# Multimodal Biomarkers For Oropharyngeal Cancer

> **NIH NIH R01** · UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN · 2020 · $416,519

## Abstract

Abstract
Head and neck cancers are the fifth most common cancer type in the United States, with an overall survival
rate lower than 50%. Although the incidence of other sub-sites of head and neck cancer has decreased
steadily in past decades, the number of oropharyngeal squamous cell carcinoma (OPSCC) cases has
increased significantly. Most OPSCC patients receive standard cancer therapy.4 However, the clinical
outcomes vary significantly and are difficult to predict. Predicting early in treatment whether a tumor is likely to
respond to treatment is one of the most difficult yet important tasks in providing individualized cancer care.
 Human papillomavirus (HPV) is a known driving oncogenic factor in oropharyngeal cancer, as well as a
significant prognostic biomarker for patient survival. Retrospective studies conducted by the International Head
and Neck Cancer Epidemiology Consortium (INHANCE) have demonstrated that clinical biomarkers have
prognostic value in helping stratify OPSCC patients into groups with differing risks of death or disease
progression. However, HPV-positive oropharyngeal cancer patients have similar rates of metastatic spread to
HPV-negative patients. The same is true for patient groups stratified with other clinical biomarkers. More robust
prognostic biomarkers are needed to accurately stratify patients for optimally effective treatment.
 MicroRNAs (miRNAs) are a family of small non-coding RNA molecules that collectively control the
expression of thousands of protein-coding genes. Multiple studies indicate that miRNAs are promising cancer
biomarkers and play critical regulatory roles in oropharyngeal cancer. Imaging features extracted from medical
images are an exciting new class of cancer biomarkers for characterizing tumor habitats. For several tumor
sites, imaging biomarkers have shown promise in accurately separating favorable and unfavorable prognosis
patients. However, current efforts to utilize high-dimensional multimodal biomarkers for treatment outcome
prediction have been compromised by small patient numbers relative to the feature space dimensionality;
feature redundancy, heterogeneity, and uncertainty; and patient cohorts with unbalanced outcomes. The
correlation, independence, and complementary nature of multimodal biomarkers (imaging, miRNA, HPV,
clinical, and histopathologic biomarkers) remains unexplored as well.
 The major goal of this research is to develop a multimodal biomarker-based model that can reliably predict
subsets of OPSCC patients with low and high risks for treatment failure. The model will serve as a clinical
decision-making tool. Specifically, we propose a novel principle and systematic machine learning-based
strategy to effectively identify and seamlessly combine prognostic information carried by multimodal
biomarkers. Aim 1: Identify prognostic multimodal biomarkers, given OPSCC patient data. Aim 2: Develop and
test a comprehensive multimodal biomarker-based model for predicting OPSCC treat...

## Key facts

- **NIH application ID:** 9994253
- **Project number:** 5R01CA233873-02
- **Recipient organization:** UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN
- **Principal Investigator:** Hua Li
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $416,519
- **Award type:** 5
- **Project period:** 2019-08-12 → 2024-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9994253

## Citation

> US National Institutes of Health, RePORTER application 9994253, Multimodal Biomarkers For Oropharyngeal Cancer (5R01CA233873-02). Retrieved via AI Analytics 2026-05-28 from https://api.ai-analytics.org/grant/nih/9994253. Licensed CC0.

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