# Inference of tumor growth dynamics using genomic data

> **NIH NIH R21** · RBHS -CANCER INSTITUTE OF NEW JERSEY · 2021 · $183,202

## Abstract

ABCTRACT
Heterogeneity and evolvability are hallmarks of cancer. By the time of detection, a typical tumor comprises of
billions of malignant cells that belong to multiple distinct subclonal cell populations, which trace their
evolutionary lineage back to a single tumor initiating cell. Subclones arise at different time-points during tumor
progression, and their population sizes grow (or in some cases shrink) with time. Quantitative assessment of
subclonal growth rates of tumors can indicate the mode of disease progression, predict the risk of emergence
of resistance, and can rationally guide clinical management of the patients in the Precision Medicine setting. It
remains unclear whether the genetically distinct subclones in heterogeneous tumors tend to have major
differences in fitness and growth rates in vivo, or most subclones grow comparably, as predicted by the neutral
evolution model. This is due to a number of technical challenges. Patho-genomic profiling of biopsies and
resected tumors provide limited and incomplete snapshots of cancer progression; much of the tumor evolution
and clonal growth dynamics therein remain unobserved. Pathological assessment can indicate overall
proliferative characteristics of a tumor but cannot attribute them to individual subclones and oncogenic driver
mutations therein. Genomic approaches for delineating clonal architectures in tumors, or genetic and non-
genetic heterogeneity also do not provide direct, quantitative estimates of subclonal growth rates. Incorrect
measurements of intra-tumor subclonal properties have led to biased inference about tumor evolution and
fueled controversies on multiple occasions - highlighting the immediate need for development of reliable
resource in this area. To address this unmet need, this proposal aims to develop a novel framework to
estimate subclonal growth rates in human tumors using emerging genomic approaches, and then validate
them experimentally before applying the framework to estimate the selective advantage conferred by
oncogenic drivers during tumor progression in individual patients. The resources developed in this proposal will
enable us to revisit the ongoing debate about the neutral evolution and selection in cancer, and also help refine
clinically relevant predictive models of tumor progression to generate testable hypotheses.

## Key facts

- **NIH application ID:** 10158455
- **Project number:** 5R21CA248122-02
- **Recipient organization:** RBHS -CANCER INSTITUTE OF NEW JERSEY
- **Principal Investigator:** Subhajyoti De
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $183,202
- **Award type:** 5
- **Project period:** 2020-05-15 → 2023-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10158455

## Citation

> US National Institutes of Health, RePORTER application 10158455, Inference of tumor growth dynamics using genomic data (5R21CA248122-02). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10158455. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*