© 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 PARADIGM 1 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 Contraster 2 – 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 5 th 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-Shiong 1,4 1 NantWorks, LLC, Culver City, CA; 2 NantOmics, LLC, Santa Cruz, CA; 3 Oncoplex Diagnostics, Rockville, MD; 4 CSS Institute of Molecular Medicine, Culver City, CA Methods Background Conclusions Acknowledgment References 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% 300 250 200 150 100 50 0 0% 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 Thyroid Uterine 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 Thyroid Uterine Number of Highly Expressed Mutations (DNA and RNA) Percentage of Alterations (DNA and/or RNA) Number of Low Expressed Mutations 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 20 86 1 4 42 297 50 5 633 912 1413 7 425 5 50 110 104 73 1 1 62 344 56 6 42 363 222 29 102 106 251 209 33 DNA alteration High RNA expression 4.6% No DNA alteration High RNA expression 26% DNA alteration Normal/low RNA expression 69% Gene Panel Analysis Panomics Case Study: End-Stage Cervical Cancer Bladder Breast Ductal Breast Lobular Colon Glioblastoma Head & Neck Kidney Clear Cell Lung Adeno. Lung Squamous Ovarian Rectal Uterine Low Grade Glioma Prostate Melanoma Thyroid Basket 1 “Basal”-like Basket 2 Hormone Receptor+ Basket 3 Cyclin-B Low Basket 4 PI3K Low Basket 5 Squamous Basket 6 Brain 0 200 400 600 800 1000 1200 Number of Samples Basket 1 Basket 2 Basket 3 Basket 4 Basket 5 Basket 6 2 (High) 1 0 -2 (Low) -1 Hormone Receptors IL/Integrin Alpha p63 Activity p53 Activity E2F Transcription Cyclin-B Activity MYC Transcription EGFR Signaling VEGF Signaling PI3K Pathway NOTCH Signaling Integrated Pathway Activity 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. a In collaboration with Vall d’Hebron University Hospital. 3 WGS Genomics: DNA Transporter Contraster Predictive Proteomics PARADIGM Targeted Proteomics OncoPlex • 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 RARA FYN NTRK1 PGR TLR7 ABCC1 ALOX5 AR FLT1 FLT3 MTOR RET AOX1 ERBB2 KDR CSF1R DHFR EGFR ABL1 BRAF Acute myeloid leukemia Bladder Bladder (Denver) Breast Cervical Colon Glioblastoma multiforme Head & neck Kidney clear cell Kidney papillary cell Low-grade glioma Lung adenocarcinoma Lung squamous Melanoma Ovarian Prostrate Rectal Stomach Thyroid Uterine Report Report DNA Sequencing, RNASeq, Pathway Analysis Proteomics • A number of drugs targeting tumor mutations are approved in cancer indications Survival Probability 0.0 0.2 0.4 0.6 0.8 1.0 1 2 3 4 5 6 7 0 Time (Years) 27 19 12 8 6 0 0 28 40 29 22 13 7 2 0 40 Disease-Free Survival by HER2 Survival Probability 0.0 0.2 0.4 0.6 0.8 1.0 1 2 3 4 5 6 0 Time (Years) 28 21 13 11 4 0 28 40 25 21 11 5 1 40 Overall Survival by HER2 a Number at Risk <2200 ≥2200 (amol/μg HER2) Number at Risk <2200 ≥2200 (amol/μg HER2) Protein Target 11,322 Test Result HCS-12-17127 (C0608) 150-26,000 Observed Range: Preclinical Expression Levels amol/μg of Tissue Protein 150 Assay Limit of Detection (LOD) HER2 HPV Type 18 (Entire Genome, ~7kb) Chromosome 17 L1 Gene Patient’s Altered HPV Type 18 ERBB2 L1 Copy Number C (~10 copies/tumor cell) HR (95% CI) = 0.22 (0.06-0.81); p=0.013 HR (95% CI) = NA (NA); p=0.001