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HHS Recovery Act Recipient Reporting Readiness Tool

Step 4. Review and Copy the Grant Awards Data

TAGGS provides some – but not all – of the data needed for the Recipient Report. Recipients are responsible for directly collecting and reporting all required data to FederalReporting.gov. Data that HHS does not currently collect are highlighted in yellow. Do not copy this highlighted information. Please enter the appropriate data for your organization in these required fields. For assistance with entering these data please contact FederalReporting.gov.

You may capture the data HHS does provide by copying data from this screen and pasting it into the reporting format of your choice, such as the Excel spreadsheet template, the XML template, or by logging into the online form. For assistance with copying and pasting these data please contact our help desk at 866-814-5703.

 

Award Detail for: HETEROGENEOUS CANCER PROGRESSION FROM MICROARRAY DATA
CARNEGIE-MELLON UNIVERSITY
DUNS Number: 052184116
5000 FORBES AVENUE
PITTSBURGH, PA 15213-3815
Recipient Report: Grant or Loan
Prime Recipient

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Reporting Information
Award Type Award Number Final Report
Grant 5R01CA140214-02 Recipient responsible for this data

Award Recipient Information
Recipient DUNS Number Recipient Account Number Recipient Congressional District
052184116 Recipient responsible for this data 14

Award Information
Funding Agency Code Awarding Agency Code Award Date
7529 7529 05-11-2010
Amount of Award Sub Account Number for Program Source (TAS)  
$ 294,535 Recipient responsible for this data
Program Source (TAS)* CFDA Number 
750850 93.701
Total Number of Sub Awards to Individuals Total Amount of Sub Awards to Individuals
Recipient responsible for this data Recipient responsible for this data
Total Number of Payments to Vendors less than $25,000/award Total Amount of Payments to Vendors less than $25,000/award
Recipient responsible for this data Recipient responsible for this data
Total Number of Sub Awards less than $25,000/award Total Amount of Sub Awards less than $25,000/award
Recipient responsible for this data Recipient responsible for this data
Award Description
DESCRIPTION (provided by applicant): Heterogeneous Cancer Progression from Microarray Data the class of diseases collectively known as cancer could in principle be produced by a limitless number of combinations of mutations. Nonetheless, it has become apparent that most cancers can be grouped into a few common "sub-types," each characterized by a common way in which the controls on cell growth become disabled. By identifying these common sub-types and the particular sequences of genetic abnormalities that produce them, we can identify patient sub-populations who may respond to different treatments than the general population, find genes that may be useful targets for new anti-cancer drugs, and develop diagnostic tests to better predict patient outcomes and suggest which drugs will benefit which patients. Great progress has been made by examining gene expression within tumors, as different cancer sub-types have characteristic patterns of overly active or overly inactive genes. Trying to interpret these expression data is, however, a difficult problem for which sophisticated computer models have proven invaluable. One class of computer models - phylogenetic (evolutionary tree) models - has provided a powerful method for interpreting likely pathways by which different cell types evolve within tumors. There are two important variants of this phylogenetic approach: one using data gathered from gene expression microarrays, which assay thousands of genes averaged over large tumor samples, and another using data gathered from cytometric studies, which assay small numbers of genes in individual cells isolated from tumors. Each has advantages, the former in allowing a far more complete picture of overall gene activity and the latter in providing valuable clues about tumor evolution by identifying which cell types co-occur in individual tumors. The proposed work will develop new computer models for these problems in order to develop a single approach with the advantages of both methods. The work will first develop approaches to infer the existence of common cell types from bulk microarray measurements of tumors sampled across patient populations. It will then build on prior methods to infer evolutionary similarity between these tumor states. It will, finally, adapt methods for cytometric tumor phylogenetics to the problem of inferring evolutionary sequences from these microarray states. The result will be a unified approach for inferring evolution among individual cell states, as in a cytometric study, but assayed on thousands of genes, as in a microarray study. The unified approach will be validated on breast cancer data, for which both microarray and cytometric measurements are available, and applied to the discovery of common progression pathways in breast cancer populations. The study can be expected to uncover distinct stages in the breast cancer progression that would not be apparent by existing methods, aiding in the identification of new patient sub-populations, drug targets, and diagnostic tests. The methods to be developed are likely to have broader applicability to solid tumor progression in general and to related problems of analyzing cell differentiation in mixed samples.

Project Information
Project Name or
Project/Program Title
Project Status Total Federal Amount ARRA Funds
Received/Invoiced
HETEROGENEOUS CANCER PROGRESSION FROM MICROARRAY DATA Recipient responsible for this data Recipient responsible for this data
Number of Jobs Description of Jobs Created
Recipient responsible for this data Recipient responsible for this data
Quarterly Activities/Project Description
Recipient responsible for this data
 
Activity Code (NAICS or NTEE-NPC)
1Recipient responsible for this data2Recipient responsible for this data
3Recipient responsible for this data4Recipient responsible for this data
5Recipient responsible for this data6Recipient responsible for this data
7Recipient responsible for this data8Recipient responsible for this data
9Recipient responsible for this data10Recipient responsible for this data
Total Federal Amount of ARRA
Expenditure
Total Federal ARRA
Infrastructure Expenditure
Infrastructure Contact Name
Recipient responsible for this data Recipient responsible for this data Recipient responsible for this data
Infrastructure Contact Email Infrastructure Contact Phone Infrastructure Contact Phone Ext.
Recipient responsible for this data Recipient responsible for this data Recipient responsible for this data
Infrastructure Contact Street Address 1 Infrastructure Contact Street Address 2 Infrastructure Contact Street Address 3
5000 FORBES AVENUE Not Available Recipient responsible for this data
Infrastructure City Infrastructure State Infrastructure ZIP Code+4
PITTSBURGH PA 15213-3815
Infrastructure Purpose and Rationale
Recipient responsible for this data

Primary Place of Performance
Street Address 1 Street Address 2 City
5000 FORBES AVE Recipient responsible for this data PITTSBURGH
State Zip Code+4 Congressional District
PA 15213 14
Country  
US

Recipient Highly Compensated Officers
Prime Recipient Indication of Reporting Applicability # Officer Name Officer Compensation
Recipient responsible for this data 1 Recipient responsible for this data Recipient responsible for this data
2 Recipient responsible for this data Recipient responsible for this data
3 Recipient responsible for this data Recipient responsible for this data
4 Recipient responsible for this data Recipient responsible for this data
5 Recipient responsible for this data Recipient responsible for this data

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USE IN THE RECIPIENT REPORT

The information provided by this tool is baseline data that the Recipient should include in the Recipient Report that must be submitted to FederalReporting.gov beginning October 1, 2009. The data from this tool can be cut and pasted directly into the Recipient Report.