The Role of The Statisticians in Personalized Medicine: An Overview of Statistical Methods in Bioinformatics
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This document discusses the role of statisticians in personalized medicine and provides an overview of statistical methods used in bioinformatics. It describes how statisticians are involved in all stages of drug development from discovery through clinical trials. Personalized medicine aims to determine an individual's unique characteristics to select the best treatment. Advanced technologies like microarrays and next-generation sequencing generate large genomic datasets that require statistical analysis for applications like disease classification, biomarker discovery, and identifying disease subtypes and targeted therapies. The document outlines statistical methods used for tasks like microarray data analysis, RNA sequencing, and finding subtype-specific genes and transcripts.
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The Role of The Statisticians in Personalized Medicine: An Overview of Statistical Methods in Bioinformatics
- 1. The Role of Statisticians in Personalized Medicine: An Overview of Statistical Methods in Bioinformatics Setia Pramana STIS Jakarta, 8 August 2014 Setia Pramana 1
- 2. Outline • Drug Development • Personalized Medicine • Central Dogma • Microarray Data Analysis • Next Generation Sequencing • Summary Setia Pramana 2
- 3. Drug Developments • Takes 10-15 years • Cost millions USD • Who: Pharmaceutical, biotechnology, device companies, Universities and government research agencies • Regulatory: The US Food and Drug Administration, BP POM • 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 3
- 4. Drug Development • The 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 4
- 6. 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 6
- 7. 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 7
- 8. Personalized Medicine • The ability to determine an individual's unique molecular characteristics and to use those genetic distinctions to diagnose more finely an individual's disease, select treatments that increase the chances of a successful outcome and reduce possible adverse reactions. • Personalized medicine also is the ability to predict an individual's susceptibility to diseases and thus to try to shape steps that may help avoid or reduce the extent to which an individual will experience a disease Setia Pramana 8
- 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 9
- 10. Subgroup Identification and Targeted Treatment Genotype Phenotype Intervention Outcome Mutations/SNP Gene/Protein Expression Epigenetics Diseases Disability Etc. Drugs Therapies Regimes Personalized medicine Setia Pramana 10
- 11. Advanced Biomedical Technologies • High-throughput microarrays and molecular imaging to monitor SNPs, gene and protein expressions • Next-Generation Sequencing Setia Pramana 11
- 12. First…. Bit Biology Setia Pramana 12
- 14. 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 14
- 15. 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 15
- 17. Microarray • DNA microarrays are biotechnologies which allow the monitoring of expression of thousand genes. Setia Pramana 17
- 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, etc. • Appropriate treatment for genetic signature • Potential new drug targets Setia Pramana 18
- 19. Microarray Overview of the process of generating high throughput gene expression data using microarrays. Setia Pramana 19
- 20. 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 20
- 21. Microarray Data Structure Setia Pramana 21
- 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 22
- 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 23
- 24. Type of Microarray Data Analysis • 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 24
- 25. Gene Selection • Modified t-test • Significance Analysis of Microarray (SAM) • Limma (Linear model for microarrays ) • Linear Mixed model • Logistics Regression • Lasso (least absolute selection and shrinkage operator) • Elastic-net • Etc, Setia Pramana 25
- 26. Visualization • Dimensionality reduction • PCA (Principal Component Analysis) • Biplot • Heatmap • Multi dimensional scaling • Etc Setia Pramana 26
- 27. Clustering • Cluster the genes • Cluster the arrays/conditions • Cluster both simultaneously • K-means • Hierarchical • Biclustering algorithms Setia Pramana 27
- 28. Clustering • Cluster or Classify genes according to tumors • Cluster tumors according to genes Setia Pramana 28
- 30. Classification • Linear Discriminant Analysis • K nearest Neighbor • Logistic regression • L1 Penalized Logistic Regression • Neural Network • Support Vector Machines • Random forest • etc Setia Pramana 30
- 31. Aim: To improve understanding of host protein profiles during disease progression especially in children.
- 32. Classification of Malaria Subtypes •Identify panel of proteins which could distinguish between different subtypes. •Implement L1-penalized logistic regression
- 33. 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. 35
- 34. Penalized Logistic Regression Logistic model: Maximize the log-likelihood: •-Penalization (Lasso): • 36
- 35. • 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. 37 L1 Penalized Logistic Regression
- 36. Classification of Severe Malaria Anemia vs. Uncomplicated Malaria group 38 AUC: 0.86
- 37. Dose-response Microarray Studies Setia Pramana 39
- 38. Dose-response Microarray Studies Setia Pramana 40 Implemented in R package IsoGene and IsoGeneGUI.
- 39. Dose-response Microarray Studies Setia Pramana 41
- 40. Gene Signature for Prostate Cancer Setia Pramana 42
- 41. Gene Signature for Prostate Cancer Setia Pramana 43
- 42. Gene Signature for Prostate Cancer Setia Pramana 44
- 43. Next Generation Sequencing Setia Pramana 45
- 44. Next Generation Sequencing Setia Pramana 46 Reading the order of bases of DNA fragments
- 45. NGS used for: • Whole genome re-sequencing • Metagenomics • Cancer genomics • Exome sequencing (targeted) • RNA-sequencing • Chip-seq • Genomic Epidemiology Setia Pramana 48
- 46. Next Generation Sequencing Setia Pramana 49 • Produce Massive Data and fast • Problem is storage and analysis
- 47. RNA-seq Pipeline • Align to a reference genome using Tophat. Reference Pramana, et.al 50NBBC 2013 Source: Trapnell et.al, 2010
- 48. 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 51NBBC 2013 Source: Trapnell et.al, 2013
- 49. Setia Pramana 52
- 50. 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 53
- 51. Subtype-specific Transcripts/Isoforms • To discover transcripts/isoforms which are only significantly (high/low) expressed in a certain cancer subtype. Pramana, et.al 54NBBC 2013
- 52. 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 55NBBC 2013
- 56. 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 59
- 57. My Current Research • Integration of Somatic Mutation, Expression and Functional Data Reveals Potential Driver Genes Predictive of Breast Cancer Survival (KI, Ewha Univ, Brescia Univ). • Molecular Subtyping of Breast Cancers using RNA- Sequence Data (KI, Ewha Univ, Brescia Univ). • The genomic surveillance of drug-resistant tuberculosis (FKUI, NUS). • Genomics screening for prostate cancer (KI) • Molecular subtyping of Malaria (KI, Scilab, Eijkman Inst.) • Health Technology Assessment (FKUI, Depkes) Setia Pramana 60
- 58. 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
- 59. Thank you for your attention…. Setia Pramana 62
Notas do Editor
- 37
- From the sample tissue, RNA is extracted and chopped into million of small pieces call fragments. The length of these fragments are around 400 bp. These RNA fragments are then converted to cDNA. Then these cDNA fragments are sequenced or read from both ends, so it so called paired-end reads These reads are then mapped to a reference genome using package Tophat.
- Next step is to measure gene expression for each gene and transcript by a so called FPKM. For example here is a gene with three exons and this gene have two transcripts or isoforms. I have to remind you that some genes are actually do not produce a single protein instead a single gene could produce different forms of protein by so called alternative splicing which produces different version of RNA from a single gene. For example in this gene have three exons and transcript one comes from first and third exon, and second transcripts are produced by exon 1 and 2. The small dots here are the mapped cDNA, so for each transcript we can obtain number of fragments that are mapped to that transcript. The abudance of RNA propotionate to the number of reads. FPKM is actually number of fragments devided by the gene length and the total number op mapped reads. After for each transcripts FPKM is obtain, a gene level FPKM can be obtained by summing up FPKM of all transcript of the gene.
- For the analysis, we split the data into two sets, a discovery and validation set. In the discovery set, we start with classification of the samples into molecular subtypes using Swedish microarray data as training using K nearest neighbor. Since the gene expression measurement in the two platform are different, before classification we normalized them together using median and variance normalization. Then we select genes which can classify the sample best. Once the subtypes are obtained, next is to obtain subtype specific transcript using transcript level FPKM. Here in order to obtain transcript that are up or down regulated in a specific subtype, robust contrast tests were performed.