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.

Public Health Relevance

HER2+ breast cancer is an aggressive subtype of breast cancer with targeted drugs available, but not all of these patients respond to HER2-directed therapies. Our project leverage an experimental approach to determine why cancer cells do not respond uniformly when their molecular pathways are perturbed by stimuli or drugs. The results from this work could one day lead to combination therapies that increase the percentage of cancer patients who respond to anti-cancer agents.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Research Project--Cooperative Agreements (U01)
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Special Emphasis Panel (ZCA1)
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Hughes, Shannon K
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University of Virginia
Biomedical Engineering
Biomed Engr/Col Engr/Engr Sta
United States
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Borten, Michael A; Bajikar, Sameer S; Sasaki, Nobuo et al. (2018) Automated brightfield morphometry of 3D organoid populations by OrganoSeg. Sci Rep 8:5319
Bajikar, Sameer S; Wang, Chun-Chao; Borten, Michael A et al. (2017) Tumor-Suppressor Inactivation of GDF11 Occurs by Precursor Sequestration in Triple-Negative Breast Cancer. Dev Cell 43:418-435.e13