Bioinformatics: A personal perspective

Transcription

1 Bioinformatics: A personal perspective Jake Chen, Ph.D. Associate Director & Professor The Informatics Institute UAB School of Medicine jakechen@uab.edu 1

2 Challenges in analyzing Genomic big data DNA Microarrays NGS Genome Sequencing RNA-SEQ Clustering Network Analysis Pathway analysis Gene set enrichment ana Format Conversation Data Extractio n Noise Reductio n Extension of sequencing pipeline Automated, Mature, CPU/Disk intensive Upstream Analysis Statistical Analysis Biological Data Analysis Modeling & Hypothesi s Generatio n Timeconsuming Many fragmented tools Specialized bioinformaticia n required Wet Lab Validatio n Systems Immature Biology Modeling Publicati on & Sharing Downstream Analysis 2

3 False Negatives happen more frequently in Proteomics than in Genomics Structural proteins Enzymes, kinases, receptors High-abundance proteins up to 10-4 commonly seen and quantified Mid-abundance proteins Tricky statistics from proteomics results Signaling molecules Low-abundance proteins Often missed by proteomics instruments Copyright Dr. Jake Chen,

4 High levels of protein expression variability CV= Coefficient of Variation. N. L.Anderson and N.G. Anderson (2002) Molecular & Cellular Proteomics 1:

5 Small fold changes are common in quantitative proteomics Candidate Cancer Protein Biomarkers Fold Change ANKRD USP TPTE VAX2 Ventral anterior homeobox 1.36 G protein coupled receptor 126 (GPR126) 1.48 ORM SHC TMPRSS EPHA ANKMY NOD TRRAP 1.10 AKAP IGF Breast cancer 40 case vs 40 controls, plasma proteomics results 5

6 Certainty of death. Small chance of success What are we waiting for?

7 Bioinformatics: A hierarchical biosystem perspective Abstract, Complex, Translational, Lean model Concrete, Reduced complexity, Basic research, Big data

8 The scales goes from molecular networks to phenotypic and patient networks Clinical Informatics Clinical records Disease symptoms Prescriptions Lab tests Imaging data Literature, notes Bioinformatics Molecular measurements Reactions Gene modules Pathways networks

9 Translational Bioinformatics in comparison with bioinformatics in other context NIH consensus definition of translational work Model organisms Humans Population Individual patients Research lab Clinical/Industry Practice Canonical Bioinformatics Translational Bioinformatics Applied Bioinformatics Human Multivariate analysis Independent variables Dependent variables varies Scope of analysis lab data only lab data + related public big data available lab data Heterogeneity of data types Low High (through data integration) available data types Patients Data analysis granularity At the cohort or aggregated level At the individual level Available data collected Practice Diversity of collaboration Limited (basic researchers) Broad (basic/clinical researchers) Limited (clinical researchers) Primary interest Algorithms, advancement of knowledge to the field Biomarkers, drugs, clinical findings, databases, tools Biomarkers, drugs, clinical findings

10 Bioinformatics: A systems science perspective System structures Network of gene interactions, biochemical pathways, and associative relationships between genes, environmental stimuli System dynamics How a system behaves over time or under various conditions Multidimensional feature space System controls Describe the cellular automata are modulated Mechanisms that systematically control the state of the cell can be modulated to minimize malfunctions and maximize therapeutic effects System design Modify or construct new biological systems, through simulation

11 Bioinformatics: Examples from systems biology examples Systems Control Systems Design Systems Structure Systems Dynamics

12 What can Bioinformatics do? Bioinformatics systems Level Structure Dynamics Control Design Omics Molecules Sequencing, sequence comparisons e.g., BLAST, MSA Gene/protein/RNA expression profiling analysis Gene regulatory relationship predictions Mutational impact analysis, protein engineering, genome editing, drug dev. Reactions Metabolic pathways, protein-interactions Tissue-specific or disease-specific interactions Transcriptional or post-translational control studies TF, RNAi of regulatory circuits Systems Biology Modules Networks Protein complex, signaling pathways Disease network construction Time-series activation of gene signatures Disease network evolution Upstream regulator of gene signatures Network centrality and choke analysis Personalized medicine at the gene signature (cmaps) level Lead compound optimization based on simulation of efficacy Bioengineering Intracellular models Immune system model development T cell signaling activation network Regulatory models that controls development, cancer metastasis, immune response Immunotherapy modeling and prediction Epidemiology Physiology Population Construction of Organlevel engineering models Risk factor identification and analysis Epidemiology of infectious diseases Public policy and preventive measures to stop disease Simulation/modeling of engineered artificial organs Vaccine dev.

13 Good Bioinformatics Collaboration Models BioIT programmers Biostatistician Bioinformatics data analysts Biomedical Researchers Adapted from

14 A Method for Identifying Discriminative Isoform-Specific Peptides for Clinical Proteomics Applications Fan Zhang and Jake Y. Chen

15 Clinical Proteomics Applications: Finding Disease Diagnosis Biomarkers in Bodily Fluids The application of proteomic technology to clinical research and molecular medicine. Compare with Clinical Genomics Applications Early disease detection Disease pathogenesis Disease monitoring In: Nature Reviews Drug Discovery 1,

16 Opportunities in using Protein Isoforms Regular Protein? A protein may carry out diverse functions More specificity Protein Isoforms?Rare protein isoforms may be indicative of unusual conditions 1. Genetic polymorphism Protein: PRB SNP ID: rs GTTTCGACCATACCTATC -> VSTIPI GTTTCGGCCATACCTATC -> VSAIPI 3. Post-translational Modifications 2. Alternative Splicing Isoforms

17 Collection of Clinical Samples Plasma protein profiles from breast cancer patients Study A and Study B Similar demography and clinical distribution Each contained 40 plasma samples from women diagnosed with breast cancer and 40 plasma samples from healthy age-matched volunteer women as control. Most breast cancers are diagnosed with a stage II or III. Most patients had previously been treated with chemotherapy. All samples were collected with the same standard operating procedure Goal: Find breast cancer blood sample protein biomarkers that can be used to develop early diagnosis tests.

18 p independent breast cancer patient cohorts # of patients Ethnicity Metastasis Cancer Type * Mean Tumor Size ** ER+/PR+ HER2+ ER, PR, HER2 Age Age Age > (Caucasian) 3 (African) 28 (Caucasian) 1 (Hispanic) 3 (No) 21 (No) 7 (Yes) 1 (Unknown) 2 (INV) 1 (DCIS) Study A (N=40) 24 (INV) 5 (DCIS) 8 (Caucasian) 6 (No) 2 (Yes) 4 (INV) 4 (DCIS) m = 1.15 m = 1.97 m = (Caucasian) 1 (African) 1 (others) 3 No 1 Yes 1 unknown 1 (INV) 3 (DCIS) 1 (Unknown) Study B (N=40) 34 (Caucasian) 1 (Unknown) 17 No 10 Yes 8 Unknown 22 (INV) 5 (DCIS) 8 (Unknown) m = 3 m = * - INV: invasive; DCIS: ductal carcinoma in situ

19 1. Peptide Search Database Construction Obtain gene structures of all protein-coding genes from the human genome Compile in silico isoform junction peptides Validate those peptides in current protein knowledgebase Genome Browser Gene exon/intron structure extractor dbsn P Short junction peptide generator s.iupui.edu/peppi/ In BMC Bioinformatics Oct 7;11 Suppl 6:S7

20 Known/Theoretical protein isoform junction peptide Normal exon Normal junction Exon Extension (Intron Retaining) Intron Extension (Exon Retaining) Exon skipping

21 RESULT: ~5M Isoform Junction Peptides Constructed Peptide Type Number of Peptides E_E Type Number of Peptides EXON_KB Normal E_E E_E_KB Skipping E_E E_E_TH Peptide Length (aa) E_I_TH Longest Exon 6057 I_E_TH Average Exon 48 Longest Junction 140 Total Average Junction 64

22 2. Isoform Junction Peptide Identification and Quantification Proteins were prepared and subjected to LC/MS/MS triple-play mode: MS scan, Zoom scan, and MS/MS scan Searched the OMSSA against the protein isoform database (PEPPI) to identify matched peptides Standard analysis uses NCBInr + Uniprot database Calculation of the Statistical Significance Chi-Square Goodness-of-Fit test to calculate the p value (also called false discovery rate). Calculated the FDR-adjusted p value. Calculated the FDR q value using the Storey-Tibshirani method. Apply a filter q<0.05 to select peptides of which we estimated significant differences in the health and breast cancer samples.

23 Results: Types of Protein Isoform Junction Peptides Identified a) Normal splice junction d) Intron Extension (Exon Retaining) c) Exon splicing b) Normal single Exon e) Exon Extension (Intron Retaining) IKBKB E5 i5 YLNQFENCCGLREGAILTLLSDI(G)K YLNQFENCCGLREGAILTLLSDI(G)K YLNQFENCCGLREGAILTLLSDI(G)K YLNQFENCCGLREGAILTLLSDI(G)K C012 C025 C027 C051

24 Results: Distribution of isoform junction peptides as candidate markers between H and C samples 90 isoform junction peptides are found in 38 genes Health (H) Cancer (C) Protein Isoform (exon skipping 7 60 or intron retention) class Single exon class 22 1 total p-value=3.2e-13 using χ 2 test The exon skipping and intron retention events are more likely to take place in cancer samples than in healthy samples. Exon missing events are more likely to take place in cancer samples.

25 Results: A Heatmap view of isoform protein markers for breast cancer classification in the blood (Study A, 100% classification) WRN_E18 FOXO4_E1_E3 ACSL3_E11 NCOA2_E9 BUB1_i17 KTN1_E22_E43 RPS6KB1_E12 ATM_E3_E27 NCOA2_E13 ERCC6_E17 CAT_E3 BUB1_E21 ATM_E3_E59 ERCC6_E13 COL1A1_E19 FLNA_E25 WRN_E16 NCOR1_E29_E44 NCOA2_E22 ATM_E7 MET_E5 GNAS_E10 CYTSB_E2 COL1A1_E15_E49 MLLT6_E10 MLLT6_E7 TP53BP1_E23 ATM_E51 CREBBP_E6 PMS2_E2_E11 SMO_E4_E12 COL1A1_E3_E33 PLK3_i3 TP53BP1_E2_E10 ARHGAP26_E3_E22 JAK1_i14 MPL_E8_E10 MET_E3_E10 KDM6A_E5_E16 COL1A1_E18_E36 IKBKB_E5_i5 ATM_E19_E50 PDE4DIP_E2_E12 BCR_E7_E18 BUB1_E5_E7 ERCC6_i3 CBFA2T3_E1_E6 KDM6A_E17_E27 MET_i6 ERCC6_E7_E18 WRN_E20_E28 ATM_E6_E15 FLNA_E1_E44 NCOA2_E3_E23 ATM_E51_E54 NOTCH2_E2_E8 NCOR1_E13_E28 TP53BP1_i23 NCOR1_E24_E34 TP53BP1_E4_E20 NCOR1_E9_E34 TP53BP1_E16_E22 NOTCH2_E7_E10 CARS_E1_E11 FLNA_E9_E14 CARS_E1_E5 CBFA2T3_i9_E10 ATM_E36_E45 ATM_E8_E23 COL1A1_E17_E44 CREBBP_E21_E27 COL1A1_E2_E31 FLNA_E4_E40 BID_i3 TOP1_E12_E15 KTN1_E16_E44 RPS6KB1_E5_E15 CARD11_E8_E19 WRN_E3_E35 NCOR1_E35_E43 ATIC_E2_E14 JAK1_E16 BCR_i18 ATM_E42_E50 BCR_E2_E10 COL1A1_E1_E30 TP53BP1_E4_E13 ATM_E3_E30 FGFR1_E11_E15 ELN_E4_E14 H748 H739 H735 H724 H722 H710 H700 H696 H665 H661 H647 H633 H631 H629 H628 H625 H623 H622 H621 H615 H610 H605 H604 H600 H599 H598 H594 H593 H585 H583 H582 H575 H564 H557 H553 H550 H538 H537 H536 H032 C051 C049 C048 C046 C045 C044 C043 C041 C040 C039 C037 C036 C032 C031 C030 C029 C028 C027 C026 C025 C024 C023 C022 C021 C020 C019 C018 C017 C016 C013 C012 C011 C010 C009 C008 C007 C006 C004 C002 C001

26 3. Validation: Explainable Index as an indicator of the candidate biomarker gene Assumption: in cancer, exon skipping takes place more frequently than in normal con # conc 1 # conh 1 # inc 1 # inc 1 C H i j k #con: the number of consistent peptide markers #inc: the number of inconsistent peptide markers consistent inconsistent i k j or i k or j C label: cancer marker set H label: healthy marker set If α > 1, the gene is more explainable If α < 1, the gene is less explainable

27 Results: Validation by Explainable Index 36 out of 38 genes are more explainable except for two genes JAK1 and KTN1 with explainable index of 1. Index value distribution Mean = , Median = 2, Maximum = 12

28 Validation: Pathway Enrichment Analysis (Study A) N PATHWAYID PATHWAYNAME PATHWAYCLASS NN SYMBOL 8 hsa05200 Pathways in cancer Human Diseases; Cancers 351 BCR,BID,CREBBP,FGFR1,IKBKB,JAK1,MET,SMO 3 hsa04010 MAPK signaling pathway Environmental Information Processing; Signal Transduction 275 FGFR1,FLNA,IKBKB 3 hsa04110 Cell cycle Cellular Processes; Cell Growth and Death 118 ATM,BUB1,CREBBP 3 hsa04210 Apoptosis Cellular Processes; Cell Growth and Death 89 ATM,BID,IKBKB 3 hsa04510 Focal adhesion Cellular Processes; Cell Communication 204 COL1A1,FLNA,MET 3 hsa04520 Adherens junction Cellular Processes; Cell Communication 77 CREBBP,FGFR1,MET 3 hsa04630 Jak STAT signaling pathway Environmental Information Processing; Signal Transduction 155 CREBBP,JAK1,MPL 3 hsa05215 Prostate cancer Human Diseases; Cancers 100 CREBBP,FGFR1,IKBKB 2 hsa04060 Cytokine cytokine receptor Environmental Information Processing; Signaling Molecules and interaction Interaction 263 MET,MPL 2 hsa04115 p53 signaling pathway Cellular Processes; Cell Growth and Death 69 ATM,BID 2 hsa04330 Notch signaling pathway Environmental Information Processing; Signal Transduction 47 CREBBP,NOTCH2 2 hsa04350 TGF beta signaling pathway Environmental Information Processing; Signal Transduction 87 CREBBP,RPS6KB1 2 hsa04660 T cell receptor signaling pathway Cellular Processes; Immune System 112 CARD11,IKBKB 2 hsa04662 B cell receptor signaling pathway Cellular Processes; Immune System 79 CARD11,IKBKB 2 hsa04910 Insulin signaling pathway Cellular Processes; Endocrine System 142 IKBKB,RPS6KB1 2 hsa04916 Melanogenesis Cellular Processes; Endocrine System 109 CREBBP,GNAS 2 hsa04920 Adipocytokine signaling pathway Cellular Processes; Endocrine System 74 ACSL3,IKBKB 2 hsa05014 Amyotrophic lateral sclerosis (ALS) Human Diseases; Neurodegenerative Diseases 65 BID,CAT Epithelial cell signaling in Helicobacter pylori 2 hsa05120 infection Human Diseases; Infectious Diseases 75 IKBKB,MET 2 hsa05211 Renal cell carcinoma Human Diseases; Cancers 73 CREBBP,MET 2 hsa05212 Pancreatic cancer Human Diseases; Cancers 74 IKBKB,JAK1 2 hsa05218 Melanoma Human Diseases; Cancers 72 FGFR1,MET 2 hsa05220 Chronic myeloid leukemia Human Diseases; Cancers 76 BCR,IKBKB 2 hsa05221 Acute myeloid leukemia Human Diseases; Cancers 61 IKBKB,RPS6KB1

29 Validation: Pathway Enrichment Analysis (Study B) N PATHWAYI D PATHWAYNAME PATHWAYCLASS NN SYMBOL APC,BIRC3,CBLB,CTBP1,ERBB2,FLT3,HDAC1,HS 12hsa05200 Pathways in cancer Human Diseases; Cancers 351P90AA1,HSP90AB1,IKBKB,PTCH1,TPR 5hsa05215 Prostate cancer Human Diseases; Cancers 100CREB3L2,ERBB2,HSP90AA1,HSP90AB1,IKBKB 4hsa04010 MAPK signaling pathway Environmental Information Processing; Signal Transduction 275ELK4,IKBKB,MAP3K1,NF1 4hsa05220 Chronic myeloid leukemia Human Diseases; Cancers 76CBLB,CTBP1,HDAC1,IKBKB 3hsa04110 Cell cycle Cellular Processes; Cell Growth and Death 118ATM,BUB1,HDAC1 3hsa04120 Ubiquitin mediated proteolysis "Genetic Information Processing; Folding, Sorting and Degradation" 137BIRC3,CBLB,MAP3K1 3hsa04210 Apoptosis Cellular Processes; Cell Growth and Death 89ATM,BIRC3,IKBKB 3hsa04310 Wnt signaling pathway Environmental Information Processing; Signal Transduction 153APC,CTBP1,PPP2CA 3hsa04330 Notch signaling pathway Environmental Information Processing; Signal Transduction 47CTBP1,HDAC1,NOTCH2 3hsa05016 Huntington's disease Human Diseases; Neurodegenerative Diseases 188CREB3L2,GNAQ,HDAC1 2hsa04012 ErbB signaling pathway Environmental Information Processing; Signal Transduction 91CBLB,ERBB2 2hsa04020 Calcium signaling pathway Environmental Information Processing; Signal Transduction 189ERBB2,GNAQ 2hsa04060 Cytokine-cytokine receptor interaction Environmental Information Processing; Signaling Molecules and Interaction 263FLT3,IL6ST 2hsa04144 Endocytosis Cellular Processes; Transport and Catabolism 192CBLB,EPS15 2hsa04270 Vascular smooth muscle contraction Cellular Processes; Circulatory System 139ARHGEF12,GNAQ 2hsa04360 Axon guidance Cellular Processes; Development 129ARHGEF12,SRGAP3 2hsa04510 Focal adhesion Cellular Processes; Cell Communication 204BIRC3,ERBB2 2hsa04612 Antigen processing and presentation Cellular Processes; Immune System 89HSP90AA1,HSP90AB1 2hsa04630 Jak-STAT signaling pathway Environmental Information Processing; Signal Transduction 155CBLB,IL6ST 2hsa04660 T cell receptor signaling pathway Cellular Processes; Immune System 112CBLB,IKBKB 2hsa04722 Neurotrophin signaling pathway Cellular Processes; Nervous System 131IKBKB,MAP3K1 2hsa04730 Long-term depression Cellular Processes; Nervous System 80GNAQ,PPP2CA 2hsa04810 Regulation of actin cytoskeleton Cellular Processes; Cell Motility 221APC,ARHGEF12 2hsa04910 Insulin signaling pathway Cellular Processes; Endocrine System 142CBLB,IKBKB 2hsa04912 GnRH signaling pathway Cellular Processes; Endocrine System 107GNAQ,MAP3K1 2hsa04916 Melanogenesis Cellular Processes; Endocrine System 109CREB3L2,GNAQ 2hsa04920 Adipocytokine signaling pathway Cellular Processes; Endocrine System 74ACSL3,IKBKB 2hsa05212 Pancreatic cancer Human Diseases; Cancers 74ERBB2,IKBKB 2hsa05213 Endometrial cancer Human Diseases; Cancers 53APC,ERBB2 2hsa05217 Basal cell carcinoma Human Diseases; Cancers 56APC,PTCH1 2hsa05221 Acute myeloid leukemia Human Diseases; Cancers 61FLT3,IKBKB 2hsa05222 Small cell lung cancer Human Diseases; Cancers 89BIRC3,IKBKB

30 Validation by independent Affymetrix exon array Array analysis was performed using R and BioConductor libraries. Probeset in the exon array to the peptide sequence in our database was performed using the exon s starting and ending positions in each transcript. Because of the limitation of the exon array, we can only validate the 23 single exon markers and test if those markers are more likely to be expressed in the same group as in our proteomics result. The validation results show that 21 of 23 single exon markers were confirmed by the exon array

31 Application: Test of candidate biomarkers obtained (from Study A, 38 genes) in a separate cohort (Study B, 26 genes) The cancer vs healthy classification performance using 26 gene biomarker overlapped is 82.5% Healthy cluster Cancer cluster