© 2015 NantOmics, LLC. All Rights Reserved. 2015 ASCO Annual Meeting; May 29-June 2, 2015; Chicago, Illinois

• Next-generation sequencing (NGS) and quantitative proteomics enable the timely identification of a cancer patient's unique molecular signature, independent of anatomical tumor type, allowing the identification of clinically relevant targets for informed treatment selection

• Gene panels comprised of <500 genes are most often used to guide treatment selection; however, panels do not provide insights into altered protein expression

• To predict the downstream effects of gene alterations, orthogonal technologies such as RNAseq and proteomics are needed

– RNAseq confirms the expression of mutated genes and enables the quantitation of gene expression and when integrated with DNA sequencing data using pathway-based modeling algorithms such as PARADIGM1 can be used to infer protein expression within actionable signaling pathways

– Mass spectrometry-based proteomics allows the quantitative measurement of expressed proteins that influence disease progression and sensitivity and resistance to therapeutics

• An important step in the evolution of precision cancer medicine is to utilize a comprehensive panomic approach to select therapies for patients

• We have developed a platform that integrates whole exome/whole genome sequencing data of patient matched tumor-normal samples with RNAseq, quantitative proteomics, and pathway analysis to identify clinically relevant targets

Study Population and Data Set• A sequencing data set of patient matched tumor-normal samples was analyzed from The Cancer

Genome Atlas (TCGA) CG-Hub database (https://cghub.ucsc.edu/) • Whole exome sequencing data/RNAseq data were available for 3783 patients

Data Analysis• Transporter software platform encrypted and securely transfered unassembled data from sequencer to

supercomputer used for analysis• DNA sequencing data were processed using Contraster2

– Gene panel analysis was limited to 328 genes• RNAseq data confirmed the presence of gene mutations

– Variants classified into high-expressed (>0.9 allele fraction) and low-expressed (<0.1 allele fraction)– RNAseq expression values calculated using a normal distribution across adjacent normal samples in TCGA;

“high” expression was Z-score ≥3• Associations between gene mutations and expression were determined:

– Highly expressed gene mutations (whole exome versus gene panel)– Low/no expression of gene mutations (gene panel)– Highly expressed non-mutated genes (gene panel)

• PARADIGM used to reveal shared pathways among patients– Copy-number alterations derived from segmented data produced by Contraster– Transcriptomic data normalized to pool of normal samples across tissues, with transcript abundance identified as

up- or down-regulated regulated when falling above the upper or below the lower 5th percentile for each gene– Clustering of PARADIGM activity values was performed using a custom version of Cluster 3.0.9

Proteomics• Tissues were microdissected, solubilized, and enzymatically digested• Peptides unique to proteins of interest were identified and labeled peptides were synthesized • Absolute quantitation of protein targets was performed using selected reaction monitoring mass

spectrometry

• Mutations in genes targeted by drugs approved based on anatomy are prevalent in other cancers independent of tissue type

• Expression matters• Quantitative measurement of HER2 >2200 amol/μg is predictive of longer survival • Panomic platform integrating genomic sequencing with quantitative protein expression analysis

informed effective treatment for patient with end-stage cervical cancer with a drug not approved in that tissue type

• Precision cancer medicine will require reclassification of cancers based on their molecular profile and not on tissue type

Genomics, Transcriptomics, and Proteomics in the Clinical Setting: Integrating Whole Genome and RNA Sequencing With Quantitative Proteomics to Better Inform Clinical Treatment Selection

Shahrooz Rabizadeh,1-4 Stephen C Benz,2 Sheeno Thyparambil,2,3 Todd Hembrough,2,3 J Zachary Sanborn,2 Charles J Vaske,2 Patrick Soon-Shiong1,4

1NantWorks, LLC, Culver City, CA; 2NantOmics, LLC, Santa Cruz, CA; 3Oncoplex Diagnostics, Rockville, MD; 4CSS Institute of Molecular Medicine, Culver City, CA

Methods

Background

Conclusions

AcknowledgmentReferences

Panomic Approach to Precision Cancer Medicine

Abstract #11093

1. Sanborn JZ, Salama SR, Grifford M, et al. Cancer Res. 2013;73:6036-6045.2. Sedgewick AJ, Benz SC, Rabizadeh S, Soon-Shiong P, Vaske CJ. Bioinformatics.

2013;29:i62-70.3. Nuciforo P, Thyparambil S, Aura C, et al. Cancer Research. 2014;75:4-11.

Mutated Targetable/Actionable Genes Across Cancer Types

Classification of Tumors Based on Shared Pathways

Highly Expressed Mutant Alleles: Whole Exome vs Panel

Low/No Expression of Mutant Alleles: Panel

Expression of Mutated and Non-Mutated Genes: Panel

Predictive Value of Proteomics: HER2 as an Example

Transporter Contraster PARADIGM

100%

3002502001501005000%

20%

40%

60%

80%

0

200

400

600

800

1000

1200

1400

1600

Bladder

Breast Ductal

Breast Lobular

Colorectal

Glioblastoma

Head & Neck

Kidney Clear Cell

Lung Adeno

Lung Squamous

Melanoma

Prostate

ThyroidUterine

Gene Panel Analysis (328 genes)Whole Exome Analysis (>20,000 genes)

0

50

100

150

200

250

300

350

400

0

Bladder

Breast Ductal

Breast Lobular

Colorectal

Glioblastoma

Head & Neck

Kidney Clear Cell

Lung Adeno

Lung Squamous

Melanoma

Prostate

ThyroidUterine

Numb

er of

High

ly Ex

pres

sed M

utatio

ns(D

NA an

d RNA

)Pe

rcenta

ge of

Alte

ratio

ns(D

NA an

d/or R

NA)

Numb

er of

Low

Expr

esse

d Muta

tions

Genes in the 328-Gene Panel

Number of Patients:N=1404

out of 3783

101 209 14 38 4 136 22 167 303 274 5 7 124

Number of Patients:N=908

out of 3783

36 205 38 22 4 63 67 127 109 107 25 30 75

54

277

80

782

2 26 2086

1 4 42

297

505

633

912

1413

7

425

550110 104 73

11

62

344

56

6

42

363

222

29

102 106

251

209

33

DNA alterationHigh RNA expression4.6%

No DNA alterationHigh RNA expression26%

DNA alterationNormal/low RNA expression69%

Gene Panel Analysis

Panomics Case Study: End-Stage Cervical Cancer

BladderBreast DuctalBreast LobularColonGlioblastoma

Head & NeckKidney Clear CellLung Adeno.Lung SquamousOvarian

RectalUterineLow Grade GliomaProstateMelanomaThyroid

Basket 1“Basal”-like

Basket 2HormoneReceptor+

Basket 3Cyclin-B

Low

Basket 4PI3K Low

Basket 5Squamous

Basket 6Brain

020

040

060

080

010

0012

00

Numb

er of

Sam

ples

Basket 1

Basket 2

Basket 3

Basket 4Basket 5

Basket 6

2 (High)

1

0

-2 (Low)

-1

Hormone

Recepto

rs

IL/Inte

grin Alph

a

p63 Activ

ity

p53 Activ

ity

E2F Transcr

iption

Cyclin-B

Activity

MYC Transcr

iption

EGFR Signalin

g

VEGF Signalin

g

PI3K Path

way

NOTCH Signalin

g

Integrated PathwayActivity Level

1400

The results published here are in whole or part based upon data generated by The Cancer Genome Atlas project established by the NCI and NHGRI. Information about TCGA and the investigators and institutions who constitute the TCGA research network can be found at “http://cancergenome.nih.gov”.Kathryn Boorer, PhD of NantHealth, LLC provided writing assistance.

*Hazard ratio for OS cannot be determined because all patients with >2200 amol/μg HER2 are alive after 6 years of anti-HER2 therapy. NA = not available.aIn collaboration with Vall d’Hebron University Hospital.3

WGS Genomics: DNATransporter

Contraster

Predictive ProteomicsPARADIGM

Targeted ProteomicsOncoPlex

• HER2 gene amplified 8-fold due to insertion of HPV DNA into patient’s genome at chromosome 17• Patient treated with anti-HER2 therapy; disease stabilization for 1.5 years

RARAFYN

NTRK1PGR

TLR7ABCC1ALOX5

ARFLT1FLT3

MTORRET

AOX1ERBB2

KDRCSF1RDHFREGFRABL1BRAF Acute myeloid leukemia

BladderBladder (Denver)BreastCervicalColonGlioblastoma multiformeHead & neckKidney clear cellKidney papillary cellLow-grade gliomaLung adenocarcinomaLung squamousMelanomaOvarianProstrateRectalStomachThyroidUterine

Report

Report

DNA Sequencing, RNASeq, Pathway Analysis

Proteomics

• A number of drugs targeting tumor mutations are approved in cancer indications

Survi

val P

roba

bility

0.0

0.2

0.4

0.6

0.8

1.0

1 2 3 4 5 6 70Time (Years)

27 19 12 8 6 0 02840 29 22 13 7 2 040

Disease-Free Survival by HER2

Survi

val P

roba

bility

0.0

0.2

0.4

0.6

0.8

1.0

1 2 3 4 5 60Time (Years)

28 21 13 11 4 02840 25 21 11 5 140

Overall Survival by HER2a

Number at Risk

<2200≥2200

(amol/μg HER2)Number at Risk

<2200≥2200

(amol/μg HER2)

Protein Target11,322

Test ResultHCS-12-17127 (C0608)

150-26,000

Observed Range: PreclinicalExpression Levels

amol/μg of Tissue Protein

150

Assay Limit of Detection(LOD)

HER2

HPV Type 18 (Entire Genome, ~7kb)

Chromosome 17L1

Gene

Patient’s AlteredHPV Type 18

ERBB2L1

Copy NumberC

(~10 copies/tumor cell)

HR (95% CI) = 0.22 (0.06-0.81); p=0.013 HR (95% CI) = NA (NA); p=0.001