2025.08.26.672478v1.full-html.html

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IN BRIEF

In this study, we developed patients’ iPSC-derived prostate organoids (iPROS) with or without a pathogenic

BRCA2 germline mutation that display human-prostate like morphology and function. MUT_BRCA2 iPROS

displayed disrupted morphology, early tumorigenic changes, and formed tumors in mice. Upon carcinogen

exposure, they showed markers of aggressive prostate cancer. This platform models early prostate

tumorigenesis and enables personalized studies of cancer initiation and therapeutic response.

SUMMARY

The lack of physiologically relevant in vitro prostate models has impeded studies of organ development and

prostate tumorigenesis. We reprogrammed peripheral blood mononuclear cells (PBMCs) from individuals with

and without pathogenic-germline BRCA2 mutation (MUT_BRCA2, CON_BRCA2) into induced pluripotent stem

cells (iPSCs), which showed no differences in morphology, proliferation, or pluripotency markers.

Differentiation of MUT_BRCA2 iPSCs into prostate organoids (iPROS) using defined growth factors and

signaling molecules resulted in disrupted morphology, impaired polarity, increased proliferation, and elevated

prostate-specific antigen (PSA) secretion compared to CON_BRCA2 iPROS. Transcriptomic profiling revealed

early prostate cancer (PCa) signatures. Upon exposure to dietary carcinogens, MUT_BRCA2 iPROS showed

further PSA elevation, enhanced proliferation, AMACR upregulation, p63 reducetion are markers of aggressive

PCa. In vivo, MUT_BRCA2 iPROS formed tumors in immunodeficient mice. This patient-derived iPROS-

platform recapitulates human-prostate mopphology and function, models early tumorigenesis events, and

provides a valuable tool for studying PCa biology and enabling personalized drug discovery.

KEYWORDS:

In vitro preclinical model, Human-prostate organoid, Prostate Cancer (PC), Disease modeling,

Oncogenic stressors.

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INTRODUCTION

Prostate cancer (PCa) is the most commonly diagnosed cancer in men globally, with over 1.2 million new

cases and 350,000 deaths annually

. In prostate epithelium, genetic and epigenetic alterations, together with

microenvironmental factors and oncogenic stress, often disrupt androgen receptor signaling, promoting PCa

development and progression

2,3

. Among these alterations, germline mutations in DNA repair genes—

particularly BRCA2—pose a significant challenge in managing PCa

. About 12% of men with metastatic PCa

harbor such mutations, more than those with localized disease, and are less responsive to treatment

Pathogenic-BRCA2 mutation carriers have an 8.6-fold increased PCa risk, especially before age 65, and show

poorer prognosis even with low-grade tumors. They also experience worse metastasis-free and PCa-specific

survival following surgery or radiotherapy

6,7,8

. These tumors exhibit aggressive features, higher genomic

instability, unique molecular profiles, and castration resistance, underscoring the need for translational models

to guide therapy.

Although rodent models have provided insights into prostate development and cancer, significant anatomical

and cellular differences with human prostate limit their translational value

. The human prostate is organized

into distinct zones with a balanced basal-luminal cell ratio, whereas rodents have separate lobes and a

luminal-dominant profile

. Additionally, access to fetal and adult prostate tissue is limited, and available cell

lines are suboptimal for modeling human PCa

. Induced pluripotent stem cell (iPSC)-derived organoids offer

an alternative by enabling the study of organogenesis, tumor initiation, and drug response in genetically

defined, scalable systems

. Unlike tumor-derived organoids, which reflect late-stage disease and harbor pre-

existing heterogeneity, iPSC-based models can recapitulate early oncogenic events by introducing genetic and

non-genetic changes stepwise. They also allow generation of matched normal controls from the same genetic

background, enhancing precision oncology applications

. Earlier models required rodent urogenital

mesenchyme (UGM) for prostate specification from human pluripotent cells, limiting their preclinical utility

14,15

Other embryonic stem cell-derived models without UGM don’t show any functional maturity

. In our study, we

differentiated iPSCs generated from human peripheral blood mononuclear cells into prostate-like organoids

(iPROS) using a rodent-UGM free, chemically defined system. These iPROS recapitulated key morphological,

transcriptional, and functional features of the human prostate and further matured with vascularization upon

xenotransplantation into immunodeficient mice

A major barrier in PCa research is modeling tumor initiation in vitro

. Controlled, human-relevant systems to

define specific drivers of transformation are critical for risk prediction and therapeutic development

. To

address this, we used dietary carcinogens to induce tumorigenesis in iPROS. PhIP, a heterocyclic amine found

in cooked meat, and MNU, a potent DNA alkylating agent, are known rodent PCa inducers

20,21

. PhIP

undergoes P450-mediated activation, forming DNA adducts that drive mutations and genomic instability

while MNU introduces O6-methylguanine lesions that mispair during DNA replication, causing G-to-A

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RESULTS

Development of prostate organoids (iPROS) from patient iPSCs

To establish a robust model for prostate organoid differentiation, we began with three control iPSC lines

derived from PBMCs in the Cedars Sinai iPSC core facility (Table S1). Prostate originates from urogenital

sinus (UGS)—a caudal extension of the hindgut—formed from definitive endoderm (DE) during late

embryogenesis (gestational weeks 10 to 12) and completes maturation at puberty

10,26

. Given the known role of

signaling pathway modulation in embryonic-development, we designed a stepwise differentiation protocol from

iPSC to DE, hindgut endoderm (HGE), and subsequently, iPROS (Fig. 1A). To efficiently induce DE, we

treated hiPSC with CHIR99021 (a GSK-3

inhibitor), Activin A, and progressively increasing serum

concentrations. Since UGS arises from hindgut, we directed DE toward hindgut lineage by activating WNT3A

and FGF4 signaling

27,28

. FGF10 and WNT10B are essential during prostate development and branching

morphogenesis, with FGF10 driving early bud formation and WNT10B potentially aiding in prostate

specification

. After 48 hours of HGE induction, we supplemented culture with FGF10 and WNT10B while

reducing FGF4 to promote prostate-specific specification over the next 48 hours, formed tiny-3D spheroids. 3D

spheroids were them embedded in Matrigel and cultured them for 5 days with high levels of Dihydroxy

testosterone (DHT), and andromedin factors FGF7, and FGF10, mimicking the urogenital mesenchyme signals

that drive in vivo prostate budding and urogenital epithelial (UGE) development

26,30

. Additionally, we included

SAG (a Sonic Hedgehog agonist) to support UGE differentiation into basal and luminal cells

. From day 15

onwards, until Hayflick limit achieved (~14-16 weeks)

, we cultured them in a low-dose DHT medium

alongside various growth factors, activators, and inhibitors detailed in the Methods. Media formulation was

informed by prostate-development literature and adapted from previous studies

30,16,33,14,15

. After day 15

mechanical dissociation of large-organoid-mass (Fig. S1A), round-organoids grow singly for weeks, then form

complex structures, merging into masses that periodically require mechanical separation. Prostate epithelial

buds typically emerge from UGS and branch to form glandular ducts comprising luminal and basal layers.

iPROS recapitulated such ductal structures at around 8-week (Fig. 1B). Immunohistochemistry and

immunofluorescence (IF) confirmed expression of prostate-specific markers—Androgen Receptor (AR),

NKX3.1, Prostate Specific Antigen (PSA), and lineage-specific markers: CK8-18 (luminal), p63 (basal), and

Chromogranin A (neuroendocrine) (Fig. 1C,D and S1B). PSA, a hallmark luminal secretion product

appeared in the media by week 6, increasing to ~60 pg/mL by week 12, indicating organ-maturation (Fig. 1E).

We next validated prostate-specific gene expression in iPROS via RT-qPCR (Fig. 1F). Compared to GAPDH

(Avg. Cq = 18), target genes AR, NKX3.1, p63, KLK3 (PSA), and CK18 displayed Cq values of 20–35,

reflecting moderate to high RNA abundance. To assess transcriptomic similarity between iPROS and native

prostate, we performed mRNA-seq on iPROS and compared the data with 282 normal prostate samples from

the GTEx Portal

. A panel of 30 highly expressed non-ribosomal genes showed expression patterns

resembling those of adult prostate tissues (Fig. 1G), with 80% transcript overlap confirmed (Fig. 1H)

(hypergeometric test, p-value = 4.445679e-10). Further comparisons using two independent adult prostate

mRNA-seq datasets from GEO revealed ~70% similarity, reinforcing the relevance of the model (Fig. 1H).

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Additionally, we queried the Human Protein Atlas and identified 126 genes showing >4-fold higher expression

in prostate vs. other tissues (Prost. Tissue Enhanced Genes), with 15 “Prost. Tissue Enriched Genes” uniquely

elevated in prostate as “tissue-specific genes”. Of these, 68 and 12 genes, respectively, were expressed in

iPROS, with significant overlap (pValue = 1.115628e-06 and 2.322562e-10, respectively) (Fig. 1I). Lastly, we

compared iPROS transcript profiles to cell-type specific mRNAs identified by total RNA-seq of adult prostate

epithelium and stroma

. Heatmaps show normalized expression of luminal, basal, neuroendocrine, and

stromal fibroblast markers in iPROS (Fig. S1C). These demonstrate that we developed a human prostate

organoid model that mimics cell-specific morphology, gene expression, and organ function.

iPROS with germline BRCA2 mutations manifest tumorigenic morphology and molecular signatures

Pathogenic-BRCA2 germline variations are a known genetic risk factor for aggressive and metastatic PCa

. To

assess their functional impact on iPROS morphology, gene expression, and PSA secretion, we generated

three additional iPSC lines from PBMCs of three consenting PCa patients from Northwestern University

(IRB#STU00018651-MOD0018) harboring pathogenic BRCA2 germline mutations. Fig. 2A (Fig. S2A and

Table S1) presents patient identification, mutation types, ISUP_Gleason-grade-group (GG) scores, and H&E-

stained prostatectomy sections. Patients 1 and 2 share same 5946delT mutation in exon-10 but had differing

GG scores: patient 1 (MUT1_BRCA4i; pT2) had GG2; patient 2 (MUT2_BRCA2A; T2) had GG5. Patient 3

(MUT1_BRCA3i) had a c.9513_9516 deletion in exon-11, GG5, and a pathology stage of T3bN1M1, with

lymph node metastasis and histology revealing densely packed glandular lumens with fibrotic stroma. All

mutations remained present in iPSCs and subsequent iPROS (Fig. 2B).

iPSC morphology, stemness, or proliferation was not alter by BRCA2 mutations. No changes were observed in

the fluorescence of stemness markers SOX2, OCT4, and SSE4 (Fig. S2B), nor in OCT4 (Fig. S2C) and SOX2

(Fig. S2D) gene expression. Although BRCA2 gene expression was reduced (Fig. S2E), BRCA1 expression

remained unaffected (Fig. S2F). Ki67 nuclear staining also confirmed unchanged iPSC proliferation (Fig.

S2G,H). These BRCA2-mutated iPSCs were differentiated into iPROS alongside three wild-type controls for 8

weeks. Reduced BRCA2 expression persisted in mutant iPROS, consistent with a haploinsufficiency due to

loss-of-function mutation (Fig. 2C).

We analyzed eight-week-old iPROS morphology using immunohistochemistry. In 72% of MUT_BRCA2 iPROS,

we found AR-positive cell layers filling glandular lumens—similar to prostatic intraepithelial neoplasia, a

dysplasia precursor

—compared to only 11% in CON_BRCA2 [pValue < 0.001] (Fig. 2D). MUT_BRCA2

iPROS displayed thicker, distorted luminal epithelia and increased PSA fluorescence (Fig. 2E), consistent with

PSA gene regulation by AR and its role as a prostate tumorigenesis marker

. PSA release was elevated in

MUT_BRCA2 (Fig. 2F), along with higher expression of AR, NKX3.1, p63, CK18, and KLK3 (Fig. 2G).

IF analysis of MUT_BRCA2 iPROS showed increased Ki67-positive nuclei, indicating enhanced proliferation

(Fig. 2H,I), confirmed by Ki67 flow cytometry (Fig. 2J). Additionally, MUT_BRCA2 iPROS exhibited

cytoskeletal disorganization, loss of epithelial polarity, and diffuse localization of CK8-18 and p63 (Fig. S2I),

with elevated and mislocalized p63 suggesting early neoplastic changes

39,40

. Signs of epithelial-mesenchymal

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transition (EMT) were evident through increased Vimentin (VIM) expression (Fig. 2G), and enhanced

fluorescence of Vimentin and

-smooth muscle actin (SMA) (Fig. 2K, L), indicating an early EMT onset

Transcriptomic profile of iPROS with germline BRCA2 mutations correlated with PCa gene expressions

Transcriptomic profiling of MUT_BRCA2 iPROS offers critical insights into molecular pathways associated with

PCa development and cellular dysfunctions linked to BRCA2 mutations

42,43

. To explore these differences, we

performed total-mRNA sequencing on eight-week-old MUT_BRCA2 iPROS from three independent

differentiation experiments and compared them to CON_BRCA2 total-mRNAseq datasets. Initial transcriptome

analysis revealed batch effects across differentiation sets (Fig. S3A), commonly observed in iPSC

differentiation due to stochastic variation in cell-type composition or patient-specific genetic backgrounds. After

batch correction, unsupervised PCA separated MUT_BRCA2 and CON_BRCA2 iPROS along PC1 and PC4

(Fig. 3A), as guided by eigencore correlation plots analyzing batch, cell line, and genotype as co-variants (Fig.

3B). Differential expression analysis (DESeq2: fold-change >1.5, adjP < 0.05) identified 869 DEGs—330

upregulated and 177 downregulated in MUT_BRCA2 compared to CON_BRCA2 (Fig. 3C). Normalized

expression showed altered levels signature genes implicated in early PCa; increased expression of AR,

FOXA1, EGFR, AMACR and loss of PTEN and RB1 and others

8,44,42,45

(Fig. 3D, S3B). GO and KEGG

geneset-enrichment-analysis on the DEGs identified activated signaling pathway_enrichment in MUT_BRCA2

iPROS, including glutathione metabolism, receptor clustering, lipid metabolism, and DNA adduct signaling,

while cell adhesion, ECM anchoring, insulin resistance, and APP catabolism were suppressed—suggesting an

oncogenic transcriptomic shift (Fig. 3E,F, S3C,D). Hallmark50_NES showed elevated Androgen Response,

INF, KRAS, and MYC signaling, and loss of apical junctional signaling suggesting Pro-PCa signaling activation

in MUT_BRCA2 iPROS

(Fig. 3H).

To assess BRCA2 mutation-specific PCa-gene expression, we analyzed TCGA_PRAD mRNAseq-data

[TCGA, Cell, 2015]. 42 genes were upregulated in BRCA2-mutated patients (n=5) vs Non-BRCA2 patients

(n=328), of which 22 overlapped with MUTvs.CON_BRCA2-iPROS DEGs, and 12 upregulated in

MUT_BRCA2 (p-value = 1.116389e-06) (Fig. 3H). In TCGA-PanCancer mRNAseq-dataset

, 4705 DEGs were

found between 494_PRAD and 51_normal patient-samples; 229 genes overlapped with MUTvs.CON_BRCA2-

iPROS DEGs. 30 most upregulated genes in PRAD were similarly elevated in MUT_BRCA2 iPROS (Fig. 3I).

Additionally, 394 DEGs were found in Primary_vs._Metastasis-PCa PDXO mRNAseq-dataset

, 339 of which

matched MUTvs.CON_BRCA2-iPROS DEGs (p-value = 1.77289e-10), with the top 30 also upregulated in

MUT_BRCA2 (Fig. 3J), reinforcing their oncogenic transcriptomic profile.

To evaluate patient-specific transcriptomes, we analyzed DEGs from each MUT_BRCA2 iPROS line—BRCA4i

(GG2), BRCA2A (GG5), and BRCA3i (GG5 with metastasis)—against CON_BRCA2. BRCA3i, BRCA2A, and

BRCA4i had 1362, 862, and 105 DEGs respectively, showing stratified gene-expression by disease stage (Fig.

3K). When matched these individual DEGs with TCGA_PRADvs.Normal DEGs, BRCA3ivs.CON-DEG and

BRCA3ivs.CON-DEG had significant gene-overlapping with 413 and 263 respectively, whereas

BRCA4ivs.CON-DEG showed only 33 gene-overlap, reflecting disease-stage-specific transcriptional fidelity

(Fig. 3L). Finally, MUTvs.CON_BRCA2-iPROS DEG comparison was done with four PCa-genesets from

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MSigDB: M6698 (RAMASWAMY_METASTASIS_UP, 67-genes_upregulated in metastatic_vs_primary-PCa),

M4691 (LIU_PROSTATE_CANCER_UP, 100-genes_upregulated in PCa_vs_benign-tissue), M11504

(TOMALINS_PROSTATE_CANCER_DN, 41-genes_downregulated in PCa_vs_benign-tissue), and M10319

(WALLACE_PROSTATE_CANCER_RACE_UP, 305-genes_up-regulated in PCa tissues from African-

American patients compared to those from the European-American patients). For M6698, M4691, and M10319

DEGs, 36, 55, and 100 DEG-matched genes were upregulated respectively in MUT_BRCA2. Conversely, for

M11504, 21 genes were downregulated in MUT_BRCA2 (Fig. S3E). These gene-expression similarity across

these datasets (

≥

50% for most sets) supports a pro-PCa transcriptional environment in MUT_BRCA2 iPROS.

Induction of tumorigenic transformation in iPROS model with dietary carcinogens

To induce PCa in vitro using our experimental iPROS model, we exposed 8-week-old iPROS to two dietary

carcinogens—PhIP (2-Amino-1-methyl-6-phenylimidazo[4,5-b]pyridine) and MNU (N-methyl-N-nitrosourea)—

for three weeks (Fig. 4A). Initially, were incubated them with a dose gradient (100

M to 1

M over 7 days) of

PhIP and MNU to optimize cytotoxicity. Based on LDH cytotoxicity assays, two concentrations, 1

M (PhIP1

and MNU1) and 5

M (PhIP5 and MNU5), were selected for three-week exposures in iPROS with and without

BRCA2 mutations. These treatments resulted in 10–20% cytotoxicity (Fig. S4A). After removing the stressors

at week-3, no significant differences in proliferation or PSA secretion were observed between stressed and

control groups. Since carcinogenesis often shows latency, ranging from days to decades depending on

carcinogen type, dosage, and genetic/immunogenic factors

, we extended the culture for an additional 4

weeks, assessing LDH cytotoxicity at weeks 2 and 4 (Fig. 4B,C). By week 4, control-iPROS treated with higher

PhIP and MNU concentrations exhibited notable cell death, which was absent in BRCA2-mutant iPROS.

Unstressed CON_ and MUT_BRCA2 iPROS maintained viability compared to stressed counterparts.

We hypothesized that prolonged PhIP and MNU exposure caused extensive double-strand breaks (DSBs)

surpassing homologous recombination (HR) repair capacity, triggering cell death in CON_iPROS. However,

MUT_BRCA2 iPROS, deficient in HR due to BRCA2 loss of function, compensate by engaging error-prone

non-homologous end joining (NHEJ)

. Post-stress, these mutants showed reduced DSBs, evident from

decreased

H2AX fluorescence (Fig. 4D, S4B), a sensitive DSB damage marker

. While NHEJ facilitates

recovery, it is inaccurate, causing indels and genome instability

, which promotes survival and

tumorigenesis

. This was corroborated by elevated Ki67 nuclei index (by IF) and higher Ki67 mean

fluorescence (by flow cytometry) in MUT_BRCA2 iPROS treated with PhIP5 and MNU5 (Fig. 4E,F, S4C).

Given that PhIP and MNU induce PCa in rodents, we next examined if iPROS could mimic this tumorigenesis

in vitro. At week 4 post-stress, PSA levels were elevated (Fig. 4G). Diagnostic PCa detection using tissue

microarrays often employs a 3-antibody panel: AMACR (

-methylacyl coenzyme A racemase), 34

E12 (high

molecular weight cytokeratin), and p63

. AMACR is overexpressed in PCa

, while loss of 34

E12 and p63

supports diagnosis. Mutant iPROS treated with PhIP5 and MNU5 exhibited increased AMACR staining and

reduced p63, suggesting PCa-like features (Fig. 4H). RT-qPCR showed upregulation of AR, AMACR, ERG,

TMPRSS2, and FOXA1, and downregulation of p63

56,57,58

(Fig. 4I), indicating tumorigenic transformation in

MUT_BRCA2 iPROS.

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Upregulated AR and its downstream genes suggest AR-driven oncogenesis. Accordingly, PhIP5- and MNU5-

stressed organoids were treated with 5

M enzalutamide, an AR inhibitor

, for 2 weeks post-stress. This

treatment elevated cytotoxicity and reduced proliferation in MUT_BRCA2 iPROS (Fig. S4D,E). Additionally, 10

M Olaparib, a PARP inhibitor inducing synthetic lethality by targeting NHEJ in MUT_BRCA2 iPROS cells

caused selective cytotoxicity and increased

H2AX staining these iPROS (Fig. S4F,G). These findings affirm

the iPROS model as a reliable human-relevant in vitro platform for studying prostate tumorigenesis.

Xenotransplanted BRCA2 mutant iPROS in immunodeficient mice promote tumorigenesis

Hayflick limits the long-term expansion of organoids in vitro, making it difficult to fully model tumorigenesis

Moreover, tumor-stromal interactions and vascularization are critical for tumor progression, maturation, and

metastasis

. iPROS cultures ceased expanding after 14–16 weeks and began to die (data not shown); with

PhIP and MNU 3 weeks treatment, this extended up to 18-22 weeks before they die out. To support further

growth in a host environment with a more favorable tissue microenvironment, we conducted a xenograft study

by injecting iPROS either subcutaneously or into the sub-renal capsules of 6–8-week-old NOD scid gamma

mice (Fig. 4J). We injected CON_ and MUT_BRCA2 iPROS, a human tumor cell line (positive control, human-

urinary-bladder cancer cell-line 5637), and Matrigel alone. The renal capsule is an advantageous ectopic site

for prostate tissue xenografting

62,15

Four weeks post-injection, large tumors formed in mice receiving the tumor cell line subcutaneously, and three

small tumors developed in MUT_BRCA2-iPROS-injected mice (Fig. 4K). Subcutaneous tumor growth curves

were tracked for 12 weeks, after which mice were euthanized and tumors collected. The positive control mouse

reached a tumor-size of 300 mm³ by week 6 and was euthanized earlier. No tumors formed in CON_BRCA2-

iPROS injected mice. Alongside hematoxylin & eosin (H&E) staining (Fig. 4L, S4H) we identified the

integration of both human-tumor cells and iPROS cells into the mouse subcutaneous tumor using human-

specific anti-mitochondria antibody (AMA, 113-1), which stains explicitly human mitochondria (a specifc

“spaghetti-like” staining) and does not cross-react with mouse or rat tissues (Fig. 4L)

. Only MUT_BRCA2

iPROS tumors expressed NKX3.1, a prostate-specific marker. In sub-renal capsule xenografts, LTL-negative

(kidney marker), but NKX3.1 positive, large-mass outgrew only in MUT_BRCA2 iPROS and positive control-

tumor cell injected kidneys (Fig. 4M, S4H). PCa markers ERG and PSMA fluorescence were elevated in

MUT_BRCA2 iPROS-transplanted kidneys (Fig. S4M), supporting in vivo tumorigenesis by MUT_BRCA2

iPROS. Positive control, CON_ and MUT_BRCA2 iPROS transplated kidney sections were positive for AMA

confirming human prostate cell integration (Fig. 4N).

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DISCUSSION

Patients’ iPSC-derived organoids are emerging as valuable tools for modeling organ development and disease

progression, including cancers

. While prior models with human iPSCs showed prostate-like organoids, these

were reliant on co-culture with rodent UGM

14,15

, reducing their utility in pre-clinical studies. In this study, we

developed a pre-clinical, rodent cell–free prostate organoid (iPROS) model that reliably recapitulates human

prostate organ morphology, gene expression, and function

. iPROS with BRCA2 risk variants displayed

functional implications in morphological and molecular plasticity during prostate tumorigenesis

. Our results

demonstrate the potential of iPROS as an effective platform for BRCA2-related PCa risk prediction,

personalized biomarker evaluation, and therapeutic screening. iPROS differentiation from iPSCs followed a

well-characterized protocol mimicking prostate glandular development

, enabling detailed mapping of

epithelial-mesenchymal interactions from definitive endoderm through hindgut, prostate progenitor, and

epithelial budding stages—circumventing the limitations of adult tissue-derived organoids.

The use of defined small molecules and pathway modulators enabled generation of 3D prostate organoids

mimicking prostate lobes and ducts, showing both basal and luminal epithelial structures. iPROS expressed

key prostate markers including Androgen Receptor (AR), NKX3.1, and p63, critical for prostate development.

PSA secretion increased over time indicating androgen responsiveness and functional maturation. Detection of

PSA confirmed the ability of iPROS to recapitulate key physiological features in vitro. Importantly, iPROS

reproduced human prostate-like architecture, including luminal, basal, and neuroendocrine epithelial lineages,

as confirmed by IF/Immunohistochemistry showing CK8-18, p63, and Chromogranin A expression.

Transcriptomic analysis further validated physiological relevance by aligning closely with human adult prostate

tissue gene profiles.

Pathogenic BRCA2 mutations are common in aggressive, metastatic PCa with high-GG tumors. We derived

iPSCs from three patients carrying pathogenic BRCA2 mutations with different GG and stages, and

differentiated them into iPROS. This enabled us to study the impact of germline BRCA2 mutations on organoid

development and tumorigenesis

. As a tumor suppressor, BRCA2 maintains DNA integrity during cell division.

Its mutation impairs DNA repair, leading to genomic instability and uncontrolled proliferation. These

deficiencies can disrupt other genes linked to growth and survival, promoting tumorigenesis

. Our findings

support this: BRCA2 mutations aligned with altered prostate epithelial morphology and gene expression but did

not affect iPSC proliferation or stemness, suggesting their influence manifests post-differentiation.

Increased Vimentin and SMA levels, along with disrupted epithelial polarity in MUT_BRCA2 iPROS, are

consistent with EMT transition, indicating predisposition to tumorigenic transformation. Transcriptomic profiling

revealed enrichment of PCa signature genes in MUT_BRCA2 iPROS. GSEA showed pro-oncogenic AR, MYC,

MAPK, and Hippo signaling upregulation, cell-peripheral signaling, cell-cell adhesion loss, and extracellular

matrix (ECM) remodeling, all hallmarks of prostate-epithelial tumorigenesis

42,68

. MUT_BRCA2 iPROS shared

significant transcriptomic overlap with prostate adenocarcinoma (PRAD), further supporting its relevance as a

disease model. The varying degrees of transcriptomic similarity between different GG-MUT_BRCA2 iPROS

and TCGA_PRAD datasets underscore the iPROS model’s potential to study primary and advanced PCa in a

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patient-specific context

. Finally, in vivo xenotransplantation confirmed higher tumorigenic potential of BRCA2-

mutated iPROS in both subcutaneous and renal capsule microenvironments.

We found that PhIP and MNU, cause genotoxic stress and induce PCa in rodent models

20,21

, also induced DNA

damage in iPROS. Despite cytotoxic stress, unrepaired or mismatch-repaired DNA allowed MUT_BRCA2

iPROS to survive, intensifying their oncogenic potential. Importantly, the carcinogen-treated iPROS responded

like human PCa: elevated expression of PCa-specific genes (AR, AMACR, ERG, TMPRSS2) and loss of the

basal marker p63 indicated onset of aggressive-PCa transformation. Furthermore, iPROS with BRCA2

mutations exhibited increased sensitivity to AR inhibitor enzalutamide and PARP inhibitor olaparib, confirming

their transformation state and dependence on AR signaling and DNA repair pathways.

In conclusion, we developed patient-specific prostate organoids (iPROS) that mimic human prostate

morphology and function. iPROS with pathogenic-BRCA2 mutation forms tumors in vivo, and show dietary-

carcinogen vulnerability, providing a robust preclinical platform to study mutation-specific-PCa and advance

precision oncology therapies.

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FIGURE LEGENDS

Figure 1: Differentiation and characterization of iPROS.

(A) Prostate organoid differentiation-schema from

iPSC_to_iPROS. (B) Representative images of iPSC and iPROS at day 60. (C) Chromogenic images showing

AR, NKX3.1, CK8/18, p63, and CgA. (D) IF-images showing AR, NKX3.1, CK8/18, p63, PSA, and PSMA with

DAPI. (E) Quantification of PSA release over weeks. (F) RT-qPCR Cq plot for prostate-specific genes. (G)

Heatmap of 30 highly expressed prostate-specific genes from GTEx. (H–I) Venn diagrams showing gene

overlaps of iPROS with GTEx, GEO, and Human Protein Atlas. Significance tested with ANOVA (E); **p <

0.01, and ****p < 0.0001.

Figure 2: Morphological and Molecular Plasticity in iPROS with BRCA2 mutations.

(A) H&E staining of

prostatectomy sections from 3-patients; black-arrows indicate normal/tumor sites. (B) Sanger-sequencing

showing BRCA2 mutations in iPSCs and iPROS. (C) qPCR of BRCA2 expression in CON_ and MUT_BRCA2

iPROS. (D) Chromogenic images of AR. (E) IF-images of PSA and F-actin. (F) PSA release at week-8. (G)

qPCR of prostate-specific genes in CON_ and MUT_BRCA2 iPROS. (H–I) Ki67 nuclei-index; image and

quantification. (J) Flow cytometry of Ki67 mean fluorescence. (K–L) IF-images of Vimentin and

-SMA.

Significance tested with pairwise comparisons (t-test with Welch correction; C,D,I,G). *p < 0.05, **p < 0.01, ***p

< 0.001, ****p < 0.0001.

Figure 3: Transcriptomic characterization of iPROS with BRCA2 mutations.

(A–B) PCA and eigencore

correlation plots showing variance among CON_ and MUT_BRCA2 iPROS. (C) Volcano plot of top 10

significantly altered genes. (D) Normalized expression of prostate- and PCa-specific genes. (E–G) GSEA

analysis for GO, KEGG, and Hallmark50 pathways with significant DEGs of MUT_BRCA2 vs CON_BRCA2

iPROS. (G) Pie chart of transcript overlap with MSigDB PCa gene sets. (H–J) Heatmaps of PCa-specific genes

from public-PCa-mRNAseq-datasets. (K–L) Pie chart showing DEGs of each MUT_BRCA2 vs CON_BRCA2,

Venn diagrams comparing them with TCGA_PRAD datasets.

Figure 4: Neoplasia induction in iPROS with dietary carcinogens.

(A) Structures of PhIP and MNU. (B–C)

LDH assay shows cytotoxicity in CON_ and MUT_BRCA2 iPROS after 2- and 4-week of PhIP (B) and MNU

H2AX flowcytometry indicating DNA damage after a 3-week exposure. (E) Representative

IF-images of Ki67-positive nuclei in 15-week-old iPROS post-treatment. (F) Ki67 flow cytometry after 4-week

stress-withdrawal. (G) PSA release comparison. (H) Immunofluorescence for AMACR and p63. (I) qPCR of

PCa-specific genes. (J) Illustration of subcutaneous and sub-renal capsules xenograft into NOD scid gamma

mice (K) Tumor growth curve shows progressive size increase over 12 weeks post-injection. (L) H&E and IF-

images of AMA and NKX3.1 in subcutaneous-tumors. (M–N) Renal capsule engraftment showing H&E and IF

with LTL, NKX3.1, and AMA. Scale-bar: 100

M (L) and significant 2-way ANOVA (multiple comparisons); *p <

0.05, **p < 0.01, ****p < 0.0001.

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SUPPLEMENTAL INFORMATION

Document S1: Key resources table, Supplementary-Figure Legends, Materials and Methods

Table S1: iPSCs details.

STAR METHODS

•

KEY RESOURCES TABLE

•

RESOURCE

AVAILABILITY

Lead

contact

Materials

availability

Data and code availability

•

SUPPLEMENTARY TABLE: Experimental models; iPSC cell line details

•

METHOD

DETAILS

Differentiation of iPSCs to iPROS

RNA Extraction and Quantitative PCR Analysis

Immunocytochemistry, Immunohistochemistry, image acquisition, and analysis

iPROS xenografts in mice

RNA processing, mRNA-sequencing, and quality control

•

QUANTIFICATION AND STATISTICAL ANALYSIS

RNA-seq data processing and differential expression testing

Statistical analysis and significance tests

KEY RESOURCES TABLE

REAGENTS OR RESOURCE

SOURCE

IDENTIFIER

Antibodies (anti-Human)

(Catalog No#)

Anti-Androgen Receptor

BD biosciences

554224

Anti-NKX3.1

Abcam and R&D Systems

ab196020, AF6080

Anti-Prostate Specific Antigen

Biogenix and R&D Systems

MU014-5UC, AF1344

Anti-p63

Abcam and R&D Systems

ab124762 and AF1916

Anti-PSMA/GCPII

Proteintech

13163-1-AP

Anti-Cytokeratin 8/18

ThermoFisher

MA5-14088

Anti-AMACR Monoclonal Antibody
(2A10F3)

ThermoFisher MA5-15360

Anti-

Chromogranin A Monoclonal

Antibody (PHE5)

ThermoFisher MA5-13281

Anti-

α−

smooth muscle Actin

Abcam ab7817

Anti-Mitochondrial Antibody

Abcam

Ab92824

Anti-ERG antibody [EPR3864]

Abcam

ab92513

Anti-

PSMA/GCPII Monoclonal

antibody

Proteintech 66678-1-Ig

Anti-Vimentin Antibody

R&D Systems

MAB2105

Anti-Ki67/MKI67 Antibody

R&D Systems

AF7617

Lotus tetragonolobus Lectin (LTL) -
Biotinylated

Vector Laboratories

B-1325-2

CoraLite®594-Phalloidin (red)

Proteintech

PF00003

Chemicals & Kits

Human Recombinant Activin A

R&D Systems, Stem Cell Technologies

338-AC-010, 78132

Human Recombinant FGF10

R&D Systems, Stem Cell Technologies

345-FG-025

Human Recombinant EGF

R&D Systems, Stem Cell Technologies

236-EG-200, 78006

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Human recombinant Noggin

R&D Systems, Stem Cell Technologies

3344-NG-050, 78060

Human recombinant WNT10B

R&D Systems

7196-WN-010

Human Recombinant bFGF (FGF2)

Stem Cell Technologies

78003

Human Recombinant FGF-4

Stem Cell Technologies

78103

Human Recombinant R-Spondin1

R&D Systems, Stem Cell Technologies

4645-RS-100,

mTeSR™1 Complete Kit

Stem Cell Technologies

85850

mTeSR™ Complete Plus Kit

Stem Cell Technologies

100-1130

ReLeSR™ Stem

Cell

Technologies

100-0483

ACCUTASE™ Stem

Cell

Technologies 07922

A83-01 Stem

Cell

Technologies

72022

SB202190 Stem

Cell

Technologies

72632

Prostaglandin E2

Stem Cell Technologies

72192

Insulin-Transferrin-Selenium (ITS -G)
(100X)

ThermoFisher Scientific

41400045

Human Wnt-7a Protein (120-31-
15UG) in Functional

ThermoFisher Scientific

120-31-15UG

Advanced DMEM/F-12

ThermoFisher Scientific

12634010

RPMI 1640

ThermoFisher Scientific

11875093

HEPES (1 M)

ThermoFisher Scientific

15630080

Critical commercial assays

CyQUANT™ LDH Cytotoxicity Assay

ThermoFisher Scientific

C20300

Human PSA (Total)/KLK3 ELISA Kit

Invitrogen EHKLK3T

MNU (N-Nitroso-N-methylurea)

Selleck Chemicals

E0158

Smoothened Agonist (SAG) HCl [2
mg]

Selleck Chemicals

S7779

PhIP (2-Amino-1-methyl-6-
phenylimidazo(4,5-b)pyridine)

Millipore Sigma

SMB01383

ATRA Millipore

Sigma R2625

-Dihydrotestosterone (DHT)

solution

Millipore Sigma

D-073-1ML

CHIR-99021 monohydrochloride

MedChemExpress

CT99021

Enzalutamide (MDV3100)

MedChemExpress

HY-70002

Olaparib (AZD2281; KU0059436)

MedChemExpress

HY-10162

Software and Algorithms

R Project for Statistical Computing

CRAN RRID:SCR_001905

tidyverse CRAN

RRID:SCR_019186

DESeq2 Bioconductor

RRID:SCR_015687

edgeR Bioconductor

RRID:SCR_012802

biomaRt

Bioconductor

RRID:SCR_019214

tximport Bioconductor

RRID:SCR_016752

PCAtools Bioconductor

Salmon Bioconductor

RRID:SCR_017036

STAR aligner

Github

RRID:SCR_004463

GSEA_ClusterProfiler Bioconductor

RRID:SCR_001905

Samtools htslib.org

RRID:SCR_002105

DAVID Bioinformatics Resource

david.ncifcrf.gov

RRID:SCR_001881

jVenn Bioconductor

RRID:SCR_016343

qPCR primers list

Human Genes

Direction (5'- 3'), F = Forward, R= Reverse

hSOX2-F GGGAAATGGGAGGGGTGCAAAAGAGG IDT

Technologies

hSOX2-R TTGCGTGAGTGTGGATGGGATTGGTG

IDT

Technologies

hRUNX1-F CCCTAGGGGATGTTCCAGAT

IDT

Technologies

hRUNX1-R TGAAGCTTTTCCCTCTTCCA

IDT

Technologies

hSOX17-F CTCTGCCTCCTCCACGAA

IDT

Technologies

hSOX17-R CAGAATCCAGACCTGCACAA IDT

Technologies

hFOXA2-F GCACTCGGCTTCCAGTATGC

IDT

Technologies

hFOXA2-R GCGTTCATGTTGCTCACGGA

IDT

Technologies

hP63-F GTGAGCCACAGTACACGAACC

IDT

Technologies

hP63-R GAGCATCGAAGGTGGAGCTGG

IDT

Technologies

hAR-F TGTCCATCTTGTCGTCTTCG

IDT

Technologies

hAR-R ATGGCTTCCAGGACATTCAG

IDT

Technologies

hNKX3.1-F GGCCTGGGAGTCTTTGACTCCACTAC

IDT

Technologies

hNKX3.1-R ATGTGGAGCCCAAACCACAGAAAATG

IDT

Technologies

hCK18-F CAGCAGATTGAGGAGAGCAC

IDT

Technologies

hCK18-R TCGATCTCCAAGGACTGGAC

IDT

Technologies

hRPL13-F GTCTCCACGTGGTGTGTTTC

IDT

Technologies

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hRPL13-R CAGGGCTTGGACTGTCTTTC

IDT

Technologies

hVimentin-F TTGACAATGCGTCTCTGGCAC

IDT

Technologies

hVimentin-R CCTGGATTTCCTCTTCGTGGAG

IDT

Technologies

hKLK3-F CGCAAGTTCACCCTCAGAAGGT

IDT

Technologies

hKLK3-R GACGTGATACCTTGAAGCACACC

IDT

Technologies

hBRCA1-F GAAACCGTGCCAAAAGACTTC

IDT

Technologies

hBRCA1-R CCAAGGTTAGAGAGTTGGACAC

IDT

Technologies

hBRCA2-F CACCCACCCTTAGTTCTACTGT

IDT

Technologies

hBRCA2-R CCAATGTGGTCTTTGCAGCTAT

IDT

Technologies

hTMPRSS2-F TATGAGAACCACGGGTATCAGT

IDT

Technologies

hTMPRSS2-R CGTTGTAATCCTCGGAGCATACT

IDT

Technologies

hAMACR_F ACGTCTTGCTCGAGATGTGA

IDT

Technologies

hAMACR_R AATCCAGCAGGTCAGCAAAG

IDT

Technologies

hERG_F CGCAGAGTTATCGTGCCAGCAGAT

IDT

Technologies

hERG_R CCATATTCTTTCACCGCCCACTCC

IDT

Technologies

hFOXA1_F GCTGGACTTCAAGGCATACGA

IDT

Technologies

hFOXA1_R GCTGGACTTCAAGGCATACGA

IDT

Technologies

hPAcP_F CGGGATCCCGATGAGAGCTGCACCCCTC

IDT

Technologies

hPAcP_R CGGGATCCCGCTAATCTGTACTGTCCTCAGT

IDT

Technologies

POU5F1-F AAGCTGGAGAAGGAGAAGCTG

IDT

Technologies

POU5F1-R AATAGAACCCCCAGGGTGAG

IDT

Technologies

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SUPPLEMENTARY TABLE

iPSC Line

Mutation

Code

used in

Manuscri

Age at

PBMC

Draw

Prostate
Cancer
Diagnosi
s

Gleaso
n Grade
Group
(ISUP)

Path
Stage if
available

Lymph
Node
Metastas
es

Distant
Metastas
es

Curr.
Status

Cancer
aggressiven
ess

0002iBRCA2 c.5946de

BRAC2A

68 1 5 T2

0 NED High

N0U3iPRC c.9513_9

516del

BRAC3i 59

5 T3bN1M1 1

High

C0S4iPRC c.5946de

BRAC4i 70

pT2

0 NED

Low

CSEDI022iC

NA CONT.1 79 0 NA NA NA NA

NA NA

CSEdi028CR

NA CONT.2 79 0 NA NA NA NA

NA NA

CSEdi037iCR

NA CONT.3 80 0 NA NA NA NA

NA NA

Table S1: List of lines with identifier and other details used in the current study

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MATERIALS AND METHODS

iPS Cell Lines:

All iPSC lines with BRCA2 mutations were generated at the iPSC Core at Cedars-Sinai

Medical Center. Patients peripheral blood mononuclear cells (PBMCs) were transfected with a non-integrating

episomal plasmid expressing seven factors: OCT4, SOX2, KLF4, L-MYC, LIN28, SV40LT, and p53 shRNA

(pEP4 E02S ET2K, pCXLEhOCT3/4-shp53-F, pCXLE-hUL, and pCXLE-hSK). All cell lines and protocols in

this study were conducted in compliance with the guidelines approved by the Stem Cell Research Oversight

Committee (SCRO) and Institutional Review Board (IRB) under IRBSCRO Protocols Pro00032834 (iPSC Core

Repository and Stem Cell Program) and Pro00021505 (Svendsen Stem Cell Program). Three existing male

control iPSC lines with wild-type BRCA2 were selected from the Cedars-Sinai Biomanufacturing Center iPSC

Core. These control lines were reprogrammed from healthy donor PBMCs, namely CSEDi022A, CSEDi028A,

and CSEDi037A. The presence of BRCA2 heterozygous mutations in these iPSC lines was confirmed through

DNA sequencing analysis. BRCA2 mutations identified were specific to each patient line: CSN0U3iPRC

(c.9513_9516del, located in exon 11), CS0002iBRCA2 (c.5946delT, located in exon 19), and CSC0S4iPRC

(c.5946delT, located in exon 19). These mutations aligned with the patients’ clinical diagnoses.

iPSC Culture:

Control and BRCA2-mutated iPSCs were cultured in mTeSR®Plus medium on growth factor-

reduced Matrigel™ Matrix (BD Biosciences)-coated plates at 37°C in a 5% CO2 incubator. When human iPSC

colonies reached 70–90% confluence, they were washed with Versine and gently lifted with ReLeSR

(STEMCELL), and then replated at a 1:6 ratio. All cell lines were tested for mycoplasma contamination

monthly.

Differentiation of iPROS from iPSCs:

iPROS differentiation from iPSCs involved three major steps: iPSC to

Definitive Endoderm (DE) differentiation, followed by differentiation hindgut endoderm (HG) and prostate

progenitor specification in 3D culture, and finally the induction of AR signaling in progenitor population for

further differentiation.

DE Differentiation (3-day Protocol):

iPSCs growing in mTeSR®Plus medium until DE induction. In a confluent

well of a 6-well plate (approximately 1.2x10^6 cells), cells are dissociated using Accutase

in mTSer1 media

containing 10

M Y-drug and plated in a 24-well plate pre-coated with Matrigel (0.25-0.5 mg/ml). On Day 1, the

medium is replaced with 1 mL of DE media (RPMI, 1x P/S, 1x L-glutamine, 3

M Chir99203, and 100 ng/ml

Activin). On Day 2, the media is changed to a fresh Day 2 formulation (RPMI, 1x Pen/Strep, 0.2% FBS, 100

ng/ml Activin A), and on Day 3, the media is switched to Day 3 media (RPMI, 1x Pen/Strep, 2% FBS, 100

ng/ml Activin).

Hindgut Endoderm & Prostate Progenitor Differentiation (2+2 Days Protocol):

Following DE differentiation, the

media is replaced with HG media for 4 days, refreshing the media daily. On Days 1 and 2, cells are exposed to

AdvDMEM/F12 (with 200 mM L-glu, 1x P/S, 15 mM Hepes) supplemented with 2% FBS, 500 ng/ml FGF4, and

500ng/ml WNT3B. On Days 3 and 4, the media is changed to a formulation containing 2% FBS, 200 ng/ml

FGF4, 500 ng/ml FGF10, and 500 ng/ml WNT10B. At this stage, 3D aggregates form beneath the monolayer,

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which are collected by scraping, spun at 200g for 3 minutes, and preferably separated as 3D aggregates for

subsequent steps.

Inducing of AR Signaling:

Matrigel is prepared by adding 20

l of 1x B27, 1.5

l of 100

g/ml EGF, and 1.5

of 100

g/ml Noggin to 1 ml of stock Matrigel (~10 mg/ml). The cell pellet is mixed 1:1 with this Matrigel

cocktail, with each drop of the mixture not exceeding 70

l (ideally 50

l) per well of a 24-well plate. After 3-4

minutes in the hood, the plate is flipped upside down, allowing the cells to hang in the Matrigel drop for 9

minutes in the incubator. Following this, 500

l of media is added to each Matrigel dome well. For the next 5-7

days these prostate progenitors were cultured in media consisting of AdvMEM12 (with 200 mM L-glu, 1x P/S,

15 mM Hepes), 1x B27, and 2% ITS, along with 500 ng/ml R-Spondin1, 100 ng/ml Noggin, 100 ng/ml EGF, 10

nM ATAR, 1.5

M DHT, 2

M CHIR-99021, 10 nM SAG (SHH agonist), and 100 ng/ml FGF10. Media should

be replaced every third day.

Long-term iPROS Culture:

From Week 2 onwards, organoids are maintained in a modified media formulation:

AdvMEM12 with 200 mM L-glu, 1x P/S, 15 mM Hepes, 1x B27, 2% ITS, 1.25 mM N-acetylcysteine, 1

prostaglandin E2, 10 mM nicotinamide, 0.5

M A83-01 (TGF

/Smad inhibitor), 10

M SB202190 (p38 MAPK

inhibitor), 500 ng/ml R-Spondin1, 100 ng/ml Noggin, 100 ng/ml EGF, 100 ng/ml FGF2, 100 ng/ml FGF10, 10

nM ATAR, 10 nM DHT, 10 nM SAG, 2

M CHIR-99021, and 10

M Y-drug. After Week 3, Y-drug and ATAR

are removed, but they should be reintroduced when organoids are split. Organoids were passaged every 2-3

weeks. To passage, organoids are dissociated with Express-TrypLE to single cells, counted, and mixed with a

1:1 Matrigel cocktail to regrow organoids with even cell numbers. This method ensures a consistent number of

organoids per dome.

Cryopreservation and Revival:

For cryopreservation, Matrigel is removed, and Cryostor media is added to the

organoids. After mechanical disruption with a pipette, the organoids are frozen with Cryostor cell freezing

media (STEMCELL). For the revival, Cryostor is replaced with the 1:1 Matrigel cocktail, and after one week,

organoids can be re-split and counted to ensure even distribution.

RNA Extraction and Quantitative PCR Analysis:

Total RNA was extracted from cells using the QIAGEN

Rneasy Mini Kit, following the manufacturer’s instructions. One microgram of the purified RNA was then used

to synthesize cDNA using the Quantitect Reverse Transcription Kit (QIAGEN). Quantitative real-time PCR was

carried out with SYBR Select Master Mix (Applied Biosystems). Gene expression levels were normalized to

GAPDH and RPL13 housekeeping genes expressed as fold changes relative to control samples. All

experiments were conducted in triplicate, and the primers used are listed in the reagent table. At least three

independent experiments were performed for all genes with three technical repeats.

Fluorescence-Immunohistochemistry of iPROS and Immunocytochemistry of iPSC Cells:

iPROS were

fixed in 4% paraformaldehyde (PFA) in phosphate buffered saline (PBS; with Ca2+ and Mg2+) for one hour at

room temperature (RT), then rinsed three times with PBS. After fixation, they were cryoprotected overnight in

30% sucrose at 4°C and embedded in OCT compound (Tissue-Tek). Frozen sections, 10 µm thick, were cut

using a cryostat, mounted on glass slides, and stored at –20°C. Before staining, sections were rehydrated,

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permeabilized, and blocked with PBS with 0.5% Triton X-100 and 10% normal human serum in PBS for one

hour at room temperature. Sections were then incubated overnight at 40C with primary antibodies diluted the

blocking solution containing 0.05% Triton X-100 (PBS-T). The secondary antibody was diluted (1:1000) with

5% Normal Donkey Serum in PBS-T for 1 hour. iPSCs were fixed in 4% PFA in PBS (with Ca2+ and Mg2+) for

20 min at RT. This was followed by permeabilization for 5 minutes with 0.25% Triton X-100, then blocking with

5% Normal Donkey Serum in PBS-T for two hours in RT, and then the secondary antibody was diluted

(1:1000) in 5% Normal Donkey Serum in PBS-T for 1 hour. Nuclei were counterstained with DAPI (4’,6-

diamidino-2-phenylindole). Finally, the slides were mounted with antifade mounting media and dried before

imaging with a Nikon-Ti Confocal microscope. Each image represents at least three independent experiments.

The primary antibodies used in this study are listed in reagent table.

iPROS Xenografts:

All procedures involving animals and their care were approved by the Institutional Animal

Care and Use Committee of CSHS (IACUC008253) in accordance with institutional and National Institutes of

Health guidelines. Male immunodeficient (NGS) mice (n=1-2/group), 6-8 weeks old were injected, either

subcutaneously into the flanks or underneath the renal capsule, bilaterally as follows. Each administration site

received 1 million (1 × 10^6 cells) positive control tumor cells (positive control group), matrigel alone (negative

control group) or iPSC derived organoids [200 organoids (~150-300 mm in diameter, consists of ~ 2 million

single cells, uninformedly sheared with 1 ml pipette tips,) mixed with cold Matrigel (50:50)] for CON_BRCA2

and MUT_BRCA2 groups. Briefly, animals were anesthetized with induced with isoflurane (1-3%) and

maintained on a nose cone, placed on a heating pad (37˚C) ear tagged, skin was shaved and aseptically

prepped using betadine and 70% alcohol. For subcutaneous delivery, a single puncture hole was used to

deliver the cells into the subcutaneous tissue of both flanks via a 18-gauge needle. For subrenal capsule

delivery, a ~1cm midline incision was made in the back in between the kidneys, the incision was moved over

the flank, a small incision was made in the muscle, and the kidney gently exposed through the incision. The

kidney was kept hydrated using 0.9% sterile saline. A small incision was made in the kidney capsule using a

23-gauge needle, using an elevator the capsule was separated from the kidney to create a small pocket. The

cells or iPROS were delivered in 10-50µL of Matrigel using PE-50 tubing. The kidney was gentrly placed back

into position through the incision, the muscle layer sutured with absorbable 5-0 suture and the skin incision

closed with wound-clips. Buprenex (0.1mg/kg) and Carprofen (5mg/kg) were given for analgesia post-

operatively. Animals were monitoried regularly for signs of tumor growth. Mice were euthanized 6 months after

the graft implant or when the tumour size reached

≥

300-500 mm

Immunohistochemistry of Xenograft iPROS Tissues:

Dissected xenografted tissue was fixed in 10%

formalin for 1 hour at room temperature, then overnight at 4°C, washed with PBS, and stored in 70% ethanol

overnight. Samples were processed by the Cedars-Sinai Biobank Core for paraffin embedding and sectioned

at 5 µm. Slides were deparaffinized with xylene (3x, 10 min each), then rehydrated through graded ethanol

solutions (100%, 95%, 75%, 50%) for 5 minutes each. After rinsing twice with tap water, antigen retrieval was

performed by microwaving in Vector Unmasking Solution (citric acid-based) and then cooling for 30 minutes.

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Endogenous peroxidase activity was blocked with 0.3% H

₂

in methanol for 30 minutes. Slides were blocked

with 3% BSA in PBS-T for 1 hour, then incubated overnight at 4°C with primary antibody (1:200). After PBS

washes, sections were treated with a biotinylated secondary antibody, followed by ABC reagent and DAB

development. Slides were counterstained with hematoxylin, dehydrated, cleared in xylene, and mounted with

coverslips. All H&E staining of original patient tissues and xenografted iPROS tissues was done in the biobank

core. The slides were stained with hematoxylin (ImmunoMaster Hematoxylin, American MasterTech Scientific,

Inc.) for 8 minutes, followed by a 5-minute rinse under running tap water. They were then briefly stained with

eosin (Eosin Y Phloxine B, American MasterTech Scientific, Inc.) for 10 seconds and rinsed again with tap

water. Afterward, the slides were dehydrated through a graded ethanol series—50%, 75%, 95%, and 100%—

and cleared in xylene, each step lasting 1 minute. Finally, the tissues were mounted with a glass coverslip

using Richard-Allan Scientific Mounting Medium.

Ki67 Positive Nuclei Count by Image Segmentation and Particle Analysis:

iPROS sections or iPSCs were

stained for Ki67 using a standard immunofluorescence protocol, and nuclei were counterstained with DAPI.

Fluorescence images were acquired at 20x magnifications to ensure optimal resolution of Ki67-positive nuclei.

Images were processed using Fiji-ImageJ. First, they were converted to 8-bit grayscale, background noise was

reduced by applying a median filter (radius 2-3 pixels). After binary water shading, the fluorescence signal

corresponding to both DAPI and Ki67 staining was thresholded using the “Threshold” tool to musk the nuclei

and exclude non-specific background staining, ensuring accurate detection of the Ki67-positive signal. Then,

using the “Analyze Particles” function in Fiji-ImageJ, the segmented DAPI and Ki67-positive areas were further

analyzed. The parameters for particle analysis were set to 100 to 1500 mm2, and circularity 0.4-1 which

exclude artifacts and only count particles of a defined size range corresponding to individual nuclei. The “Show

Results” option provided a count of both DAPI and Ki67-positive nuclei in the image. The number of Ki67-

positive cells was expressed as a percentage of total DAPI-positive nuclei to account for variations in cell

density. Statistical Analysis: Data from multiple images (n

≥

3 per condition and at least six segments) were

used for statistical analysis. Ki67-positive cell counts were normalized to the total number of DAPI-positive

nuclei per image, and the results were presented as the mean percentage of Ki67-positive nuclei ± SEM.

mRNA-seq Experiment and Analysis for Transcriptional Profiling of iPROS:

Total RNA was extracted

from samples using the QIAGEN Rneasy Mini Kit according to the manufacturer’s instructions. RNA

concentration and purity were measured using a NanoDrop spectrophotometer, and RNA integrity was

assessed using the Agilent 2100 Bioanalyzer. Only RNA samples with an RNA Integrity Number (RIN) greater

than 8 were used for sequencing. Standarised cDNA library preparation (poly A enrichment) was done as

suggested by manufacturer, using the Illumina TruSeq Stranded mRNA Library Preparation Kit. Libraries were

quantified using a Qubit fluorometer and assessed for quality on the Bioanalyzer. Pooled libraries were

sequenced on an Illumina NovaSeq X Plus (PE150) platform with pair-end reads at a depth of ~25 million

reads per sample. Base calling and demultiplexing were performed using Illumina bcl2fastq software. Raw

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reads were quality-checked using FastQC and trimmed using Trimmomatic to remove adapters and low-quality

bases.

High-quality reads were pseudoaligned to the GRCh38.p13 human reference genome using Salmon (v1.4.0)

with default parameters. Transcript-level abundances were imported and summarized to the gene level using

the R package tximport. Genes with an average read count below 3 across all samples or lacking HGNC

annotation were excluded. Normalization and differential expression analysis were conducted using the

DESeq2 package in R. Raw counts were transformed using the variance stabilizing transformation (VST) for

downstream analysis and visualization. Gene expression patterns, heatmaps were generated using

the tidyverse and ggplot2 R package. Principal component analysis (PCA) was used to assess sample

clustering and variance across conditions. Gene symbols were added via the addIDs() function. All rows

without a corresponding gene symbol were removed. All genes with total counts less than the number of

samples were removed from subsequent analysis. Samples were processed and analyzed using DESeq2.

Gene expression was normalized using variance stabilizing transformation, and batches were corrected using

limma::removeBatchEffect. Volcano plots were generated using the Enhanced Volcano package and PCA was

performed with PCAtools in R. Gene set enrichment analysis was conducted with Clusterprofiler with GO and

KEGG biological process gene set. Differential gene expression analysis was performed using DESeq2 in R.

Counts were normalized using DESeq2’s median-of-ratios method, and differentially expressed genes were

identified based on an adjusted p-value < 0.05. Data visualization, including principal component analysis

(PCA) and heatmaps, was conducted using the ggplot2 package. All plots were generated in R (version 4.2.0).

Gene Set Enrichment Analysis (GSEA) was performed using the pre-ranked method in the

GSEA_ClusterProfiler2.R and GSEA_preranked_list.R, querying the MsigDB collections. Enriched pathways

and gene sets were considered significant at a false discovery rate (FDR) of < 0.05.The whole pipeline,

including code and parameters, is available upon request. For, Venn diagram building, Jvenn software were

used

PSA ELISA:

iPROS culture medium was collected at indicated time points and centrifuge at 1,000 x g for 10

minutes to remove any cellular debris. Store the supernatant at -80°C until further analysis. We used Human

PSA (Total)/KLK3 ELISA kit from Thermo-Fisher, and prepared reagents according to the manufacturer’s

instructions, including PSA standards, wash buffer and detection reagent. Add 50 µL of each standard and 50

µL of the sample supernatant to the corresponding wells in the ELISA plate. Include blanks and controls as

needed. The plate was incubated at room temperature (RT) for 2-3 hours to allow antigen-antibody binding.

Wash the plate three times with wash buffer to remove unbound substances. Add 100 µL of enzyme-

conjugated detection antibody added to each well and incubate for 1 hour at RT. After washing, substrate

solution was added and incubated for 15 minutes. Finally, we added the stop solution as recommended.

Absorbance was measured at 450 nm using a microplate reader, constructed a standard curve using the

known PSA standards, and calculated the concentration of PSA in the samples. Samples were normalized

based on total protein concentration using a BCA assay and represented as PSA release per µg of protein.

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Lactate Dehydrogenase (LDH) Cytotoxicity Assay:

Cell membrane integrity and cytotoxicity were assessed

using a LDH release assay, which was performed using the CyQUANT-LDH Cytotoxicity Assay

Kit (ThermoFisher, USA.) according to the manufacturer’s instructions. Briefly, ~200 iPROS were embedded in

matrigel and allowed to adhere for 2 days before compound treatment. Following treatment with test

compounds at indicated concentrations and time points, 25 µL of the culture supernatant from each well was

transferred to a new flat-bottom 96-well plate. An equal volume (25 µL) of LDH reaction mixture was added to

each well and incubated at room temperature in the dark for 30 minutes. The enzymatic reaction converts

lactate to pyruvate, resulting in the reduction of a tetrazolium salt to a red formazan product. The absorbance

was measured at 490 nm, with a reference wavelength of 680 nm, using a microplate reader. Cytotoxicity (%)

was calculated using the following formula:

Cytotoxicity (%)=[(Experimental LDH release−Spontaneous LDH release)/(Maximum LDH release−Spontaneo

us LDH release)]×100.

Spontaneous LDH release is when cells/organoids are incubated with medium only, and Maximum LDH

release is obtained when iPROS were treated with lysis buffer provided in the kit. All experiments were

performed in triplicate and repeated at least three times independently. Data are expressed as mean ± SEM.

Statistical Analysis:

Statistical analyses were conducted using Prism software (GraphPad Software, La Jolla,

California). Quantitative data are presented as mean values ± Standard Error of the Mean (SEM) and analyzed

using student t-test with Welch correction or with one-way or 2-way ANOVA (for unequal variances) with

multiple pair comparisons; analysis was done across three biological replicates, if not it is otherwise mentioned

in figure legends. Statistical significance was determined with *p

≤

0.05, **p

≤

0.01, ***p

≤

0.001, and ****p

≤

0.0001.

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SUPPLEMENTRAY FIGURE LEGENDS

Figure S1:

(A) Evos-Brightfield images showing the stages of iPROS differentiation. (B) Represented

immunofluorescence images showing prostate tissue-specific makers in iPROS: AR, NKX3.1, CK8/18, p63,

PSA, and PSMA with DAPI counterstain, insets are separately shown in Fig.1D. (C) Heatmaps showing the

TPM normalized expression of genes in iPROS. Genelist generated from Human prostate epithelial (Luminal,

Basal, and NE) and stromal cell-specific transcripts. The top 100 highly expressed genes were selected from

each cell type for comparison.

Figure S2:

(A) H&E staining of prostatectomy sections from 3 patients; full slide image, insets are separately

shown in Fig.2A. (B) Representative images showing the Sox2, Oct4, and SSE4 immunostaining in CON_ and

MUT_BRCA2 iPSCs. (C-D) Relative gene expression profiles (qPCR) of iPSC-specific genes; OCT4 and

SOX2 in CON_ and MUT_BRCA2 iPSCs. (E-F) Relative gene expression profiles (qPCR) of both BRCA2 and

BRCA1 in CON_ and MUT_BRCA2 iPSCs. (G-H) Ki67 positive proliferating nuclei analysis in CON_ and

MUT_BRCA2 iPSCs, representative images, and quantitation. (I) Represented IF images showing the F-actin

organization, localization of apical (CK8/18), and basal (p63) polarity markers in CON_ and MUT_BRCA2

iPROS. Scale bars: 100 mM (A, F) and 40 mM (J). (n = 3 different biological replicates). Significance

calculated with pairwise comparison (t-test; Welch correction); ns= p>0.05, *p < 0.05.

Figure S3:

(A) PCA analysis and batch correction showing the variance among samples (explained in the

result).

(B) Normalized expression of prostate- and PCa-specific genes. (C) GSEA ridge map with significant DEGs

between CON_ and MUT_BRCA2 iPROS samples. (D) GSEA barcode plots with significant DEGs between

CON_ and MUT_BRCA2 iPROS samples. (E) Pie chart showing gene commonality/overlapping in

MUT_BRCA2 with four different MSigdb PCa datasets on significant DEGs between MUT_ vs. CON_BRCA2

iPROS samples.

Figure S4:

(A) Bar-plot showing the cytotoxicity of CON_ and MUT_BRCA2 iPROS after 3 weeks of

incubations with PhIP and MNU. (B-C) Histograms showing the flow cytometric analysis of

H2AX and Ki67 in

PhIP and MNU treated CON_ and MUT_BRCA2 iPROS. (D-E) Boxplots showing the cytotoxicity and Bar-plot

showing the proliferation of CON_ and MUT_BRCA2 iPROS at 4

week following 2 weeks after PhIP and MNU

treatment withdrawal and addition of Enzalutamide for 2wks., by LDH cytotoxic assay, and mean-fluorescence

obtained by flow cytometry. (F) LDH cytotoxic assay showing the cytotoxicity of CON_ and MUT_BRCA2

iPROS at 4

week following 2 weeks after PhIP and MNU treatment withdrawal and addition of Olaparib for 2

weeks. (G) Flow cytometric analysis of

H2AX for detecting DNA damage in iPROS with and without Olaparib

treatment at 4

week. (H) Representative H&E staining of whole mount of subcutaneous and renal capsule

tumor sections obtained from positive control and MUT_BRCA2 iPROS. Boxed sections are separately shown

in Fig.4K (I) Representative IF staining of whole mount of kidney with PSMA and ERG, counterstained with

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Bardou, P., Mariette, J., Escudie, F., Djemiel, C., and Klopp, C. (2014). jvenn: an interactive Venn
diagram viewer. BMC Bioinformatics 15, 293. 10.1186/1471-2105-15-293.

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this version posted August 31, 2025.

bioRxiv preprint

Figure 1: Acharya et. al.

PBMC

iPSC

Day 4

Day 8

HGE

Day 60

iPROS

Day 0

Day 15

Activin A

FBS

FGF4

WNT3A

FGF10

WNT10A

FGF10

FGF7

SAG
DHT

iPROS

Media

(A)

(B)

(C)

(D)

(E)

(F)

(G)

(H)

Prostate Specific Genes

(The Human Protein Atlas)

(I)

80% similarity

~70% similarity

iPSC

iPROS-60 days

4x 10x

NKX3

-1

CK8/18

PSMA

PSA

p63

Prostate Epithelia

NKX3-1

CK8/18

p63

CgA

Prostate Specific Genes

(normalised expression)

GTEx Geneset

iPROS

(

)

The copyright holder for this preprint

this version posted August 31, 2025.

bioRxiv preprint

Figure 2: Acharya et. al.

(A)

AAAA

G-GG

AAAA

MUT1_BRCA4i-iPSC

MUT1_BRCA4i-iPROS

AAAA

MUT2_BRCA2A-iPSC

MUT2_BRCA2A-iPROS

TTT

----

MUT2_BRCA3i-iPSC

MUT2_BRCA3i-iPROS

(B)

(C)

(D)

(E)

(F)

(G)

(H)

(I)

(J)

iPROS

CON_BRCA2

iPROS

MUT_BRCA2

Ki67

DAPI

Vimentin

DAPI

iPROS

CON_BRCA2

iPROS

MUT_BRCA2

SMA

DAPI

iPROS

CON_BRCA2

iPROS

MUT_BRCA2

(K)

(L)

iPROS

CON_BRCA2

iPROS

MUT_BRCA2

MUT1_BRCA4i

5946delT, GG2, pT2

MUT2_BRCA2A

5946delT, GG5, T2

MUT3_BRCA3i

c.9513_9516del

GG5, T3b1M1

iPROS

CON_BRCA2

PSA

iPROS

MUT_BRCA2

DAPI

PSA

F-actin

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this version posted August 31, 2025.

bioRxiv preprint

GO-Enriched Pathways

KEGG-Enriched Pathways

Figure 3: Acharya et. al.

Log2 (fold change)

-Log10 (adjusted p-value)

(A)

(B)

(C)

(D)

(E)

(F)

CON1

CON2

CON3

MUT1_BRCA4i

MUT2_BRCA2A

MUT3_BRCA3i

CON1

CON2

CON3

MUT1_BRCA4i

MUT2_BRCA2A

MUT3_BRCA3i

CON1

CON2

CON3

MUT1_BRCA4i

MUT2_BRCA2A

MUT3_BRCA3i

(I)

(J)

(H)

(L)

DEG = 862

DEG = 1362

DEG = 104

(K)

Rank: p-adjust

Hallmark50-Enriched Pathways

(G)

GG2-pT2: 33 gene matched

DOWN-DEG

TCGA_PRAD vs. CON.

hypergeometric p-value = 0.2436368

DEG

MUT2_BRCA4i vs. CON.

UP-DEG

TCGA_PRAD vs. CON.

1661 0 2258

GG5-T2: 263 gene matched

DOWN-DEG

TCGA_PRAD vs. CON.

hypergeometric p-value = 1.045108e-16

DEG

MUT2_BRCA2A vs. CON.

UP-DEG

TCGA_PRAD vs. CON.

1615 0 2093

191

599

GG5-T3bN1M1: 413 gene matched

DOWN-DEG

TCGA_PRAD vs. CON.

hypergeometric p-value = 2.582221e-25

DEG

MUT2_BRCA3i vs. CON.

UP-DEG

TCGA_PRAD vs. CON.

1585 0 1973

102 311

909

Cell Lines

MUT2_BRCA2A
MUT3_BRCA3i
MUT1_BRCA4i
CON1
CON2
CON3

PC4, 7.61% variation

PC1, 33.28% variation

variation

MUT_BRCA2

CON_BRCA2

MUT_BRCA2

CON_BRCA2

MUT_BRCA2

CON_BRCA2

MUT_BRCA2

CON_BRCA2

MUT_BRCA2

CON_BRCA2

MUT_BRCA2

CON_BRCA2

MUT_BRCA2

CON_BRCA2

***

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this version posted August 31, 2025.

bioRxiv preprint

Figure S3: Acharya et. al.

(A)

(B)

(C)

(D)

(E)

MUT_BRCA2

CON_BRCA2

MUT_BRCA2

CON_BRCA2

MUT_BRCA2

CON_BRCA2

MUT_BRCA2

CON_BRCA2

Before Batch Correction

Batch Effect

After Batch Correction

Rank

ed List Metr

FHOOïFHOOMXQFWLRQ

3RVLWLRQLQWKH5DQNHG/LVWRI*HQHV

5XQQLQJ(QU

LFKPHQW6FRUH

Rank

ed List Metr

FHOOSHULSKHU\

3RVLWLRQLQWKH5DQNHG/LVWRI*HQHV

5XQQLQJ(QU

LFKPHQW6FRUH

Rank

ed List Metr

LPPXQHUHFHSWRUDFWLYLW\

3RVLWLRQLQWKH5DQNHG/LVWRI*HQHV

5XQQLQJ(QU

LFKPHQW6FRUH

Rank

ed List Metr

OLSLGPHWDEROLFSURFHVV

3RVLWLRQLQWKH5DQNHG/LVWRI*HQHV

5XQQLQJ(QU

LFKPHQW6FRUH

NES: -0.5761558
padj: 0.0033255736614566

NES: 0.374764660085977
padj: 0.00149476831091181

NES: 0.813369726983327
padj: 0.00174852964552535

NES: 0.454041311466977
padj: 0.00415851272015656

Prost. Cancer_msigdb Genesets

36(67)

55(100)

100(305)

21(41)

MUT_BRCA2

CON_BRCA2

MUT_BRCA2

CON_BRCA2

MUT_BRCA2

CON_BRCA2

***

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this version posted August 31, 2025.