Analyse des Big Data en recherche clinique. Rodolphe Thiébaut INSERM U897, INRIA team SISTM, ISPED Bordeaux

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1 Analyse des Big Data en recherche clinique Rodolphe Thiébaut INSERM U897, INRIA team SISTM, ISPED Bordeaux

2 Big Data (google.com/trends) 90% des données dans le monde ont été créées au cours des deux dernières années (Nate Silver) 2

3 Quels Big Data? 3

4 Alain Viari 04/2013

5 Alain Viari 04/2013

6 Point de vue du biostatstcien X Y Z 6

7 Point de vue du clinicien Essai Nb de pa<ents Taille de la base ALBI ANRS Ko DALIA Go 7

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10 Qu apporte les Big data en recherche clinique? Du bruit ou de l informaton? (GIGO) Très / trop spécifique? (taux d antcorps vs. Genomics, proteomics, cytomics ) Du Data driven vs. Hypothesis driven? Une augmentaton du budget d analyse? (3 mois ETP bioinfo + 3 mois ETP biostat vs. 1 mois ETP biostat)? 10

11 Protéomique pour le pronostc 46 patents (25 bons répondeurs à la radiochimiothérapie et 21 mauvais répondeurs) inclusion T1 T2 T3 T4 T5 T6 : veille de chirurgie P P P P Traitement par radio- chimiothérapie 1 fois par semaine pendant 5 semaines 4 à 8 semaines sans traitement P 11

12 Comparaison globale des échantllons Abondances non nulles Q3 Q2 Q1 ProporTon d abondances nulles EchanTllons 12

13 Abondances non nulles Q3 Q2 Q1 ProporTon Abondances nulles 13

14 ModélisaTon Mathieu Raimbault 14

15 ModélisaTon 15

16 DALIA TRIAL (DendriTc cells And Lipo5 ImmunizaTon against Aids) Vaccina<on of HIV- 1 infected pa<ents with ex- vivo generated interferon- α dendri<c cells loaded with HIV- 1 lipopep<des and ac<vated with lipopolysaccharide in addi<on to an<retroviral treatment: Exploratory phase I study

17 DALIA TRIAL (DendriTc cells And Lipo5 ImmunizaTon against Aids) 17

18 Pour commencer 18

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20 Les (big) data probes (18 temps x sondes) 26 Mo beads (22 billes/ sonde) 6 Go No antigen Gag peptides SEA SEB - 30 populatons cellulaires 0.05 Mo antcorps (18 temps x 15 tubes x 8 antcorps) pour 2.6 Go CD Total number of CD69+ cells Cell count mesures/temps Mo CD69 IFN-γ Gene profiling e-3 IL Epitope mapping IFN-γ Cell responses Responses of CD3+CD4+ cells Responses of CD3+CD4- cells Viral changes/ adapta<on Cytokines % of total CD4 % of total CD8 PPi, H +, séquences 20 Mo Stimulation with: No antigen Gag peptid IL-2 20

21 Clinical Core JD. Lelièvre V. Rieux A.L. Ross + investgators of clinical site M.I.C C. Lacabaratz M. Surenaud A. Wiedemann S. Hüe H. Hocini Teams Baylor/U899 G. Zurawski S. Zurawski K. Palucka M. Montes J. Banchereau Virology Core NRF For Viral Hepa<<s/HIV JM. Pawlotsky S.Chevaliez D. Challine M.Bouvier- Alias C. Delaugerre J. Corneille Al. Soulier Clinical trial unit G. Chêne C. Fagard P. Reboud V. Arnold C. Boucherie C. Grondin + C. Durier Biosta<s<cs / Bioinforma<cs R. Thiébaut B. Liquet R. Genuer L. Richert B. Hejblum M. Raimbault H. Bonnabau NHP R. Le Grand N. Bosquet A. Cosma B. Targat AS. Beignon

22 Réponses cytokiniques Laura Richert 22

23 MulTplicité des tests NEJM 2006;354:

24 MulTplicité des tests Declared non-significant Declared significant total True null hypotheses U V m 0 Non-true null hypotheses T S m - m 0 m-r R m Family Wise Error Rate FWER = P(V>0) False Discorery Rate FDR = E(V/R) = espérance du taux de faux positifs Benjamini, Y. and Hochberg, Y. (1995) Controlling the false discovery rate: a practcal and powerful approach to multple testng. J. R. StaTst. Soc. B, 57,

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26 Réduire la dimension Wirkowski Stat Med 2004 Richert et al. IAS 2012

27 Quel lien? 27

28 Réponses cytokiniques & virus 28

29 Expression génique 29

30 Analyse gène par gène 30

31 Analyse par Gene Set Subramanian PNAS 2005; 102:

32 1.0 Time-course Gene Set Analysis 0.5 B.P. Hejblum et al Median of standardized gene expression W24 W25 W26 W27 W28 Time Fig. 1. Exemple of a homogeneous gene set (Module M1.2 Interferon ). Each line is the median expression of a gene inside this particular gene set across all the patients. The expression of the genes inside this gene set is quite homogeneous and it is easy to identify a global time trend, displayed by the dashed black line (smoothed median). sion 1.0 W32 W36 W40 W44 Median of standardized gene expression W24 W25 W26 W27 W28 Time Fig. 1. Exemple of a homogeneous gene set (Module M1.2 Interferon ). Each line is the median expression of a gene inside this particular gene set across all the patients. The expression Boris of Hejblum the genes inside this gene set is quite homogeneous and it is easy to identify a global time trend, displayed by the dashed black line (smoothed median). heterogeneity 1.0 inside a gene set. It relies on a Principal Component Median of standardized gene expression Analysis (PCA) of each gene set. This PCA step is followed by a linear regression of the significant principal components (i.e. that 0.5 have a variation above the mean gene variance) over time. However, they did not considered time-course experiments with replicates (such as 0.0 several patients). The choice of the null hypothesis is one of the decisive components of a gene set analysis algorithm. Those can be 0.5 classified into two main types of hypothesis: i) the competitive null hypothesis, that tests the genes inside a given gene set against 1.0 all the other genes outside the gene set; ii) the self-contained null hypothesis, that only uses the genes inside the gene set of interest Time (Tian et al., 2005; Goeman and Bühlmann, 2007; Ackermann and Strimmer, 2009). This latter type of hypothesis includes the global Fig. 2. Exemple of a heterogeneous geneset (Module M4.16 cell cycle ). null hypothesis (the expression of at least one gene inside a gene Each line is the median expression of a gene inside this particular gene set set is changing over time) and the central null hypothesis (the mean across all the patients. The expression of the genes inside this gene set is rather expression heterogeneous. of thethis geneses makesincluded it difficultintoaidentify gene set anyistime changing trend, asover the mean time). expression In the present insidepaper, this gene weset look is stays at a sub-kind close to zero. of self-contained However a closer null look hypothesis, reveals twothe distinct mixed timenull trends, hypothesis : displayed by the two respectively dashed and dotted black lines (smoothed medians). W24 W25 W26 W27 W28 W32 W32 W36 W36 W40 W40 W44 W44 heter Anal linea have they (such Th comp class null all th hypo (Tian Strim null set is expre time) null Thro both He meth signi heter and m estim is cru This thoug this m show Paler reach

33 . Hejblum et al Time-course Gene Set Analysis Median of standardized gene expression W24 W25 W26 W27 W28 Time W32 W36 W40 W44 Cluster 1 Median of standardized gene expression W24 W25 W26 W27 W28 Time W32 W36 W40 W44 Cluster 1 M 1.2: Interferon ression 1.0 ression 2

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35 PopulaTons cellulaires 8 antcorps = 256 populatons différentes / tube

36 PopulaTons cellulaires «GaTng» manuel s SEA SEB Reconnaissance automatque Total number of CD69+ cells Cell count J Comp Graph Stat 2010, 9:

37 DALIA Des données nombreuses mais spécifiques nécessitant une approche multdisciplinaire Test d hypothèse ET fouille de données Ne pas ignorer l informaton mais prudence et réplicaton nécessaire 37

38 VAC 18 trial 38

39 VAC 18 trial 39

40 VAC 18 trial 40

41 Vac 18 trial: ELISPOT Ifn- γ assay 41

42 VAC 18 trial: substudy Richert et al. AIDS 2013

43 VAC 18 trial: substudy W0 (Before vaccinaton) In vitro W14 (Awer vaccinaton) Gene expression (awer 6/24h stmulaton) by Illumina Human HT- 12v4 Expression BeadChips Cytokine producton (awer 11 days stmulaton) by Luminex

44 VAC 18 trial: substudy STmulaTon with LIPO5

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46 VAC 18 trial: substudy Comment faire le lien entre ces données (expression génique & producton de cytokines)? Difficultés: Mesures répétées Plusieurs stmulaton Données multvariées de grande dimension 46

47 VAC 18: methodology Benoit Liquet

48 VAC 18: methodology 48

49 VAC 18: discriminant analysis Par<al Least Square (PLS) Same idea than PCA but taking into account the correlaton with Y Exist for: multvariate Y (PLS2) qualitatve outcome (Discriminant analysis) X = X 1 Explained by t 1 To be explained Y = Y 1 49

50 VAC 18: discriminant analysis Par<al Least Square (PLS) Same idea than PCA but taking into account the correlaton with Y Exist for: multvariate Y (PLS2) qualitatve outcome (Discriminant analysis) X ~ = X 2 Explained by t 1 Explained by t 2 Remain to explain Y ~ = y 2 50

51 VAC 18: discriminant analysis X- variate 1: 30 genes X- variate 2: 137 genes X- variate 3: 123 genes 51

52 VAC 18: discriminant analysis 52

53 VAC 18: integratve analysis Cytokine producton Gene expression

54 VAC 18: integratve analysis Th2 response Th17 response 54

55 Qu apporte les Big data en recherche clinique? Difficultés Intérêts Bruit Interdisciplinarité Plusieurs questons Conso de temps InformaTon Interdisciplinarité Plusieurs réponses Gain de temps 55

56 Mais prudence Car les erreurs sont quelques fois moins évidentes avec ces données et les questons sous jacentes sont souvent causales Beaucoup d hypothèses peuvent être spécifiées a priori Calcul de NSN pour données omiques réalisables Nécessité de répliquer 56

57 Remerciements 57

58 Equipe BiostaTsTque INSERM U897 Bourse de thèse/postdoc disponible bordeaux2.fr 58

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