Tumor Diversity As A Biomarker For Colorectal Cancer

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DESCRIPTION (provided by applicant): 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.