Browsing by Author "Parwani, Anil"

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    GATA3 expression in clear cell adenocarcinoma of the lower urinary tract: a potential diagnostic pitfall
    (Springer, 2022-11-01) Akgul, Mahmut; Humble, Robert; Osme, Abdullah; Yuce, Servet; Kocak, Elif N.; Najafzade, Parisa; Sangoi, Ankur; Pattnaik, Niharika; Mishra, Sourav; Sharma, Shivani; Shaker, Nada; Kaushal, Seema; Baisakh, Manas; Lightle, Andrea R.; Balzer, Bonnie L.; Xiao, Guang-Qian; MacLennan, Gregory T.; Osunkoya, Adeboye O.; Parwani, Anil; Cheng, Liang; Bellizzi , Andrew; Mohanty, Sambit K.; Pathology and Laboratory Medicine, School of Medicine
    Background Clear cell adenocarcinoma of the lower urinary tract (CCACLUT) is a rare primary malignant neoplasm with heterogenous morphology. There is a paucity of data in the literature regarding its immunohistochemical profile. Methods The immunohistochemical features (extent and intensity) of a multinational cohort of CCACLUT were evaluated with comparison between clear cell adenocarcinoma of the female genital tract (CCACFGT, tissue microarray) and nephrogenic adenoma (NA). Results 33 CCACLUT (24 female, 9 male; mean age 59 years) were collected. CCACLUT most commonly arose from the urinary bladder (26/33, 78%), particularly from the trigone (10/33, 30.3%) followed by the urethra (8/33, 22%). All 12 NA cases were located at the urinary bladder, whereas the most common CCACFGT location was the ovary (29/56, 52%). None of the CCACLUT patients had, intestinal metaplasia, NA, or urothelial carcinoma. One patient had concurrent endometriosis of the sigmoid colon. Most frequently observed morphology in CCACLUT was papillary/tubulocystic (9/3; 27.3%), followed by papillary/tubular (6/33; 18.2%) and papillary/solid (5/33; 15.2%). GATA3 expression was significantly higher in CCACLUT (18/33, 54.5%) and NA (6/12, 50%), when compared to CCACFGT cases 6/56, 11.7%)(p = 0.001 and p = 0.022, respectively). The extent of GATA3 was significantly higher in CCACLUT group (19.2 ± 16.6%) than the other groups (9.6 ± 22.5% in NA and 2.6 ± 9% in CCACFGT group) (p = 0.001). 4/33 patients (12.1) had weak, 10/33 patients (30.3%) had moderate, and 4/33 patients (12.1%) had strong GATA3 intensity in CCACLUT group. In NA group, one patient (8.3%, 1/12) had weak, one patient (8.3%, 1/12) had moderate and 4 patients (33.3%, 4/12) had strong GATA3 intensity. Most cases (CCACLUT 29/33, 88%; NA 11/12, 92%; CCACFGT 46/56, 82.1%) had positive Napsin A expression, by which CCACLUT had significantly more cases with Napsin A expression (p = 0.034). p63 was consistently negative in all cases (30/33 (91.9%) CCACLUT; 12/12 (100%) NA; 42/56 (75%) CCACFGT. Ki67 (MIB) proliferation index was significantly higher in CCACLUT group (54.6 ± 21%) when compared to NA group (4.5 ± 2.7%) and CCACFGT group (35.5 ± 25.8%) (p = 0.001). Conclusion CCACLUT has consistent GATA3 expression, which may cause challenge in the diagnosis of urothelial carcinoma but can be used to distinguish CCACLUT from CCACFGT.
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    Identification of Topological Features in Renal Tumor Microenvironment Associated with Patient Survival
    (Oxford, 2018-03) Cheng, Jun; Mo, Xiaokui; Wang, Xusheng; Parwani, Anil; Feng, Qianjin; Huang, Kun; Medicine, School of Medicine
    Motivation As a highly heterogeneous disease, the progression of tumor is not only achieved by unlimited growth of the tumor cells, but also supported, stimulated, and nurtured by the microenvironment around it. However, traditional qualitative and/or semi-quantitative parameters obtained by pathologist’s visual examination have very limited capability to capture this interaction between tumor and its microenvironment. With the advent of digital pathology, computerized image analysis may provide a better tumor characterization and give new insights into this problem. Results We propose a novel bioimage informatics pipeline for automatically characterizing the topological organization of different cell patterns in the tumor microenvironment. We apply this pipeline to the only publicly available large histopathology image dataset for a cohort of 190 patients with papillary renal cell carcinoma obtained from The Cancer Genome Atlas project. Experimental results show that the proposed topological features can successfully stratify early- and middle-stage patients with distinct survival, and show superior performance to traditional clinical features and cellular morphological and intensity features. The proposed features not only provide new insights into the topological organizations of cancers, but also can be integrated with genomic data in future studies to develop new integrative biomarkers.
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    Integrative Analysis of Histopathological Images and Genomic Data Predicts Clear Cell Renal Cell Carcinoma Prognosis
    (AACR, 2017-11) Cheng, Jun; Zhang, Jie; Han, Yatong; Wang, Xusheng; Ye, Xiufen; Meng, Yuebo; Parwani, Anil; Han, Zhi; Feng, Qianjin; Huang, Kun; Medicine, School of Medicine
    In cancer, both histopathologic images and genomic signatures are used for diagnosis, prognosis, and subtyping. However, combining histopathologic images with genomic data for predicting prognosis, as well as the relationships between them, has rarely been explored. In this study, we present an integrative genomics framework for constructing a prognostic model for clear cell renal cell carcinoma. We used patient data from The Cancer Genome Atlas (n = 410), extracting hundreds of cellular morphologic features from digitized whole-slide images and eigengenes from functional genomics data to predict patient outcome. The risk index generated by our model correlated strongly with survival, outperforming predictions based on considering morphologic features or eigengenes separately. The predicted risk index also effectively stratified patients in early-stage (stage I and stage II) tumors, whereas no significant survival difference was observed using staging alone. The prognostic value of our model was independent of other known clinical and molecular prognostic factors for patients with clear cell renal cell carcinoma. Overall, this workflow and the shared software code provide building blocks for applying similar approaches in other cancers.