iomics clinical & omnia

iOMICS Clinical & OmniaOmics based (CDx)-drug-(Rx) solution

Asoke K Talukder, PhD & Mohamood AdhilInterpretOmics

iOMICS Clinical: OverviewiOMICS Clinical & Omnia is a (CDx)-drug-(Rx) software solution for Biomarker and Drug target discovery. It helps Pharmaceutical companies and Hospitals to associate targeted drugs/companion diagnostic pairs seamlessly

iOMICS Clinical (Big Data Analytics for Biomarker discovery):• Discovery: Phenotype Modeling, Drug Target Identification and Validation • Pre-Clinical: Toxicogenomics • Clinical Trial: Trial Stratification and Companion Diagnostics (CDx)

Omnia (Knowledge Base):• Disease centric Multi-omics knowledge base• Integration and Functional annotation for identification of significant Biomarkers

© Interpretomics www.interpretomics.co

Currently 42% of all drugs and 73% of oncology drugs in development are targeted drugs. This market is worth approximately $42 billion and should be worth over $60 billion by 2019.

(The Journal of Precision Medicine Vol1 Issue 2 Page no 31)

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iOMICS Clinical

© Interpretomics www.interpretomics.co 3

Uses Top-down and Bottom-up approach for Biomarker identification

In Top-down, user generated High-throughput data is used to scale down the molecular information based on Statistical characteristics

In Bottom-up, reference Biological databases and in-house database (Omnia) are used to annotate, analyse and integrate

Finally, Integrative approach is used to identify significant biomarkers using results from Top-down and Bottom-up approach

iOMICS Clinical: Systems Biology Approach


iOMICS Clinical: Target Identification - 3 Step Process

Experimental Data Analysis (Case/Control):

> Gene Expression > Metabolomics

> Proteomics

Can also use experimental results stored in Omnia

Integrative Analysis

> Metabolic Pathways > Signaling Pathways > Protein interaction

Networks

Flux balance analysis Metabolic flux comparison Expression level changes

Network neighborhood analysis Key pathway identification Central pathway genes

Protein Functional Analysis In-silico knockout Validation based scoring using properties such as:

Protein class Localization Frequency Network centrality

Data

Analysis

Validation

> Omnia (Knowledge base) > Literature Mining


iOMICS Clinical: Lung Cancer Case StudyData: Lung Squamous Cell Carcinoma (PMID: 25189482)

Data Analysis and Network Based Target Identification

Target Assessment Protein class Protein localization Protein structure Contribution in interaction network Protein-disease association

Statistics in Omnia Frequency of gene-disease association Functional association of gene with disease Population-wide statistics of variations in

the gene and mutation prioritization Molecular signature based clustering for

target population identification Existing drugs for the molecule

Based on networks including the protein interaction network, cellular signaling network and human metabolic network

Networks are calibrated according to the cell types

Network properties are studied to obtain the genes


iOMICS Clinical: Patient Stratification

Clinical Variation Gene/Protein Expression

• Clinical marker identification for phenotype classification (example: responders and non-responders)

• Grouping clinical variables into Demographics, Environmental, and Phenotypes

• Supports all types of variables such as Continous, Categorical, Discrete, and Binary

• Statistical tools used for modeling are Linear regression, Logistic regression, Cox regression, and Decision tree

• Molecular marker (Mutation) identification for Phenotype classification (example: responders and non-responders)

• Supports raw (FASTQ), BAM, and VCF files for DNA data

• Integrates Clinical with Variation data for Biomarker identification, Functional analysis, and Annotation with Omnia

• Statistical tools used for Modeling are Cox regression and Binomial-naive Bayes classifier

Molecular marker (Gene Expression) Identification for group classification (example: responders and non-responders)

Supports raw (FASTQ and CEL), BAM, and CSV for RNA data

Integrates Clinical with Gene expression data for Biomarker identification, Functional analysis, and Annotation with Omnia

Statistical tools used for Modeling are Cox regression and LASSO


iOMICS Clinical: Patient Stratification - Illustration

Sample Labeling

Feature Selection

Model Selection

Data: Lung Squamous Cell Carcinoma (PMID: 25189482)

Three significant clinical features ('TNM', 'Chemotherapy-Neoadjuvant therapy', 'Tobacco-Smoking') separating two groups (responders and non-responders)

Data: Lung Squamous Cell Carcinoma (PMID: 25189482)

Bio-markers for drug response: 10 genes (RNFT2, RHPN2, PROX1, CNTN1, FGFBP1, TINCR, OLFML2A, CYP26B1, SALL3, AREG)

Responders Group

Non-Responders Group

Data: Uveal Melanoma (PMID: 21051595)

Bio-markers for group separation (Class 2 and Metastatic): 22 significant variations (Few are chr16_g.69373414T>C, chr21_g.46330674C>A, chr19_g.46281745A>G, chr12_g.65141588C>T)

Clinical Variation Gene/Protein Expression


Omnia contains curated Omics data (Variation, Expression, GO, Pathway, Drug, and Pharmacogenomics) along with subjects’ clinical data such as Demographics, Environmental, Phenotype and other attributes

Curation is based on Data mining and Text mining techniques using Manual curation and Manual validation pipelines by PhD quality biologists

Fields in Omnia are populated based on controlled vocabulary like HGNC, OMIM, UMLS, ICD10, and MeSH terms

Omnia contains 316 disease types for four disease groups: Neurology, Metabolic, Paediatric, and Oncology

Currently, Omnia contains more than 200,000 Variations, 100 Genomic experiments and 5000 Curated papers for Genotype-Phenotype relationships


Omnia KB

Variation

Expression

Gene Ontology

Pathways

Drug

Pharmacogenomics

External DatabasesExAC, ESP, dbSNP, SRA, COXPRESSdb,Recon X, UMLS, Reactome, GEO, GO consortium, Drug Bank ....

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Forecast of Omnia

High-throughput Experiments

Omnia – Knowledge Base

Summary

Best-in-Class solution with analytical platform and knowledge base for biomarker discovery

Support for every stage of the drug development process using big data analytics Contains multi-omics multi-scale data in the knowledge base Genome-phenome and Phenome-genome modeling Supports multi-experiment type

- Features

- Benefits


Identification of Right Target and Right Patient Reduced Cost and Improved TAT (Turn around time) Increased success rate of drug discovery

Corporate Office: India Corporate Office: USAInterpretOmics India Pvt. Ltd., InterpretOmics, Inc.#15 Shezan Levelle, Walton Road #5 Parker Street, LexingtonBangalore – 560 001 MA 02421, USAEmail: [email protected]: +91 80 46623 800 Telephone: 415-800-4515URL: www.interpretomics.co URL: www.interpretomics.co

Genomic Sequencing Lab: IndiaInterpretOmics Center for Next Generation Sequencing#329 7th Main, 80 Ft Road, Indiranagar, HAL II StageBangalore – 5600 08/

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iomics clinical & omnia

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