# Reverse Sensitivity Analysis for Identifying Predictive Proteomics Signatures of Cancer

> **NIH NIH U01** · BATTELLE PACIFIC NORTHWEST LABORATORIES · 2021 · $597,684

## Abstract

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 ...

## Key facts

- **NIH application ID:** 10148720
- **Project number:** 5U01CA227544-03
- **Recipient organization:** BATTELLE PACIFIC NORTHWEST LABORATORIES
- **Principal Investigator:** Wei-Jun Qian
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $597,684
- **Award type:** 5
- **Project period:** 2019-05-01 → 2024-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10148720

## Citation

> US National Institutes of Health, RePORTER application 10148720, Reverse Sensitivity Analysis for Identifying Predictive Proteomics Signatures of Cancer (5U01CA227544-03). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10148720. Licensed CC0.

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