Multiplexed biomarker analysis is more powerful in reflecting the biological behaviors of a tumor than single biomarker analysis, but its standardization and quantification is still a challenge. Furthermore, most computer software does not provide methods for imaging and analyzing subcellular localization of biomarkers and correlating them with biological and clinical information. The objective of this project is to develop a platform which combines imaging and quantification of multiplexed immunostaining plus bioinformatics for the prediction of lymph node metastases (LNM) from the primary tumor (PT) of squamous cell carcinoma of the head and neck (SCCHN). LNM of SCCHN is a precisely defined biological phenomenon which is an ideal model to be utilized to develop this multiplexed biomarker platform (MBP). Based on our preliminary studies, we aim to test the hypothesis that that the MBP can be developed to identify the subcellular distribution and expression of multiple metastasis-related biomarkers simultaneously in PTs. Accurate quantification of these biomarkers will facilitate the prediction of metastasis from PTs. Three emerging technologies, quantum dot (QD)-based immunohistofluorescence (IHF), multispectral imaging, and machine learning will be used to test this hypothesis. Using these approaches, a platform that combines quantifying multiplexed immunostaining with biostatistics will be developed and tested for its sensitivity, specificity, and prediction power for use in the clinic. Therefore, this project fits appropriately to the scope of the NCI program announcement """"""""Developmental Research in Cancer Prognosis and Prediction"""""""" (PA-09-159).
Three aims are proposed in the study. (1) To develop a multiplexed biomarker system and method based on a bulk tissue model for prediction of LNM in SCCHN PT tissues.
This Aim will establish and validate an analysis methodology for multiplexed quantification of membrane and cytoplasmic staining using a new function in InForm software where subcellular localization of certain biomarkers will be specifically analyzed. Prediction of LNM based on this bulk tissue model will be achieved. (2) To develop a per-cell quantification method based on a sub-population model for prediction of LNM in SCCHN PT tissue. The per-cell analysis results will quantified as the percentage of high risk cells from the multiplexed biomarker analyses in the same PTs. The high risk population will be correlated with LNM. The sensitivity and specificity of the prediction by the sub-population model will be compared with that of the bulk tissue model. (3) To develop and validate a nomogram with software combining clinical characterizations of metastasis as a working platform for the prediction of LNM. While the primary endpoint of Aim 1 and 2 is to correlate the three biomarkers with metastasis, other clinical factors such as differentiation status, tumor stage, and site, etc. may also correlate with LNM. The most predictive biomarker set combined with relevant clinical factors will constitute a platform with computer software that will be validated in an additional 100 SCCHN samples for prediction of LNM.

Public Health Relevance

Imaging and quantifying expression and subcellular localization of multiplexed biomarkers is currently a challenge in cancer research and clinical application. This project aims to develop a platform which combines imaging and quantifying multiplexed biomarkers plus their correlation with lymph node metastasis of squamous cell carcinoma of the head and neck. This platform can be used for an assessment of LNM which is paramount for appropriate treatment planning.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Exploratory/Developmental Grants Phase II (R33)
Project #
Application #
Study Section
Cancer Biomarkers Study Section (CBSS)
Program Officer
Kim, Kelly Y
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
Emory University
Internal Medicine/Medicine
Schools of Medicine
United States
Zip Code
Hu, Zhongliang; Qian, Guoqing; Müller, Susan et al. (2016) Biomarker quantification by multiplexed quantum dot technology for predicting lymph node metastasis and prognosis in head and neck cancer. Oncotarget 7:44676-44685
Qian, Guoqing; Hu, Zhongliang; Xu, Hong et al. (2016) A novel prediction model for human papillomavirus-associated oropharyngeal squamous cell carcinoma using p16 and subcellular ?-catenin expression. J Oral Pathol Med 45:399-408
Qian, Guoqing; Wang, Dongsheng; Magliocca, Kelly R et al. (2016) Human papillomavirus oncoprotein E6 upregulates c-Met through p53 downregulation. Eur J Cancer 65:21-32
Block, Keith I; Gyllenhaal, Charlotte; Lowe, Leroy et al. (2015) Designing a broad-spectrum integrative approach for cancer prevention and treatment. Semin Cancer Biol 35 Suppl:S276-S304
Hu, Zhongliang; Müller, Susan; Qian, Guoqing et al. (2015) Human papillomavirus 16 oncoprotein regulates the translocation of ?-catenin via the activation of epidermal growth factor receptor. Cancer 121:214-25
Xu, Hong; Xu, Jing; Wang, Xu et al. (2013) Quantum dot-based, quantitative, and multiplexed assay for tissue staining. ACS Appl Mater Interfaces 5:2901-7
Li, Rui; You, Shuo; Hu, Zhongliang et al. (2013) Inhibition of STAT3 by niclosamide synergizes with erlotinib against head and neck cancer. PLoS One 8:e74670
Xu, Jing; Müller, Susan; Nannapaneni, Sreenivas et al. (2012) Comparison of quantum dot technology with conventional immunohistochemistry in examining aldehyde dehydrogenase 1A1 as a potential biomarker for lymph node metastasis of head and neck cancer. Eur J Cancer 48:1682-91
Wang, Dongsheng; Müller, Susan; Amin, A R M Ruhul et al. (2012) The pivotal role of integrin ?1 in metastasis of head and neck squamous cell carcinoma. Clin Cancer Res 18:4589-99