This proposed CTDD program will help translate the enormous resource of high- throughput cancer genome characterizations into functionally validated, cancer-genotype based therapeutic targets. Over the past several years, members of this project have developed powerful tools and strategies for functionally annotating cancer genomes. These have enabled the identification and validation over fifty cancer genes, several of which are compelling therapeutic targets. Here, we propose to synthesize and optimize those strategies into a unified blueprint that can be applied across many tumor types. At the core of our philosophy is the use of powerful computational tools to narrow the extent of the genome that must be surveyed functionally. This enables the use of more precise human and mouse models to assess drivers and dependencies and approaches to combinatorial interactions that could not be carried out on a genome wide scale. We will apply these tools to select cancer types and gene sets in order to identify new oncogenic drivers, genotype-specific cancer dependencies, and to test strategies for the systematic identification of targets for combination therapies. We envision the impact of our project not only as the identification of genomically informed targets for several tumor types but also as providing tools and strategies that can be used throughout the consortium and the community.

Public Health Relevance

Technological breakthroughs in genomics have enabled a comprehensive characterization of the genetic abnormalities in many human tumor types. What is needed now are equally powerful methods for translating these genomic characterizations into functionally validated cancer therapeutic targets and ultimately new treatments. We propose to synthesize and optimize informatics, functional, and in vivo strategies into a unified blueprint that can be applied across many tumor types.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project--Cooperative Agreements (U01)
Project #
3U01CA168409-01S1
Application #
8593329
Study Section
Special Emphasis Panel (ZCA1-SRLB-V (J1))
Program Officer
Gerhard, Daniela
Project Start
2012-05-01
Project End
2017-04-30
Budget Start
2012-05-01
Budget End
2013-04-30
Support Year
1
Fiscal Year
2013
Total Cost
$250,000
Indirect Cost
$36,893
Name
Cold Spring Harbor Laboratory
Department
Type
DUNS #
065968786
City
Cold Spring Harbor
State
NY
Country
United States
Zip Code
11724
Tiriac, Herve; Bucobo, Juan Carlos; Tzimas, Demetrios et al. (2018) Successful creation of pancreatic cancer organoids by means of EUS-guided fine-needle biopsy sampling for personalized cancer treatment. Gastrointest Endosc 87:1474-1480
Tiriac, Hervé; Belleau, Pascal; Engle, Dannielle D et al. (2018) Organoid Profiling Identifies Common Responders to Chemotherapy in Pancreatic Cancer. Cancer Discov 8:1112-1129
Senturk, Serif; Shirole, Nitin H; Nowak, Dawid G et al. (2017) Rapid and tunable method to temporally control gene editing based on conditional Cas9 stabilization. Nat Commun 8:14370
Öhlund, Daniel; Handly-Santana, Abram; Biffi, Giulia et al. (2017) Distinct populations of inflammatory fibroblasts and myofibroblasts in pancreatic cancer. J Exp Med 214:579-596
Roe, Jae-Seok; Hwang, Chang-Il; Somerville, Tim D D et al. (2017) Enhancer Reprogramming Promotes Pancreatic Cancer Metastasis. Cell 170:875-888.e20
Chio, Iok In Christine; Tuveson, David A (2017) ROS in Cancer: The Burning Question. Trends Mol Med 23:411-429
Feigin, Michael E; Garvin, Tyler; Bailey, Peter et al. (2017) Recurrent noncoding regulatory mutations in pancreatic ductal adenocarcinoma. Nat Genet 49:825-833
Fang, Han; Bergmann, Ewa A; Arora, Kanika et al. (2016) Indel variant analysis of short-read sequencing data with Scalpel. Nat Protoc 11:2529-2548
Cancer Target Discovery and Development Network (2016) Transforming Big Data into Cancer-Relevant Insight: An Initial, Multi-Tier Approach to Assess Reproducibility and Relevance. Mol Cancer Res 14:675-82
Andreou, Chrysafis; Neuschmelting, Volker; Tschaharganeh, Darjus-Felix et al. (2016) Imaging of Liver Tumors Using Surface-Enhanced Raman Scattering Nanoparticles. ACS Nano 10:5015-26

Showing the most recent 10 out of 53 publications