# Integrated Cancer Modeling: A New Dimension

> **NIH NIH R03** · YALE UNIVERSITY · 2020 · $83,750

## Abstract

Project Summary
Significant effort has been devoted to cancer modeling, exploring statistical models that can more accurately
describe cancer outcomes/phenotypes. Taking advantage of data collected at Yale University and other institutes
and publicly available data (especially TCGA), we have conducted extensive research on integrated cancer
modeling using various types of omics data. In the clinical practice of most if not all cancers, imaging techniques
have been routinely used. Specifically, radiologists use CT/MRI/PET and other techniques, generate radiological
images, and report the “macro” features of tumors. Then with samples retrieved via biopsy, pathologists review
the slides of representative sections of tissues and make definitive diagnosis – this procedure generates
pathological (diagnostic) images, which contain rich information on the “micro” features of tumors. Omics and
pathological imaging data have been separately analyzed and established as highly effective for cancer modeling.
However, a critical and practically highly relevant question, which remains unanswered, is “for more accurate
cancer modeling, is it necessary to integrate omics and pathological imaging data?”.
 Our ultimate goal is to more effectively model cancer outcomes/phenotypes by integrating multiple
sources/types of data, so as to advance cancer research and practice. In this study, we will take advantage of
data recently collected under multiple Yale studies and TCGA, significantly expand the integrated analysis
paradigm developed for omics data, and innovatively integrate various types of omics and pathological imaging
data for cancer modeling. Three highly interconnected aims have been designed to comprehensively and
complementarily study different perspectives of data integration. Aim 1: Assess the degree of overlapping
information in cancer-associated omics and imaging features/models. This analysis will reveal whether
overall omics and imaging data contain independent information and its degree, which is the foundation of data
integration. Aim 2: Identify individual imaging (omics) features that are independently associated with
cancer beyond omics (imaging) features. This analysis will identify the most important imaging/omics features,
which are likely to have the highest scientific, clinical, and statistical value. Aim 3: Construct integrated models
using all omics and imaging features. This analysis can lead to “mega” models, which are superior to those
constructed using omics and imaging data separately, as well as rigorous measures of improvement. Such
results will have high clinical relevance.
 This study will deliver an innovative analysis pipeline and multiple novel methods for integrating omics
and pathological imaging data. With omics and pathological imaging data now routinely collected in cancer
research and practice, this study will open a new venue and have a high and long-lasting impact.

## Key facts

- **NIH application ID:** 9965900
- **Project number:** 5R03CA241699-02
- **Recipient organization:** YALE UNIVERSITY
- **Principal Investigator:** Shuangge Ma
- **Activity code:** R03 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $83,750
- **Award type:** 5
- **Project period:** 2019-07-01 → 2022-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9965900

## Citation

> US National Institutes of Health, RePORTER application 9965900, Integrated Cancer Modeling: A New Dimension (5R03CA241699-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9965900. Licensed CC0.

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