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© 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