Genomic Characterization of Cancer-Adjacent …...Genomic Characterization of Cancer-Adjacent...
Transcript of Genomic Characterization of Cancer-Adjacent …...Genomic Characterization of Cancer-Adjacent...
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Genomic Characterization of Cancer-Adjacent Breast: Evidence of field effects and expression subtypes
Melissa Troester, PhD, MPH
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What Predicts Breast Cancer Recurrence?
• Recurrence rates are higher for breast conserving therapy.
• Local recurrence commonly occurs in the lumpectomy bed.
• Local recurrence rates are higher among basal-like breast cancers.
Veronesi et al. (2002) NEJM, 347(16): 1227.
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Field carcinogenic events
• Slaughter et al. (1953) observed abnormal tissue surrounding oral squamous cell carcinoma – Field cancerization explains the development of
multiple primaries and local recurrences.
patch field cancer
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How does cancer-adjacent tissue respond to tumor?
• Response to wounding
• Stress response
• Immune response
• Angiogenesis
• Extracellular matrix
• Chemotaxis
cancer-adjacent reduction mammoplasty
Troester et al. (2009) Clin Cancer Res
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RNAseq, microarray
M. Troester, K. Hoadley, M. D’Arcy, UNC
Copy Number Alterations
A. Cherniack, Broad
Exome Seq D. Koboldt, L. Ding, WashU
Methylation H. Shen, S. Mahurkar,
P. Laird, USC
microRNASeq G. Robertson, BCCA
RNA and DNA from 40 triplets: blood <- normal breast -> tumor
40+ tumor-normal pairs normal -> tumor
Double Normal Breast Committee Chair: Melissa Troester, UNC
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RNAseq, microarray
M. Troester, K. Hoadley, M. D’Arcy, UNC
Copy Number Alterations
A. Cherniack, Broad
Exome Seq D. Koboldt, L. Ding, WashU
Methylation H. Shen, S. Mahurkar,
P. Laird, USC
microRNASeq G. Robertson, BCCA
Q1. Detectable field effects?
Q2. Detectable tumor cells?
Double Normal Breast Committee Chair: Melissa Troester, UNC
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Tumor-like copy number alterations
Chromosome 8
Chromosome 17
Erbb2
Focal peak in chromosome 10 is also seen in normal.
MYC
Normal Tumor
Normal Tumor
Courtesy of Andy Cherniack
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Tumor-like copy number alterations
7% with ‘field effect’ OR tumor contamination
Basal-like LumB LumA
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Courtesy of Dan Koboldt/Li Ding
10 cases (25%) had strong evidence of field effect (many mutations with VAF >=2% in the adjacent normal).
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Tumor-like mutations
7% with ‘field effect’ OR tumor contamination 25% with ‘field effect’ OR tumor contamination
CN
Basal-like LumB LumA
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HM27
Exp Str Epi
21 Tumors 21 Adjacent Normals
PathAveEpi (Epi): 0 - 15
PathAveStroma (Str): 0 - 100
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Active Inactive Expression signature (Exp)
Courtesy of Swapna Mahurkar
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HM450 22 Tumors 22 Adjacent Normals
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Active Inactive
Exp Str Epi
PathAveEpi (Epi): 0 - 15
PathAveStroma (Str): 0 - 100
Expression signature (Exp)
Courtesy of Swapna Mahurkar
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7% with ‘field effect’ OR tumor contamination 25% with ‘field effect’ OR tumor contamination 7-10% with ‘field effect’ OR tumor contamination
CN
Exome-Seq
Tumor-like methylation patterns
Basal-like LumB LumA
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RNAseq, microarray
M. Troester, K. Hoadley, M. D’Arcy, UNC
Copy Number Alterations
A. Cherniack, Broad
Exome Seq D. Koboldt, L. Ding, WashU
Methylation H. Shen, S. Mahurkar,
P. Laird, USC
microRNASeq G. Robertson, BCCA
Q1. Detectable field effects?
Q2. Detectable tumor cells?
Double Normal Breast Committee Chair: Melissa Troester, UNC
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DNA data types: Comparison & Validation
A ‘positive control’ – all three DNA platforms detected the sample with tumor contamination
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Histopathologic Assessment
SCORING: Pathology (tumor, benign) Immune infiltrations Percent Composition: e.g. 30% Stroma 63% Adipose 7% Epithelium
Melissa Troester, UNC Rupninder Sandhu, UNC Andy Beck, Harvard Nicole Johnson, Harvard Kim Allison, U of Wash
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Methylation Reflecting Composition
• Epithelial Content on HM450 platform (qvalue<0.05).
– 13000 probes were positively correlated
– 12500 probes were negatively correlated
• Stromal Content on HM450 platform (qvalue<0.05):
– 5700 probes were positively correlated
– 2300 probes were negatively correlated
• Correlation composition and DNA methylation on 27k was weak. This needs further investigation.
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RNAseq, microarray
M. Troester, K. Hoadley, M. D’Arcy, UNC
Copy Number Alterations
A. Cherniack, Broad
Exome Seq D. Koboldt, L. Ding, WashU
Methylation H. Shen, S. Mahurkar,
P. Laird, USC
microRNASeq G. Robertson, BCCA
Q1. Detectable field effects? Normal vs. blood Normal vs. tumor
Double Normal Breast Committee Chair: Melissa Troester, UNC
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RNAseq, microarray
M. Troester, K. Hoadley, M. D’Arcy, UNC
Copy Number Alterations
A. Cherniack, Broad
Exome Seq D. Koboldt, L. Ding, WashU
Methylation H. Shen, S. Mahurkar,
P. Laird, USC
microRNASeq G. Robertson, BCCA
Q2. Detectable tumor cells?
Q3. Other heterogeneity?
Double Normal Breast Committee Chair: Melissa Troester, UNC
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decreased cell adhesion differentiation cell-cell contact
increased cell movement inflammation fibrosis chemotaxis
Active Inactive
Two Subtypes of Cancer-Adjacent Tissue
Roman-Perez et al. (2012) Breast Cancer Res
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Cancer-Adjacent Subtype vs. Tumor Subtype
ER status Tumor subtype
Active microenvironment occurs in all tumor subtypes
Active Inactive
Roman-Perez et al. (2012) Breast Cancer Res
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Active Microenvironment Predicts Survival
Roman-Perez et al. (2012) Breast Cancer Res
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• RNA expression clusters – Two main clusters by microRNA-seq – Two main clusters by RNA-seq
• RNA and miRNA concordance
• Tumor characteristics (ER status, intrinsic subtype, etc.) not strongly associated with main clusters
• ‘Probable contamination’ samples not readily detected.
mRNA and microRNA subtypes
Courtesy of Gordon Robertson
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RNA Expression Subtype vs. Composition %
Ad
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Active Inactive Active Inactive Active Inactive
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Conclusions & Future Directions
• DNA shows field effects/tumor contamination RNA identifies expression subtypes
• Distinguishing field effects vs. tumor cells
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Acknowledgments
UNC: Melissa Troester, Katie Hoadley, Monica D’Arcy, Rupan Sandhu, Chuck Perou
Buck Institute: Christopher Benz, Christina Yau Broad: Andrew Cherniack Wash U: Dan Koboldt, Li Ding BCGSC: Gordon Robertson USC: Peter Laird, Swapna Mahurkar, Hui Shen Harvard: Andy Beck, Nicole Johnson U of Washington: Kim Allison NCI: Margi Sheth, Jay Bowen, Kenna Shaw