Browsing by Author "Tamboli, Pheroze"

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    Effective combinatorial immunotherapy for penile squamous cell carcinoma
    (Springer Nature, 2020-05-01) Huang, Tianhe; Cheng, Xi; Chahoud, Jad; Sarhan, Ahmed; Tamboli, Pheroze; Rao, Priya; Guo, Ming; Manyam, Ganiraju; Zhang, Li; Xiang, Yu; Han, Leng; Shang, Xiaoying; Deng, Pingna; Luo, Yanting; Lu, Xuemin; Feng, Shan; Ferrer, Magaly Martinez; Wang, Y. Alan; DePinho, Ronald A.; Pettaway, Curtis A.; Lu, Xin; Medicine, School of Medicine
    Penile squamous cell carcinoma (PSCC) accounts for over 95% of penile malignancies and causes significant mortality and morbidity in developing countries. Molecular mechanisms and therapies of PSCC are understudied, owing to scarcity of laboratory models. Herein, we describe a genetically engineered mouse model of PSCC, by co-deletion of Smad4 and Apc in the androgen-responsive epithelium of the penis. Mouse PSCC fosters an immunosuppressive microenvironment with myeloid-derived suppressor cells (MDSCs) as a dominant population. Preclinical trials in the model demonstrate synergistic efficacy of immune checkpoint blockade with the MDSC-diminishing drugs cabozantinib or celecoxib. A critical clinical problem of PSCC is chemoresistance to cisplatin, which is induced by Pten deficiency on the backdrop of Smad4/Apc co-deletion. Drug screen studies informed by targeted proteomics identify a few potential therapeutic strategies for PSCC. Our studies have established what we believe to be essential resources for studying PSCC biology and developing therapeutic strategies.
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    A generative adversarial approach to facilitate archival-quality histopathologic diagnoses from frozen tissue sections
    (Elsevier, 2022) Falahkheirkhah, Kianoush; Guo, Tao; Hwang, Michael; Tamboli, Pheroze; Wood, Christopher G.; Karam, Jose A.; Sircar, Kanishka; Bhargava, Rohit; Pathology and Laboratory Medicine, School of Medicine
    In clinical diagnostics and research involving histopathology, formalin-fixed paraffin-embedded (FFPE) tissue is almost universally favored for its superb image quality. However, tissue processing time (>24 h) can slow decision-making. In contrast, fresh frozen (FF) processing (<1 h) can yield rapid information but diagnostic accuracy is suboptimal due to lack of clearing, morphologic deformation and more frequent artifacts. Here, we bridge this gap using artificial intelligence. We synthesize FFPE-like images ("virtual FFPE") from FF images using a generative adversarial network (GAN) from 98 paired kidney samples derived from 40 patients. Five board-certified pathologists evaluated the results in a blinded test. Image quality of the virtual FFPE data was assessed to be high and showed a close resemblance to real FFPE images. Clinical assessments of disease on the virtual FFPE images showed a higher inter-observer agreement compared to FF images. The nearly instantaneously generated virtual FFPE images can not only reduce time to information but can facilitate more precise diagnosis from routine FF images without extraneous costs and effort.