Prostate cancer is the second most frequent cancer diagnosed in men worldwide. Localized disease can be successfully treated, but advanced cases are more problematic. After initial effectiveness of androgen deprivation therapy, resistance quickly occurs. Therefore, we aimed to investigate the role of Hedgehog-GLI (HH-GLI) signaling in sustaining androgen-independent growth of prostate cancer cells. We found various modes of HH-GLI signaling activation in prostate cancer cells depending on androgen availability. When androgen was not deprived, we found evidence of non-canonical SMO signaling through the SRC kinase. After short-term androgen deprivation canonical HH-GLI signaling was activated, but we found little evidence of canonical HH-GLI signaling activity in androgen-independent prostate cancer cells. We show that in androgen-independent cells the pathway ligand, SHH-N, non-canonically binds to the androgen receptor through its cholesterol modification. Inhibition of this interaction leads to androgen receptor signaling downregulation. This implies that SHH-N activates the androgen receptor and sustains androgen-independence. Targeting this interaction might prove to be a valuable strategy for advanced prostate cancer treatment. Also, other non-canonical aspects of this signaling pathway should be investigated in more detail and considered when developing potential therapies.

Background: Despite significant progress in therapy, melanoma still has a rising incidence worldwide, and novel treatment strategies are needed. Recently, researchers have recognized the involvement of the Hedgehog-GLI (HH-GLI) signaling pathway in melanoma and its consistent crosstalk with the MAPK pathway. In order to further investigate the link between the two pathways and to find new target genes that could be considered for combination therapy, we set out to find transcriptional targets of all three GLI proteins in melanoma. Methods: We performed RNA sequencing on three melanoma cell lines (CHL-1, A375, and MEL224) with overexpressed GLI1, GLI2, and GLI3 and combined them with the results of ChIP-sequencing on endogenous GLI1, GLI2, and GLI3 proteins. After combining these results, 21 targets were selected for validation by qPCR. Results: RNA-seq revealed a total of 808 differentially expressed genes (DEGs) for GLI1, 941 DEGs for GLI2, and 58 DEGs for GLI3. ChIP-seq identified 527 genes that contained GLI1 binding sites in their promoters, 1103 for GLI2 and 553 for GLI3. A total of 15 of these targets were validated in the tested cell lines, 6 of which were detected by both RNA-seq and ChIP-seq. Conclusions: Our study provides insight into the unique and overlapping transcriptional output of the GLI proteins in melanoma. We suggest that our findings could provide new potential targets to consider while designing melanoma-targeted therapy.

Background: Melanoma represents the deadliest skin cancer due to its cell plasticity which results in high metastatic potential and chemoresistance. Melanomas frequently develop resistance to targeted therapy; therefore, new combination therapy strategies are required. Non-canonical signaling interactions between HH-GLI and RAS/RAF/ERK signaling were identified as one of the drivers of melanoma pathogenesis. Therefore, we decided to investigate the importance of these non-canonical interactions in chemoresistance, and examine the potential for HH-GLI and RAS/RAF/ERK combined therapy. Methods: We established two melanoma cell lines resistant to the GLI inhibitor, GANT-61, and characterized their response to other HH-GLI and RAS/RAF/ERK inhibitors. Results: We successfully established two melanoma cell lines resistant to GANT-61. Both cell lines showed HH-GLI signaling downregulation and increased invasive cell properties like migration potential, colony forming capacity, and EMT. However, they differed in MAPK signaling activity, cell cycle regulation, and primary cilia formation, suggesting different potential mechanisms responsible for resistance occurrence. Conclusions: Our study provides the first ever insights into cell lines resistant to GANT-61 and shows potential mechanisms connected to HH-GLI and MAPK signaling which may represent new hot spots for noncanonical signaling interactions.

Prostate cancer (PC) is the third most frequently diagnosed cancer worldwide and the second most frequent in men. Several risk factors can contribute to the development of PC, and those include age, family history, and specific genetic mutations. So far, drug testing in PC, as well as in cancer research in general, has been performed on 2D cell cultures. This is mainly because of the vast benefits these models provide, including simplicity and cost effectiveness. However, it is now known that these models are exposed to much higher stiffness; lose physiological extracellular matrix on artificial plastic surfaces; and show changes in differentiation, polarization, and cell–cell communication. This leads to the loss of crucial cellular signaling pathways and changes in cell responses to stimuli when compared to in vivo conditions. Here, we emphasize the importance of a diverse collection of 3D PC models and their benefits over 2D models in drug discovery and screening from the studies done so far, outlining their benefits and limitations. We highlight the differences between the diverse types of 3D models, with the focus on tumor–stroma interactions, cell populations, and extracellular matrix composition, and we summarize various standard and novel therapies tested on 3D models of PC for the purpose of raising awareness of the possibilities for a personalized approach in PC therapy.

Tumors are three-dimensional (3D) entities characterized by complex structural architecture which is necessary for adequate intercellular, intracellular and cell-to-matrix interactions among the aberrant cells in cancer. In the field of cancer research, 2D cell cultures are traditionally used for decades in the majority of experiments. The reasons for this are the vast benefits these models provide, including simplicity and cost effectiveness. However, it is now known that these models are exposed to much higher stiffness, they lose physiological extracellular matrix (ECM) on artificial plastic surfaces as well as differentiation, polarization and cell-cell communication. This leads to the loss of crucial cellular signaling pathways and changes in cell responses to stimuli when compared to in vivo conditions. Moreover, they cannot adequately mimic the complexity and dynamic interactions of the tumor microenvironment (TME) which is of great importance in anticancer drug treatments. 3D models seem more biomimetic compared to 2D cell monolayers because they offer the opportunity to model the cancer mass together with its environment which seems the key factor in promoting and directing cancer invasion. 3D cell culture with its additional dimensionality makes the difference in cellular responses because it influences the spatial and physical aspects of the cells in 3D culture. This affects the signal transduction and makes the behavior of 3D-cultured cells more physiologically relevant and reflective of in vivo cellular responses. This review focuses on major differences between 2D and 3D cell cultures, highlighting the importance of considering bioengineering humanized 3D cancer models as the future in cancer research. Additionally, it presents diverse 3D models currently used in cancer research, outlining their benefits and limitations. Precisely, this review highlights the differences between the 3D models with the focus on tumor stroma interactions, cell population and extracellular matrix composition providing methods and examples for each model from the studies done so far.