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Combined Imaging and RNA Analyses to Predict Head and Neck Cancer Recurrence

Award Number: R56DE034973
ORGANIZATION: NATIONAL INSTITUTE OF DENTAL AND CRANIOFACIAL RESEARCH
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)
PERIOD OF PERFORMANCE START DATE: 09/17/2026
PERIOD OF PERFORMANCE END DATE: 09/16/2028

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Combined Imaging and RNA Analyses to Predict Head and Neck Cancer Recurrence - Title: Combined Imaging and RNA Analyses to Predict Head and Neck Cancer Recurrence ABSTRACT Head and neck squamous cell carcinomas (HNSCCs) are biologically diverse and clinically aggressive, with high recurrence rates and poor treatment outcomes. Approximately 50% of patients experience recurrence, and prognosis is particularly poor for HPV-negative tumors, where median overall survival following recurrence is only 8.9 months. This proposal seeks to address this critical gap by developing integrative multimodal models that combine imaging, genomic, and clinical data to improve early recurrence prediction and personalize therapy in HPV-negative HNSCC. We and others have shown that human ribonucleic acids (RNAs) are promising biomarkers and play critical regulatory roles in HNSCC. Radiologic imaging biomarkers derived from PET, CT, and MR imaging data have shown promise in stratifying patients with favorable and unfavorable prediction for treatment response. Their non-invasive characteristics also allow for convenient and longitudinal monitoring of tumor progression and heterogeneous response during the treatment course. Histologic images provide key information about tumor architecture, cellular features, patterns of invasion, and associated tumor microenvironment. Recent reports and our preliminary studies have shown that histologic imaging biomarkers can aid in decision-making by identifying metastases, subtyping and grading tumors, and predicting clinical outcomes. Clinicopathologic biomarkers (e.g., tumor stage, nodal involvement) have shown prognostic value in retrospective studies. Still, many HPV-negative HNSCC patients have cancer recurrence and poor survival outcomes despite favorable prognosis by these biomarkers individually. The major goal of this study is to develop a comprehensive and robust computational model to enable early prediction of treatment failure and tumor recurrence in HPV-negative HNSCC. We will integrate our recently developed advanced learning-based techniques to build prognostic models using about 1,000 patient cases collected from two institutions. The prognostic model will form a solid basis for individualized care of HNSCC patients based on predicted treatment outcomes. Moreover, our work is expected to discover the correlations among multimodal data, leading to dynamic patient stratification to support adaptive treatment strategies.


Issue Date FY	Funding FY	Legal Entity Name	Legal Entity Address	Legal Entity City	Legal Entity State	Legal Entity Zip Code	Legal Entity COUNTY	Legal Entity COUNTRY	Assistance Listing	Award Code	Budget Year	Action Date	Action Type	Action Amount

Issue Date FY: 2026 ( Subtotal = $1,315,306 )
2026	2026	WASHINGTON UNIVERSITY, THE	1 BROOKINGS DR	SAINT LOUIS	MO	63130	SAINT LOUIS	USA	Oral Diseases and Disorders Research	000	1	9/17/2026	NEW	$1,315,306
														Subtotal = $1,315,306

Grand Total All Awards = $1,315,306

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Combined Imaging and RNA Analyses to Predict Head and Neck Cancer Recurrence

Award Number: R56DE034973

ORGANIZATION: NATIONAL INSTITUTE OF DENTAL AND CRANIOFACIAL RESEARCH

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)

PERIOD OF PERFORMANCE START DATE: 09/17/2026

PERIOD OF PERFORMANCE END DATE: 09/16/2028

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