# Integrating bioinformatics into multiscale models for hepatocellular carcinoma

> **NIH NIH U01** · JOHNS HOPKINS UNIVERSITY · 2020 · $43,359

## Abstract

Project Summary
Hepatocellular carcinoma (HCC) causes the 3rd most cancer related deaths worldwide, only a 5-
year survival rate of 12%, and over 70% of HCC cases are diagnosed late-stage. In late stage,
HCC has developed a complex molecular and cellular network in order to survive. The
complexity of this system makes treatment of HCC difficult. Understanding the molecular and
cellular processes in HCC opens the door to possible treatment methods.
To aid in the understanding of HCC function and therapeutic response, the parent proposal
embeds cell signaling networks with states determined by genomic data in multi-scale
mathematical models. Preliminary work by Dr. Fertig and Dr. Popel has used such a cellular
signaling network of the HGF/Met pathway to model HCC [1]. Independently, Dr. Fertig has
demonstrated that alternative splicing can dysregulate cellular signaling pathways in absence of
mutations or copy number alterations [2]. This supplement to the associated parent grant will
provide a framework for evaluating alternative splicing events and their relation to functional
changes in HCC to enhance the signaling networks in the mathematical models.

## Key facts

- **NIH application ID:** 10154624
- **Project number:** 3U01CA212007-03S1
- **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:** $43,359
- **Award type:** 3
- **Project period:** 2020-05-01 → 2023-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10154624

## Citation

> US National Institutes of Health, RePORTER application 10154624, Integrating bioinformatics into multiscale models for hepatocellular carcinoma (3U01CA212007-03S1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10154624. Licensed CC0.

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