Chemotherapy is successful in some patients, but it has been difficult to predict which individual patients will benefit. We propose that tumor diversity levels can predict therapeutic responses because more diverse tumors more likely contain the rare pre-existing resistant variant cells commonly thought to be responsible for recurrence (Goldie-Coldman hypothesis). A cancer may be initially homogeneous and sensitive to chemotherapy, but with time becomes polymorphic and more likely to acquire resistant variant cells. There are currently no methods that quantify cancer diversity, and we propose to translate well-established population genetics approaches to measure Stage III colorectal cancer diversity. Because somatic mutations are relatively rare in human cancers, more easily detected epigenetic DNA methylation pattern variation at neutral CpG rich loci (""""""""passenger methylation"""""""") will be measured. By sampling multiple epialleles from different parts of the same cancer, tumor diversity can be quantified using pairwise distances that compare methylation status at homologous CpG sites. More diverse cancers should have more heterogeneous passenger methylation patterns and greater average pairwise distances. Because population geneticists seldom rely on a single gene to quantify diversity, we propose to develop a set of ten different passenger methylation loci. The average diversity of 50 Stage III colorectal cancers will be measured at multiple passenger loci to retrospectively test whether higher diversity levels correlate with recurrence. Diversity levels may predict which tumors more likely contain pre-existing resistant variant cells and therefore identify individual patients more likely to remain in remission after chemotherapy.

Public Health Relevance

Pre-existing resistant variant cells are thought to be responsible for relapse after chemotherapy - more diverse tumors are more likely to contain chemoresistant variant cells. We propose to develop a method to quantify tumor diversity to test whether higher diversity is a biomarker for poorer outcomes. Such a diversity biomarker may better predict which patients would more likely benefit from chemotherapy.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Exploratory/Developmental Grants (R21)
Project #
1R21CA149990-01
Application #
7874806
Study Section
Cancer Biomarkers Study Section (CBSS)
Program Officer
Thurin, Magdalena
Project Start
2010-04-01
Project End
2012-03-31
Budget Start
2010-04-01
Budget End
2011-03-31
Support Year
1
Fiscal Year
2010
Total Cost
$211,737
Indirect Cost
Name
University of Southern California
Department
Pathology
Type
Schools of Medicine
DUNS #
072933393
City
Los Angeles
State
CA
Country
United States
Zip Code
90089
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Zhao, Junsong; Siegmund, Kimberly D; Shibata, Darryl et al. (2014) Ancestral inference in tumors: how much can we know? J Theor Biol 359:136-45
Kang, Haeyoun; Shibata, Darryl (2013) Direct measurements of human colon crypt stem cell niche genetic fidelity: the role of chance in non-darwinian mutation selection. Front Oncol 3:264
Kreso, Antonija; O'Brien, Catherine A; van Galen, Peter et al. (2013) Variable clonal repopulation dynamics influence chemotherapy response in colorectal cancer. Science 339:543-8
Shibata, Darryl (2012) Cancer. Heterogeneity and tumor history. Science 336:304-5
Shibata, Darryl (2011) Molecular tumor clocks to study the evolution of drug resistance. Mol Pharm 8:2050-4
Siegmund, Kimberly D; Marjoram, Paul; Tavaré, Simon et al. (2011) High DNA methylation pattern intratumoral diversity implies weak selection in many human colorectal cancers. PLoS One 6:e21657
Shibata, Darryl (2011) Mutation and epigenetic molecular clocks in cancer. Carcinogenesis 32:123-8
Hong, You Jin; Marjoram, Paul; Shibata, Darryl et al. (2010) Using DNA methylation patterns to infer tumor ancestry. PLoS One 5:e12002
Calabrese, Peter; Shibata, Darryl (2010) A simple algebraic cancer equation: calculating how cancers may arise with normal mutation rates. BMC Cancer 10:3

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