Recurrent gene fusions and internal tandem duplications are among the most tumor-specific molecular markers known and can provide the potential for therapeutic targets. With a few notable exceptions, however, relatively common recurrent gene fusions have not been identified in commonly occurring carcinomas, which often have multiple, complex chromosomal rearrangements that are difficult to analyze by traditional cytogenetic approaches. Complex tumor karyotpes make it difficult to identify gene fusions using cytogenetics, but suggest the possibility that recurrent rearrangements producing fusions or internal tandem duplications (ITDs) may be prevalent. This proposal aims to use deep sequencing and the novel analytic techniques described to study aspects of the serous ovarian cancer genome and transcriptome which have remained hidden due to limitations in technology or analytical methods, and to test intra- individual and inter-individual selective pressures on tumors. The aspects of this proposal are as follows 1) to further investigate the extent of gene rearrangements in ovarian cancer, focusing on discovering local rearrangements transcribed into RNA;2) to determine the composition of a group of novel circular transcripts that I have recently found to be expressed at relatively high levels in normal and pathogenic human cells;3) to characterize double minutes in ovarian cancer, combining bioinformatics to determine rearrangements in their sequence composition and statistical analysis to determine evolutionary pressures on their composition exerted by the tumors. The applicant has a track-record of success in discovering novel gene fusions with ultra-high throughput sequencing (the ESRRA-C11orf20 fusion), as well as designing original rigorous statistical and bioinformatic methods for ultra high throughput data. Under the mentorship of Dr. Patrick O. Brown, a pioneer in high throughput genomic technologies and statistical methods for analyzing them, the applicant will continue career development and training.
The first aim of this project will be performed during the mentoring phase, and experiments for aims 2 and 3 will be piloted. The K99/R00 award will support the applicant in her development into an independent investigator who combines statistical and experimental approaches to study cancer genetics.

Public Health Relevance

Ovarian cancer is estimated to kill more than 140,000 women every year and has a poor prognosis once it presents with clinical symptoms. Discovery of truly tumor- specific molecular markers or identification of early and selected amplifications may be essential for effective early detection of serous tumors, which account for the majority of ovarian cancer deaths. While this proposal is focused on ovarian cancer, the methods are applicable to any cancer, and thus have broad significance. The consolidating theme of this proposal is to use deep sequencing and the novel analytic techniques described to study aspects of the serous ovarian cancer genome and transcriptome which have remained hidden due to limitations in technology or analytical methods. The experimental and analytical methods developed will be applicable to all tumor types and hence of broad relevance to the study of cancer.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Career Transition Award (K99)
Project #
1K99CA168987-01
Application #
8354071
Study Section
Subcommittee G - Education (NCI)
Program Officer
Schmidt, Michael K
Project Start
2012-09-01
Project End
2013-12-31
Budget Start
2012-09-01
Budget End
2013-12-31
Support Year
1
Fiscal Year
2012
Total Cost
$110,132
Indirect Cost
$8,158
Name
Stanford University
Department
Biochemistry
Type
Schools of Medicine
DUNS #
009214214
City
Stanford
State
CA
Country
United States
Zip Code
94305
Wang, Peter L; Bao, Yun; Yee, Muh-Ching et al. (2014) Circular RNA is expressed across the eukaryotic tree of life. PLoS One 9:e90859
Salzman, Julia; Chen, Raymond E; Olsen, Mari N et al. (2013) Cell-type specific features of circular RNA expression. PLoS Genet 9:e1003777