Personalized cancer medicine involves identifying clinically actionable cancer mutations and genomic aberrations in any given tumor. As we recently published in the November 2011 issue of Nature Biotechnology, Oligonucleotide-Selective Sequencing (OS-Seq) is a novel targeted resequencing approach that fundamentally improves the detection of cancer mutations from clinical samples. This technology has the potential for enabling the rapid, accurate detection of cancer mutations for both translational research studies and potentially, "personalized" cancer diagnostics. OS-Seq provides a number of advantages to make personalized cancer analysis accessible, rapid, robust and accurate. The overall workflow is simplified such that the majority of preparative steps take place on a standard fluidics device and the actual experimental manipulation is limited. The performance is improved compared to the current commercially available methods for targeted, gene-specific cancer sequencing analysis. Based on our empirical analysis and subsequent refinements in designing capture probes, we demonstrate very specific capture of genomic targets with less variance than other methods. We achieve a high level of sequencing coverage on our targets that permit sensitive and specific detection of cancer mutations. With recent improvements in sequencing technology speed, OS-Seq can potentially be adapted to analyze large number of cancer genes in a matter of days which includes the time that genomic DNA is extracted from a biopsy to the completion of the targeted sequencing run. This holds the possibility of rapidly identifying cancer mutations from clinical samples. Our proposal is focused on development of the OS-Seq technology for identifying cancer mutations, rearrangements, copy number alterations and potential cancer-related infectious agents from clinical tumor samples. To achieve this goal, we will develop key aspects of OS-Seq technology for integrated detection of cancer mutations and genomic aberrations with simple protocols that are reliable, rapid and with high accuracy.

Public Health Relevance

Personalized cancer medicine involves assessing an individual patient's tumor for mutation and genomic aberrations and subsequently using this invaluable genetic data to determine appropriate therapy. There have been isolated successes as of late in the endeavor of personalized medicine based on cancer genetics. To insure that all cancer patients have access and benefit from this new clinical paradigm we propose developing an innovative and potentially disruptive technology that enables personalized genetic analysis of their tumors in a rapid, robust and accurate fashion with potential for diagnostic application.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Exploratory/Developmental Grants Phase II (R33)
Project #
1R33CA174575-01
Application #
8472073
Study Section
Special Emphasis Panel (ZCA1-SRLB-J (J1))
Program Officer
Divi, Rao L
Project Start
2013-05-01
Project End
2016-04-30
Budget Start
2013-05-01
Budget End
2014-04-30
Support Year
1
Fiscal Year
2013
Total Cost
$358,520
Indirect Cost
$112,083
Name
Stanford University
Department
Internal Medicine/Medicine
Type
Schools of Medicine
DUNS #
009214214
City
Stanford
State
CA
Country
United States
Zip Code
94305
Hopmans, Erik S; Natsoulis, Georges; Bell, John M et al. (2014) A programmable method for massively parallel targeted sequencing. Nucleic Acids Res 42:e88