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The Role of Statistician in Personalized Medicine: An Overview of Statistical Methods in Bioinformatics

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Information about The Role of Statistician in Personalized Medicine: An Overview of...
Education

Published on March 13, 2014

Author: hafidztio

Source: slideshare.net

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Invited lecturer given at Teknik Fisika, Institut Teknologi Sepuluh Nopember Surabaya.
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The Role of The Statisticians in Personalized Medicine: An Overview of Statistical Methods in Bioinformatics Setia Pramana Teknik Fisika Fakultas Teknik Industri Institut Teknologi Sepuluh Nopember Surabaya, 12 March 2014 Setia Pramana 1

Educational Background • Universitas Brawijaya Malang, FMIPA, Statistics department, 1995-1999. • Hasselt Universiteit, Belgium, MSc in Applied Statistics 2005-2006. • Hasselt Universiteit, Belgium, MSc in Biostatistics 2006- 2007. • Hasselt Universiteit, Belgium, PhD Statistical Bioinformatics, 2007-2011. • Medical Epidemiology And Biostatistics Dept. Karolinska Institutet, Sweden, Postdoctoral, 2011-2014

Now? • Lecture and Researcher at Sekolah Tinggi Ilmu Statistik, Jakarta. • Adjunct Faculty at Medical Epidemiology and Biostatistics Dept, Karolinska Institutet, Stockholm.

Outline • Personalized Medicine • Central Dogma • Microarray Data Analysis • Next Generation Sequencing • Summary Setia Pramana 4

Personalized Medicine • Drug Development: – Takes 10-15 years – Cost millions USD • Who: Pharmaceutical, biotechnology, device companies, Universities and government research agencies • Regulatory: The US Food and Drug Administration (FDA) • Evaluate: – Safety – can people take it? – Efficacy – does it do anything in humans? – Effectiveness – is it better or at least as good as what is currently available? – Do the benefits outweigh the risks? Setia Pramana 5

Personalized Medicine • Drug Development Stages: - Drug Discovery - Pre-clinical Development - Clinical Development 4 Phases • Statisticians are involved in all stages • Stages are highly regulated • Result is based on most of patients • But .. Patients are created differently! Setia Pramana 6

Patients Heterogeneity Setia Pramana 7

Patients Heterogeneity • We’re all different in - Physiological, demographic characteristics - Medical history - Genetic/genomic characteristics • What works for a patient with one set of characteristics might not work for another! Setia Pramana 8

Patients Heterogeneity • “One size does not fit all” • Use a patient’s characteristics to determine best treatment for him/her • Genomic information is a great potential -- > Personalized medicine: “The right treatment for the right patient at the right time” Setia Pramana 9

Subgroup identification and targeted treatment • Determine subgroups of patients who share certain characteristics and would get better on a particular treatment • Discover biomarkers which can identify the subgroup • Focus on finding and treating a subgroup Setia Pramana 11

Subgroup identification and targeted treatment Genotype Phenotype Intervention Outcome Mutations/SN Ps Gene/Protein Expression Epigenetics Diseases Disability etc Drug Regimes Personalized medicine Setia Pramana 12

Advanced Biomedical Technologies • High-throughput microarrays and molecular imaging to monitor SNPs, gene and protein expressions • Next-Generation Sequencing Setia Pramana 13

Microarrays Setia Pramana 14

Central Dogma Central Dogma http://compbio.pbworks.com Setia Pramana 15

Gene • The full DNA sequence of an organism is called its genome • A gene is a segment that specifies the sequence of one or more protein. Setia Pramana 16

Genomics • The study of all the genes of a cell, or tissue, at : – the DNA (genotype), e.g., GWAS SNP, CNV etc… – mRNA (transcriptomics), Gene expression, – or protein levels (proteomics). • Functional Genomics: study the functionality of specific genes, their relations to diseases, their associated proteins and their participation in biological processes. Setia Pramana 17

Microarray • DNA microarrays are biotechnologies which allow the monitoring of expression of thousand genes. Setia Pramana 18

Applications • High efficacy and low/no side effect drug • Genes related disease. • Biological discovery – new and better molecular diagnostics – new molecular targets for therapy – finding and refining biological pathways • Molecular diagnosis of leukemia, breast cancer, etsc. • Appropriate treatment for genetic signature • Potential new drug targets Setia Pramana 19

Microarray Overview of the process of generating high throughput gene expression data using microarrays. Setia Pramana 20

The Pipeline • Experiment design  Lab work  Image processing • Signal summarization (RMA, GCRMA) • Normalization • Data Analysis: – Differentially Expressed genes – Clustering – Classification – Etc. • Network / Pathways (GSEA etc..) • Biological interpretations Setia Pramana 21

Microarray Data Structure Setia Pramana 22

Preprocessed Data Genes C1 C2 C3 T1 T2 T3 G8522 6.78 6.55 6.37 6.89 6.78 6.92 G8523 6.52 6.61 6.72 6.51 6.59 6.46 G8524 5.67 5.69 5.88 7.43 7.16 7.31 G8525 5.64 5.91 5.61 7.41 7.49 7.41 G8526 4.63 4.85 5.72 5.71 5.47 5.79 G8528 7.81 7.58 7.24 7.79 7.38 8.60 G8529 4.26 4.20 4.82 3.11 4.94 3.08 G8530 7.36 7.45 7.31 7.46 7.53 7.35 G8531 5.30 5.36 5.70 5.41 5.73 5.77 G8532 5.84 5.48 5.93 5.84 5.73 5.75Setia Pramana 23

Challenges • Mega data, difficult to visualize • Too few records (columns/samples), usually < 100 • Too many rows(genes), usually > 10,000 • Too many genes likely leading to False positives • For exploration, a large set of all relevant genes is desired • For diagnostics or identification of therapeutic targets, the smallest set of genes is needed • Model needs to be explainable to biologists Setia Pramana 24

Microarray Data Analysis Types • Gene Selection –find genes for therapeutic targets • Classification (Supervised) –identify disease (biomarker study) –predict outcome / select best treatment • Clustering (Unsupervised) –find new biological classes / refine existing ones –Understanding regulatory relationship/pathway –exploration Setia Pramana 25

Gene Selection • Modified t-test • Significance Analysis of Microarray (SAM) • Limma (Linear model for microarrays ) • Linear Mixed model • Lasso (least absolute selection and shrinkage operator) • Elastic-net • Etc, Setia Pramana 26

Visualization • Dimensionality reduction • PCA (Principal Component Analysis) • Biplot • Heatmap • Multi dimensional scaling • Etc Setia Pramana 27

Clustering • Cluster the genes • Cluster the arrays/conditions • Cluster both simultaneously • K-means • Hierarchical • Biclustering algorithms Setia Pramana 28

Clustering • Cluster or Classify genes according to tumors • Cluster tumors according to genes Setia Pramana 29

Classification • Linear Discriminat Analysis • K nearest neighbour • Logistic regression • L1 Penalized Logistric regression • Neural Network • Support vector machines • Random forest • etc Setia Pramana 31

Aim: To improve understanding of host protein profiles during disease progression especially in children.

Classification of Malaria Subtypes •Identify panel of proteins which could distinguish between different subtypes. •Implement L1-penalized logistic regression

Penalized Logistic Regression •Logistic regression is a supervised method for binary or multi-class classification. •In high-dimensional data (e.g., microarray): More variables than the observations  Classical logistic regression does not work. •Other problems: Variables are correlated (multicolinierity) and over fitting. •Solution: Introduce a penalty for complexity in the model. 36

Penalized Logistic Regression Logistic model: Maximize the log-likelihood: •-Penalization (Lasso): • 37

• Shrinks all regression coefficients () toward zero and set some of them to zero. • Performs parameter estimation and variable selection at the same time. • The choice of λ is crucial and chosen via k-fold cross-validation procedure. • The procedure is implemented in an R package called penalized. 38 L1 Penalized Logistic Regression

Classification of Severe Malaria Anemia vs. Uncomplicated Malaria group 39 AUC: 0.86

Dose-response Microarray Studies Setia Pramana 40

Dose-response Microarray Studies Setia Pramana 41 Implemented in R package IsoGene and IsoGeneGUI.

Dose-response Microarray Studies Setia Pramana 42

Gene Signature for Prostate Cancer Setia Pramana 43

Gene Signature for Prostate Cancer Setia Pramana 44

Gene Signature for Prostate Cancer Setia Pramana 45

Next Generation Sequencing Setia Pramana 46

Next Generation Sequencing Setia Pramana 47 Reading the order of bases of DNA fragments

NGS used for: • Whole genome re-sequencing • Metagenomics • Cancer genomics • Exome sequencing (targeted) • RNA-sequencing • Chip-seq • Genomic Epidemiology Setia Pramana 49

Next Generation Sequencing Setia Pramana 50 • Produce Massive Data and fast • Problem is storage and analysis

RNA-seq Pipeline • Align to a reference genome using Tophat. Reference Pramana, et.al 51NBBC 2013 Source: Trapnell et.al, 2010

RNA-seq Pipeline • Measure gene expression using Cufflinks: FPKM (Fragments Per Kilobase of transcript per Million mapped reads). Reference Gene Transcript 2 Transcript 1 Isoform/Transcript FPKM Gene FPKM Sample 1 Sample 2 Sample 3 Pramana, et.al 52NBBC 2013 Source: Trapnell et.al, 2013

Setia Pramana 53

Subtype-specific Transcripts/Isoforms • Breast invasive carcinoma (BRCA) from the Cancer Genome Atlas Project (TCGA). • 329 tumor samples. • Platform: illumina • Paired-end reads (length 50 bp). • 20 -100 million reads Setia Pramana 54

Subtype-specific Transcripts/Isoforms • To discover transcripts/isoforms which are only significantly (high/low) expressed in a certain cancer subtype. Pramana, et.al 55NBBC 2013

Analysis Flow 329 samples TCGA Discovery set 179 samples Validation set - TCGA 150 samples - External samples Classification to mol-subtypes - Use Swedish microarray data as training data. - Based on gene level FPKM - Median and variance normalization - K-nearest neighbor - Classifier genes selection Subtype-specific Transcript - Transcript level FPKM of all genes - For each transcript: Robust contrast tests. - Multiple testing adjustment. Pramana, et.al 56NBBC 2013

Subtype-specific Transcripts/Isoforms Setia Pramana 57

Subtype-specific Transcripts/Isoforms Setia Pramana 58

Subtype-specific Transcripts/Isoforms Setia Pramana 59

Software? • R now is growing, especially in bioinformatics – Statistics, data analysis, machine learning – Free – High Quality – Open Source – Extendable (you can submit and publish your own package!!) – Can be integrated with other languages (C/C++, Java, Python) – Large active user community – Command-based (-) Setia Pramana 60

Summary • Statistics plays important roles in developing personalized medicine • Multidisciplinary field  need collaboration with different experts. • Bioinformaticians is one of the sexiest job • Big Data in Medicine: Numerous opportunities to be explored and discovered. Setia Pramana 61

Thank you for your attention…. Setia Pramana 62

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