#498 Meta-Analysis of Genomic Aberrations Identified in CTCs and ctDNA in Triple Negative Breast CancerKellie Howard1, Sharon Austin1, Fang Yin Lo1, Arturo B. Ramirez2, Debbie Boles3, John Pruitt3, Elisabeth Mahen4, Heather Collins1, Amanda Leonti1, Lindsey Maassel1, Christopher Subia1, Tuuli Saloranta1, Nicole Christopherson1, Kerry Deutsch1, Jackie L. Stilwell2, Eric P. Kaldjian2, Michael Dorschner4, Sibel Blau4,5, Anthony Blau4, Marcia Eisenberg3, Steven Anderson6 and Anup Madan11Covance, Seattle, WA; 2RareCyte, Inc., Seattle, WA; 3Laboratory Corporation of America® Holdings, Research Triangle Park, NC; 4Center for Cancer Innovation, University of Washington, Seattle, WA; 5Northwest Medical Specialties, Puyallup, WA; 6Covance, Durham, NC

AbstractTechnological innovation and scientific advances in understanding cancer at the molecular level have accelerated the discovery and development of both diagnostics and therapeutics. Circulating tumor cells (CTCs) and plasma circulating tumor DNA (ctDNA) are non-invasive prognostic markers that have been associated with metastatic and aggressive disease. Both CTCs and ctDNA allow molecular characterization of a tumor that is inaccessible or too risky to biopsy. The analysis of genomic aberrations in both sample types provides insights into drug resistance and can help determine appropriate, targeted cancer treatments. Mutations found in the primary or metastatic tumor can be identified in both CTCs and ctDNA as well as novel mutations that may reflect intratumoral and intermetastatic heterogeneity. When collected and evaluated over an extended period of time, changes in the CTC and/or ctDNA mutational profile can offer guidance into the effectiveness of a treatment, indicate the progression of disease, and detect recurrence of disease earlier.

We have performed whole exome sequencing of CTCs and ctDNA from a metastatic triple negative breast cancer (TNBC) patient to better understand the evolution of tumor heterogeneity during therapy. The patient was enrolled in the Intensive Trial of OMics in Cancer clinical Trial (ITOMIC-001) and initially received weekly cisplatin infusions followed by additional targeted therapy. Longitudinal peripheral blood samples were collected over a period of 272 days following enrollment in the clinical trial. CTCs were identified using the AccuCyte-CyteFinder® system (RareCyte, Seattle, WA).

We used next generation sequencing and computational biology tools to analyze genomic DNA from multiple CTCs, white blood cells (WBCs) and ctDNA from various time points. We observed similar genomic aberrations in both CTCs and ctDNA that could be classified into three groups: a) a static group that remains unchanged during the course of therapy, b) a sample-specific group that is unique to each time point and c) an intermediate group that has variants that are short-lived but are present across multiple time points. Variants identified in the liquid biopsy samples were compared with variants observed in primary breast tumor, metastatic bone marrow tumor and publically available pan-cancer datasets. We then performed meta-analysis on somatic variants to identify changes in affected networks in response to therapy over time. Several key nodes were identified that could rationally have been targeted for therapy using compounds currently in clinical trials. We then compared and combined the perturbed networks obtained from the CTCs and ctDNA to better understand the etiology of TNBC. These studies represent the first step of a synergistic partnership between the genetic information obtained from the analysis of CTCs and ctDNA with innovative health care for patients with metastatic breast cancer.

Patient History▶ The patient was a 56-year-old woman with metastatic triple negative breast

cancer (TNBC).1

▶ In October 2013, she consented to enrollment in the Intensive Trial of OMics in Cancer clinical Trial (ITOMIC).2

▶ During the study period the patient underwent weekly chemotherapy treatments and her CTC/cfDNA were collected.

Presented at AACR 2016Covance is the drug development business of Laboratory Corporation of America Holdings (LabCorp). Content of this material was developed by scientists who at the time were affiliated with LabCorp Clinical Trials or Tandem Labs, now part of Covance.

Figure 1. Genomic analysis of CTCs and cfDNA from different time points. CTCs were regularly enumerated over the study period. CTCs were isolated using the AccuCyte–CyteFinder® system from RareCyte Inc., Seattle, WA and Whole Exome Sequencing (WES) was performed after Whole Genome Amplification (WGA). CTCs with available sequence data are indicated with arrows (n=6). Whole Genome Sequencing was also performed on cfDNA isolated from the plasma at the same time points.

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Figure  1.  Genomic  analysis  of  CTCs  and  cfDNA  from  different  time  points.  CTCs  were  regularly  enumerated  over  the  study  period.  CTCs  were  isolated  using  the  AccuCyte–CyteFinder   system from  RareCyte   Inc.,  Seattle,  WA  and  sequencing  was  performed  after  Whole  Genome  Amplification  (WGA).    CTCs  with  available   sequence  data  are  indicated  with  arrows  (n=6).    Whole  Genome  Sequencing  was  also  performed  on  cfDNA  isolated   from  the  plasma  at  the  same  time  points.

• The  patient  was  a  56-‐year-‐old  woman  with  metastatic   triple  negative  breast  cancer  (TNBC).1

• In  October  2013,  she  consented   to  enrollment   in  the  Intensive   Trial  of  OMics  in  Cancer  clinical  Trial  (ITOMIC).2

• During  the  study  period   the  patient  underwent  weekly  chemotherapy   treatments  and  her  CTC/cfDNA  were  collected.

Patient  History

Clinical  Site-Northwest  Medical  Specialties

Study  Enrollment  -Seattle  Cancer  Care  Alliance

CTC  Assessments-RareCyte

NGS  and  Data  Analysis-UW  and  Covance  Genomics  Lab

Figure 2. Using genomic tools for a better understanding of TNBC etiology.

Variant  Lists

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Variants  present  in  least  two  different  CTCs/cfDNA

Figure 3. Number of variants identified in various CTCs at various time points using bio-informatic filtering. Sequencing data was aligned against the hg19 reference sequence using bwa. Samtools were used to call variants.

Confidential  – For  Internal  Use  Only

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Figure  3.  Number  of  variants  identified  in  various  CTCs  at  various   time  points  using  bio-informatic   filtering  described. Sequencing   data  was  aligned  against  hg19   reference  using  bwa  and  samtools  were  used  to  call  variants.  

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Figure 4. Detection of variants across various time points from individual CTCs. Each column indicates a different CTC. The larger columns indicate time points for which there are multiple CTCs. Each row represents a mutation identified in the WES data. Those variants that are detected in cfDNA are represented by red (same time point) and blue (different time point). Variants that are shared across all time points are at the top, variants that evolve over time are at the bottom of the figure.

Confidential  – For  Internal  Use  Only

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6 91 167 200 216 258

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Figure 5. Identified variants occupy key nodal points of cancer associated pathways. Various cancer driver variants were mapped across known pathways using Ingenuity. The colors are described in the key to the left.

Figure  5.  Identified  variants  occupy  key  nodal  points  of  cancer  associated  pathways.  Various  cancer  driver  variants  were  mapped  across  known  pathways  using  Ingenuity.  The  colors  are  described   in  the  key  above.  

Table1. Top Three Ranked Common Pathways Associated

with Cancer Driver Variants Identified in Individual CTCs

DAY 6

CTC1 Protein Kinase A Signaling 1.64E-04CTC1 CDK5 Signaling 6.65E-03CTC1 IL-1 Signaling 6.82E-03CTC2 Protein Ubiquitination Pathway 4.39E-05CTC2 RAR Activation 1.51E-03CTC2 BMP Signaling Pathway 2.29E-02

CTC3 Wnt/β-catenin Signaling 2.27E-06CTC3 Breast Cancer Regulation by Stathmin1 3.70E-06CTC3 Cell Cycle Regulation by BTG Family Proteins 1.83E-05CTC4 IL-17 Signaling 7.80E-03CTC4 Role of Tissue Factors in Cancer 1.40E-02CTC4 Leukocyte Extravasation Signaling 2.10E-02CTC5 Cell Cycle Control of Chromosomal Replication 3.37E-03CTC5 STAT3 Pathway 7.88E-03CTC5 VEGF Family Ligand-Receptor Interactions 8.77E-03

DAY 91

CTC1 Regulation of the Epithelial-Mesenchymal Transition Pathway 5.04E-07CTC1 Colorectal Cancer Metastasis Signaling 1.78E-06

CTC1 NF-κB Signaling 4.27E-05CTC2 Molecular Mechanisms of Cancer 3.66E-06

CTC2 Wnt/β-catenin Signaling 4.15E-06CTC2 Renal Cell Carcinoma Signaling 5.79E-06

CTC3 NF-κB Signaling 8.38E-06CTC3 EGF Signaling 5.24E-04CTC3 FGF Signaling 9.57E-04CTC4 Protein Kinase A Signaling 5.34E-05CTC4 Role of Oct4 in Mammakuan Embyonic Stem Cell Pluripotency 1.91E-03CTC4 Myc Mediated Apoptosis Signaling 1.09E-03

DAY 167

CTC1 PTEN Signaling 1.45E-05CTC1 PI3K/AKT Signaling 2.13E-05CTC1 Non-Samll Cell Lung Cancer Signaling 1.21E-04CTC2 Cell Cycle: G2/M DNA Damage Checkpoint Regulation 3.38E-04CTC2 Protein Kinase A Signaling 9.05E-04CTC2 p53 Signaling 1.13E-03CTC3 Protein Kinase A Signaling 6.48E-09CTC3 Colorectal Cancer Metastasis Signaling 7.46E-07CTC3 FAK Signaling 1.26E-06CTC4 STAT3 Pathway 1.30E-04CTC4 PDGF Signaling 2.06E-04CTC4 Colorectal Cancer Metastasis Signaling 1.58E-03CTC5 Role of BRCA1 in DNA Damage Response 7.10E-04CTC5 DNA Methylation and Transcriptional Repression Signaling 1.01E-02CTC5 EGF Signaling 3.03E-02CTC6 STAT3 Pathway 5.31E-04CTC6 HER-2 Signaling in Breast Cancer 6.97E-04CTC6 PI3K/AKT Signaling 2.68E-03

DAY 200

CTC1 ErbB2-ErbB3 Signaling 1.19E-04CTC1 Cell Cycle: G1/S Checkpoint Regulation 1.54E-04CTC1 Bladder Cancer Signaling 3.69E-04CTC3 IL-17 Signaling 1.55E-02CTC3 Renal Cell Carcinoma Signaling 1.71E-02CTC3 PDGF Signaling 1.97E-02CTC4 Hypoxia Signaling in the Cardiovascular System 8.77E-03CTC4 ERK5 Signaling 9.13E-03CTC4 ATM Signaling 9.86E-03CTC5 Crosstalk between Dendritic Cells and Natural Killer Cells 4.39E-03CTC5 Natural Killer Cell Signaling 4.76E-03CTC5 Graft-versus Host Disease Signaling 4.93E-03

DAY 216

CTC1 TNFR1 Signaling 0.00293CTC1 Renal Cell Carcinoma Signaling 0.004304CTC1 ErbB Signaling 0.00488CTC2 G-Protein Coupled Receptor Signaling 4.96E-04CTC2 DNA Double-Strand Break Repair by Homologous Recombination 2.11E-03CTC2 DNA Methylation and Transcriptional Repression Signaling 2.89E-03CTC3 Protein Kinase A Signaling 5.42E-03CTC3 Transcriptional Regulatory Network in Embryonic Stem Cells 1.28E-02CTC3 IL-17 Signaling 2.03E-02

DAY 258

CTC1 Glutathione-mediated Detoxification 3.77E-05CTC1 Chemokine Signaling 1.74E-02CTC1 Role of BRCA1 in DNA Damage Response 1.87E-02CTC2 p70S6K Signaling 1.24E-08CTC2 PI3K Signaling in B Lymphocytes 2.68E-08CTC2 Protein Kinase A Signaling 3.60E-06CTC3 Ceramide Signaling 4.83E-05CTC3 UVC-Induced MAPK Signaling 1.07E-02CTC3 NGF Signaling 1.44E-02CTC4 Protein Kinase A Signaling 6.10E-13CTC4 Cdc42 Signaling 1.83E-05CTC4 Pancreatic Adenocarcinoma Signaling 2.53E-05CTC5 Hepatic Fibrosis / Hepatic Stellate Cell Activation 9.01E-11CTC5 PTEN Signaling 3.50E-10CTC5 G-Protein Coupled Receptor Signaling 1.77E-08

p-values are shown in the column along with enriched pathways. Investigations of pathways perturbed in individual CTCs shed light on tumor heterogeneity and help better understand tumor etiology. 1 Blau et al. A Distributed Network for Intensive Longitudinal Monitoring in Metastatic Triple Negative Breast Cancer.

J. Natl. Compr. Canc Network 2016; 14(1):8-17;

2 ITOMIC-001; ClinicalTrials.gov ID NCT01957514