Anton Yuryev describes how to identify optimal molecular targets and drugs for personalized cancer treatment using network and pathway analysis of transcriptomics profiles from tumor biopsies. The approach involves determining the most active targets using network analysis, finding cancer hallmark pathways enriched with these targets, and identifying FDA-approved drugs targeting the most active hallmarks. Sub-Network Enrichment Analysis is used to calculate regulator activity from downstream targets in patient profiles. Pathway Studio contains cancer pathway models built from literature to map patient profiles and find druggable targets. The approach is validated for stage IV cancer patients and aims to optimize treatment by targeting multiple identified regulators with drug combinations.

1. Anton Yuryev, Ph.D.
March 29 2018
AAPS Webinar
Personalized Medicine:
Matching cancer drugs with mechanism.

2. Session Description and Objectives
Slide 2
www.aaps.org
I describe how to identify the best molecular targets and drugs for
anti-cancer treatment using combination of network and pathway
analysis of transcriptomics profile measured from tumor biopsy.
• Determination of the most active targets using network analysis
• Finding cancer hallmark pathways enriched with most active targets
• Finding druggable targets using most active cancer hallmarks
• Finding FDA approved drugs to optimize personalized anti-cancer therapy

3. Central dogma augmented
How to calculate protein activity
from tumor molecular profile?
Slide 3
www.aaps.org
Copyright: RSC Publishing - Royal Society of Chemistry
Protein
activity
Biological
processes

4. Sub-Network Enrichment Analysis
(aka SNEA, Causal Reasoning)
Calculates regulator activity from the changes observed
in the downstream targets (activity biomarkers)
• SNEA builds networks from all genes/proteins
measured in the experiment using all relations in the
database.
• SNEA can include indirect regulation i.e. expression
regulatory cascades consisting of 2-3 steps
• Significant network centers may be found that are
not measured in the primary dataset
• No prior curation of gene sets is required.
• Can work with partial information about regulators
targets. Does not require knowledge about all targets
• P-value is sensitive to the size of the chip
Slide 4
www.aaps.org
Most active
Least active

5. Network data is extracted by
Elsevier Deep Reading
technology from 80% of all
biomedical literature
Deep Reading technology = NLP +
1) Understanding protein mutation, modification
sites, domains and complexes
2) Co-reference resolution
3) Discourse analysis
4) Understanding experimental techniques used in
the original research articles
Deep Reading can extract comprehensive
summary of an scientific article
NLP can extract some facts from an article
Slide 5
www.aaps.org

6. Our approach is
validated on Stage IV
cancer patients
www.wakeforest-personalized-hemonc.com
Personalized Hematology-Oncology of Wake Forest
11635 Northpark Drive, Suite 250, Wake Forest, NC 27587
Luminita Castillos, PhD, MBA,
Francisco Castillos, III, MD
Slide 6
www.aaps.org

7. How to measure
approach success?
Comparison with standard of care patient
survival curve
Comparison with patient survival curves from
other approaches
Approach cannot be used on Stage 2-3 cancer
patients due to standard of care restrictions
Slide 7
www.aaps.org

8. SNEA results from
the molecular profile
of patient tumor
• SNEA can find major expression
regulators and rank them by activity
• SNEA can find cancer hallmark
processes and rank them by activity
• Which regulator drives which cancer
hallmark?
• How to inhibit major regulator(s)?
Slide 8
www.aaps.org

9. Next step of analysis:
Mapping SNEA regulators
on cancer hallmark
pathways
• Red colored proteins- mRNA is upregulated
• Blue colored proteins- mRNA is downregulated
• Red highlight – activated expression regulators
identified by SNEA
• Blue highlight – repressed expression
regulators identified by SNEA
• Note: Red highlight does not always
coincide with red color
• Cancer hallmark pathways are built manually by
Ph.D. level curators from relations extracted by
Elsevier deep reading technology using review
and original research articles
Slide 9
www.aaps.org

10. Cancer hallmarks
pathways collection is
available in Pathway
Studio
• Collection has >180 pathways for 10 cancer
hallmarks. Additional >150 pathways for 25
individual cancers
• Hanahan & Weinberg. Hallmarks of cancer:
the next generation. Cell 2011;144(5):646-74
• Each hallmark has several fundamental
mechanisms that may have slightly different
components in one of 250 human tissues
• Current collection explains 80-90% of top 100
SNEA regulators for every new cancer patient
• Effort is on-going: if tumor molecular profile
cannot be explained by pathways in the
collection new pathways are built from patients
SNEA regulators
Slide 10
www.aaps.org

11. Common misconception
addressed by SNEA
Pathway activity
Differential Expression of its components
Pathway activity
Differential Expression of its expression
targets
Cancer hallmarks activity is
determined based on the molecular
profile of the biomarker in patient
tumor
Slide 11
www.aaps.org

12. Finding drugs for
personalized
cancer treatment
• Red colored proteins- mRNA is upregulated
• Blue colored proteins- mRNA is
downregulated
• Red highlight – activated expression regulators
identified by SNEA
• Blue highlight – repressed expression
regulators identified by SNEA
• The most active regulators identified by SNEA
may not be druggable
• Pathways in Cancer hallmark collection helps
to find druggable targets
• Note: drugs target proteins that are
upstream of SNEA regulator in the pathway
Slide 12
www.aaps.org

13. Pathways allow to find drugs
with best efficacy and to
optimize combinatorial therapy
• Pathway may contain several SNEA regulators
identified in a patient tumor molecular profile
• Drugs may target multiple SNEA regulators
(e.g. kinase inhibitors)
• The most potent drug should targets the biggest
amount of SNEA regulators
• In this screenshot:
cediranib=vandetanib=erlotinib>AEE788>roc
iletinib>bosutinib
• The same logic can be used in designing drug
combination for a patient
Slide 13
www.aaps.org
Hepatocellular carcinoma polypharmacology

14. Drug optimization can be
also done using the
translational data from the
animal and in-vitro studies
• Pathway Studio knowledgebase also
contains drug effects reported against
cancer cell lines, cancer animal models
and allografts, clinical case reports for
individual patients
• This information can be used to further
optimize drug selection by boosting the
confidence with translational data
Slide 14
www.aaps.org

15. The Future: Oncology 3.0
EUROPEAN SOCIETY FOR MEDICAL ONCOLOGY
http://www.esmo.org/Oncology-News/Precision-Oncology-3.0
“The emerging new generation of precision oncology, which
the authors call Precision Oncology 3.0, uses broad-spectrum
panomics and sophisticated network-based statistical reverse
engineering methods to hypothesize the putative driver
networks for a given patient's tumor. Once these are
computed, they are combined with important contextual
features (such as the patient's treatment history, status, and
preferences, as well as knowledge of available drugs and
drug interactions) to hypothesize a treatment plan that attacks
the tumor drivers with combination of narrowly targeted
therapies.
The heart of Precision Oncology 3.0 is driver network
analysis and clinical targeting and treatment planning. Driver
network analysis identifies key genes, which modulate
established cancer hallmarks. By charting the trajectory of a
tumour's molecular profile over time, it might be possible to
anticipate how a cancer is likely to evolve, and to take
proactive steps to block it from doing so.
In Precision Oncology 3.0, every treatment event is a probe,
simultaneously treating the patient and providing an
opportunity to test and improve molecular understanding of
the disease…”
Slide 15
www.aaps.org

16. Slide 16
www.aaps.org
LIVE POLLING QUESTION #1
• Can personalized oncology use case be generalized to any other
disease?

17. Slide 17
www.aaps.org
LIVE POLLING QUESTION #2
• What is the difference between precision and personalized
medicine?

18. Contact Information
Slide 18
www.aaps.org
Anton Yuryev, Director of Professional Services
a.yuryev@elsevier.com
http://www.pathwaystudio.com

19. References
Slide 19
www.aaps.org
Construction of cancer pathways for personalized medicine:
https://www.slideshare.net/AntonYuryev/construction-of-cancer-
pathways-for-personalized-medicine
The Success of Personalized Anti-Cancer Therapies
https://pharma.elsevier.com/pharma-rd/success-personalized-
anti-cancer-therapies/
Personalized vs. Precision Medicine
https://pharma.elsevier.com/pharma-rd/personalized-vs-
precision-medicine/

20. Acknowledgements
Slide 20
www.aaps.org
• Luminita Castillos, Personalized Hematology-Oncology of Wake Forest,
USA, info@wakeforest-personalized-hemonc.com
• Francisco Antonio Castillos III MD, Personalized Hematology-Oncology of
Wake Forest, USA, info@wakeforest-personalized-hemonc.com
• Sergey Sozin Ph.D., Elsevier, sergey_sozin@yahoo.com
https://www.elsevier.com/__data/assets/pdf_file/0020/124625/R_D-
Solutions_Pharma_White-Paper_Disease-Pathway_DIGITAL.pdf