U24CA271076
Cooperative Agreement
Overview
Grant Description
IPGDAC, an integrative proteogenomic data analysis center for CPTAC - project summary. By combining mass spectrometry (MS)-based proteomics with genomics, epigenomics, and transcriptomics, proteogenomics holds great potential to better illuminate cancer complexities than individual 'omes. During the past 10 years, the Clinical Proteomics Tumor Analysis Consortium (CPTAC) has performed comprehensive proteogenomic characterization of >1,500 tumors across 10 cancer types.
These studies not only yield novel biological and clinical insights into different cancer types but also produce valuable datasets and computational tools that can be further used by the broad scientific community. The next phase of the CPTAC program seeks to expand the current success to more cancer types and translational research focusing on clinically relevant questions.
Our integrative proteogenomic data analysis center (IPGDAC) is one of the current CPTAC funded PGDACs. We have participated in the studies of all CPTAC cancer types and have played a leading role in data analysis for several cancer types. This application seeks to continue and enhance our contribution to the next phase of the CPTAC program.
The overarching goal of our PGDAC is to accelerate the translation of cancer proteogenomic data into a better understanding of cancer biology and improved cancer treatment. We will continue developing and improving our computing tools, workflows, and web portals that have already been successfully used in the CPTAC studies for sequence-based and pathway/network-based proteogenomic data integration.
In addition, we will address unmet needs in post-translational modification (PTM)-related analyses by using protein sequence and natural language-based deep learning techniques to improve PTM peptide identification, to predict genomic variant impact on PTMs, and to connect PTM sites to existing knowledge.
Using unique tools from our team and cutting-edge statistical inference and machine learning algorithms, we will perform integrated analysis on proteogenomic data from the CPTAC studies to: 1) create a comprehensive molecular and cellular portrait for each patient's tumor; 2) identify and characterize molecular and tumor microenvironment/immune subtypes; 3) prioritize functional genomic aberrations using proteogenomic data; 4) reveal molecular mechanisms of cancer phenotypes; and 5) develop predictive models for patient prognosis and treatment response.
Our PGDAC brings to the CPTAC network a fully integrated, completely established program with expertise in all the critical areas specified by the RFA. We have a proven track record of leadership in computational proteogenomics and successful collaboration in the CPTAC network, and we expect to broadly advance the field through this project.
These studies not only yield novel biological and clinical insights into different cancer types but also produce valuable datasets and computational tools that can be further used by the broad scientific community. The next phase of the CPTAC program seeks to expand the current success to more cancer types and translational research focusing on clinically relevant questions.
Our integrative proteogenomic data analysis center (IPGDAC) is one of the current CPTAC funded PGDACs. We have participated in the studies of all CPTAC cancer types and have played a leading role in data analysis for several cancer types. This application seeks to continue and enhance our contribution to the next phase of the CPTAC program.
The overarching goal of our PGDAC is to accelerate the translation of cancer proteogenomic data into a better understanding of cancer biology and improved cancer treatment. We will continue developing and improving our computing tools, workflows, and web portals that have already been successfully used in the CPTAC studies for sequence-based and pathway/network-based proteogenomic data integration.
In addition, we will address unmet needs in post-translational modification (PTM)-related analyses by using protein sequence and natural language-based deep learning techniques to improve PTM peptide identification, to predict genomic variant impact on PTMs, and to connect PTM sites to existing knowledge.
Using unique tools from our team and cutting-edge statistical inference and machine learning algorithms, we will perform integrated analysis on proteogenomic data from the CPTAC studies to: 1) create a comprehensive molecular and cellular portrait for each patient's tumor; 2) identify and characterize molecular and tumor microenvironment/immune subtypes; 3) prioritize functional genomic aberrations using proteogenomic data; 4) reveal molecular mechanisms of cancer phenotypes; and 5) develop predictive models for patient prognosis and treatment response.
Our PGDAC brings to the CPTAC network a fully integrated, completely established program with expertise in all the critical areas specified by the RFA. We have a proven track record of leadership in computational proteogenomics and successful collaboration in the CPTAC network, and we expect to broadly advance the field through this project.
Awardee
Funding Goals
TO IMPROVE SCREENING AND EARLY DETECTION STRATEGIES AND TO DEVELOP ACCURATE DIAGNOSTIC TECHNIQUES AND METHODS FOR PREDICTING THE COURSE OF DISEASE IN CANCER PATIENTS. SCREENING AND EARLY DETECTION RESEARCH INCLUDES DEVELOPMENT OF STRATEGIES TO DECREASE CANCER MORTALITY BY FINDING TUMORS EARLY WHEN THEY ARE MORE AMENABLE TO TREATMENT. DIAGNOSIS RESEARCH FOCUSES ON METHODS TO DETERMINE THE PRESENCE OF A SPECIFIC TYPE OF CANCER, TO PREDICT ITS COURSE AND RESPONSE TO THERAPY, BOTH A PARTICULAR THERAPY OR A CLASS OF AGENTS, AND TO MONITOR THE EFFECT OF THE THERAPY AND THE APPEARANCE OF DISEASE RECURRENCE. THESE METHODS INCLUDE DIAGNOSTIC IMAGING AND DIRECT ANALYSES OF SPECIMENS FROM TUMOR OR OTHER TISSUES. SUPPORT IS ALSO PROVIDED FOR ESTABLISHING AND MAINTAINING RESOURCES OF HUMAN TISSUE TO FACILITATE RESEARCH. SMALL BUSINESS INNOVATION RESEARCH (SBIR) PROGRAM: TO EXPAND AND IMPROVE THE SBIR PROGRAM, TO INCREASE PRIVATE SECTOR COMMERCIALIZATION OF INNOVATIONS DERIVED FROM FEDERAL RESEARCH AND DEVELOPMENT, TO INCREASE SMALL BUSINESS PARTICIPATION IN FEDERAL RESEARCH AND DEVELOPMENT, AND TO FOSTER AND ENCOURAGE PARTICIPATION OF SOCIALLY AND ECONOMICALLY DISADVANTAGED SMALL BUSINESS CONCERNS AND WOMEN-OWNED SMALL BUSINESS CONCERNS IN TECHNOLOGICAL INNOVATION. SMALL BUSINESS TECHNOLOGY TRANSFER (STTR) PROGRAM: TO STIMULATE AND FOSTER SCIENTIFIC AND TECHNOLOGICAL INNOVATION THROUGH COOPERATIVE RESEARCH AND DEVELOPMENT CARRIED OUT BETWEEN SMALL BUSINESS CONCERNS AND RESEARCH INSTITUTIONS, TO FOSTER TECHNOLOGY TRANSFER BETWEEN SMALL BUSINESS CONCERNS AND RESEARCH INSTITUTIONS, TO INCREASE PRIVATE SECTOR COMMERCIALIZATION OF INNOVATIONS DERIVED FROM FEDERAL RESEARCH AND DEVELOPMENT, AND TO FOSTER AND ENCOURAGE PARTICIPATION OF SOCIALLY AND ECONOMICALLY DISADVANTAGED SMALL BUSINESS CONCERNS AND WOMEN-OWNED SMALL BUSINESS CONCERNS IN TECHNOLOGICAL INNOVATION.
Grant Program (CFDA)
Awarding / Funding Agency
Place of Performance
Texas
United States
Geographic Scope
State-Wide
Related Opportunity
Analysis Notes
Amendment Since initial award the total obligations have increased 184% from $896,717 to $2,549,304.
Baylor College Of Medicine was awarded
iPGDAC, An Integrative Proteogenomic Data Analysis Center for CPTAC
Cooperative Agreement U24CA271076
worth $2,549,304
from National Cancer Institute in June 2022 with work to be completed primarily in Texas United States.
The grant
has a duration of 5 years and
was awarded through assistance program 93.394 Cancer Detection and Diagnosis Research.
The Cooperative Agreement was awarded through grant opportunity Proteogenomic Data Analysis Centers (PGDACs) for Clinical Proteomic Tumor Analysis Consortium (U24 Clinical Trial Not Allowed).
Status
(Ongoing)
Last Modified 3/5/25
Period of Performance
6/1/22
Start Date
5/31/27
End Date
Funding Split
$2.5M
Federal Obligation
$0.0
Non-Federal Obligation
$2.5M
Total Obligated
Activity Timeline
Subgrant Awards
Disclosed subgrants for U24CA271076
Transaction History
Modifications to U24CA271076
Additional Detail
Award ID FAIN
U24CA271076
SAI Number
U24CA271076-348712617
Award ID URI
SAI UNAVAILABLE
Awardee Classifications
Private Institution Of Higher Education
Awarding Office
75NC00 NIH National Cancer Institute
Funding Office
75NC00 NIH National Cancer Institute
Awardee UEI
FXKMA43NTV21
Awardee CAGE
9Z482
Performance District
TX-90
Senators
John Cornyn
Ted Cruz
Ted Cruz
Budget Funding
Federal Account | Budget Subfunction | Object Class | Total | Percentage |
---|---|---|---|---|
National Cancer Institute, National Institutes of Health, Health and Human Services (075-0849) | Health research and training | Grants, subsidies, and contributions (41.0) | $1,760,801 | 100% |
Modified: 3/5/25