# Integrating multi-omics, imaging, and longitudinal data to predict radiation response in cervical cancer

> **NIH NIH R01** · WASHINGTON UNIVERSITY · 2024 · $481,595

## Abstract

PROJECT SUMMARY/ABSTRACT
Cervical cancer is among the most common cancer diagnoses among women, and treatment failure of standard
of care chemoradiation therapy (CRT) for locally advanced cervical cancer (LACC) is as high as 30-50%. Since
recurrent and metastatic diseases are not curable, there is a pressing need to identify patients at risk of treatment
failure as early as possible to allow for personalized treatment, rather than after a failure and progression. While
TCGA’s molecular stratification of cervical cancer using genomic data failed to associate to patient outcomes,
we recently published on integrating genomic and imaging data to improve LACC risk stratification after CRT.
Therefore, in this study we intend to use multi-omics data to define and validate LACC risk groups and identify
group-specific treatment targets. Based on our preliminary data that indicate distinct biological mechanisms drive
CRT resistance in patients with different levels of lymph node (LN) involvement at presentation, we will stratify
patients by LN status to develop and validate novel radiogenomic biomarkers. Prognostic models will be
developed using gene expression data from pre-treatment tumor biopsy and radiomic features from pre-
treatment PET imaging data. Upstream driver and/or feature genes will be validated at the RNA and protein
levels by qRT-PCR, Western blotting, and tissue microarray (TMA). One such gene identified from our
preliminary data using a radiogenomic approach is nuclear factor erythroid 2–related factor 2 (NRF2), which has
not been previously characterized in LACC, since it is not frequently mutated in cervical cancer. We will perform
functional analysis to study NRF2 biology in LACC via clonogenic survival assay and other standard assays. In
addition to pre-treatment biomarkers, we will leverage radiomic features from our time course MR images and
on-treatment gene expression data to develop novel radiogenomic biomarkers to assess a patient's evolving risk
of treatment failure over the course of CRT, informing adjustment of therapy at mid-treatment. The pre-treatment
model will be further refined by applying deep learning to identify predictive features for CRT outcome directly
from clinical PET images to inform intensified treatment from the beginning. Finally, we will apply multi-omics
approaches (scRNA-seq, proteomics, metabolomics) to characterize the biology related to LACC CRT
radiogenomic biomarkers. Taken together, we expect fulfillment of these aims will create a series of optimized,
validated recurrence biomarkers at presentation and over the course of 6 weeks of CRT treatment, and will
indicate targets for personalized alternative treatment regimens. Beyond the specific application to LACC, our
proposal will generate novel methods to integrate multi-omics data to improve hypothesis-driven cancer research.

## Key facts

- **NIH application ID:** 10923978
- **Project number:** 5R01CA276955-02
- **Recipient organization:** WASHINGTON UNIVERSITY
- **Principal Investigator:** Jin Zhang
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $481,595
- **Award type:** 5
- **Project period:** 2023-09-07 → 2028-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10923978

## Citation

> US National Institutes of Health, RePORTER application 10923978, Integrating multi-omics, imaging, and longitudinal data to predict radiation response in cervical cancer (5R01CA276955-02). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10923978. Licensed CC0.

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