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#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
Confidence Filter
Common Variant Filter
Predicted Deleterious
Genetic Analysis
Biological Context
Pathway Analysis
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Cancer Driver Variants based on literature
Whole genomes projects/WBCs
Greater than Quality score of 20 and at least 10X coverage
Pathogenic/SIFT/PolyPhen-‐2
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.
Days
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
Days
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