# Modeling heterogeneity of a cancer-signaling cascade using biomimetic cells

> **NIH NIH R21** · UNIVERSITY OF CALIFORNIA AT DAVIS · 2021 · $226,787

## Abstract

PROJECT SUMMARY
Modeling heterogeneity of a cancer-signaling cascade using biomimetic cells, PI Tan
Developing biomimetic systems that capture the heterogeneity of cancer-signaling cascades will enhance the
development of effective anti-cancer therapy and reduce the attrition rate of drug candidates. A cancer-
signaling cascade consists of receptors that propagate signals to protein networks, which then modulate
expression profiles of gene networks. A signaling cascade can exhibit tremendous heterogeneity in cancers due
to variation in the concentration, composition, and sequence of its protein constituents. Such heterogeneity has
been known to diminish the efficacy of certain anti-cancer drugs. To date, however, there is a general lack of
engineered systems that emulate the heterogeneity of cancer-signaling cascades directly. Even though cell
cultures and xenografts capture physical structure of tumors, they do not directly control the heterogeneity of a
targeted signaling cascade. Here, we propose to overcome the bottleneck by engineering a biomimetic-cell
approach that reconstitutes variants of a cancer-signaling cascade to mimic its heterogeneity. Each biomimetic
cell is a synthetic system that will be constructed from the bottom-up by mimicking cell membranes and one
instance of a heterogeneous cancer-signaling cascade. As a proof of concept, we will investigate the proposed
idea using the Ras signaling cascade activated by the platelet-derived-growth-factor receptor beta (PDGFRβ).
The proposal consists of two main steps 1) Reconstitute the core PDGFRβ signaling cascade inside biomimetic
cells. 2) Model heterogeneity of the PDGFRβ signaling cascade inside biomimetic cells. We will construct a
library of biomimetic cells, each containing one unique instance of the signaling cascade, using a bio-printer
that will mix each protein constituents of the signaling cascade at well-defined composition and concentrations.
The biomimetic-cell library and a computational model will be integrated to study robustness of anti-cancer
drugs in inhibiting the heterogeneous signaling cascade. The proposed idea challenges the main paradigm in
studies of cancer-signaling cascades by reconstituting (partially or fully) at least 100,000 unique and
physiologically relevant instances of a cancer-signaling cascade inside biomimetic cells, which will be
miniaturized for high-throughput drug screening. The proposed work is significant because it enables multi-
dimensional screening of drugs against well-defined heterogeneity of the targeted cancer-signaling cascade. If
successful, this study will yield a validated, generalizable approach for studying the impact of heterogeneous
cancer pathways on the efficacy of anti-cancer drugs.

## Key facts

- **NIH application ID:** 10085223
- **Project number:** 5R21EB025938-03
- **Recipient organization:** UNIVERSITY OF CALIFORNIA AT DAVIS
- **Principal Investigator:** Cheemeng Tan
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $226,787
- **Award type:** 5
- **Project period:** 2019-03-15 → 2023-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10085223

## Citation

> US National Institutes of Health, RePORTER application 10085223, Modeling heterogeneity of a cancer-signaling cascade using biomimetic cells (5R21EB025938-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10085223. Licensed CC0.

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