Genomics2 Phenomics Complete
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This document summarizes the analysis of multi-omics data from a study of lung squamous cell carcinoma. The analysis integrated genomic, transcriptomic, clinical and phenotypic data from the study using various bioinformatics tools. Key findings included the identification of genes associated with patient survival outcomes and the stratification of patients into prognosis groups. Differential expression and mutational analysis revealed molecular differences between the basaloid and squamous cell carcinoma subtypes. Integration across molecular levels identified genes whose expression changed due to mutations. Pathway and network analysis provided functional insight. Integration with metabolic models highlighted potentially druggable targets. The multi-omics, multi-scale analysis framework generated novel biological insights from the data.
![Big Data in Biomedicine
The 7 Vs of Genomic Big Data
• Volume is defined in terms of the physical volume of the data that need to be online, like giga-byte
(10^9), tera-byte (10^12), peta-byte (10^15) or exa-byte (10^18) or even beyond.
• Velocity is about the data-retrieval time or the time taken to service a request. Velocity is also
measured through the rate of change of the data volume.
• Variety relates to heterogeneous types of data like text, structured, unstructured, video, audio
etcetera.
• Veracity is another dimension to measure data reliability - the ability of an organization to trust the
data and be able to confidently use it to make crucial decisions.
• Vexing covers the effectiveness of the algorithm. The algorithm needs to be designed to ensure that
data processing time is close to linear and the algorithm does not have any bias; irrespective of the
volume of the data, the algorithm is able to process the data in reasonable time.
• Variability is the scale of data. Data in biology is multi-scale, ranging from sub-atomic ions at
picometers, macro-molecules, cells, tissues and finally to a population [9] at thousands of kilometers.
• Value is the final actionable insight or the functional knowledge. The same mutation in a gene may
have a different effect depending on the population or the environmental factors.
9March 3-5, 2016
Reference: Talukder AK, Genomics 3.0: Big Data in Precision Medicine, Big Data Analytics, Springer, LNCS9498, 2015](https://image.slidesharecdn.com/genomics2phenomicscomplete-160311114044/85/Genomics2-Phenomics-Complete-9-320.jpg)
Genomics2 Phenomics Complete
- 1. 18th June 2001017th December 2009 Genomics to Phenomics: The Complex Journey in Big Data Biomedicine Asoke K Talukder, PhD InterpretOmics, Bangalore Indian Society of Human Genetics 41st Annual Meeting, Sankara Nethralaya, Chennai, 3-5 March, 2016
- 2. Acknowledgement • Organizing Committee, ISHG2016 • Authors & Agencies Making their Research articles, and Data available in the open domain and Internet • Authors of Open source Software • NCBI, NIH, Wikipedia, Google & other Internet sites that believe in Bhikshu Economy by making their contents open in the Cloud 2March 3-5, 2016
- 3. Hunting the “Dwarfing” Gene? 3March 3-5, 2016 Palm Oil – Activate the Dwarfing Gene (Genomics)Teak – Repress the Dwarfing Gene (Genomics)
- 4. The Human Genome – Decoding the Book of Life A Milestone for Humanity – the Human genome Human Genome Completed, 26 June, 2000 Francis CollinsBill ClintonJ Craig Ventor Craig Venter Bill Clinton Francis Collins 4March 3-5, 2016
- 5. Trillion-Dollar Science to Trillion-Dollar Industry 5March 3-5, 2016
- 6. The relationship between the number of stem cell divisions in the lifetime of a given tissue and the lifetime risk of cancer in that tissue Reference: Cristian Tomasetti, and Bert Vogelstein, Jan 2 Science 2015;347:78-81 6March 3-5, 2016 Reference: Norbert Stefan, et al, Divergent associations of height with cardiometabolic disease and cancer: epidemiology, pathophysiology, and global implications. The Lancet Diabetes & Endocrinology, 2016; DOI:
- 7. Reduction Vs Integration 7March 3-5, 2016 Genomics (System) (Genetics) Talukder AK, Genomics 3.0, Big Data Analytics, Springer, 2015
- 8. Evidence Based Science (Biology & Medicine) 8March 3-5, 2016 Genetics Genomics Confirmatory Exploratory Hypothesis Driven Hypothesis Creating Component Holistic Biology Statistical Data Mining
- 9. Big Data in Biomedicine The 7 Vs of Genomic Big Data • Volume is defined in terms of the physical volume of the data that need to be online, like giga-byte (10^9), tera-byte (10^12), peta-byte (10^15) or exa-byte (10^18) or even beyond. • Velocity is about the data-retrieval time or the time taken to service a request. Velocity is also measured through the rate of change of the data volume. • Variety relates to heterogeneous types of data like text, structured, unstructured, video, audio etcetera. • Veracity is another dimension to measure data reliability - the ability of an organization to trust the data and be able to confidently use it to make crucial decisions. • Vexing covers the effectiveness of the algorithm. The algorithm needs to be designed to ensure that data processing time is close to linear and the algorithm does not have any bias; irrespective of the volume of the data, the algorithm is able to process the data in reasonable time. • Variability is the scale of data. Data in biology is multi-scale, ranging from sub-atomic ions at picometers, macro-molecules, cells, tissues and finally to a population [9] at thousands of kilometers. • Value is the final actionable insight or the functional knowledge. The same mutation in a gene may have a different effect depending on the population or the environmental factors. 9March 3-5, 2016 Reference: Talukder AK, Genomics 3.0: Big Data in Precision Medicine, Big Data Analytics, Springer, LNCS9498, 2015
- 10. 21st Century Biomedicine is a Multi-Scale Challenge Genome Transcriptome Proteome Cellular Structure and Function Tissue Structure and Function Organ Structure and Function Patient Molecular Scale ηm~μm μm~mm mm~cm 1mηm ηs ηs-μs μs-s s~hour hour~day years Molecular events (Eg: Ion-channel gating Diffusion and cell signaling Motility Mitosis Protein turnover Human lifetime 10March 3-5, 2016 Genomics Phenomics
- 11. ‘OMICS’ (High-throughput) Big Data Domains GWAS Population GeneticsMicroarray Systems Biology Phenomics ChIp-Seq DNA-Seq RNA-Seq Exome-Seq Repli-Seq Small RNA-Seq Metabolic Networks Proteomics Metagenomics 11March 3-5, 2016
- 12. Multi Omics Big Data 12March 3-5, 2016 Reference: Talukder AK, Genomics 3.0: Big Data in Precision Medicine, Big Data Analytics, Springer, LNCS9498, 2015
- 13. Hypothesis Creating Multi-Omics Big Data Analytics 13March 3-5, 2016 Genomic Big Data Statistics (Exploratory Data Analysis) Phenomic & Environmental Knowledgebase Systems Biology Reference: Talukder AK, Genomics 3.0: Big Data in Precision Medicine, Big Data Analytics, Springer, LNCS9498, 2015
- 14. Lung Cancer: A Multi-Omics Multi-Scale Big Data Case Study using iOMICS Pipelines We have taken a Lung squamous cell carcinoma study and reanalysed its data using iOMICS pipelines to unleash novel knowledge The reanalysis is for a Lung Squamous Cell Carcinoma (SCC) 18 years Longitudinal clinical research published in PMID: 25189482. The data consist of Omics data for 93 tumor patients and 16 healthy individuals. DNA level genotype data: 64 tumor samples, 373,398 DNA sites RNA level gene expression data: 109 samples, 20,117 genes Clinical data: General and clinical information (where applicable) for all 109 individuals in the study. Survival information was also available in the form of overall and disease recurrence free survival 14March 3-5, 2016
- 15. Multi-Omics Based Multi-Scale Analytics Framework Data from patient is integrated with existing knowledgebases using a 3 step analysis framework Top-down Exploratory Data Analysis: Analysis of experimental data for molecular information such as DNA mutations and gene expression Multi-scale Integrative Analysis: Integration of molecular scale data such as DNA, RNA level results for mechanistic modeling Bottom-up Integrative and Network Analysis: Integration of experimental data analysis results with existing knowledgebases for generalizability and improved quality results Results from the framework can be used to power clinical decision support systems for treatment strategies and drug design 15March 3-5, 2016 Reference: Agarwal M, Adhil M, Talukder AK, Multi-Omics Multi-Scale Big data Analytics for Cancer Genomics, Big Data Analytics, Springer, LNCS9498, 2015
- 16. Patient Stratification Integration of gene expression with recurrence free survival of patients Recurrence free survival known for 87 samples Used Cox regression to model survival time as a function of gene expression Stratified patients into 3 response groups: Good, Average and Poor prognosis Aim: Markers of Patient Survival Survival Probability Curves for Stratified Prognosis Groups Top Significant Genes separating Poor and Average prognosis are: EIF5A, SCEL, ABCA11P, VAV2 Top Significant Genes separating Good and Average prognosis are: SLC7A11, G6PD, ALDH3A1, NQO1, SOST Top Significant Genes separating Good and Poor prognosis are: SCEL, VAV2, PPP1R26, ZNF77, EIF5A 16March 3-5, 2016 Reference: Agarwal M, Adhil M, Talukder AK, Multi-Omics Multi-Scale Big data Analytics for Cancer Genomics, Big Data Analytics, Springer, LNCS9498, 2015
- 17. Phenotype Based Patient Stratification 17© InterpertOmics Data: Lung Squamous Cell Carcinoma with Basaloid Histology Study (PubMed-ID: 25189482) Clinical Data. Overall Survivability: The basaloid tumor samples have poor overall survival (OS) compared to the other samples. Fig 1 in the original paper Recurrence Free Survivability: Basaloid tumors show distinctly poor recurrence- free survival (RFS) compared to other samples Age factor: Patients diagnosed at an age of 53 or less showed better prognosis compared to those diagnosed later Adjuvant Radiotherapy Factor: Patients who did not receive adjuvant radiotherapy (Age ≤ 53) show better overall survival, compared to those who did Unique Findings: - The basaloid subtypes showed distinctly poor prognosis compared to the other samples - Adjuvant radiotherapy is not very effective for improving patient survival in these cases - For patients diagnosed before 53 years of age, administration of adjuvant radiotherapy represents worse long term overall survival Aim: Markers of Patient Survival
- 18. Differential Gene Expression Key differentially expressed protein- coding genes between the 2 cancer subtypes were identified 106 differentially expressed genes were identified based on the filtering criteria Key differentially expressed genes were: KLHL23, IVL, MPZL2, KCNK6, SPRR3, ELL2, MALL, RPRD1A, ZNF124 p-value criteria ≤ 0.0001 and absolute log fold change > 0.6 Aim: Basaloid vs. SCC Molecular Comparison 18March 3-5, 2016 Reference: Agarwal M, Adhil M, Talukder AK, Multi-Omics Multi-Scale Big data Analytics for Cancer Genomics, Big Data Analytics, Springer, LNCS9498, 2015
- 19. Mutation Association with Cancer Identified DNA sequence sites with different genotypes between the two lung carcinoma subtypes (basaloid and SCC) After linkage analysis and filtering, the 373,398 sites were reduced to 735 disease type associated DNA loci These mapped to 558 unique genes Aim: Basaloid vs. SCC Molecular Comparison Karyotype Plot for Mutation Locations across Chromosomes p-value criteria ≤ 0.001 and odds ratio criteria ≥ 3 19March 3-5, 2016 Reference: Agarwal M, Adhil M, Talukder AK, Multi-Omics Multi-Scale Big data Analytics for Cancer Genomics, Big Data Analytics, Springer, LNCS9498, 2015
- 20. We characterized the 558 mutated genes identified from the DNA level analysis using XomPathways Results indicated the key pathways differentiating the tumor subtypes such as cell signaling and adhesion Functional Characterization Aim: Basaloid vs. SCC Molecular Comparison p-value criteria ≤ 0.001 for pathway enrichment 20March 3-5, 2016 Reference: Agarwal M, Adhil M, Talukder AK, Multi-Omics Multi-Scale Big data Analytics for Cancer Genomics, Big Data Analytics, Springer, LNCS9498, 2015 Pathway-Pathway Network Gene-Gene Network Genes-Pathway Bipartite Network
- 21. Multi-Scale Integrative Biology (Expression QTL) Aim: Basaloid vs. SCC Molecular Comparison The DNA level variants identified for the basaloid histology comparisons were compared with Gene Expression levels to view the effect of mutations from the DNA to RNA level Expression levels of some of the associated genes were altered with a large fold change Interesting genes include: CLCA2, CENPF, SHROOM3, ELL2, ATP10B, CASC15, TIAM2, PROX1, EYA1, C10orf54, HOXC9, SCEL, BCL2, FUT3, YPEL1, PATZ1, CAV2 21March 3-5, 2016 Reference: Agarwal M, Adhil M, Talukder AK, Multi-Omics Multi-Scale Big data Analytics for Cancer Genomics, Big Data Analytics, Springer, LNCS9498, 2015
- 22. Multi-Scale Integration Mutations associated with expression level changes were identified These were associated with up or down-regulation of gene expression Genes-Mutations Integration 22March 3-5, 2016 Reference: Agarwal M, Adhil M, Talukder AK, Multi-Omics Multi-Scale Big data Analytics for Cancer Genomics, Big Data Analytics, Springer, LNCS9498, 2015
- 23. Functional Characterization Aim: Basaloid vs. SCC Molecular Comparison Functional Enrichment highlights key pathways involved. For the top differentially expressed genes between tumor and normal samples. The pathways and processes involved in epidermal and epithelial cell differentiation Together, the functional analysis results show that the primary differences between the basaloid and SCC subtypes are associated with tissue structure This is consistent with histology based distinction between the two subtypes Genes -Biological Processes Bipartite Network 23March 3-5, 2016 Reference: Agarwal M, Adhil M, Talukder AK, Multi-Omics Multi-Scale Big data Analytics for Cancer Genomics, Big Data Analytics, Springer, LNCS9498, 2015
- 24. Metabolic and Biochemical Reactions Integration Aim: Identification of Potential Drug Targets Genomic level alterations translate into protein and metabolism changes, which finally affect phenotype at a cellular and tissue level Using expression data, metabolic network models were constructed for healthy and lung cancer samples Recon X was taken as a reference genome scale model Genes associated with maximum metabolic alterations can serve as effective targets Carbohydrate Metabolism Pathways Image source: Khazaei, T., McGuigan, A., Mahadevan, R.: Ensemble modeling of cancer metabolism. Frontiers in physiology 3 (2012) 24March 3-5, 2016 Reference: Agarwal M, Adhil M, Talukder AK, Multi-Omics Multi-Scale Big data Analytics for Cancer Genomics, Big Data Analytics, Springer, LNCS9498, 2015
- 25. Solve Constrained Based Differential Equations Aim: Identification of Potential Drug Targets Three Step process Step I: Model initiation using constraint based modeling Cancer state optimized for maximum growth Healthy state optimized for maximum energy production Step II: Identification of highly altered reactions and associated genes Step III: Extension of gene list to include first degree PPI interactions as potential targets Step I Step II Step III 25March 3-5, 2016 Reference: Agarwal M, Adhil M, Talukder AK, Multi-Omics Multi-Scale Big data Analytics for Cancer Genomics, Big Data Analytics, Springer, LNCS9498, 2015
- 26. Metabolic and Protein Network Integration Aim: Identification of Potential Drug Targets Identified Metabolic Reactions Network Protein-protein interactions for an identified gene (EIF1B) 26March 3-5, 2016 Reference: Agarwal M, Adhil M, Talukder AK, Multi-Omics Multi-Scale Big data Analytics for Cancer Genomics, Big Data Analytics, Springer, LNCS9498, 2015
- 27. Potential targets were identified as genes with large association with altered reactions High degree in the human protein interaction network for these genes indicates that effect of targeting these will impact more pathways and may be toxic to the cell Identified potential drug targets include: NME2, GSR, YWHAZ, TGM2, JAM2, STAT3, TIMP2, RHOB, GIT2 and TK1 Systems Biology and the Small Molecule Targets Aim: Identification of Potential Drug Targets 27March 3-5, 2016 Reference: Agarwal M, Adhil M, Talukder AK, Multi-Omics Multi-Scale Big data Analytics for Cancer Genomics, Big Data Analytics, Springer, LNCS9498, 2015
- 28. Conclusions from the Cancer/iOMICS Case Study Molecular differences between basaloid and SCC lung carcinoma subtypes: Based on DNA and RNA level comparisons, we were able to identify genes involved in the differentiation of the two cancer subtypes. We tracked the mutations in genes such as SHROOM3, PROX1, CLCA2 etc. to gene expression alterations. The molecular level differences between the two subtypes were able to predict the cellular and tissue level differences seen between the subtypes Molecular states associated with poor patient survival: Identified genes involved in poor patient survival probabilities such as VAV2, EIF5A, SCEL etc. Identified a hidden molecular subtype within the pure basaloid subgroup, having particularly poor prognosis Identification of potential drug targets: Based on the translation of gene expression to metabolic fluxes, we identified key altered metabolic pathways, reactions and associated genes which are putative drug targets All analysis results were validated using extensive bibliomic data 28March 3-5, 2016
- 29. Omnia Knowledgebase & Clinical Decision Support System 29March 3-5, 2016 Patient Specific Survival for breast cancer based on the patient age, sex, grade and stage. There are 2,613 individuals with breast cancer of age group 45-49, from SEER within Omnia • For adjuvant therapeutic intervention A+B, overall QALYs (Quality Adjusted Life Year) is around 8 years and cost per QALY is ₹2,00,000; with likely disease burden of ~₹16,00,000 for 8 years of life. • For drug A, the overall QALYs is around 6 years and cost per QALY is ₹80,000; with likely burden of ~₹4,80,000 for 6 years of life. • Using this prognostic information, informed decision can be made by considering the QALYs and the total cancer burden. Drugs with detailed description report for breast cancer type chr16_g.69373414T>C (NIP7) Omnia contains curated Multi-Omics data (Variation, Expression, GO, Pathway, Drug, and Pharmacogenomics) along with subjects’ clinical data such as Demographics, Environmental, Phenotype and other attributes like HGNC, OMIM, UMLS, ICD10, SEER, and MeSH terms. Currently, Omnia contains more than 200,000 Variations, 100 Genomic experiments and 5000 Curated papers for Genotype-Phenotype relationships. Reference: Adhil M, Talukder AK, Gandham S, Agarwal M, CuraEx: Clinical Expert System Using Big data for Precision Medicine, Big Data Analytics, Springer, LNCS9498, 2015
- 30. iOMICS – the MultiOmics Platform 30March 3-5, 2016
- 31. iOMICS App Store 31March 3-5, 2016
- 32. Enterprises Disrupting Biomedical Industries InterpretOmics (http://www.interpretomics.co) Revolutionizing Genomics through Big data Multi-Scale Multi-Omics Solutions Singapore Life Sciences Transforming Life Sciences and Precision Medicine Applied Genetics Diagnostics (http://www.appgendx.com) The Next Generation Healthsciences company offering Genetic Diagnostic Services 32March 3-5, 2016
- 33. JNCASR Some Of Our Collaborators/Customers 33March 3-5, 2016
- 34. iOMICS Accelerate Your Biomedical Research – Making it Quicker, Reliable, and Affordable InterpretOmics Office: Shezan Lavelle, 5th Floor, #15 Walton Road, Bengaluru 560001 Sequencing Center: #329, 7th Main, HAL 2nd Stage, Indiranagar, Bengaluru 560008 Phone: +91(80)46623800