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.

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Prime Recipient Report

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

Reporting Information

Award Type*: Grant

Award Number*: 5R01CA140214-02

Final Report*: Recipient responsible for this data

Award Recipient Information

Recipient DUNS Number*: 052184116

Recipient Account Number: Recipient responsible for this data

Recipient Congressional District*: 14

Award Information

Funding Agency Code*: 7529

Awarding Agency Code*:7529

Award Date*: 05-11-2010

Amount of Award*: $ 294,535

Program Source (TAS)*: 750850

CFDA Number*: 93.701

Sub Account Number for Program Source (TAS)*: Recipient responsible for this data

Total Number of Sub Awards to Individuals*: Recipient responsible for this data

Total Amount of Sub Awards to Individuals*: Recipient responsible for this data

Total Number of Payments to Vendors less than $25,000/award*: Recipient responsible for this data

Total Amount of Payments to Vendors less than $25,000/award*: Recipient responsible for this data

Total Number of Sub Awards less than $25,000/award*: Recipient responsible for this data

Total Amount of Sub Awards less than $25,000/award*: 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*: HETEROGENEOUS CANCER PROGRESSION FROM MICROARRAY DATA

Project Status*: Recipient responsible for this data

Total Federal Amount of ARRA Funds Received/Invoiced*: Recipient responsible for this data

Number of Jobs*: Recipient responsible for this data

Description of Jobs Created*: Recipient responsible for this data

Quarterly Activities/Project Description*: Recipient responsible for this data

Activity Code (NAICS or NTEE-NPC)*: Recipient responsible for this data

Total Federal Amount of ARRA Expenditure* (Enter the cumulative total amount of Recovery Funds received that were expended to projects or activities. Refer to the Data Model for details on how to calculate this amount.): Recipient responsible for this data

Total Federal ARRA Infrastructure Expenditure Recipient responsible for this data

Infrastructure Contact Name: Recipient responsible for this data

Infrastructure Contact Email: Recipient responsible for this data

Infrastructure Contact Phone: Recipient responsible for this data

Infrastructure Contact Phone Ext: Recipient responsible for this data

Infrastructure Contact Street Address 1: 5000 FORBES AVENUE

Infrastructure Contact Street Address 2: Not Available

Infrastructure Contact Street Address 3: Recipient responsible for this data

Infrastructure City: PITTSBURGH

Infrastructure State: PA

Infrastructure ZIP Code+4: 15213-3815

Infrastructure Purpose and Rationale (If applicable, enter an explanation about how the infrastructure investment will contribute to one or more purposes of the Recovery Act. Refer to the Data Model for details on what to report. 4000 characters or less.): Recipient responsible for this data

Primary Place of Performance

Street Address 1: 5000 FORBES AVEWH 411

Street Address 2: PITTSBURGH

City*: PITTSBURGH

State*: PA

ZIP Code+4*: 15213

Congressional District*: 14

Country*: US

Recipient Highly Compensated Officers

Prime Recipient Indication of Reporting Applicability*: Recipient responsible for this data

1. Officer Name and Compensation: Recipient responsible for this data
2. Officer Name and Compensation: Recipient responsible for this data
3. Officer Name and Compensation: Recipient responsible for this data
4. Officer Name and Compensation: Recipient responsible for this data
5. Officer Name and Compensation: Recipient responsible for this data

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