Expert-guided Visual Exploration (EVE) for patient stratification. Hamid Bolouri, Lue-Ping Zhao, Eric C. Holland

Transcription

1 Expert-guided Visual Exploration (EVE) for patient stratification Hamid Bolouri, Lue-Ping Zhao, Eric C. Holland

2 Oncoscape.sttrcancer.org Paul Lisa Ken Jenny Desert Eric

3 The challenge Given - patient clinical records and genome-wide data for one or more of - mrna, mirna, protein expression - DNA single nucleotide and copy number alterations (SNAs, CNAs) - Methylated DNA regions per gene Identify clinically-relevant patient sub-groups

4 Classical approaches to finding disease sub-types: 1) Supervised stratification - Divide patients by known criteria (e.g. fusion genes) - Test for enrichment of markers (e.g. mutations) within groups limited applicability 2) Guided clustering - Cluster samples by most variable or candidate genes/probes - Find optimum number of clusters and cluster boundaries - Find genes/probes that best distinguish among clusters 3) Biomarker-based - Find genes/probes correlated with one or more phenotypes sophisticated methods, complex to generalize

5 Motivation: - Empower disease experts, exploit their biomedical knowledge/insights - Leverage users pattern recognition and reasoning skills - Use methods that can easily be adapted to all cancers - Use methods applicable to all genome-wide data types - Enable integration of large-scale and in-house data - Allow for nonlinear gene-gene interactions Overview of EVE Step 1: Step 2: Step 3: Step 4: Reduce genome-wide data to informative features (gene/probe sets) Calculate sample similarities/distances using each feature Visualize feature-based distance matrices as 2/3D scatter plots Simultaneously color-in samples with shared properties across all plots Useful side effects - Can use published gene/probe sets - Methods available for automated feature and parameter searching

6 Feature-based pattern-recognition Discover/define features cluster by features find informative clusters Features End point : cluster archetypes (representatives) Two types of features: local (e.g. eye color) global (e.g. head shape) Example feature relationships: distance between eyes relative size of mouth/nose

7 MGMT probe methylation levels compared to all probes genome-wide (in a single GBM patient) Molecular features to cluster by: sets of genes / probes measuring expression, methylation, DNA-variation, genomic location,... associated with a particular clinical phenotype cellular process cell type... promoter (<1Kbp of TSS) all MGMT probes

8 Example available gene sets (similarity/distance features )

9 Example distance measures

10 1) similaritysna inner product of per-gene indicator vectors 2) similaritycna inner product if per-gene thresholded GISTIC scores 3) joint.sna.cna sum of normalized SNA and CNA similarity scores 4) joint.exp.cna inner product of per-gene expression Z-scores & thresholded GISITC scores 5) me.top8000 top 8000 methylation probes by Median Absolute Deviation 6) me.marker.probes 55 probes reported to be differentially methylated in gliomas (14 genes) 7) me.cimp 1444 of 1503 G-CIMP classifier probes (TCGA 2010) 8) me. ingene 291,151 me-probes within gene bodies 9) me.promotercpg 120,560 me-probes within CpG islands <1000bp of transcription start sites 10) corr.me.exp correlation(top 8000 MAD(per gene methylation, per gene expression)) 11) glioma.gene.exp 162 glioma-associated genes from the literature 12) stemness.exp 396 stemness marker genes 13) metabolic.exp PCA(1157/1240 KEGG Hs. metabolic genes excl. GBM subtype classifiers) 14) GSEA.C2 Manhattan distance(ssgsea of MSIGDB C2 (pathways) gene set) 15) GSEA.C7 Manhattan distance(ssgsea of MSIGDB C7 (immunologic) gene set) 16) exp.os PCA(expression of 46 genes reported to predict Overall Survival)

11 Example (user-defined) sample similarity measures I SNA ( gene i ) = 1 if gene i is mutated 0 otherwise I CNA ( gene i ) = -2 if ploidy = 0-1 if ploidy = 1 0 if ploidy = 2 +1 if ploidy = 3 +2 if ploidy > 3 I SNA ( gene 1 ) I cna ( gene 1 ) I SNA ( gene 2 ) I cna ( gene 2 ) s i = sample SNA vector =.. c i = sample CNA vector =.... I SNA ( gene 20K ) I cna ( gene 20K ) SNA similarity (s 1, s 2 ) = s 1. s 2 CNA similarity (c 1, c 2 ) = c 1. c 2 Joint SNA:CNA similarity = s/sum(s) + c/sum(c)

12 I ll use data for 1105 TCGA glioma samples as example Single Nucleotide Alterations (SNAs) from exome-sequencing Copy Number Alterations (CNAs) from SNP6.0 arrays DNA methylation from Infinium 450K arrays mrna-seq Clinical data, but ~ 2/3 rd of lower grade gliomas were alive at data collection ~ 1/5 th have no status information

13 Example views of sample similarity Lower Grade Glioma (LGG) GBM (grade 4 glioma) published CpG-island methylator phenotype (CIMP) GBM

14 non-cimp LGGs have GBM-like survival CIMP non-cimp

15 CIMP LGGs noncimp LGGs mutation frequency GBMs CIMP LGGs noncimp LGGs GBMs noncimp LGGs have no del(1p19q) & GBM-like freq(cnvs) in CDKN2A/2B, EGFR, PDGFA, MET PTEN EGFR 17 authors

16 non-cimp LGGs are GBM-like in DNA sequence, but distinct in expression & a subset of DNA-me extent of anti-correlation between promoter-me & expression

17 Gliomas can be divided into eight distinct genomic clusters (empirical p-value <0.0001)

18 P-value calculation for GMB SNA:CNA cluster 1

19 Density based clustering verifies genomic clusters 1) density-based clustering 2) density contours 4) cluster members within selected contour 3) points inside a selected contour

20 97 genes shared by Vogelstein et al & UW Oncoplex 274 genes from Vogelstein et al & UW Oncoplex change in sum(all distances) 274 cancer-causing genes are sufficient to capture sample similarities IDH1 top 20 genes not sufficient TP53 ATRX Impact of leaving 1 of 274 genes out impact of leaving ATRX out

21 Robustness: the location of a tumor can be estimated from its similarity to other samples (sample leave-one-out experiment) Sample locations vs. centroid of 3 nearest neighbors Left out sample s location estimated by 3-neighbor centroid

22 % of HER2+ or HER2p+ samples cluster 3 all cluster 3 all cluster 3 all Clusters 1-8 can be used to identify patient groups highly enriched for candidate drugs e.g. cluster 3 is ~ 3-fold enriched for EGFR signaling HER2+ HER2p+ combined

23 Genomic clusters have distinct DNA-methylation & gene expression profiles differential methylation analysis clustering by 4,500 methylation probes HDAC1 a g5,6 REST/NRSF d REST/NRSF mrna expression z-score mrna expression z-score b g7,8 c

24 Automated searching identifies distinct groups of marker-genes (example 1: ZNF821)

25 Automated searching identifies distinct groups of marker-genes (example 2: GPG5)

26 Expression clusters were ranked by the number of misclassifications by a linear SVM PCA of all samples for 396 stemness markers genes PC2 low mrna high mrna PC1

27 frequency in 631 discovered genes 631 genes whose low/high expression segregate metabolic space cluster into 3 groups Expression levels of groups 1 & 3 are ~ anti-correlated. Group 2 is orthogonal to 1 and 3. angle of direction vector

28 High cofilin-1 levels correlate with cisplatin resistance in lung adenocarcinomas. Becker M, et al. Tumour Biol, 2014 Expression levels of some genes delineate specific features (example: CFL1)

29 Dist. to > density To come: combine clustering & marker detection to auto-rank genes sets cluster detection Local density c.f. Science, 27 June 2014 hierarchical complete K-means

30 The self correcting nature of EVE

31 EVE detection of a sequencing batch effect in TCGA lung adenocarcinoma SNA data

32 Sample purity confounding effects in TCGA prostate adenocarcinomas mrna DNA-me mirna

33 Methylation batch effects were corrected with Functional Normalization (R/Bioconductor package minfi ) Expression batch effects were corrected with ComBat (R/Bioconductor package swamp ) Before batch-effect correction After ComBat batch-effect correction p-value(linear-fit of per-gene expression to batch ID)

34 15 OS marker genes, 1 (TAZ) is on ChrX (4 out of 59 probes) Same plot with TAZ removed

35 Expert-guided Visual Exploration (EVE) for patient stratification Hamid Bolouri, Lue-Ping Zhao, Eric C. Holland

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37 1) density-based clustering 2) density contours 4) density-based cluster members within selected contour 3) points inside a selected (green) contour (10%) above

38 non-cimp LGGs are GBM-like CIMP LGG non-cimp LGG non-cimp GBM DNA-me verified CIMP GBM GBM, no DNA-me data

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