Niclosamide enhances abiraterone treatment via inhibition of androgen receptor variants in castration resistant prostate cancer.

Considerable evidence from both clinical and experimental studies suggests that androgen receptor variants, particularly androgen receptor variant 7 (AR-V7), are critical in the induction of resistance to enzalutamide and abiraterone. In this study, we investigated the role of AR-V7 in the cross-resistance of enzalutamide and abiraterone and examined if inhibition of AR-V7 can improve abiraterone treatment response. We found that enzalutamide-resistant cells are cross-resistant to abiraterone, and that AR-V7 confers resistance to abiraterone. Knock down of AR-V7 by siRNA in abiraterone resistant CWR22Rv1 and C4-2B MDVR cells restored their sensitivity to abiraterone, indicating that AR-V7 is involved in abiraterone resistance. Abiraterone resistant prostate cancer cells generated by chronic treatment with abiraterone showed enhanced AR-V7 protein expression. Niclosamide, an FDA-approved antihelminthic drug that has been previously identified as a potent inhibitor of AR-V7, re-sensitizes resistant cells to abiraterone treatment in vitro and in vivo. In summary, this preclinical study suggests that overexpression of AR-V7 contributes to resistance to abiraterone, and supports the development of combination of abiraterone with niclosamide as a potential treatment for advanced castration resistant prostate cancer

Lin28 Promotes Growth of Prostate Cancer Cells and Activates the Androgen Receptor

Prostate cancer (CaP) progresses to a castration-resistant state assisted by multifold molecular changes, most of which involve activation of the androgen receptor (AR). Having previously demonstrated the importance of the Lin28/let-7/Myc axis in CaP, we tested the hypothesis that Lin28 is overexpressed in CaP and that it activates AR and promotes growth of CaP cells. We analyzed human clinical CaP samples for the expression of Lin28 by quantitative real-time RT-PCR, Western blot analysis, and IHC. Growth characteristics of CaP cell lines transiently and stably expressing Lin28 were examined. The clonogenic ability of CaP cells expressing Lin28 was determined by colony formation and soft agar assays. Increase in expression of AR and subsequent increase in transcription of AR-target genes were analyzed by quantitative real-time RT-PCR, luciferase assays, and ELISA. LNCaP cells stably expressing Lin28 were injected into nude mice, and tumorigenesis was monitored. We found that Lin28 is overexpressed in clinical CaP compared to benign prostates. Overexpression of Lin28 enhanced, while down-regulation reduced, growth of CaP cells. Lin28 enhanced the ability of CaP cells to form colonies in anchorage-dependent and anchorage-independent conditions. LNCaP cells stably expressing Lin28 exhibited significantly higher tumorigenic ability in vivo. Lin28 induced expression of the AR and its target genes such as PSA and NKX3.1. Collectively, our findings demonstrate a novel role for Lin28 in CaP development and activation of the AR axis

Lin28 Promotes Growth of Prostate Cancer Cells and Activates the Androgen Receptor

Prostate cancer (CaP) progresses to a castration-resistant state assisted by multifold molecular changes, most of which involve activation of the androgen receptor (AR). Having previously demonstrated the importance of the Lin28/let-7/Myc axis in CaP, we tested the hypothesis that Lin28 is overexpressed in CaP and that it activates AR and promotes growth of CaP cells. We analyzed human clinical CaP samples for the expression of Lin28 by quantitative real-time RT-PCR, Western blot analysis, and IHC. Growth characteristics of CaP cell lines transiently and stably expressing Lin28 were examined. The clonogenic ability of CaP cells expressing Lin28 was determined by colony formation and soft agar assays. Increase in expression of AR and subsequent increase in transcription of AR-target genes were analyzed by quantitative real-time RT-PCR, luciferase assays, and ELISA. LNCaP cells stably expressing Lin28 were injected into nude mice, and tumorigenesis was monitored. We found that Lin28 is overexpressed in clinical CaP compared to benign prostates. Overexpression of Lin28 enhanced, while down-regulation reduced, growth of CaP cells. Lin28 enhanced the ability of CaP cells to form colonies in anchorage-dependent and anchorage-independent conditions. LNCaP cells stably expressing Lin28 exhibited significantly higher tumorigenic ability in vivo. Lin28 induced expression of the AR and its target genes such as PSA and NKX3.1. Collectively, our findings demonstrate a novel role for Lin28 in CaP development and activation of the AR axis

MicroRNA let-7c Is Downregulated in Prostate Cancer and Suppresses Prostate Cancer Growth

Prostate cancer (PCa) is characterized by deregulated expression of several tumor suppressor or oncogenic miRNAs. The objective of this study was the identification and characterization of miR-let-7c as a potential tumor suppressor in PCa.Levels of expression of miR-let-7c were examined in human PCa cell lines and tissues using qRT-PCR and in situ hybridization. Let-7c was overexpressed or suppressed to assess the effects on the growth of human PCa cell lines. Lentiviral-mediated re-expression of let-7c was utilized to assess the effects on human PCa xenografts.We identified miR-let-7c as a potential tumor suppressor in PCa. Expression of let-7c is downregulated in castration-resistant prostate cancer (CRPC) cells. Overexpression of let-7c decreased while downregulation of let-7c increased cell proliferation, clonogenicity and anchorage-independent growth of PCa cells in vitro. Suppression of let-7c expression enhanced the ability of androgen-sensitive PCa cells to grow in androgen-deprived conditions in vitro. Reconstitution of Let-7c by lentiviral-mediated intratumoral delivery significantly reduced tumor burden in xenografts of human PCa cells. Furthermore, let-7c expression is downregulated in clinical PCa specimens compared to their matched benign tissues, while the expression of Lin28, a master regulator of let-7 miRNA processing, is upregulated in clinical PCa specimens.These results demonstrate that microRNA let-7c is downregulated in PCa and functions as a tumor suppressor, and is a potential therapeutic target for PCa

Detection of androgen receptor (AR) and AR-V7 in small cell prostate carcinoma: Diagnostic and therapeutic implications

Objective: Small cell prostate carcinoma (SCPC) is a rare and highly malignant subtype of prostate cancer. SCPC frequently lacks androgen receptor (AR) and prostate-specific antigen (PSA) expression, and often responds poorly to androgen deprivation therapy (ADT). AR splice variant-7 (AR-V7) is a truncated AR protein implicated in resistance to AR-targeting therapies. AR-V7 expression in castration-resistant prostate cancers has been evaluated extensively, and blood-based detection of AR-V7 has been associated with lack of response to abiraterone and enzalutamide. However, whether AR-V7 is expressed in SCPC is not known. Methods: Using validated antibodies, we performed immunohistochemistry (IHC) assay for the full-length AR (AR-FL) and (AR-V7) on post-ADT surgical SCPC specimens. Results: Seventy-five percent (9/12) of the specimens showed positive staining for the AR-FL with various intensities. Thirty-three percent (4/12) of the specimens showed positive staining for AR-V7. Among the specimens with positive AR-V7 staining, two samples displayed very weak staining, one sample showed weak-to-moderate staining, and one sample showed strong staining. All positive specimens displayed a heterogeneous pattern of AR-FL/AR-V7 staining. All specimens positive for AR-V7 were also positive for AR-FL. Conclusion: The study findings support the existence of measurable AR-FL and AR-V7 proteins in SCPC specimens. The results also have implications in detection of AR-V7 in specimens obtained through systemic sampling approaches such as circulating tumor cells. A positive AR-V7 finding by blood-based tests is not impossible in patients with SCPC who often demonstrate low PSA values. Keywords: Small cell prostate carcinoma, Androgen receptor, Androgen receptor splice variant-7, Immunohistochemistr

Data augmentation using conditional generative adversarial networks for leaf counting in arabidopsis plants

Deep Learning models are being applied to address plant phenotyping problems such as leaf segmentation and leaf counting. Training these models requires large annotated datasets of plant images, which, in many cases, are not readily available. We address the problem of data scarcity by proposing a data augmentation approach based on generating artificial images using conditional Generative Adversarial Networks (cGANs). Our model is trained by conditioning on the leaf segmentation mask of plants with the aim to generate corresponding, realistic, plant images. We also provide a novel method to create the input masks. The proposed system is thus capable of generating realistic images by first producing a mask, and subsequently using the mask as input to the cGANs. We evaluated the impact of the data augmentation on the leaf counting performance of the Mask R-CNN model. The average leaf counting error is reduced by 16.67% when we augment the training set with the generated data

Data augmentation using conditional generative adversarial networks for leaf counting in arabidopsis plants

Deep Learning models are being applied to address plant phenotyping problems such as leaf segmentation and leaf counting. Training these models requires large annotated datasets of plant images, which, in many cases, are not readily available. We address the problem of data scarcity by proposing a data augmentation approach based on generating artificial images using conditional Generative Adversarial Networks (cGANs). Our model is trained by conditioning on the leaf segmentation mask of plants with the aim to generate corresponding, realistic, plant images. We also provide a novel method to create the input masks. The proposed system is thus capable of generating realistic images by first producing a mask, and subsequently using the mask as input to the cGANs. We evaluated the impact of the data augmentation on the leaf counting performance of the Mask R-CNN model. The average leaf counting error is reduced by 16.67% when we augment the training set with the generated data