# Computational Prediction of Genetic Drivers of Breast Cancer Metastases

> **NIH NIH F33** · JOHNS HOPKINS UNIVERSITY · 2020 · $62,848

## Abstract

Project Summary
While metastasis is the main cause of cancer mortality, many of its molecular requirements remain unknown.
Computational methods that trace the biological networks from upstream metastasis drivers to downstream
effectors have the potential to identify new points of intervention for cancer therapies. Developing these types of
methods requires individuals with expertise in statistical network models and deep engagement with cancer
experimental systems. This fellowship will train the PI to predict the flow of both transcriptional and signaling
information through biological networks, connecting these changes to phenotypic and behavioral consequences
for cells and tissue derived from metastatic and non-metastatic breast cancer organoids. The Bader
(computational) and Ewald (experimental) labs are jointly funded by the National Cancer Institute as a Cancer
Target Discovery and Development (CTD2) Center focused on breast cancer metastasis. This center provides
a uniquely powerful environment of mentorship, resources, and infrastructure that will enable the PI to use his
formal training in statistical physics as the foundation for developing and applying new methods for computational
oncology. Research will exploit three-dimensional organotypic cell culture and experimental methods motivated
by population genetics to identify metastasis driver and effector genes in genetically engineered mouse models
and in primary tumor specimens from an ongoing IRB-approved human subjects study. Genes identified by RNA-
Seq will be analyzed with novel network perturbation theory to connect upstream drivers to downstream
effectors. These inferred networks will in turn be connected to phenotypic and behavioral consequences for
mammary organoids and tissues. Although outside the scope of this proposal, the JHU CTD2 Center has the
mission and resources to validate findings with clinical potential for preventing or treating metastatic breast
cancer, and potentially other invasive or metastatic cancers with similar molecular mechanisms. The PI’s
background in biological and statistical physics, including computational methods, enables the mathematical and
computational aspects of the proposed research. The fellowship will provide essential training that will permit
the PI to lead independent research that combines physical sciences methods with experimental innovations
and data-rich -omics measurements for cancer basic research and to aid therapeutic advances. The PI will have
robust opportunities to collaborate with Hopkins and other institutions in future and will be an effective mentor
for training computational oncology researchers in his own lab.

## Key facts

- **NIH application ID:** 9910689
- **Project number:** 1F33CA247344-01
- **Recipient organization:** JOHNS HOPKINS UNIVERSITY
- **Principal Investigator:** Michael G. Lerner
- **Activity code:** F33 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $62,848
- **Award type:** 1
- **Project period:** 2020-02-27 → 2020-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9910689

## Citation

> US National Institutes of Health, RePORTER application 9910689, Computational Prediction of Genetic Drivers of Breast Cancer Metastases (1F33CA247344-01). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/9910689. Licensed CC0.

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