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Browsing by Author "Cheng, Huiwen"
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Item Applying Small Molecule Signal Transducer and Activator of Transcription-3 (STAT3) Protein Inhibitors as Pancreatic Cancer Therapeutics(American Association for Cancer Research, 2016-05) Arpin, Carolynn C.; Mac, Stephen; Jiang, Yanlin; Cheng, Huiwen; Grimard, Michelle; Page, Brent D. G.; Kamocka, Malgorzata M.; Haftchenary, Sina; Su, Han; Ball, Daniel; Rosa, David A.; Lai, Ping-Shan; Gómez-Biagi, Rodolfo F.; Ali, Ahmed M.; Rana, Rahul; Hanenberg, Helmut; Kerman, Kagan; McElyea, Kyle C.; Sandusky, George E.; Gunning, Patrick T.; Fishel, Melissa L.; Pediatrics, School of MedicineConstitutively activated STAT3 protein has been found to be a key regulator of pancreatic cancer and a target for molecular therapeutic intervention. In this study, PG-S3-001, a small molecule derived from the SH-4-54 class of STAT3 inhibitors, was found to inhibit patient-derived pancreatic cancer cell proliferation in vitro and in vivo in the low micromolar range. PG-S3-001 binds the STAT3 protein potently, Kd = 324 nmol/L by surface plasmon resonance, and showed no effect in a kinome screen (>100 cancer-relevant kinases). In vitro studies demonstrated potent cell killing as well as inhibition of STAT3 activation in pancreatic cancer cells. To better model the tumor and its microenvironment, we utilized three-dimensional (3D) cultures of patient-derived pancreatic cancer cells in the absence and presence of cancer-associated fibroblasts (CAF). In this coculture model, inhibition of tumor growth is maintained following STAT3 inhibition in the presence of CAFs. Confocal microscopy was used to verify tumor cell death following treatment of 3D cocultures with PG-S3-001. The 3D model was predictive of in vivo efficacy as significant tumor growth inhibition was observed upon administration of PG-S3-001. These studies showed that the inhibition of STAT3 was able to impact the survival of tumor cells in a relevant 3D model, as well as in a xenograft model using patient-derived cells.Item Mitotic cell detection in H&E stained meningioma histopathology slides(2019-12) Cheng, Huiwen; Tsechpenakis, Gavriil; Tuceryan, Mihran; Jiang, Yu zhengMeningioma represent more than one-third of all primary central nervous system (CNS) tumors, and it can be classified into three grades according to WHO (World Health Organization) in terms of clinical aggressiveness and risk of recurrence. A key component of meningioma grades is the mitotic count, which is defined as quantifying the number of cells in the process of dividing (i.e., undergoing mitosis) at a specific point in time. Currently, mitosis counting is done manually by a pathologist looking at 10 consecutive high-power fields (HPF) on a glass slide under a microscope, which is an extremely laborious and time-consuming process. The goal of this thesis is to investigate the use of computerized methods to automate the detection of mitotic nuclei with limited labeled data. We built computational methods to detect and quantify the histological features of mitotic cells on a whole slides image which mimic the exact process of pathologist workflow. Since we do not have enough training data from meningioma slide, we learned the mitotic cell features through public available breast cancer datasets, and predicted on meingioma slide for accuracy. We use either handcrafted features that capture certain morphological, statistical, or textural attributes of mitoses or features learned with convolutional neural networks (CNN). Hand crafted features are inspired by the domain knowledge, while the data-driven VGG16 models tend to be domain agnostic and attempt to learn additional feature bases that cannot be represented through any of the handcrafted features. Our work on detection of mitotic cells shows 100% recall , 9% precision and 0.17 F1 score. The detection using VGG16 performs with 71% recall, 73% precision, and 0.77 F1 score. Finally, this research of automated image analysis could drastically increase diagnostic efficiency and reduce inter-observer variability and errors in pathology diagnosis, which would allow fewer pathologists to serve more patients while maintaining diagnostic accuracy and precision. And all these methodologies will increasingly transform practice of pathology, allowing it to mature toward a quantitative science.