Faculty Bibliography

Background & Objectives: Ciliated muconodular papillary tumour (CPMT) is a rare benign lesion, which occurs in the periphery of the lung and is characterised by papillary architecture, intra-alveolar mucin and the presence of non-atypical ciliated-columnar, basal and mucous cells. Since its introduction in 2002 by Ishikawa et al, this entity counts a few reports in the literature, which have primarily occurred in East Asia. Although not in the 2015WHO classification, CPMT represents a frank neoplastic process, harbouring genetic alterations including BRAF and EGFR mutations. The goal of this study was to characterise the clinicopathologic features of CPMT diagnosed at a tertiaty care institution in the U.S.
Method(s): Nine cases with characteristic features of classic and nonclassic CPMT from New York University Tisch Hospital and Bellevue Hospital from 2016 to 2019 were identified. Clinical and pathologic data were reviewed.
Result(s): CPMTs were identified in 3 Male and 6 Female patients, whose age ranged from 47 to 82 years old. The lesions were predominantly found in the right lung and had a median size of 6 mm. Six (6) cases represented classic CPMTs, while 3 cases lacked the full spectrum of CPMT diagnostic features, and were classified as non-classic CPMTs. While in 7 cases, surgical resection was performed due to radiologic diagnosis of a peripheral nodule, in 2 cases, small CPMTs were incidental findings in resections performed for a dominant lesion, which was either adenocarcinoma, non-mucinous type (case 2) or atypical carcinoid (case 8). All lesions showed the characteristic immunohistochemical TTF-1 stain of columnar cells and p63 or p40 stain of basal cells. Two out of four (2/4) cases stained for BRAF-V600E showed uniform staining of all 3 cellular components of the lesion. The data are summarized on Table 1.
Conclusion(s): CPMTs although infrequent are now increasingly recognized and their incidence appears higher than reported in western literature. Interestingly, this self-limited tumour shares similar driver mutations with lung adenocarcinoma. Further studies will include a comprehensive immunohistochemical and molecular characterization of these nine cases and comparison with morphologically similar non-neoplastic processes, like bronchiolar metaplasia

Fungi of the Paracoccidioides genus are the etiological agents of paracoccidioidomycosis (PCM), a systemic mycosis restricted to the countries of Latin America. Currently, the Paracoccidioides complex is represented by Paracoccidioides lutzii, Paracoccidioides americana, Paracoccidioides brasiliensis, Paracoccidioides restrepiensis, and Paracoccidioides venezuelensis. Even with advances in techniques used for diagnosing fungal diseases, high rates of false-positive results for PCM are still presented. Additionally, there is no efficient antigen that can be used to follow up the efficiency of patient treatment. The immunoproteomic is considered a powerful tool for the identification of antigens. In addition, antigens are molecules recognized by the immune system, which make them excellent targets for diagnostic testing of diseases caused by microorganisms. In this vein, we investigated which antigens are secreted by species representing Paracoccidioides complex to increase the spectrum of molecules that could be used for future diagnostic tests, patient follow-up, or PCM therapy. To identify the profile of antigens secreted by Paracoccidioides spp., immunoproteomic approaches were used combining immunoprecipitation, followed by antigen identification by nanoUPLC-MS^E-based proteomics. Consequently, it was possible to verify differences in the exoantigen profiles present among the studied species. Through a mass spectrometry approach, it was possible to identify 79 exoantigens in Paracoccidioides species. Using bioinformatics tools, two unique exoantigens in P. lutzii species were identified, as well as 44 epitopes exclusive to the Paracoccidioides complex and 12 unique antigenic sequences that can differentiate between Paracoccidioides species. Therefore, these results demonstrate that Paracoccidioides species have a range of B-cell epitopes exclusive to the complex as well as specific to each Paracoccidioides species. In addition, these analyses allowed us the identification of excellent biomarker candidates for epidemiology screening, diagnosis, patient follow-up, as well as new candidates for PCM therapy.

Background/UNASSIGNED:Mammalian cells express a single functional heparanase, an endoglycosidase that cleaves heparan sulfate and thereby promotes tumor metastasis, angiogenesis, and inflammation. Malignant mesothelioma is highly aggressive and has a poor prognosis because of the lack of markers for early diagnosis and resistance to conventional therapies. The purpose of this study was to elucidate the mode of action and biological significance of heparanase in mesothelioma and test the efficacy of heparanase inhibitors in the treatment of this malignancy. Methods/UNASSIGNED:The involvement of heparanase in mesothelioma was investigated by applying mouse models of mesothelioma and testing the effect of heparanase gene silencing (n = 18 mice per experiment; two different models) and heparanase inhibitors (ie, PG545, defibrotide; n = 18 per experiment; six different models). Synchronous pleural effusion and plasma samples from patients with mesothelioma (n = 35), other malignancies (12 non-small cell lung cancer, two small cell lung carcinoma, four breast cancer, three gastrointestinal cancers, two lymphomas), and benign effusions (five patients) were collected and analyzed for heparanase content (enzyme-linked immunosorbent assay). Eighty-one mesothelioma biopsies were analyzed by H-Score for the prognostic impact of heparanase using immunohistochemistry. All statistical tests were two-sided. Results/UNASSIGNED:Mesothelioma tumor growth, measured by bioluminescence or tumor weight at termination, was markedly attenuated by heparanase gene silencing (P = .02) and by heparanase inhibitors (PG545 and defibrotide; P < .001 and P = .01, respectively). A marked increase in survival of the mesothelioma-bearing mice (P < .001) was recorded. Heparanase inhibitors were more potent in vivo than conventional chemotherapy. Clinically, heparanase levels in patients' pleural effusions could distinguish between malignant and benign effusions, and a heparanase H-score above 90 was associated with reduced patient survival (hazard ratio = 1.89, 95% confidence interval = 1.09 to 3.27, P = .03). Conclusions/UNASSIGNED:Our results imply that heparanase is clinically relevant in mesothelioma development. Given these preclinical and clinical data, heparanase appears to be an important mediator of mesothelioma, and heparanase inhibitors are worthy of investigation as a new therapeutic modality in mesothelioma clinical trials.

Visual inspection of histopathology slides is one of the main methods used by pathologists to assess the stage, type and subtype of lung tumors. Adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC) are the most prevalent subtypes of lung cancer, and their distinction requires visual inspection by an experienced pathologist. In this study, we trained a deep convolutional neural network (inception v3) on whole-slide images obtained from The Cancer Genome Atlas to accurately and automatically classify them into LUAD, LUSC or normal lung tissue. The performance of our method is comparable to that of pathologists, with an average area under the curve (AUC) of 0.97. Our model was validated on independent datasets of frozen tissues, formalin-fixed paraffin-embedded tissues and biopsies. Furthermore, we trained the network to predict the ten most commonly mutated genes in LUAD. We found that six of them-STK11, EGFR, FAT1, SETBP1, KRAS and TP53-can be predicted from pathology images, with AUCs from 0.733 to 0.856 as measured on a held-out population. These findings suggest that deep-learning models can assist pathologists in the detection of cancer subtype or gene mutations. Our approach can be applied to any cancer type, and the code is available at https://github.com/ncoudray/DeepPATH .