# An Integrated Systems Approach for Incompletely Penetrant Onco-phenotypes.

> **NIH NIH U01** · UNIVERSITY OF VIRGINIA · 2020 · $517,131

## Abstract

PROJECT SUMMARY/ABSTRACT
Perturbation of cancer cells often leads to heterogeneous outcomes, in that most cells exhibit a dominant
phenotype, but the rest appear resistant or hypersensitive to the perturbation. If the penetrance of such a
phenotype is heritably incomplete, then it becomes extremely difficult to decipher the upstream molecular
events that heterogenize the population and cause response variability. By combining quantitative
measurements with dynamical models, systems approaches should be useful if provided with a core network
of important biomolecules. The daunting hurdle lies in identifying phenotype-relevant regulatory
heterogeneities that define the network for penetrance at the single-cell level. Our proposal seeks to exploit a
new approach, called stochastic frequency matching (SFM), for elaborating the molecular networks upstream
of incompletely penetrant phenotypes. SFM identifies and parameterizes single-cell heterogeneities—which
emerge after a uniform perturbation but before the appearance of a variable phenotype—to hone in on
regulatory states corresponding to future penetrance. For an onco-phenotype incompletely triggered by ErbB
receptor tyrosine kinase signaling in 3D cultured breast epithelia, we implemented SFM using microarrays to
uncover a network of critical nucleocytoplasmic regulators. The goals of this proposal are to apply systems
approaches to the ErbB nucleocytoplasmic network and adapt SFM more broadly to RNA sequencing of breast
cancer patients with ErbB amplification. Based on our provisional SFM results, we hypothesize that ErbB
signaling heterogeneously reconfigures the nucleocytoplasmic shuttling state of cells to determine incomplete
penetrance of the onco-phenotype. The aims are to: 1) Identify network-level mechanisms for the
incompletely penetrant ErbB1:ErbB2 phenotype. 2) Determine whether drivers of incomplete penetrance in 3D
define shuttling states in human cancers and promote ErbB2-driven mammary tumors in mice. 3) Sequence
and parameterize regulatory-state heterogeneity in HER2+ breast cancers to assemble patient-specific network
models of shuttling variability and sensitivity. Drivers of incomplete penetrance are important for understanding
transitions during tumor initiation-progression and for developing therapeutic interventions with more reliable
patient outcomes. SFM gives the Cancer Systems Biology Consortium a means to identify driver networks in a
comprehensive and hypothesis-driven way.

## Key facts

- **NIH application ID:** 9964690
- **Project number:** 5U01CA215794-04
- **Recipient organization:** UNIVERSITY OF VIRGINIA
- **Principal Investigator:** Kevin A Janes
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $517,131
- **Award type:** 5
- **Project period:** 2017-09-15 → 2022-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9964690

## Citation

> US National Institutes of Health, RePORTER application 9964690, An Integrated Systems Approach for Incompletely Penetrant Onco-phenotypes. (5U01CA215794-04). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9964690. Licensed CC0.

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