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

>

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

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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