| Literature DB >> 35866337 |
Xiaozhuo Liu1, Wen Jess Li1,2, Igor Puzanov3, David W Goodrich1,2, Gurkamal Chatta3, Dean G Tang1,2.
Abstract
Cancer progression is characterized and driven by gradual loss of a differentiated phenotype and gain of stem cell-like features. In prostate cancer (PCa), androgen receptor (AR) signaling is important for cancer growth, progression, and emergence of therapy resistance. Targeting the AR signaling axis has been, over the decades, the mainstay of PCa therapy. However, AR signaling at the transcription level is reduced in high-grade cancer relative to low-grade PCa and loss of AR expression promotes a stem cell-like phenotype, suggesting that emergence of resistance to AR-targeted therapy may be associated with loss of AR signaling and gain of stemness. In the present mini-review, we first discuss PCa from the perspective of an abnormal organ with increasingly deregulated differentiation, and discuss the role of AR signaling during PCa progression. We then focus on the relationship between prostate cancer stem cells (PCSCs) and AR signaling. We further elaborate on the current methods of using transcriptome-based stemness-enriched signature to evaluate the degree of oncogenic dedifferentiation (cancer stemness) in pan-cancer datasets, and present the clinical significance of scoring transcriptome-based stemness across the spectrum of PCa development. Our discussions highlight the importance to evaluate the dynamic changes in both stem cell-like features (stemness score) and AR signaling activity across the PCa spectrum.Entities:
Keywords: androgen receptor; cancer stem cells; prostate cancer; stemness; therapy resistance
Mesh:
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Year: 2022 PMID: 35866337 PMCID: PMC9484140 DOI: 10.1042/EBC20220003
Source DB: PubMed Journal: Essays Biochem ISSN: 0071-1365 Impact factor: 7.258
Figure 1A schematic illustrating spectrum of PCa initiation, development, and progression
Left: The normal human prostate is a pseudostratified two-layer epithelial glandular organ that contains an inner layer of secretory luminal epithelial cells surrounded by a layer of basal cells with rare neuroendocrine (NE) cells scattered in between. Also depicted are rare luminal progenitor cells in the luminal cell layer.
Middle: Localized PCa (i.e., tumors restricted to the primary organ, prostate). PCa gains more aggressiveness across the spectrum of PCa initiation, progression, relapse, metastasis, and therapeutic resistance. Primary PCa is commonly graded by the combined Gleason score (GS) system, which relies on the architectural pattern of cancerous glands, with Gleason pattern 1—representing most well-differentiated glandular structures and Gleason pattern 5—representing the most poorly differentiated cells (lack of glandular structures). Given that PCa is often multifocal, the combined GS is the sum of the two most prevalent patterns, with GS ≤ 7—representing low-grade PCa and GS ≥ 8—representing high-grade PCa. Illustrated here are two representative tumor glands with a decrease/loss in basal cells and expansion of luminal progenitor-like cells.
Right: PCa metastasis. Depicted is a metastatic castration-resistant PCa (mCRPC) with increased cellular heterogeneity (i.e., cells at a variety of epigenetic and phenotypic states) and increased stem-like and NE-like cells.
Below: Depending on the severity of the disease, current treatment options for PCa include active surveillance, surgery by prostatectomy, radiation therapy, hormonal therapy, or chemotherapy. Up to one-third of patients with a localized disease eventually fail on local therapies and progress to advanced-stage or metastatic PCa within 10 years. For locally advanced (GS ≥ 9) and metastatic PCa, androgen-deprivation therapy (ADT) is the standard of care, which uses LHRH agonists/antagonists to block testicular androgen synthesis. Although the majority of patients initially respond, most tumors become resistant to primary hormonal therapy within 14–30 months. Tumors that have failed this first-line therapy are termed CRPC and are further treated with AR signaling inhibitors such as enzalutamide (Enza) that interferes with AR functions. Enza only extends CRPC patients’ lives by 4–5 months before tumor recurrence. For men with mCRPC, the median survival in phase III studies range from 15 to 19 months. For several years, the chemotherapeutic drug docetaxel was the only treatment option for mCRPC. Most mCRPC, including both CRPC-adenocarcinoma (CRPC-adeno) and CRPC-NE subtypes, remains lethal. Figure is modified from [85] and [86].
Bottom: The trajectory of PCa development and progression is accompanied by loss of differentiation with increasing malignancy and aggressiveness at both cellular and molecular levels as well as at the tumor level. Could PCa progression be associated with increasing stemness? This is an outstanding question elaborated in the present mini-review.
Figure 2A schematic illustrating the cellular architecture of the prostate epithelium and ECM and stromal cells in the normal prostate gland
The luminal epithelial cells are defined by expression of CK8 and CK18 and AR. The basal epithelial cells express high levels of CK5 and p63 and very low levels of AR. Neuroendocrine cells are a small population of endocrine–paracrine cells. Neuroendocrine cells express neuroendocrine markers such as synaptophysin and chromogranin A and do not express AR [86]. The stroma is populated by fibroblasts, immune cells, smooth muscle cells and varies subtypes of neural cells, such as TH+, NES+, and β-Tubulin III+ nerve fibers and abundant GFAP+ cells [83]. Figure is modified from [85] and [83].
Figure 3Schematic presentation of transcriptome-based stemness quantification methods
Several transcriptome-based methods for stemness quantification have been developed [5,14,15,76,77], which generally include three steps: (1) development of stemness-enriched signatures from training datasets by comparing transcriptomic data from normal stem cells with differentiated progeny; (2) validation of the stemness signature in validation datasets by its ability to distinguish stem cells from their differentiated counterparts; and (3) application of the stemness signature on cancer sample datasets. Malta et al. utilized a one-class logistic regression (OCLR) machine-learning algorithm to develop the stemness-enriched signature score, namely, mRNAsi [15]. Smith et al. applied a rank–rank hypergeometric overlap (RRHO) algorithm to develop an adult stem cell (ASC) signature [5]. Miranda et al. conducted single-sample gene set enrichment analysis (ssGSEA) on the 109 stemness-related genes to calculate the stemness index (HSC signature score) by using GSVA package in R [14]. Finally, Zheng et al. calculated the stemness index based on relative expression ordering (REOs) of gene pairs within a sample [76].