FOXA1 loss drives basal/squamous de-differentiation of prostate cancer and induces an immunosuppressive tumor microenvironment

Domain: biology
Published: 2026-03-28
Editorial outcome: major_revision

Abstract
FOXA1 is a prostate lineage-specifying transcription factor that is frequently dysregulated or
mutated in prostate cancer (PCa). While FOXA1 has been reported to exhibit both PCa-
promoting and -inhibitory functions, its role within an immune-proficient PCa context remains
unclear. Here, we show that prostate-specific deletion of Foxa1 in Pten-deficient mice drives
tumor progression by reprogramming luminal PCa cells toward a basal/squamous-like state and
promoting an immunosuppressive tumor microenvironment. Histological and transcriptomic
analyses reveal aggressive tumors with extensive basal/squamous features, a reactive stroma,
and disorganized tissue architecture. Mechanistically, FOXA1 directly represses
basal/squamous and inflammatory genes, which become activated upon its depletion. This is
accompanied by an accumulation of immunosuppressive myeloid cells, dysfunctional T cells,
and immunosuppressive cytokine signaling. Together, these findings demonstrate a tumor-
suppressive role for FOXA1 as an enforcer of luminal identity, such that its loss drives
basal/squamous de-differentiation, inflammatory response, and immunosuppression.

Introduction
Forkhead box A1 (FOXA1) is a master regulator of utmost importance in prostate cancer (PCa)
progression and is one of the most frequently mutated genes in PCa1-4. It is a pioneering factor
which critically recruits the androgen receptor (AR) to lineage-specific enhancers to induce
prostatic gene expression5-7. FOXA1 is initially upregulated in primary prostate cancer (PCa) vs.
benign prostate, but is ultimately downregulated in late-stage castration-resistant PCa (CRPC),
wherein AR, in contrast, is amplified and overexpressed8-12. In this low milieu of FOXA1, AR has
been shown to become unregulated by FOXA1 and thus gain broad binding events, some of
which are at oncogenic sites6. In addition, FOXA1 loss further induces epithelial-to-mesenchymal
transition (EMT) and neuroendocrine differentiation, at least in part, via upregulating cytokines,
such as TGFB3 and IL89-11,13. Accordingly, FOXA 1 has shown context-dependent anti- or pro-
tumor roles as its depletion inhibits growth in hormone-sensitive PCa3,10 but induces aggressive
phenotypes in CRPC6,10. Moreover, FOXA1 harbors a wide spectrum of mutations that also show
distinct functional consequences, including pro- or anti-luminal differentiation and pro- or anti-
tumor progression2,3. Recently, FOXA1 loss was shown to induce macrophage infiltration in an in
vitro co-culture system under hypoxic conditions mimicking the tumor environment14. In contrast,
using cell culture and subcutaneous allograft models treated with IFN inducers, such as poly(I:C),
He et al. reported increased IFN response and T cell infiltration in FOXA1-knockdown cells15.
However, a comprehensive analysis of FOXA1 in an immune-competent and autochthonous PCa
setting is still lacking.

In recent years, many previously hard-to-treat or treatment-resistant cancer types,
including melanoma and lung cancers, have benefited greatly from immunotherapies, such as
immune checkpoint inhibitors (ICIs). Unfortunately, PCa patients have shown an overall poor
response to ICIs due to the immune-cold nature of the disease16-19. Recent studies have
demonstrated that PCa is highly infiltrated by T regulatory cells (Tregs), myeloid-derived
suppressor cells (MDSCs), and tumor-associated macrophages (TAMs) that promote
immunosuppression and enable tumor progression20,21. Moreover, metastatic CRPC, which has
increased lineage plasticity, has been shown to exhibit a particularly immune-suppressive tumor
immune microenvironment (TIME), with increased TGF-β signaling and increased abundance of
T helper 17 (Th17) cells, exhausted cytotoxic T lymphocytes (CTLs), and tumor-promoting
myeloid populations, such as M2-macrophages and inflammatory monocytes22-25.

Emerging evidence suggests that certain genetic and epigenetic traits of PCa critically
regulate the composition of tumor immune infiltrates in its TIME26-30. For example, several studies
have explored the effects of PTEN loss, a frequently altered tumor suppressor gene that is
inactivated in ~20-30% of primary PCa and up to ~60% of CRPC12,31,32. Notably, prostate-specific
knockout (KO) of Pten in mice, which leads to PCa initiation, induced the expression of
immunosuppressive cytokines CXCL1, CXCL2, IL1 0 and IL6 via JAK/STAT3 signaling, leading to
increased prostate tumor infiltration by MDSCs and decreased accumulation of T cells26,33,34.
Moreover, other genetic aberrations, such as Trp53 KO, Arid1a KO, Zbtb7a KO, and Pml KO,
have also been shown to alter the TIME of PCa26,35. However, it remains unclear whether there
are prostate lineage-defining factors that uniquely predispose PCa to an immunosuppressive
TIME.

Here, we generated a genetically engineered mouse model with prostate-specific deletion
of Foxa1 in the Pten-null mice. Combining mouse histology with single-cell and spatial
transcriptomics and Cytek spectral flow analysis, we report that Foxa1 loss induces luminal-to-
basal/squamous lineage switch and remodels the TIME in PCa, promoting tumor infiltration by
immunosuppressive myeloid cells and dysfunctional T cells, which was validated in human PCa.
We present FOXA1 as a master regulator of luminal cell lineage and the PCa immune landscape.

Results
Depletion of Foxa1 in PCa epithelial cells leads to basal/squamous de-differentiation, a
loss of prostate tissue architecture, and a reactive stroma

Prostate-specific deletion of Foxa1 in the PbCre4:Foxa1f/f mouse (denoted as F mouse),
which places the Cre recombinase gene under the control of the Pbsn gene promoter and directs
Cre expression to prostate epithelial cells36, has been shown to induce prostatic hyperplasia, but
not tumorigenesis37. To examine FOXA1 function in the context of PCa, we crossed the F mouse
with the Pten-null transgenic mouse (P mouse), a well-established model that develops PCa,
representing a large number of human PCa with PTEN loss32, and generated the
PbCre4:Ptenf/fFoxa1f/f (PF mouse) model with prostate-specific deletion of both Pten and Foxa1
genes. As mice are known to naturally exhibit low androgen levels, closer to those observed in
patients undergoing standard androgen deprivation therapy (ADT)38, the PF tumors arise in an
androgen-low environment that parallels CRPC, a setting in which FOXA1 is commonly
downregulated8-11, often co-occurring with PTEN loss (Figure S1A). Genotyping PF mice using
allele-specific primers confirmed Foxa1 and Pten deletions, whereas immunohistochemistry (IHC)
staining validated the depletion of PTEN and/or FOXA1 proteins in the respective mice, compared
to the PbCre4-negative wild type (WT) mice (Figure 1A-B).

To understand the tumorigenic effects of the genetic deletions in P vs. PF mice, we first
examined the prostates of 15-week (wk)-old mice by hematoxylin and eosin (H&E) staining
(Figure 1C). Pten-null prostates exhibited cribriform morphology and intraductal carcinoma, being
consistent with previous reports32,37,39. Notably, concurrent deletion of Foxa1 in the context of Pten
KO in the PF mice led to dramatic alterations to all lobes (AP, DLP, and VP) of the prostate
(Figure 1C & S1B). Compared to the typical glandular architecture of PCa observed in the P
mice, the tumors of the PF mice exhibited remarkably disordered cell arrangements with a loss of
glandular architecture, suggesting more aggressive behavior. Even as early as 9 wks of age, the
PF prostates exhibited a largely disorganized and aggressive phenotype compared to P mice
(Figure S1C). Further evaluating PF mice across different timepoints, we observed that PF
prostate tumors exhibited abundant evidence of PCa invasion, necrosis, and a reactive stroma,
characterized by extracellular matrix remodeling and inflammation40 (Figure 1D & S1C).

Interestingly, PF tumors also exhibited a massive gain in adeno-squamous differentiation
with keratinization, displaying large keratin pools (Figure 1D & S1D), suggesting that Foxa1
deletion in the prostate leads to epithelial cell de-differentiation and induces lineage plasticity.
Indeed, IHC staining of 18-wk-old prostates revealed a widespread gain in the expression of basal
marker p63 in tumor epithelial cells in the PF mice compared to the P mice, accompanied by
partial loss of luminal marker AR and KRT8 (Figure 1E). Notably, PF prostate epithelial cells also
exhibited increased staining for the proliferation marker Ki67, suggesting increased proliferation
with Foxa1 loss. Lastly, in line with our findings of poorly differentiated and highly proliferative
tumors in the PF prostates, we observed that the survival of the PF mice was greatly shortened
as compared to P mice (Figure 1F). Further investigation of the anatomy of the PF mice revealed
that they likely died from bladder outlet obstruction, a common complication of locally advanced
prostate cancer also found in humans41.

To further investigate how Foxa1 loss influences tumor progression and phenotype under
a more advanced, profoundly androgen-depleted CRPC-like context, P and PF mice were
castrated at 12 - 18 wks old and harvested 3 - 12 months later. Castrated P and PF mice are
referred to as XP and XPF, moving forward. H&E and IHC staining for Ki67 in XP and XPF
prostate tissues demonstrated reduced epithelial cell growth and proliferation in XP mice as
compared to XPF, as seen in intact mice. Meanwhile, XPF prostate tissues exhibited a disordered
and poorly differentiated tumor phenotype (Figure 1G & H, Figure S1E), with a loss of luminal
identity (indicated by reduced AR and KRT8 staining), and widespread gain in basal phenotype
(indicated by p63 staining). Moreover, XPF tumors were found to be significantly larger than XP
tumors post castration, suggesting some survival advantage of Foxa1 loss (Figure S1F).
Histopathological evaluation of tissue sections from lymph nodes, lung, and/or liver of 9 intact PF
mice, 7 intact P mice, and 5 castrated XP mice revealed no evidence of metastasis, while 2 out
of 5 evaluated XPF mice exhibited evidence of metastasis at distant organs (Figure S1G). Thus,
it appears Foxa1 loss promotes aggressive castration-resistant disease in our GEMMs.
Altogether, our histopathological analysis of the P and PF mice tumors found that Foxa1 loss led
to luminal cell de-differentiation, a loss of prostate glandular architecture, and aggressive
epithelial growth patterns, which are further exacerbated by castration.

Foxa1 loss in PCa cells induces lineage plasticity and a basal/squamous cell population

To understand the molecular basis underlying the marked histological changes of Foxa1-
depleted PCa, we performed 10x Genomics scRNA-seq analysis of whole prostates dissected
from the P and PF mice at an early 12wk (P12 vs. PF12) and a late 18wk (P18 vs. PF18)
timepoints, as well as mice castrated at 18wk and harvested 12wk post castration (XP vs. XPF).
Clustering of all single cells using the Louvain algorithm and Uniform Manifold Approximation and
Projection (UMAP) visualization resulted in 8 major cell type clusters: Krt8-high luminal-like
epithelial cells, Krt5-high basal-like epithelial cells, stromal cells, monocytes, macrophages, B
cells, T/NK cells, and Pate4-high cells of seminal vesicles that are likely due to their incomplete
separation from the prostates (Figure 2A). Of these, Epcam+ prostate epithelial cells, including
luminal-like and basal-like clusters, comprised the majority of cells of the prostate (Figure 2A-B).
Within the luminal-like cluster, we further identified four subclusters, consisting of Wfdc2-high,
Pbsn-high, Spink1-high, and Tff3-high populations, whereas there were three Trp63-high (basal),
Mki67-high (proliferative), or Krt6a-high (squamous) subclusters within the basal-like cluster
(Figure S2A-D).

A comparison of cells derived from the P vs PF mice revealed a strong increase of basal-
like epithelial cells in the PF prostates, which was also accompanied by an increase in immune
cells (especially myeloid cells) (Figure 2C-D). Visualization of gene expression intensity on the
UMAP confirmed a drastic increase of Krt5 (basal) and Krt6a (squamous) expression in epithelial
cells derived from PF12 and PF18 prostates compared to P12 and P18, while the expression of
luminal marker Krt8 was not substantially affected (Figure 2E). XPF prostate epithelial cells also
exhibited a dramatic increase in basal/squamous phenotype (Figure 2D & S2E ).To further
validate this, we quantified the major luminal (Foxa1, Ar, Krt8, Krt18, Cd24a), basal (Trp63, Krt5,
Krt14, Lgals7), and squamous (Krt6a, Krt13, Krt16) marker gene expression within all epithelial
cells of each mouse. Our data showed that basal/squamous gene expression was markedly
upregulated by Foxa1 KO across both intact and castrated conditions, while luminal genes were
only slightly reduced in PF compared to P prostate epithelial cells (Figure 2F). Additionally, we
found increased expression of EMT genes (Vim, Snai2, Twist1) upon Foxa1 deletion, especially
under castrated conditions, suggesting increased invasive potential (Figure S2F).

To further confirm that Foxa1 loss induces basal/squamous de-differentiation, we
performed trajectory analyses of epithelial cells within all samples and created a joint pseudotime
(Figure S2G). Plotting key luminal (Krt8), squamous (Krt6a), and basal (Krt14, Krt5, Trp63) genes
along the joint pseudotime, we observed marked differences in their expression across the
luminal-to-basal trajectory in P vs. PF samples (Figure 2G). For instance, PF18 tumors appear
to exhibit a much earlier gain in basal gene expression along the trajectory, with a lot more cells
within the “early” luminal populations expressing basal markers. PF tumors also harbor a more
prominent transitionary (basal/squamous) population and have a major increase of “final” basal
population, as compared to P18 tumors. A very similar pattern was also observed for PF12 vs.
P12 and XP vs. XPF epithelial cells on trajectory analyses (Figure S2H-I). Altogether, these
results suggest that FOXA1 is required to suppress basal/squamous and EMT gene expression
in PCa cells and that Foxa1 loss leads to lineage plasticity.

FOXA1 binds the chromatin to directly induce luminal genes, but repress basal/squamous
and inflammatory genes

To gain further insights into the dysregulated signaling pathways underlying this observed
basal/squamous de-differentiation in PF vs. P prostates, we further analyzed the scRNA-seq data
solely focusing on prostatic epithelial cells. Using a two-fold cutoff, we identified 676 and 587
genes that are respectively up- and down-regulated in the epithelial cells of PF12 vs. P12 mice
and 635 and 786 genes that are respectively up- and down-regulated in the epithelial cells of
PF18 vs. P18 mice. Gene Ontology (GO) analyses42 of Hallmark gene sets revealed that PF12-
and PF18-upregulated genes were strongly enriched for inflammatory pathways, cytokine
signaling, EMT, and hypoxia (Figure 3A), being consistent with the observed highly reactive
stroma in the PF prostates and previous reports of FOXA1 in suppressing EMT and hypoxia in
human PCa10,13,14. On the other hand, genes downregulated in the epithelial cells of PF prostates
were enriched for hormonal response, fatty acid metabolism, and adipogenesis, concordant with
the role of FOXA1 as a pioneer factor of AR, which regulates androgen response and various
aspects of cellular metabolism6,7 (Figure 3B). In agreement, GO analyses of Biological Processes
(BP) revealed that PF12- and PF18-upregulated genes were highly enriched for BP pathways
related to Skin Development, Keratinocyte Differentiation, and Cell/Tissue Migration, whereas
PF12/PF18-downregulated genes significantly overlap with genes involved in various metabolic
pathways (Figure S3A-B).

As FOXA1 is a transcription factor that directly binds to DNA through consensus FKHD
motif, we next attempted to address whether FOXA1 directly regulates genes of the above noted
pathways. We performed FOXA1 ChIP-seq in three biological replicates of whole prostates from
age-matched 18-wk-old P and PF mice. ChIP-seq analysis identified a total of 10,541 consensus
FOXA1 binding peaks on the chromatin. Motif analysis confirmed FKHD as the most highly
enriched motif at FOXA1 binding sites. As expected, almost all of the FOXA1 binding events were
lost in the PF mice (Figure 3C-D). Next, to further understand the function of the genes that are
bound by FOXA1, we assigned each FOXA1 binding event to the nearest gene and performed
GO analysis. We found significant enrichment of androgen response genes among FOXA1-bound
genes (Figure 3E), in line with the well-established role of FOXA1 as an AR co-factor in gene
activation5. Notably, multiple pathways related to cytokine signaling and inflammation, such as
Inflammatory Response, TNFα signaling, and IL2-STAT5 Signaling, were also strongly enriched
for FOXA1 binding. Together with RNA-seq data showing upregulated expression of these
pathway genes in PF epithelial cells (Figure 3A), this data suggests that they may be directly
repressed by FOXA1. Indeed, genome browser track view of ChIP-seq data demonstrated
FOXA1 binding at enhancers proximal to luminal genes (Krt8, Krt18) in the P mice, which was
abolished in the PF mice, accompanied by a reduction of H3K27Sac at the target gene promoters
(Figure 3F & S3C), suggesting their being FOXA1-induced genes. Importantly, strong FOXA1
binding events were also detected at regulatory elements near basal/squamous genes (Krt6a,
Trp63) and inflammatory genes (Hif1a, Tnfaip2, Ifitm2/3/6) in the P mice, which was also lost in
the PF mice (Figure 3G-H & S3D-E). However, H3K27ac at the promoters or enhancers of these
genes were increased, supporting their being FOXA1-repressed genes. This data supports that
FOXA1 directly binds luminal gene enhancers to promote their expression, while it binds at
basal/squamous and inflammatory genes to repress their expression.

To further validate this in human PCa, we evaluated previously published gene expression
microarray analysis of human PCa LNCaP cells with FOXA1 KO and rescue with WT or mutant
FOXA1 (G87R, D226N, M253K, 254-257FENG>C(del), F266S, L388M)13. Interestingly, we found
that FOXA1 KO leads to an increase in KRT14 and KRT6A expression. Rescue with WT FOXA1
subsequently repressed KRT14 and KRT6A, while rescue with FOXA1 forkhead domain mutants
(226, 253, 254-7FENG, and 266), which have been shown to exhibit impaired chromatin binding,
had reduced ability to repress KRT14 and KRT6A expression (Figure S3F). In accordance,
evaluation of previously published FOXA1 ChIP-seq data in LNCaP control vs. FOXA1 KD cells
uncovered FOXA1 binding peaks near basal/squamous lineage genes such as KRT14 and
KRT6A/C (GSE55007)6 (Figure S3G). A similar trend was observed for inflammatory gene HIF1A
(Figure S3F-G), as previously published14. Taken together, these findings support our mouse
data showing FOXA1 as a transcriptional repressor of squamous and inflammatory gene
expression in human PCa.

Foxa1 loss remodels the tumor immune microenvironment, leading to tumor infiltration by
immunosuppressive myeloid cells and dysfunctional T cells

Our scRNA-seq analyses have revealed that, in addition to basal/squamous cells, immune
cells were also increased in the prostates of intact PF vs. P mice (Figure 2D). Considering that
castration itself has profound effects on tumor-immune infiltration and composition43-45, we
decided to focus our analyses on the TIME of intact P vs PF mice. To delve deeper into how
Foxa1 loss affects the TIME, we isolated immune cells (CD45+) from the whole prostates of P15
and PF15 mice, a middle stage between P12 and P18 mice used for 10x scRNA-seq above, for
PIP-seq scRNA-seq analysis. UMAP analyses and immune cell marker annotation identified
several major immune populations, including Monocytes, Macrophages, Dendritic Cells (DC), B
cells, CD8 T cells, CD4 T cells, and NK cells in the TIME of PCa (Figure 4A-B). Of note,
monocytes were poorly captured by PIP-seq as compared to 10x Genomics scRNA-seq, and
there were more total immune cells captured from the PF relative to P mouse prostates (Figure
S4A). Next, we focused on the CD8 and CD4 T cell populations and identified four CD8 T cell
subclusters, including cycling CD8 T cells with high expression of cell cycle genes and three other
cytotoxic T lymphocyte populations (CTL1, CTL2, and CTL3), and three major subclusters within
the CD4 population, including Foxp3+ Tregs, Ccr7+ naïve/memory T helper, and Rorc+ Th17
cells (Figure S4B-C). Interestingly, we found that PF prostates contained a greater proportion of
CD4 T cells, as compared to CD8 T cells, a large percentage of which were of the
immunosuppressive Treg or Th17 phenotype (Figure 4C) that have been associated with
impaired anti-tumor immune response in PCa21-23. Moreover, the CD8 T cell population in PF
prostate tumors showed a decreased cytotoxic signature gene expression compared to P (Figure
4D). On the other hand, we observed an increase in immunosuppressive M2 signature gene
expression, along with a slight decrease in M1 anti-tumor signature gene expression, in tumor-
associated macrophages (TAMs) of PF compared to P mice (Figure 4E). In aggregates, these
data suggest that Foxa1 loss in PCa epithelial cells leads to T-cell dysfunction and promotes M2
polarization of TAMs.

To validate these results and further evaluate tumor immune infiltrates at protein levels,
we performed multiparameter FACS staining and Cytek spectral flow analysis of whole prostates
from several P (n=6) and PF (n=7) mice between the ages of 18 – 20 weeks (Figure S4D). We
observed substantially increased monocytic myeloid-derived suppressor cell (M-MDSC) and
polymorphonuclear MDSC (PMN-MDSC) populations as a percentage of total CD45+ immune
cells in PF compared to P tumors (Figure 4F). Further, we noticed statistically significant
increases in PD-L1 expression by M-MDSCs and PMN-MDSCs, along with DCs and TAMs, in PF
tumors, suggesting that Foxa1 loss drives myeloid populations to a more immunosuppressive
state (Figure 4G). Moreover, TAMs of PF tumors were found to exhibit higher expression of M2-
like marker CD163 and lower expression of M1-like marker CD80, as compared to P tumors
(Figure 4H), further supporting that Foxa1 loss promotes M2 polarization of TAMs. Next, we
evaluated CD8 T cell phenotypes and found that PF tumors were more highly infiltrated by
terminally exhausted T cells (TOX+/PD1+ and CD101+/PD1+ subsets), while P tumors have more
progenitor exhausted CD8 T cells (CD69+/Ly108+ and PD1+/Ly108+ subsets)46, suggesting that
Foxa1 loss promotes terminal exhaustion of CD8 T cells within the PCa TIME (Figure 4I).
Accordingly, there was a decrease of TNFα+/IFNγ+ effector CD8 T cells upon Foxa1 loss in their
tumors, although not significant (Figure 4J). Altogether, our data indicate immunosuppressive
myeloid cell enrichment and a weakened CD8 T cell response or dysfunction in Foxa1-depleted
PCa.

Spatial Transcriptomics analyses revealed massive myeloid infiltration but constrained T-
cell activities in Foxa1-depleted PCa

To understand the spatial organization of PCa and immune cells, we performed 10x Genomics
Visium Spatial Transcriptomics (ST) analysis of prostate tissue sections derived from age-
matched 12wk- and 18wk-old PF and P mice. First, ST data confirmed depleted Foxa1 expression
across the entire prostate section of the PF18 mouse (Figure S5A). We next utilized the TESLA
machine-learning framework47, which integrates spatial gene expression with tissue histology
information to provide pixel-level resolution for annotating and characterizing different cell types
within the TIME. We found that most cells in the P prostates were called luminal cells by the
program, with minimal basal cells identified, as expected for prostate tissues (Figure 5A). In line
with our histopathology analyses, P prostate tumors appear to maintain their glandular
architecture, with tumor epithelial cell regions surrounded by mesenchymal stromal cells (Figure
5A & S5B-C). Critically, PF18 tumors exhibited a striking increase in basal-like cell density
compared to P18 tumors, whereas luminal cells remained largely the same across genotypes,
because many cells that were identified as basal cells in the PF tumors continued to express
luminal genes and were simultaneously called luminal cells by TESLA, representing a mixed
basal-luminal lineage. Interestingly, TESLA analysis for mesenchymal populations in PF tumors
provided further evidence of lineage plasticity, with broad regions of tumor epithelial cells
identified to express mesenchymal markers (Figure 5A & S5C). Altogether, our ST data
demonstrated that Foxa1 loss leads to lineage plasticity and luminal-to-basal de-differentiation of
PCa cells, being consistent with histology and scRNA-seq data shown earlier.

Next, we sought to evaluate the spatial distribution of immune cells in the TIME of PCa.
Using TESLA to call immune cell densities across PF18 and P18 tissue sections, we identified
striking increases in the accumulation of both myeloid and lymphoid cells upon Foxa1 loss (Figure
5B). Specifically, the abundance of TAMs, as a percentage of the total captured area, increased
from 2.8% in P18 to 17.2% in PF18 PCa tissues. Similarly, monocytes increased from 12.8% in
P18 to 26.1% in PF18 samples. We also observed a clear increase in T cell accumulation upon
Foxa1 depletion, from 2.4% CD4+ T cells and 0.9% CD8+ T cells in P18 to 19.5% and 19.7% in
the PF tissue, respectively. However, we found that myeloid and T cell populations exhibited
distinct distribution patterns in the TIME of PF mice. While T cells largely localized to stromal
regions adjacent to tumor cell nests, macrophages and monocytes were more widely distributed
throughout and into the tumor regions (Figure 5C). Similar trends were also observed in the
prostates of the 12-week-old mice (Figure S5D-E). Moreover, these findings were confirmed by
IHC staining for F4/80+ macrophages, CD8+ T cells, and CD4+ T cells, which again showed that
T cell populations were largely restricted within intratumoral stromal regions whereas
macrophages were well infiltrated into tumor cell nests (Figure 5DS). This suggests that, although
Foxa1 loss induced both macrophages and T cells, it promotes an “excluded” phenotype of T
cells, which could have strongly limited T-cell anti-tumor activities. Finally, further phenotyping of
the major immune populations identified in our ST analysis confirmed that PF immune infiltrates
exhibit an immunosuppressive phenotype. For instance, the majority of macrophages in PF
tumors were found to express M2 markers (Figure 5E & S5F), and a larger proportion of CD8 T
cells expressed a lot more exhaustion markers compared to cytotoxic markers (Figure 5F & S5G).
Altogether, ST data confirmed luminal-to-basal de-differentiation of PCa cells and showed an
immunosuppressive TIME caused by massive myeloid infiltration in the tumors but spatially
constrained T-cell activities in the Foxa1-depleted PCa.

Foxa1 loss greatly enhanced immunosuppressive cytokine signaling, primarily from basal
cells to macrophages

We next sought to understand how Foxa1 deletion within epithelial cells leads to the
immunosuppressive phenotype observed in PF tumors. To study the communication between
cancer cells and immune populations in the TIME, we utilized the bioinformatics tool CellChat
48,49, which estimates ligand-receptor interactions between cell groups from scRNA-seq data
based on gene expression and literature on ligand-receptor pairs. We focused our search on the
“Secreted Signaling” subset of CellChat’s ligand-receptor database, considering the important
roles of cytokine signaling in regulating tumor immunity. First, comparing the number of secreted
signaling interactions among epithelial, immune, and stromal cells between PF and P prostates,
we observed a major increase in epithelial-to-immune/stroma interactions upon Foxa1 loss at
both 12-wk and 18-wk timepoints (Figure 6A). Further, by investigating individual secreted
signaling pathways in PF vs. P tumors, we found that a vast majority of these pathways have
increased interactions among cell types in PF tumors, some of which, such as PTN, KIT, and
IL6/1, are exclusive to PF tumors, indicating that they only mediate interactions between cell
groups in PF tumors (Figure 6B & S6A). To focus on the pathways through which FOXA1 might
regulate how epithelial cells interact with immune cells, we cross-referenced the PF-enriched
pathways with those originating from luminal and basal-like populations and identified three major
pathways of interest: TGF-β, PTN, and MIF (Figure 6C & S6B). Notably, signaling through these
pathways appears to be more strongly contributed by the basal-like populations than luminal,
suggesting a link between Foxa1-loss-induced basal phenotype and increased secreted
signaling.

To further decipher how epithelial and/or immune populations may be signaling with each
other through these pathways in Foxa1-deficient tumors, we determined their signaling networks
among major cell types, including luminal, basal, T cell, B cell, macrophage, monocyte, and
stromal populations in the PF mice. Critically, we observed dominant interactions originating from
basal cells to other cell types for all 3 pathways, with PTN signaling originating solely from basal
cells and sending signals to all other cell types, including macrophages and monocytes, and MIF
signaling originating from basal/luminal epithelial cells to mostly macrophages (Figure S6C),
being consistent with their reported roles in recruiting tumor-promoting neutrophils and
macrophages, respectively50,51. Importantly, TGF-β signaling stands out as a major pathway
through which Foxa1-depleted prostate epithelial cells, especially the basal-like population, sent
and also received signals from immune and stromal cell populations in the TIME, with dominant
signaling from basal to luminal, stroma, and macrophage cells (Figure 6D). Accordingly, TGF-β
ligands, including TGF-β1, 2, 3, are well-known immunosuppressive cytokines that prompt M2
polarization, Treg induction, and effector T cell inhibition52-55 and induce reactive stroma in PCa40.
Next, we evaluated the expression of TGF-β cytokines in our scRNA-seq data and found
that Foxa1 loss drastically induced the expression of Tgfb3, Tgfb2, and Tgfb1 in luminal and
basal-like cells of the prostate tumors (Figure 6E-F & S6D). Tgfb2 and Tgfb3 were mainly
expressed in epithelial cells and stroma, while Tgfb1 was most highly expressed in immune cells,
such as macrophages and monocytes, supporting TGF-β signaling originated from these various
cell types in CellChat analyses. As TGF-β cytokines signal through their receptors, we also
examined receptor expression and confirmed that both Tgfbr1/2 were expressed in almost all
cells of P and PF tumors, with immune cells and stroma having the highest expression (Figure
S6E), in agreement with their primary roles as receivers in cell-cell communication. To further
confirm this finding that TGF-β cytokine genes are induced upon Foxa1 loss, we evaluated the
ST analysis P18 vs PF18 tumor sections and observed a significant upregulation of Tgfb3, Tgfb2,
and Tgfb1 cytokine genes in PF18 compared to P18 tumors (Figure S6F-G). Moreover, ChIP-
seq data revealed some FOXA1 binding events at or near TGF-β cytokine genes in P tumors,
which were diminished in the PF tumors (Figure S6H), suggesting that FOXA1 directly represses
TGF-β cytokine gene expression. Finally, to confirm this phenomenon at the protein level, we
stained P18 vs. PF18 tumor sections for pSMAD2, a downstream readout for TGF-β signal
transduction. As expected, pSMAD2 staining was drastically increased in PF18 tumors, as
compared to P18 (Figure 6G). Further, staining for F4/80+ macrophages demonstrated increased
macrophage accumulation in pSMAD2-high PF18 tumors, and reduced macrophage
accumulation pSMAD2-low P18 tumors (Figure 6G), further supporting increased TGF-β
signaling and macrophage accumulation upon Foxa1 loss. Taken together, our data revealed
drastically increased cell-cell communication in PF tumors, in particular, immunosuppressive
cytokine signaling from basal cells to macrophages, suggesting that Foxa1 loss leads to an
immunosuppressive TIME through increased cytokine signaling that is likely caused by luminal-
to-basal cell de-differentiation.

To directly investigate whether FOXA1 loss drives macrophage infiltration through a TGF-
β-mediated pathway we performed an in vitro co-culture assay using a prostate cancer cell line
(PC3) that was transfected with either and empty vector (PC3 – WT) or FOXA1 knockdown vector
(PC3 – FOXA1 KD). We utilized the Stacks cell culture platform, which is an open, reconfigurable
microfluidic cell culture platform that has been validated for the investigation of the TIME56-58. PCa
cells were cultured with primary monocyte-derived macrophages (MDMs) derived from peripheral
blood samples of patients with PCa, in the presence or absence of the TGF-β receptor I inhibitor
(TGFBi), Galunisertib. After 24h, the MDMs were analyzed for tumor-directed migration, and PC3
– WT and PC3 – FOXA1 KD cells were analyzed for mRNA expression.

Analysis of the mRNA expression within the tumor cells confirmed downregulation of
FOXA1 mRNA expression and supported the upregulation of CCL2, MIF, and TGFB3 in tumor
cells with FOXA1 loss (Figure S6I), in line with our mouse model scRNA-seq analyses and
previous findings9,14. Moreover, analysis of MDM migration using in-chip, fluorescence confocal
microscopy demonstrated a significant increase in the number of MDMs that migrated when
cultured with the PC3 – FOXA1 KD PCa cells compared with the PC3 – WT cells. Furthermore,
the increase in MDM migration in the PC3 – FOXA1 KD co-culture condition was abrogated in the
wells that were treated with TGFBi (Figure 6H). These findings provide direct evidence that loss
of FOXA1 in tumor cells drives tumor-directed migration of primary, PCa-associated MDMs
through a TGF-β-mediated process.

Finally, to evaluate whether targeting FOXA1-loss induced TGF-β signaling reduces tumor
growth in vivo, we treated mice harboring subcutaneous PF tumor-derived allografts with TGFBi
(Galunisertib) or a vehicle control for three weeks. Notably, TGFBi treatment led to a reduction in
tumor weight compared to vehicle control, though not significant, suggesting TGFBi treatment
inhibits Foxa1-deficient PCa tumor growth (Figure S6J). Taken together, these results support
that Foxa1 loss drives an immunosuppressive TIME and PCa progression by enhancing TGF-β
signaling.

FOXA1 downregulation in human PCa is associated with epithelial lineage switch and
immunosuppression

To assess the clinical relevance of our mouse model findings in PCa patients, we
examined gene expression in 179 clinical primary PCa samples 59 and found that FOXA1
expression is indeed negatively correlated with the expression of immune markers, such as CD68,
CD8A, CD4, and FOXP3, as well as TGF-β cytokine genes (Figure 7A). To further probe
FOXA1’s relation to immune infiltration in CRPC, we utilized the CIBERSORTx60 deconvolution
tool to analyze the Stand-up-to-cancer (SU2C) CRPC dataset61 to estimate the abundance of
immune cell types in each of the 208 CRPC samples. Interestingly, our results revealed that
FOXA1 expression was significantly negatively correlated with the abundance of various immune
cell populations, supporting an overall trend towards increased immune cell accumulation in
FOXA1-low tumors (Figure 7B). Notably, M2 macrophage was the immune cell type that is the
most significantly negatively correlated with FOXA1, in line with our mouse data that showed
increased M2-polarization of TAMs in Foxa1-depleted tumors. This bulk RNA-seq data also
showed an increase of T cells, but mostly Tregs and naïve/memory CD4 T cells. Moreover, T
cells might also be spatially restricted in the stroma surrounding the tumor regions, further limiting
their anti-tumor activities, as we have found in mouse models.

Next, to determine how FOXA1 expression in epithelial cells relates to lineage-specific
gene expression and cytokine signaling in human PCa, we analyzed scRNA-seq data of 17
human localized PCa tumor samples (GSE181294)21. We focused on gene expression within the
epithelial cell populations and stratified the 17 PCa samples into FOXA1-high (n = 8) and FOXA1-
low (n = 9) groups based on epithelial FOXA1 expression in each sample (Figure S7A). We found
that androgen signaling-related genes such as AR and KLK3 were downregulated, while
basal/squamous genes TP63, KRT5, and KRT13 were upregulated in the epithelial cells of
FOXA1-low PCa (Figure 7C). Overall, pathways related to lineage plasticity or squamous
differentiation, for instance, EMT and Skin Development gene sets, were upregulated in the
FOXA1-low group, while androgen response pathways were downregulated (Figure 7D),
supporting our findings in mouse models that Foxa1 loss induces basal/squamous de-
differentiation. Moreover, TGF-β signaling, Inflammatory response, TNFα signaling, and Hypoxia
Hallmark gene sets were more highly expressed by epithelial populations in the FOXA1-low group
(Figure 7E & S7B), validating our observations in mice that Foxa1 loss increases
immunosuppressive cytokine signaling. Altogether, our patient data analysis supports our
observations of epithelial lineage switch, increased inflammatory and cytokine signaling, tumor
immune cell accumulation, and immunosuppression upon FOXA1 loss.

Methods
Ethics Statement
In vitro co-culture studies utilizing patient-derived monocytes were conducted in compliance with
the Declaration of Helsinki. Patients were enrolled under institutional IRB‐approved biospecimen
protocols (1202‐1214 and 2020‐0915). Written informed consent was obtained from all
participants before enrollment. The institutional biospecimen collection protocol does not allow
unrestricted public access to the raw data to maintain protection of patient privacy. Therefore,
data sharing requests must be submitted to the William S Middleton Memorial Veterans Hospital
for review and approval. Source data are provided with this paper.

All mouse work was approved by the Institutional Animal Care and Use Committee (IACUC) at
Northwestern University and Emory University in compliance with all relevant ethical
regulations.

Generating genetically engineered mouse models
The PbCre4 transgene was utilized in this model, which enables expression of Cre recombinase
under control of the probasin promoter, to allow Cre-Lox mediated deletion of target genes in
prostate epithelial cells36. The PbCre4 mouse was first crossed with the Foxa1f/f mouse,
generously shared by Dr. David Degraff (Penn State Hershey Medical Center), to generate
PbCre4:Foxa1f/f mouse with prostate specific Foxa1 KO 37. Next, we crossed this mouse with the
Ptenf/f mouse to generate the PbCre4:Ptenf/fFoxa1f/f mouse. And simultaneously crossed PbCre4
mouse with the Ptenf/f mouse to generate PbCre4:Ptenf/f mouse. The PbCre4 gene was only
transmitted through male breeder mice. Only PbCre4 negative females were used for breeding,
as maternally inherited PBCre4 can lead to nonspecific recombination of loxP-flanked alleles in
other tissues74. Only heterozygous Pten KO male mice were used for breeding due to poor fertility
of Pten homozygous KO male mice. Mice are of C57BL/6 background. Humane endpoints were
defined by clinical criteria (e.g., weight loss, body condition, mobility, or distress), as tumors were
not externally measurable. No animals exceeded approved tumor burden limits. Mice were
maintained under standard housing conditions, including a 12-hour light/12-hour dark cycle,
ambient temperature maintained at ~19–23 °C, and relative humidity of 30–70%, with ad libitum
access to food and water. The primers used for genotyping and the expected band sizes for wild-
type and mutant alleles are as follows: Pten (F, CAA GCA CTC TGC GAA CTG AG; R, AAG TTT
TTG AAG GCA AGA TGC; mutant=328 bp; heterozygote= 156 bp and 328 bp; WT= 156 bp),
Foxa1 (F, CTG TGG ATT ATG TTC CTG ATC; R, GTG TCA GGA TGC CTA TCT GGT;
mutant=480 bp; heterozygote= 290 bp and 480 bp; WT= 290 bp), and PBCre4 (F, CTG AAG
AAT GGG ACA GGC ATT G; R, CAT CAC TCG TTG CAT CGA CC; Internal positive control F,
CAA ATG TTG CTT GTC TGG TG; Internal positive control R, GTC AGT CGA GTG CAC AGT
TT; Tg=393 bp; internal control= 200 bp) (Supplementary Data 4).

FFPE mouse tissue harvesting & processing
Mouse prostate tissues were harvested at desired timepoints and immediately placed in 10%
formalin for fixation ~20-24h, then stored in 70% ethanol until proceeding with paraffin processing
and embedding. Tissues were paraffin processed and embedded through the Mouse Histology &
Phenotyping Laboratory (MHPL) at Northwestern University. Tissue blocks were sectioned in
house (5um thickness).

Hematoxylin and eosin (H&E) Staining
FFPE tissue sections were deparaffinized by 5 min in xylene (3x), followed by 5 min in 100%
Ethanol (2x), 5min in 95% ethanol, 5min in 70% ethanol, and transfer to DI water. Slides were
stained in Hematoxylin (Hematoxylin Gill III MilliporeSigma 6506775) for ~40s and immediately
transferred to a beaker with running water for several minutes. Sections were then counterstained
with Eosin (VWR Eosin Stain Premium 95057-848) for ~40s. Next, Tissues were dehydrated by
dips through 70% Ethanol, 95% Ethanol, and 100% Ethanol (2x), followed by 2 min in Xylene
(3x). Finally, coverslips were mounted using permount (Fisher Chemical™ Permount™ Mounting
Medium SP15-500).

IHC Staining
Tissue sections were deparaffinized by dips in xylene for 5min (3x), 100% Ethanol for 5min (2x),
95% ethanol for 5min, and 70% ethanol for 5min. Tissue sections were transferred to TBS. Next,
tissue sections were transferred into a slide container with pre-heated sodium citrate buffer in a
pressure cooker for heat-induced antigen retrieval. Slides in the sodium citrate buffer were heated
in the pressure cooker on a 15min steam program. When complete, the slide container was placed
into ice cold water for 1h to cool. Next, Slides were rinsed 3 times x 5min in TBS. A pap pen was
then used to draw a circle around each tissue, slides were placed into a humidifier slide case, and
0.5% Triton X-100 was pipetted onto each tissue for tissue permeabilization for 15min. Following
tissue permeabilization, tissues were washed 3 times x 5min in TBS. Next, we used Vector Lab
Impress anti-Rabbit or M.O.M. (mouse-on-mouse) kit depending on primary antibody host and
followed kit specific instructions. Finally, we counterstained with hematoxylin ~35-40sec and
washed in a beaker with running water. Finally, we dehydrated the tissue by 3min in 50% ethanol,
3min in 70% ethanol, 3min in 100% ethanol (2x), and 3 min in xylene (3x). Coverslips were
mounted using permount. The following antibodies were used for IHC staining: Recombinant Anti-
FOXA1 antibody [EPR10881] ab170933 Rabbit (1:1000), PTEN Cell Signaling Technology (D4.3)
XP® Rabbit mAb #9188 (1:75), Recombinant Anti-Androgen Receptor antibody [ER179(2)]
(ab108341) (1:1000), Anti-p63 Antibody (D-9) Santa Cruz sc-25268 Ms (1:250), Purified anti-
Cytokeratin 8 Antibody Biolegend 904804 Ms (1:400), pSMAD2 (MilliporeSigma; AB3849;
1:1000), F4/80 (CST 70076; 1:500), CD4 (CST 25229; 1:100), and CD8 (CST-98941; 1:400).

Single cell RNA-seq of whole mouse prostate tumors with 10X Genomics
Chromium Next GEM Single Cell 3' Kit v3.1 (Dual Index) was used for single cell RNA-seq analysis
of whole prostate tumor tissues (P12, PF12, P18, PF18, XP, and XPF). Working space and tools
were prepared with 70% ethanol and RNase-Zap (Invitrogen). Mouse prostate tissue was resected
and immediately processed for downstream scRNA-seq analysis. Prostate tumors were cleaned
to remove excess fat, seminal vesicles, and vans deferens. Tissue was dissociated using the
Worthington-Biochem papain dissociation kit (according to manufacturer instructions). In brief,
clean mouse prostate tissue was minced into small pieces and added to papain solution containing
20 units/ml papain, 0.005% DNase, and 10uM Y-27632 in EBSS (Earle's Balanced Salt Solution).
The tube was incubated in a water bath at 37C, shaking every 5-10min, for 1h. The tissue was
then triturated with a wide bore tip 1ml pipet and filtered through a 70um cell strainer. The cell
suspension was centrifuged at 150-300g for 5min, the supernatant was aspirated, and the cell
pellet was resuspended in a DNase dilute albumin-inhibitor solution consisting of 850ul EBSS,
100ul reconstituted albumin-ovomucoid inhibitor solution, and 0.005% DNase. A discontinuous
density gradient was prepared to separate membrane fragments from dissociated cell pellet by
layering the cell suspension on top of 5ml albumin-inhibitor solution and centrifuging at 70g for
6min. Supernatant was discarded, and the pellet was resuspended in TrypLE + 10uM Y-27632
and incubated at 37C for 15min, pipetting the cell suspension every 5min. The suspension was
passed through a 40um strainer (Flowmi) and TrypLE was inactivated by the addition of RPMI
+10% FBS. Following dissociation, cell count, and viability were measured by Trypan blue using
Cell Countess II to ensure single cell suspension and viability >80%. If doublets/multiplets
remained, the TrypLE dissociation steps were repeated. Finally, cells were centrifuged at 300g for
5min and resuspended in RPMI+10%FBS for loading into the 10X Chip G, with a targeted cell
recovery of 4000-5000 cells. A special thanks to Dr. Alexander Misharin for generously sharing
their lab’s Chromium Controller for GEM generation. The Biorad C1000 Touch or MyCyler Thermal
Cycler system was utilized for protocol steps requiring thermal cycler incubation, depending on
volume requirements. Amplified cDNA and final libraries were analyzed by Qubit dsDNA HS Assay
and Agilent BioAnalyzer (NUSeq core). Libraries were sequenced on Illumina HiSeq2000 PE150
through FSU or NovaSEQ-SP-100 through UC.

Single cell RNA-seq of CD45+ sorted mouse prostate tumors with PIP-seq
PIPseq T20 3ʹ Single Cell RNA Kit v3.0 was used for single cell analysis of CD45+ tumor
infiltrating immune cells (samples P15 and PF15). Mouse prostate tumor tissues were dissociated
as described in the 10X scRNA-seq methods. For live/dead staining, cells were stained with
Zombie Aqua Live/Dead stain (Biolegend Zombie Aqua™ Fixable Viability Kit # 423101, 1:500)
for 30min at 4C. For CD45 staining, Fc block (TruStain FcX™ PLUS Antibody # 156603, 1:150)
was applied for 5-10min, followed by CD45+ APC stain (Biolegend APC anti-mouse CD45
Antibody # 147707, 1:100) for 30min. Cells were washed and resuspended in 2%FBS + 25mM
HEPES PBS for cell sorting. Live CD45+ cells were sorted into 20%FBS collection media using
the FACS Aria II through the Emory Pediatrics' + Winship Flow Cytometry Core. After sorting,
cells were washed with PIP-seq cell resuspension buffer and continued with the PIP-seq protocol
as described in the user guide. Amplified cDNA and final libraries were analyzed by Qubit dsDNA
HS Assay and Agilent BioAnalyzer (Emory Integrated Genomics Core). Libraries were sequenced
on Illumina HiSeq2000 PE150 through FSU.

Single-cell RNA-Seq Analysis
For 10x Genomics single-cell RNA-seq, libraries were aligned to hg38 using CellRanger 75. Low-
quality cells with low UMI counts and high mitochondrial content within each mouse were filtered
out. The individual samples were normalized using SCTransform v2, and then integrated with
castrated mice samples using the Seurat V3 integration pipeline76. Clustering was performed at
resolution 0.2, yielding 17 clusters (0-16). Clusters were subsequently annotated based on cell
type marker gene expression, with several clusters combined based on similar marker gene
expression and proximity in the UMAP (Figure S2A-D). Visualizations and differential gene
expression analysis utilized LogNormalized data. For PIP-seq single-cell RNA-seq, libraries were
aligned using PIPseeker to hg38 and sensitivity 5 results were chosen. Further removal of low-
quality cells and integration were performed in the same manner as 10x single-cell RNA-Seq. For
the trajectory analysis, only epithelial cells were selected from the integrated object and re-
integrated to create a new UMAP. Trajectory graph and pseudotime calculation was performed
with this UMAP using Monocle3. Dittoseq was utilized to visualize cell type proportions77.
Differentially expressed (DE) genes between P and PF conditions were determined utilizing
Seurat FindMarkers() function and the MAST package for DE testing78, with |log2 fold change|
cutoff > 1. The overlap of PF upregulated or downregulated genes with GO BP (biological
processes) or Hallmark gene sets in MSigDB were determined42,79,80. Seurat AddModuleScore()
function was utilized to evaluate module scores for gene expression programs. Signature gene
programs utilized to evaluate immune cell phenotypes are found in Supplementary Data 1.
Signature gene programs were modified from existing signature gene sets in the literature, for
analysis of mouse or human data 21.

CellChat analysis of 10x scRNA-seq data
Cell-cell interactions were evaluated using the CellChat v2 R package48,49, which estimates cell-
cell interactions based on the expression level of a ligand and its corresponding receptor on the
cells. The CellChat ligand-receptor database is informed by literature knowledge and includes
subsets for Secreted Signaling L-R pairs (cytokines, chemokines, growth factors, etc.), ECM-
Receptor, Cell-Cell Contact, and Non-protein signaling. Data analysis was performed following
the tutorial for comparison analysis of multiple datasets (https://github.com/jinworks/CellChat).
Only the Secreted Signaling subset was utilized to focus our analysis on evaluating the role of
FOXA1 in regulating cytokine signaling interactions. Population size was set to true to take the
cell proportions into account in the probability calculation.

Spatial Transcriptomics analysis with 10x Genomics Visium
Spatial transcriptomic analysis was performed using the 10x Genomics Visium platform on tumor
sections from P12, PF12, P18, and PF18 mice (one biological pair per time point; total n = 2
pairs). The limited number of replicates reflects the cost and technical complexity of the assay.
Mouse prostate tissues were harvested at desired timepoints and formalin fixed for ~20-24h.
Tissues were paraffin processed and embedded by the Mouse Histology & Phenotyping
Laboratory (MHPL) at Northwestern University. Tissue histology was analyzed prior to Visium
experiment by H&E and IHC staining to confirm representative samples were chosen and to select
regions for tissue scoring. Tissue blocks were scored, sectioned (5µm) and placed onto the
Visium capture areas in lab, using RNaseZap treated tools/equipment. Visium slides were then
immediately sent to the NUseq core for sample processing according to the 10x Visium for FFPE
guide. Libraries were sequenced through NUseq core using Novaseq6000 Sequencer.

TESLA analysis of Visium data
We utilized a machine learning method, TESLA 47, to annotate cell type distribution om Visium
spatial transcriptomics data. Briefly, TESLA takes the selected cell type marker genes as inputs,
enhances the expression of cell-type-specific marker genes to the same pixel resolution as H&E
images, and integrates these data to determine the abundance of target cell types. The method
assigns each pixel a score between 0 and 1 that reflects the relative enrichment of a given cell
type at that pixel across the whole slide. To identify regions enriched for a specific cell type,
pixels with enrichment scores >0.3 were classified as enriched for that cell type and used to quantify the
corresponding area. Notably, because these scores represent relative enrichment rather than
exclusive assignment, a pixel may have non-zero scores for multiple cell types, capturing their
colocalization pattern. Cell type marker genes used for our analyses are summarized in
Supplementary Data 2. The identified immune populations were overlaid with epithelial tumor
regions to visualize immune infiltration within tumor areas.

ChIP-seq of mouse prostate tumors
For ChIP-seq analysis of mouse prostate tissues, 40mg of flash frozen 18wk timepoint P and PF
prostate tumors (n = 3 per genotype) were utilized for the FOXA1 ChIP experiments and 20mg
for H3K27ac. Tissues were ground using agate mortar and pestle and homogenized using the
BeadBug benchtop homogenizer. Tissues were then double cross linked with 2mM DSG for
10min, followed by 1% formaldehyde for 10min, at room temperature. Crosslinking was quenched
with 0.125 M glycine for 5min at room temperature. Chromatin was fragmented using an E220
focused ultrasonicator (Covaris), 50 ng of Drosophila chromatin was added to each sample as
spike-in DNA, and samples were pre-cleared with protein A agarose beads (Millipore) for 1hr.
Then samples were incubated with the following antibodies overnight at 4C with rotation: FOXA1
ab23738 (5µg; Abcam) and H3K27ac CST 8173S (1:100). 1 µg drosophila H3 antibody (Active
motif Cat# 61686) was also added per sample. Then, protein A agarose beads were added and
incubated for 2h at 4C, followed by washing the beads with 1×dialysis buffer (2mM EDTA, 50mM
Tris-Cl, pH 8.0) twice, and IP wash buffer (100mM Tris-Cl, pH 9.0, 500mM LiCl, 1% NP40, 1%
deoxycholate) four times. Finally, protein-DNA complexes were eluted (50mM NaHCO3, 1%
SDS), crosslinks were reversed, and DNA was purified using DNA Clean & Concentrator-5 kit
(ZYMO Research). Total 10 ng ChIP DNA was used to prep library (NEB E7645L).

ChIP-seq data analysis
Duplicate reads were identified and removed by Picard (v3.0.0) and the adaptor reads removal
process was performed with Trimmomatic V0.39. The reads were aligned against the Mus
musculus reference genome mm10 and the D. melanogaster reference genome Dmel A4.10
using Bowtie2 (v2.5.1). The spike-in normalization was performed by counting D. melanogaster
reads in each sample to calculate a spike-in normalization factor, and down-sampling was
performed by samtools (v1.17) by random down-sampling to the smallest spike-in counts.
HOMER (v4.11) was utilized for ChIP-seq peak calling with default cutoff. Overlap of FOXA1
ChIP-seq peaks was determined using HOMER default setting. To evaluate enrichment of peak,
the number of unique aligned reads overlapping each peak in each sample was calculated by
subread (v 2.0.6). The differential peak enrichment was analyzed by R Bioconductor package
DEseq2 at the indicated FDR-adjusted p-value and log fold-change cutoffs (p <0.05, |log fold-
change|>1). ChIP-seq heatmaps were generated with deepTools (v3.5.4) and show normalized
read counts 5kb before and after peak center. ChIP-seq peaks were assessed for annotations
among genes near ChIP-seq peaks, assigning each peak to the TSS of nearest gene using
HOMER. The R package clusterProfiler was used to perform enrichment analysis on Gene
Ontology and Hallmark gene set from MSigDB42.

Flow cytometry and CyTEK analysis
The single-cell suspensions underwent stimulation with phorbol 12-myristate 13-acetate (50
ng/ml), ionomycin (5 μg/ml), and brefeldin A (10 μg/ml) to induce IFNγ and TNFα production. To
block non-specific antibody binding to Fc receptors on immune cells, the stimulated single-cell
suspensions were incubated with anti-mouse CD16/32 antibody (2.4G2). Following this, live/dead
dye and fluorophore-conjugated anti-mouse antibodies targeting cell surface markers were
added. After PBS washing, cells were fixed with 1x Fixation/Permeabilization reagent
(eBioscienceTM, 00-5223-56) for 20 minutes at room temperature. Subsequently, cells were
washed twice with 1x Permeabilization/wash buffer (eBioscienceTM, 00-8333-56) and subjected
to intracellular staining (ICS) with an antibody cocktail for 45 minutes at room temperature. Finally,
cells were washed and suspended in phosphate-buffered saline (PBS) for acquisition using a
three-laser CyTEK AURORA spectral flow cytometry or LSRII flow cytometry. Single staining of
each antibody with corresponding fluorescence on splenocytes served as reference for spectral
unmixing (CyTEK). Data analysis was performed using either the OMIQ web application
(https://www.omiq.ai/) or the FlowJo application, according to previously described methods
(PMID: 35671108; PMID: 38530357). Antibody information is listed in Supplementary Data 3.

CIBERSORTx analysis of publicly available patient datasets
CIBERSORTx analysis 60 was performed on the SU2C mCRPC RNA-seq dataset 61 to impute cell
fractions. The following parameters were used for the run: LM22 signature matrix 81, B-mode
batch correction, absolute run mode, 100 permutations. Pearson correlation between FOXA1
expression and immune infiltrate levels were determined.

Peripheral blood mononuclear cells (PBMC) isolation
Blood samples were collected from patient donors with prostate cancer after receiving written
informed consent under a protocol approved by the Institutional Review Board at the William S
Middleton Memorial Veteran’s Hospital, Madison, WI in accordance with the Declaration of
Helsinki. Blood specimens were collected in vacutainer tubes (BD Biosciences, Franklin Lake,
NJ, USA) with EDTA anticoagulant. Whole blood was diluted 1:1 with Hank’s balanced salt
solution (HBSS, Lonza Group, Basel, Switzerland) before being underlaid with 10 mL of Ficoll-
Paque PLUS (GE Healthcare, Cat# 45-001-750) for gradient centrifugation. CD14+ monocytes
were enriched from PBMCs using LS MACS columns following incubation with anti-CD14
magnetic beads (Miltenyi Biotec Inc., Bergisch Gladbach, North Rhine-Westphalia, Germany).

Cell Culture & TGFB Receptor Inhibitor Treatment
Transfected PC-3 cells, control and FOXA1 knockdown (pGIPZ-shCtrl and pGIPZ-shFOXA1
(target sequence: TGGACAGACTTCCAAGATG), obtained from Open Biosystems), were cultured in
RPMI1640 media with L-Glutamine (CorningTM Thermo Fisher Scientific, Waltham, MA, USA),
10% FBS (GibcoTM, Thermo Fisher Scientific, Waltham, MA, USA) and 2%
penicillin/streptomycin (HycloneTM, VWR, Radnor, PA, USA). 1ug/mL Puromycin (GibcoTM,
Thermo Fisher Scientific, Waltham, MA, USA) was added to the media to select for transfected
cells and was refreshed with new media daily. PC-3 cell line was obtained from ATCC and
periodically tested for potential mycoplasma contamination. Patient-derived monocytes were
cultured in RPMI1640 media with L-Glutamine, 10% FBS, 5% Glutamax (GibcoTM, Thermo
Fisher Scientific, Waltham, MA, USA), and 2% penicillin/streptomycin. Single cell suspensions of
PC-3 cells were harvested at log phase and were seeded onto Stacks hydrogel wells at a
concentration of 150k cells per mL. Using established protocols (PMID 36083096, 39414902,
40605246), CD14+ monocytes were differentiated into macrophages using 50 ng/mL colony
stimulating factor 1 (CSF1) (RnD systems, Minneapolis, MN, USA), administered on Days 1 and
4, and were seeded in Stacks at a concentration of 3x106 cells per mL. PC-3 and patient-derived
primary macrophages were cultured in Stacks by seeding as a monolayer on a collagen-
fibronectin matrix, consisting of 79% collagen I (Advanced BioMatrix, Carlsbad, CA, USA #5005),
1.5% fibronectin (Sigma-Aldrich, Millipore Sigma, Burlington, MA, USA #F1141) prepared
according to manufacturer guidelines. Moisture was retained by storing Stacks inside of a
humidifying chamber.

Prior to treatment and stacking, the PC-3 wells were washed twice with a 30-minute
incubation using plain media to remove the puromycin. TGF-β receptor I inhibitor Galunisertib
(LY2157299) at a stock concentration of 10 mM was diluted at a 1:2000 dilution in media to 5 μM
solution. Treatment was applied for 24h as a 20 μL of media with 5 μM Galunisertib or vehicle
control on day 8.

Stacks devices
Prior to culture, Stacks plates (Protolabs, Maple Plain, MN, US #1121-5161-007) were prepared
by sonication in 100% isopropanol for 60 minutes, and air dried inside a BSC on a pre-sterilized
rack for 10-20 minutes before rotating and drying for an additional 10-20 minutes. Stacks devices,
3D holders, NuncTMOmnitraysTM (Thermo Fisher Scientific, Waltham, MA, USA), and non-tissue
culture-treated BioAssay dish (245mm square; Corning Inc., Corning, NY, USA) were sterilized
with 70% ethanol followed by 20 minutes UV light treatment on each side. Cells were plated in
the Stacks device and then allowed to adhere or migrate through the matrix as applicable. When
stacking devices, media was removed from the top and bottom leaving only a small volume of
residual media to prevent gas bubble formation during stacking. Stack devices were placed in a
three-layer humidifying chamber including a sterile sponge soaked in sterile ddH2O in a
NuncTMOmnitrayTM(Thermo Fisher Scientific, Waltham, MA, USA).

Nucleic Acid Extraction and Quantitative RT-PCR
Cells were removed from hydrogel matrix via application of 5 μL of 1 mg/mL collagenase I (Sigma-
Aldrich, Millipore Sigma, Burlington, MA, USA) for matrix digestion prior to transferring single cell
suspensions to 96-well plates for subsequent lysis containing 40μl of lysis/binding buffer solution
+ 10 μL of LB washed beads per well. mRNA isolation was performed using InvitrogenTM
DynabeadsTM mRNA DIRECT Kit (Thermo Fisher Scientific, Waltham, MA, USA). The lysate was
washed with 100μL Buffer A x 2 and 100 μL Buffer B x 1, then suspended in 16 μL of nuclease-
free H2O (PromegaTM, Madison, WI, USA).

The mRNA elution sample was reverse transcribed using 4 μL SuperscriptTM IV VILOTM
Master Mix (InvitrogenTM, Thermo Fisher Scientific, Waltham, MA, USA), according to
manufacturer’s directions using Bio-Rad C1000 TouchTM thermocycler (Bio-Rad Laboratories,
Hercules, CA, USA). The subsequent cDNA solution (6.3 μL) was then amplified using 12.5 μL
TaqManTM PreAmp master mix (Applied BiosystemsTM, Thermo Fisher Scientific, Waltham, MA,
USA) plus 6.25 μL of a Primer mix. The primer mix contained 20 μL of each available primer and
TE buffer to bring the mix up to 2000 μL final volume. 10 cycles of pre-amplification were
performed according to manufacturer’s directions, and the final reaction volume was then diluted
by adding 200μL nuclease-free H2O. For TaqManTM qPCR assays, 2.5 μL of diluted cDNA
template was mixed with 5μL TaqMan FastTM master mix (Bio-Rad, USA), 0.5 μL TaqManTM Gene
Expression Assay C-C motif chemokine ligand 2 (CCL2) (HS00234140_m1), transforming growth
factor beta 3 (TGFB3) (Hs01086000_m1), forkhead box A1 (FOXA1) (Hs04187555_m1),
macrophage migration inhibitory factor (MIF) (Hs00236988_g1), and housekeeping genes:
ribosomal protein lateral stalk subunit P0 (RPLP0) (Hs00420895_gH) and RNA polymerase II
subunit A (POLR2A) (Hs00172187_m1) (Life Technologies, USA)) and 2 μL nuclease-free water.
Each reaction was amplified in duplicate using a QuantStudioTM 5 Real-Time PCR system via the
Comparative CT Fast protocol available on the machine. Analysis was performed using the
Thermo Fisher ConnectTM online application.

Confocal Microscopy Analysis
For confocal microscopy analysis, cells were then fixed at their location within the collagen-
fibronectin matrix (eBioscience, Thermo Fisher Scientific, Waltham, MA, USA; # 00-5521-00),
washed, and then stored at 4°C. Staining was performed via overnight incubation at 4°C of a 1x
PBS/10% FBS solution comprised of 20mM Hoechst 33342 (1:250) and anti-CD14 (BD
Biosciences, USA # 561708) (1:50). Spinning disk confocal microscopy was performed at the
University of Wisconsin–Madison Biochemistry Optical Core (RRID:SCR_023952), which was
established with support from the University of Wisconsin–Madison Department of Biochemistry
Endowment, at 10x magnification with 10um z-layers, for a total of 101 layers. Images were
analyzed with NIS-Elements software (RRID:SCR_014329; Nikon Instruments, Melville, NY,
USA). Individual cells were identified using the 3D spot detection function via a recipe generated
with the General Analysis 3 (GA3) module in the Hoechst channel to identify each nucleus.
Migrated cells are classified as cells found below 200 μm, which is the approximate depth of the
biomatrix meniscus within the Stacks well.

PF tumor allografts
To establish PF tumor allografts, prostate tumors from 15 – 18 week old PF transgenic mice were
harvested, minced, and subcutaneously engrafted in the right flank of immunodeficient NSG mice
to expand. Tumors that successfully engrafted in NSG mice (reached at least 300mm3) were
harvested, minced and subcutaneously engrafted into the right flanks of three pairs of NOD-SCID
mice, which lack T and B cells but retain macrophages, for treatment with vehicle or 75 mg/kg
TGF-β receptor I inhibitor Galunisertib (LY2157299) twice daily for three weeks.

Statistics & Reproducibility
The statistical significance of differences in gene expression between conditions in the scRNA-
seq data was performed using the ggbetweenstats function within the ggstatsplot package, with
a two-sided Mann-Whitney nonparametric test82. Statistical analyses for Cytek spectral flow data,
tumor size differences, mouse survival, qRT-PCR, and co-culture assays were performed using
GraphPad Prism. Descriptions of the statistical tests used in the experiments can be found in the
corresponding figure legends. All H&E and IHC images are representative of independent
biological replicates as indicated in the figure legends (≥3 per genotype). No statistical method
was used to predetermine sample size. No data was excluded from the analyses. The
investigators were not blinded to allocation during experiments and outcome assessment. All
animal experiments and human analyses included only male subjects, as the prostate is a male-
specific organ. Consequently, sex was not included as a variable in the study design or statistical
analysis.

Data Availability
All scRNA-seq, ChIP-seq, and Visium spatial transcriptomics data generated for this study have
been deposited in the Gene Expression Omnibus (GSE282726) at
https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE282726. Previously published
microarray data for LNCaP cells with FOXA1 KD and rescue with WT or mutant FOXA1 constructs
is deposited in GSE12888213
(https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE128882). Previously published data
for FOXA1 ChIP-seq in LNCaP cells is deposited in GSE550076
(https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE55007). Publicly available human
PCa datasets analyzed in this study can be found at GSE18129421
(https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE181294) and GSE2103459
(https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE21034).

Code Availability
The code for next-generation sequencing analysis performed in this paper has been uploaded to
https://github.com/JYULAB/FOXA1_immune_project.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Brea and colleagues describe a new genetically engineered mouse model of PCa bearing the concomitant prostate specific loss of Pten and Foxa1. By coupling classical histopathological studies with approaches of single cell sequencing and spatial transcriptomics, the authors describe a critical oncosuppressive role of Foxa1 in Pten-loss driven mouse prostate tumorigenesis. Their findings highlight two major phenotypes associated with the loss of Foxa1: the skewing of the luminal lineage versus the basal/squamous lineage of the prostatic epithelium combined with the generation of an immunosuppressive tumor microenvironment.\n\nThe manuscript is well-written, the topic is clinically relevant, and the results are interesting.\nBelow few major aspects that Authors should carefully consider and address:\n1. To better understand the relevance of the genetic model established in mirroring human disease, the Authors should define the incidence of PTEN-FOXA1 double null condition in human prostate cancer considering primary, metastatic and castration resistant stages of the disease.\n2. Absence of basal cell markers such as TP63 and CK5 is generally coupled to H&E staining in PCa diagnosis. Authors should indicate if and how the expansion of TP63 staining (and maybe also CK5) recapitulates specific genetic or histological subtypes of human PCa.\n3. Figure 1 nicely describes the histology of the different lobes of the mouse prostate in the wild type, Pten-null and Pten-Foxa1 double null model, while 1F defines the lethality of the PF condition compared to P only. Although bladder obstruction may most likely be the reason for lethality, the authors should present a careful analysis of proximal lymph nodes, distant organs (e.g., liver and lung), and bone (e.g., femurs) to exclude or perhaps include metastatic disease progression, which, given the paucity of metastatic GEMMs for PCa, would greatly increase the relevance of the study.\n4. Bulk and scRNAseq studies demonstrate a different immune profile of P and PF prostate tumors with PF described as more immune suppressive/exhausted than P. A critical aspect of these analyses is related to the substantial different stage of P and PF tumors. As clearly described in Figure 1, PF tumors are much more aggressive and advanced than P tumors at the same time points. Therefore, it is difficult, if not impossible, to conclude whether it is the loss of Foxa1 or the stage of the tumor that defines immune suppression/exhaustion. These analyses should be performed on histopathological similar -no age matched- P and PF tumors (i.e., P24 vs PF12).\n5. Based on the data shown in Figure 4 immune cells appear to share common areas of PF tumor. Are these areas characterized by specific CAFs/extracellular matrix conditions. Fibroblasts are weirdly neglected in this work.\n6. Figure 4 describe the spatial distribution of immune cells into the diseased prostate gland of P and PF mice. Are the different percentages described an average of n=? different P and PF tumors or the comparison of just a single P versus a single PF tumors. Are the data consistent across different set (i.e 100 um/10 sections away from those in Figure 4) of sections of the same couple of P and PF tumors?\n7. Figure 5 describes the ability of PF tumors to generate an immune suppressor tumor microenvironment by modulating extracellular signalling. However, the conclusions are completely inferred from the differential RNA expression profiles, which is not sufficient to support the conclusion. Intratumor quantification of the most interesting factors through MS or ELISA methodologies should be included. As well, the immune suppressor role of specific factors (at least one) should be functionally demonstrated in vivo through the injection of specific inhibitors. Interestingly, based on panel B PF tumors express very low levels of Activin (Activin A?) compared to P tumors. Activin A has been described plays a very important role in controlling prostate tissue homeostasis in mouse adult prostate progenitors by promoting quiescence (Cambuli et al., 2022 EMBO Reports). Organoids that were able to spontaneously overcome the barrier by reducing Activin A expression/secretion experience genomic instability and generate CK8/CK5 double positive hyper proliferating dysplastic lesions when transplanted orthotopically into the prostate of adult wild type syngeneic mice. Authors should comment on the possible implication of reduced Activin expression in PF versus P prostate tumors.\n8. A recent paper (De Felice et al., 2024) describes the role of Retinoic Acid signaling in promoting luminal lineage of mouse prostate progenitors by promoting Foxa1 expression. Lack of RA-Foxa1 activity as well as specific mutant forms of FOXA1 associated with human PCa determine AR deregulation and a basal/squamous commitment of prostate progenitor cells. Authors should carefully discuss this paper in their manuscript.", "reviewer_2": "FOXA1 is a critical gene in prostate cancer development and therapeutic resistance, with functions that appear to be highly context-dependent. It is frequently mutated and overexpressed in prostate cancer, rarely deleted, but appears to be downregulated in castration-resistant prostate cancer (CRPC). Its primary role is as a pioneer factor for the androgen receptor (AR), and it has been implicated in AR-independent CRPC evolution through mechanisms such as cellular plasticity.\n\nThis complexity has led to seemingly conflicting findings, underscoring the multifaceted role that FOXA1 plays in prostate cancer pathogenesis. In this context, the generation and characterization of the PTEN-FOXA1 prostate knockout model presented in this manuscript is of considerable interest. The authors have previously investigated FOXA1 downregulation in human cells, primarily using shRNA, observing reduced cell proliferation in hormone-sensitive lines and upregulation of the epithelial-to-mesenchymal transition (EMT) program.\n\nInterestingly, in this mouse model, co-deletion of PTEN and FOXA1 results in an aggressive prostate cancer phenotype with pronounced basal and squamous features and an immune infiltrate rich in myeloid cells. This suggests a potentially immunosuppressive tumor microenvironment characterized by dysfunctional T cells. Although basal/squamous prostate cancers are relatively rare in patients, I find this model highly intriguing, as it aligns with reports suggesting that this phenotype—more commonly observed in mice—may represent a plastic intermediate state in patients.\n\nThe authors employ a multimodal characterization of these tumors, which may have broader implications beyond the specific role of FOXA1. This is a significant strength of the manuscript, and I believe the study would benefit greatly from additional analyses and functional experiments.\n\nMajor Points:\nCastration Studies\nFOXA1 downregulation occurs later in prostate cancer progression, particularly during CRPC evolution, as noted by the authors. Characterizing these models post-castration—whether through surgical means or degarelix administration—is essential to fully demonstrate their relevance. The single-cell RNA sequencing (scRNA-seq) data indicate that the luminal compartment remains substantial. The impact of castration on these cells, as well as on the immune microenvironment, may influence the cancer phenotype and progression.\n\nMetastatic Potential\nConsistent with their previous studies in human cell lines, the authors report EMT upregulation upon FOXA1 deletion in these models. This raises an important question: do these tumors develop metastases? It is unclear whether the authors have investigated this aspect. Analyzing metastatic spread in both intact and castrated mice would provide a more comprehensive characterization of the model.\n\nAddressing Discrepancies in Tumor Immune Microenvironment (TIME) Analysis\nThe authors provide an extensive characterization of the TIME using multiple single-cell resolution technologies, including scRNA-seq, PIP-seq, Cytek spectral flow cytometry, and spatial transcriptomics. However, while scRNA-seq and spatial transcriptomics reveal prominent macrophage and monocyte infiltration, flow cytometry identifies polymorphonuclear myeloid-derived suppressor cells (PMN-MDSCs) as the dominant immune cell type. PMN-MDSCs are of great interest in prostate cancer, as they play a crucial role in immune suppression in both mouse models and patients. The discrepancy between methods raises concerns. One possible explanation is methodological bias introduced by cell dissociation and tissue preparation. On the other hand, the flow cytometry panel used may not be optimal for distinguishing MDSCs subsets. The standard antibody combination for M-MDSCs and PMN-MDSCs includes Ly6C (HK1.4) and Ly6G (1A8). However, the authors appear to have used Ly6C (HK1.4) in combination with the Ly6G/C antibody (RB6-8C5). When this antibody is used instead of a Ly6G-specific antibody such as 1A8, it stains both M-MDSCs and PMN-MDSCs, resulting in the majority of CD11b-positive cells being positive for this staining, as shown in Fig. S3. This could potentially lead to an overestimation of the number of PMN-MDSCs. Revisiting the staining strategy including additional validation with alternative antibodies is recommended.\n\nFunctional Experiments\nThe manuscript is largely descriptive. The authors acknowledge in the discussion that targeting cytokines such as TGF-β could influence immune checkpoint inhibitor (ICI) responses. This reasoning could also be extended to the observed TIME components. Performing a cell depletion assay or cytokine-targeting experiments—even without ICI combination—would provide functional evidence for the importance of at least one of these TIME components.\n\nAdditional Major Considerations:\nThroughout the manuscript, including the title and discussion, the authors repeatedly emphasize how their data support the role of FOXA1 in prostate cancer progression through its regulation of prostate epithelial cell lineage identity and the tumor immune microenvironment. While the data may be consistent with this hypothesis, the manuscript lacks direct evidence—either genetic (e.g., lineage tracing) or mechanistic (e.g., ChIP-seq)—to prove it. Alternative explanations cannot be ruled out, making this the weakest aspect of the study.\n\nTo address this, I suggest two possible courses of action:\n- The authors could revise the text to reduce emphasis on this aspect, ensuring that the conclusions are appropriately cautious.\n- If the editors and authors believe this hypothesis is central to the study, additional experiments are necessary to provide stronger evidence.\n\nDetermining the Luminal or Basal Origin of the Tumor\nThe initial study describing the PB-Cre mice used in this study reported that \"there was no evidence of Cre expression in the basal cells of any of the prostate lobes examined\" (Wu et al., Mechanisms of Development, 2001). However, subsequent studies have shown that this model is not luminal-specific and that Cre is expressed in both basal and luminal epithelial cells (Wang et al., PNAS, 2006 [https://doi.org/10.1073/pnas.051065210]). Thus, to support the claim that FOXA1 drives dedifferentiation, the authors should investigate the tumor cell of origin in greater detail. The current data do not exclude a basal origin, and the two selected time points (12 and 18 weeks) do not clearly demonstrate a phenotypic switch. To determine whether luminal or basal cells initiate oncogenic transformation in this model, several approaches are available:\n- Lineage tracing: This can be achieved using transgenic mouse models with CreERT2 systems more specific to either basal (e.g., CK5-CreERT2, TP63-CreERT2) or luminal (e.g., CK8-CreERT2, NKX3.1-CreERT2) epithelial cells, which would be more appropriate than the PB-Cre model.\n- In vitro modeling: Isolating the different prostate epithelial cells (e.g., as organoids), transforming them genetically, and studying their oncogenic potential could provide more direct mechanistic insights. Performing similar experiments in human cells would further strengthen the study.\n\nChromatin Immunoprecipitation (ChIP)\nThe authors acknowledge that FOXA1’s role is highly context-dependent. While the upregulation of EMT is consistent with previous findings in human cancer cell lines, the observed basal/squamous phenotype and cytokine signaling are novel insights specific to this model. To establish FOXA1’s role in this process, ChIP-seq analysis in the relevant cell population is crucial. This would clarify:\n- Whether FOXA1 directly regulates luminal differentiation.\n- Whether the observed immunosuppressive TIME is a result of direct transcriptional repression of cytokines by FOXA1 or merely a consequence of the basal/squamous PCa phenotype, which may have its own distinct transcritional network and immune landscape.\n\nIn conclusion, I see significant value in publishing a study describing the characterization of this PTEN/FOXA1 prostate cancer model, which represents a rare but intriguing subtype of prostate cancer observed in patients. However, I would recommend a more comprehensive characterization, including castration of the mice—either surgically or through degarelix treatment—as the genetic alterations in this model are intended to mimic castration-resistant prostate cancer. Additionally, an analysis of potential metastases should be included.\n\nWhile I understand the inclination to link FOXA1 to cellular plasticity and propose it as a direct regulator of the tumor immune microenvironment through transcriptional control of the chemokine/cytokine milieu, this interpretation remains speculative. Although the characterization may suggest such a role, the study does not provide definitive proof. If the authors wish to pursue this hypothesis—which I agree is of considerable interest—they should conduct a combination of genetic, molecular, and functional analyses to substantiate their claims. Otherwise, they should adjust the manuscript’s tone and conclusions accordingly.", "reviewer_3": "Overview:\nThis manuscript (ms) explores the tumorigenic effects of Foxa1 depletion in prostate cancer by comparing single-cell and spatial transcriptomic profiles between Pten-null transgenic mice (P) and Pten/Foxa1 double-knockout mice (PF). Comprehensive bioinformatics analyses were conducted using various software tools to support the study’s findings.\nOverall, this manuscript is well-written, with data and analysis results clearly presented. However, several issues need to be addressed.\n\nMajor Comments:\n1. The study primarily analyzes four samples: P12, PF12, P18, and PF18 — representing two conditions at two different time points. A key limitation of the study is the lack of biological replicates for each combination of condition and time point. This is particularly concerning for the spatial analysis using TESLA, as different tissue structures of replicate samples could lead to varying conclusions.\n2. In the scRNA-seq integration analysis, there seem to be six samples in total: P12, PF12, P18, PF18, and two castrated samples (XP and XFP). If the two castrated samples are indeed part of the integration analysis (as indicated in the analysis code), their absence from the UMAP visualization is unclear. Furthermore, there is no mention or representation of these castrated samples in any analysis or figures throughout the ms.\n3. In the scRNA-seq integration analysis, several cell clusters co-express luminal and basal genes. For instance, the small basal (red) cluster in the center of Fig.2A shows high Krt8 expression in Fig.2E, and similar patterns are seen in the \"basal squamous\" and \"basal proliferating\" clusters in Fig.S2A and S2B. Could these clusters represent doublets?\n4. What does the UMAP plot in Fig.S2D look like when coloured by sample? Providing this visualization would help clarify sample-specific clustering and potential batch effects.\n5. In the pseudotime analysis, both the “Basal proliferating” and “Tff3 Lum” clusters are assigned a zero pseudotime (Fig.2D). Does this imply that these clusters were excluded from the Monocle3 pseudotime trajectory analysis? The UMAP also suggests that luminal cells are closer to basal cells than basal proliferating cells, which seems counterintuitive.\n6. How was the starting point chosen for the Monocle3 pseudotime analysis? Have the authors tested the robustness of their results by selecting different starting points — for example, the basal cluster on the left side of the UMAP?\n7. The TESLA tool was used to annotate Visium data at the pixel level, but it appears that individual pixels do not have unique cell-type annotations. For example, in Fig.4A (PF18), there is substantial overlap between luminal and basal cells. Does this suggest that both luminal and basal cells occupy the same H&E image pixels? This ambiguity complicates the interpretation of cell-type proportions, as seen in Fig.4B, where macrophages, monocytes, and T cells together constitute over 80% of the tissue — a proportion unlikely to reflect the true composition.\n8. Stromal cell populations are not clearly presented in the Visium profiles. Are stromal cells present in the analyzed tissues, and if so, where are they located?\n\nMinor Comments:\n1. Consistent colours for cell-type granularity across Fig.S2D and S2E would improve visual clarity and aid in the interpretation of results."}, "author_response": "Response to Referees\nManuscript Title: FOXA1 loss drives basal/squamous de-differentiation of prostate cancer and\ninduces an immunosuppressive tumor microenvironment\nManuscript ID: NCOMMS-25-07398A-Z\nDear Reviewers,\nThank you for your careful evaluation of our manuscript and for the thoughtful and constructive comments\nprovided. We have carefully considered all suggestions and revised the manuscript accordingly. Below, we\npresent a point-by-point response to each comment, detailing the changes made and where they appear\nwithin the revised manuscript. All changes are highlighted in yellow in the revised manuscript. In addressing\nthese comments, we were able to further validate and solidify the conclusions of the study, which we\nbelieve substantially strengthened the manuscript. We sincerely appreciate the time and effort dedicated\nto reviewing our submission and hope that the revisions satisfactorily address all concerns raised.\nReviewer #1 (Remarks to the Author)\nBrea and colleagues describe a new genetically engineered mouse model of PCa bearing the concomitant\nprostate specific loss of Pten and Foxa1. By coupling classical histopathological studies with approaches\nof single cell sequencing and spatial transcriptomics, the authors describe a critical oncosuppressive role\nof Foxa1 in Pten-loss driven mouse prostate tumorigenesis. Their findings highlight two major phenotypes\nassociated with the loss of Foxa1: the skewing of the luminal lineage versus the basal/squamous lineage\nof the prostatic epithelium combined with the generation of an immunosuppressive tumor\nmicroenvironment.\nThe manuscript is well-written, the topic is clinically relevant, and the results are interesting.\nBelow few major aspects that Authors should carefully consider and address:\n1. To better understand the relevance of the genetic model established in mirroring human disease, the\nAuthors should define the incidence of PTEN-FOXA1 double null condition in human prostate cancer\nconsidering primary, metastatic and castration resistant stages of the disease.\nR: Thank you for this important suggestion. When generating these genetically engineered mouse models, Pten\ndeletion served as a general tumor driving event in order to study the role of FOXA1 within a PCa context. We\nchose Pten deletion as our tumor driving event because Pten null mice are very commonly used to study PCa\nand phenotypically recapitulates the human disease well (PMID: 14522255).\nTo better understand the incidence of PTEN-FOXA1 double null condition in human PCa, we analyzed\npublicly available datasets containing primary prostate adenocarcinoma or metastatic castration-resistant\nprostate cancer (CRPC) cases for correlations between PTEN copy-number and FOXA1 mRNA expression level.\nWhile we did not observe any significant overall correlation, we observe some cases in which patients with PTEN\ncopy-number loss also exhibit low FOXA1 expression (Quadrant III below), signifying PTEN and FOXA1 loss are\nnot mutually exclusive and the PTEN-FOXA1 low condition is observed in human prostate cancer. This data has\nbeen added to Figure S1A.\nAdditionally, adult male mice have been shown to have lower endogenous androgen levels as compared to\nhuman, such that mice exhibit similar androgen levels as humans who have undergone androgen ablation\ntherapy (PMCID: PMC4009979). Taken together, Foxa1 loss in the context of Pten deletion in our model\nrepresents more advanced PCa with lineage plasticity.\n1\n2. Absence of basal cell markers such as TP63 and CK5 is generally coupled to H&E staining in PCa\ndiagnosis. Authors should indicate if and how the expansion of TP63 staining (and maybe also CK5)\nrecapitulates specific genetic or histological subtypes of human PCa.\nR: Thank you for this comment. Intraductal prostate adenocarcinoma is typically identified by the presence\nof an intact basal cell barrier surrounding glandular luminal structures, as observed in our Pten-null mice,\nand in previous studies on Pten-null mice. Typically, invasive carcinoma is identified by a loss of p63/KRT5+\nbasal layer and invasion of luminal epithelial cells into stromal regions (PMID: 28389514). However, in our\nmodel, when FOXA1 is deleted in the context of Pten KO, we interestingly observed a broad gain in basal\ngene expression by tumor epithelial cells. Moreover, we observe a gain in squamous differentiation and\ngene expression. Thus, our model doesn’t represent majority of primary PCa, but rather models advanced\nPCa with lineage plasticity and squamous differentiation. Rather, the gained p63 and squamous\ndifferentiation points to the de-differentiation of luminal tumor cells and lineage plasticity upon Foxa1 loss.\nSuch phenomena of lost luminal identity and increased lineage plasticity can be observed in advance\ncastration-resistant prostate cancer as cells de-differentiate and lose their dependence on AR. Moreover,\nA previous study analyzing metastatic castration-resistant prostate cancer samples identified a subset of\ndouble negative prostate cancer tumors with squamous differentiation. Squamous adenocarcinoma of the\nprostate, though rare, is associated with treatment resistance and aggressive disease (PMID: 17407588\nPMID: 15105655). Thus, we believe our model represents a castration-resistant subtype of prostate cancer\nwith gained lineage plasticity.\n3. Figure 1 nicely describes the histology of the different lobes of the mouse prostate in the wild type, Pten-\nnull and Pten-Foxa1 double null model, while 1F defines the lethality of the PF condition compared to P\nonly. Although bladder obstruction may most likely be the reason for lethality, the authors should present a\ncareful analysis of proximal lymph nodes, distant organs (e.g., liver and lung), and bone (e.g., femurs) to\nexclude or perhaps include metastatic disease progression, which, given the paucity of metastatic GEMMs\nfor PCa, would greatly increase the relevance of the study.\nR: Thank you for inquiring about this important point. To address the incidence of metastasis in our novel\nGEMM models, we examined tissue sections from lymph nodes, lung, and liver by IHC for AR and H&E\nstaining for evidence of metastatic PCa cells. Organs of 9 intact PF mice and 7 intact P mice between the\nages of 15-21wks were evaluated, and none were found to exhibit evidence of metastasis. However, we\nobserved evidence of metastasis following castration in XPF mice. 2 out of 5 XPF mice demonstrated\nevidence of metastasis, one in the lung and one in lymph node tissue, while 0 out of 5 XP mice evaluated\nexhibited evidence of metastasis by IHC/H&E analysis (as summarized in the table below now found in\nFigure S1G).\nEvaluated Metastasis Incidence (%)\nPF (intact) 9 0 0%\nP (intact) 7 0 0%\nXPF (castrated) 5 2 40%\nXP (castrated) 5 0 0%\n4. Bulk and scRNAseq studies demonstrate a different immune profile of P and PF prostate tumors with PF\ndescribed as more immune suppressive/exhausted than P. A critical aspect of these analyses is related to\nthe substantial different stage of P and PF tumors. As clearly described in Figure 1, PF tumors are much\nmore aggressive and advanced than P tumors at the same time points. Therefore, it is difficult, if not\nimpossible, to conclude whether it is the loss of Foxa1 or the stage of the tumor that defines immune\nsuppression/exhaustion. These analyses should be performed on histopathological similar -no age\nmatched- P and PF tumors (i.e., P24 vs PF12).\nR: The reviewer brings up a great point. Indeed, it is difficult to determine whether the immune phenotype\nobserved is due to direct regulation by Foxa1 or tumor stage. To address this question, we have compared\nP18 vs PF12 samples shown below. We observed that PF12 mice still show a greater abundance of\nmacrophages, monocyte-like, CD8 T cell, and CD4 T cells as compared to P18, and that these populations\nare largely skewed to an immunosuppressive phenotype. Furthermore, it is important to note that Foxa1\nloss leads to a histologically distinct phenotype characterized by basal-squamous de-differentiation, which\nis not observed in Pten-null mice, regardless of timepoint. Thus, an early timepoint PF tumor will not be\nhistologically similar to a late timepoint P tumor.\n5. Based on the data shown in Figure 4 (now Figure 5) immune cells appear to share common areas of PF\ntumor. Are these areas characterized by specific CAFs/extracellular matrix conditions. Fibroblasts are\nweirdly neglected in this work.\nR: CAFs/extracellular matrix conditions are certainly important players in the TME. We observe regions of\nreactive stroma in the PF mice by histopathology analysis, as discussed in Fig 1. However, in this paper we\ndecided to focus our attention on the immune compartment. We believe the T cell localization in the stromal\n3\nregions surrounding tumor epithelial cells reflects its exclusion from properly infiltrating into the tumor cell\nbeds. Nevertheless, we evaluated the proportion of stromal/fibroblast populations in P vs PF scRNA-seq\ndata, and did not find observed consistent marked differences between genotypes (data shown below). An\nin-depth analysis of what role CAF populations, in particular, may be playing in contributing to the phenotype\nmerits further investigation in future studies, but is beyond the scope of this paper. We will plan to evaluate\nthe relationship among Foxa1-null PCa cells and fibroblasts in further depth in follow-up studies.\n6. Figure 4 (now Figure 5) describe the spatial distribution of immune cells into the diseased prostate gland of\nP and PF mice. Are the different percentages described an average of n=? different P and PF tumors or the\ncomparison of just a single P versus a single PF tumors. Are the data consistent across different set (i.e\n100 um/10 sections away from those in Figure 4) of sections of the same couple of P and PF tumors?\nR: Thank you for these questions. The percentage refers to the percent of total captured area determined\nto contain that cell type by TESLA analysis, within each sample. (We’ve now added this description to the\nFigure legend.) In the Visium spatial transcriptomics data, we are comparing a pair of P12 vs PF12 and P18\nvs PF18 mouse tumors. Though our number of replicates for our spatial transcriptomics analysis are low,\ndue to the expense and time required for these experiments (a key limitation of this study), our results\nbetween P vs PF genotypes are consistent across timepoints. Moreover, we also performed IHC staining to\nconfirm the observed phenotype, as shown in Figure 5D.\n7. Figure 5 describes the ability of PF tumors to generate an immune suppressor tumor microenvironment by\nmodulating extracellular signaling. However, the conclusions are completely inferred from the differential\nRNA expression profiles, which is not sufficient to support the conclusion. Intratumor quantification of the\nmost interesting factors through MS or ELISA methodologies should be included.\ngained TGFB cytokine signaling in PF compared to P tumors, we evaluated our Visium spatial\ntranscriptomics data, as a means of intratumor quantification of TGFB cytokine expression. In accordance\nwith our scRNA-seq data, we observed increased expression of Tgfb1/2/3 in PF Visium data compared to\nP (Figure S6F-G). To further validate these data at the protein level, we performed IHC staining of PF vs.\nP tumor tissues for pSMAD2 as an indicator of TGFB signaling. In line with our RNA level data, we observed\nincreased pSMAD2 staining in PF tumors compared to P (Figure 6G). Altogether, we have observed\nincreased TGFB signaling in PF tumors compared to P tumors, confirmed by several modalities.\n8. As well, the immune suppressor role of specific factors (at least one) should be functionally demonstrated\nin vivo through the injection of specific inhibitors.\nR: We agree it is important to functionally demonstrated the immunosuppressive effect of FOXA1-loss\ninduced cytokine pathways. To this end, we decided to focus on the TGFB pathway, and performed a co-\nculture assay with WT or FOXA1 KD PC3 prostate cancer cells co-cultured with monocyte derived\nmacrophages (MDMs). Treatment with TGF-β receptor I inhibitor Galunisertib (TGFBi) in this co-culture\nsystem led to a reduction in macrophage infiltration, suggesting FOXA1-loss induced TGFB signaling recruit\nmacrophages to the tumor immune microenvironment. This data has now been added to Figure 6H.\nvivo, we treated PF tumor-derived allograft mouse models with 75 mg/kg TGFBi twice daily, for three weeks,\nand found that TGFBi led to a reduction in tumor weight (though not significant). This data has now been\nadded to Figure S6J.\n9. Interestingly, based on panel B PF tumors express very low levels of Activin (Activin A?) compared to P\ntumors. Activin A has been described plays a very important role in controlling prostate tissue homeostasis\nin mouse adult prostate progenitors by promoting quiescence (Cambuli et al., 2022 EMBO Reports).\nOrganoids that were able to spontaneously overcome the barrier by reducing Activin A\nexpression/secretion experience genomic instability and generate CK8/CK5 double positive hyper proliferating dysplastic\nlesions when transplanted orthotopically into the prostate of adult wild type syngeneic mice. Authors should\ncomment on the possible implication of reduced Activin expression in PF versus P prostate tumors.\nR: Thank you for this suggestion. Indeed, activin A plays important roles in prostate tissue homeostasis.\nHowever, we only found Activin A downregulated in PF mice at the 18wk timepoint, not the 12wk timepoint.\nWe chose to only discuss pathways found consistently enriched or de-enriched across both 12wk and 18wk\ntimepoints, to support more robust conclusions.\n10. A recent paper (De Felice et al., 2024) describes the role of Retinoic Acid signaling in promoting luminal\nlineage of mouse prostate progenitors by promoting Foxa1 expression. Lack of RA-Foxa1 activity as well\nas specific mutant forms of FOXA1 associated with human PCa determine AR deregulation and a\nbasal/squamous commitment of prostate progenitor cells. Authors should carefully discuss this paper in\ntheir manuscript.\nR: Thank you for bringing this recent relevant paper to our attention. We have now added a discussion of\nthis paper to our discussion section.\nFOXA1 is a critical gene in prostate cancer development and therapeutic resistance, with functions that\nappear to be highly context-dependent. It is frequently mutated and overexpressed in prostate cancer,\nrarely deleted, but appears to be downregulated in castration-resistant prostate cancer (CRPC). Its\nprimary role is as a pioneer factor for the androgen receptor (AR), and it has been implicated in AR-\nindependent CRPC evolution through mechanisms such as cellular plasticity.\nThis complexity has led to seemingly conflicting findings, underscoring the multifaceted role that FOXA1\nplays in prostate cancer pathogenesis. In this context, the generation and characterization of the PTEN-\nFOXA1 prostate knockout model presented in this manuscript is of considerable interest. The authors\nhave previously investigated FOXA1 downregulation in human cells, primarily using shRNA, observing\nreduced cell proliferation in hormone-sensitive lines and upregulation of the epithelial-to-mesenchymal\ntransition (EMT) program.\nInterestingly, in this mouse model, co-deletion of PTEN and FOXA1 results in an aggressive prostate\ncancer phenotype with pronounced basal and squamous features and an immune infiltrate rich in\nmyeloid cells. This suggests a potentially immunosuppressive tumor microenvironment characterized by\ndysfunctional T cells. Although basal/squamous prostate cancers are relatively rare in patients, I find this\nmodel highly intriguing, as it aligns with reports suggesting that this phenotype—more commonly\nobserved in mice—may represent a plastic intermediate state in patients.\nThe authors employ a multimodal characterization of these tumors, which may have broader implications\nbeyond the specific role of FOXA1. This is a significant strength of the manuscript, and I believe the\nstudy would benefit greatly from additional analyses and functional experiments.\nMajor Points:\nCastration Studies\nFOXA1 downregulation occurs later in prostate cancer progression, particularly during CRPC evolution, as\nnoted by the authors. Characterizing these models post-castration—whether through surgical means or\ndegarelix administration—is essential to fully demonstrate their relevance. The single-cell RNA sequencing\n(scRNA-seq) data indicate that the luminal compartment remains substantial. The impact of castration on\nthese cells, as well as on the immune microenvironment, may influence the cancer phenotype and\nprogression.\nR: Thank you for this suggestion. It is important to note that intact adult male mice naturally exhibit low androgen\nlevels similar to those observed in patients undergoing standard androgen deprivation therapy (ADT), whereas\ncastrated mice exhibit extremely low androgen levels similar to abiraterone-treated patients (PMID: 23775398).\nThus, our intact PF mice exist in a low-androgen environment that resembles the androgen-low conditions under\nwhich many post-ADT CRPC tumors arise, wherein FOXA1 is often found downregulated. Castration of PF\nmice creates an even more androgen-depleted setting, similar to the more advanced CRPC context following\ntreatment with abiraterone. To assess the role of FOXA1 loss under these more severe androgen-deprived\nconditions, we have now evaluated a cohort of castrated P versus PF mice (abbreviated XP and XPF),\nas suggested by the reviewer. We found Foxa1 loss promotes castration resistance in surgically castrated PF vs.\nP mice. Prostate tumors of castrated PF mice (XPF) show an aggressive, invasive, poorly differentiated and\ndisordered cellular phenotype, with further loss of luminal identity and gained basal-squamous phenotype.\nMoreover, Foxa1 depleted tumors show an enhanced proliferative capacity as compared to P tumors following\nsurgical castration, with XPF tumors showing larger tumor size and greater Ki67 staining. These findings have\nbeen incorporated into Figures 1 & 2, and are also shown below with key data.\n7\nConsistent with their previous studies in human cell lines, the authors report EMT upregulation upon FOXA1\ndeletion in these models. This raises an important question: do these tumors develop metastases? It is\nunclear whether the authors have investigated this aspect. Analyzing metastatic spread in both intact and\ncastrated mice would provide a more comprehensive characterization of the model.\nR: As described in response to Reviewer 1 Question 3, to address the incidence of metastasis in our novel\nGEMM models, we examined tissue sections from lymph nodes, lung, and liver by IHC for AR and H&E staining\nfor evidence of metastatic PCa cells. 9 intact PF mice and 7 intact P mice between the ages of 15-21wks were\nevaluated, and none were found to exhibit evidence of metastasis. However, we observed evidence of\nmetastasis following castration in XPF mice. 2 out of 5 XPF mice demonstrated evidence of metastasis, one in\nthe lung and one in lymph node tissue, while 0 out of 5 XP mice evaluated exhibited evidence of metastasis by\nIHC/H&E analysis. This information has been added to a summary table in Figure S1G.\nAddressing Discrepancies in Tumor Immune Microenvironment (TIME) Analysis\nThe authors provide an extensive characterization of the TIME using multiple single-cell resolution\ntechnologies, including scRNA-seq, PIP-seq, Cytek spectral flow cytometry, and spatial transcriptomics.\nHowever, while scRNA-seq and spatial transcriptomics reveal prominent macrophage and monocyte\ninfiltration, flow cytometry identifies polymorphonuclear myeloid-derived suppressor cells (PMN-MDSCs) as\nthe dominant immune cell type. PMN-MDSCs are of great interest in prostate cancer, as they play a crucial\nrole in immune suppression in both mouse models and patients. The discrepancy between methods raises\nconcerns. One possible explanation is methodological bias introduced by cell dissociation and tissue\npreparation. On the other hand, the flow cytometry panel used may not be optimal for distinguishing MDSCs\nsubsets. The standard antibody combination for M-MDSCs and PMN-MDSCs includes Ly6C (HK1.4) and\nLy6G (1A8). However, the authors appear to have used Ly6C (HK1.4) in combination with the Ly6G/C\nantibody (RB6-8C5). When this antibody is used instead of a Ly6G-specific antibody such as 1A8, it stains\nboth M-MDSCs and PMN-MDSCs, resulting in the majority of CD11b-positive cells being positive for this\nstaining, as shown in Fig. S3. This could potentially lead to an overestimation of the number of PMN-MDSCs.\nRevisiting the staining strategy including additional validation with alternative antibodies is recommended.\nR: We thank the reviewer for pointing out the issue regarding the flow cytometry panel for defining MSDC\nsubsets. We agree that the standard antibody combination for M-MDSCs and PMN-MDSCs includes Ly6C\n(HK1.4) and Ly6G (1A8). We apologize for the incorrect labeling of “Ly6G” as “Ly6G_C” in the original Fig S3.\nThe error has now been corrected. As the percentage of Ly6G⁺ cells (PMN-MDSCs) among CD11b⁺ cells varied\nacross individual samples, a more representative staining panel is now presented now in Figure\nS4D. Additionally, the anti-Ly6G antibody information in Table S3 has been updated from Ly6G/C (RB6-8C5) to\nLy6G (1A8).\nWith regards to the apparent discrepancy between the 10X Genomics scRNA-seq, PIP-seq, flow\ncytometry, and spatial transcriptomics data, we believe several factors may be at play. First, different tissue\ndissociation methods and analysis modalities can indeed influence the relative abundance of captured cell\ntypes, as shown in PMID: 36750562. Also, discussions with the PIPseq technical support indicated that PIPseq\nsingle-cell analysis does not efficiently capture monocytic populations, hence leading to our low recovery of\nmonocytes by PIPseq. Additionally, PMN-MDSCs are known to be poorly captured by scRNA-seq platforms,\ndue to their low RNA content, high RNase activity, and membrane fragility, explaining why they were not\nrepresented in our transcriptomics analyses. Lastly, monocytes and MDSCs share several myeloid lineage\nmarkers in common and can be challenging to distinguish based solely on RNA expression. We annotated\nmonocyte-like populations based on expression of S100a8, S100a9, and Cd14 genes. However, MDSCs also\nexpress these markers. Thus, it is likely this “monocyte” population includes M-MDSCs.\nFunctional Experiments\nThe manuscript is largely descriptive. The authors acknowledge in the discussion that targeting cytokines\nsuch as TGF-β could influence immune checkpoint inhibitor (ICI) responses. This reasoning could also be\nextended to the observed TIME components. Performing a cell depletion assay or cytokine-targeting\nexperiments—even without ICI combination—would provide functional evidence for the importance of at least\none of these TIME components.\nR: As addressed in Reviewer 1 Question 8, we agree it is important to functionally demonstrated the\nimmunosuppressive effect of FOXA1-loss induced cytokine pathways. To this end, we performed a functional\nassay with WT or FOXA1-KD PC3 prostate cancer cells co-cultured with monocyte derived\nmacrophages (MDMs) from PCa patients. Treatment with TGFBi in this co-culture system led to a reduction in\nmacrophage infiltration, suggesting FOXA1-loss induced TGFB signaling recruit macrophages to the tumor\nimmune microenvironment. This data has now been added to Figure 6H, also shown below.\nAdditional Major Considerations:\nThroughout the manuscript, including the title and discussion, the authors repeatedly emphasize how their\ndata support the role of FOXA1 in prostate cancer progression through its regulation of prostate epithelial cell\nlineage identity and the tumor immune microenvironment. While the data may be consistent with this\nhypothesis, the manuscript lacks direct evidence—either genetic (e.g., lineage tracing) or mechanistic (e.g.,\nChIP-seq)—to prove it. Alternative explanations cannot be ruled out, making this the weakest aspect of the\nstudy.\nTo address this, I suggest two possible courses of action:\n- The authors could revise the text to reduce emphasis on this aspect, ensuring that the conclusions are\nappropriately cautious.\n- If the editors and authors believe this hypothesis is central to the study, additional experiments are\nnecessary to provide stronger evidence.\nR: Thank you for this comment. To further support our claim that FOXA1 regulates epithelial lineage identity and\ninflammatory signaling, we performed ChIP-seq for FOXA1 and H3K27ac. Notably, ChIP-seq analysis revealed\nFOXA1 chromatin binding at/near lineage and cytokine/inflammatory genes of interest, with corresponding\nchanges in H3K27ac, suggesting their direct transcriptional regulation by FOXA1. This data has now been\nincorporated into the manuscript in Figures 3F-H, S3C-E, & S6H.\nThe initial study describing the PB-Cre mice used in this study reported that \"there was no evidence of Cre\nexpression in the basal cells of any of the prostate lobes examined\" (Wu et al., Mechanisms of Development,\n2001). However, subsequent studies have shown that this model is not luminal-specific and that Cre is\nexpressed in both basal and luminal epithelial cells (Wang et al., PNAS, 2006\n[https://doi.org/10.1073/pnas.051065210]). Thus, to support the claim that FOXA1 drives dedifferentiation,\nthe authors should investigate the tumor cell of origin in greater detail. The current data do not exclude a\nbasal origin, and the two selected time points (12 and 18 weeks) do not clearly demonstrate a phenotypic switch. To\ndetermine whether luminal or basal cells initiate oncogenic transformation in this model, several approaches\nare available:\n11\n- Lineage tracing: This can be achieved using transgenic mouse models with CreERT2 systems more specific\nto either basal (e.g., CK5-CreERT2, TP63-CreERT2) or luminal (e.g., CK8-CreERT2, NKX3.1-CreERT2)\nepithelial cells, which would be more appropriate than the PB-Cre model.\n- In vitro modeling: Isolating the different prostate epithelial cells (e.g., as organoids), transforming them\ngenetically, and studying their oncogenic potential could provide more direct mechanistic insights. Performing\nsimilar experiments in human cells would further strengthen the study.\nR: Though PbCre may be expressed in both luminal and basal epithelial cells, FOXA1 is only expressed by\nluminal epithelial cells. Thus, FOXA1 deletion should only be occurring in luminal cells. Nevertheless, to better\nsupport our claim that FOXA1 loss drives de-differentiation of prostate luminal epithelial cells to a\nbasal/squamous phenotype, we evaluated ChIP-seq data of FOXA1 in LNCaP cells and found FOXA1 binding\npeaks at/near basal/squamous lineage genes such as KRT14 and KRT6A/B/C (GSE55007) (Figure S3G). To\nfurther understand how FOXA1 loss regulates epithelial lineage in human PCa, we evaluated previously\npublished gene expression microarray analysis of LNCaP cells with FOXA1 KO and rescue with WT or mutant\nFOXA1 (G87R, D226N, M253K, 254-257FENG>C(del), F266S, L388M) (PMID: 31324884). We found that\nFOXA1 KO leads to an increase in KRT14 and KRT6A expression. Rescue with WT FOXA1 subsequently\nrepressed KRT14 and KRT6A, while rescue with FOXA1 forkhead mutants, which are known to exhibit reduced\nchromatin binding, had reduced ability to repress KRT14 and KRT6A expression (Figure S3F). Taken together,\nthese findings suggest several squamous genes are direct transcriptional targets of FOXA1 in human PCa.\nChromatin Immunoprecipitation (ChIP)\nThe authors acknowledge that FOXA1’s role is highly context-dependent. While the upregulation of EMT is\nconsistent with previous findings in human cancer cell lines, the observed basal/squamous phenotype and\ncytokine signaling are novel insights specific to this model. To establish FOXA1’s role in this process, ChIP-\nseq analysis in the relevant cell population is crucial. This would clarify:\n- Whether FOXA1 directly regulates luminal differentiation.\n- Whether the observed immunosuppressive TIME is a result of direct transcriptional repression of cytokines\nby FOXA1 or merely a consequence of the basal/squamous PCa phenotype, which may have its own distinct\ntranscriptional network and immune landscape.\nR: As suggested, we performed FOXA1 ChIP-seq in P vs. PF prostate tumors to identify specific FOXA1 binding\nevents on the chromatin. We observed FOXA1 chromatin binding at/near luminal and basal/squamous lineage\ngenes of interest, suggesting FOXA1 directly regulates their expression and functions as a master regulator of\nepithelial lineage in prostate cancer cells. This data is now included in Figure 3F-H & S3C-E. We also observe\nFOXA1 chromatin binding at/near immunosuppressive genes, such as TGFB cytokine genes, now included in\nFigure S6H.\nPTEN/FOXA1 prostate cancer model, which represents a rare but intriguing subtype of prostate cancer\nobserved in patients. However, I would recommend a more comprehensive characterization, including\ncastration of the mice—either surgically or through degarelix treatment—as the genetic alterations in this\nmodel are intended to mimic castration-resistant prostate cancer. Additionally, an analysis of potential\nmetastases should be included.\nWhile I understand the inclination to link FOXA1 to cellular plasticity and propose it as a direct regulator\nof the tumor immune microenvironment through transcriptional control of the chemokine/cytokine milieu,\nthis interpretation remains speculative. Although the characterization may suggest such a role, the study\ndoes not provide definitive proof. If the authors wish to pursue this hypothesis—which I agree is of\nconsiderable interest—they should conduct a combination of genetic, molecular, and functional analyses\nto substantiate their claims. Otherwise, they should adjust the manuscript’s tone and conclusions\naccordingly.\nR: We appreciate the reviewers’ careful and thoughtful evaluation of our work and are grateful for the\nconstructive feedback. In response to the comments, we have added substantial experiments and analyses,\nincluding expanding our analyses to the castrated condition, evaluating metastasis, and performing ChIP-seq\nand additional functional experiments, as described in our point-by-point response. We believe these\nadditions significantly strengthen the manuscript and more robustly support our conclusions. We have also\nrevised the title of our manuscript.\nOverview:\nThis manuscript (ms) explores the tumorigenic effects of Foxa1 depletion in prostate cancer by\ncomparing single-cell and spatial transcriptomic profiles between Pten-null transgenic mice (P) and\nPten/Foxa1 double-knockout mice (PF). Comprehensive bioinformatics analyses were conducted using\nvarious software tools to support the study’s findings. Overall, this manuscript is well-written, with data\nand analysis results clearly presented. However, several issues need to be addressed.\nMajor Comments:\n1. The study primarily analyzes four samples: P12, PF12, P18, and PF18 — representing two conditions\nat two different time points. A key limitation of the study is the lack of biological replicates for each\ncombination of condition and time point. This is particularly concerning for the spatial analysis using\nTESLA, as different tissue structures of replicate samples could lead to varying conclusions.\nR: We agree with the reviewers that the lack of biological replicates for our scRNA-seq and spatial\ntranscriptomics analyses, due to their high cost, are a limitation of our study. However, we have\nconfirmed our major conclusions via a variety of modalities, including scRNA-seq, spatial transcriptomics,\nIHC, and flow cytometry analyses. Moreover, we observe consistent phenotypes across 12wk and 18wk\ntimepoints, further validating the robustness of our findings.\n2. In the scRNA-seq integration analysis, there seem to be six samples in total: P12, PF12, P18, PF18,\nand two castrated samples (XP and XFP). If the two castrated samples are indeed part of the integration\nanalysis (as indicated in the analysis code), their absence from the UMAP visualization is unclear.\nFurthermore, there is no mention or representation of these castrated samples in any analysis or figures\nthroughout the ms.\nR: Thank you for this comment. We’ve re-incorporated the castrated samples in our UMAPs and\nmanuscript discussions, considering reviewer comments and the important roles of FOXA1 loss in\ncastration-resistant disease. Data on castrated mice has now been incorporated into Figures 1 & 2.\n3. In the scRNA-seq integration analysis, several cell clusters co-express luminal and basal genes. For\ninstance, the small basal (red) cluster in the center of Fig.2A shows high Krt8 expression in Fig.2E, and\nsimilar patterns are seen in the \"basal squamous\" and \"basal proliferating\" clusters in Fig.S2A and S2B.\nCould these clusters represent doublets?\nR: Thank you for this comment. Our data processing pipeline includes doublet removal, so it is unlikely\nthese represent doublets. We believe this overlap in luminal and basal markers may suggest a state of\nlineage plasticity or some basal-luminal intermediate state which is promoted by Foxa1 depletion.\n4. What does the UMAP plot in Fig.S2D look like when coloured by sample? Providing this visualization\nwould help clarify sample-specific clustering and potential batch effects.\nR: Thank you for this suggestion. As suggested, we have recolored the UMAP plot in Fig. S2D (now Figure\nS2F) by sample, as shown below. Based on this visualization, there does not appear to be any clear batch\neffects, the samples appear well integrated.\n14\n5. In the pseudotime analysis, both the “Basal proliferating” and “Tff3 Lum” clusters are assigned a zero\npseudotime (Fig.2D). Does this imply that these clusters were excluded from the Monocle3 pseudotime\ntrajectory analysis? The UMAP also suggests that luminal cells are closer to basal cells than basal proliferating\ncells, which seems counterintuitive.\nR: Thank you for this question. “Basal proliferating” and “Tff3 Lum” clusters were assigned to a separate\ntrajectory path that’s not part of the main trajectory. We utilized Monocle3’s ability to produce separate\npartitions/trajectories via clustering. Monocle3 does not enforce that all cells must descend from a common\ntranscriptional ancestor and are assigned pseudotime of 0 by default. The basal proliferating cells may have\nvery distinct transcript of cycling genes and may represent a functionally separate pool of cells.\n6. How was the starting point chosen for the Monocle3 pseudotime analysis? Have the authors tested the\nrobustness of their results by selecting different starting points — for example, the basal cluster on the left side\nof the UMAP?\nR: Thank you for this question. Yes, we have tested various starting points before choosing this particular one.\nWe tried using the luminal population only (the one we submitted), the Pbsn luminal population, as well as\nvarious others (not shown). While choosing Pbsn as the starting point puts the population first, the rest of the\ntrajectory remains similar, with Basal population at the end.\n15\n7. The TESLA tool was used to annotate Visium data at the pixel level, but it appears that individual pixels do\nnot have unique cell-type annotations. For example, in Fig.4A (PF18), there is substantial overlap between\nluminal and basal cells. Does this suggest that both luminal and basal cells occupy the same H&E image pixels?\nThis ambiguity complicates the interpretation of cell-type proportions, as seen in Fig.4B, where macrophages,\nmonocytes, and T cells together constitute over 80% of the tissue — a proportion unlikely to reflect the true\ncomposition.\nR: Thank you for this comment. Because Visium is not single cell resolution, but rather each barcoded spot\nmay contain 5-10 cells, then each barcoded spot may contain several different cell types, and thus, each spot\nmay express genes corresponding to several different cell types. Thus, TESLA, which annotates Visium data\nbased on a combination of gene expression data and H&E staining, may annotate overlapping regions as\ncontaining multiple cell types. The percentages depicted for immune populations in the manuscript should not\nbe interpreted as absolute proportions of the tumor, but rather % of captured spots in the tissue section with\nhigh probability of containing the indicated cell type. This clarification has now been added to the figure\n16\nlegend. With regards to the luminal and basal overlap, we believe the vast overlap of luminal and basal-like\nregions identified in the PF tumors could be indicative of a mixed luminal-basal phenotype and lineage\nplasticity, as observed in our scRNA-seq analyses as well.\n8. Stromal cell populations are not clearly presented in the Visium profiles. Are stromal cells present in the\nanalyzed tissues, and if so, where are they located?\nR: Thank you for this question. Stromal cells are present in the tissues analyzed by Visium. To visualize the\nlocation of mesenchymal stromal cells in the analyzed tissue sections, we performed TESLA analysis of the\nVisium tissue sections based on expression of mesenchymal markers Vim, Col1a1, Col3a1, Col6a1, Col6a2,\nDcn, Tagln. Notably, P prostate tissue sections appeared to maintain their glandular architecture, with tumor\nepithelial cell regions surrounded by well-defined mesenchymal stromal cell regions. Meanwhile, in PF tissue\nsections, TESLA analysis for mesenchymal stromal populations further pointed towards a gain in lineage\nplasticity in PF tumors, as broad regions of tumor epithelial cells appeared to express these mesenchymal\nmarkers. This data has now been added to Figure S5C.\nMinor Comments:\n1. Consistent colours for cell-type granularity across Fig.S2D and S2E would improve visual clarity and aid in\nthe interpretation of results.\nR: As suggested, we have recolored the UMAP in Figure S2F to match color in the pseudotime plots.\nManuscript Title: FOXA1 loss drives basal/squamous de-differentiation of prostate cancer and\ninduces an immunosuppressive tumor microenvironment\nManuscript ID: NCOMMS-25-07398A-Z\nDear Reviewers,\nWe thank you for the continued evaluation of our manuscript and for the helpful feedback provided. In\nthis revision, we have addressed the remaining concerns by further clarifying our bioinformatics methods\nand confirming the robustness of our findings. Below, we provide a point-by-point response to the\ncomments raised, with all changes highlighted in the revised manuscript.\nReviewer #1 (Remarks to the Author):\nThanks to the authors for addressing the main issues.\nThe work is now suitable for publication.\nR: We thank the reviewer for their careful re-evaluation and positive assessment of the revised\nmanuscript.\nReviewer #2 (Remarks to the Author):\nThe authors have carefully and thoroughly addressed all of the points raised in my previous review. The\nvast majority of the additional experiments requested have been performed, and the resulting data\nstrengthen the overall conclusions of the study. Importantly, these new analyses have improved both\nthe clarity and the robustness of the manuscript. I am pleased to see that the manuscript represents a\nsubstantial improvement over the original submission.\nR: We thank the reviewer for the careful re-evaluation of the manuscript and for the positive and\nencouraging feedback. We are grateful that the additional experiments and analyses were found to\nstrengthen the conclusions and improve the clarity and robustness of the study.\nReviewer #3 (Remarks to the Author):\nI appreciate the authors' efforts in addressing the comments. Most concerns have been adequately\naddressed. However, several important issues remain.\nMajor comment #3:\nThere is no clear description of doublet removal in the data analysis pipeline. The only mention of\ndoublets appears in the TrypLE dissociation step during sample preparation, which does not guarantee\nthe removal of all doublets for the downstream analysis. Proper doublet detection and filtering should\nbe performed computationally during data processing, using established tools such as scDblFinder or\nDoubletFinder.\nR: Thank you for bringing this question to our attention. Indeed, doublet removal was not performed as\npart of the data analysis pipeline, as there was no clear consensus on doublet removal at the time this\ndata was collected and analyzed.\nIn light of these comments and developments in doublet-removal tools, we reprocessed our\nscRNA-seq data using scDblFinder, as suggested, to assess its effect on downstream analyses and\nconclusions. Notably, doublets were found to represent less than 5% of total identified cells in this dataset,\nand the percentage of doublets was very similar across all samples (Rebuttal Table 1, Rebuttal Figure\n1). Importantly, overall percentages of cell types/clusters do not change appreciably whether doublets\n1\nare removed or included (Rebuttal Tables 2-4). Taken together, doublet removal does not appear to have\nmuch effect on this dataset and should not affect conclusions drawn by our original analysis.\nRebuttal Fig. 1\nRebuttal Table 1: Percentage of doublets across samples\nSample % doublets\nP12 4.33%\nPF12 4.58%\nP18 4.89%\nPF18 5.05%\nXP 4.74%\nXPF 5.08%\nOverall 4.81%\nRebuttal Table 2: Major cell type percentages with or without doublet removal\nCell types major singlets % cell type with singlets % cell type\ndoublet removal + without doublet\ndoublets removal\nLuminal 7013 38.25% 7537 39.13%\nBasal 7066 38.54% 7378 38.30%\nSeminal Vesicles 1069 5.83% 1105 5.74%\nStroma 570 3.11% 584 3.03%\nMonocytes 1389 7.58% 1406 7.30%\nMacrophage 769 4.19% 790 4.10%\nB Cells 258 1.41% 260 1.35%\nT/NK Cells 201 1.10% 202 1.05%\nCell types singlets % cell type singlets + % cell type\ngranular with doublet doublets without\nremoval doublet\nremoval\nLuminal 4639 25.30% 5108 26.52%\nSpink1 Lum 1349 7.36% 1361 7.07%\nPbsn Lum 681 3.71% 702 3.64%\nTff3 Lum 344 1.88% 366 1.90%\nSeminal Vesicles 1069 5.83% 1105 5.74%\nBasal 4683 25.54% 4822 25.03%\nBasal Squamous 1893 10.32% 2009 10.43%\nBasal Proliferating 490 2.67% 547 2.84%\nFibroblasts 470 2.56% 483 2.51%\nEndothelial 100 0.55% 101 0.52%\nMonocytes 1389 7.58% 1406 7.30%\nMacrophage 769 4.19% 790 4.10%\nT/NK cells 201 1.10% 202 1.05%\nB cells 258 1.41% 260 1.35%\nRebuttal Table 4: Cluster percentages (resolution 0.2) with or without doublet removal\nres0.2 singlets % cell type singlets + % cell type\ncluster with doublet doublets without doublet\nremoval removal\n0 4639 25.30% 5108 26.52%\n1 3322 18.12% 3446 17.89%\n2 1893 10.32% 2009 10.43%\n3 1389 7.58% 1406 7.30%\n4 1349 7.36% 1361 7.07%\n5 1219 6.65% 1229 6.38%\n6 829 4.52% 855 4.44%\n7 769 4.19% 790 4.10%\n8 681 3.71% 702 3.64%\n9 490 2.67% 547 2.84%\n10 470 2.56% 483 2.51%\n11 344 1.88% 366 1.90%\n12 258 1.41% 260 1.35%\n13 240 1.31% 250 1.30%\n14 201 1.10% 202 1.05%\n15 142 0.77% 147 0.76%\n16 100 0.55% 101 0.52%\nThe current cell-type annotation is unconvincing. In Fig S2A, two clusters that are far apart from each\nother on UMAP are both annotated as basal cells (and similarly for the two well-separated Spink1\nluminal clusters). According to the code on GitHub, clustering at a resolution of 0.2 yields 17 clusters\n(0–16). It is unclear why multiple, transcriptionally distant clusters (e.g., clusters 1, 5, and 15) are\nassigned the same basal cell identity. What marker genes distinguish these clusters (e.g., cluster 1 vs\n5)?\nFurthermore, the UMAP from the epithelial integration analysis (in the monocle3 analysis) suggests the\npresence of two transcriptionally distinct basal cell subsets (as well as two distinct Spink1 luminal\nsubsets). These observations indicate meaningful biological heterogeneity that is not sufficiently\nexplained by the current annotations. The rationale for assigning identical cell-type labels to these\ndistinct clusters should be clearly justified, and the annotations revised if necessary.\nR: Thank you for this thoughtful comment. Indeed, the tumor epithelium is quite heterogeneous and\nclusters into several sub-populations (Rebuttal Fig 2). However, our focus in our scRNA-seq analysis\nwas to evaluate how Foxa1 loss dysregulates epithelial cell lineage, signaling, and the tumor immune\nmicroenvironment, rather than identifying granular epithelial subclusters. Thus, we combined some\nepithelial subclusters identified by resolution 0.2 clustering based on canonical cell type marker gene\nexpression to reduce the number of clusters and focus on the main takeaways from this data.\nTo begin, evaluating the expression of major cell type marker genes across resolution 0.2 clusters\n(Rebuttal Fig 3 right panel, also added to Supplementary Figure 2A), we decided to combine 13 & 6\ndue to their shared elevated expression of seminal vesicle marker gene Pate4, as well as their proximity\nin the UMAP. Next, cluster 15 was combined with cluster 1 due to similar expression of key basal marker\ngenes and proximity in the UMAP. Cluster 5 was also combined with 1 in the basal population due to the\ngained expression of key basal genes, which were not expressed in most luminal clusters other than\nSpink1+ cells, and the reduced expression of luminal markers (Rebuttal Figure 3 & new Supplementary\nFigure 2C). Thus, Cluster 5 might represent an intermediate basal population, which hasn’t fully lost\nluminal gene expression but has begun to gain basal gene expression. This has now been added to\nSupplementary Figure 2A,C. Moreover, recoloring the pseudotime trajectory by resolution 0.2 clusters,\nwe observed clusters 15 & 5 largely appear within a similar “pseudotime” as cluster 1 (Rebuttal Fig 4),\nsupporting their incorporation as part of the \"basal\" population.\nRegarding cluster 4 (the Spink1+ luminal population), though it appears as two physically\nseparate clusters on the UMAP, it was transcriptionally similar enough to be identified as a single cluster\nat resolution 0.2, likely due to the 2D UMAP visualization not accurately reflecting their global distance.\nFurther, prostate cells that are strongly Spink1+ are well known as a special luminal cell type associated\nwith aggressive prostate cancer with enhanced lineage plasticity, supported by co-expression of luminal\nand basal genes in our data. Because the focus of this manuscript was not to granularly subtype epithelial\npopulations, we did not further subcluster or explore these populations.\nRebuttal Fig. 2\nRebuttal Fig 4\nMajor comment #7:\nRegarding the statement \"... but rather % of captured spots in the tissue section with high probability of\ncontaining the indicated cell type,\" the definition of \"high probability\" is unclear. What probability\nthreshold is used to classify a pixel as containing a given cell type? Or what exactly is the criterion for\ncounting pixels for each cell type? While it is correct that Visium spots do not have single-cell resolution\nand may contain multiple cells, this argument does not apply at the pixel level, as image pixels are\nsubstantially smaller than individual cells and should therefore mostly correspond to a single cell type.\nR: We thank the reviewer for this thoughtful question. In TESLA analysis, the reported values are not\ndeconvolution-style probabilities of cell-type presence at the pixel level. Instead, as described in the\noriginal TESLA publication, the method assigns each pixel a score between 0 and 1 that reflects the\nrelative enrichment of a given cell type at that pixel across the whole slide. To identify regions enriched\nfor a specific cell type, pixels with enrichment scores >0.3 were classified as enriched for that cell type\nand used to quantify the corresponding area. Notably, because these scores represent relative\n5\nenrichment rather than exclusive assignment, a pixel may have non-zero scores for multiple cell types,\ncapturing their colocalization pattern.\nThe advantages of using the score from TESLA over spot-level deconvolution are threefold:\n1. TESLA incorporates H&E image information, providing additional morphological context to improving\ncell-type annotation compared with deconvolution approaches based solely on spot-level gene\nexpression.\n2. TESLA enables annotation across the entire tissue section, rather than being restricted to measured\nVisium spots, which typically cover < 50% of the captured tissue area.\n3. Pixel-level scores provide improved visualization and quantification of spatial co-localization among\ndifferent cell types compared with spot-level analysis.\nWe have clarified this point in the methods and legends of the revised manuscript to avoid confusion.\nAdditional point related to Major comment #7:\nIn the statement \"... we believe the vast overlap of luminal and basal-like regions identified in the PF\ntumours could be indicative of a mixed luminal–basal phenotype and lineage plasticity, as observed in\nour scRNA-seq analyses as well,\" it is unclear which specific scRNA-seq clusters in the integration\nanalysis represent this \"mixed luminal–basal phenotype.\" The authors should explicitly identify the\nrelevant clusters and provide supporting evidence for this interpretation.\nR: We thank the reviewer for this question and opportunity to clarify this statement. This statement here\nwas not made in reference to a particular scRNA-seq cluster, but rather a trend we observed of gained\nbasal phenotype among PF tumor epithelial cells, without complete loss of luminal gene expression,\nsuggesting a “mixed” identity or lineage plasticity, which was supported by several pieces of evidence\nfrom our scRNA-seq analysis. Firstly, evaluation of Krt5 (basal), Krt6a (squamous), and Krt8 (luminal)\nexpression on the UMAP for PF tumors showed evidence of epithelial cells with gained basal/squamous\ngene expression but limited loss of luminal marker Krt8 expression (Figure 2E). Additionally, our lineage\ntrajectory analysis revealed that PF tumors exhibit a much earlier gain in basal gene expression along\nthe trajectory, with more cells within the “early” luminal populations expressing basal marker genes, as\ncompared to P tumors (Figure 2G). Moreover, we observed increased expression of epithelial-\nmesenchymal transition (EMT) genes among epithelial cells in PF tumors (Fig S2F & Fig 3A), further\nsupporting Foxa1 loss induces lineage plasticity. Nevertheless, as described in response to comment #3\nabove, further evaluation of canonical luminal and basal cell type marker genes across resolution 0.2\nclusters suggests cluster 5 may represent a mixed or intermediate basal state.\nMinor comment: It might be better to include Fig S2C in Fig 2D.\nR: We have now included FigS2C in Fig2D, as suggested.\nReviewer #3 (Remarks on code availability):\nThe code on the GitHub is well organized and easy to follow.\nR: Thank you.\nWe confirm that there are no additional reviewer comments to address.\nWe sincerely thank the reviewers and the editors for their careful evaluation of our\nmanuscript and for their thoughtful and constructive feedback. Their insights have\nsignificantly strengthened our work, and we greatly appreciate the time and expertise\ninvested in the review process."}, {"round": 2, "reviewer_comments": {"reviewer_1": "Thanks to the authors for addressing the main issues.\nThe work is now suitable for publication.", "reviewer_2": "The authors have carefully and thoroughly addressed all of the points raised in my previous review. The vast majority of the additional experiments requested have been performed, and the resulting data strengthen the overall conclusions of the study. Importantly, these new analyses have improved both the clarity and the robustness of the manuscript. I am pleased to see that the manuscript represents a substantial improvement over the original submission.", "reviewer_3": "I appreciate the authors' efforts in addressing the comments. Most concerns have been adequately addressed. However, several important issues remain.\n\nMajor comment #3:\nThere is no clear description of doublet removal in the data analysis pipeline. The only mention of doublets appears in the TrypLE dissociation step during sample preparation, which does not guarantee the removal of all doublets for the downstream analysis. Proper doublet detection and filtering should be performed computationally during data processing, using established tools such as scDblFinder or DoubletFinder.\n\nContinuation of Major comment #3:\nThe current cell-type annotation is unconvincing. In Fig S2A, two clusters that are far apart from each other on UMAP are both annotated as basal cells (and similarly for the two well-separated Spink1 luminal clusters). According to the code on GitHub, clustering at a resolution of 0.2 yields 17 clusters (0–16). It is unclear why multiple, transcriptionally distant clusters (e.g., clusters 1, 5, and 15) are assigned the same basal cell identity. What marker genes distinguish these clusters (e.g., cluster 1 vs 5)?\n\nFurthermore, the UMAP from the epithelial integration analysis (in the monocle3 analysis) suggests the presence of two transcriptionally distinct basal cell subsets (as well as two distinct Spink1 luminal subsets). These observations indicate meaningful biological heterogeneity that is not sufficiently explained by the current annotations. The rationale for assigning identical cell-type labels to these distinct clusters should be clearly justified, and the annotations revised if necessary.\n\nMajor comment #7:\nRegarding the statement \"... but rather % of captured spots in the tissue section with high probability of containing the indicated cell type,\" the definition of \"high probability\" is unclear. What probability threshold is used to classify a pixel as containing a given cell type? Or what exactly is the criterion for counting pixels for each cell type? While it is correct that Visium spots do not have single-cell resolution and may contain multiple cells, this argument does not apply at the pixel level, as image pixels are substantially smaller than individual cells and should therefore mostly correspond to a single cell type.\n\nAdditional point related to Major comment #7:\nIn the statement \"... we believe the vast overlap of luminal and basal-like regions identified in the PF tumours could be indicative of a mixed luminal–basal phenotype and lineage plasticity, as observed in our scRNA-seq analyses as well,\" it is unclear which specific scRNA-seq clusters in the integration analysis represent this \"mixed luminal–basal phenotype.\" The authors should explicitly identify the relevant clusters and provide supporting evidence for this interpretation.\n\nMinor comment: It might be better to include Fig S2C in Fig 2D."}, "author_response": "Response to Referees\nManuscript Title: FOXA1 loss drives basal/squamous de-differentiation of prostate cancer and\ninduces an immunosuppressive tumor microenvironment\nManuscript ID: NCOMMS-25-07398A-Z\nDear Reviewers,\nWe thank you for the continued evaluation of our manuscript and for the helpful feedback provided. In\nthis revision, we have addressed the remaining concerns by further clarifying our bioinformatics methods\nand confirming the robustness of our findings. Below, we provide a point-by-point response to the\ncomments raised, with all changes highlighted in the revised manuscript.\nReviewer #1 (Remarks to the Author):\nThanks to the authors for addressing the main issues.\nThe work is now suitable for publication.\nR: We thank the reviewer for their careful re-evaluation and positive assessment of the revised\nmanuscript.\nReviewer #2 (Remarks to the Author):\nThe authors have carefully and thoroughly addressed all of the points raised in my previous review. The\nvast majority of the additional experiments requested have been performed, and the resulting data\nstrengthen the overall conclusions of the study. Importantly, these new analyses have improved both\nthe clarity and the robustness of the manuscript. I am pleased to see that the manuscript represents a\nsubstantial improvement over the original submission.\nR: We thank the reviewer for the careful re-evaluation of the manuscript and for the positive and\nencouraging feedback. We are grateful that the additional experiments and analyses were found to\nstrengthen the conclusions and improve the clarity and robustness of the study.\nReviewer #3 (Remarks to the Author):\nI appreciate the authors' efforts in addressing the comments. Most concerns have been adequately\naddressed. However, several important issues remain.\nMajor comment #3:\nThere is no clear description of doublet removal in the data analysis pipeline. The only mention of\ndoublets appears in the TrypLE dissociation step during sample preparation, which does not guarantee\nthe removal of all doublets for the downstream analysis. Proper doublet detection and filtering should\nbe performed computationally during data processing, using established tools such as scDblFinder or\nDoubletFinder.\nR: Thank you for bringing this question to our attention. Indeed, doublet removal was not performed as\npart of the data analysis pipeline, as there was no clear consensus on doublet removal at the time this\ndata was collected and analyzed.\nIn light of these comments and developments in doublet-removal tools, we reprocessed our\nscRNA-seq data using scDblFinder, as suggested, to assess its effect on downstream analyses and\nconclusions. Notably, doublets were found to represent less than 5% of total identified cells in this dataset,\nand the percentage of doublets was very similar across all samples (Rebuttal Table 1, Rebuttal Figure\n1). Importantly, overall percentages of cell types/clusters do not change appreciably whether doublets\n1\nare removed or included (Rebuttal Tables 2-4). Taken together, doublet removal does not appear to have\nmuch effect on this dataset and should not affect conclusions drawn by our original analysis.\nRebuttal Fig. 1\nRebuttal Table 1: Percentage of doublets across samples\nSample % doublets\nP12 4.33%\nPF12 4.58%\nP18 4.89%\nPF18 5.05%\nXP 4.74%\nXPF 5.08%\nOverall 4.81%\nRebuttal Table 2: Major cell type percentages with or without doublet removal\nCell types major singlets % cell type with singlets % cell type\ndoublet removal + without doublet\ndoublets removal\nLuminal 7013 38.25% 7537 39.13%\nBasal 7066 38.54% 7378 38.30%\nSeminal Vesicles 1069 5.83% 1105 5.74%\nStroma 570 3.11% 584 3.03%\nMonocytes 1389 7.58% 1406 7.30%\nMacrophage 769 4.19% 790 4.10%\nB Cells 258 1.41% 260 1.35%\nT/NK Cells 201 1.10% 202 1.05%\nCell types singlets % cell type singlets + % cell type\ngranular with doublet doublets without\nremoval doublet\nremoval\nLuminal 4639 25.30% 5108 26.52%\nSpink1 Lum 1349 7.36% 1361 7.07%\nPbsn Lum 681 3.71% 702 3.64%\nTff3 Lum 344 1.88% 366 1.90%\nSeminal Vesicles 1069 5.83% 1105 5.74%\nBasal 4683 25.54% 4822 25.03%\nBasal Squamous 1893 10.32% 2009 10.43%\nBasal Proliferating 490 2.67% 547 2.84%\nFibroblasts 470 2.56% 483 2.51%\nEndothelial 100 0.55% 101 0.52%\nMonocytes 1389 7.58% 1406 7.30%\nMacrophage 769 4.19% 790 4.10%\nT/NK cells 201 1.10% 202 1.05%\nB cells 258 1.41% 260 1.35%\nRebuttal Table 4: Cluster percentages (resolution 0.2) with or without doublet removal\nres0.2 singlets % cell type singlets + % cell type\ncluster with doublet doublets without doublet\nremoval removal\n0 4639 25.30% 5108 26.52%\n1 3322 18.12% 3446 17.89%\n2 1893 10.32% 2009 10.43%\n3 1389 7.58% 1406 7.30%\n4 1349 7.36% 1361 7.07%\n5 1219 6.65% 1229 6.38%\n6 829 4.52% 855 4.44%\n7 769 4.19% 790 4.10%\n8 681 3.71% 702 3.64%\n9 490 2.67% 547 2.84%\n10 470 2.56% 483 2.51%\n11 344 1.88% 366 1.90%\n12 258 1.41% 260 1.35%\n13 240 1.31% 250 1.30%\n14 201 1.10% 202 1.05%\n15 142 0.77% 147 0.76%\n16 100 0.55% 101 0.52%\nThe current cell-type annotation is unconvincing. In Fig S2A, two clusters that are far apart from each\nother on UMAP are both annotated as basal cells (and similarly for the two well-separated Spink1\nluminal clusters). According to the code on GitHub, clustering at a resolution of 0.2 yields 17 clusters\n(0–16). It is unclear why multiple, transcriptionally distant clusters (e.g., clusters 1, 5, and 15) are\nassigned the same basal cell identity. What marker genes distinguish these clusters (e.g., cluster 1 vs\n5)?\nFurthermore, the UMAP from the epithelial integration analysis (in the monocle3 analysis) suggests the\npresence of two transcriptionally distinct basal cell subsets (as well as two distinct Spink1 luminal\nsubsets). These observations indicate meaningful biological heterogeneity that is not sufficiently\nexplained by the current annotations. The rationale for assigning identical cell-type labels to these\ndistinct clusters should be clearly justified, and the annotations revised if necessary.\nR: Thank you for this thoughtful comment. Indeed, the tumor epithelium is quite heterogeneous and\nclusters into several sub-populations (Rebuttal Fig 2). However, our focus in our scRNA-seq analysis\nwas to evaluate how Foxa1 loss dysregulates epithelial cell lineage, signaling, and the tumor immune\nmicroenvironment, rather than identifying granular epithelial subclusters. Thus, we combined some\nepithelial subclusters identified by resolution 0.2 clustering based on canonical cell type marker gene\nexpression to reduce the number of clusters and focus on the main takeaways from this data.\nTo begin, evaluating the expression of major cell type marker genes across resolution 0.2 clusters\n(Rebuttal Fig 3 right panel, also added to Supplementary Figure 2A), we decided to combine 13 & 6\ndue to their shared elevated expression of seminal vesicle marker gene Pate4, as well as their proximity\nin the UMAP. Next, cluster 15 was combined with cluster 1 due to similar expression of key basal marker\ngenes and proximity in the UMAP. Cluster 5 was also combined with 1 in the basal population due to the\ngained expression of key basal genes, which were not expressed in most luminal clusters other than\nSpink1+ cells, and the reduced expression of luminal markers (Rebuttal Figure 3 & new Supplementary\nFigure 2C). Thus, Cluster 5 might represent an intermediate basal population, which hasn’t fully lost\nluminal gene expression but has begun to gain basal gene expression. This has now been added to\nSupplementary Figure 2A,C. Moreover, recoloring the pseudotime trajectory by resolution 0.2 clusters,\nwe observed clusters 15 & 5 largely appear within a similar “pseudotime” as cluster 1 (Rebuttal Fig 4),\nsupporting their incorporation as part of the \"basal\" population.\nRegarding cluster 4 (the Spink1+ luminal population), though it appears as two physically\nseparate clusters on the UMAP, it was transcriptionally similar enough to be identified as a single cluster\nat resolution 0.2, likely due to the 2D UMAP visualization not accurately reflecting their global distance.\nFurther, prostate cells that are strongly Spink1+ are well known as a special luminal cell type associated\nwith aggressive prostate cancer with enhanced lineage plasticity, supported by co-expression of luminal\nand basal genes in our data. Because the focus of this manuscript was not to granularly subtype epithelial\npopulations, we did not further subcluster or explore these populations.\nRebuttal Fig. 2\nRebuttal Fig 4\nMajor comment #7:\nRegarding the statement \"... but rather % of captured spots in the tissue section with high probability of\ncontaining the indicated cell type,\" the definition of \"high probability\" is unclear. What probability\nthreshold is used to classify a pixel as containing a given cell type? Or what exactly is the criterion for\ncounting pixels for each cell type? While it is correct that Visium spots do not have single-cell resolution\nand may contain multiple cells, this argument does not apply at the pixel level, as image pixels are\nsubstantially smaller than individual cells and should therefore mostly correspond to a single cell type.\nR: We thank the reviewer for this thoughtful question. In TESLA analysis, the reported values are not\ndeconvolution-style probabilities of cell-type presence at the pixel level. Instead, as described in the\noriginal TESLA publication, the method assigns each pixel a score between 0 and 1 that reflects the\nrelative enrichment of a given cell type at that pixel across the whole slide. To identify regions enriched\nfor a specific cell type, pixels with enrichment scores >0.3 were classified as enriched for that cell type\nand used to quantify the corresponding area. Notably, because these scores represent relative\n5\nenrichment rather than exclusive assignment, a pixel may have non-zero scores for multiple cell types,\ncapturing their colocalization pattern.\nThe advantages of using the score from TESLA over spot-level deconvolution are threefold:\n1. TESLA incorporates H&E image information, providing additional morphological context to improving\ncell-type annotation compared with deconvolution approaches based solely on spot-level gene\nexpression.\n2. TESLA enables annotation across the entire tissue section, rather than being restricted to measured\nVisium spots, which typically cover < 50% of the captured tissue area.\n3. Pixel-level scores provide improved visualization and quantification of spatial co-localization among\ndifferent cell types compared with spot-level analysis.\nWe have clarified this point in the methods and legends of the revised manuscript to avoid confusion.\nAdditional point related to Major comment #7:\nIn the statement \"... we believe the vast overlap of luminal and basal-like regions identified in the PF\ntumours could be indicative of a mixed luminal–basal phenotype and lineage plasticity, as observed in\nour scRNA-seq analyses as well,\" it is unclear which specific scRNA-seq clusters in the integration\nanalysis represent this \"mixed luminal–basal phenotype.\" The authors should explicitly identify the\nrelevant clusters and provide supporting evidence for this interpretation.\nR: We thank the reviewer for this question and opportunity to clarify this statement. This statement here\nwas not made in reference to a particular scRNA-seq cluster, but rather a trend we observed of gained\nbasal phenotype among PF tumor epithelial cells, without complete loss of luminal gene expression,\nsuggesting a “mixed” identity or lineage plasticity, which was supported by several pieces of evidence\nfrom our scRNA-seq analysis. Firstly, evaluation of Krt5 (basal), Krt6a (squamous), and Krt8 (luminal)\nexpression on the UMAP for PF tumors showed evidence of epithelial cells with gained basal/squamous\ngene expression but limited loss of luminal marker Krt8 expression (Figure 2E). Additionally, our lineage\ntrajectory analysis revealed that PF tumors exhibit a much earlier gain in basal gene expression along\nthe trajectory, with more cells within the “early” luminal populations expressing basal marker genes, as\ncompared to P tumors (Figure 2G). Moreover, we observed increased expression of epithelial-\nmesenchymal transition (EMT) genes among epithelial cells in PF tumors (Fig S2F & Fig 3A), further\nsupporting Foxa1 loss induces lineage plasticity. Nevertheless, as described in response to comment #3\nabove, further evaluation of canonical luminal and basal cell type marker genes across resolution 0.2\nclusters suggests cluster 5 may represent a mixed or intermediate basal state.\nMinor comment: It might be better to include Fig S2C in Fig 2D.\nR: We have now included FigS2C in Fig2D, as suggested.\nReviewer #3 (Remarks on code availability):\nThe code on the GitHub is well organized and easy to follow.\nR: Thank you.\nWe confirm that there are no additional reviewer comments to address.\nWe sincerely thank the reviewers and the editors for their careful evaluation of our\nmanuscript and for their thoughtful and constructive feedback. Their insights have\nsignificantly strengthened our work, and we greatly appreciate the time and expertise\ninvested in the review process."}, {"round": 3, "reviewer_comments": {"reviewer_3": "I appreciate the authors' additional analyses addressing all the concerns, which have substantially strengthened the\nmanuscript."}, "author_response": "Response to Referees\nWe confirm that there are no additional reviewer comments to address.\nWe sincerely thank the reviewers and the editors for their careful evaluation of our\nmanuscript and for their thoughtful and constructive feedback. Their insights have\nsignificantly strengthened our work, and we greatly appreciate the time and expertise\ninvested in the review process."}]

Decision letter