Statistical Issues in Translational Cancer Research

Transcription

1 Statistical Issues in Translational Cancer Research Martin Bøgsted Department of Haematology Aalborg University Hospital and Department of Clinical Medicine Aalborg University The only useful function of a statistician is to make predictions and thus to provide a basis for action Deming, WE Statistical issues Klitgaarden 2013 November 13, / 28

2 Programme Presentation 1/2 hour Journal club 1/2 hour Wright et al., 2000, Nature Hans et al., 2004, Blood Hernandalez-Illizaliture, 2011, Cancer Discussion 1/2 hour How did ABC/GCB found its way into clinical trials? Could this have been done more efficiently? What do we do in the future to speed up the translational process? Could better statistical insight have helped during the process? Statistical issues Klitgaarden 2013 November 13, / 28

3 Outline Motivation The biomarker vocabulary Predictive biomarkers and classifiers Phases in clinical trials The translational pathway A personalized drug development strategy (our) Unsupervised cluster analysis Differentially expressed genomic features Build the classifier Feature selection Assay development Validation Design of clinical trials Discussion Statistical issues Klitgaarden 2013 November 13, / 28

4 Motivation The recognition of the heterogeneity of tumors of the same primary site, availability of the tools of genomics for characterizing tumors, and focus on molecularly targeted drugs, has resulted in increased interest in predictive classification problems and the need for new clinical trial designs Statistical issues Klitgaarden 2013 November 13, / 28

5 The biomarker vocabulary Traditional biomarkers are measured to track the pace of a disease increasing as the disease progresses and decresing as it regresses (surrogate endpoints). Prognostic biomarkers are measured before treatment to indicate which patients receiving standard treatment have sufficiently good prognosis that they do not need additional treatment. Predictive biomarkers are measured before treatment to identify who is likely or unlikely to benefit from a particular treatment. Statistical issues Klitgaarden 2013 November 13, / 28

6 Predictive biomarkers and classifiers Taken from: Richard Simon, NCI Most prognostic factors are not used because they are not therapeutically relevant Most prognostic factor studies are not focused on a clear objective They use a convenience sample of patients for who tissue is available Often the patients are too heterogeneous to support therapeutically relevant conclustions Most cancer treatments benefit only a minority of patients to whom they are administered Being able to predict which patients are likely to benefit would save patients from unnecessary toxicity, and enhance their chance of receiving a drug that helps them Help control medical costs Medicine needs predicitve not prognostic biomarkers A predictive classifier is a method which on basis of a number of predictive biomarkers measured before treatment can predict whether a particular treatment is likely to be beneficial Statistical issues Klitgaarden 2013 November 13, / 28

7 Phases in clinical trials Drug validation Biomarker validation Phase I Stage I Phase II Stage II Phase III Stage III Phase IV Stage IV Statistical issues Klitgaarden 2013 November 13, / 28

8 The translational pathway Assay Development Validation of predictive models Design of clinical trials Statistical issues Klitgaarden 2013 November 13, / 28

9 A personalised drug development strategy Tumor biopsies from cancer patients (n) Tumour genomic analysis Supervised/unsupervised cluster analysis Partition patients into new clusters Differentially expressed features (p) Build classifier (p >>n) Feature enrichment Drug target identification Assay development Clinical trials Statistical issues Klitgaarden 2013 November 13, / 28

10 Unsupervised cluster analysis The algorithm Given a set of n items to be clustered, and an n n distance (or similarity) matrix, the basic process of hierarchical clustering is this: 1 Start by assigning each item to its own cluster, so that if you have n items, you now have n clusters, each containing just one item. Let the distances (similarities) between the clusters equal the distances (similarities) between the items they contain. 2 Find the closest (most similar) pair of clusters and merge them into a single cluster, so that now you have one less cluster. 3 Compute distances (similarities) between the new cluster and each of the old clusters. 4 Repeat steps 2 and 3 until all items are clustered into a single cluster of size n. Statistical issues Klitgaarden 2013 November 13, / 28

11 Unsupervised cluster analysis Examples Alizadeh et al., 2000, Nature Statistical issues Klitgaarden 2013 November 13, / 28

12 A personalised drug development strategy Tumor biopsies from cancer patients (n) Tumour genomic analysis Supervised/unsupervised cluster analysis Partition patients into new clusters Differentially expressed features (p) Build classifier (p >>n) Feature enrichment Drug target identification Assay development Clinical trials Statistical issues Klitgaarden 2013 November 13, / 28

13 Differentially expressed genomic features Difference in expression detected by variations of the t-test Multiple test correction (Bonferroni, FDR, etc.) Advances in biotechnology requires new approaches like linear and linear mixed models, generalized linear mixed models, Bayesian approaches etc. Kloster et al., 2012, BMC Genomics Statistical issues Klitgaarden 2013 November 13, / 28

14 A personalised drug development strategy Tumor biopsies from cancer patients (n) Tumour genomic analysis Supervised/unsupervised cluster analysis Partition patients into new clusters Differentially expressed features (p) Build classifier (p >>n) Feature enrichment Drug target identification Assay development Clinical trials Statistical issues Klitgaarden 2013 November 13, / 28

15 LMO2 CD53 CD40 IRF8 REL BACH2 MALT1 TFRC TP53 ETS1 CD81 CD86 STAT5B FAS CD22 MS4A1 CD72 CR2 PAX5 NFKB1 POU2F2 CXCR5 CD24 SPI1 CXCR4 FOXP1 MKI67 BCL6 SERPINA9 CCNB1 POU2AF1 AICDA SOX5 FOXO1 PTPRC FNIP1 STAT3 RUNX1 GCET2 MTA3 ADA TCF3 IKZF1 CD38 CD9 PTK2B PLCG2 RAG1 IL2RA RELA SOX4 BCL3 ICAM1 PRKDC MITF JUN GPR183 FOS PECAM1 MUM1 XBP1 SDC1 PRDM1 IRF4 LGALS1 RUNX2 SPN CCR6 BCL2 CD44 CDKN1A LGALS8 KLF9 CD200 KLF4 CD48 MIR155HG MYC FCER2 ITGA4 TNFRSF8 ZBTB16 CD5 NKX2 3 TLR9 RAG2 Build a classifier Demonstrating statistical significance of prognostic factors is NOT the same as demonstrating predictive accuracy. Statisticians (and other scientists) are used to inference, not prediction Most statistical methods were not developed for p >> n prediction problems Color Key Subpopulation profiles 2 2 Row Z Score Centroblast B250 Centroblast B233 Centroblast B236 Centroblast B238 Centroblast B249 Centroblast B234 Centroblast B235 Centrocyte B233 Centrocyte B250 Centrocyte B235 Centrocyte B236 Centrocyte B249 Centrocyte B234 Centrocyte B238 Plasmablast B238 Plasmablast B233 Plasmablast B237 Plasmablast B235 Plasmablast B236 Plasmablast B249 Plasmablast B234 Memory B235 Memory B234 Memory B249 Memory B233 Memory B236 Memory B250 Naive B237 Naive B233 Naive B249 Naive B250 Naive B234 Naive B235 Genes Samples Statistical Dybkær issues et al., 2013, in prep. Klitgaarden 2013 November 13, / 28

16 Introduction to classifier training Classification begins with a specification of two spaces: X = R p p-dimensional Euclidean space of feature vectors Y = {1,..., k} k classes or class labels Assume {X i, Y i } n i=1 is a collection of training data. Then the empirical risk is defined as ˆR n (f ) = 1 n l(f (X i ), Y i ). n i=1 where l is a loss function used to measure the loss of errornous decisions. Statistical learning can now be formulated as minimizing the empirical risk, i.e. ˆf n = arg min ˆR n (f ). f F Consider e.g. the the 0-1 loss l(ˆf n (X ), Y ) = 1{ˆf n (X ) Y } Statistical issues Klitgaarden 2013 November 13, / 28

17 Model Assessment y x Polynomial degree Prediction Error empirical risk underfitting Best Model overfitting true risk Complexity Robert Nowak, 2011 Statistical issues Klitgaarden 2013 November 13, / 28

18 Strategies to Avoid Overfitting Use e.g. Dimension reduction (methods of sieves) Penalization (shrinkage) Bayesian methods In combination with Complicated mathematics (min-max lower bounds) Hold out methods (e.g. leave-one-out-cross-validation) Statistical issues Klitgaarden 2013 November 13, / 28

19 Model Assessment Normally one work with The sample/data The population Ideally one splits the sample into the following data sets: Training set: A sub-sample used for learning, that is to fit the parameters (i.e., weights) of the classifier. Validation set: A sub-sample used to tune the parameters (i.e., architecture, not weights) of a classifier. Test set: A sub-sample used only to assess the performance (generalization) of a fully-specified classifier. Note that the training and validation data sets are often combined and tuning as well as assessment are done by cross-validation over the training set. Statistical issues Klitgaarden 2013 November 13, / 28

20 A personalised drug development strategy Tumor biopsies from cancer patients (n) Tumour genomic analysis Supervised/unsupervised cluster analysis Partition patients into new clusters Differentially expressed features (p) Build classifier (p >>n) Feature enrichment Drug target identification Assay development Clinical trials Statistical issues Klitgaarden 2013 November 13, / 28

21 Feature selection Differential coexpression ABC vs. GCB (LMPP) Gene ontology analysis Weighted network analysis Statistical issues Klitgaarden 2013 November 13, / 28

22 A personalised drug development strategy Tumor biopsies from cancer patients (n) Tumour genomic analysis Supervised/unsupervised cluster analysis Partition patients into new clusters Differentially expressed features (p) Build classifier (p >>n) Feature enrichment Drug target identification Assay development Clinical trials Statistical issues Klitgaarden 2013 November 13, / 28

23 Assay development Microarrays: Typically not much in use. qpcr Flow cytometry Immunohistochemestry Hans, 2004, Blood Statistical issues Klitgaarden 2013 November 13, / 28

24 Validation Drug target identification Cell line models Animal models Retrospective data analysis Phase II/Stage II clinical trials Falgreen et al., 2013, submitted Statistical issues Klitgaarden 2013 November 13, / 28

25 Validation The classifier Retrospective data analysis Phase II/Stage II clinical trials Statistical issues Klitgaarden 2013 November 13, / 28

26 A personalised drug development strategy Tumor biopsies from cancer patients (n) Tumour genomic analysis Supervised/unsupervised cluster analysis Partition patients into new clusters Differentially expressed features (p) Build classifier (p >>n) Feature enrichment Drug target identification Assay development Clinical trials Statistical issues Klitgaarden 2013 November 13, / 28

27 Design of clinical trials (phase III/stage III) End point Tumour acitivity Time to pregression Overall survival Design issues Enrichment designs etc. (Maitournam and Simon, 2005, Statis. Med.) Sample size calculations allows us to determine the sample size required to estimate the performance of a classifier with a given precision allows us to determine the sample size required to detect an effect of a given size with a given degree of confidence. The protocol Analysis plan Sample size calculations Statistical issues Klitgaarden 2013 November 13, / 28

28 Discussion Statistical issues Klitgaarden 2013 November 13, / 28