# Network Analysis for a Data-Driven Approach to Cancer Care

> **NIH NIH U01** · VANDERBILT UNIVERSITY MEDICAL CENTER · 2020 · $336,986

## Abstract

Project Summary
The treatment of cancer has dramatically increased in complexity over the last several decades. Five-year
relative cancer survival for all cancers has improved from 49% in the 1970’s to 68% in the 2000’s. This improved
outlook is the result of careful and systematic evaluation of many drug combinations. However, cancer remains
the second-leading cause of death in the United States and the leading cause of years of potential life lost. The
explosion of treatment options for any given cancers has introduced a complexity of choice which is troubling for
most clinicians. As just one example, there are at least 70 distinct regimens that have been evaluated in
randomized trials for the postoperative (adjuvant) treatment of breast cancer. Given the number of possible drug
combinations for nearly every subtype of cancer, a full comparison of all treatment options in the clinical trial
setting can never be done. Adding to this complexity is that the very definition of cancer is changing, as genomic
information regarding prognosis and treatment selection is brought into the clinic. This deluge of information
has exceeded the cognitive capacity of cancer clinicians. One solution to this information problem is to
increasingly rely on expert-driven guidelines or proscribed pathways. However, many cancer scenarios are
already complex enough that guidelines do not, for example, provide ranked recommendations for treatment
options. With few exceptions, published guidelines also rarely incorporate tumor biology. We have previously
introduced new information theoretic methods to quantitatively compare the value of treatment regimens that
have never been compared directly, as well as network analytic methods to begin to tie tumor biology into the
clinical setting. Our group also has experience with advanced data extraction techniques and experience
developing standardized software applications that can be utilized by clinicians and researchers. In response to
PAR-15-332 (Early Stage Development of Informatics Technologies for Cancer Research and Management),
we will: 1) Produce a comprehensive ontology of chemotherapy regimen concepts; 2) Determine a treatment
preference hierarchy based on information theoretic network analysis, and present the results to clinicians and
cancer researchers; and 3) Present preference hierarchy-modulated genomic treatment options. Our software
will be evaluated on two common cancers: breast cancer and multiple myeloma and will be made freely available
for non-commercial uses. We believe that the software developed as a result of this work will be widely
applicable to a variety of cancer types, to the benefit of clinicians and the patients that they care for.

## Key facts

- **NIH application ID:** 9991804
- **Project number:** 5U01CA231840-03
- **Recipient organization:** VANDERBILT UNIVERSITY MEDICAL CENTER
- **Principal Investigator:** Jeremy L. Warner
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $336,986
- **Award type:** 5
- **Project period:** 2018-09-18 → 2022-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9991804

## Citation

> US National Institutes of Health, RePORTER application 9991804, Network Analysis for a Data-Driven Approach to Cancer Care (5U01CA231840-03). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9991804. Licensed CC0.

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