Download Meta-Analysis of Genomic Aberrations Identified in CTCs

#498 Meta-Analysis of Genomic Aberrations Identified in CTCs and ctDNA in Triple Negative Breast Cancer
Kellie 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 Madan1
1Covance, 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
Abstract
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.
Nucleated CTCs (per mL)
Technological 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.
Table1. Top Three Ranked Common Pathways Associated
10000
In October 2013, she consented to enrollment in the Intensive Trial of OMics in
▶(TNBC).
Cancer
clinical
(ITOMIC).2 to enrollment in the Intensive Trial of OMics in • In October 2013, Trial
she consented 1
Cancer clinical Trial (ITOMIC).2
•
▶ During the study period the patient underwent weekly chemotherapy treatments
and her CTC/cfDNA
were
collected.
During the study period the patient underwent weekly chemotherapy treatments and her CTC/cfDNA were collected.
Clinical Site
-­Northwest M edical Specialties
Study Enrollment -­Seattle Cancer Care Alliance
CTC Assessments
-­RareCyte
NGS and Data Analysis
-­UW and Covance Genomics Lab
Need new figure
7000
6000
5000
4000
3000
2000
0
DAY 6
-­10
40
90
140
190
240
290
Study Day
Figure 1. G enomic analysis of CTCs and cfDNA from different time points. CTCs were regularly enumerated over period. CTCs were isolated sing the Aand
ccuCyte–CyteFinder system
from RareCyte Inc., Seattle, WA and Figurethe 1.study Genomic
analysis
ofuCTCs
cfDNA from
different
time
points.
sequencing was performed after Whole Genome Amplification (WGA). CTCs with available sequence data are CTCs indicated were regularly
overSequencing the study
CTCs
isolated
with arrows enumerated
(n=6). Whole Genome was period.
also performed on cwere
fDNA isolated from using
the plasma at the same time points. ® system from RareCyte Inc., Seattle, WA and Whole
the AccuCyte–CyteFinder
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.
Variant Lists
Greater than Quality score of 20 and at least 10X coverage
Common Variant Filter
6
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
Presented at AACR 2016
167
200
216
258
Days
DAY 91
Whole genomes projects/WBCs
DAY 167
Predicted Deleterious
Pathogenic/SIFT/PolyPhen-­‐2
Genetic Analysis
Cancer Driver Variants based on literature
Biological Context
Pathway Analysis
s
Figure 2. Using genomic tools for a better understanding of TNBC etiology.
350
DAY 200
Deleterious
Cancer Driver
300
250
DAY 216
200
150
100
50
0
6
91
167
200
Days
216
258
DAY 258
Figure 3. Number of variants identified in various CTCs at various time points Figure 3.
Number of variants
identified
in various
CTCsdata was aligned at various timeagainst points
using bio-­informatic filtering described.
Sequencing using bio-informatic
filtering. Sequencing
data was alignedvagainst
hg19 reference using bwa and samtools were used to call ariants. the hg19
reference sequence using bwa. Samtools were used to call variants.
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.
Confidential – For I nternal Use Only
1
91
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
Confidential – For I nternal Use Only
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.
Variants present in least two different CTCs/cfDNA
Confidence Filter
Number of Variants
•
8000
1000
Patient History
Patient History
▶ The patient was a 56-year-old woman with metastatic triple negative breast
cancer (TNBC).1 56-­‐year-­‐old woman with metastatic triple negative breast cancer The patient was a with Cancer Driver Variants Identified in Individual CTCs
9000
Protein Kinase A Signaling
CDK5 Signaling
IL-1 Signaling
Protein Ubiquitination Pathway
RAR Activation
BMP Signaling Pathway
Wnt/β-catenin Signaling
Breast Cancer Regulation by Stathmin1
Cell Cycle Regulation by BTG Family Proteins
IL-17 Signaling
Role of Tissue Factors in Cancer
Leukocyte Extravasation Signaling
Cell Cycle Control of Chromosomal Replication
STAT3 Pathway
VEGF Family Ligand-Receptor Interactions
Regulation of the Epithelial-Mesenchymal Transition Pathway
Colorectal Cancer Metastasis Signaling
NF-κB Signaling
Molecular Mechanisms of Cancer
Wnt/β-catenin Signaling
Renal Cell Carcinoma Signaling
NF-κB Signaling
EGF Signaling
FGF Signaling
Protein Kinase A Signaling
Role of Oct4 in Mammakuan Embyonic Stem Cell Pluripotency
Myc Mediated Apoptosis Signaling
PTEN Signaling
PI3K/AKT Signaling
Non-Samll Cell Lung Cancer Signaling
Cell Cycle: G2/M DNA Damage Checkpoint Regulation
Protein Kinase A Signaling
p53 Signaling
Protein Kinase A Signaling
Colorectal Cancer Metastasis Signaling
FAK Signaling
STAT3 Pathway
PDGF Signaling
Colorectal Cancer Metastasis Signaling
Role of BRCA1 in DNA Damage Response
DNA Methylation and Transcriptional Repression Signaling
EGF Signaling
STAT3 Pathway
HER-2 Signaling in Breast Cancer
PI3K/AKT Signaling
ErbB2-ErbB3 Signaling
Cell Cycle: G1/S Checkpoint Regulation
Bladder Cancer Signaling
IL-17 Signaling
Renal Cell Carcinoma Signaling
PDGF Signaling
Hypoxia Signaling in the Cardiovascular System
ERK5 Signaling
ATM Signaling
Crosstalk between Dendritic Cells and Natural Killer Cells
Natural Killer Cell Signaling
Graft-versus Host Disease Signaling
TNFR1 Signaling
Renal Cell Carcinoma Signaling
ErbB Signaling
G-Protein Coupled Receptor Signaling
DNA Double-Strand Break Repair by Homologous Recombination
DNA Methylation and Transcriptional Repression Signaling
Protein Kinase A Signaling
Transcriptional Regulatory Network in Embryonic Stem Cells
IL-17 Signaling
Glutathione-mediated Detoxification
Chemokine Signaling
Role of BRCA1 in DNA Damage Response
p70S6K Signaling
PI3K Signaling in B Lymphocytes
Protein Kinase A Signaling
Ceramide Signaling
UVC-Induced MAPK Signaling
NGF Signaling
Protein Kinase A Signaling
Cdc42 Signaling
Pancreatic Adenocarcinoma Signaling
Hepatic Fibrosis / Hepatic Stellate Cell Activation
PTEN Signaling
G-Protein Coupled Receptor Signaling
1.64E-04
6.65E-03
6.82E-03
4.39E-05
1.51E-03
2.29E-02
2.27E-06
3.70E-06
1.83E-05
7.80E-03
1.40E-02
2.10E-02
3.37E-03
7.88E-03
8.77E-03
5.04E-07
1.78E-06
4.27E-05
3.66E-06
4.15E-06
5.79E-06
8.38E-06
5.24E-04
9.57E-04
5.34E-05
1.91E-03
1.09E-03
1.45E-05
2.13E-05
1.21E-04
3.38E-04
9.05E-04
1.13E-03
6.48E-09
7.46E-07
1.26E-06
1.30E-04
2.06E-04
1.58E-03
7.10E-04
1.01E-02
3.03E-02
5.31E-04
6.97E-04
2.68E-03
1.19E-04
1.54E-04
3.69E-04
1.55E-02
1.71E-02
1.97E-02
8.77E-03
9.13E-03
9.86E-03
4.39E-03
4.76E-03
4.93E-03
0.00293
0.004304
0.00488
4.96E-04
2.11E-03
2.89E-03
5.42E-03
1.28E-02
2.03E-02
3.77E-05
1.74E-02
1.87E-02
1.24E-08
2.68E-08
3.60E-06
4.83E-05
1.07E-02
1.44E-02
6.10E-13
1.83E-05
2.53E-05
9.01E-11
3.50E-10
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.
Covance 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 5. Identified variants occupy key nodal points of cancer associated pathways. Various cancer driver CTC1
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