During disease progression from primary towards metastatic prostate cancer (PCa), and in particular bone metastases, the tumor microenvironment (TME) evolves in parallel with the cancer clones, altering extracellular matrix composition (ECM), vasculature architecture, and recruiting specialized tumor-supporting cells that favor tumor spread and colonization at distant sites. We introduce the clinical profile of advanced metastatic PCa in terms of common genetic alterations. Findings from recently developed models of PCa metastatic spread are discussed, focusing mainly on the role of the TME (mainly matrix and fibroblast cell types), at distinct stages: premetastatic niche orchestrated by the primary tumor towards the metastatic site and bone metastasis. We report evidence of premetastatic niche formation, such as the mechanisms of distant site conditioning by extracellular vesicles, chemokines and other tumor-derived mechanisms, including altered cancer cell-ECM interactions. Furthermore, evidence supporting the similarities of stroma alterations among the primary PCa and bone metastasis, and contribution of TME to androgen deprivation therapy resistance are also discussed. We summarize the available bone metastasis transgenic mouse models of PCa from a perspective of pro-metastatic TME alterations during disease progression and give an update on the current diagnostic and therapeutic radiological strategies for bone metastasis clinical management.

Therapy resistance and metastatic processes in prostate cancer (PCa) remain undefined, due to lack of experimental models that mimic different disease stages. We describe an androgen-dependent PCa patient-derived xenograft (PDX) model from treatment-naïve, soft tissue metastasis (PNPCa). RNA and whole-exome sequencing of the PDX tissue and organoids confirmed transcriptomic and genomic similarity to primary tumor. PNPCa harbors BRCA2 and CHD1 somatic mutations, shows an SPOP/FOXA1-like transcriptomic signature and microsatellite instability, which occurs in 3% of advanced PCa and has never been modeled in vivo. Comparison of the treatment-naïve PNPCa with additional metastatic PDXs (BM18, LAPC9), in a medium-throughput organoid screen of FDA-approved compounds, revealed differential drug sensitivities. Multikinase inhibitors (ponatinib, sunitinib, sorafenib) were broadly effective on all PDX- and patient-derived organoids from advanced cases with acquired resistance to standard-of-care compounds. This proof-of-principle study may provide a preclinical tool to screen drug responses to standard-of-care and newly identified, repurposed compounds.

Tumors strongly depend on their surrounding tumor microenvironment (TME) for growth and progression, since stromal elements are required to generate the optimal conditions for cancer cell proliferation, invasion, and possibly metastasis. Prostate cancer (PCa), though easily curable during primary stages, represents a clinical challenge in advanced stages because of the acquisition of resistance to anti-cancer treatments, especially androgen-deprivation therapies (ADT), which possibly lead to uncurable metastases such as those affecting the bone. An increasing number of studies is giving evidence that prostate TME components, especially cancer-associated fibroblasts (CAFs), which are the most abundant cell type, play a causal role in PCa since the very early disease stages, influencing therapy resistance and metastatic progression. This is highlighted by the prognostic value of the analysis of stromal markers, which may predict disease recurrence and metastasis. However, further investigations on the molecular mechanisms of tumor–stroma interactions are still needed to develop novel therapeutic approaches targeting stromal components. In this review, we report the current knowledge of the characteristics and functions of the stroma in prostate tumorigenesis, including relevant discussion of normal prostate homeostasis, chronic inflammatory conditions, pre-neoplastic lesions, and primary and metastatic tumors. Specifically, we focus on the role of CAFs, to point out their prognostic and therapeutic potential in PCa.

Bladder Cancer (BLCa) inter-patient heterogeneity is the primary cause of treatment failure, suggesting that patients could benefit from a more personalized treatment approach. Patient-derived organoids (PDOs) have been successfully used as a functional model for predicting drug response in different cancers. In our study, we establish PDO cultures from different BLCa stages and grades. PDOs preserve the histological and molecular heterogeneity of the parental tumors, including their multiclonal genetic landscapes, and consistently share key genetic alterations, mirroring tumor evolution in longitudinal sampling. Our drug screening pipeline is implemented using PDOs, testing standard-of-care and FDA-approved compounds for other tumors. Integrative analysis of drug response profiles with matched PDO genomic analysis is used to determine enrichment thresholds for candidate markers of therapy response and resistance. Finally, by assessing the clinical history of longitudinally sampled cases, we can determine whether the disease clonal evolution matched with drug response.

Resistance acquisition to androgen deprivation treatment and metastasis progression are a major clinical issue associated with prostate cancer (PCa). The role of stroma during disease progression is insufficiently defined. Using transcriptomic and proteomic analyses on differentially aggressive patient-derived xenografts (PDXs), we investigated whether PCa tumors predispose their microenvironment (stroma) to a metastatic gene expression pattern. RNA sequencing was performed on the PCa PDXs BM18 (castration-sensitive) and LAPC9 (castration-resistant), representing different disease stages. Using organism-specific reference databases, the human-specific transcriptome (tumor) was identified and separated from the mouse-specific transcriptome (stroma). To identify proteomic changes in the tumor (human) versus the stroma (mouse), we performed human/mouse cell separation and subjected protein lysates to quantitative Tandem Mass Tag labeling and mass spectrometry. Tenascin C (TNC) was among the most abundant stromal genes, modulated by androgen levels in vivo and highly expressed in castration-resistant LAPC9 PDX. The tissue microarray of primary PCa samples (n = 210) showed that TNC is a negative prognostic marker of the clinical progression to recurrence or metastasis. Stroma markers of osteoblastic PCa bone metastases seven-up signature were induced in the stroma by the host organism in metastatic xenografts, indicating conserved mechanisms of tumor cells to induce a stromal premetastatic signature. A 50-gene list stroma signature was identified based on androgen-dependent responses, which shows a linear association with the Gleason score, metastasis progression and progression-free survival. Our data show that metastatic PCa PDXs, which differ in androgen sensitivity, trigger differential stroma responses, which show the metastasis risk stratification and prognostic biomarker potential.

Cripto is a small glycosylphosphatidylinisitol (GPI)-anchored and secreted oncofetal protein that plays important roles in regulating normal physiological processes, including stem cell differentiation, embryonal development, and tissue growth and remodeling, as well as pathological processes such as tumor initiation and progression. Cripto functions as a co-receptor for TGF-β ligands such as Nodal, GDF1, and GDF3. Soluble and secreted forms of Cripto also exhibit growth factor-like activity and activate SRC/MAPK/PI3K/AKT pathways. Glucose-Regulated Protein 78 kDa (GRP78) binds Cripto at the cell surface and has been shown to be required for Cripto signaling via both TGF-β and SRC/MAPK/PI3K/AKT pathways. To provide a comprehensive overview of the scientific literature related to Cripto, we performed, for the first time, a bibliometric analysis of the biological roles of Cripto as reported in the scientific literature covering the last 10 years. We present different fields of knowledge in comprehensive areas of research on Cripto, ranging from basic to translational research, using a keyword-driven approach. Our ultimate aim is to aid the scientific community in conducting targeted research by identifying areas where research has been conducted so far and, perhaps more importantly, where critical knowledge is still missing.