# Integrating bioinformatics into multiscale models for hepatocellular carcinoma

> **NIH NIH U01** · JOHNS HOPKINS UNIVERSITY · 2020 · $608,149

## Abstract

Project Summary
Liver cancer is a major global health problem, responsible for the 3rd most cancer deaths worldwide. Diagnosis
often occurs at late stages, at which point liver tumors have complex tumor/stroma interactions across multiple
spatial and temporal scales. The resulting multiscale interactions drive tumor progression and therapeutic
response. The proposed project will develop new mathematical/computational techniques to model molecular,
cellular, tumor, and organ scales to elucidate the mechanisms driving liver cancer progression and to predict
the response to targeted therapeutics. The investigator team is uniquely suited to develop the proposed
multiscale models of hepatocellular carcinoma (HCC), the most common type of liver cancer. The expertise of
the four PIs/PDs is synergistic, combining a state of the art multiscale computational models of cancer (Dr.
Popel) with molecular and cellular features inferred from bioinformatics analysis (Dr. Fertig) using state of the
art 3D in vitro organoid models (Dr. Ewald) and in vivo mouse models of HCC (Dr. Tran). The well-integrated
experimental/computational design of the proposal will result in new algorithms for predictive computational
modeling of therapeutic response in HCC. We include extensive experimental studies for model development,
parameter tuning, and validation. Specific Aim 1 will infer bioinformatically the signaling pathways important in
crosstalk between cancer and stromal cells, integrate models of intracellular signaling and 3D extracellular
ligand transport and biochemical reactions and embed them into the cell fate decision rules of an agent-based
model of cellular agents resulting in a multiscale hybrid model. The model will be parameterized with phospho-
proteomic data under relevant ligand stimulations identified by the bioinformatics analysis and with growth,
invasion, proteomic, and genomic data from co-cultured cancer and stromal cells and organoids; independent
data will be used for model validation. We will use this model to predict outcomes in a 3D in vitro organoid
model of HCC. Specific Aim 2 will extend and adapt this hybrid model to model the tumor microenvironment
and to account for the drug pharmacokinetic and pharmacodynamic, the 3D geometry of the liver, molecular
interactions in vivo and cellular composition inferred from bioinformatics analysis. Finally, Specific Aim 3 will
develop new bioinformatics analysis algorithms to initialize the model with distribution of cellular agents and
molecular states from The Cancer Genome Atlas (TCGA) genomic and proteomic data to predict the efficacy
of targeted therapeutics in the diverse genetic backgrounds of human liver cancer. The project will develop
innovative computational techniques to integrate features at both the molecular and cellular scales from
genomics and proteomics analysis with multiscale computational models to predict therapeutic response. The
resulting computational algorithms will address the...

## Key facts

- **NIH application ID:** 9891969
- **Project number:** 5U01CA212007-03
- **Recipient organization:** JOHNS HOPKINS UNIVERSITY
- **Principal Investigator:** Andrew Josef Ewald
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $608,149
- **Award type:** 5
- **Project period:** 2018-04-17 → 2023-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9891969

## Citation

> US National Institutes of Health, RePORTER application 9891969, Integrating bioinformatics into multiscale models for hepatocellular carcinoma (5U01CA212007-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9891969. Licensed CC0.

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