# "Novel Mouse Models for Quantitative Understanding of Baseline and Therapy-Driven Evolution of Prostate Cancer Metastasis"

> **NIH NIH R01** · WEILL MEDICAL COLL OF CORNELL UNIV · 2024 · $605,237

## Abstract

PROJECT SUMMARY / ABSTRACT
On average, a man dies from PCa every 16 minutes, mainly due to development of secondary malignant
growths outside of the primary cancer site, known as metastases. The cornerstone of PCa treatment is
androgen deprivation therapy (ADT). ADT temporarily halts PCa, but leads to resistance in nearly all cases,
resulting in castration-resistant PC (CRPC). CRPC then undergoes further evolution of metastatic subclones
and results in incurable disease. Research techniques revealing resistance mechanisms and clonal evolution
of metastatic PCa are lacking due to the limited capacity of current animal models to mimic PCa evolution in its
native microenvironment as well as inefficient methods for tracing subclonal evolution.
Therefore, we developed EvoCaP (!Evolution in Cancer of the Prostate”), a mouse model of endogenous
metastasis that recapitulates human PCa genetically, by using PTEN/TP53 co-deletions enriched in metastatic
patients, and phenotypically, by focal initiation of primary disease progressing to bones, lungs, lymph nodes
and liver metastases. Our model uses a lentiviral platform - LV.CreBC10 carrying: (1) Cre (Pten/Trp53 co-
deletions; activation of Cas9, fluorescence and luminescence markers); (2) Barcode with ten sites for marking
by Cas9 (BC10); (3) RNA guide specifically marking BC10; and (4) guide or short hairpin RNA for testing
metastatic drivers. Luminescence (FLuc) permits continuous tracking of disease progression and fluorescence
(eGFP) allows for specific sorting of cancer cells. BC10 represents a synthetic array of on-target sites, in order
of decreasing activity, for the RNA guide that attracts Cas9 to generate subsequently specific edits. To
streamline barcode analysis, we have established an R package - EvoTraceR. This comprehensive system
enables: (1) the profiling of cancer cells based on shared mutational patterns in primary and metastasis; and
(2) the building of phylogenetic trees to track evolution toward metastases in a robust and flexible way.
Our central hypothesis is that differences in distinct molecular and phenotypical clonal architectures will be
precisely detected between primary and metastatic sites depending on therapy status, enabling the inhibition of
metastasis and/or resistance promoting genes and pathways. Our analyses will establish and mechanistically
validate drivers of metastatic clonal expansion caused by Pten/Tp53-loss (basal) and also investigate how
evolutionary pressure from therapy (ADT), applied at different stages of PCa, leads to the emergence of
resistant clones. We will then use Cas9/guide (g)RNA and inducible short hairpins to target genes altered in
those expanding clones to identify drivers of both treatment-naive and treatment-induced PCa metastasis.
EvoCaP can feasibly track molecular evolution and validate targets for drug development, which may lead to
identification of novel metastatic driver genes and pathways. Thus, therapies could be applied in: (1) primary
dise...

## Key facts

- **NIH application ID:** 10817078
- **Project number:** 5R01CA272466-02
- **Recipient organization:** WEILL MEDICAL COLL OF CORNELL UNIV
- **Principal Investigator:** Dawid Grzegorz Nowak
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $605,237
- **Award type:** 5
- **Project period:** 2023-04-01 → 2028-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10817078

## Citation

> US National Institutes of Health, RePORTER application 10817078, "Novel Mouse Models for Quantitative Understanding of Baseline and Therapy-Driven Evolution of Prostate Cancer Metastasis" (5R01CA272466-02). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10817078. Licensed CC0.

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