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Int. J. Mol. Sci. 2020, 21, 905; doi:10.3390/ijms21030905 www.mdpi.com/journal/ijms
Review
Engineering Prostate Cancer from Induced
Pluripotent Stem Cells—New Opportunities to
Develop Preclinical Tools in Prostate and Prostate
Cancer Studies
Anastasia C Hepburn 1,*,†, CH Cole Sims 1,†, Adriana Buskin 1,† and Rakesh Heer 1,2,*
1 Newcastle University Centre for Cancer, Translational and Clinical Research Institute, Paul O’Gorman
building, Newcastle University, Newcastle upon Tyne NE2 4HH, UK; Cole.Sims@newcastle.ac.uk (C.C.S.),
adriana.buskin@ncl.ac.uk (A.B.)
2 Department of Urology, Freeman Hospital, The Newcastle upon Tyne Hospitals NHS Foundation Trust,
Newcastle upon Tyne NE7 7DN, UK.
* Correspondence: anastasia.hepburn@ncl.ac.uk (A.C.H.), rakesh.heer@ncl.ac.uk (R.H.);
Tel.: +44-191-2084300 (R.H.)
† These authors contributed equally to this work.
Received: 16 December 2019; Accepted: 28 January 2020; Published: 30 January 2020
Abstract: One of the key issues hampering the development of effective treatments for prostate
cancer is the lack of suitable, tractable, and patient-specific in vitro models that accurately
recapitulate this disease. In this review, we address the challenges of using primary cultures and
patient-derived xenografts to study prostate cancer. We describe emerging approaches using
primary prostate epithelial cells and prostate organoids and their genetic manipulation for disease
modelling. Furthermore, the use of human prostate-derived induced pluripotent stem cells (iPSCs)
is highlighted as a promising complimentary approach. Finally, we discuss the manipulation of
iPSCs to generate ‘avatars’ for drug disease testing. Specifically, we describe how a conceptual
advance through the creation of living biobanks of “genetically engineered cancers” that contain
patient-specific driver mutations hold promise for personalised medicine.
Keywords: prostate cancer; induced pluripotent stem cells; organoids; patient-derived xenografts;
primary culture; cell lines; preclinical model
1. Introduction
The development of experimental models that accurately recapitulate cancer is crucial for the
study of cancer biology and development of therapeutic treatments. This is a daunting challenge
given the complexity and heterogeneity seen in many cancers, including prostate cancer, which
results in variation in the curative effects from person to person [1,2]. Prostate cancer is the second
commonest male cancer worldwide, accounting for 1.3 million new cases and 630,000 deaths in 2018
[3]. It is an androgen-dependent disease whose growth and progression depends on the
transcriptional activity of the androgen receptor (AR), also a master regulator of normal prostate
epithelial cell differentiation [4]. Therapeutic options for men with localised prostate cancer include
active surveillance, surgery or radiotherapy with curative intent [5–8]. For men with advanced
prostate cancer, the initial mainstay for many is androgen deprivation therapy [9,10]. Despite initial
favourable response, most patients progress and succumb to lethal castration-resistant prostate
cancer (CRPC), the second leading cause of male cancer deaths [3,11,12]. Though next-generation
hormonal treatments, such as enzalutamide and abiraterone, and chemotherapeutics, such as
Int. J. Mol. Sci. 2020, 21, 905 2 of 15
docetaxel, have been demonstrated to extend survival, CRPC remains a major clinical problem [13–
16].
Prostate cancer is a biologically heterogeneous disease and its complex nature provides a
significant challenge for its clinical management. The nature of prostate cancer heterogeneity is
characterised by interpatient, intertumoural (multifocal disease), intratumoural, genomic and
epigenetic heterogeneity, which raises considerable challenges when developing therapies [17].
Nevertheless, several genomic landscape studies of primary and metastatic prostate cancer have
identified distinct molecular subtypes and potentially actionable genomic driver events [18–22]. A
recurring major event is the acquired treatment resistance to hormonal approaches due to
reactivation of the AR signalling pathway through AR amplification, mutations, splice variants or
bypass mechanisms [21,23–25]. Furthermore, 10–20% of these CRPC tumours can go on to lose AR
dependence altogether and exhibit small-cell neuroendocrine carcinoma characteristics (CRPC-NE)
[26,27]. Therefore, development of preclinical models that can recreate this patient heterogeneity and
resistance phenotypes is of upmost urgency to develop successful prostate cancer treatments.
Induced pluripotent stem cell (iPSC)-based disease modelling has proven to be a powerful tool
in biomedical research and personalised regenerative medicine by improving the understanding of
the disease pathophysiology of various human inherited disorders at the cellular level. The
emergence of three-dimensional multi-layered organoids has attracted widespread interest and has
presented a unique opportunity for high throughput drug discovery which, combined with genome
editing, has become an attractive model for cancer research. This review focuses on current prostate
cancer preclinical models and how recent developments and the potential manipulation of human
induced pluripotent stem cells (iPSCs) could hold promise for prostate disease modelling and
personalised medicine.
2. Challenges in Current Preclinical Prostate Cancer Models
2.1. Cell Lines
A large proportion of basic cancer research today is still undertaken in cancer cell lines (Table
1). Whilst this method remains suitable for many basic research endeavours and particularly those
studies focusing on molecular interactions, cell line models are not ideal for translational studies.
Although many human cell lines have been propagated from clinical cancer, over time these lines
accumulate multiple additional mutations that then move the genotype and phenotype away from
those originally seen in the tissue from which the cells were derived [28,29]. In addition, for prostate
cancer there are only a limited number of cell lines available (LNCaP, PC-3, LAPC-3, LAPC-4, VCaP.
NCI-H660, MDA PCa 2a, MDA PCa 2b, CWR22Rv1, DU 145) [30–37]. Adding to the problems
associated with prostate cancer cell lines is that some lack important characteristics that make their
use difficult to generalise back into clinical practice. For example, PC-3 cells do not express AR [31]
and NCI-H660 cells are the only line to express chromogranin A, a marker of neuroendocrine
differentiation [34]. A step closer to more faithful in vitro modelling of prostate cancer may be found
in using primary cultures from cells taken directly from patient tumours.
Table 1. Advantages and disadvantages of current models of prostate cancer.
Model
Advantages
Disadvantages
Cancer cell
lines
Easy and cheap to grow; Useful for basic science;
High throughput drug screening
Limited to 2D; Mutation accumulation over
time; Limited number available
Primary cells
Derived from patients; Initial drug studies; Use for
PDXs, PDOs and iPSCs
Difficult to grow; Tissue accessibility; Limited
to 2D; Mutation accumulation over time
Patient-derived
xenografts
(PDXs)
Retain 3D tissue architecture; Intact endocrine
system; Disease stage-specific models available
Time consuming and expensive; Low
engraftment efficiencies; Mouse has deficient
immunity and different microenvironment
Patient-derived
organoids
(PDOs)
Retain 3D tissue architecture; Histological and
molecular resemblance to tissue of origin; Drug
testing responses more accurate
At present only established from aggressive
prostate cancer specimens; Low establishment
rate; Lack microenvironment and immune
influence
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iPSC-derived
organoids
(iDOs)
Retain 3D tissue architecture; Unlimited source of
iPSCs; Isogenic lines; Gene editing to introduce
patient-specific mutations; High throughput drug
screening; ‘avatar’ for precision medicine
Lack microenvironment and immune
influence
2.2. Primary Culture Cells
Primary benign human prostate cells can be cultured in vitro with relative ease from fresh
patient biopsies. Isolating primary human epithelium (which grows from either acini structures or
single cell digestions) and stroma (a mesenchymal cell mixture which supports the epithelial acini)
has long been a mainstay of translational research scientists [38,39]. However, primary culture of
prostate cancers is more challenging. Localised cancers are very difficult to grow, and it is those from
metastatic disease that are most likely to be viable in vitro. These issues are further compounded by
the small amounts of tissue available from cancer biopsies. Additionally, biopsy acquisition itself is
not accessible for many researchers who lack contacts with clinical departments, and although
biobanks (e.g., King’s Health Partners’ Prostate Cancer Biobank (KHP PCaBB)) are helping to provide
non-clinical researchers with access to clinically relevant samples, access to fresh tissue remains
difficult for most researchers [40]. In those cases where cancer primary cultures are established, ex
vivo primary cells undergo spontaneous mutations in culture and deviate away from the initially
derived patient genotype similar to the issue described in classical cell line models above [41].
Historically, primary prostate cells were grown in 2D and it had become recognised that their gene
expression profiles and proliferation rates were different compared to counterparts grown in 3D [42].
This problem has been overcome with both 3D “tissue” like growth in xenografts and in vitro as
organoids.
2.3. Patient-Derived Xenografts (PDXs)
Prostate cancer-derived xenografts (PDXs) involve implantations of cells or tissue from a patient
into immuno-deficient mice. The efficiency is improved with co-engraftment with mesenchymal
tissue (such as embryonic urogenital mesenchyme) and into a vascular niche (typically into the sub-
renal capsule space) [43,44]. PDXs solve one of the limitations of conventional 2D culture as the
prostate cells grow in 3D within the graft. Though PDXs are considered to be the gold standard for
many cancers, their use in prostate cancer is more trying as there are low engraftment rates (15-20%)
and is expensive [45]. Variability in the engraftment rate of PDXs is further compounded by location
of grafting. Sub-renal, subcutaneous and orthotopic grafts have different growth yields but are also
associated with increasing skill requirements and technical proficiency [46]. The process is also slow,
as once the PDXs are implanted, there is a significant wait for tumour growth (median of 22 months
[45]). Furthermore, growth of PDXs is determined by the type of sample engrafted, with metastatic
prostate cancer cells more likely to result in successful engraftment than low-risk, non-metastasising
cells [47]. In some cases, there can be a lack of serial transplantability of PDXs with engraftments
remaining as first-generation [48]. Those that possess the ability of serial transplantation allow
researchers to undertake modelling of disease (e.g., LTL331 tumour tissue line [49]). The problem of
stochastic mutation accumulation with time, as described above, can lead to significant genotypic
shifts away from the patient genome of which the model is designed to represent. Nevertheless, PDXs
remain a major tool in the prostate cancer researcher’s armoury. Aside from PDXs, there are other
additional complementary approaches to modelling 3D tissue architecture and the main advances
have been around in vitro cultures as organoids.
2.4. Patient-Derived Organoids (PDOs)
Organoids are in vitro 3D structures which authentically recapitulate the in vivo architecture,
molecular make-up and function of the tissue of origin [50]. Organoids differ from spheroids as there
is at least pseudo-stratification of cell types within an organoid whereas spheroids are a mass of cells
grown as a dense sphere that lack the organisation seen in vivo. Since the 2010s, organoids have
emerged as a leading method for in vitro 3D modelling of various organs. Many cell types can be
Int. J. Mol. Sci. 2020, 21, 905 4 of 15
grown as organoids including intestine, stomach, lung and mammary gland [51–53]. The
development of organoids from human prostate cells has similarly been shown [54–56]. This opened
the door for a patient focussed, precision medicine approach to in vitro prostate cancer modelling.
However, use of patient-derived organoids (PDOs) is limited by similar drawbacks described for
primary culture. Whilst there is potential to create numerous prostate cancer lines at various stages
of disease progression, organoid generation has low efficiencies and in cases where access to primary
prostate cancer tissue/samples is limited this method may not be widely applicable. In established
lines, intra-tumour diversification of genetic, epigenetic and transcriptome states evolve in a cell-
autonomous fashion [57]. These differences can lead to markedly altered sensitivities to anticancer
drugs between even closely related cells derived from the same tumour [57].
Failure to translate findings from preclinical models into the patient setting has been one of the
factors contributing to the low success rate of anti-cancer drugs making it from the bench to the clinic,
and highlights the need for more accurate experimental models [58]. The use of organoid technology
to model cancer for drug discovery, drug testing and precision medicine has become of great interest
to the cancer field [59]. The fundamental idea behind precision medicine is to tailor medical treatment
to the genetic composition of each patient and cancer is a major focus of this initiative [60]. PDOs may
be used to more precisely select patients for targeted therapy.
PDOs have served as a platform for cancer drug screening with studies demonstrating
correlation between in vitro drug sensitivities and patient tumour molecular profiles. Gao et al.
studied drug response in seven new human prostate cancer organoid lines derived from metastatic
and circulating tumour cells expressing disease-specific mutations such as TMPRSS2-ERG fusion,
PTEN loss, TP53 loss, SPOP mutations, FOXA1 mutations and CHD1 loss [55]. Correlation of
response with the mutational landscape of the tumour was observed following treatment of the
tumour organoids with antiandrogen enzalutamide and PI3K inhibitors currently in clinical trials for
CRPC. A broad spectrum of AR levels was also observed recapitulating AR-dependent and AR-
negative/neuroendocrine phenotypes. However, it was noted that the efficiency of generating
organoids from metastatic samples was <20% and reproducibly maintained for only 1–2 months with
many cultures overtaken by normal epithelial cells present in the biopsy samples. Van de Wetering
et al. established a ‘living’ biobank of colorectal cancer organoids representative of the major
molecular subtypes seen in colorectal cancer and performance of a drug screen of 83 compounds
including drugs in clinical use detected gene-drug associations that could potentially facilitate
personalised therapy [61]. For example, they confirmed resistance to anti-EGFR inhibitors in the
setting of KRAS mutant organoids whilst loss-of function mutations of the tumour suppressor TP53
were associated with resistance to MDM2 inhibition. Interestingly, they found RNF43 mutant
organoids to be exquisitely sensitive to Wnt secretion inhibitors, potentially identifying a treatment
option for patients carrying this mutation. Another drug screen study performed on PDOs
discovered novel therapeutic options for endometrial, uterine and colorectal cancer patients [62].
Such studies have paved the way for the development of precision medicine, but the limitations
associated with access to primary cultures further highlight the need for additional approaches to
improve the generation of PDOs.
Organoid cultures can be expanded long term relatively fast and cryopreserved enabling
generation of organoid biobanks, such as the Hubrecht Organoid Technology (HUB) ‘‘living’’
biobank which generates organoids from a vast number of tumour specimens including prostate
cancer aiding access to the scientific community [63]. This is a well-characterised library with genome
sequencing, expression profiling and drug sensitivity screening data available. Recent studies of
living biobanks of patient-derived tumour organoids have reported they retain the genetic landscape
of the original tumour and drug responses correspond to patient outcomes. Generation of a “living
biobank” of >100 primary and metastatic breast cancer organoid lines captured disease heterogeneity
and aided assessment of drug response in a personalised fashion. These efforts highlight the potential
of tumour organoid biobanks for high throughput drug screening and precision medicine approaches
[61,64–66].
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However, as advances in models for prostate biology have been hindered by the many
challenges associated with primary prostate culture, use of iPSCs presents a promising
complimentary approach. Yet, generation of prostate iPSCs has lagged behind that reported from
other tissues. Therefore, we describe the significant advances made from using iPSCs to generate
models in other systems to demonstrate how the prostate field could take lead from them.
3. Human iPSCs for Disease Modelling
In 2006, Takahashi and Yamanaka demonstrated that stem cells with the same characteristics as
embryonic stem cells (ESCs) could be generated from adult somatic cells by the simultaneous
introduction of just four transcription factors (Oct3/4, Sox2, Klf4, c-Myc), known as Yamanaka factors
[67]. iPSCs have the ability to differentiate into almost every cell type of the body, making them
powerful tools for disease modelling, regenerative medicine and drug screening [68]. Unlike primary
cultures, iPSCs are capable of sustained self-renewal, providing unlimited cell source to investigate
diseases, at molecular, cellular and functional levels. Successful reprogramming of prostate tissue
and prostate-directed differentiation of iPSCs has been demonstrated, providing scope for such
studies in the prostate field [69,70]. Many differentiation protocols towards 2D disease-relevant cell
types have been described [71–76]. Furthermore, iPSCs are patient-specific and can mimic patients’
phenotypes. In addition to the possibility of genome editing, via knock-in or correction of disease-
specific mutations, or knock-out of target genes, iPSCs are becoming one of the preferred choices for
disease modelling and personalised medicine approaches.
Despite the huge advances in identifying cell-level phenotypes across a range of diseases, 2D
models are restricted to cell culture-based systems and are mostly relevant to the cellular level, failing
to represent complex diseases and tumour biology. Due to the significant interest in generating iPSCs
from cancer cells to help elucidate the molecular mechanisms of cancer development, various cancer
cell lines have been reprogrammed, including prostate cancer [77], melanoma [77,78], breast cancer
[79], gastrointestinal cancer [80], chronic myeloid leukaemia [81], glioblastoma [82] and lung cancer
[83]. Although reprogramming of cells with cancer genomes is not impossible, it is inefficient and it
requires non-integrating methods to avoid increased tumourigenesis and a different combination of
transcription factors, alternatively or in addition to the Yamanaka cocktail. Further barriers to
generate iPSCs from cancer cell lines and in turn differentiate these cells into the cell-types of interest
in 2D or 3D models include cancer-associated mutations, epigenetic modification and high levels of
DNA damage [84].
3.1. Human iPSC-Derived Tissue and Organ Models
iPSCs have the potential to differentiate into multiple cell lineages and provide a promising
source of specialised cells. The differentiation process to the target cell type is a pivotal step in the
development of a model. Having gained insights from normal development, many strategies have
been explored to guide the direct differentiation of iPSCs mainly by introducing cocktails of growth
factors and/or small molecules at defined times and concentrations. In turn, iPSCs have provided
models recapitulating single-cell and more complex multi-cell type tissues. For example, generation
of human adipocytes (white, brown and beige) from iPSCs, exhibiting mature morphological and
functional properties characteristic of in vivo fat tissue, has shed light on adipogenesis and provided
a platform for the development of obesity- and diabetes- related therapies [85,86]. TGFβ-driven
differentiation of human iPSCs into chondrocytes capable of forming cartilage has opened up new
strategies for cartilage tissue engineering, disease modelling and pharmacological drug studies for
osteoarthritis [87]. In contrast, the adult human heart harbours several different cell types. Functional
cardiomyocytes generated from iPSCs hold significant promise as an autologous cell source for
cardiac repair [88]. However, cardiomyocytes comprise only 25% of all cardiac cells even though they
occupy approximately 75% of normal myocardial tissue volume, whilst atrial and ventricular
cardiomyocytes have distinct action potentials [89]. As such, presently, generation of a fully beating
heart is a long distance away. Although such studies have been informative, they also have
limitations mainly that differentiation strategies are based on 2D cultures.
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More recently, iPSC-derived cancer organoids have been described. Using standard iPSC
differentiation techniques coupled with recent advances in bioengineering, xenotransplantation and
genome editing, iPSCs present new opportunities for the study of human cancer [90]. These methods
increase the generation of cells to undertake high throughput assessment that have otherwise been
previously challenging, such as wide scale in vitro organoid drug testing and organ-on-chip assays.
In this context, 3D iPSC-derived organoids (iDOs) have now been generated for multiple organs due
to their resemblance to stratified cell organisation and organ structure, which better represents
human physiology and development (Table 2). iDOs provide new tools for the study of human
development, disease and drug testing from which the prostate field can learn from and are
successfully generated in nearly every differentiation run.
Table 2. Generation of iPSC-derived organoids.
Tissue/Organ
Method
Key Small Molecules
References
Brain
Self-organisation by embryoid bodies formation,
and the addition of temporal small molecules
IWR1 and SB431542 [91,92]
Eye
Self-organisation by embryoid bodies formation,
and the addition of temporal small molecules
BMP4 and IGF1 [93–95]
Intestine
Extracellular support matrix and culture medium
supplemented with pro-intestine growth factors
Activin A, WNT3A
and FGF4
[96]
Liver
Co-culture of iPSCs with mesenchymal and
endothelial cells followed by self-organisation by
cell-to-cell contact or self-organisation by embryoid
bodies formation on 3D perfusable chip
Activin-A, bFGF and
HGF [97,98]
Kidney
Mesoderm induction step followed by self-
organisation in 3D culture
CHIR99021 and FGF9 [99]
Lung
Endoderm induction, addition of temporal small
molecules and culture in extracellular support
matrix or transwell inserts
Activin A, Noggin,
SB431542, SAG, FGF4,
CHIR99021 and FGF10
[96,100–
102]
Prostate
Endoderm induction step and co-culture of iPSCs
with rodent urogenital sinus mesenchyme (UGM),
followed by self-organisation by cell-to-cell contact
in extracellular support matrix
Activin A, EGF, R-
spondin1, Noggin, and
A83-01
[69]
3.1.1. Cerebral iDOs
The groundbreaking work described by Yoshiki Sasai and colleagues, in a seminal paper [103],
demonstrated how serum-free suspension culture of embryoid bodies, and the temporal addition of
small molecules to generate forebrain neural precursors, led to an in vitro model of brain-like
organoids [91]. These 3D models, called cerebral organoids, contained regions that resembled various
compartments of brain regions, such as cortical-like regions, similar to that of the human embryo
cortex. The addition of temporal inductive signals to these 3D models in subsequent studies, was
shown to drive dorsal and ventral fore-brain differentiation [104], containing a variety of cell types
present in the human cerebral cortex [105]. Ventricular-like zones of cerebral organoids containing
neural stem cells expressed markers of deep- and superficial-layer neurons and outer radial glial cells,
only present in humans [106]. This has brought about a huge interest in organoid technology as a
means to investigate human-specific conditions and the development of the human brain [92].
3.1.2. Retinal iDOs
Further work from Yoshiki Sasai described the self-organisation of pluripotent stem cells to form
optic cup structures, via temporal application of small molecules [93], displaying features of multi-
layered retinal architecture that resembled foetal human neural retinas. Importantly, some iPSC-
derived retinal organoids have been shown to be light responsive, with immature signals simulating
those observed in the neonatal retina [107]. Retinal organoids have been used to interrogate and treat
a variety of eye-degenerating conditions as well as cancer [94,95,108].
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3.1.3. Intestine iDOs
Intestine organoids can also be derived from iPSCs in a 3D culture system by the use of
extracellular support matrix and culture medium supplemented with pro-intestine growth factors
[96]. These organoids contained defined 3D structures reflecting the villus and crypts of the small
intestine and were capable of self-renewal and self-organisation for prolonged cultures [109,110].
iPSC-derived colonic organoids have been used for the modelling of colorectal cancer [111].
3.1.4. Liver iDOs
iPSC-derived liver organoids have been generated by co-culture of iPSCs, mesenchymal and
endothelial cells. Liver buds were formed by self-organisation and cell-to-cell contact combined with
paracrine signalling, resulting in induction of hepatic genes and expression of bile salt export pumps
[97]. More recently, generation of liver organoids on a 3D perfusable chip has been reported [98].
These organoids had higher cell viability and expressed endodermal and mature hepatic genes.
Transplantation of liver buds in mouse models of liver failure partially rescued hepatic function [112].
3.1.5. Kidney iDOs
iPSCs can also be differentiated into kidney organoids using a mesoderm induction step
followed by 3D culture to promote self-organisation leading to organoid formation. These kidney
organoids contained segmented nephrons connected to collecting ducts, surrounded by renal
interstitial cells. Proximal tubules within the organoids were able to carry out endocytosis, as
evidence of functional maturity [99]. Kidney organoids can be used as a platform to develop new
drugs to treat chronic kidney disease [113].
3.1.6. Lung iDOs
Lung organoids have also been successfully generated and used to study lung development and
disease [114,115]. Lung organoids can be derived from iPSCs using an endoderm induction step
followed by the addition of key defined growth factors, and further passage into a 3Ds system using
extracellular support matrix, in order to promote branching morphogenesis, growth, and alveolar
cell formation [96,100–102]. Those airway progenitors contain cell types and structures similar to
those of bronchi/bronchioles of early lung development and express alveolar-cell markers.
Importantly, the iPSC-derived models have been benchmarked with human foetal tissue and the
transcriptomics of organoids have been shown to be similar to that of foetal lung [102].
4. Emerging Approaches in Preclinical Prostate Cancer Research
4.1. Transformation of Primary Prostate Cells
In a seminal paper, it was shown that primary benign human basal prostate epithelium can
initiate prostate cancer in immune-deficient mice and that the derived tumours realistically recreate
histology of in situ human prostate cancer [116]. This has been the basis for an interesting strategy to
overcome some of the problems of primary prostate cancer culture, where researchers can now
transform easier to grow benign prostate epithelium to generate prostate cancer organoids [117].
These studies lay the platform for a new paradigm, where benign cells are converted into “designer”
cancers harbouring specific mutations of interest. These can be repeatedly generated whilst faithfully
maintaining the genotypes of interest avoiding the ever evolving subclonal progressions affecting
long term culture of primary derived cancers [57]. Despite the promise of this “tumour engineering”
approach there are limitations to maintaining even benign prostate epithelial cultures and a more
ready supply of cells to manipulate are ideally required. In this respect, the emergence of easy to
expand and immortalised iPSCs and the ability to differentiate these in the tissue type of interest
offers a new way forward.
4.2. Prostate iDOs
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Recently, a high throughput model of generating human prostate organoids from iPSCs has also
been described, involving co-culturing iPSCs with rodent urogenital sinus mesenchyme (UGM). This
simple differentiation protocol results in glandular structures in vitro that faithfully mimic prostate
tissue histology and express key prostate markers such as AR, prostate specific homeobox protein
NKX3.1 and secretory prostate specific antigen (PSA) [69]. This approach built on previous data
showing the generation of prostate tissue in xenograft studies from ESCs [118]. Differentiation from
iPSCs avoids many ethical and regulatory restrictions relating to ESCs and enable greater access to
organoid generation to groups worldwide culture [119,120]. Previous in vitro human prostate
organoid approaches, from either tissue-derived cells or ESCs, do not recreate the full breadth of in
situ prostate differentiation as they do not contain neuroendocrine cells particularly relevant in light
of emerging data showing that neuroendocrine differentiation drives treatment-resistant prostate
cancer [56,121,122]. Additionally, it would be of interest to determine whether following maturation
of prostate iDOs there is a switch to a somatic stem cell mode of homeostasis, identified by the
presence of DLK1-enriched basal stem cells, to sustain long-term culture [123]. High-throughput
iPSC-derived human prostate tissue generation provides unparalleled scope for in vitro disease
modelling and drug discovery without the constraints of tissue accessibility and long-standing
difficulties associated with primary culture.
4.3. Genome Editing Technology and Precision Medicine
Genome editing technology has emerged as an extremely powerful tool that can greatly advance
organoid-based research for the development of better targeted therapies [124]. CRISPR-Cas9
genome editing induces double-stranded DNA breaks at specific loci adjacent to a protospacer-
adjacent motif (PAM) using a complementary single-guide RNA sequence (sgRNA) and Cas9
endonuclease [125]. DNA repair then takes place by either non-homologous end joining, resulting in
insertions/deletions (INDELs) or homology-directed repair with a donor template. In 2013, Schwank
et al. reported the first successful therapeutic CRISPR-Cas9 gene editing in human tissue, by
correcting the CFTR locus in intestinal organoids from patients with cystic fibrosis (CF), making CF
treatment a possible reality [126]. Since then, CRISPR-Cas9 has further been used to reproduce genetic
mutations that occur in cancers including prostate cancer. In 2017, for the first time, the use of
CRISPR/Cas9 to create endogenous gene fusions in organoids was reported [127]. Mouse prostate
organoids were modified to carry the TMPRSS2-ERG fusion, a genetic alteration present in more than
50% of prostate cancers that leads to high ERG expression driven from the androgen-responsive
promoter of the TMPRSS2 gene [127–129]. Previously this fusion had been modelled by artificial ERG
overexpression and studied in human prostate cancer cell lines and mouse models, but this approach
for the first time allows investigation of its effect in a wildtype background [127,130].
5. Final Remarks
Effective treatment development for prostate cancer is hampered by the lack of patient-specific
in vitro models that accurately recapitulate this disease. The prostate-derived iPSC generation of
human prostate tissue both in vivo and in vitro is a new complimentary approach to established
primary culture and PDX models [69]. Together with genome editing technologies such as
CRISPR/Cas9, this model opens up new avenues to recreate the genetic make-up of individual
patients and correlate drug sensitivity in vitro in a personalised fashion. Introduction of patient-
specific mutations into iPSCs to generate “designer” cancer organoids could lead to the creation of
organoid biobanks covering the spectrum of prostate cancer mutations and facilitate the design of
powerful screening platforms. Proof of concept is already established showing that benign prostate
cells can be transformed into prostate cancers [116]. This approach would overcome a major problem
with the low efficiency of prostate cancer organoid culture, issues with significant genetic drift
associated with long-term primary culture and has the ability to reproduce, with high fidelity,
isogenic cultures time after time. In the future, routine genomic testing would define patient-specific
profiles and the biobank would provide that reference genotype for new drug testing or known
Int. J. Mol. Sci. 2020, 21, 905 9 of 15
sensitivity to pre-tested standards of care to allow clinicians to tailor treatments options to improve
outcomes in cancer patients.
Funding: We acknowledge funding from Prostate Cancer Foundation (Grand Challenge Award) and John Black
Charitable Foundation.
Conflicts of Interest: The authors declare no conflict of interest.
Abbreviations
AR
Androgen receptor
CRPC
Castration-resistant prostate cancer
CRPC-NE
Castration-resistant prostate cancer neuroendocrine
CF
Cystic fibrosis
ESC
Embryonic stem cells
iPSCs
Induced-pluripotent stem cells
iDOs
iPSC-derived organoids
PAM
Protospacer-adjacent motif
PDOs
Patient-derived organoids
PDXs
Patient-derived xenografts
PSA
Prostate-specific antigen
sgRNA
Single-guide RNA sequence
UGM
Urogenital mesenchyme
References
1. Boyd, L.K.; Mao, X.; Lu, Y.J. The complexity of prostate cancer: Genomic alterations and heterogeneity. Nat.
Rev. Urol. 2012, 9, 652–664.
2. Meacham, C.E.; Morrison, S.J. Tumour heterogeneity and cancer cell plasticity. Nature 2013, 501, 328–337.
3. Bray, F.; Ferlay, J.; Soerjomataram, I.; Siegel, R.L.; Torre, L.A.; Jemal, A. Global cancer statistics 2018:
Globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin.
2018, 68, 394–424.
4. Shen, M.M.; Abate-Shen, C. Molecular genetics of prostate cancer: New prospects for old challenges. Genes
Dev. 2010, 24, 1967–2000.
5. Bill-Axelson, A.; Holmberg, L.; Garmo, H.; Rider, J.R.; Taari, K.; Busch, C.; Nordling, S.; Haggman, M.;
Andersson, S.O.; Spangberg, A., et al. Radical prostatectomy or watchful waiting in early prostate cancer.
N. Engl. J. Med. 2014, 370, 932–942.
6. Klotz, L.; Zhang, L.; Lam, A.; Nam, R.; Mamedov, A.; Loblaw, A. Clinical results of long-term follow-up of
a large, active surveillance cohort with localized prostate cancer. J. Clin. Oncol. 2010, 28, 126–131.
7. Wilt, T.J.; Brawer, M.K.; Jones, K.M.; Barry, M.J.; Aronson, W.J.; Fox, S.; Gingrich, J.R.; Wei, J.T.; Gilhooly,
P.; Grob, B.M.; et al. Radical prostatectomy versus observation for localized prostate cancer. N. Engl. J. Med.
2012, 367, 203–213.
8. Hamdy, F.C.; Donovan, J.L.; Lane, J.A.; Mason, M.; Metcalfe, C.; Holding, P.; Davis, M.; Peters, T.J.; Turner,
E.L.; Martin, R.M.; et al. 10-year outcomes after monitoring, surgery, or radiotherapy for localized prostate
cancer. N. Engl. J. Med. 2016, 375, 1415–1424.
9. Chen, Y.; Sawyers, C.L.; Scher, H.I. Targeting the androgen receptor pathway in prostate cancer. Curr. Opin.
Pharm. 2008, 8, 440–448.
10. Huggins, C.; Hodges, C.V. Studies on prostatic cancer: I. The effect of castration, of estrogen and of
androgen injection on serum phosphatases in metastatic carcinoma of the prostate. 1941. J. Urol. 2002, 168,
9–12.
11. Mateo, J.; Fizazi, K.; Gillessen, S.; Heidenreich, A.; Perez-Lopez, R.; Oyen, W.J.G.; Shore, N.; Smith, M.;
Sweeney, C.; Tombal, B.; et al. Managing nonmetastatic castration-resistant prostate cancer. Eur. Urol. 2019,
75, 285–293.
12. Nuhn, P.; De Bono, J.S.; Fizazi, K.; Freedland, S.J.; Grilli, M.; Kantoff, P.W.; Sonpavde, G.; Sternberg, C.N.;
Yegnasubramanian, S.; Antonarakis, E.S. Update on systemic prostate cancer therapies: Management of
metastatic castration-resistant prostate cancer in the era of precision oncology. Eur. Urol. 2019, 75, 88–99.
Int. J. Mol. Sci. 2020, 21, 905 10 of 15
13. Clarke, N.W.; Ali, A.; Ingleby, F.C.; Hoyle, A.; Amos, C.L.; Attard, G.; Brawley, C.D.; Calvert, J.;
Chowdhury, S.; Cook, A.; et al. Addition of docetaxel to hormonal therapy in low- and high-burden
metastatic hormone sensitive prostate cancer: Long-term survival results from the stampede trial. Ann.
Oncol. 2019, 30, 1992–2003.
14. de Bono, J.S.; Logothetis, C.J.; Molina, A.; Fizazi, K.; North, S.; Chu, L.; Chi, K.N.; Jones, R.J.; Goodman,
O.B., Jr.; Saad, F.; et al. Abiraterone and increased survival in metastatic prostate cancer. N. Engl. J. Med.
2011, 364, 1995–2005.
15. Hoyle, A.P.; Ali, A.; James, N.D.; Cook, A.; Parker, C.C.; de Bono, J.S.; Attard, G.; Chowdhury, S.; Cross,
W.R.; Dearnaley, D.P.; et al. Abiraterone in “high-” and “low-risk” metastatic hormone-sensitive prostate
cancer. Eur. Urol. 2019, 76, 719–728.
16. Scher, H.I.; Fizazi, K.; Saad, F.; Taplin, M.E.; Sternberg, C.N.; Miller, K.; de Wit, R.; Mulders, P.; Chi, K.N.;
Shore, N.D.; et al. Increased survival with enzalutamide in prostate cancer after chemotherapy. N. Engl. J.
Med. 2012, 367, 1187–1197.
17. Frame, F.M.; Noble, A.R.; Klein, S.; Walker, H.F.; Suman, R.; Kasprowicz, R.; Mann, V.M.; Simms, M.S.;
Maitland, N.J. Tumor heterogeneity and therapy resistance - implications for future treatments of prostate
cancer. J. Cancer Metastasis Treat. 2017, 3, 302–314.
18. Armenia, J.; Wankowicz, S.A.M.; Liu, D.; Gao, J.; Kundra, R.; Reznik, E.; Chatila, W.K.; Chakravarty, D.;
Han, G.C.; Coleman, I.; et al. The long tail of oncogenic drivers in prostate cancer. Nat. Genet. 2018, 50, 645–
651.
19. Cancer Genome Atlas Research, N. The molecular taxonomy of primary prostate cancer. Cell 2015, 163,
1011–1025.
20. Grasso, C.S.; Wu, Y.M.; Robinson, D.R.; Cao, X.; Dhanasekaran, S.M.; Khan, A.P.; Quist, M.J.; Jing, X.;
Lonigro, R.J.; Brenner, J.C.; et al. The mutational landscape of lethal castration-resistant prostate cancer.
Nature 2012, 487, 239–243.
21. Robinson, D.; Van Allen, E.M.; Wu, Y.M.; Schultz, N.; Lonigro, R.J.; Mosquera, J.M.; Montgomery, B.;
Taplin, M.E.; Pritchard, C.C.; Attard, G.; et al. Integrative clinical genomics of advanced prostate cancer.
Cell 2015, 161, 1215–1228.
22. Taylor, B.S.; Schultz, N.; Hieronymus, H.; Gopalan, A.; Xiao, Y.; Carver, B.S.; Arora, V.K.; Kaushik, P.;
Cerami, E.; Reva, B.; et al. Integrative genomic profiling of human prostate cancer. Cancer Cell 2010, 18, 11–
22.
23. Bluemn, E.G.; Coleman, I.M.; Lucas, J.M.; Coleman, R.T.; Hernandez-Lopez, S.; Tharakan, R.; Bianchi-Frias,
D.; Dumpit, R.F.; Kaipainen, A.; Corella, A.N.; et al. Androgen receptor pathway-independent prostate
cancer is sustained through fgf signaling. Cancer Cell 2017, 32, 474–489 e476.
24. Dehm, S.M.; Schmidt, L.J.; Heemers, H.V.; Vessella, R.L.; Tindall, D.J. Splicing of a novel androgen receptor
exon generates a constitutively active androgen receptor that mediates prostate cancer therapy resistance.
Cancer Res. 2008, 68, 5469–5477.
25. Visakorpi, T.; Hyytinen, E.; Koivisto, P.; Tanner, M.; Keinanen, R.; Palmberg, C.; Palotie, A.; Tammela, T.;
Isola, J.; Kallioniemi, O.P. In vivo amplification of the androgen receptor gene and progression of human
prostate cancer. Nat. Genet. 1995, 9, 401–406.
26. Aggarwal, R.; Huang, J.; Alumkal, J.J.; Zhang, L.; Feng, F.Y.; Thomas, G.V.; Weinstein, A.S.; Friedl, V.;
Zhang, C.; Witte, O.N.; et al. Clinical and genomic characterization of treatment-emergent small-cell
neuroendocrine prostate cancer: A multi-institutional prospective study. J. Clin. Oncol. 2018, 36, 2492–2503.
27. Davies, A.H.; Beltran, H.; Zoubeidi, A. Cellular plasticity and the neuroendocrine phenotype in prostate
cancer. Nat. Rev. Urol. 2018, 15, 271–286.
28. Gibas, Z.; Becher, R.; Kawinski, E.; Horoszewicz, J.; Sandberg, A.A. A high-resolution study of chromosome
changes in a human prostatic carcinoma cell line (lncap). Cancer Genet. Cytogenet. 1984, 11, 399–404.
29. Esquenet, M.; Swinnen, J.V.; Heyns, W.; Verhoeven, G. Lncap prostatic adenocarcinoma cells derived from
low and high passage numbers display divergent responses not only to androgens but also to retinoids. J.
Steroid Biochem. Mol. Biol. 1997, 62, 391–399.
30. Horoszewicz, J.S.; Leong, S.S.; Kawinski, E.; Karr, J.P.; Rosenthal, H.; Chu, T.M.; Mirand, E.A.; Murphy,
G.P. Lncap model of human prostatic carcinoma. Cancer Res. 1983, 43, 1809–1818.
31. Kaighn, M.E.; Lechner, J.F.; Narayan, K.S.; Jones, L.W. Prostate carcinoma: Tissue culture cell lines. Natl.
Cancer Inst. Monogr. 1978, 49, 17–21.
Int. J. Mol. Sci. 2020, 21, 905 11 of 15
32. Klein, K.A.; Reiter, R.E.; Redula, J.; Moradi, H.; Zhu, X.L.; Brothman, A.R.; Lamb, D.J.; Marcelli, M.;
Belldegrun, A.; Witte, O.N.; et al. Progression of metastatic human prostate cancer to androgen
independence in immunodeficient scid mice. Nat. Med. 1997, 3, 402–408.
33. Korenchuk, S.; Lehr, J.E.; McLean, L.; Lee, Y.G.; Whitney, S.; Vessella, R.; Lin, D.L.; Pienta, K.J. Vcap, a cell-
based model system of human prostate cancer. In Vivo 2001 15, 163–168.
34. Mertz, K.D.; Setlur, S.R.; Dhanasekaran, S.M.; Demichelis, F.; Perner, S.; Tomlins, S.; Tchinda, J.; Laxman,
B.; Vessella, R.L.; Beroukhimt, R.; et al. Molecular characterization of tmprss2-erg gene fusion in the nci-
h660 prostate cancer cell line: A new perspective for an old model. Neoplasia 2007, 9, 200–IN203.
35. Navone, N.M.; Olive, M.; Ozen, M.; Davis, R.; Troncoso, P.; Tu, S.M.; Johnston, D.; Pollack, A.; Pathak, S.;
von Eschenbach, A.C.; et al. Establishment of two human prostate cancer cell lines derived from a single
bone metastasis. Clin. Cancer Res. 1997, 3, 2493–2500.
36. Sramkoski, R.M.; Pretlow, T.G.; Giaconia, J.M.; Pretlow, T.P.; Schwartz, S.; Sy, M.-S.; Marengo, S.R.; Rhim,
J.S.; Zhang, D.; Jacobberger, J.W. A new human prostate carcinoma cell line, 22rv1. In Vitro Cell. Dev. Biol.
Anim. 1999, 35, 403–409.
37. Stone, K.R.; Mickey, D.D.; Wunderli, H.; Mickey, G.H.; Paulson, D.F. Isolation of a human prostate
carcinoma cell line (du 145). Int. J. Cancer 1978, 21, 274–281.
38. Lechner, J.F.; Narayan, K.S.; Ohnuki, Y.; Babcock, M.S.; Jones, L.W.; Kaighn, M.E. Replicative epithelial cell
cultures from normal human prostate gland: Brief communication2. J. Natl. Cancer Inst. 1978, 60, 797–801.
39. Niranjan, B.; Lawrence, M.G.; Papargiris, M.M.; Richards, M.G.; Hussain, S.; Frydenberg, M.; Pedersen, J.;
Taylor, R.A.; Risbridger, G.P. Primary culture and propagation of human prostate epithelial cells. Methods
Mol. Biol. 2013, 945, 365–382.
40. Saifuddin, S.R.; Devlies, W.; Santaolalla, A.; Cahill, F.; George, G.; Enting, D.; Rudman, S.; Cathcart, P.;
Challacombe, B.; Dasgupta, P.; et al. King’s health partners’ prostate cancer biobank (khp pcabb). BMC
Cancer 2017, 17, 784.
41. Kim, M.; Rhee, J.K.; Choi, H.; Kwon, A.; Kim, J.; Lee, G.D.; Jekarl, D.W.; Lee, S.; Kim, Y.; Kim, T.M. Passage-
dependent accumulation of somatic mutations in mesenchymal stromal cells during in vitro culture
revealed by whole genome sequencing. Sci. Rep. 2017, 7, 1–10.
42. Souza, A.G.; Silva, I.B.B.; Campos-Fernandez, E.; Barcelos, L.S.; Souza, J.B.; Marangoni, K.; Goulart, L.R.;
Alonso-Goulart, V. Comparative assay of 2d and 3d cell culture models: Proliferation, gene expression and
anticancer drug response. Curr. Pharm. Des. 2018, 24, 1689–1694.
43. Toivanen, R.; Berman, D.M.; Wang, H.; Pedersen, J.; Frydenberg, M.; Meeker, A.K.; Ellem, S.J.; Risbridger,
G.P.; Taylor, R.A. Brief report: A bioassay to identify primary human prostate cancer repopulating cells.
Stem Cells 2011, 29, 1310–1314.
44. Wang, Y.; Wang, J.X.; Xue, H.; Lin, D.; Dong, X.; Gout, P.W.; Gao, X.; Pang, J. Subrenal capsule grafting
technology in human cancer modeling and translational cancer research. Differentiation 2016, 91, 15–19.
45. Lin, D.; Wyatt, A.W.; Xue, H.; Wang, Y.; Dong, X.; Haegert, A.; Wu, R.; Brahmbhatt, S.; Mo, F.; Jong, L.; et
al. High fidelity patient-derived xenografts for accelerating prostate cancer discovery and drug
development. Cancer Res. 2014, 74, 1272–1283.
46. Toivanen, R.; Taylor, R.A.; Pook, D.W.; Ellem, S.J.; Risbridger, G.P. Breaking through a roadblock in
prostate cancer research: An update on human model systems. J. Steroid Biochem. Mol. Biol. 2012, 131, 122–
131.
47. Lin, D.; Bayani, J.; Wang, Y.; Sadar, M.D.; Yoshimoto, M.; Gout, P.W.; Squire, J.A.; Wang, Y. Development
of metastatic and non-metastatic tumor lines from a patient’s prostate cancer specimen-identification of a
small subpopulation with metastatic potential in the primary tumor. Prostate 2010, 70, 1636–1644.
48. Risbridger, G.P.; Toivanen, R.; Taylor, R.A. Preclinical models of prostate cancer: Patient-derived
xenografts, organoids, and other explant models. Cold Spring Harb. Perspect. Med. 2018, 8, a030536–a030536.
49. Lin, D.; Dong, X.; Wang, K.; Wyatt, A.W.; Crea, F.; Xue, H.; Wang, Y.; Wu, R.; Bell, R.H.; Haegert, A.; et al.
Identification of dek as a potential therapeutic target for neuroendocrine prostate cancer. Oncotarget 2015,
6, 1806–1820.
50. Clevers, H. Modeling development and disease with organoids. Cell 2016, 165, 1586–1597.
51. Barker, N.; Huch, M.; Kujala, P.; van de Wetering, M.; Snippert, H.J.; van Es, J.H.; Sato, T.; Stange, D.E.;
Begthel, H.; van den Born, M.; et al. Lgr5+ve stem cells drive self-renewal in the stomach and build long-
lived gastric units in vitro. Cell Stem Cell 2010, 6, 25–36.
Int. J. Mol. Sci. 2020, 21, 905 12 of 15
52. Desai, T.J.; Brownfield, D.G.; Krasnow, M.A. Alveolar progenitor and stem cells in lung development,
renewal and cancer. Nature 2014, 507, 190–194.
53. Linnemann, J.R.; Miura, H.; Meixner, L.K.; Irmler, M.; Kloos, U.J.; Hirschi, B.; Bartsch, H.S.; Sass, S.; Beckers,
J.; Theis, F.J.; et al. Quantification of regenerative potential in primary human mammary epithelial cells.
Development 2015, 142, 3239–3251.
54. Chua, C.W.; Shibata, M.; Lei, M.; Toivanen, R.; Barlow, L.J.; Bergren, S.K.; Badani, K.K.; McKiernan, J.M.;
Benson, M.C.; Hibshoosh, H.; et al. Single luminal epithelial progenitors can generate prostate organoids
in culture. Nat. Cell Biol. 2014, 16, 951–961.
55. Gao, D.; Vela, I.; Sboner, A.; Iaquinta, P.J.; Karthaus, W.R.; Gopalan, A.; Dowling, C.; Wanjala, J.N.; Undvall,
E.A.; Arora, V.K.; et al. Organoid cultures derived from patients with advanced prostate cancer. Cell 2014,
159, 176–187.
56. Karthaus, W.R.; Iaquinta, P.J.; Drost, J.; Gracanin, A.; Van Boxtel, R.; Wongvipat, J.; Dowling, C.M.; Gao,
D.; Begthel, H.; Sachs, N.; et al. Identification of multipotent luminal progenitor cells in human prostate
organoid cultures. Cell 2014, 159, 163–175.
57. Roerink, S.F.; Sasaki, N.; Lee-Six, H.; Young, M.D.; Alexandrov, L.B.; Behjati, S.; Mitchell, T.J.; Grossmann,
S.; Lightfoot, H.; Egan, D.A.; et al. Intra-tumour diversification in colorectal cancer at the single-cell level.
Nature 2018, 556, 457–462.
58. Rubin, E.H.; Gilliland, D.G. Drug development and clinical trials--the path to an approved cancer drug.
Nat. Rev. Clin. Oncol. 2012, 9, 215–222.
59. Drost, J.; Clevers, H. Organoids in cancer research. Nat. Rev. Cancer 2018, 18, 407–418.
60. Aronson, S.J.; Rehm, H.L. Building the foundation for genomics in precision medicine. Nature 2015, 526,
336–342.
61. van de Wetering, M.; Francies, H.E.; Francis, J.M.; Bounova, G.; Iorio, F.; Pronk, A.; van Houdt, W.; van
Gorp, J.; Taylor-Weiner, A.; Kester, L.; et al. Prospective derivation of a living organoid biobank of
colorectal cancer patients. Cell 2015, 161, 933–945.
62. Pauli, C.; Hopkins, B.D.; Prandi, D.; Shaw, R.; Fedrizzi, T.; Sboner, A.; Sailer, V.; Augello, M.; Puca, L.;
Rosati, R.; et al. Personalized in vitro and in vivo cancer models to guide precision medicine. Cancer Discov.
2017, 7, 462–477.
63. Weeber, F.; Ooft, S.N.; Dijkstra, K.K.; Voest, E.E. Tumor organoids as a pre-clinical cancer model for drug
discovery. Cell Chem. Biol. 2017, 24, 1092–1100.
64. Fujii, M.; Shimokawa, M.; Date, S.; Takano, A.; Matano, M.; Nanki, K.; Ohta, Y.; Toshimitsu, K.; Nakazato,
Y.; Kawasaki, K.; et al. A colorectal tumor organoid library demonstrates progressive loss of niche factor
requirements during tumorigenesis. Cell Stem Cell 2016, 18, 827–838.
65. Sachs, N.; de Ligt, J.; Kopper, O.; Gogola, E.; Bounova, G.; Weeber, F.; Balgobind, A.V.; Wind, K.; Gracanin,
A.; Begthel, H.; et al. A living biobank of breast cancer organoids captures disease heterogeneity. Cell 2017.
66. Yan, H.H.N.; Siu, H.C.; Law, S.; Ho, S.L.; Yue, S.S.K.; Tsui, W.Y.; Chan, D.; Chan, A.S.; Ma, S.; Lam, K.O.;
et al. A comprehensive human gastric cancer organoid biobank captures tumor subtype heterogeneity and
enables therapeutic screening. Cell Stem Cell 2018, 23, 882–897 e811.
67. Takahashi, K.; Yamanaka, S. Induction of pluripotent stem cells from mouse embryonic and adult fibroblast
cultures by defined factors. Cell 2006, 126, 663–676.
68. Sayed, N.; Liu, C.; Wu, J.C. Translation of human-induced pluripotent stem cells: From clinical trial in a
dish to precision medicine. J. Am. Coll. Cardiol. 2016, 67, 2161–2176.
69. Hepburn, A.; Curry, E.; Moad, M.; Steele, R.; Franco, O.; Wilson, L.; Singh, P.; Crawford, S.; Gaughan, L.;
Mills, I. High-throughput propagation of human prostate tissue from induced-pluripotent stem cells.
bioRxiv 2019, 637876.
70. Moad, M.; Pal, D.; Hepburn, A.C.; Williamson, S.C.; Wilson, L.; Lako, M.; Armstrong, L.; Hayward, S.W.;
Franco, O.E.; Cates, J.M.; et al. A novel model of urinary tract differentiation, tissue regeneration, and
disease: Reprogramming human prostate and bladder cells into induced pluripotent stem cells. Eur. Urol.
2013, 64, 753–761.
71. Jacob, A.; Morley, M.; Hawkins, F.; McCauley, K.B.; Jean, J.C.; Heins, H.; Na, C.L.; Weaver, T.E.; Vedaie,
M.; Hurley, K.; et al. Differentiation of human pluripotent stem cells into functional lung alveolar epithelial
cells. Cell Stem Cell 2017, 21, 472–488 e410.
Int. J. Mol. Sci. 2020, 21, 905 13 of 15
72. Kim, D.S.; Lee, J.S.; Leem, J.W.; Huh, Y.J.; Kim, J.Y.; Kim, H.S.; Park, I.H.; Daley, G.Q.; Hwang, D.Y.; Kim,
D.W. Robust enhancement of neural differentiation from human es and ips cells regardless of their innate
difference in differentiation propensity. Stem Cell Rev. Rep. 2010, 6, 270–281.
73. Lam, A.Q.; Freedman, B.S.; Bonventre, J.V. Directed differentiation of pluripotent stem cells to kidney cells.
Semin. Nephrol. 2014, 34, 445–461.
74. Spence, J.R.; Mayhew, C.N.; Rankin, S.A.; Kuhar, M.F.; Vallance, J.E.; Tolle, K.; Hoskins, E.E.; Kalinichenko,
V.V.; Wells, S.I.; Zorn, A.M.; et al. Directed differentiation of human pluripotent stem cells into intestinal
tissue in vitro. Nature 2011, 470, 105–109.
75. Tohyama, S.; Fujita, J.; Fujita, C.; Yamaguchi, M.; Kanaami, S.; Ohno, R.; Sakamoto, K.; Kodama, M.;
Kurokawa, J.; Kanazawa, H.; et al. Efficient large-scale 2d culture system for human induced pluripotent
stem cells and differentiated cardiomyocytes. Stem Cell Rep. 2017, 9, 1406–1414.
76. Xia, Y.; Carpentier, A.; Cheng, X.; Block, P.D.; Zhao, Y.; Zhang, Z.; Protzer, U.; Liang, T.J. Human stem cell-
derived hepatocytes as a model for hepatitis b virus infection, spreading and virus-host interactions. J.
Hepatol. 2017, 66, 494–503.
77. Lin, S.L.; Chang, D.C.; Chang-Lin, S.; Lin, C.H.; Wu, D.T.; Chen, D.T.; Ying, S.Y. Mir-302 reprograms human
skin cancer cells into a pluripotent es-cell-like state. RNA 2008, 14, 2115–2124.
78. Utikal, J.; Maherali, N.; Kulalert, W.; Hochedlinger, K. Sox2 is dispensable for the reprogramming of
melanocytes and melanoma cells into induced pluripotent stem cells. J. Cell Sci. 2009, 122, 3502–3510.
79. 79. Corominas-Faja, B.; Cufi, S.; Oliveras-Ferraros, C.; Cuyas, E.; Lopez-Bonet, E.; Lupu, R.; Alarcon, T.;
Vellon, L.; Iglesias, J.M.; Leis, O.; et al. Nuclear reprogramming of luminal-like breast cancer cells generates
sox2-overexpressing cancer stem-like cellular states harboring transcriptional activation of the mtor
pathway. Cell Cycle 2013, 12, 3109–3124.
80. Miyoshi, N.; Ishii, H.; Nagai, K.; Hoshino, H.; Mimori, K.; Tanaka, F.; Nagano, H.; Sekimoto, M.; Doki, Y.;
Mori, M. Defined factors induce reprogramming of gastrointestinal cancer cells. Proc. Natl. Acad. Sci. USA
2010, 107, 40–45.
81. Carette, J.E.; Pruszak, J.; Varadarajan, M.; Blomen, V.A.; Gokhale, S.; Camargo, F.D.; Wernig, M.; Jaenisch,
R.; Brummelkamp, T.R. Generation of ipscs from cultured human malignant cells. Blood 2010, 115, 4039–
4042.
82. Stricker, S.H.; Feber, A.; Engstrom, P.G.; Caren, H.; Kurian, K.M.; Takashima, Y.; Watts, C.; Way, M.; Dirks,
P.; Bertone, P.; et al. Widespread resetting of DNA methylation in glioblastoma-initiating cells suppresses
malignant cellular behavior in a lineage-dependent manner. Genes Dev. 2013, 27, 654–669.
83. Mathieu, J.; Zhang, Z.; Zhou, W.; Wang, A.J.; Heddleston, J.M.; Pinna, C.M.; Hubaud, A.; Stadler, B.; Choi,
M.; Bar, M.; et al. Hif induces human embryonic stem cell markers in cancer cells. Cancer Res. 2011, 71, 4640–
4652.
84. Kim, J.J. Applications of ipscs in cancer research. Biomark Insights 2015, 10, 125–131.
85. Ahfeldt, T.; Schinzel, R.T.; Lee, Y.K.; Hendrickson, D.; Kaplan, A.; Lum, D.H.; Camahort, R.; Xia, F.; Shay,
J.; Rhee, E.P.; et al. Programming human pluripotent stem cells into white and brown adipocytes. Nat Cell
Biol. 2012, 14, 209–219.
86. Su, S.; Guntur, A.R.; Nguyen, D.C.; Fakory, S.S.; Doucette, C.C.; Leech, C.; Lotana, H.; Kelley, M.; Kohli, J.;
Martino, J.; et al. A renewable source of human beige adipocytes for development of therapies to treat
metabolic syndrome. Cell Rep. 2018, 25, 3215–3228 e3219.
87. Diederichs, S.; Klampfleuthner, F.A.M.; Moradi, B.; Richter, W. Chondral differentiation of induced
pluripotent stem cells without progression into the endochondral pathway. Front Cell Dev. Biol. 2019, 7, 270.
88. Zhang, J.; Wilson, G.F.; Soerens, A.G.; Koonce, C.H.; Yu, J.; Palecek, S.P.; Thomson, J.A.; Kamp, T.J.
Functional cardiomyocytes derived from human induced pluripotent stem cells. Circ. Res. 2009, 104, e30–
e41.
89. Grandi, E.; Pandit, S.V.; Voigt, N.; Workman, A.J.; Dobrev, D.; Jalife, J.; Bers, D.M. Human atrial action
potential and ca2+ model: Sinus rhythm and chronic atrial fibrillation. Circ. Res. 2011, 109, 1055–1066.
90. Papapetrou, E.P. Patient-derived induced pluripotent stem cells in cancer research and precision oncology.
Nat. Med. 2016, 22, 1392–1401.
91. Lancaster, M.A.; Renner, M.; Martin, C.A.; Wenzel, D.; Bicknell, L.S.; Hurles, M.E.; Homfray, T.; Penninger,
J.M.; Jackson, A.P.; Knoblich, J.A. Cerebral organoids model human brain development and microcephaly.
Nature 2013, 501, 373–379.
Int. J. Mol. Sci. 2020, 21, 905 14 of 15
92. Gonzalez, C.; Armijo, E.; Bravo-Alegria, J.; Becerra-Calixto, A.; Mays, C.E.; Soto, C. Modeling amyloid beta
and tau pathology in human cerebral organoids. Mol. Psychiatry 2018, 23, 2363–2374.
93. Nakano, T.; Ando, S.; Takata, N.; Kawada, M.; Muguruma, K.; Sekiguchi, K.; Saito, K.; Yonemura, S.;
Eiraku, M.; Sasai, Y. Self-formation of optic cups and storable stratified neural retina from human escs. Cell
Stem Cell 2012, 10, 771–785.
94. Buskin, A.; Zhu, L.; Chichagova, V.; Basu, B.; Mozaffari-Jovin, S.; Dolan, D.; Droop, A.; Collin, J.; Bronstein,
R.; Mehrotra, S.; et al. Disrupted alternative splicing for genes implicated in splicing and ciliogenesis causes
prpf31 retinitis pigmentosa. Nat. Commun. 2018, 9, 4234.
95. Saengwimol, D.; Rojanaporn, D.; Chaitankar, V.; Chittavanich, P.; Aroonroch, R.; Boontawon, T.;
Thammachote, W.; Jinawath, N.; Hongeng, S.; Kaewkhaw, R. A three-dimensional organoid model
recapitulates tumorigenic aspects and drug responses of advanced human retinoblastoma. Sci. Rep. 2018,
8, 15664.
96. Aurora, M.; Spence, J.R. Hpsc-derived lung and intestinal organoids as models of human fetal tissue. Dev.
Biol. 2016, 420, 230–238.
97. 106. Takebe, T.; Sekine, K.; Kimura, M.; Yoshizawa, E.; Ayano, S.; Koido, M.; Funayama, S.; Nakanishi, N.;
Hisai, T.; Kobayashi, T.; et al. Massive and reproducible production of liver buds entirely from human
pluripotent stem cells. Cell Rep. 2017, 21, 2661–2670.
98. Wang, Y.; Wang, H.; Deng, P.; Chen, W.; Guo, Y.; Tao, T.; Qin, J. In situ differentiation and generation of
functional liver organoids from human ipscs in a 3d perfusable chip system. Lab Chip 2018, 18, 3606–3616.
99. Forbes, T.A.; Howden, S.E.; Lawlor, K.; Phipson, B.; Maksimovic, J.; Hale, L.; Wilson, S.; Quinlan, C.; Ho,
G.; Holman, K.; et al. Patient-ipsc-derived kidney organoids show functional validation of a ciliopathic
renal phenotype and reveal underlying pathogenetic mechanisms. Am. J. Hum. Genet. 2018, 102, 816–831.
100. Chen, Y.W.; Huang, S.X.; de Carvalho, A.; Ho, S.H.; Islam, M.N.; Volpi, S.; Notarangelo, L.D.; Ciancanelli,
M.; Casanova, J.L.; Bhattacharya, J.; et al. A three-dimensional model of human lung development and
disease from pluripotent stem cells. Nat. Cell Biol. 2017, 19, 542–549.
101. Dye, B.R.; Hill, D.R.; Ferguson, M.A.; Tsai, Y.H.; Nagy, M.S.; Dyal, R.; Wells, J.M.; Mayhew, C.N.; Nattiv,
R.; Klein, O.D.; et al. In vitro generation of human pluripotent stem cell derived lung organoids. Elife 2015,
4.
102. Miller, A.J.; Dye, B.R.; Ferrer-Torres, D.; Hill, D.R.; Overeem, A.W.; Shea, L.D.; Spence, J.R. Generation of
lung organoids from human pluripotent stem cells in vitro. Nat. Protoc. 2019, 14, 518–540.
103. Watanabe, K.; Kamiya, D.; Nishiyama, A.; Katayama, T.; Nozaki, S.; Kawasaki, H.; Watanabe, Y.; Mizuseki,
K.; Sasai, Y. Directed differentiation of telencephalic precursors from embryonic stem cells. Nat. Neurosci.
2005, 8, 288–296.
104. Kadoshima, T.; Sakaguchi, H.; Nakano, T.; Soen, M.; Ando, S.; Eiraku, M.; Sasai, Y. Self-organization of
axial polarity, inside-out layer pattern, and species-specific progenitor dynamics in human es cell-derived
neocortex. Proc. Natl. Acad. Sci. USA 2013, 110, 20284–20289.
105. Velasco, S.; Kedaigle, A.J.; Simmons, S.K.; Nash, A.; Rocha, M.; Quadrato, G.; Paulsen, B.; Nguyen, L.;
Adiconis, X.; Regev, A.; et al. Individual brain organoids reproducibly form cell diversity of the human
cerebral cortex. Nature 2019, 570, 523–527.
106. Hansen, D.V.; Lui, J.H.; Parker, P.R.; Kriegstein, A.R. Neurogenic radial glia in the outer subventricular
zone of human neocortex. Nature 2010, 464, 554–561.
107. Hallam, D.; Hilgen, G.; Dorgau, B.; Zhu, L.; Yu, M.; Bojic, S.; Hewitt, P.; Schmitt, M.; Uteng, M.; Kustermann,
S.; et al. Human-induced pluripotent stem cells generate light responsive retinal organoids with variable
and nutrient-dependent efficiency. Stem Cells 2018, 36, 1535–1551.
108. Gasparini, S.J.; Llonch, S.; Borsch, O.; Ader, M. Transplantation of photoreceptors into the degenerative
retina: Current state and future perspectives. Prog. Retin. Eye Res. 2019, 69, 1–37.
109. Barker, N. Adult intestinal stem cells: Critical drivers of epithelial homeostasis and regeneration. Nat. Rev.
Mol. Cell Biol. 2014, 15, 19–33.
110. Sato, T.; Vries, R.G.; Snippert, H.J.; van de Wetering, M.; Barker, N.; Stange, D.E.; van Es, J.H.; Abo, A.;
Kujala, P.; Peters, P.J.; et al. Single lgr5 stem cells build crypt-villus structures in vitro without a
mesenchymal niche. Nature 2009, 459, 262–265.
111. Crespo, M.; Vilar, E.; Tsai, S.Y.; Chang, K.; Amin, S.; Srinivasan, T.; Zhang, T.; Pipalia, N.H.; Chen, H.J.;
Witherspoon, M.; et al. Colonic organoids derived from human induced pluripotent stem cells for modeling
colorectal cancer and drug testing. Nat. Med. 2017, 23, 878–884.
Int. J. Mol. Sci. 2020, 21, 905 15 of 15
112. Huch, M.; Dorrell, C.; Boj, S.F.; van Es, J.H.; Li, V.S.; van de Wetering, M.; Sato, T.; Hamer, K.; Sasaki, N.;
Finegold, M.J.; et al. In vitro expansion of single lgr5+ liver stem cells induced by wnt-driven regeneration.
Nature 2013, 494, 247–250.
113. Ramos, S.; Carlos, A.R.; Sundaram, B.; Jeney, V.; Ribeiro, A.; Gozzelino, R.; Bank, C.; Gjini, E.; Braza, F.;
Martins, R.; et al. Renal control of disease tolerance to malaria. Proc. Natl. Acad. Sci. USA 2019, 116, 5681–
5686.
114. Korogi, Y.; Gotoh, S.; Ikeo, S.; Yamamoto, Y.; Sone, N.; Tamai, K.; Konishi, S.; Nagasaki, T.; Matsumoto, H.;
Ito, I.; et al. In vitro disease modeling of hermansky-pudlak syndrome type 2 using human induced
pluripotent stem cell-derived alveolar organoids. Stem Cell Rep. 2019, 13, 235.
115. Leibel, S.L.; Winquist, A.; Tseu, I.; Wang, J.; Luo, D.; Shojaie, S.; Nathan, N.; Snyder, E.; Post, M. Reversal
of surfactant protein b deficiency in patient specific human induced pluripotent stem cell derived lung
organoids by gene therapy. Sci. Rep. 2019, 9, 13450.
116. Goldstein, A.S.; Huang, J.; Guo, C.; Garraway, I.P.; Witte, O.N. Identification of a cell of origin for human
prostate cancer. Science 2010, 329, 568–571.
117. Unno, K.; Roh, M.; Yoo, Y.A.; Al-Shraideh, Y.; Wang, L.; Nonn, L.; Abdulkadir, S.A. Modeling african
american prostate adenocarcinoma by inducing defined genetic alterations in organoids. Oncotarget 2017,
8, 51264–51276.
118. Taylor, R.A.; Cowin, P.A.; Cunha, G.R.; Pera, M.; Trounson, A.O.; Pedersen, J.; Risbridger, G.P. Formation
of human prostate tissue from embryonic stem cells. Nat. Methods 2006, 3, 179–181.
119. Robertson, J.A. Human embryonic stem cell research: Ethical and legal issues. Nat. Rev. Genet. 2001, 2, 74–
78.
120. Halevy, T.; Urbach, A. Comparing esc and ipsc-based models for human genetic disorders. J. Clin. Med.
2014, 3, 1146–1162.
121. Bishop, J.L.; Thaper, D.; Vahid, S.; Davies, A.; Ketola, K.; Kuruma, H.; Jama, R.; Nip, K.M.; Angeles, A.;
Johnson, F.; et al. The master neural transcription factor brn2 is an androgen receptor-suppressed driver of
neuroendocrine differentiation in prostate cancer. Cancer Discov. 2017, 7, 54–71.
122. Calderon-Gierszal, E.L.; Prins, G.S. Directed differentiation of human embryonic stem cells into prostate
organoids in vitro and its perturbation by low-dose bisphenol a exposure. PLoS ONE 2015, 10, e0133238.
123. Moad, M.; Hannezo, E.; Buczacki, S.J.; Wilson, L.; El-Sherif, A.; Sims, D.; Pickard, R.; Wright, N.A.;
Williamson, S.C.; Turnbull, D.M.; et al. Multipotent basal stem cells, maintained in localized proximal
niches, support directed long-ranging epithelial flows in human prostates. Cell Rep. 2017, 20, 1609–1622.
124. Driehuis, E.; Clevers, H. Crispr/cas 9 genome editing and its applications in organoids. Am. J. Physiol.
Gastrointest. Liver Physiol. 2017, 312, G257–G265.
125. Cong, L.; Ran, F.A.; Cox, D.; Lin, S.; Barretto, R.; Habib, N.; Hsu, P.D.; Wu, X.; Jiang, W.; Marraffini, L.A.;
et al. Multiplex genome engineering using crispr/cas systems. Science 2013, 339, 819–823.
126. Schwank, G.; Koo, B.K.; Sasselli, V.; Dekkers, J.F.; Heo, I.; Demircan, T.; Sasaki, N.; Boymans, S.; Cuppen,
E.; van der Ent, C.K.; et al. Functional repair of cftr by crispr/cas9 in intestinal stem cell organoids of cystic
fibrosis patients. Cell Stem Cell 2013, 13, 653–658.
127. Driehuis, E., C.H. Crispr-induced tmprss2-erg gene fusions in mouse prostate organoids. JSM Biotechnol.
Biomed. Eng. 2017, 4, 1076.
128. Demichelis, F.; Fall, K.; Perner, S.; Andren, O.; Schmidt, F.; Setlur, S.R.; Hoshida, Y.; Mosquera, J.M.;
Pawitan, Y.; Lee, C.; et al. Tmprss2:Erg gene fusion associated with lethal prostate cancer in a watchful
waiting cohort. Oncogene 2007, 26, 4596–4599.
129. Tomlins, S.A.; Rhodes, D.R.; Perner, S.; Dhanasekaran, S.M.; Mehra, R.; Sun, X.W.; Varambally, S.; Cao, X.;
Tchinda, J.; Kuefer, R.; et al. Recurrent fusion of tmprss2 and ets transcription factor genes in prostate
cancer. Science 2005, 310, 644–648.
130. Tomlins, S.A.; Laxman, B.; Varambally, S.; Cao, X.; Yu, J.; Helgeson, B.E.; Cao, Q.; Prensner, J.R.; Rubin,
M.A.; Shah, R.B.; et al. Role of the tmprss2-erg gene fusion in prostate cancer. Neoplasia 2008, 10, 177–188.
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