My talk at Discovery On Target on September 26th 2017

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Presented By
Date
Profiling how Immune inhibitors Secreted by Melanoma
affect NK & other immune cells
Discovery On Target, Boston, Sept 26th
Anton Yuryev

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Gaining insights from internal and external/public
information sources is time and resource consuming
We have over 500 information solutions, we
don’t want 501. We want to consolidate our
solutions and increase information
discoverability.
Pers. Comm. Head of Medicinal
Chemistry at aTop5 Pharma
The challenge is in putting together different
data sources and seeing patterns.
Former Pharma COO
There is lots of locked away data—if that could
be made available, it would be highly valuable.
CIO of Biotech
64 % of data management effort and time
is spent finding and profiling data sources
55-75 % of data collected
by businesses go unused
Unstructured
data
Structured
data
Source: Forrester Research Survey, Global Databerg Report

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Gain critical insights from current,
integrated, knowledge helps to better
inform your critical workflows:
 Identify novel immunotherapy targets
 Find potentially new immunomodulatory
drugs through drug repurposing
 Create models that improve
understanding of combinatorial
treatment interactions
 Better match patients with treatments
Discussion Summary

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Elsevier’s Solutions can help streamline critical workflows with
integrated data
Discovery & Lead ID & Valid Pre-clinical Clinical Post-launch
• Monitoring adverse
events
• Lead prioritization for safety, delivery and efficacy
• Translational medicine/research
• Lead identification and
characterization
• Synthesis optimization
• Bioactivity
• Disease modeling
• Target identification
• Biomarker discovery
• Drug repositioning
Elsevier and non-Elsevier
textual information
Public and proprietary
databasesDisparate Data/Content
Examples
Supported
Applications
Use-case centered integration & customization focus on customer outcomes
Expertise/ Capabilities Data extraction, Data normalization, Data integration
Elsevier Text MiningTechnology & data structure Dictionaries & taxonomies
Outcomes
+

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• Human γδ T cells lyse melanomas and other cancerous epithelial
cells in a perforin-mediated manner.
• Indeed, melanoma cell lines are subject to lysis by γδ T-cells,
which produce perforin and exhibit strong cytolytic activity upon
exposure.
• Both in vitro and in xenograft models, γδ lymphocyte-mediated
cytotoxicity against melanoma cells has been reported.
• Our results suggest that a natural immune response mediated by
γδ T lymphocytes may contribute to the immunosurveillance of
melanoma.
• Killer cell inhibitory receptors for MHC class I molecules regulate
lysis of melanoma cells mediated by gamma delta T
lymphocytes.
Transform text to facts using Elsevier Deep Reading technology
gamma delta T cell melanoma
negative
regulation

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Find the
right data
NLP information extraction data model: 2017
46 relation types; >7,138,122 relations; >32,658,982 facts
Relation Type Stats
Binding 324,913
ProtModification 57,830
DirectRegulation 38,562
PromoterBinding 34,104
microRNAEffect 39,181
MolTransport 40,515
Expression 286,648
Regulation 382,111
Type Stats
Binding 75,911
MolSynthesis 24,688
MolTransport 30,222
ChemReaction 76,744
Expression 340,815
MolTransport 11,736
DirectRegulation 87,312
Regulation 413,152
Type Stats
Regulation 826,471
Regulation 438,389
Expression 16,069
MolTransport 1,730
MolSynthesis 16,102
MolTransport 5,618
Clinicaltrial 4,067
Regulation 127,515
Protein->Clinical
parameter
Type Stats
Regulation 616,577
Regulation 566,444
ClinicalTrial 78,713
QuantitativeChange 250,475
GeneticChange 193,858
StateChange 16,291
Biomarker 73,677
QuantitativeChange 29,300
Biomarker 9,025
FunctionalAssociation 410,856
FunctionalAssociation 169,586
Type Stats
FunctionalAssociation 5,062
Regulation 13,854
Regulation 57,179
MolTransport 15,834
CellExpression 432,591
MolTransport 51,908
Biomarker 4,016
QuantitativeChange 8,112
StateChange 9,328
Regulation 102,435
Regulation 135,789
CellEffectTM
for cancer
immuno-
therapy

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Epitope
Normalization of cell identifiers:
From inconsistent names to standard names
Basic cell“Attribute”
CD4+ CD25+ regulatory T cell
T-lymphocyte leukocyte
T-cell leucocyte
hemopoetic
hemopoietic
haemopoetic
haemopoietic
hematopoetic
hematopoietic
haematopoetic
haematopoietic
regulatory
immunoregulatoryCD4+CD25+
CD25+FOXP3+
CD4+ CD25+ FOXP3+
CD3+CD4+CD25+
Standard
cell name

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Example of a “synonym” list

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Provide greater context by adding cell processes to
each cell type
Allows more flexibility to address emerging topics
• More cell processes in the database provides greater breadth of information
• Find cell processes relevant to rare and cell types critical to biology of disease
• Automatic tracking of changes in literature trends to keep pace with evolving biology
proliferation of
death of
migration of human polarization
cytotoxicity
quantity
Standard
cell
name

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Modeling to identify immunotherapy melanoma targets in
Pathway Studio – graphical query in Pathway Studio
May help some cancers to grow

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Expanding the query:
Melanoma-related concepts in Pathway Studio
178 melanoma
cell lines
32 melanoma
Disease concepts

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Expanding the query:
Concepts related to immune suppression
291 concepts that can be
positively regulated to
suppress immune response
1376 concepts that can be
negatively regulated to
suppress immune response

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Known melanoma Immune suppression mechanisms
• 226 proteins secreted or expressed on the cell surface by melanoma that can inhibit
activation of immune system
• 142 proteins secreted or expressed on the cell surface by melanoma that can activate
immune tolerance
CellExpression MolTransport relation types
Regulation Effect=negative or positive relation types
1. Extracted from more than 20,000 articles and network created in several hours
2. Each target requires at least two publications:
 describing its expression in melanoma
 describing its immune system suppressive function
Only NLP extraction allows search
across several articles

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Main result of inspecting potential
immunotherapy targets:
Many mechanisms, not one.
Examples of targets found by curation of the results of
expanded queries
13

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Example why Keytruda
may not work:
PDCD1 is not the only
mechanism activating
immune tolerance
Example of novel
target with no drugs
Examples of known and novel targets

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Example of drug repurposing: Galectin-1; VEGFA/C

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Well-known drug may be used in combinatorial immunotherapy with
Keytruda
CD73 –
Oleclumab target

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More novel targets for immunotherapy
TEW-7197 target

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Multiple immuno-modulatory mechanisms can help
identify new, potentially, safer drug combinations

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Alternative mechanism for immuno-suppression in the tumor
and low PDCD1 expression in a patient should predict no
response to Keytruda

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Find personalized immunotherapy option targeting a
mechanism activated in a single patient.
Precision Oncology 3.0
(2020)

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Gain critical insights from current,
integrated, knowledge helps to better
inform your critical workflows:
 Identify novel immunotherapy targets
 Find potentially new immunomodulatory
drugs through drug repurposing
 Create models that improve
understanding of combinatorial
treatment interactions
 Better match patients with treatments
Discussion Summary

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www.elsevier.com/rd-solutions
Thank you for your attention.
Acknowledgements:
Maria Shkrob, PhD
Stephen Sharp, PhD
Mathew Clark, PhD