# A phylodynamic time machine for solid tumors

> **NIH NIH DP2** · UNIVERSITY OF WASHINGTON · 2022 · $1,399,500

## Abstract

Solid tumors progress towards metastasis as their cells diversify in space and genotype. Unfortunately, by the
time nearly all solid tumors are discoverable, the dynamics governing this diversification are obscured. To
overcome this limitation, I will develop methodology that enables both reconstruction of key diversification
dynamics throughout a tumor’s past and robustly predicts which current tumor cellular lineages, if unmitigated,
will permit tumor progression towards poor clinical outcomes; i.e., a tumor ‘time machine’.
In this proposal, I describe how I will construct such a time machine from phylodynamic methodologies, which
synthesize phylogenetic and population dynamic models and were originally developed for the study of viral
evolution and geographic spread. To make such methods suitable for the analysis of tumor sequencing data, I
will extend these models to incorporate multiple attributes of tumor growth dynamics. I will apply these new
tools at scale to analyze cancer evolutionary processes, and develop novel diagnostic tools to yield clinically
actionable insights into tumor progression. Crucially, application of these methods relies on cancer single cell
DNA sequencing data, which has recently gained prominence and reveals a much more detailed view of
cancer evolution than previous tumor sequencing technologies. I hypothesize that these data will yield still
greater insight when subjected to the rigorous, quantitative examinations enabled by the cancer-calibrated
phylodynamic methodology I will develop.
In applying these new tools, I will derive precisely-calibrated clinical timelines, including estimates of tumor
initiation and the age of important clonal subpopulations, which will help us understand personalized tumor
progression. I will disentangle heterogeneous cancer growth rates from within a single tumor, enabling both the
prediction of the clinically most important variants and a new approach for discovering driver mutations. These
phylodynamic methods will also illuminate how and when cancer cells disperse within a primary tumor as a
function of genotype and/or environment, enabling us to chart the development of metastatic lineages.
In addition to answering fundamental questions about the driving forces shaping tumorigenesis and cancer
progression, this proposal aims to provide a powerful new set of methodologies that will drive a paradigm shift
in the analysis of tumor single cell sequencing data in diverse basic science, clinical and translational cancer
settings.

## Key facts

- **NIH application ID:** 10471549
- **Project number:** 1DP2CA280623-01
- **Recipient organization:** UNIVERSITY OF WASHINGTON
- **Principal Investigator:** Alison F Feder
- **Activity code:** DP2 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $1,399,500
- **Award type:** 1
- **Project period:** 2022-09-09 → 2025-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10471549

## Citation

> US National Institutes of Health, RePORTER application 10471549, A phylodynamic time machine for solid tumors (1DP2CA280623-01). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10471549. Licensed CC0.

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