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1. PINK1-Mediated Mitochondrial Activity Confers Olaparib Resistance in Prostate Cancer Cells

Author

Schaaf, Zachary A, Ning, Shu, Leslie, Amy R, Sharifi, Masuda, Gao, Richard Y, Maine, James P, Lou, Wei, Lombard, Alan P, Liu, Chengfei, Yu, Ai-Ming, Mitsiades, Nicholas, and Gao, Allen C

Subjects
Biomedical and Clinical Sciences, Oncology and Carcinogenesis, Aging, Urologic Diseases, Cancer, Prostate Cancer, 2.1 Biological and endogenous factors, Humans, Male, Phthalazines, Piperazines, Prostatic Neoplasms, Drug Resistance, Neoplasm, Mitochondria, Cell Line, Tumor, Protein Kinases, Poly(ADP-ribose) Polymerase Inhibitors
Abstract

SignificanceOlaparib, a PARP inhibitor, is effective against various cancers, including prostate cancer. However, resistance to olaparib poses a significant challenge. This study uncovers that mitochondrial alterations and PINK1 gene overexpression contribute to this resistance in prostate cancer cells. Enhanced mitochondrial functionality and increased PINK1 expression in olaparib-resistant cells underscore the importance of targeting mitochondrial dynamics and PINK1 to develop more effective treatments for overcoming olaparib resistance in prostate cancer.

Published
2024

2. Cell Graph Transformer for Nuclei Classification

Author

Lou, Wei, Li, Guanbin, Wan, Xiang, and Li, Haofeng

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Nuclei classification is a critical step in computer-aided diagnosis with histopathology images. In the past, various methods have employed graph neural networks (GNN) to analyze cell graphs that model inter-cell relationships by considering nuclei as vertices. However, they are limited by the GNN mechanism that only passes messages among local nodes via fixed edges. To address the issue, we develop a cell graph transformer (CGT) that treats nodes and edges as input tokens to enable learnable adjacency and information exchange among all nodes. Nevertheless, training the transformer with a cell graph presents another challenge. Poorly initialized features can lead to noisy self-attention scores and inferior convergence, particularly when processing the cell graphs with numerous connections. Thus, we further propose a novel topology-aware pretraining method that leverages a graph convolutional network (GCN) to learn a feature extractor. The pre-trained features may suppress unreasonable correlations and hence ease the finetuning of CGT. Experimental results suggest that the proposed cell graph transformer with topology-aware pretraining significantly improves the nuclei classification results, and achieves the state-of-the-art performance. Code and models are available at https://github.com/lhaof/CGT, Comment: AAAI 2024, Code and models are available at https://github.com/lhaof/CGT

Published
2024

4. IGFBP3 promotes resistance to Olaparib via modulating EGFR signaling in advanced prostate cancer

Author

Leslie, Amy R, Ning, Shu, Armstrong, Cameron M, D’Abronzo, Leandro S, Sharifi, Masuda, Schaaf, Zachary A, Lou, Wei, Liu, Chengfei, Evans, Christopher P, Lombard, Alan P, and Gao, Allen C

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Oncology and Carcinogenesis, Urologic Diseases, Prostate Cancer, Cancer, Genetics, 6.1 Pharmaceuticals, 5.1 Pharmaceuticals, 2.1 Biological and endogenous factors, Cell biology, Transcriptomics
Abstract

Olaparib is a pioneering PARP inhibitor (PARPi) approved for treating castration-resistant prostate cancer (CRPC) tumors harboring DNA repair defects, but clinical resistance has been documented. To study acquired resistance, we developed Olaparib-resistant (OlapR) cell lines through chronic Olaparib treatment of LNCaP and C4-2B cell lines. Here, we found that IGFBP3 is highly expressed in acquired (OlapR) and intrinsic (Rv1) models of Olaparib resistance. We show that IGFBP3 expression promotes Olaparib resistance by enhancing DNA repair capacity through activation of EGFR and DNA-PKcs. IGFBP3 depletion enhances efficacy of Olaparib by promoting DNA damage accumulation and subsequently, cell death in resistant models. Mechanistically, we show that silencing IGFBP3 or EGFR expression reduces cell viability and resensitizes OlapR cells to Olaparib treatment. Inhibition of EGFR by Gefitinib suppressed growth of OlapR cells and improved Olaparib sensitivity, thereby phenocopying IGFBP3 inhibition. Collectively, our results highlight IGFBP3 and EGFR as critical mediators of Olaparib resistance.

Published
2024

5. Multi-stream Cell Segmentation with Low-level Cues for Multi-modality Images

Author

Lou, Wei, Yu, Xinyi, Liu, Chenyu, Wan, Xiang, Li, Guanbin, Liu, Siqi, and Li, Haofeng

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Cell segmentation for multi-modal microscopy images remains a challenge due to the complex textures, patterns, and cell shapes in these images. To tackle the problem, we first develop an automatic cell classification pipeline to label the microscopy images based on their low-level image characteristics, and then train a classification model based on the category labels. Afterward, we train a separate segmentation model for each category using the images in the corresponding category. Besides, we further deploy two types of segmentation models to segment cells with roundish and irregular shapes respectively. Moreover, an efficient and powerful backbone model is utilized to enhance the efficiency of our segmentation model. Evaluated on the Tuning Set of NeurIPS 2022 Cell Segmentation Challenge, our method achieves an F1-score of 0.8795 and the running time for all cases is within the time tolerance., Comment: The second place in NeurIPS 2022 cell segmentation challenge (https://neurips22-cellseg.grand-challenge.org/), released code: https://github.com/lhaof/CellSeg

Published
2023

6. Diffusion-based Data Augmentation for Nuclei Image Segmentation

Author

Yu, Xinyi, Li, Guanbin, Lou, Wei, Liu, Siqi, Wan, Xiang, Chen, Yan, and Li, Haofeng

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

Nuclei segmentation is a fundamental but challenging task in the quantitative analysis of histopathology images. Although fully-supervised deep learning-based methods have made significant progress, a large number of labeled images are required to achieve great segmentation performance. Considering that manually labeling all nuclei instances for a dataset is inefficient, obtaining a large-scale human-annotated dataset is time-consuming and labor-intensive. Therefore, augmenting a dataset with only a few labeled images to improve the segmentation performance is of significant research and application value. In this paper, we introduce the first diffusion-based augmentation method for nuclei segmentation. The idea is to synthesize a large number of labeled images to facilitate training the segmentation model. To achieve this, we propose a two-step strategy. In the first step, we train an unconditional diffusion model to synthesize the Nuclei Structure that is defined as the representation of pixel-level semantic and distance transform. Each synthetic nuclei structure will serve as a constraint on histopathology image synthesis and is further post-processed to be an instance map. In the second step, we train a conditioned diffusion model to synthesize histopathology images based on nuclei structures. The synthetic histopathology images paired with synthetic instance maps will be added to the real dataset for training the segmentation model. The experimental results show that by augmenting 10% labeled real dataset with synthetic samples, one can achieve comparable segmentation results with the fully-supervised baseline. The code is released in: https://github.com/lhaof/Nudiff, Comment: MICCAI 2023, released code: https://github.com/lhaof/Nudiff

Published
2023

7. The Multi-modality Cell Segmentation Challenge: Towards Universal Solutions

Author

Ma, Jun, Xie, Ronald, Ayyadhury, Shamini, Ge, Cheng, Gupta, Anubha, Gupta, Ritu, Gu, Song, Zhang, Yao, Lee, Gihun, Kim, Joonkee, Lou, Wei, Li, Haofeng, Upschulte, Eric, Dickscheid, Timo, de Almeida, José Guilherme, Wang, Yixin, Han, Lin, Yang, Xin, Labagnara, Marco, Gligorovski, Vojislav, Scheder, Maxime, Rahi, Sahand Jamal, Kempster, Carly, Pollitt, Alice, Espinosa, Leon, Mignot, Tâm, Middeke, Jan Moritz, Eckardt, Jan-Niklas, Li, Wangkai, Li, Zhaoyang, Cai, Xiaochen, Bai, Bizhe, Greenwald, Noah F., Van Valen, David, Weisbart, Erin, Cimini, Beth A., Cheung, Trevor, Brück, Oscar, Bader, Gary D., and Wang, Bo

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Quantitative Biology - Quantitative Methods
Abstract

Cell segmentation is a critical step for quantitative single-cell analysis in microscopy images. Existing cell segmentation methods are often tailored to specific modalities or require manual interventions to specify hyper-parameters in different experimental settings. Here, we present a multi-modality cell segmentation benchmark, comprising over 1500 labeled images derived from more than 50 diverse biological experiments. The top participants developed a Transformer-based deep-learning algorithm that not only exceeds existing methods but can also be applied to diverse microscopy images across imaging platforms and tissue types without manual parameter adjustments. This benchmark and the improved algorithm offer promising avenues for more accurate and versatile cell analysis in microscopy imaging., Comment: NeurIPS22 Cell Segmentation Challenge: https://neurips22-cellseg.grand-challenge.org/ . Nature Methods (2024)

Published
2023
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10. The multimodality cell segmentation challenge: toward universal solutions

Author

Ma, Jun, Xie, Ronald, Ayyadhury, Shamini, Ge, Cheng, Gupta, Anubha, Gupta, Ritu, Gu, Song, Zhang, Yao, Lee, Gihun, Kim, Joonkee, Lou, Wei, Li, Haofeng, Upschulte, Eric, Dickscheid, Timo, de Almeida, José Guilherme, Wang, Yixin, Han, Lin, Yang, Xin, Labagnara, Marco, Gligorovski, Vojislav, Scheder, Maxime, Rahi, Sahand Jamal, Kempster, Carly, Pollitt, Alice, Espinosa, Leon, Mignot, Tâm, Middeke, Jan Moritz, Eckardt, Jan-Niklas, Li, Wangkai, Li, Zhaoyang, Cai, Xiaochen, Bai, Bizhe, Greenwald, Noah F., Van Valen, David, Weisbart, Erin, Cimini, Beth A., Cheung, Trevor, Brück, Oscar, Bader, Gary D., and Wang, Bo

Published
2024
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11. Single-stage resection of uterine fibroids and intravascular leiomyomatosis: a case report

Author

Jing-Xiao Li, Wei-Qi Zhang, Chao-Hai Lv, Jian-Lin Wen, Chun-Lou Wei, Jing Qian, Xiao-Chun Zeng, Liu-Liu Huang, Bao-Shi Zheng, Hua-Fu Zhou, and Ting Zhou

Subjects
Uterine fibroids, Intravascular leiomyomatosis, Heart failure, Gynecology and obstetrics, RG1-991, Public aspects of medicine, RA1-1270
Abstract

Abstract Background Uterine Fibroids (UFs) are common benign tumors in the female reproductive tract, but their progression to intravascular leiomyomatosis (IVL) is rare. Presently, there are few reports on single-stage resection of UFs and IVL. Case presentation A 47-year-old woman, G2P2, had been diagnosed multiple UFs four years ago and now developed heart failure. Imaging examinations revealed that UFs had invaded the right iliac vein and extended into the right atrium through the inferior vena cava. Through multidisciplinary collaboration and a single-stage resection, the patient has survived for over 24 months post-surgery, and her heart function has significantly improved compared to preoperative levels, with no recurrence of UFs observed. Conclusions Single-stage resection of IVL and UF is feasible and advantageous for this case, and selecting the appropriate surgical approach is crucial.

Published
2024
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12. Structure Embedded Nucleus Classification for Histopathology Images

Author

Lou, Wei, Wan, Xiang, Li, Guanbin, Lou, Xiaoying, Li, Chenghang, Gao, Feng, and Li, Haofeng

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Nuclei classification provides valuable information for histopathology image analysis. However, the large variations in the appearance of different nuclei types cause difficulties in identifying nuclei. Most neural network based methods are affected by the local receptive field of convolutions, and pay less attention to the spatial distribution of nuclei or the irregular contour shape of a nucleus. In this paper, we first propose a novel polygon-structure feature learning mechanism that transforms a nucleus contour into a sequence of points sampled in order, and employ a recurrent neural network that aggregates the sequential change in distance between key points to obtain learnable shape features. Next, we convert a histopathology image into a graph structure with nuclei as nodes, and build a graph neural network to embed the spatial distribution of nuclei into their representations. To capture the correlations between the categories of nuclei and their surrounding tissue patterns, we further introduce edge features that are defined as the background textures between adjacent nuclei. Lastly, we integrate both polygon and graph structure learning mechanisms into a whole framework that can extract intra and inter-nucleus structural characteristics for nuclei classification. Experimental results show that the proposed framework achieves significant improvements compared to the state-of-the-art methods.

Published
2023

13. Which Pixel to Annotate: a Label-Efficient Nuclei Segmentation Framework

Author

Lou, Wei, Li, Haofeng, Li, Guanbin, Han, Xiaoguang, and Wan, Xiang

Subjects
Computer Science - Computer Vision and Pattern Recognition, I.4.6
Abstract

Recently deep neural networks, which require a large amount of annotated samples, have been widely applied in nuclei instance segmentation of H\&E stained pathology images. However, it is inefficient and unnecessary to label all pixels for a dataset of nuclei images which usually contain similar and redundant patterns. Although unsupervised and semi-supervised learning methods have been studied for nuclei segmentation, very few works have delved into the selective labeling of samples to reduce the workload of annotation. Thus, in this paper, we propose a novel full nuclei segmentation framework that chooses only a few image patches to be annotated, augments the training set from the selected samples, and achieves nuclei segmentation in a semi-supervised manner. In the proposed framework, we first develop a novel consistency-based patch selection method to determine which image patches are the most beneficial to the training. Then we introduce a conditional single-image GAN with a component-wise discriminator, to synthesize more training samples. Lastly, our proposed framework trains an existing segmentation model with the above augmented samples. The experimental results show that our proposed method could obtain the same-level performance as a fully-supervised baseline by annotating less than 5% pixels on some benchmarks., Comment: IEEE TMI 2022, Released code: https://github.com/lhaof/NuSeg

Published
2022
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16. Fast Heterogeneous Federated Learning with Hybrid Client Selection

Author

Shen, Guangyuan, Gao, Dehong, Song, Duanxiao, Yang, Libin, Zhou, Xukai, Pan, Shirui, Lou, Wei, and Zhou, Fang

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence
Abstract

Client selection schemes are widely adopted to handle the communication-efficient problems in recent studies of Federated Learning (FL). However, the large variance of the model updates aggregated from the randomly-selected unrepresentative subsets directly slows the FL convergence. We present a novel clustering-based client selection scheme to accelerate the FL convergence by variance reduction. Simple yet effective schemes are designed to improve the clustering effect and control the effect fluctuation, therefore, generating the client subset with certain representativeness of sampling. Theoretically, we demonstrate the improvement of the proposed scheme in variance reduction. We also present the tighter convergence guarantee of the proposed method thanks to the variance reduction. Experimental results confirm the exceed efficiency of our scheme compared to alternatives., Comment: arXiv admin note: text overlap with arXiv:2201.05762

Published
2022

19. Therapeutic Resistance Models and Treatment Sequencing in Advanced Prostate Cancer

Author

Schaaf, Zachary A, Ning, Shu, Leslie, Amy R, Sharifi, Masuda, Han, Xianrui, Armstrong, Cameron, Lou, Wei, Lombard, Alan P, Liu, Chengfei, and Gao, Allen C

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Oncology and Carcinogenesis, Genetics, Minority Health, Prostate Cancer, Urologic Diseases, Biotechnology, Cancer, Health Disparities, 5.1 Pharmaceuticals, 6.1 Pharmaceuticals, Good Health and Well Being, prostate, cancer, resistance, enzalutamide, abiraterone, darolutamide, apalutamide, docetaxel, olaparib, Oncology and carcinogenesis
Abstract

Current common treatments for castration-resistant prostate cancer (CRPC) typically belong to one of three major categories: next-generation anti-androgen therapies (NGAT) including enzalutamide, abiraterone acetate, apalutamide, and darolutamide; taxane therapy represented by docetaxel; and PARP inhibitors (PARPi) like olaparib. Although these treatments have shown efficacy and have improved outcomes for many patients, some do not survive due to the emergence of therapeutic resistance. The clinical landscape is further complicated by limited knowledge about how the sequence of treatments impacts the development of therapeutic cross-resistance in CRPC. We have developed multiple CRPC models of acquired therapeutic resistance cell sublines from C4-2B cells. These include C4-2B MDVR, C4-2B AbiR, C4-2B ApaR, C4-2B DaroR, TaxR, and 2B-olapR, which are resistant to enzalutamide, abiraterone, apalutamide, darolutamide, docetaxel, and olaparib, respectively. These models are instrumental for analyzing gene expression and assessing responses to various treatments. Our findings reveal distinct cross-resistance characteristics among NGAT-resistant cell sublines. Specifically, resistance to enzalutamide induces resistance to abiraterone and vice versa, while maintaining sensitivity to taxanes and olaparib. Conversely, cells with acquired resistance to docetaxel exhibit cross-resistance to both cabazitaxel and olaparib but retain sensitivity to NGATs like enzalutamide and abiraterone. OlapR cells, significantly resistant to olaparib compared to parental cells, are still responsive to NGATs and docetaxel. Moreover, OlapR models display cross-resistance to other clinically relevant PARP inhibitors, including rucaparib, niraparib, and talazoparib. RNA-sequencing analyses have revealed a complex network of altered gene expressions that influence signaling pathways, energy metabolism, and apoptotic signaling, pivotal to cancer's evolution and progression. The data indicate that resistance mechanisms are distinct among different drug classes. Notably, NGAT-resistant sublines exhibited a significant downregulation of androgen-regulated genes, contrasting to the stable expression noted in olaparib and docetaxel-resistant sublines. These results may have clinical implications by showing that treatments of one class can be sequenced with those from another class, but caution should be taken when sequencing drugs of the same class.

Published
2023

24. Risk factors for one-year mortality following discharge in patients with acute aortic dissection: development and validation of a predictive model in a cross-sectional study

Author

Ting Zhou, Jing-Xiao Li, Chao-Yong Zhang, Yu-Gui Li, Jun Peng, Chun-Lou Wei, Meng-Hua Chen, and Hua-Fu Zhou

Subjects
Acute aortic dissection, Public database, Risk prediction model, Diseases of the circulatory (Cardiovascular) system, RC666-701
Abstract

Abstract Purpose This study was aimed to identify the risk factors that influence the mortality risk in patients with acute aortic dissection (AAD) within one year after discharge, and aimed to construct a predictive model for assessing mortality risk. Methods The study involved 320 adult patients obtained from the Medical Information Mart for Intensive Care (MIMIC) database. Logistic regression analysis was conducted to identify potential risk factors associated with mortality in AAD patients within one year after discharge and to develop a predictive model. The performance of the predictive model was assessed using the receiver operating characteristic curve (ROC), calibration curve, and decision curve analysis (DCA). To further validate the findings, patient data from the First Affiliated Hospital of Guangxi Medical University (157 patients) were analyzed. Results Univariate and multivariate logistic regression analyses revealed that gender, length of hospital stay, highest blood urea nitrogen (BUN_max), use of adrenaline, and use of amiodarone were significant risk factors for mortality within one year after discharge (p

Published
2024
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26. Bioengineered BERA-Wnt5a siRNA Targeting Wnt5a/FZD2 Signaling Suppresses Advanced Prostate Cancer Tumor Growth and Enhances Enzalutamide Treatment.

Author

Ning, Shu, Liu, Chengfei, Lou, Wei, Yang, Joy C, Lombard, Alan P, D'Abronzo, Leandro S, Batra, Neelu, Yu, Ai-Ming, Leslie, Amy R, Sharifi, Masuda, Evans, Christopher P, and Gao, Allen C

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Oncology and Carcinogenesis, Genetics, Aging, Cancer, Prostate Cancer, Urologic Diseases, Biotechnology, 5.1 Pharmaceuticals, Androgen Antagonists, Benzamides, Cell Line, Tumor, Drug Resistance, Neoplasm, Frizzled Receptors, Humans, Male, Nitriles, Phenylthiohydantoin, Prostatic Neoplasms, Prostatic Neoplasms, Castration-Resistant, RNA, Small Interfering, Receptors, Androgen, Wnt Signaling Pathway, Wnt-5a Protein, Pharmacology and Pharmaceutical Sciences, Oncology & Carcinogenesis, Biochemistry and cell biology, Oncology and carcinogenesis
Abstract

The next-generation antiandrogen drugs such as enzalutamide and abiraterone extend survival times and improve quality of life in patients with advanced prostate cancer. However, resistance to both drugs occurs frequently through mechanisms that are incompletely understood. Wnt signaling, particularly through Wnt5a, plays vital roles in promoting prostate cancer progression and induction of resistance to enzalutamide and abiraterone. Development of novel strategies targeting Wnt5a to overcome resistance is an urgent need. In this study, we demonstrated that Wnt5a/FZD2-mediated noncanonical Wnt pathway is overexpressed in enzalutamide-resistant prostate cancer. In patient databases, both the levels of Wnt5a and FZD2 expression are upregulated upon the development of enzalutamide resistance and correlate with higher Gleason score, biochemical recurrence, and metastatic status, and with shortened disease-free survival duration. Blocking Wnt5a/FZD2 signal transduction not only diminished the activation of noncanonical Wnt signaling pathway, but also suppressed the constitutively activated androgen receptor (AR) and AR variants. Furthermore, we developed a novel bioengineered BERA-Wnt5a siRNA construct and demonstrated that inhibition of Wnt5a expression by the BERA-Wnt5a siRNA significantly suppressed tumor growth and enhanced enzalutamide treatment in vivo. These results indicate that Wnt5a/FZD2 signal pathway plays a critical role in promoting enzalutamide resistance, and targeting this pathway by BERA-Wnt5a siRNA can be developed as a potential therapy to treat advanced prostate cancer.

Published
2022

27. Variance-Reduced Heterogeneous Federated Learning via Stratified Client Selection

Author

Shen, Guangyuan, Gao, Dehong, Yang, Libin, Zhou, Fang, Song, Duanxiao, Lou, Wei, and Pan, Shirui

Subjects
Computer Science - Machine Learning, Computer Science - Distributed, Parallel, and Cluster Computing
Abstract

Client selection strategies are widely adopted to handle the communication-efficient problem in recent studies of Federated Learning (FL). However, due to the large variance of the selected subset's update, prior selection approaches with a limited sampling ratio cannot perform well on convergence and accuracy in heterogeneous FL. To address this problem, in this paper, we propose a novel stratified client selection scheme to reduce the variance for the pursuit of better convergence and higher accuracy. Specifically, to mitigate the impact of heterogeneity, we develop stratification based on clients' local data distribution to derive approximate homogeneous strata for better selection in each stratum. Concentrating on a limited sampling ratio scenario, we next present an optimized sample size allocation scheme by considering the diversity of stratum's variability, with the promise of further variance reduction. Theoretically, we elaborate the explicit relation among different selection schemes with regard to variance, under heterogeneous settings, we demonstrate the effectiveness of our selection scheme. Experimental results confirm that our approach not only allows for better performance relative to state-of-the-art methods but also is compatible with prevalent FL algorithms., Comment: there is something wrong in the experiment

Published
2022

31. Olaparib-Induced Senescence Is Bypassed through G2–M Checkpoint Override in Olaparib-Resistant Prostate Cancer

Author

Lombard, Alan P, Armstrong, Cameron M, D'Abronzo, Leandro S, Ning, Shu, Leslie, Amy R, Sharifi, Masuda, Lou, Wei, Evans, Christopher P, Dall'Era, Marc, Chen, Hong-Wu, Chen, Xinbin, and Gao, Allen C

Subjects
Biological Sciences, Biomedical and Clinical Sciences, Oncology and Carcinogenesis, Cancer, Prostate Cancer, Genetics, Urologic Diseases, 2.1 Biological and endogenous factors, 5.1 Pharmaceuticals, Good Health and Well Being, Cell Cycle Checkpoints, Cell Line, Tumor, Humans, Male, Phthalazines, Piperazines, Poly(ADP-ribose) Polymerase Inhibitors, Prostatic Neoplasms, Pharmacology and Pharmaceutical Sciences, Oncology & Carcinogenesis, Biochemistry and cell biology, Oncology and carcinogenesis
Abstract

PARP inhibition represents the dawn of precision medicine for treating prostate cancer. Despite this advance, questions remain regarding the use of PARP inhibitors (PARPi) for the treatment of this disease, including (i) how specifically do PARPi-sensitive tumor cells respond to treatment, and (ii) how does PARPi resistance develop? To address these questions, we characterized response to olaparib in sensitive LNCaP and C4-2B cells and developed two olaparib-resistant derivative cell line models from each, termed LN-OlapR and 2B-OlapR, respectively. OlapR cells possess distinct morphology from parental cells and display robust resistance to olaparib and other clinically relevant PARPis, including rucaparib, niraparib, and talazoparib. In LNCaP and C4-2B cells, we found that olaparib induces massive DNA damage, leading to activation of the G2-M checkpoint, activation of p53, and cell-cycle arrest. Furthermore, our data suggest that G2-M checkpoint activation leads to both cell death and senescence associated with p21 activity. In contrast, both LN-OlapR and 2B-OlapR cells do not arrest at G2-M and display a markedly blunted response to olaparib treatment. Interestingly, both OlapR cell lines harbor increased DNA damage relative to parental cells, suggesting that OlapR cells accumulate and manage persistent DNA damage during acquisition of resistance, likely through augmenting DNA repair capacity. Further impairing DNA repair through CDK1 inhibition enhances DNA damage, induces cell death, and sensitizes OlapR cells to olaparib treatment. Our data together further our understanding of PARPi treatment and provide a cellular platform system for the study of response and resistance to PARP inhibition.

Published
2022

32. Dynamic-OFA: Runtime DNN Architecture Switching for Performance Scaling on Heterogeneous Embedded Platforms

Author

Lou, Wei, Xun, Lei, Sabet, Amin, Bi, Jia, Hare, Jonathon, and Merrett, Geoff V.

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Mobile and embedded platforms are increasingly required to efficiently execute computationally demanding DNNs across heterogeneous processing elements. At runtime, the available hardware resources to DNNs can vary considerably due to other concurrently running applications. The performance requirements of the applications could also change under different scenarios. To achieve the desired performance, dynamic DNNs have been proposed in which the number of channels/layers can be scaled in real time to meet different requirements under varying resource constraints. However, the training process of such dynamic DNNs can be costly, since platform-aware models of different deployment scenarios must be retrained to become dynamic. This paper proposes Dynamic-OFA, a novel dynamic DNN approach for state-of-the-art platform-aware NAS models (i.e. Once-for-all network (OFA)). Dynamic-OFA pre-samples a family of sub-networks from a static OFA backbone model, and contains a runtime manager to choose different sub-networks under different runtime environments. As such, Dynamic-OFA does not need the traditional dynamic DNN training pipeline. Compared to the state-of-the-art, our experimental results using ImageNet on a Jetson Xavier NX show that the approach is up to 3.5x (CPU), 2.4x (GPU) faster for similar ImageNet Top-1 accuracy, or 3.8% (CPU), 5.1% (GPU) higher accuracy at similar latency., Comment: Accepted at CVPR ECV Workshop 2021

Published
2021

33. Wntless expression promotes lineage plasticity and is associated with neuroendocrine prostate cancer.

Author

D'Abronzo, Leandro S, Lombard, Alan P, Ning, Shu, Armstong, Cameron M, Leslie, Amy R, Sharifi, Masuda, Schaaf, Zachary A, Lou, Wei, and Gao, Allen C

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Oncology and Carcinogenesis, Aging, Cancer, Urologic Diseases, Prostate Cancer, 2.1 Biological and endogenous factors, Prostate cancer, WLS, lineage plasticity, neuroendocrine, enzalutamide
Abstract

Resistance to androgen receptor (AR) targeted therapies remains as the main reason for most prostate cancer related deaths. Lineage plasticity resulting in altered, treatment insensitive prostate tumor cell phenotypes such neuroendocrine differentiated prostate cancer is a common manifestation within resistant tumors upon AR-targeted therapies. The mechanisms responsible for lineage plasticity in prostate cancer remain incompletely understood. Here we demonstrate that the enzalutamide resistant MDVR cell line possesses lineage plastic characteristics associated with overexpression of the Wnt transporter Wntless (WLS). Furthermore, we present evidence that overexpression of WLS is common in varying cell line models of lineage plastic prostate cancer, is higher in neuroendocrine patient samples, and positively correlates with the neuroendocrine marker SYP in clinical data. Targeting WLS in lineage plastic cellular models reduces viability and represses lineage plasticity associated gene expression. Our study provides insight into the importance of WLS to the development of lethal resistant prostate cancer and provides a potential target for the treatment of advanced disease.

Published
2022

34. Activation of neural lineage networks and ARHGEF2 in enzalutamide-resistant and neuroendocrine prostate cancer and association with patient outcomes

Author

Ning, Shu, Zhao, Jinge, Lombard, Alan P, D’Abronzo, Leandro S, Leslie, Amy R, Sharifi, Masuda, Lou, Wei, Liu, Chengfei, Yang, Joy C, Evans, Christopher P, Corey, Eva, Chen, Hong-Wu, Yu, Aiming, Ghosh, Paramita M, and Gao, Allen C

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Oncology and Carcinogenesis, Genetics, Clinical Research, Stem Cell Research, Precision Medicine, Prostate Cancer, Aging, Urologic Diseases, Cancer, 4.1 Discovery and preclinical testing of markers and technologies, Prostate, Prostate cancer
Abstract

BackgroundTreatment-emergent neuroendocrine prostate cancer (NEPC) after androgen receptor (AR) targeted therapies is an aggressive variant of prostate cancer with an unfavorable prognosis. The underlying mechanisms for early neuroendocrine differentiation are poorly defined and diagnostic and prognostic biomarkers are needed.MethodsWe performed transcriptomic analysis on the enzalutamide-resistant prostate cancer cell line C4-2B MDVR and NEPC patient databases to identify neural lineage signature (NLS) genes. Correlation of NLS genes with clinicopathologic features was determined. Cell viability was determined in C4-2B MDVR and H660 cells after knocking down ARHGEF2 using siRNA. Organoid viability of patient-derived xenografts was measured after knocking down ARHGEF2.ResultsWe identify a 95-gene NLS representing the molecular landscape of neural precursor cell proliferation, embryonic stem cell pluripotency, and neural stem cell differentiation, which may indicate an early or intermediate stage of neuroendocrine differentiation. These NLS genes positively correlate with conventional neuroendocrine markers such as chromogranin and synaptophysin, and negatively correlate with AR and AR target genes in advanced prostate cancer. Differentially expressed NLS genes stratify small-cell NEPC from prostate adenocarcinoma, which are closely associated with clinicopathologic features such as Gleason Score and metastasis status. Higher ARGHEF2, LHX2, and EPHB2 levels among the 95 NLS genes correlate with a shortened survival time in NEPC patients. Furthermore, downregulation of ARHGEF2 gene expression suppresses cell viability and markers of neuroendocrine differentiation in enzalutamide-resistant and neuroendocrine cells.ConclusionsThe 95 neural lineage gene signatures capture an early molecular shift toward neuroendocrine differentiation, which could stratify advanced prostate cancer patients to optimize clinical treatment and serve as a source of potential therapeutic targets in advanced prostate cancer.

Published
2022

37. Towards a global partnership model in interprofessional education for cross-sector problem-solving

Author

Ganotice, Jr, Fraide, Zheng, Binbin, Ng, Pauline Yeung, Leung, Siu Chung, Barrett, Elizabeth Ann, Chan, Hoi Yan Celia, Chan, Chad W. N., Chan, Kit Wa Sherry, Chan, Linda, Chan, M. K. Karen, Chan, Siu Ling Polly, Chan, So Ching Sarah, Chan, Esther W. Y., Chen, Julie, Cheuk, Yuet Ying Jessica, Chong, Yin Kei Doris, Chow, Yin Man Amy, Chu, Kwok Pui Jody, Chung, Hon Yin Brian, Ho, Shun Yee Amy, Jen, Julienne, Jin, Jingwen, Khoo, Ui Soon, Lam, Ho Yan Angie, Lam, May P. S., Lam, Suk Fun Veronica, Lee, Pamela Pui-Wah, Lee, Jetty Chung-Yung, Leung, Chung Yin Feona, Leung, Anna K. Y., Lin, Xiang, Liu, Rebecca K. W., Lou, Wei Qun Vivian, Luk, Pauline, Ng, Lai Han Zoe, Ng, Yee Man Alina, Ng, Tin Wai Terry, See, Lok Man Mary, Shen, Jiangang, Shen, Xiaoai, Szeto, Grace, Tam, Eliza Y. T., To, Kelvin Kai-Wang, Tso, Wan-Yee Winnie, Vackova, Dana, Wang, Ning, Wang, Runjia, Wong, Hoi Yan Gloria, Wong, K. T. Janet, Wong, M. Y. Anita, Wong, Yuen Ha Janet, Yuen, Kwan Yuk Jacqueline, Yuen, Wai Yee Grace, Orlu, Mine, and Tipoe, George L.

Published
2023
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38. Activation of the ABCB1-amplicon promotes cellular viability and resistance to docetaxel and cabazitaxel in castration-resistant prostate cancer

Author

Lombard, Alan P, Lou, Wei, Armstrong, Cameron M, D'Abronzo, Leandro S, Ning, Shu, Evans, Christopher P, and Gao, Allen C

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Oncology and Carcinogenesis, Prostate Cancer, Urologic Diseases, Aging, Cancer, Genetics, Biotechnology, ATP Binding Cassette Transporter, Subfamily B, Antineoplastic Combined Chemotherapy Protocols, Apoptosis, Biomarkers, Tumor, Cell Proliferation, Docetaxel, Drug Resistance, Neoplasm, Gene Expression Regulation, Neoplastic, Humans, Male, Prostatic Neoplasms, Castration-Resistant, Taxoids, Tumor Cells, Cultured, Pharmacology and Pharmaceutical Sciences, Oncology & Carcinogenesis, Biochemistry and cell biology, Oncology and carcinogenesis
Abstract

Docetaxel and cabazitaxel-based taxane chemotherapy are critical components in the management of advanced prostate cancer. However, their efficacy is hindered due to de novo presentation with or the development of resistance. Characterizing models of taxane-resistant prostate cancer will lead to creation of strategies to overcome insensitivity. We have previously characterized docetaxel-resistant C4-2B and DU145 cell line derivatives, TaxR and DU145-DTXR, respectively. In the present study, we characterize cabazitaxel-resistant derivative cell lines created from chronic cabazitaxel exposure of TaxR and DU145-DTXR cells, CabR and CTXR, respectively. We show that CabR and CTXR cells are robustly resistant to both taxanes but retain sensitivity to antiandrogens. Both CabR and CTXR cells possess increased expression of ABCB1, which is shown to mediate resistance to treatment. Interestingly, we also present evidence for coordinated overexpression of additional genes present within the 7q21.12 gene locus where ABCB1 resides. This locus, known as the ABCB1 amplicon, has been demonstrated to be amplified in multidrug-resistant tumor cells, but little is known regarding its role in prostate cancer. We show that two ABCB1-amplicon genes other than ABCB1, RUNDC3B and DBF4, promote cellular viability and treatment resistance in taxane-resistant prostate cancer models. We present evidence that coordinated amplification of ABCB1-amplicon genes is common in a subset of prostate cancer patients. These data together suggest that ABCB1-amplicon activation plays a critical role in taxane resistance.

Published
2021

39. ARVib suppresses growth of advanced prostate cancer via inhibition of androgen receptor signaling

Author

Liu, Chengfei, Armstrong, Cameron M, Ning, Shu, Yang, Joy C, Lou, Wei, Lombard, Alan P, Zhao, Jinge, Wu, Chun-Yi, Yu, Aiming, Evans, Christopher P, Tepper, Clifford G, Li, Pui-kai, and Gao, Allen C

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Oncology and Carcinogenesis, Prostate Cancer, Cancer, Urologic Diseases, 5.1 Pharmaceuticals, 2.1 Biological and endogenous factors, Male, Humans, Receptors, Androgen, Signal Transduction, Animals, Prostatic Neoplasms, Castration-Resistant, Mice, Cell Line, Tumor, Xenograft Model Antitumor Assays, Cell Proliferation, Androgen Receptor Antagonists, Gene Expression Regulation, Neoplastic, Prostatic Neoplasms, Phenylthiohydantoin, Benzamides, Oncology & Carcinogenesis, Biochemistry and cell biology, Oncology and carcinogenesis
Abstract

Targeting androgen signaling with the second-generation anti-androgen drugs, such as enzalutamide (Enza), abiraterone (Abi), apalutamide (Apal), and darolutamide (Daro), is the mainstay for the treatment of castration-resistant prostate cancer (CRPC). While these treatments are effective initially, resistance occurs frequently. Continued expression of androgen receptor (AR) and its variants such as AR-V7 despite AR-targeted therapy contributes to treatment resistance and cancer progression in advanced CRPC patients. This highlights the need for new strategies blocking continued AR signaling. Here, we identify a novel AR/AR-V7 degrader (ARVib) and found that ARVib effectively degrades AR/AR-V7 protein and attenuates AR/AR-V7 downstream target gene expression in prostate cancer cells. Mechanistically, ARVib degrades AR/AR-V7 protein through the ubiquitin-proteasome pathway mediated by HSP70/STUB1 machinery modulation. ARVib suppresses HSP70 expression and promotes STUB1 nuclear translocation, where STUB1 binds to AR/AR-V7 and promotes its ubiquitination and degradation. ARVib significantly inhibits resistant prostate tumor growth and improves enzalutamide treatment in vitro and in vivo. These data suggest that ARVib has potential for development as an AR/AR-V7 degrader to treat resistant CRPC.

Published
2021

44. Steroid Sulfatase Stimulates Intracrine Androgen Synthesis and is a Therapeutic Target for Advanced Prostate Cancer.

Author

Armstrong, Cameron M, Liu, Chengfei, Liu, Liangren, Yang, Joy C, Lou, Wei, Zhao, Ruining, Ning, Shu, Lombard, Alan P, Zhao, Jinge, D'Abronzo, Leandro S, Evans, Christopher P, Li, Pui-Kai, and Gao, Allen C

Subjects
Cell Line, Tumor, Humans, Prostatic Neoplasms, Benzamides, Nitriles, Phenylthiohydantoin, Androstenes, Dehydroepiandrosterone, Dehydroepiandrosterone Sulfate, Androgen Antagonists, Steryl-Sulfatase, Androgens, Neoplasm Staging, Cell Proliferation, Gene Expression Regulation, Neoplastic, Drug Resistance, Neoplasm, Male, Prostatic Neoplasms, Castration-Resistant, Carcinogenesis, RNA-Seq, Aging, Genetics, Clinical Research, Urologic Diseases, Cancer, Prostate Cancer, 5.1 Pharmaceuticals, Development of treatments and therapeutic interventions, Oncology and Carcinogenesis, Oncology & Carcinogenesis
Abstract

PurposeMost patients with prostate cancer receiving enzalutamide or abiraterone develop resistance. Clinical evidence indicates that serum levels of dehydroepiandrosterone sulfate (DHEAS) and biologically active DHEA remain in the high range despite antiandrogen treatment. The conversion of DHEAS into DHEA by steroid sulfatase (STS) may contribute to sustained intracrine androgen synthesis. Here, we determine the contribution of STS to treatment resistance and explore the potential of targeting STS to overcome resistance in prostate cancer.Experimental designSTS expression was examined in patients and cell lines. In vitro, STS activity and expression were modulated using STS-specific siRNA or novel STS inhibitors (STSi). Cell growth, colony formation, androgen production, and gene expression were examined. RNA-sequencing analysis was conducted on VCaP cells treated with STSi. Mice were treated with STSis with or without enzalutamide to determine their effects in vivo.ResultsSTS is overexpressed in patients with castration-resistant prostate cancer (CRPC) and resistant cells. STS overexpression increases intracrine androgen synthesis, cell proliferation, and confers resistance to enzalutamide and abiraterone. Inhibition of STS using siRNA suppresses prostate cancer cell growth. Targeting STS activity using STSi inhibits STS activity, suppresses androgen receptor transcriptional activity, and reduces the growth of resistant C4-2B and VCaP prostate cancer cells. STSis significantly suppress resistant VCaP tumor growth, decrease serum PSA levels, and enhance enzalutamide treatment in vitro and in vivo.ConclusionsThese studies suggest that STS drives intracrine androgen synthesis and prostate cancer proliferation. Targeting STS represents a therapeutic strategy to treat CRPC and improve second-generation antiandrogen therapy.

Published
2020

45. Cross-Resistance Among Next-Generation Antiandrogen Drugs Through the AKR1C3/AR-V7 Axis in Advanced Prostate Cancer

Author

Zhao, Jinge, Ning, Shu, Lou, Wei, Yang, Joy C, Armstrong, Cameron M, Lombard, Alan P, D'Abronzo, Leandro S, Evans, Christopher P, Gao, Allen C, and Liu, Chengfei

Subjects
Cancer, Clinical Research, Urologic Diseases, Prostate Cancer, Aging, Development of treatments and therapeutic interventions, 5.1 Pharmaceuticals, Good Health and Well Being, Aldo-Keto Reductase Family 1 Member C3, Alternative Splicing, Androgen Receptor Antagonists, Apoptosis, Biomarkers, Tumor, Cell Proliferation, Drug Resistance, Neoplasm, Gene Expression Regulation, Neoplastic, Humans, Male, Prostatic Neoplasms, Castration-Resistant, Receptors, Androgen, Tumor Cells, Cultured, Oncology and Carcinogenesis, Pharmacology and Pharmaceutical Sciences, Oncology & Carcinogenesis
Abstract

The next-generation antiandrogen drugs, XTANDI (enzalutamide), ZYTIGA (abiraterone acetate), ERLEADA (apalutamide) and NUBEQA (darolutamide) extend survival times and improve quality of life in patients with advanced prostate cancer. Despite these advances, resistance occurs frequently and there is currently no definitive cure for castration-resistant prostate cancer. Our previous studies identified that similar mechanisms of resistance to enzalutamide or abiraterone occur following treatment and cross-resistance exists between these therapies in advanced prostate cancer. Here, we show that enzalutamide- and abiraterone-resistant prostate cancer cells are further cross-resistant to apalutamide and darolutamide. Mechanistically, we have determined that the AKR1C3/AR-V7 axis confers this cross-resistance. Knockdown of AR-V7 in enzalutamide-resistant cells resensitize cells to apalutamide and darolutamide treatment. Furthermore, targeting AKR1C3 resensitizes resistant cells to apalutamide and darolutamide treatment through AR-V7 inhibition. Chronic apalutamide treatment in C4-2B cells activates the steroid hormone biosynthesis pathway and increases AKR1C3 expression, which confers resistance to enzalutamide, abiraterone, and darolutamide. In conclusion, our results suggest that apalutamide and darolutamide share similar resistant mechanisms with enzalutamide and abiraterone. The AKR1C3/AR-V7 complex confers cross-resistance to second-generation androgen receptor-targeted therapies in advanced prostate cancer.

Published
2020

49. AKR1C3 Promotes AR-V7 Protein Stabilization and Confers Resistance to AR-Targeted Therapies in Advanced Prostate Cancer

Author

Liu, Chengfei, Yang, Joy C, Armstrong, Cameron M, Lou, Wei, Liu, Liangren, Qiu, Xiaomin, Zou, Binhao, Lombard, Alan P, D'Abronzo, Leandro S, Evans, Christopher P, and Gao, Allen C

Subjects
Biochemistry and Cell Biology, Biomedical and Clinical Sciences, Biological Sciences, Clinical Sciences, Oncology and Carcinogenesis, Aging, Urologic Diseases, Prostate Cancer, Cancer, 5.1 Pharmaceuticals, 2.1 Biological and endogenous factors, Administration, Oral, Aldo-Keto Reductase Family 1 Member C3, Alternative Splicing, Animals, Benzamides, Cell Line, Tumor, Drug Resistance, Neoplasm, Humans, Indomethacin, Male, Mice, SCID, Molecular Targeted Therapy, Neoplasm Staging, Nitriles, Phenylthiohydantoin, Prostatic Neoplasms, Protein Binding, Protein Stability, Receptors, Androgen, Signal Transduction, Steroids, Pharmacology and Pharmaceutical Sciences, Oncology & Carcinogenesis, Biochemistry and cell biology, Oncology and carcinogenesis
Abstract

The mechanisms resulting in resistance to next-generation antiandrogens in castration-resistant prostate cancer are incompletely understood. Numerous studies have determined that constitutively active androgen receptor (AR) signaling or full-length AR bypass mechanisms may contribute to the resistance. Previous studies established that AKR1C3 and AR-V7 play important roles in enzalutamide and abiraterone resistance. In the present study, we found that AKR1C3 increases AR-V7 expression in resistant prostate cancer cells through enhancing protein stability via activation of the ubiquitin-mediated proteasome pathway. AKR1C3 reprograms AR signaling in enzalutamide-resistant prostate cancer cells. In addition, bioinformatical analysis of indomethacin-treated resistant cells revealed that indomethacin significantly activates the unfolded protein response, p53, and apoptosis pathways, and suppresses cell-cycle, Myc, and AR/ARV7 pathways. Targeting AKR1C3 with indomethacin significantly decreases AR/AR-V7 protein expression in vitro and in vivo through activation of the ubiquitin-mediated proteasome pathway. Our results suggest that the AKR1C3/AR-V7 complex collaboratively confers resistance to AR-targeted therapies in advanced prostate cancer.

Published
2019

50. Overexpressed ABCB1 Induces Olaparib-Taxane Cross-Resistance in Advanced Prostate Cancer.

Author

Lombard, Alan P, Liu, Chengfei, Armstrong, Cameron M, D'Abronzo, Leandro S, Lou, Wei, Chen, Hongwu, Dall'Era, Marc, Ghosh, Paramita M, Evans, Christopher P, and Gao, Allen C

Subjects
Aging, Urologic Diseases, Genetics, Cancer, Prostate Cancer, Development of treatments and therapeutic interventions, 5.1 Pharmaceuticals, Evaluation of treatments and therapeutic interventions, 6.2 Cellular and gene therapies, Biochemistry and Cell Biology, Clinical Sciences, Oncology and Carcinogenesis, Oncology & Carcinogenesis
Abstract

Castration-resistant prostate cancer remains as an incurable disease. Exploiting DNA damage repair defects via inhibition of poly (ADP-ribose) polymerase (PARP) is becoming an attractive therapeutic option. The TOPARP-A clinical trial demonstrated that the PARP inhibitor olaparib may be an effective strategy for treating prostate cancer. However, several unanswered questions regarding the use of olaparib remain: 1) How do we best stratify patients for olaparib treatment? 2) Where do we place olaparib in the treatment sequence paradigm? 3) Is there cross-resistance between olaparib and currently used therapies? Here, we tested putative cross-resistance between current therapies and olaparib in treatment-resistant castration-resistant prostate cancer models. Docetaxel-resistant cells exhibited robust resistance to olaparib which could be attributed to blunted PARP trapping in response to olaparib treatment. Upregulated ABCB1 mediates cross-resistance between taxanes and olaparib, which can be overcome through decreasing ABCB1 expression or inhibiting ABCB1 using elacridar or enzalutamide. We also show that combining olaparib with enzalutamide is more effective in olaparib-sensitive cells than either single agent. Our results demonstrate that cross-resistance between olaparib and other therapies could blunt response to treatment and highlight the need to develop strategies to maximize olaparib efficacy.

Published
2019

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