THECANCERCELLMAPINITIATIVE: ANATIONALRESEARCHCENTERFORCANCERSYSTEMSBIOLOGY OVERALLSUMMARY TheCancerGenomeAtlasandsisterprojectshavenowcompletedanalysisofover10,000tumorgenomes, providing a catalog of the gene mutations, copy number variants and other genetic alterations that cause cancer.Inmanycasesitremainsunclear,however,whicharethekeydrivermutationsordependenciesina givencancerandhowtheseinfluencepathogenesisandresponsetotherapy.Althoughtumorsofsimilartypes andclinicaloutcomescanhavepatternsofmutationsthatarestrikinglydifferent,itisbecomingapparentthat these mutations recurrently hijack the same hallmark molecular pathways and networks. For this reason, cancer research and treatment is increasingly dependent on knowledge of biological networks of multiple types, including physical interactions among proteins and syntheticlethal and epistatic interactions among genes. Here we seek support for a new effort, The Cancer Cell Map Initiative (CCMI), aimed at comprehensivelydetailingthesecomplexinteractionsamongcancergenesandproteinsandhowtheydiffer between diseased and healthy states. The CCMI is a multicampus initiative of the University of California, centeredatUCSanFranciscoandUCSanDiego,whichleveragesadvancednetworkmapping,computational analysisandcancerresearchplatformsdevelopedbymultipleCCMIinvestigatorsoverthepastdecade.Thus primed,theseplatformswillbeturnedtoefficientlygenerate,assembleandanalyzecancermolecularnetworks withaviewtowardspathwayandnetworkbasedpersonalizedtherapy.Specifically,overthenextfiveyears theCCMIwillseektocatalyzemajorphasetransitionsincancerresearchandtherapyby(1)Comprehensively mapping the networks of physical interactions among cancer proteins, revealing the protein complexes and higherorder molecular units under selection in cancer? (2) Mapping the parallel networks of syntheticlethal andepistaticinteractionsamongcancergenes,revealingthefunctionallogicofcancer?(3)Establishingthe robust computational methodology, enduser software, and databases for assembly and use of cancer cell networkmapsinbothbasicandclinicalmodalities?(4)Buildingacriticalmassofleadingcancerinvestigators worldwide to expand CCMI into a global coordinated partnership? and (5) Training the current and nextgenerationofscientistsinNetworkBiologyanditsapplicationstocancerresearch.

Public Health Relevance

THECANCERCELLMAPINITIATIVE: ANATIONALRESEARCHCENTERFORCANCERSYSTEMSBIOLOGY OVERALLNARRATIVE Although much attention has been devoted to mapping the tumor genome, understanding cancer involves morethancataloguingitscomponentgenes.Itiscriticaltounderstandthemanyinteractionsbetweenthese genes and the corresponding proteins, and how these complex networks gives rise to tumor initiation, progression and metastasis. The Cancer Cell Map Initiative will apply systematic approaches to comprehensivelymapthemolecularnetworksthatunderliecancerandwillusethesemapsasakeyresource forprecisionmedicine.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Specialized Center--Cooperative Agreements (U54)
Project #
1U54CA209891-01A1
Application #
9351146
Study Section
Special Emphasis Panel (ZCA1-RTRB-R (J2))
Program Officer
Hughes, Shannon K
Project Start
2017-05-11
Project End
2022-04-30
Budget Start
2017-05-11
Budget End
2018-04-30
Support Year
1
Fiscal Year
2017
Total Cost
$1,929,032
Indirect Cost
$417,773
Name
University of California San Francisco
Department
Pharmacology
Type
Schools of Medicine
DUNS #
094878337
City
San Francisco
State
CA
Country
United States
Zip Code
94118
Eckhardt, Manon; Zhang, Wei; Gross, Andrew M et al. (2018) Multiple Routes to Oncogenesis Are Promoted by the Human Papillomavirus-Host Protein Network. Cancer Discov 8:1474-1489
Shen, John Paul; Ideker, Trey (2018) Synthetic Lethal Networks for Precision Oncology: Promises and Pitfalls. J Mol Biol 430:2900-2912
Wang, Sheng; Ma, Jianzhu; Zhang, Wei et al. (2018) Typing tumors using pathways selected by somatic evolution. Nat Commun 9:4159
Zhang, Wei; Ma, Jianzhu; Ideker, Trey (2018) Classifying tumors by supervised network propagation. Bioinformatics 34:i484-i493
Zhang, Wei; Bojorquez-Gomez, Ana; Velez, Daniel Ortiz et al. (2018) A global transcriptional network connecting noncoding mutations to changes in tumor gene expression. Nat Genet 50:613-620
Huang, Justin K; Jia, Tongqiu; Carlin, Daniel E et al. (2018) pyNBS: a Python implementation for network-based stratification of tumor mutations. Bioinformatics 34:2859-2861
Patrick, Kristin L; Wojcechowskyj, Jason A; Bell, Samantha L et al. (2018) Quantitative Yeast Genetic Interaction Profiling of Bacterial Effector Proteins Uncovers a Role for the Human Retromer in Salmonella Infection. Cell Syst 7:323-338.e6
Bui, Nam; Huang, Justin K; Bojorquez-Gomez, Ana et al. (2018) Disruption of NSD1 in Head and Neck Cancer Promotes Favorable Chemotherapeutic Responses Linked to Hypomethylation. Mol Cancer Ther 17:1585-1594
Huang, Justin K; Carlin, Daniel E; Yu, Michael Ku et al. (2018) Systematic Evaluation of Molecular Networks for Discovery of Disease Genes. Cell Syst 6:484-495.e5
Palovcak, Eugene; Wang, Feng; Zheng, Shawn Q et al. (2018) A simple and robust procedure for preparing graphene-oxide cryo-EM grids. J Struct Biol 204:80-84

Showing the most recent 10 out of 16 publications