# Multi-omic marker sets involving KRAS, TP53, and SMAD4 and their association with survival in pancreatic ductal adenocarcinoma

> **NIH NIH P20** · NORTH DAKOTA STATE UNIVERSITY · 2022 · $210,357

## Abstract

PROJECT SUMMARY
Fully characterizing genomic changes is a central challenge in understanding biological processes and
creating effective treatment targets for complex, multi-factorial diseases like cancer. Genomic and epigenomic
data have been used to subcategorize pancreatic ductal adenocarcinoma (PDAC) into more meaningful and
functionally important groups. This data can be used to understand unique biological and molecular tumor
signatures for treatment and improve survival. Typical multi-omics approaches use single-feature association
analysis that requires the feature to have the same behavior in all patients in each group and high homogeneity
in each subgroup. We propose a more realistic analysis using the convolution neural network (CNN) deep
learning approach to analyze multi-omics datasets. CNN is designed to identify subsets of features important
in subsets of patients. We will enrich existing PDAC omics datasets developed in our previous research
collaboration, where an omics-based approach was used to identify the PDAC-associated alterations across
omic sets (e.g., expression, methylation, sequence, etc.). We propose identifying the functionally important
genomic features associated with core gene (KRAS, SMAD4, and TP53) mutations that lead to PDAC
progression and survival. We will identify high-leverage biomarker/treatment targets with the following study
objectives. (1) Assess and refine core gene involved sufficient-component survival models for participants with
paired normal and tumor tissues, (2) use genetically engineered mouse models (KPC and PKS) to refine these
models, and (3) train deep learning methods to identify gene mutations, epigenetic, and clinical features
associated with the progression and survival of PDAC tumors. Our team is ideally suited to accomplish these
aims, as we have developed bioinformatics workflows and conducted epigenetic and genome-wide analyses
in multiple disease conditions using numerous study types in humans and mice. This proposed work will use
the CNN deep learning method to explore multi-omics data and validate combinations of clinical, genetic, and
epigenetic features associated with PDAC progression and survival previously observed by our group. We will
use genomic feature-defined subtypes of PDAC and incorporate clinical information, and the methods and
framework used in this project can be applied to other cancers. The proposed work will reveal functionally
meaningful relationships between genomic features (e.g., methylation, miRNA, lncRNA, ATAC, core gene
mutations) and core gene expression in PDAC tissue and adjacent normal samples. This will provide targets
for drug development and has the potential to benefit millions of people. This work will also lay the foundation
for integrating genomic data within the clinic to provide individualized PDAC tumor treatment plans.

## Key facts

- **NIH application ID:** 10496135
- **Project number:** 2P20GM109024-06
- **Recipient organization:** NORTH DAKOTA STATE UNIVERSITY
- **Principal Investigator:** Rick J Jansen
- **Activity code:** P20 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $210,357
- **Award type:** 2
- **Project period:** 2016-03-01 → 2027-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10496135

## Citation

> US National Institutes of Health, RePORTER application 10496135, Multi-omic marker sets involving KRAS, TP53, and SMAD4 and their association with survival in pancreatic ductal adenocarcinoma (2P20GM109024-06). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10496135. Licensed CC0.

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