big data analytics for biomedical discovery€¦ · products in development: idatamed and pathway...

Big Data Analytics for Biomedical Discovery

Knowledge Synthesis

Decisions for better medicine.

July 2016 Knowledge Synthesis Inc. 2

Biomolecular Big Data Languish

LABOR-INTENSIVE DATA ANALYSIS PLANS are slow to develop and expensive to

execute.

MANY DATA SOURCES

are not integrated.

BIOINFORMATIC EXPERTISE

NEEDED to navigate complex

biomolecular big data.

LIMITATIONS OF PUBLISHED

RESULTS

Hypothesis-driven analyses don’t

anticipate many future uses.

FAILURE TO INFORM

ONGOING MEDICAL

RESEARCH

Data are not revisited in new contexts.

DIVERSE DATA gene expression

protein expression protein modification drugs and chemicals interaction networks signaling pathways

biochemical pathways

+

DISCONNECTION

Many big data details are not related to medical research concepts, which

discourages efforts to re-use.


MEDLINE−indexed articlespublished per year

1950 1960 1970 1980 1990 2000 2010 2020

200k

400k

600k

800k

1M

1.2M

Jason Priem

drugs

patient samples

proteins

pathways

biological networks

microarrays

next-gen sequencing

in vitro models

in vivo models

disease

response

treatment

MRI image from Mehta et al. (2013). Iron is a sensitive biomarker for inflammation in multiple sclerosis lesions. PLoS One 8(3): e57573.

Problem: Vast, Disparate Data


molecular mechanisms inform R&D • biomarkers • diagnostics • drug targets • patient selection • indication selection • drug repurposing 0 20 40 60 80 100

0.0

0.2

0.4

0.6

0.8

1.0

Patients lacking standard markers,complete resection, no therapy

Time to relapse (mo)

Prop

ortio

n w

ithou

t a re

laps

ePrediction Score > 34Prediction Score < 34

breadth

depth patient samples

drugs pathways microarrays biological networks proteins

next-gen sequencing

in vivo models

in vitro models

disease

response

treatment

proprietary analyses and cross-study normalization

Solution Using proprietary methods, we integrate a broad range of different data sources and provide depth of information from many studies.


Impact Across R&D Basic Research

Prototype Design or Discovery

Preclinical Development

Clinical Development

Phase I Phase II Phase III

FDA Filing / Approval & Launch

Clinical Utility among Candidate Stratification Markers

We validate our approach at each step, for example: molecular mechanism discovery phosphorylation event predicted in chemotherapeutic

resistance confirmed in independent experiment

model-to-clinic translation cytokine therapy-response biomarker predicted from pathway analysis of mouse lung confirmed in human plasma

Patient Stratification, PD, and Efficacy Markers

Personalized Medicine

Animal Studies for Toxicology and Mechanism of Action

Target Selection

Indication Prioritization

Biomarker Panel Selection


What Is Knowledge Synthesis? Services Customized Data Analysis, Discovery Research, and Technology. Underexploited data means missed opportunities. Knowledge Synthesis will increase your return on investment by reducing data to findings that inform your actions. We integrate all types of life sciences data. We build databases, search interfaces, and data analysis tools and pipelines. We also use the technology we build and can work with your research team to get the results you need to make decisions for better medicine. Products in Development: iDataMed and Pathway Data Analytics Big Data for every life sciences investigator. With iDataMed, Knowledge Synthesis meets the challenge to tame Big Data for life sciences researchers. The key is to bring data into the right context to forge new biomedical knowledge. Straightforward text searches bring the right biomolecular findings to the right person at the right time. Pathway Data Analytics conveys pathway analysis details without compromising understandability. By using intuitive data visualization, we provide easier to navigate pathway analysis. Technology Breadth and depth for every study. Our proprietary life sciences data integration platform ensures that each data set is viewed through many facets of biological knowledge, including multiple sources of pathways and networks that link genes, proteins, and chemicals. Add to that breadth our ability to normalize statistically significant findings across multiple studies, and your understanding of cells, model systems, and clinical samples gains depth.


Knowledge Synthesis Provides Context

Genome-wide sample data!

!RNA!DNA!

protein assays!

Pathways

Functions

Protein activities

Drugs

Endogenous chemistry

microRNA activity

Chromosomal locations

Findings with statistical support and relevance to aims.

More than 250,000 biological entities and more than

14 million relationships provide context


IPF PBMCs!transcriptome

data!

Example: Idiopathic Pulmonary Fibrosis (IPF) Increased ECM function, including ITGAM (CD11b), suggesting macrophage role1

Increased 19p13 gene expression2

Decreased expression downstream of FOXP33

Increased expression related to HNE Inhibitor4

Knowledge Synthesis uses proprietary tools that link data-driven findings with relevant and corroborating literature. 1.  Abnormal lymphangiogenesis in idiopathic pulmonary fibrosis with insights into cellular and molecular mechanisms. 2009. PNAS. 2.  Genome-wide association study identifies multiple susceptibility loci for pulmonary fibrosis. 2013. Nat Genet. 3.  Global impairment of CD4+CD25+FOXP3+ regulatory T cells in idiopathic pulmonary fibrosis. 2009. Am J Respir Crit Care Med. 4.  Effects of elastase inhibitor on the epithelial cell apoptosis in bleomycin-induced pulmonary fibrosis. 2009. Exp Lung Res.

Novel Findings

KS/path

KS/net KS/chem

KS/prot


Levine DM, Dutta NK, Eckels J, Scanga C, Stein C, Mehra S, Kaushal D, Karakousis PC, Salamon H. A tuberculosis ontology for host systems biology. Tuberculosis (Edinb). 2015 Sep;95(5):570-4.

Shi L, Salamon H, Eugenin EA, Pine R, Cooper A, Gennaro ML. Infection with Mycobacterium tuberculosis induces the Warburg effect in mouse

lungs. Sci Rep. 2015 Dec 10;5:18176 Salamon H, Yamaguchi KD, Cirillo DM, Miotto P, Schito M, Posey J, Starks AM, Niemann S, Alland D, Hanna D, Aviles E, Perkins MD, Dolinger DL.

Integration of Published Information Into a Resistance-Associated Mutation Database for Mycobacterium tuberculosis. J Infect Dis. 2015 Apr 1; 211 (suppl 2): S50-S57

Salamon H, Bruiners N, Lakehal K, Shi L, Ravi J, Yamaguchi KD, Pine R, Gennaro ML. Cutting edge: Vitamin D regulates lipid metabolism in

Mycobacterium tuberculosis infection. J Immunol. 2014 Jul 1;193(1):30-4. Campbell JD, Kell SA, Kozy HM, Lum JA, Sweetwood R, Chu M, Cunningham CR, Salamon H, Lloyd CM, Coffman RL, Hessel EM. A limited CpG-

containing oligodeoxynucleotide therapy regimen induces sustained suppression of allergic airway inflammation in mice. Thorax. 2014 Jun;69(6):565-73.

Toda M, Shao Z, Yamaguchi KD, Takagi T, D'Alessandro-Gabazza CN, Taguchi O, Salamon H, Leung LL, Gabazza EC, Morser J. Differential gene

expression in thrombomodulin (TM; CD141)(+) and TM(-) dendritic cell subsets. PLoS One. 2013 Aug 23;8(8):e72392. Salamon H, Qiao Y, Cheng JC, Yamaguchi KD, Soteropoulos P, Weiden M, Gennaro ML, Pine R. Evidence for postinitiation regulation of mRNA

biogenesis in tuberculosis. J Immunol. 2013 Mar 15;190(6):2747-55. Kunnath-Velayudhan S, Davidow AL, Wang HY, Molina DM, Huynh VT, Salamon H, Pine R, Michel G, Perkins MD, Xiaowu L, Felgner PL, Flynn JL,

Catanzaro A, Gennaro ML. Proteome-scale antibody responses and outcome of Mycobacterium tuberculosis infection in nonhuman primates and in tuberculosis patients. J Infect Dis. 2012 Sep 1;206(5):697-705.

Croze E, Yamaguchi KD, Knappertz V, Reder AT, Salamon H. Interferon-beta-1b-induced short- and long-term signatures of treatment activity in

multiple sclerosis. Pharmacogenomics J. 2013 Oct;13(5):443-51. Kunnath-Velayudhan S, Salamon H, Wang HY, Davidow AL, Molina DM, Huynh VT, Cirillo DM, Michel G, Talbot EA, Perkins MD, Felgner PL, Liang X,

Gennaro ML. Dynamic antibody responses to the Mycobacterium tuberculosis proteome. Proc Natl Acad Sci U S A. 2010 Aug 17;107(33):14703-8.

Publications with Collaborators and Clients


Cost-effective biomarker discovery exploiting pre-clinical data (treatment response in plasma)

Pathways and activity more reproducible than gene expression (tuberculosis research)

Confirmation of predicted phosphorylation event (MCF-7 cells)

KS finds answers where others fail to find anything (ALS)

Cross-system pathway and activity detection (tuberculosis research)

Our pathway-focused approach improves survival prediction (lung cancer)

Examples of Molecular Discovery

Some Technology We Use Contact Hugh Salamon, Founder and Chief Executive Officer [email protected] Ken Yamaguchi, Founder and President [email protected] Ivan Plavec, Business Development [email protected]

big data analytics for biomedical discovery€¦ · products in development: idatamed and pathway...

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