# Modeling Tumor Growth to Characterize Disease Heterogeneity

> **NIH NIH P01** · UNIVERSITY OF SOUTHERN CALIFORNIA · 2020 · $280,031

## Abstract

ABSTRACT 
The overall goal of this project is to develop computational methods for studying tumor growth, and to relate 
growth parameters to patient characteristics and prognosis. We hypothesize that tumor growth parameters will 
allow us to define cancer phenotypes that help resolve cancer heterogeneity, and thereby improve power in 
analyses that try to link germline genetic variation and internal/external environment to phenotypic variation 
(Projects 1 & 3). 
When discovered, human tumors vary in size and extent of spread. Although it is impossible to look directly 
back in time to see how the tumor grew, it is possible to reconstruct the past with “molecular phylogeny”. The 
approach is analogous to reconstructing the genealogy of species using DNA sequences. In previous work, we 
developed a molecular phylogeny approach to study human cancers using DNA methylation patterns and 
found that a relatively simple exponential growth model fits most colorectal cancers. We now propose to test 
and further develop the model by integrating new independent molecular data types. The experimental data 
sample glands from opposite tumor sides and measures passenger DNA methylation patterns, chromosome 
copy number, and point mutations. Each data type provides `molecular clocks' with different rates of sequence 
evolution, such that their joint analysis permits our setting a new goal of characterizing what happens during 
the first few cell divisions following transformation, even before a tumor is clinically detectable. We hypothesize 
that abnormal cell mobility, a prerequisite for subsequent invasion and metastasis, is a phenotype that can be 
measured immediately after tumor initiation in some cancers but not benign tumors (“Born to be Bad”). This 
work will provide a new understanding of intratumor heterogeneity and cancer cell behavior, and might well be 
the catalyst for the development of new treatment or prognostic paradigms. We will use approximate Bayesian 
computation to estimate model parameters in this high-dimensional setting. This requires the development of 
software, and implementation of methods for choosing an optimal set of statistics and corresponding weights 
for parameter inference. These tools will be applicable to any ABC analysis, and not just our own. As such, we 
will make this software publicly available to the wider community. 
The present application uses data from colon cancer to develop the methods and software tools for inferring 
tumor growth, but the approach is generalizable to any adenocarcinomas, or tumors with glandular structure 
(e.g. prostate, kidney, lung, breast and more).

## Key facts

- **NIH application ID:** 9991776
- **Project number:** 5P01CA196569-05
- **Recipient organization:** UNIVERSITY OF SOUTHERN CALIFORNIA
- **Principal Investigator:** KIMBERLY D SIEGMUND
- **Activity code:** P01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $280,031
- **Award type:** 5
- **Project period:** — → —

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9991776

## Citation

> US National Institutes of Health, RePORTER application 9991776, Modeling Tumor Growth to Characterize Disease Heterogeneity (5P01CA196569-05). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9991776. Licensed CC0.

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