OBJECTIVES: The late effects of RT are not well reported in patients with oral tongue cancer (OTC). This study reports the incidence of late effects and factors associated with the development of late effects in OTC patients. METHODS: Patients with OTC treated in our institution from 2003 to 2013 were evaluated. The association between RT doses, including mandible maximum and minimum doses and total 3D maximum dose, and late toxicity, defined as development of osteoradionecrosis (ORN), percutaneous endoscopic gastrostomy (PEG) tube dependence for >6 months after treatment, and narcotic dependency >6 months posttreatment were assessed using both univariate and multivariable (MV) analysis. RESULTS: Seventy-six patients with OTC (45% males and 55% females) were treated with definitive surgical resection followed by adjuvant RT. The median follow-up was 4.3 years. Combined late toxicities were reported in 38% of patients. Thirty-four percent of the patients had narcotic dependency and, 3.9% of the patients had ORN of the mandible. Thirteen percent of patients developed PEG tube dependency that was significantly associated with a higher 3D maximum radiation dose on univariate analysis (p < 0.01). On MV analysis, 3D maximum dose remained significantly associated with long-term PEG tube dependency (p = 0.05). CONCLUSION: Patients with OTC treated with adjuvant RT are at significant risk for development of late toxicities. Increasing maximum dose is associated with long-term PEG tube dependence, and care should be taken to reduce the "hot spot" within radiation treatment plans as much as possible.

Purpose: Our previous work suggested that HER3 inhibition sensitizes head and neck squamous cell carcinoma (HNSCC) to EGFR inhibition with cetuximab. This study aimed to define the role of HER3 in cetuximab resistance and the antitumor mechanisms of EGFR/HER3 dual targeting in HNSCC. Experimental Design: We treated cetuximab-resistant HNSCC UMSCC1-C and parental UMSCC1-P cell lines with anti-EGFR antibody cetuximab, anti-HER3 antibody MM-121, and their combination. We assessed activities of HER2, HER3, and downstream signaling pathways by Western blotting and cell growth by sulforhodamine B (SRB) and colony formation assays. HER3-specific shRNA was used to confirm the role of HER3 in cetuximab response. The combined efficacy and alterations in biomarkers were evaluated in UMSCC1-C xenograft and patient-derived xenograft (PDX) models. Results: Cetuximab treatment induced HER3 activation and HER2/HER3 dimerization in HNSCC cell lines. Combined treatment with cetuximab and MM-121 blocked EGFR and HER3 activities and inhibited the PI3K/AKT and ERK signaling pathways and HNSCC cell growth more effectively than each antibody alone. HER3 knockdown reduced HER2 activation and resensitized cells to cetuximab. Cetuximab-resistant xenografts and PDX models revealed greater efficacy of dual EGFR and HER3 inhibition compared with single antibodies. In PDX tissue samples, cetuximab induced HER3 expression and MM-121 reduced AKT activity. Conclusions: Clinically relevant PDX models demonstrate that dual targeting of EGFR and HER3 is superior to EGFR targeting alone in HNSCC. Our study illustrates the upregulation of HER3 by cetuximab as one mechanism underlying resistance to EGFR inhibition in HNSCC, supporting further clinical investigations using multiple targeting strategies in patients who have failed cetuximab-based therapy.

One of the largest factors affecting disease recurrence after surgical cancer resection is negative surgical margins. Hyperspectral imaging (HSI) is an optical imaging technique with potential to serve as a computer aided diagnostic tool for identifying cancer in gross ex-vivo specimens. We developed a tissue classifier using three distinct convolutional neural network (CNN) architectures on HSI data to investigate the ability to classify the cancer margins from ex-vivo human surgical specimens, collected from 20 patients undergoing surgical cancer resection as a preliminary validation group. A new approach for generating the HSI ground truth using a registered histological cancer margin is applied in order to create a validation dataset. The CNN-based method classifies the tumor-normal margin of squamous cell carcinoma (SCCa) versus normal oral tissue with an area under the curve (AUC) of 0.86 for inter-patient validation, performing with 81% accuracy, 84% sensitivity, and 77% specificity. Thyroid carcinoma cancer-normal margins are classified with an AUC of 0.94 for inter-patient validation, performing with 90% accuracy, 91% sensitivity, and 88% specificity. Our preliminary results on a limited patient dataset demonstrate the predictive ability of HSI-based cancer margin detection, which warrants further investigation with more patient data and additional processing techniques to optimize the proposed deep learning method.

A label-free, hyperspectral imaging (HSI) approach has been proposed for tumor margin assessment. HSI data, i.e., hypercube (x,y,λ), consist of a series of high-resolution images of the same field of view that are acquired at different wavelengths. Every pixel on an HSI image has an optical spectrum. In this pilot clinical study, a pipeline of a machine-learning-based quantification method for HSI data was implemented and evaluated in patient specimens. Spectral features from HSI data were used for the classification of cancer and normal tissue. Surgical tissue specimens were collected from 16 human patients who underwent head and neck (H&N) cancer surgery. HSI, autofluorescence images, and fluorescence images with 2-deoxy-2-[(7-nitro-2,1,3-benzoxadiazol-4-yl)amino]-D-glucose (2-NBDG) and proflavine were acquired from each specimen. Digitized histologic slides were examined by an H&N pathologist. The HSI and classification method were able to distinguish between cancer and normal tissue from the oral cavity with an average accuracy of 90%±8%, sensitivity of 89%±9%, and specificity of 91%±6%. For tissue specimens from the thyroid, the method achieved an average accuracy of 94%±6%, sensitivity of 94%±6%, and specificity of 95%±6%. HSI outperformed autofluorescence imaging or fluorescence imaging with vital dye (2-NBDG or proflavine). This study demonstrated the feasibility of label-free, HSI for tumor margin assessment in surgical tissue specimens of H&N cancer patients. Further development of the HSI technology is warranted for its application in image-guided surgery.

Purpose: This study intends to investigate the feasibility of using hyperspectral imaging (HSI) to detect and delineate cancers in fresh, surgical specimens of patients with head and neck cancers. Experimental Design: A clinical study was conducted in order to collect and image fresh, surgical specimens from patients (N = 36) with head and neck cancers undergoing surgical resection. A set of machine-learning tools were developed to quantify hyperspectral images of the resected tissue in order to detect and delineate cancerous regions which were validated by histopathologic diagnosis. More than two million reflectance spectral signatures were obtained by HSI and analyzed using machine-learning methods. The detection results of HSI were compared with autofluorescence imaging and fluorescence imaging of two vital-dyes of the same specimens. Results: Quantitative HSI differentiated cancerous tissue from normal tissue in ex vivo surgical specimens with a sensitivity and specificity of 91% and 91%, respectively, and which was more accurate than autofluorescence imaging (P < 0.05) or fluorescence imaging of 2-NBDG (P < 0.05) and proflavine (P < 0.05). The proposed quantification tools also generated cancer probability maps with the tumor border demarcated and which could provide real-time guidance for surgeons regarding optimal tumor resection. Conclusions: This study highlights the feasibility of using quantitative HSI as a diagnostic tool to delineate the cancer boundaries in surgical specimens, and which could be translated into the clinic application with the hope of improving clinical outcomes in the future.

We explored potential associations of the PD-1/PD-L1/PD-L2 pathway with clinical characteristics, outcome, and expression of EGFR, HER2, HER3 in oropharyngeal squamous cell carcinoma (OPSCC) using an institutional database. Protein expression was assessed by IHC on tissue microarray sections (EGFR, HER2, HER3) or whole tissue sections (PD-1/PD-L1/PD-L2). Expression of EGFR, HER2, HER3, PD-L1, and PD-L2 was quantified on tumor cells. Maximum density of PD-1 positive lymphocytes was measured on a scale of 0 to 4 within the tumor mass and peritumoral stroma. Associations between biomarkers and patient outcomes were tested using descriptive and inferential statistics, logistic regression, and Cox proportional hazards models. We analyzed tissue samples from 97 OPSCC cases: median age 59 years, p16þ (71%), male (83.5%), never smokers (18%), stage 3 to 4 disease (77%). Twenty-five percent of cases were PD-L1 positive. The proportion of PD-L1þ tumors was higher in p16þ (29%) than p16 OPSCC (11%, P ¼ 0.047). There was no correlation between PD-L1, PD-L2, PD-1, EGFR, HER2, or HER3 expression. Positive PD-L1 status correlated with advanced nodal disease on multivariate analysis (OR 5.53; 95% CI, 1.06–28.77; P ¼ 0.042). Negative PD-L2 expression was associated with worse survival (HR 3.99; 95% CI, 1.37–11.58; P ¼ 0.011) in p16 OPSCC. Lower density of PD-1 positive lymphocytes in peritumoral stroma was associated with significantly increased risk of death on multivariate analysis (HR 3.17; 95% CI, 1.03–9.78; P ¼ 0.045) after controlling for prognostic factors such as stage and p16 status. PD-L1 expression on tumor cells correlates with p16 status and advanced nodal status in OPSCC. PD-1 positive lymphocytes in peritumoral stroma serve as an independent prognostic factor for overall survival.

Surgical cancer resection requires an accurate and timely diagnosis of the cancer margins in order to achieve successful patient remission. Hyperspectral imaging (HSI) has emerged as a useful, noncontact technique for acquiring spectral and optical properties of tissue. A convolutional neural network (CNN) classifier is developed to classify excised, squamous-cell carcinoma, thyroid cancer, and normal head and neck tissue samples using HSI. The CNN classification was validated by the manual annotation of a pathologist specialized in head and neck cancer. The preliminary results of 50 patients indicate the potential of HSI and deep learning for automatic tissue-labeling of surgical specimens of head and neck patients.

Hyperspectral imaging (HSI), a non-contact optical imaging technique, has been recently used along with machine learning technique to provide diagnostic information about ex-vivo surgical specimens for optical biopsy. The computer-Aided diagnostic approach requires accurate ground truths for both training and validation. This study details a processing pipeline for registering the cancer-normal margin from a digitized histological image to the gross-level HSI of a tissue specimen. Our work incorporates an initial affine and control-point registration followed by a deformable Demons-based registration of the moving mask obtained from the histological image to the fixed mask made from the HS image. To assess registration quality, Dice similarity coefficient (DSC) measures the image overlap, visual inspection is used to evaluate the margin, and average target registration error (TRE) of needle-bored holes measures the registration error between the histologic and HSI images. Excised tissue samples from seventeen patients, 11 head and neck squamous cell carcinoma (HNSCCa) and 6 thyroid carcinoma, were registered according to the proposed method. Three registered specimens are illustrated in this paper, which demonstrate the efficacy of the registration workflow. Further work is required to apply the technique to more patient data and investigate the ability of this procedure to produce suitable gold standards for machine learning validation.

Successful outcomes of surgical cancer resection necessitate negative, cancer-free surgical margins. Currently, tissue samples are sent to pathology for diagnostic confirmation. Hyperspectral imaging (HSI) is an emerging, non-contact optical imaging technique. A reliable optical method could serve to diagnose and biopsy specimens in real-time. Using convolutional neural networks (CNNs) as a tissue classifier, we developed a method to use HSI to perform an optical biopsy of ex-vivo surgical specimens, collected from 21 patients undergoing surgical cancer resection. Training and testing on samples from different patients, the CNN can distinguish squamous cell carcinoma (SCCa) from normal aerodigestive tract tissues with an area under the curve (AUC) of 0.82, 81% accuracy, 81% sensitivity, and 80% specificity. Additionally, normal oral tissues can be sub-classified into epithelium, muscle, and glandular mucosa using a decision tree method, with an average AUC of 0.94, 90% accuracy, 93% sensitivity, and 89% specificity. After separately training on thyroid tissue, the CNN differentiates between thyroid carcinoma and normal thyroid with an AUC of 0.95, 92% accuracy, 92% sensitivity, and 92% specificity. Moreover, the CNN can discriminate medullary thyroid carcinoma from benign multi-nodular goiter (MNG) with an AUC of 0.93, 87% accuracy, 88% sensitivity, and 85% specificity. Classical-type papillary thyroid carcinoma is differentiated from benign MNG with an AUC of 0.91, 86% accuracy, 86% sensitivity, and 86% specificity. Our preliminary results demonstrate that an HSI-based optical biopsy method using CNNs can provide multi-category diagnostic information for normal head-and-neck tissue, SCCa, and thyroid carcinomas. More patient data are needed in order to fully investigate the proposed technique to establish reliability and generalizability of the work.

Purpose: Previous studies revealed diverging results regarding the role of survivin in squamous cell carcinoma of the head and neck (SCCHN). This study aimed to evaluate the clinical significance of survivin expression in SCCHN; the function of survivin in DNA-damage repair following ionizing radiation therapy (RT) in SCCHN cells; and the potential of honokiol to enhance RT through downregulation of survivin. Experimental Design: Expression of survivin in SCCHN patient primary tumor tissues (n ¼ 100) was analyzed and correlated with clinical parameters. SCCHN cell lines were used to evaluate the function of survivin and the effects of honokiol on survivin expression in vitro and in vivo. Results: Overexpression of survivin was significantly associated with lymph nodes' metastatic status (P ¼ 0.025), worse overall survival (OS), and disease-free survival (DFS) in patients receiving RT (n ¼ 65, OS: P ¼ 0.024, DFS: P ¼ 0.006) and in all patients with SCCHN (n ¼ 100, OS: P ¼ 0.002, DFS: P ¼ 0.003). In SCCHN cells, depletion of survivin led to increased DNA damage and cell death following RT, whereas overexpression of survivin increased clonogenic survival. RT induced nuclear accumulation of survivin and its molecular interaction with g-H2AX and DNA-PKCs. Survivin specifically bound to DNA DSB sites induced by I-SceI endonuclease. Honokiol (which downregulates survivin expression) in combination with RT significantly augmented cytotoxicity in SCCHN cells with acquired radioresistance and inhibited growth in SCCHN xenograft tumors. Conclusions: Survivin is a negative prognostic factor and is involved in DNA-damage repair induced by RT. Targeting survivin using honokiol in combination with RT May provide novel therapeutic opportunities.