Fully automated 3D machine learning model for HPV status characterization in oropharyngeal squamous cell carcinomas based on CT images.

BACKGROUND: Human papillomavirus (HPV) status plays a major role in predicting oropharyngeal squamous cell carcinoma (OPSCC) survival. This study assesses the accuracy of a fully automated 3D convolutional neural network (CNN) in predicting HPV status using CT images. METHODS: Pretreatment CT images from OPSCC patients were used to train a 3D DenseNet-121 model to predict HPV-p16 status. Performance was evaluated by the ROC Curve (AUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and F1 score. RESULTS: The network achieved a mean AUC of 0.80 ± 0.06. The best-preforming fold had a sensitivity of 0.86 and specificity of 0.92 at the Youden's index. The PPV, NPV, and F1 scores are 0.97, 0.71, and 0.82, respectively. CONCLUSIONS: A fully automated CNN can characterize the HPV status of OPSCC patients with high sensitivity and specificity. Further refinement of this algorithm has the potential to provide a non-invasive tool to guide clinical management.

Rapid expansion of a left atrial myxoma caused by acute multiple internal hemorrhages: a case report and literature review.

BACKGROUND: Left atrial myxoma is the most common benign tumor, with the growth rate remaining unknown because specific symptoms do not present until the tumor grows to a certain size. Early surgical management is performed in most cases once it is detected by physicians. Despite cardiac myxomas commonly being perceived as slow-growing tumors, rapid enlargement of myxomas has been reported. CASE PRESENTATION: A 64-year-old woman was referred to our hospital with a diagnosis of a left atrial tumor. The pointed tumor changed morphologically in a few hours, and her respiratory condition, which had been normal at admission, suddenly deteriorated. Emergent surgery was performed, and the diagnosis was myxoma with multiple intratumor massive hematomas. The patient recovered uneventfully and was discharged on postoperative day 12 without any complications. CONCLUSIONS: We report an extremely rare case of left atrial myxoma rapidly expanded due to acute multiple hemorrhages within itself. Massive internal hemorrhage alters the size, shape, and fragility of the tumor. We should recognize the potential risk of internal hemorrhage that may lead to acute deterioration of the so-called "slow-growing benign" tumors, such as myxomas.

Comparing accuracy of machine learning approaches to identifying parathyroid adenomas: Lessons and new directions.

PURPOSE: The purpose of this investigation is to understand the accuracy of machine learning techniques to detect biopsy-proven adenomas from similar appearing lymph nodes and factors that influence accuracy by comparing support vector machine (SVM) and bidirectional Long short-term memory (Bi-LSTM) analyses. This will provide greater insight into how these tools could integrate multidimensional data and aid the detection of parathyroid adenomas consistently and accurately. METHODS: Ninety-nine patients were identified; 93 4D-CTs of patients with pathology-proven parathyroid adenomas were reviewed; 94 parathyroid adenomas and 112 lymph nodes were analyzed. A 2D slice through the lesions in each phase was used to perform sequence classification with ResNet50 as the pre-trained network to construct the Bi-LSTM model, and the mean enhancement curves were used to form an SVM model. The model characteristics and accuracy were calculated for the training and validation data sets. RESULTS: On the training data, the area under the curve (AUC) of the Bi-LSTM was 0.99, while the SVM was 0.95 and statistically significant on the DeLong test. The overall accuracy of the Bi-LSTM on the validation data set was 92 %, while the SVM was 88 %. The accuracy for parathyroid adenomas specifically was 93 % for the Bi-LSTM and 83 % for the SVM model. CONCLUSION: Enhancement characteristics are a distinguishing feature that accurately identifies parathyroid adenomas alone. The Bi-LSTM performs statistically better in identifying parathyroid adenomas than the SVM analysis when using both morphologic and enhancement information to distinguish between parathyroid adenomas and lymph nodes. SUMMARY STATEMENT: The Bi-LSTM more accurately identifies parathyroid adenomas than the SVM analysis, which uses both morphologic and enhancement information to distinguish between parathyroid adenomas and lymph nodes, performs statistically better.

Multiparametric machine learning algorithm for human papillomavirus status and survival prediction in oropharyngeal cancer patients.

BACKGROUND: Human papillomavirus (HPV) status influences prognosis in oropharyngeal cancer (OPC). Identifying high-risk patients are critical to improving treatment. We aim to provide a noninvasive opportunity for managing OPC patients by training multiple machine learning pipelines to determine the best model for characterizing HPV status and survival. METHODS: Multi-parametric algorithms were designed using a 492 OPC patient database. HPV status incorporated age, sex, smoking/drinking habits, cancer subsite, TNM, and AJCC 7th edition staging. Survival considered HPV model inputs plus HPV status. Patients were split 4:1 training: testing. Algorithm efficacy was assessed through accuracy and area under the receiver operator characteristic curve (AUC). RESULTS: From 31 HPV status models, ensemble yielded 0.83 AUC and 78.7% accuracy. From 38 survival models, ensemble yielded 0.91 AUC and 87.7% accuracy. CONCLUSION: Results reinforce artificial intelligence's potential to use tumor imaging and patient characterizations for HPV status and outcome prediction. Utilizing these algorithms can optimize clinical guidance and patient care noninvasively.

Optimization of Culture Conditions for the Generation of Canine CD20-CAR-T Cells for Adoptive Immunotherapy.

BACKGROUND/AIM: Chimeric antigen receptor (CAR) T cell therapy targeting CD20 has the potential to become a promising novel treatment for canine B cell lymphoid malignancy. However, the optimal approach for producing potent CAR-T cells with favorable phenotype for dogs remains unknown. In this study, we assessed several culture conditions and their effects on the phenotypic characteristics of CD20-CAR-T cells. MATERIALS AND METHODS: Canine CAR-T cells were generated by incubating with several mitogens in the presence or absence of Akt inhibitor. Gene transduction efficiency and phenotypic characteristics were determined by flow cytometry. RESULTS: Comparison of several kinds of mitogens revealed that stimulation with phytohemagglutinin has high transduction efficacy, whereas stimulation with concanavalin A was superior in memory T cell formation. Akt inhibition at the initial stage of CAR-T production tended to enhance transduction efficiency and memory T cell formation. CONCLUSION: This study provides a significant insight into the understanding of the ex vivo expansion of canine T cells in adoptive immunotherapy.

Urgency of Esophageal Foreign Body Removal: Differentiation Between Coins and Button Cell Batteries.

OBJECTIVE: Coin-shaped button battery foreign bodies have a similar initial presentation to coin ingestion, but delayed retrieval of a battery from the esophagus can have devastating consequences. Variations in timing of retrieval for children with ingestion of coin foreign bodies have been reported. The study assesses the sensitivity and specificity of conventional and digital radiographs to differentiate button batteries from coin foreign bodies. STUDY DESIGN: 3B case control study. STUDY SETTING: Tertiary academic medical center. METHODS: A radiographic study of the 12 most common commercially available button batteries and 66 coins of varying international origins was performed. Foreign bodies were placed at the cervical esophagus of a cadaver, and anteroposterior (AP) and lateral conventional radiographs of the neck were obtained. Digital AP and lateral radiographs of standalone coins and batteries were also obtained. Images were blindly read by 2 otolaryngologists and 2 radiologists. Statistical analysis was performed to determine accuracy in identifying coins vs batteries. RESULTS: Using conventional radiographs to identify button batteries yielded a sensitivity of 0.88 and a specificity of 0.92 (positive predictive value [PPV] = 0.75, negative predictive value [NPV] = 0.97). Digital radiography yielded an overall sensitivity of 0.98 and specificity of 0.97 (PPV = 0.87, NPV = 0.99). Features of button batteries were only seen on AP conventional radiographs using reverse contrast. CONCLUSIONS: Neither conventional nor digital radiographic imaging had perfect accuracy in identifying coins vs batteries. Features of common disc batteries were identified, which may aid in diagnosis. With potential devastating consequences from retained battery in the esophagus, emergent removal of any possible disc battery foreign body should be considered.

Quantitative assessment of the maxillary sinusitis using computed tomography texture analysis: odontogenic vs non-odontogenic etiology.

OBJECTIVES: The purpose of this study was to investigate computed tomography (CT) texture features of mucosal thickening of maxillary sinus mucosa to differentiate odontogenic maxillary sinusitis (OMS) from non-odontogenic maxillary sinusitis (NOMS). METHODS: Eighteen OMS patients and age- and gender-matched 18 NOMS patients who underwent sinus CT were retrospectively reviewed. OMS patients were identified by histopathological examination of tissues excised at surgery combined with CT imaging findings. Patients with mucosal thickening in the maxillary sinus without apical periodontitis or advanced periodontal bone loss near the maxillary sinus on CT were defined as NOMS. Patients with thin mucosal thickening (< 10 mm), cyst, tumor, post-operative deformity, severe metal artifact precluding visualization of the maxillary sinus, and age younger than 20 years were excluded. CT texture features of the mucosal thickening were analyzed using an in-house developed Matlab-based texture analysis program. Forty-five texture features were extracted from each segmented volume. The results were tested with the Mann-Whitney U test. RESULTS: Six histogram features (mean, median, standard deviation, entropy, geometric mean, harmonic mean) and two gray-level co-occurrence matrix features (entropy, correlation) showed significant differences between OMS and NOMS patients. CONCLUSIONS: CT texture analysis revealed the quantitative differences between OMS and NOMS. The texture features can serve as a quantitative indicator of maxillary sinusitis to differentiate between OMS and NOMS and help prevent incorrect treatment choices.

A tissue-engineered, decellularized, connective tissue membrane for allogeneic arterial patch implantation.

BACKGROUND: We have previously applied in vivo tissue-engineered vascular grafts constructed in patients' subcutaneous spaces. However, since the formation of these vascular grafts depends on host health, their application is challenging in patients with suppressed regenerative ability. Therefore, the allogeneic implantation of grafts from healthy donors needs to be evaluated. This study aimed to fabricate allogeneic cardiovascular grafts in animals. MATERIALS AND METHODS: Silicone rod molds were implanted into subcutaneous pouches in dogs; the implants, along with surrounding connective tissues, were harvested after four weeks. Tubular connective tissues were decellularized and stored before they were cut open, trimmed to elliptical sheets, and implanted into the common carotid arteries of another dog as vascular patches (n = 6); these were resected and histologically evaluated at 1, 2, and 4 weeks after implantation. RESULTS: No aneurysmal changes were observed by echocardiography. Histologically, we observed neointima formation on the luminal graft surface and graft wall cell infiltration. At 2 and 4 weeks after implantation, α-SMA-positive cells were observed in the neointima and graft wall. At 4 weeks after implantation, the endothelial lining was observed at the grafts' luminal surfaces. CONCLUSION: Our data suggest that decellularized connective tissue membranes can be prepared and stored for later use as allogeneic cardiovascular grafts.

Cell-Free HPV DNA Provides an Accurate and Rapid Diagnosis of HPV-Associated Head and Neck Cancer.

PURPOSE: HPV-associated head and neck squamous cell carcinoma (HPV+HNSCC) is the most common HPV-associated malignancy in the United States and continues to increase in incidence. Current diagnostic approaches for HPV+HNSCC rely on tissue biopsy followed by histomorphologic assessment and detection of HPV indirectly by p16 IHC. Such approaches are invasive and have variable sensitivity. EXPERIMENTAL DESIGN: We conducted a prospective observational study in 140 subjects (70 cases and 70 controls) to test the hypothesis that a noninvasive diagnostic approach for HPV+HNSCC would have improved diagnostic accuracy, lower cost, and shorter diagnostic interval compared with standard approaches. Blood was collected, processed for circulating tumor HPV DNA (ctHPVDNA), and analyzed with custom ddPCR assays for HPV genotypes 16, 18, 33, 35, and 45. Diagnostic performance, cost, and diagnostic interval were calculated for standard clinical workup and compared with a noninvasive approach using ctHPVDNA combined with cross-sectional imaging and physical examination findings. RESULTS: Sensitivity and specificity of ctHPVDNA for detecting HPV+HNSCC were 98.4% and 98.6%, respectively. Sensitivity and specificity of a composite noninvasive diagnostic using ctHPVDNA and imaging/physical examination were 95.1% and 98.6%, respectively. Diagnostic accuracy of this noninvasive approach was significantly higher than standard of care (Youden index 0.937 vs. 0.707, P = 0.0006). Costs of noninvasive diagnostic were 36% to 38% less than standard clinical workup and the median diagnostic interval was 26 days less. CONCLUSIONS: A noninvasive diagnostic approach for HPV+HNSCC demonstrated improved accuracy, reduced cost, and a shorter time to diagnosis compared with standard clinical workup and could be a viable alternative in the future.

Multiparametric Analysis of Tumor Morphological and Functional MR Parameters Potentially Predicts Local Failure in Pharynx Squamous Cell Carcinoma Patients.

Purpose : To predict local control / failure by a multiparametric approach using magnetic resonance (MR)-derived tumor morphological and functional parameters in pharynx squamous cell carcinoma (SCC) patients. Materials and Methods : Twenty-eight patients with oropharyngeal and hypopharyngeal SCCs were included in this study. Quantitative morphological parameters and intratumoral characteristics on T2-weighted images, tumor blood flow from pseudo-continuous arterial spin labeling, and tumor diffusion parameters of three diffusion models from multi-b-value diffusion-weighted imaging as well as patients' characteristics were analyzed. The patients were divided into local control / failure groups. Univariate and multiparametric analysis were performed for the patient group division. Results : The value of morphological parameter of 'sphericity' and intratumoral characteristic of 'homogeneity' was revealed respectively significant for the prediction of the local control status in univariate analysis. Higher diagnostic performance was obtained with the sensitivity of 0.8, specificity of 0.75, positive predictive value of 0.89, negative predictive value of 0.6 and accuracy of 0.79 by multiparametric diagnostic model compared to results in the univariate analysis. Conclusion : A multiparametric analysis with MR-derived quantitative parameters may be useful to predict local control in pharynx SCC patients. J. Med. Invest. 68 : 354-361, August, 2021.

Development of a monoclonal antibody for the detection of anti-canine CD20 chimeric antigen receptor expression on canine CD20 chimeric antigen receptor-transduced T cells.

Chimeric antigen receptor (CAR) CAR-T cell therapy targeting CD20 can be a novel adoptive cell therapy for canine patients with B-cell malignancy. After injection of the CAR-T cells in vivo, monitoring circulating CAR-T cells is essential to prove in vivo persistence of CAR-T cells. In this study, we developed a novel monoclonal antibody against canine CD20 CAR, whose single-chain variable fragment was derived from the our previously reported anti-canine CD20 therapeutic antibody. Furthermore, we proved that this monoclonal antibody can detect therapeutic anti-canine CD20 chimeric antibody in the serum from healthy beagle dogs injected with the therapeutic antibody for safety study. This monoclonal antibody is a useful tool for monitoring both canine CD20-CAR-T cells and anti-canine CD20 therapeutic antibody for canine lymphoma.

Prediction of the local treatment outcome in patients with oropharyngeal squamous cell carcinoma using deep learning analysis of pretreatment FDG-PET images.

BACKGROUND: This study aimed to assess the utility of deep learning analysis using pretreatment FDG-PET images to predict local treatment outcome in oropharyngeal squamous cell carcinoma (OPSCC) patients. METHODS: One hundred fifty-four OPSCC patients who received pretreatment FDG-PET were included and divided into training (n = 102) and test (n = 52) sets. The diagnosis of local failure and local progression-free survival (PFS) rates were obtained from patient medical records. In deep learning analyses, axial and coronal images were assessed by three different architectures (AlexNet, GoogLeNET, and ResNet). In the training set, FDG-PET images were analyzed after the data augmentation process for the diagnostic model creation. A multivariate clinical model was also created using a binomial logistic regression model from a patient's clinical characteristics. The test data set was subsequently analyzed for confirmation of diagnostic accuracy. Assessment of local PFS rates was also performed. RESULTS: Training sessions were successfully performed with an accuracy of 74-89%. ROC curve analyses revealed an AUC of 0.61-0.85 by the deep learning model in the test set, whereas it was 0.62 by T-stage, 0.59 by clinical stage, and 0.74 by a multivariate clinical model. The highest AUC (0.85) was obtained with deep learning analysis of ResNet architecture. Cox proportional hazards regression analysis revealed deep learning-based classification by a multivariate clinical model (P < .05), and ResNet (P < .001) was a significant predictor of the treatment outcome. In the Kaplan-Meier analysis, the deep learning-based classification divided the patient's local PFS rate better than the T-stage, clinical stage, and a multivariate clinical model. CONCLUSIONS: Deep learning-based diagnostic model with FDG-PET images indicated its possibility to predict local treatment outcomes in OPSCCs.

CT of Skull Base Fractures: Classification Systems, Complications, and Management.

As advances in prehospital and early hospital care improve survival of the head-injured patient, radiologists are increasingly charged with understanding the myriad skull base fracture management implications conferred by CT. Successfully parlaying knowledge of skull base anatomy and fracture patterns into precise actionable clinical recommendations is a challenging task. The authors aim to provide a pragmatic overview of CT for skull base fractures within the broader context of diagnostic and treatment planning algorithms. Laterobasal, frontobasal, and posterior basal fracture patterns are emphasized. CT often plays a complementary, supportive, or confirmatory role in management of skull base fractures in conjunction with results of physical examination, laboratory testing, and neurosensory evaluation. CT provides prognostic information about short- and long-term risk of cerebrospinal fluid (CSF) leak, encephalocele, meningitis, facial nerve paralysis, hearing and vision loss, cholesteatoma, vascular injuries, and various cranial nerve palsies and syndromes. The radiologist should leverage understanding of specific strengths and limitations of CT to anticipate next steps in the skull base fracture management plan. Additional imaging is warranted to clarify ambiguity (particularly for potential sources of CSF leak); in other cases, clinical and CT criteria alone are sufficient to determine the need for intervention and the choice of surgical approach. The radiologist should be able to envision stepping into a multidisciplinary planning discussion and engaging neurotologists, neuro-ophthalmologists, neurosurgeons, neurointerventionalists, and facial reconstructive surgeons to help synthesize an optimal management plan after reviewing the skull base CT findings at hand. Online supplemental material is available for this article. ©RSNA, 2021.

Cystic cervical lymph nodes of papillary thyroid carcinoma, tuberculosis and human papillomavirus positive oropharyngeal squamous cell carcinoma: utility of deep learning in their differentiation on CT.

OBJECTIVES: Cervical lymph nodes with internal cystic changes are seen with several pathologies, including papillary thyroid carcinoma (PTC), tuberculosis (TB), and HPV-positive oropharyngeal squamous cell carcinoma (HPV+OPSCC). Differentiating these lymph nodes is difficult in the absence of a known primary tumor or reliable medical history. In this study, we assessed the utility of deep learning in differentiating the pathologic lymph nodes of PTC, TB, and HPV+OPSCC on CT. METHODS: A total of 173 lymph nodes (55 PTC, 58 TB, and 60 HPV+OPSCC) were selected based on pathology records and suspicious morphological features. These lymph nodes were divided into the training set (n = 131) and the test set (n = 42). In deep learning analysis, JPEG lymph node images were extracted from the CT slice that included the largest area of each node and fed into a deep learning training session to create a diagnostic model. Transfer learning was used with the deep learning model architecture of ResNet-101. Using the test set, the diagnostic performance of the deep learning model was compared against the histopathological diagnosis and to the diagnostic performances of two board-certified neuroradiologists. RESULTS: Diagnostic accuracy of the deep learning model was 0.76 (=32/42), whereas those of Radiologist 1 and Radiologist 2 were 0.48 (=20/42) and 0.41 (=17/42), respectively. Deep learning derived diagnostic accuracy was significantly higher than both of the two neuroradiologists (P < 0.01, respectively). CONCLUSION: Deep learning algorithm holds promise to become a useful diagnostic support tool in interpreting cervical lymphadenopathy.

Usefulness of dual-layer spectral CT in follow-up examinations: diagnosing recurrent squamous cell carcinomas in the head and neck.

PURPOSE: To evaluate the usefulness of dual-energy analyses using dual-layer spectral CT (DLSCT) for diagnosing recurrent lesions of head and neck squamous cell carcinoma (HNSCC). MATERIALS AND METHODS: The study population comprised 62 patients with a history of HNSCC. Attenuation values on conventional 120-kVp images and 40-keV virtual monochromatic images (VMIs) and iodine concentration (IC) were compared between recurrent lesions and post-treatment changes or non-recurrent nodes using the Mann-Whitney U test. Receiver-operating characteristic (ROC) analysis was used to assess the ability of attenuation values and IC to diagnose recurrent lesions. RESULTS: Attenuation values for 120-kVp and 40-keV images and IC of local recurrent lesions were significantly higher than those of post-treatment changes (p < 0.001), whereas recurrent nodes showed significantly lower attenuation values for both 120 kVp and 40 keV and IC than non-recurrent nodes (p < 0.001). Area under the ROC curves for 120-kVp images, 40-keV images, and IC to diagnose local recurrences were 0.912, 0.992, and 0.984, respectively, and those to diagnose recurrent nodes were 0.819, 0.922, and 0.934, respectively. CONCLUSIONS: Dual-energy images using DLSCT, particularly 40-keV VMIs and IC, may help in diagnosing recurrent lesions of HNSCC.

Risk assessment of osteoradionecrosis associated with periodontitis using 18F-FDG PET/CT.

PURPOSE: Osteoradionecrosis (ORN) is a serious complication after radiotherapy (RT), even in the era of intensity modulated radiation therapy (IMRT). The purpose of this study was to evaluate whether 18F-FDG PET/CT can predict ORN associated with periodontal disease in patients with oropharyngeal or oral cavity squamous cell carcinoma (OP/OC SCC) undergoing RT. METHODS: One hundred and five OP/OC SCC patients treated with RT who underwent pretreatment 18F-FDG PET/CT between October 2007 and June 2016 were retrospectively reviewed. A post-treatment diagnosis of ORN was made clinically based on presence of exposed irradiated mandibular bone that failed to heal after a period of three months without persistent or recurrent tumor. The maximum standardized uptake value (SUVmax) of periodontal regions identified on PET/CT was measured for all patients. Image-based staging of periodontitis was also performed using American Academy of Periodontology staging system on CT. RESULTS: Among 105 patients, 14 (13.3 %) developed ORN. The SUVmax of the periodontal region in patients with ORN (3.35 ±â€¯1.23) was significantly higher than patients without ORN (1.92 ±â€¯0.66) (P <  .01). The corresponding CT stage of periodontitis in patients with ORN was significantly higher (2.71±0.47) than patients without ORN (1.80±0.73) (P <  .01). ROC analysis revealed the cut-off values of developing ORN were 2.1 in SUVmax, and II in CT stage of periodontitis. The corresponding AUC was 0.86 and 0.82, respectively. CONCLUSIONS: Pretreatment 18F-FDG PET/CT identification of periodontitis may be helpful to predict the future development of ORN in patients with OP/OC SCC undergoing RT.

Cystic cervical lymph nodes of papillary thyroid carcinoma, tuberculosis and human papillomavirus positive oropharyngeal squamous cell carcinoma: Comparative CT analysis for their differentiation.

PURPOSE: Cervical lymph nodes with cystic changes are an important finding seen with several pathologies including papillary thyroid carcinoma (PTC), tuberculosis (TB) and HPV-positive oropharyngeal squamous cell carcinoma (HPV + OPSCC). In the absence of known primary tumor or conclusive medical history, differentiating among these nodes is difficult. We compared the pathologic nodes of PTC, TB and HPV + OPSCC to identify imaging features useful for their differentiation. MATERIALS AND METHODS: Fifty-five PTC, 58 TB and 51 HPV + OPSCC nodes were selected based on surgical pathology records and suspicious morphological features. These nodes were compared for morphological features: long axis length, nodal shape, nodal location, presence of cystic change, area of cystic change:area of entire node ratio, Hounsfield unit of the cystic component, degree of enhancement, enhancement pattern, presence of calcification, presence of perinodal infiltration, and presence of surrounding inflammatory changes. RESULTS: PTC nodes formed calcifications more frequently and demonstrated greater enhancement (P < 0.01). TB nodes were characterized by their irregular shape (P < 0.05), irregular enhancement surrounding the cystic change (P < 0.01), greater frequencies of perinodal infiltration (P < 0.01) and surrounding inflammatory changes (P < 0.01). While no unique features were seen with HPV+OPSCC, they were characterized by the absence of those features that distinguished the other groups: these nodes tended to have smooth, circumscribed margins with no hyperenhancement, calcifications or inflammatory changes. PTC and TB nodes were more frequently identified in the lower neck, while HPV+OPSCC nodes were localized to the upper neck (P < 0.01). CONCLUSIONS: PTC, TB and HPV + OPSCC lymph nodes can be differentiated based on their morphologies and locations.

Establishment and Characterization of Monoclonal Antibody Against Canine CD8 Alpha.

Understanding of the microenvironment of cancer plays a crucial role in cancer research. A tool is needed to evaluate the immune cells surrounding the cancer cells. This study establishes and evaluates a novel monoclonal antibody against canine CD8α (cCD8α). The antibody was produced by immunization of rats with cCD8α-expressing cells. After establishment and selection of hybridoma cells, the clone F3-B2 was established. The reactivity of F3-B2 was confirmed using cCD8α-overexpressing murine cells. Flow cytometric analysis also demonstrated that F3-B2 reacts with cCD8α naturally expressed in canine peripheral blood mononuclear cells and a canine T cell lymphoma cell line. The specimens of lymphoid tissue showed immunohistochemical staining for F3-B2. Moreover, we also found that F3-B2 exhibited reactivity against feline CD8. Thus, this antibody provides a good research tool to analyze CD8-positive cytotoxic lymphocytes in canine and feline tumors.

Generation of a canine anti-canine CD20 antibody for canine lymphoma treatment.

Lymphoma is the most common hematological cancer in dogs. Canine diffuse large B cell lymphoma shows a relatively good response to treatment with multi-agent cyclophosphamide, doxorubicin, vincristine, and prednisone (CHOP) chemotherapy; however, the 2-year survival rate is as low as 20%. For human B cell type lymphoma, the anti-CD20 chimeric antibody, rituximab, was developed two decades ago. The combination of rituximab and CHOP chemotherapy was highly successful in improving patient prognosis. However, no anti-canine CD20 antibody is available for the treatment of canine lymphoma. During this study, a rat anti-canine CD20 monoclonal antibody was established. We also generated a rat-canine chimeric antibody against canine CD20 designed for clinical application. This chimeric antibody (4E1-7-B) showed in vitro antibody-dependent cell-mediated cytotoxicity (ADCC) and complement-dependent cytotoxicity (CDC) against the canine B cell lymphoma cell line CLBL-1. Moreover, to obtain stronger ADCC activity, a defucosylated 4E1-7-B antibody (4E1-7-B_f) was also generated, and it showed tenfold stronger ADCC activity compared with 4E1-7-B. 4E1-7-B_f as well as 4E1-7-B suppressed the growth of CLBL-1 tumors in an immunodeficient xenotransplant mouse model. Finally, a single administration of 4E1-7-B_f induced considerable peripheral B cell depletion in healthy beagles. Thus, 4E1-7-B_f is a good antibody drug candidate for canine B cell type lymphoma.