage effect score defined in this work will facilitate integration of copy number and expression data for identifying driver genes in cancer genomics studies. The accompanying R code is available at http://www.canevolve.org/dosageEffect/

Classify Hyperdiploidy Status of Multiple Myeloma Patients Using Gene Expression Profiles

Multiple myeloma (MM) is a cancer of antibody-making plasma cells. It frequently harbors alterations in DNA and chromosome copy numbers, and can be divided into two major subtypes, hyperdiploid (HMM) and non-hyperdiploid multiple myeloma (NHMM). The two subtypes have different survival prognosis, possibly due to different but converging paths to oncogenesis. Existing methods for identifying the two subtypes are fluorescence in situ hybridization (FISH) and copy number microarrays, with increased cost and sample requirements. We hypothesize that chromosome alterations have their imprint in gene expression through dosage effect. Using five MM expression datasets that have HMM status measured by FISH and copy number microarrays, we have developed and validated a K-nearest-neighbor method to classify MM into HMM and NHMM based on gene expression profiles. Classification accuracy for test datasets ranges from 0.83 to 0.88. This classification will enable researchers to study differences and commonalities of the two MM subtypes in disease biology and prognosis using expression datasets without need for additional subtype measurements. Our study also supports the advantages of using cancer specific characteristics in feature design and pooling multiple rounds of classification results to improve accuracy. We provide R source code and processed datasets at www.ChengLiLab.org/software

A polygenic risk score for multiple myeloma risk prediction

This work was partially supported by intramural funds of the University of Pisa, DKFZ, and University Hospital of Southern Jutland, Denmark, and by a grant of the French National Cancer Institute (INCA). The authors wish to thank Dr. Dominic Edelmann (Division of Biostatistics, DKFZ) for helpful advice about data analysis.There is overwhelming epidemiologic evidence that the risk of multiple myeloma (MM) has a solid genetic background. Genome-wide association studies (GWAS) have identified 23 risk loci that contribute to the genetic susceptibility of MM, but have low individual penetrance. Combining the SNPs in a polygenic risk score (PRS) is a possible approach to improve their usefulness. Using 2361 MM cases and 1415 controls from the International Multiple Myeloma rESEarch (IMMEnSE) consortium, we computed a weighted and an unweighted PRS. We observed associations with MM risk with OR = 3.44, 95% CI 2.53-4.69, p = 3.55 x 10(-15) for the highest vs. lowest quintile of the weighted score, and OR = 3.18, 95% CI 2.1 = 34-4.33, p = 1.62 x 10(-13) for the highest vs. lowest quintile of the unweighted score. We found a convincing association of a PRS generated with 23 SNPs and risk of MM. Our work provides additional validation of previously discovered MM risk variants and of their combination into a PRS, which is a first step towards the use of genetics for risk stratification in the general population.University of Pisa, DKFZUniversity Hospital of Southern Jutland, DenmarkInstitut National du Cancer (INCA) Franc

Heterogeneity of genomic evolution and mutational profiles in multiple myeloma.

Multiple myeloma is an incurable plasma cell malignancy with a complex and incompletely understood molecular pathogenesis. Here we use whole-exome sequencing, copy-number profiling and cytogenetics to analyse 84 myeloma samples. Most cases have a complex subclonal structure and show clusters of subclonal variants, including subclonal driver mutations. Serial sampling reveals diverse patterns of clonal evolution, including linear evolution, differential clonal response and branching evolution. Diverse processes contribute to the mutational repertoire, including kataegis and somatic hypermutation, and their relative contribution changes over time. We find heterogeneity of mutational spectrum across samples, with few recurrent genes. We identify new candidate genes, including truncations of SP140, LTB, ROBO1 and clustered missense mutations in EGR1. The myeloma genome is heterogeneous across the cohort, and exhibits diversity in clonal admixture and in dynamics of evolution, which may impact prognostic stratification, therapeutic approaches and assessment of disease response to treatment

Prévention en santé: la révolution algorithmique est en marche

International audienceHealth prevention is a national cause. Having lagged behind for decades, successive governments are now embracing the cause, with an increasing number of measures.La prévention en santé est une cause nationale. Après avoir été à la traîne pendant des décennies, les gouvernements successifs embrassent la cause, multipliant les mesures

Les hôpitaux face à la crise Covid.: Une affaire de management

International audienc

L’e-santé rend-elle la démocratie sanitaire pleinement performative ?

International audienceSince at least the 1990s, a movement quoted as the “health democracy,” has set out to establish new rights for patients, and changes current professional practices. Its dynamic can be analyzed through the lens of performativity, a whole wave of research with the aim to understand how a theory or doctrine can feasibly make real what it theorizes and encourages. “Health democracy” intends to reduce the disproportionate distribution of power in doctor/patient relationships. In parallel, different innovations related to the irruption of E-health (social networks, web applications, and other devices) are currently modifying the practices, and thereby reconstructing the relationships between patients and professionals. Based on a corpus analysis, using a scoping review method, this article explores the ways E-health modifies the process of performativity in the “health democracy”. Two effects are identified: a co-production introduced in the classic relationship between patients and healthcare professionals thanks to a better follow-up at distance, and a new form of expertise based on the information circulating on the internet. Each effect develops its own benefits and risks. In order to optimize this new added-value offered by E-health on patient engagement, many managerial consequences must be taken into account. Employing a narrative approach to the dynamics currently at play, it establishes that E-health represents a process of performativity of health democracy by “overflowing”. It also highlights a risk of counter-performativity: in that if the traditional patient/doctor relationship is less asymmetric, answering to the “health democracy”’s demand may pose another risk related to the use of internet-based information that threats this equilibrium.Depuis au moins les années 90, un mouvement cherche à introduire de nouveaux droits en faveur du patient. Connu sous le vocable de démocratie sanitaire, il aspire à changer les pratiques. Cette dynamique peut donc être analysée sous l’angle de la performativité, notion définissant tout un courant de recherche visant à comprendre comment une théorie ou une doctrine peut rendre les pratiques conformes à ce qu’elle analyse et préconise. C’est essentiellement sous l’angle d’une réduction de l’asymétrie vis-à-vis du patient que la démocratie sanitaire entend performer les pratiques médicales. Or, l’e-santé (réseaux sociaux, outils connectés, applications web) est elle aussi en train de modifier ces pratiques et de changer la relation entre le patient et les professionnels de santé. Reposant sur une analyse du corpus existant, l’article se propose d’éclairer la manière dont l’innovation numérique modifie le processus de performativité de la démocratie sanitaire. Il en ressort que l’e-santé répond aux attentes de la démocratie sanitaire en favorisant un engagement du patient dans son parcours de santé selon deux modalités: l’affirmation d’une co-production dans le cadre de la relation traditionnelle qu’il entretient avec les professionnels de santé grâce à de meilleurs suivis à distance, et l’acquisition d’une expertise autonome fondée sur les informations produites via les opérateurs privés du numérique. Chaque mode présente des avantages et des risques. Afin d’optimiser cette marge de manœuvre offerte par l’e-santé en faveur de la démocratie sanitaire, plusieurs implications managériales s’imposent.Présenté sous la forme d’une recension narrative, l’e-santé représente un processus de performativité de la démocratie sanitaire par « débordement » et révèle qu’un risque de contre-performativité est présent : alors que la relation patient/médecin se trouve rééquilibrée dans un sens plus démocratique par l’e-santé, le développement de celle-ci risque de créer des déséquilibres démocratiques d’une autre nature