# Fu - Proj 3

> **NIH NIH P20** · DARTMOUTH COLLEGE · 2021 · $325,152

## Abstract

PROJECT SUMMARY
It is of fundamental importance to understand the key mechanisms that govern the progression of cancer and
elucidate the often-unknown factors that account for treatment failures. Although they fail to cure most patients
with common metastatic solid cancers (like breast and lung), immunotherapies have had a significant impact in
a minority of late-stage lung cancer and melanoma patients. While these potentially curative cancer therapies
are being rapidly developed and tested, a major barrier is the lack of quantitative models to describe and evaluate
their efficacy. This project proposes to explore clinically relevant math and in-silico models of cancer cell
dynamics for personalized immunotherapy. We will focus on two distinct, yet strongly interconnected,
approaches of cancer therapy: (1) adoptive-cell transfer, in which in-vitro engineered and personalized tumor-
infiltrating T-cells are transfused to suppress tumor growth; and (2) checkpoint inhibitors that boost anti-tumor
activities of effector immune cells. Very recently, a wealth of immune-related biomarker data has become
available—their close integration with mechanistic, mathematical models would unleash their explanatory and
predictive power in treatment response and outcome. Here, Project 3 will take advantage of these biomarker
data to infer and quantify key parameters that govern cancer-immune interactions. Specifically, Aim 1 will
develop a quantitative mathematical framework based on the dynamical systems approach to provide practical
guidance for clinical assessment of the efficacy of adoptive cell transfer approach. Aim 2 will optimize therapeutic
strategies for checkpoint inhibitors and their potential combinations, while Aim 3 will evaluate and identify
immune-related biomarkers for melanoma cancer by closely integrating computational modeling with single-cell
sequencing data from animal models and clinical trials. This design will use a theoretical framework to assess
and compare the efficacies of different combinations, as well as to provide guidance on the minimum efficacy
and optimal dosage schedule of checkpoint inhibitors required to achieve positive clinical outcomes. This
proposal will develop clinically relevant math and in-silico models that will facilitate the way novel cancer
immunotherapeutic strategies are conceived, tested, and understood. Owing to their innate flexibility, these in-
silico models also can be readily incorporated with the specific cancer profile on the cancer-cell level, and thus
enable informed treatment decisions and predict treatment outcomes in a personalized fashion. The ultimate
goal is to use these in-silico and mathematical models to interpret lab and clinical results and to guide design
principles of future lab experiments and clinical trials, all with an eye toward model-informed personalized
immunotherapy.

## Key facts

- **NIH application ID:** 10212418
- **Project number:** 5P20GM130454-03
- **Recipient organization:** DARTMOUTH COLLEGE
- **Principal Investigator:** Feng Fu
- **Activity code:** P20 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $325,152
- **Award type:** 5
- **Project period:** 2019-08-01 → 2024-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10212418

## Citation

> US National Institutes of Health, RePORTER application 10212418, Fu - Proj 3 (5P20GM130454-03). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10212418. Licensed CC0.

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