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Pathway analysis for personalized oncology
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Transcript of Pathway analysis for personalized oncology
Presented by: Anton Yuryev
Title: Professional services, Director
Friday, October 16, 2015
Pathway Analysis for Personalized oncology
With so many drugs on the market why we
cannot cure cancer yet?
# drugs # clinical trials
Entire history 1,554 7,515
At least on trial since 2010 983 3,501
At least on trial since 2010 for top 100 most common cancers 952 3,093
At least on trial since 2010 for top 100 most common cancers
FDA approved
425 1,681
From clinicaltrials.gov
*Chemotherapy and Radiotherapy drugs are not included
Drug Trade name # cancers
bevacizumab Avastin 48
everolimus Zortress 43
sorafenib Nexavar 30
pazopanib Votrient 28
erlotinib Tarceva 26
filgrastim neopogen 25
temsirolimus Torisel 24
Vorinostat suberanilohydroxamate 23
cetuximab erbitux 23
panitumumab vectibix 22
Disease # FDA approved drugs
Breast Cancer 208
Prostate Cancer 150
Leukemia 117
Lymphoma 115
Melanoma 95
Lung Cancer 95
Colorectal Cancer 94
Multiple Myeloma 94
Ovary Cancer 69
Cancer of Head and Neck 69
Sarcoma 62
Urinary Bladder Cancer 46
Cancer of the Uterine Cervix 44
Cancer of Stomach 44
Cancer of Kidney 43
How useful is disease classification?
3
1.4 million new colon cancer cases diagnosed in 2012 (100%)
K-ras mutation (40%)
no cure
K-ras wild type (60%)
EGFR inhibitors
K-ras wild type (40-50%)
EGFR inhibitors resistance
no cure and no biomarkers
About 1 mln colon cancer patients still with
no cure with 5 years survival rate ~50%
Breast cancer sub-types
Invasive cancers:
IDC — Invasive Ductal Carcinoma (80%)
Tubular Carcinoma (<2%)
Medullary Carcinoma (3-5%)
Cribriform Carcinoma
Osteoclastic Carcinoma
Apocrine Carcinoma (1-4%)
Adenoid cystic Carcinoma (<1%)
Mucinous Carcinoma
mucinous A, mucinous B
NOS – not otherwise specified (60%) Luminal A (40%), luminal B (20%), basal-like/triple negative (15-20%), HER2+ (10-15%)
Metaplastic Carcinoma (<1%)
Neuroendocrine Carcinoma (<0.1%)
Inflammatory Breast Cancer(<0.1%)
ILC — Invasive Lobular Carcinoma (10-15%) Luminal A (40%), luminal B (20%), basal-like/triple negative (15-20%), HER2+ (10-15%)
Non-invasive breast cancers:
LCIS — Lobular Carcinoma In Situ
Paget's Disease of the Nipple
DCIS — Ductal Carcinoma In Situ
Phyllodes Tumors (<1%)
Micropapillary Carcinoma
4
Disease classification is useful but slow
Current approach
Analyze as many patients as you can get
Build disease classifier
Try to find the drug for each disease class
5
While we classify the disease patients are dying => a many patients
must die without cure before we accumulate enough statistics for
comprehensive disease classification
Knowing disease classes does not guarantee that
the new patient will belong to one of them
there is a cure for his disease class
Curse of Dimensionality of OMICs data: We will never have enough patient samples to calculate robust signatures from large
scale molecular profiling data
6
signature size # patients
error rate
Hua et al. Optimal number of features as a function of sample size for various
classification rules. Bioinformatics. 2005
Fig.3 Optimal feature size versus sample size for Polynomial SVM classifier.
nonlinear model, correlated feature, G=1, r= 0.25. s2 is set to let Bayers error be 0.05
Robust signature must have 20-30 genes
# Differentially expressed genes: 500-2000
Sequence
Target Test
Treat
Monitor
How to find the right drug for cancer patient
Precision Oncology 3.0 (2020)
In silico In vivo In vitro
Normal cell
Sequencing Machines
Microarray data
Normal cell
Cancer cell
Treated cell
Scans
Patient
Biomarkers
Adapted from NY Times and CancerCommons
Original cancer cell
Biopsies Biopsy
Panomics
Treatment Planning
Pathway and Network Analysis
Combo Therapies
Serum Markers
Pathway Studio Knowledgebase for SNEA
powered by Elsevier NLP
8
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Internal
Documents Subscribed
Titles
613 Elsevier
journals
23,641,270 Pubmed abstracts from
>9,500 journals
884 non-Elsevier
journals
Custom data can be imported
into dedicated PS instance
Public
databases
>3,500,000 full-text
articles
27,243
477,365
Expression: Protein->Protein
Promoter Binding: Protein TF-
>Protein
9
Our solution: Pathway Activity signatures identify targets for
anti-cancer drugs
Hanahan & Weinberg. Hallmarks of cancer: the next generation. Cell. 2011;144(5):646-74
1. Calculates major expression regulators from the expression of their targets
2. Maps major expression regulators on cancer pathway collection
3. Calculate pathway activity signature
1. Pathway activity signatures are short and therefore can classify patients better
2. Pathway activity allow selection of drugs inhibiting the active pathway(s) instead of
inhibiting single target
10
Major steps to calculate pathway
activity signature
Causal reasoning
(Sub-network enrichment analysis SNEA)
Common misconception:
Differential Expression of its components Pathway activity
Pathway activity Differential Expression of its expression targets
Finds activated pathway components from patient OMICs data
Pathway components
Pathway targets
12
Cancer pathways: Insights to cancer biology EGFR activation by apoptotic clearance (wound healing pathway)
Red highlight –
Major
expression
regulators in
cancer patient
Apoptotic debris
13
Avoiding immune destruction: N1->N2 polarization
Highlights – SNEA regulators from different patients
14
Example: Drugs inhibiting DREAM complex controling quiescence
www.wakeforest-personalized-hemonc.com
11635 Northpark Drive, Suite 250, Wake Forest, NC 27587
Gene expression profiling for targeted cancer treatment
Luminita Castillos1, PhD, MBA, Francisco Castillos1, III, MD and Anton Yuryev2, PhD
1Personalized Hematology-Oncology of Wake Forest, PLLC, NC 27587, USA
2Elsevier, MD 20852, USA
Second patient – tumor signaling pathway
No lung met
after treatment
8 July 2014
PET/Scan
Lung met
before treatment
12 September 2013
PET/Scan
No lung met
after treatment
8 July 2014
CT/Scan
Sequence
Target Test
Treat
Monitor
Precision Oncology 3.0 (2020)
In silico In vivo In vitro
Normal cell
Sequencing Machines
Microarray data
Normal cell
Cancer cell
Treated cell
Scans
Patient
Biomarkers
Adapted from NY Times and CancerCommons
Original cancer cell
Biopsies Biopsy
Panomics
Treatment Planning
Pathway and Network Analysis
Combo Therapies
Serum Markers
Future
19
1) We look for more medical collaborators to
continue validating our approach on live
patients
2) Algorithm improvements: More pathways,
better knowledgebase, algorithm for
pathway activity signature
3) Methodology implementation in clinical
decision support solution