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Reverse Sensitivity Analysis for Identifying Predictive Proteomics Signatures of Cancer

Award Number: U01CA227544
ORGANIZATION: NATIONAL CANCER INSTITUTE
OPDIV: NIH
AWARD CLASS: COOPERATIVE AGREEMENT
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)
PERIOD OF PERFORMANCE START DATE: 05/01/2019
PERIOD OF PERFORMANCE END DATE: 01/31/2025

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Reverse Sensitivity Analysis for Identifying Predictive Proteomics Signatures of Cancer - Title: Reverse Sensitivity Analysis for Identifying Proteomics Signatures of Cancer Abstract Cancer is a complex disease in which genetic disruptions in cell signaling networks are known to play a significant role. A major aim of cancer systems biology is to build models that can predict the impact of these genetic disruptions to guide therapeutic interventions (i.e. personalized medicine). A prominent driver of cancer cell growth is signaling pathway deregulation from mutations in key regulatory nodes and loss/gain in gene copy number (CNV). However, current mathematical modeling approaches do not adequately capture the impact of these genetic changes. Reasons for this include the poorly understood layers of regulation between gene expression and protein activity, and limitations in most modeling and protein measurement technologies. In addition, there is a paucity of overarching hypotheses that can link specific gene expression or mutation patterns to the cancer phenotype. Recent work by our group has resolved some of the technical challenges that have hindered the application of proteomics technologies to cancer systems biology research. It has also suggested a new approach for using quantitative proteomics data to understand mechanisms driving cancer cell behavior. Using an ultrasensitive, targeted proteomics platform that can measure both abundance and phosphorylation of proteins present at only hundreds of copies per cell, we found that signaling pathways appeared to be controlled by only a limited number of key nodes whose activity is tightly regulated through low abundance and feedback phosphorylation. We propose to build on these findings by critically testing the hypothesis that CNV and genetic mutations dysregulate signaling pathways in cancer by shifting control from tightly regulated nodes to poorly regulated ones. This will be done by systematically identifying key regulatory nodes of normal and cancer cells using CRISPRa/i screens, determine the relationship between protein abundance and signaling pathway activities using ultrasensitive targeted proteomics and phosphoproteomics and then use these data to semi-automatically generate mathematical models of the functional topology of the signaling pathways. Specifically, we propose to: 1) Use targeted CRISPR gene perturbation libraries to identify the regulatory topologies of signaling pathways important in cancer and how they are disrupted by common cancer mutations, 2) Use the CRISPR perturbation and proteomics data to semi-automatically build predictive models of cancer cell signaling pathways, and 3) Combine modeling and perturbation screens to understand how feedback regulation in cancer contributes to drug resistance. This work will result in simplified, computationally tractable yet mechanistic models of signaling pathways and provide network maps of feedback and crosstalk circuits that can be used to rapidly map the regulatory state of cells. Most important, it will provide a generic platform for translating protein abundance and phosphorylation patterns into a “state” snapshot of cancers that can lead to predicting their response to specific drugs.


Issue Date FY	Funding FY	Legal Entity Name	Legal Entity Address	Legal Entity City	Legal Entity State	Legal Entity Zip Code	Legal Entity COUNTY	Legal Entity COUNTRY	Assistance Listing	Award Code	Budget Year	Action Date	Action Type	Action Amount

Issue Date FY: 2025 ( Subtotal = -$6,371 )
2025	2023	BATTELLE MEMORIAL INSTITUTE	902 BATTELLE BLVD	RICHLAND	WA	99354	BENTON	USA	Cancer Biology Research	000	5	6/10/2025	NON-COMPETING CONTINUATION	-$6,371
														Subtotal = -$6,371

Issue Date FY: 2023 ( Subtotal = $564,140 )
2023	2023	BATTELLE MEMORIAL INSTITUTE	902 BATTELLE BLVD	RICHLAND	WA	99354	BENTON	USA	Cancer Biology Research	000	5	5/1/2023	NON-COMPETING CONTINUATION	$564,140
														Subtotal = $564,140

Issue Date FY: 2022 ( Subtotal = $573,846 )
2022	2022	BATTELLE MEMORIAL INSTITUTE	900 BATTELLE BLVD	RICHLAND	WA	99352	BENTON	USA	Cancer Biology Research	001	4	4/29/2022	NON-COMPETING CONTINUATION	$573,846
2022	2021	BATTELLE MEMORIAL INSTITUTE	900 BATTELLE BLVD	RICHLAND	WA	99352	BENTON	USA	Cancer Biology Research	000	3	12/13/2021	NON-COMPETING CONTINUATION	$0
														Subtotal = $573,846

Issue Date FY: 2021 ( Subtotal = $597,684 )
2021	2021	BATTELLE MEMORIAL INSTITUTE	900 BATTELLE BLVD	RICHLAND	WA	99352	BENTON	USA	Cancer Biology Research	001	3	4/21/2021	NON-COMPETING CONTINUATION	$597,684
2021	2020	BATTELLE MEMORIAL INSTITUTE	900 BATTELLE BLVD	RICHLAND	WA	99352	BENTON	USA	Cancer Biology Research	000	2	12/16/2020	NON-COMPETING CONTINUATION	$0
														Subtotal = $597,684

Issue Date FY: 2020 ( Subtotal = $715,682 )
2020	2020	BATTELLE MEMORIAL INSTITUTE	900 BATTELLE BLVD	RICHLAND	WA	99352	BENTON	USA	Cancer Biology Research	001	2	8/24/2020	NON-COMPETING CONTINUATION	$105,310
2020	2020	BATTELLE MEMORIAL INSTITUTE	900 BATTELLE BLVD	RICHLAND	WA	99352	BENTON	USA	Cancer Biology Research	000	2	4/22/2020	NON-COMPETING CONTINUATION	$610,372
														Subtotal = $715,682

Issue Date FY: 2019 ( Subtotal = $608,348 )
2019	2019	BATTELLE MEMORIAL INSTITUTE	900 BATTELLE BLVD	RICHLAND	WA	99352	BENTON	USA	Cancer Biology Research	000	1	4/30/2019	NEW	$608,348
														Subtotal = $608,348

Grand Total All Awards = $3,053,329

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Reverse Sensitivity Analysis for Identifying Predictive Proteomics Signatures of Cancer

Award Number: U01CA227544

ORGANIZATION: NATIONAL CANCER INSTITUTE

OPDIV: NIH

AWARD CLASS: COOPERATIVE AGREEMENT

AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)

PERIOD OF PERFORMANCE START DATE: 05/01/2019

PERIOD OF PERFORMANCE END DATE: 01/31/2025

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