Abstract
Tumors initiate by mutations in cancer cells, and progress through
interactions of the cancer cells with non-malignant cells of the tumor
microenvironment. Major players in the tumor microenvironment are
cancer-associated fibroblasts (CAFs), which support tumor malignancy,
and comprise up to 90% of the tumor mass in pancreatic cancer. CAFs are
transcriptionally rewired by cancer cells. Whether this rewiring is
differentially affected by different mutations in cancer cells is
largely unknown. Here we address this question by dissecting the
stromal landscape of BRCA-mutated and BRCA Wild-type pancreatic ductal
adenocarcinoma. We comprehensively analyze pancreatic cancer samples
from 42 patients, revealing different CAF subtype compositions in
germline BRCA-mutated vs. BRCA Wild-type tumors. In particular, we
detect an increase in a subset of immune-regulatory clusterin-positive
CAFs in BRCA-mutated tumors. Using cancer organoids and mouse models we
show that this process is mediated through activation of heat-shock
factor 1, the transcriptional regulator of clusterin. Our findings
unravel a dimension of stromal heterogeneity influenced by germline
mutations in cancer cells, with direct implications for clinical
research.
Subject terms: Cancer microenvironment, Pancreatic cancer
__________________________________________________________________
Cancer-associated fibroblasts are transcriptionally rewired by signals
from the cancer cells, resulting in heterogeneous populations. Here the
authors show that loss of BRCA function in pancreatic cancer cells
leads to HSF1–dependent accumulation of immune-regulatory
clusterin-positive cancer associated fibroblasts.
Introduction
Pancreatic ductal adenocarcinoma (PDAC) is one of the most aggressive
cancer types, with a 10% 5-year survival rate^[80]1. Major contributors
to this aggressiveness are cancer-associated fibroblasts
(CAFs)^[81]2,[82]3. CAFs comprise up to 90% of the cellular tumor
microenvironment (TME) in PDAC, and promote tumorigenesis by elevating
proliferation, invasion, and chemoresistance of cancer cells, and by
remodeling the extracellular matrix (ECM)^[83]4–[84]6. CAFs are
functionally and phenotypically heterogeneous, and are composed of
multiple subpopulations^[85]7–[86]11. In PDAC, a series of studies
identified three major CAF subtypes with distinct functions—a
myofibroblastic subtype that expresses α-smooth-muscle-actin (αSMA;
termed myCAF), an inflammatory subtype that expresses interleukin
6 (IL-6) and leukemia inhibitory factor (LIF; termed iCAF), and an
antigen-presenting subtype that expresses MHC class II
(apCAF)^[87]12–[88]15. Another study described four CAF subtypes with
distinct functional features and prognostic impact^[89]9, and
single-cell analysis of human PDAC identified eight fibroblast
clusters^[90]16. Moreover, cancer-associated mesenchymal stem cells
were shown to secrete granulocyte-macrophage colony-stimulating factor
(GM-CSF), acting as CAFs to support PDAC tumor progression^[91]17,
while CAFs of pancreatic stellate cells (PSC)-origin were demonstrated
to regulate specific ECM features and to contribute to tumor
stiffness^[92]18. Most recently, single-cell analysis of human PDAC
identified a subset of LRRC15^+ CAFs and showed a correlation between
elevated levels of this subset and poor response to anti-PD-L1
therapy^[93]19. These studies and others^[94]20–[95]23 exposed
additional complexity and diversity leading to the segregation of the
three main subtypes of CAFs into multiple subpopulations^[96]11.
Inflammatory CAFs, for example, were segregated into subpopulations
based on expression of distinct cytokines and immune-modulatory genes,
in addition to antigen-presenting modules^[97]20,[98]22. These studies
also highlighted the dynamic nature of CAFs, and their ability to shift
between phenotypes depending on external signals^[99]7, which could
explain previous contradictory findings of both anti- and
pro-tumorigenic effects of CAF depletion in PDAC^[100]24–[101]26.
CAFs are genomically stable, and rarely have copy number alterations or
somatic mutations leading to loss of heterozygosity^[102]27. Yet, they
are transcriptionally heterogeneous^[103]8,[104]13. This heterogeneity
is driven by different external cues received from neighboring cells
and local environmental conditions^[105]28,[106]29. For example,
hypoxia was shown to induce a pro-glycolytic transcriptional program in
CAFs^[107]30, and a metabolic switch from oxidative phosphorylation to
glycolysis was also shown in response to TGFβ and PDGF in an
IDH3α-mediated mechanism^[108]31. The stress-induced transcriptional
regulator Heat Shock Factor 1 (HSF1) was shown, by us and others, to
play a key role in shaping CAF transcription in diverse human
carcinomas, including breast, lung, gastric, and colon
cancer^[109]32–[110]36. HSF1 orchestrates a transcriptional program in
fibroblasts that enables their reprogramming into CAFs and promotes
malignancy by TGFβ and SDF1, YAP/TAZ signaling, and exosome-mediated
secretion of THBS2 and INHBA^[111]32–[112]36. CAF heterogeneity was
also proposed to stem from different cells of origin giving rise to
CAFs, including tissue-resident fibroblasts, mesenchymal stromal cells,
pericytes, and adipocytes^[113]8,[114]17,[115]37–[116]41. For example,
bone-marrow-derived CAFs in breast cancer were shown to express high
levels of Clusterin (Clu), and exhibit a distinct transcriptional
profile compared to tissue-resident CAFs^[117]40. However, it is not
known whether different germline mutations in the cancer cells lead to
differential rewiring of CAFs and contribute to CAF heterogeneity.
In PDAC, a subset of up to 7% of the general population, and up to 20%
in certain subgroups (such as patients of Ashkenazi Jewish descent),
have germline mutations in the breast cancer-1 (BRCA1) and BRCA2
genes^[118]42,[119]43, which are part of the DNA damage homologous
repair mechanism. BRCA mutations are the most prominent germline
mutations associated with increased risk of developing pancreatic
cancer^[120]44. Patients carrying these mutations, both in PDAC and in
other BRCA-associated cancers (e.g. breast cancer), exhibit a higher
response rate to platinum-based chemotherapy regimens and PARP
inhibitors, resulting in longer than expected overall
survival^[121]43,[122]45. Several cell-autonomous mechanisms by which
PARP inhibitors affect BRCA-mutant (BRCA-mut) cancer cells were
suggested^[123]46–[124]48, however, additional non-cell-autonomous
factors mediating the efficacy of these treatments may be pivotal in
PDAC. Recent studies described distinct immune microenvironments in
BRCA-mut breast, ovarian, and prostate cancers, characterized by
increased infiltration of T cells and macrophages^[125]49–[126]52.
Since cells of the TME are considered to be genomically stable^[127]27,
this rewiring is thought to be orchestrated through non-cell-autonomous
effects driven by BRCA mutations in the cancer cells. Yet the
transcriptional landscape of the fibroblastic microenvironment of
BRCA-mut PDAC remains uncharted. In breast cancer, we have recently
identified two major CAF subtypes expressing either the marker S100A4
(also known as FSP1) or podoplanin (PDPN)^[128]8. The ratio of these
two CAF subtypes was correlated with BRCA1/2 mutational status and with
disease outcome in BRCA-mut breast cancer patients.
Given that CAFs are reprogrammed by the adjacent cancer cells, we
hypothesize that different driver mutations will yield different
stromal landscapes. Here, we set out to test this hypothesis in a
comprehensive cohort of 42 BRCA-mut and BRCA-WT pancreatic cancer
patients. Using three CAF markers—Clusterin (CLU), αSMA, and MHC class
II—we identify three mutually exclusive CAF subtypes in primary
pancreatic tumor resection specimens, and show that the ratio between
these CAF subtypes is altered in BRCA-mut tumors compared to BRCA-WT
tumors. We apply laser capture microdissection (LCM) followed by RNA
sequencing to define stromal transcriptional signatures unique to
BRCA-mut vs. BRCA-WT tumors. We characterize BRCA-associated stromal
signatures by multiplexed immunofluorescence (MxIF) and second harmonic
generation signaling (SHG). We find distinct stress response activation
patterns in BRCA-mut vs. BRCA-WT tumors. In particular, we find that
HSF1 is upregulated in BRCA-mut tumors. Using cancer organoids,
co-cultures, and in-vivo models we show that loss of BRCA function in
cancer cells leads to a transcriptional shift of PSCs from
myofibroblastic to immune-regulatory Clu^+ CAFs in an HSF1-dependent
manner. Our findings portray distinct stromal compositions in BRCA-mut
and BRCA-WT PDAC tumors with far-reaching clinical implications for
early detection and for PDAC therapy.
Results
BRCA-mut and BRCA-WT tumors exhibit distinct CAF compositions
To dissect the stroma of BRCA-mut PDAC in comparison with that of
BRCA-WT PDAC, we assembled a clinical cohort of 42 patients (27 BRCA-WT
and 15 germline BRCA-mut; see Supplementary Data [129]1).
Formalin-fixed primary tumor resection tissue, and deeply annotated
demographic, clinical and pathologic data were collected for all
patients in the study. In addition, genomic (MSK-IMPACT^TM) data and
fresh-frozen tumor tissue was collected for a subset of patients. PDAC
CAFs are comprised of distinct subtypes, marked by the expression of
distinct proteins^[130]9,[131]12,[132]13. To test whether CAF
compositions are affected by the germline mutational status of the
cancer cells we assessed the distribution of several CAF markers in
primary tumor resections from BRCA-WT and BRCA-mut PDAC patients. In
particular, we stained for αSMA, podoplanin (PDPN), platelet-derived
growth factor receptor alpha (PDGFRa), human leukocyte antigen DR
isotype (HLA-DR; an MHC class II molecule), and S100A4, all of which
were previously described as CAF markers in different cancer
types^[133]8,[134]33,[135]39,[136]40. We also stained for CLU, which
was previously suggested as a marker of bone marrow-derived fibroblasts
in breast cancer^[137]4 (Fig. [138]1a, b and Supplementary
Figure [139]1a, b). Of these proteins, three marked discrete CAF
subtypes (negative for CD45 and cytokeratin), and together covered most
of the stromal cells – αSMA, CLU and HLA-DR (MHC-II; Fig. [140]1a, b
and Supplementary Figure [141]1c–e). S100A4 marked mostly CD45^+ immune
cells in this patient cohort, PDPN marked a subset of αSMA^+ CAFs, and
PDGFRa partially overlapped with other markers; therefore, these were
not chosen for further analysis (Supplementary Figure [142]1a, b). αSMA
is a well-known myofibroblastic marker in various carcinomas, including
PDAC^[143]53. MHC-II was recently suggested as a marker of apCAFs in
both breast and pancreatic cancers^[144]8,[145]12,[146]15. Clu was
shown to be expressed by αSMA^low CAFs in breast and pancreatic
cancer^[147]8,[148]13,[149]40, however, the identity of these αSMA^low
CAFs was not fully elucidated—in mouse models of PDAC Clu was shown to
be expressed by apCAFs, whereas in human patient samples it is
expressed by inflammatory CAFs^[150]12. Immunohistochemical analysis
(IHC; Fig. [151]1a) and MxIF (Fig. [152]1b) staining demonstrated a
segregation of αSMA^+, CLU^+ and HLA-DR^+ CAFs in PDAC. Automated image
analysis quantifying the relative abundance of these proteins in
stromal cells (CD45^-Cytokeratin^-) in a subcohort of 10 BRCA-mut and
15 BRCA-WT tumors confirmed that the three CAF markers clearly mark
discrete CAF subtypes (Supplementary Figure [153]1c–e), as shown by the
low co-expression of each CAF marker with the other markers.
Fig. 1. CAF compositions change between BRCA-WT and BRCA-mut PDAC tumors.
[154]Fig. 1
[155]Open in a new tab
Formalin-fixed paraffin-embedded (FFPE) tumor sections from BRCA-mut
and BRCA-WT PDAC patients were stained for hematoxylin and eosin (H&E),
IHC, and MxIF. (a) IHC was performed for αSMA, CLU and HLA-DR (Scale
bar, 200 μm). Representative images of a BRCA-WT tumor are shown
(n = 2). (b–h) MxIF was performed using antibodies for the depicted
proteins. DAPI was used to stain nuclei. Scale bar, 50 μm.
Representative images are shown in (b). Images were analyzed using
ImageJ software, CD45^− CK^− regions were defined as regions of
interest (ROIs) and the area stained by each CAF marker was calculated,
divided by the ROI and averaged for each patient sample (c–e).
Mann-Whitney test was performed. The ratio of the different CAF
subtypes is shown in (f–h) and was analyzed using Student’s t-test.
Data are presented as Mean
[MATH: ± :MATH]
SEM. ns marks p-values greater than 0.05. For IHC and H&E staining
n = 2, and for MxIF staining n = 11 BRCA-mut and n = 15 BRCA-WT. (i–n)
Single-cell RNA-seq data of fibroblasts and stellate cells from human
PDAC tumors^[156]16 was reanalyzed using the Seurat R toolkit. (i)
Uniform Manifold Approximation and Projection (UMAP) of 6,405 cells
from^[157]16, color-coded for the indicated cell clusters defined by a
local moving clustering algorithm. The clusters that differentially
express ACTA2, CLU and HLA-DR are indicated. (j) Dot plot visualization
of gene expression of the indicated CAF markers. (k–n) Single-cell
expression level of CAF markers on the UMAP shown in (i). Marker genes
of ACTA2 (k), CLU^low (l), CLU^high (m), and HLA-DR (n) clusters are
represented. Source data are provided as a Source Data file.
Next, we asked whether the global composition of immune cells, cancer
cells and CAFs is different between BRCA-mut and BRCA-WT tumors. We
quantified the number of CD45^+ (immune) cells by MxIF and found no
differences between the different genotypes (Supplementary
Figure [158]1f). Then, we used an artificial intelligence image
analysis algorithm to classify different cell populations in
H&E–stained FFPE sections from patients. We found no significant
differences in the percentage of CAF-rich, cancer-rich, and immune-rich
regions between BRCA-mut and BRCA-WT tumors (Supplementary
Figure [159]1g–j).
We then evaluated each subpopulation of CAFs separately, by quantifying
CLU^+, MHC-II^+, and αSMA^+ CAF staining in a subcohort of 26 human
PDAC patients, including 15 BRCA-WT and 11 BRCA-mut patients. αSMA^+
CAFs and HLA-DR^+ CAFs did not differ between the genotypes. CLU^+ CAFs
were significantly more abundant in BRCA-mut tumors (Fig. [160]1c–e).
Moreover, the ratio between CAF subtypes was different between BRCA-mut
and BRCA-WT tumors (Fig. [161]1f–h). Specifically, the ratio of
CLU^+/αSMA^+ CAFs and the ratio of CLU^+/HLA-DR^+ CAFs was higher in
BRCA-mut tumors compared to BRCA-WT tumors (Fig. [162]1f, g),
suggesting that germline mutations in the cancer cells alter tumor CAF
compositions.
To examine whether this characteristic is different between BRCA1 and
BRCA2 mutant tumors, we compared the relative abundance of CLU^+ CAFs
and the CLU^+/αSMA^+ CAF ratio in BRCA1 vs. BRCA2 mutant patients. We
found no significant differences (Supplementary Figure [163]1k–l),
implying that the changes in CAF compositions are shared between
different BRCA mutations.
Several recent studies associated CLU expression with neoadjuvant
therapy. One study reported elevated expression of CLU following
neoadjuvant therapy in prostate cancer^[164]54, and another suggested
that low stromal expression of CLU is predictive of better response to
neoadjuvant therapy in triple-negative breast cancer^[165]55. We
therefore compared CLU^+/αSMA^+ CAF ratios in neoadjuvant-treated vs.
non-treated patients. We found that the ratio of CLU^+/αSMA^+ CAFs was
not affected by treatment (Supplementary Figure [166]1m). Both treated
and non-treated patients had higher CLU^+/αSMA^+ CAF ratios in BRCA-mut
patients compared to WT, supporting the notion that CAF distribution is
driven by the tumor genotype and is not altered by neoadjuvant
treatment regimens (See Supplementary Data [167]1 for clinical
information).
Next, we sought to explore whether the CAF subtypes we characterized
using protein markers could also be identified at the transcriptional
level. To that end we reanalyzed data from a large and comprehensive
single-cell RNA-seq dataset of human PDAC (Peng et al.^[168]16) using
the Seurat R toolkit^[169]56. We reanalyzed only tumor samples
(excluding cells from normal controls), and within these samples
analyzed all the cells that were defined as “fibroblasts” or “stellate
cells” in the original dataset, and excluded MCAM positive cells (a
pericyte marker). Unbiased clustering of 6405 cells that passed QC (see
Methods) revealed 7 distinct CAF subtypes (Fig. [170]1i, j,
Supplementary Data [171]2). ACTA2, CLU, and HLA-DR were differentially
expressed (DE) in distinct clusters, supporting our MxIF analysis and
suggesting that not only at the protein level, but also at the
transcriptional level, these genes mark discrete CAF populations. This
segregation was evident across patients, and did not stem from
intra-patient variability (Supplementary Fig. [172]1n). To further
validate these findings, we reanalyzed an additional published
single-cell RNA-seq dataset of human PDAC^[173]12. Here we analyzed all
cells defined as iCAFs or myCAFs (HLA-DR^+ antigen-presenting CAFs were
not found in this patient dataset). Similar to the Peng dataset, in
this dataset, CLU and ACTA2 were differentially expressed in distinct
clusters (Supplementary Figure [174]1o–p, Supplementary Data [175]3).
To further study the transcriptional signatures of these clusters we
performed pathway analysis of the top DE genes in clusters that
differentially expressed ACTA2, CLU, and HLA-DR in the Peng dataset
(Fig. [176]1j–n; Supplementary Data [177]2). The ACTA2^+ (αSMA;
Fig. [178]1k, cluster 0) cluster was enriched for myofibroblastic
pathways such as ECM remodeling (collagens and MMPs), wound healing
(INHBA, THBS2), smooth muscle contraction (ACTA2 and TPM genes), and
cell-substrate adhesion (LRRC15,
ITGB5)^[179]12,[180]13,[181]19,[182]34. CLU was differentially
upregulated in two clusters—cluster 1 and cluster 2—albeit at different
levels. We defined these clusters as CLU^low (1) and CLU^high (2), to
reflect these differences. The CLU^low cluster (cluster 1;
Fig. [183]1l) was enriched with genes involved in complement and
coagulation cascades (A2M, C1R, C1S, C7), in addition to genes involved
in ECM organization (DCN, LAMA2, TIMP1). The CLU^high cluster (cluster
2; Fig. [184]1m) expressed inflammatory genes (IL-6, CXCL12, CXCL1,
NFKBIA), as well as genes involved in ECM remodeling (LIF, COL14A1,
HAS1) and angiogenesis regulation (C3, IL6)^[185]12–[186]14. The HLA-DR
cluster (cluster 3; Fig. [187]1n) was enriched for antigen presentation
(variety of HLA genes), and for T cell activation (ITGB2, S100A8).
These results indicate that CLU is a marker of a distinct CAF subset in
PDAC, characterized by an immune-regulatory and inflammation-associated
gene signature.
BRCA-WT and BRCA-mut stroma exhibit distinct transcriptional signatures
To directly map the transcriptional landscapes of BRCA-WT and BRCA-mut
stroma, we employed laser capture microdissection (LCM) followed by
RNA-sequencing on CAF-rich regions from 12 patients (5 BRCA-mut and 7
BRCA-WT; Supplementary Data [188]4). Unsupervised differential
expression analysis showed clear segregation between BRCA-WT and
BRCA-mut tumors. This analysis revealed 30 upregulated and 10
down-regulated genes in BRCA-mut vs. BRCA-WT patients (Fig. [189]2a).
CLU was not among the DE genes, however, it did show a trend of
elevation in BRCA-mut patients compared to BRCA-WT patients
(Supplementary Figure [190]2a). Pathway analysis of the differentially
upregulated genes showed enrichment of genes involved in ECM remodeling
and proteolysis (MUC5B, SERPINA1, A2ML1, S100A2, GREM1), wound healing
(TNC, CD177, WFDC1), muscle contraction (DES, KCNMA1, OXTR, CEMIP), and
regulation of cell growth (CRABP2, ROS1, WFDC1) in BRCA-mut vs. BRCA-WT
patients (Fig. [191]2a and Supplementary Data [192]4). Genes involved
in T-cell activation and migration (IRF4, TBX21, CXCL9) and tyrosine
kinase signaling (STAP1, FLT3) were downregulated in BRCA-mut vs.
BRCA-WT patients (Fig. [193]2a and Supplementary Data [194]4). To
exclude the possibility that the observed differential expression of
immune-related genes is due to higher immune-cell contamination of the
dissected CAF-rich regions in BRCA-WT stroma, we applied CIBERSORTx, a
computational deconvolution tool that estimates the relative abundance
of individual cell types in a mixed cell population based on
single-cell RNA-seq profiles^[195]57. First, we estimated the relative
abundance of fibroblasts in our samples using the single-cell human
PDAC dataset by Peng et al.^[196]16. We found that CAFs were
predominant in all our samples, comprising 74−91% of the cells in each
sample, with an average of 85%. This analysis also excluded potential
cancer cell contamination and showed that there were no differences
between the relative abundance of the tested cell types in BRCA-mut vs
BRCA-WT samples (Supplementary Figure [197]2b and Supplementary
Data [198]5). Then, we applied this tool to estimate the distribution
of immune cell subtypes within these samples. Similarly, no significant
differences were found between BRCA-WT and BRCA-mut stroma in any of
the immune cell subtypes tested (Supplementary Figure [199]2c and
Supplementary Data [200]5). Lastly, we stained a cohort of 7 BRCA-mut
and 11 BRCA-WT tumors by MxIF to assess CD3 expression at the protein
level and found no differences in its abundance (Supplementary
Figure [201]2d). This converging evidence suggests that even if immune
cells have infiltrated into the dissected stromal regions, the observed
differential expression patterns most likely originate from CAFs.
Fig. 2. The transcriptional profile of BRCA-mut stroma is different than that
of BRCA-WT stroma.
[202]Fig. 2
[203]Open in a new tab
CAF-rich regions of fresh-frozen tumor sections from 7 BRCA-WT and 5
BRCA-mut PDAC patients were laser-capture-microdissected and analyzed
by RNA-seq. (a) Heatmap showing hierarchical clustering of DE genes in
CAF-rich regions from BRCA-mut and BRCA-WT samples. Pathway analysis
was performed using Metascape; Selected significant pathways (p < 0.05)
are shown (see full list in Supplementary Data [204]4). (b, c) FFPE
tumor sections from 2 PDAC BRCA-mut patients were stained by IHC for
MUC5B and SERPINA1. Representative images are shown. Scale bar, 50 μm
(d) Human PDAC tissue-derived exosomal proteomes (n = 21) and non-tumor
adjacent tissue-derived exosomal proteomes (n = 16) were analyzed by
liquid chromatography with tandem mass spectrometry (LC-MS/MS).
Proteins found in >15% of pancreatic cancer exosomes were compared to
pancreatic adjacent tissue-derived exosomes. Log2 protein expression of
the indicated proteins is presented. P values were calculated by
Welch’s t-test for the comparison of expression level and Fisher’s
exact test for the comparison of positivity. Data are expressed as
mean ± SEM. (e–f) FFPE tumor sections from 9 BRCA-mut and 9 BRCA-WT
PDAC patients were stained by MxIF using antibodies for the indicated
proteins. DAPI was used to stain nuclei. Scale bar, 50 μm.
Representative images are shown in (e). MUC5B and SERPINA1 protein
levels were quantified by ImageJ software and the area stained by each
protein and CAF marker was measured. Quantification of MUC5B
colocalization with CLU and αSMA was analyzed by two-way ANOVA, and
presented as mean ± SEM in (f). (g) DE genes were analyzed by Ingenuity
software using the causal network tool. Schematic representation of the
predicted network is presented. Upregulated and downregulated genes in
BRCA-mut patients are marked in red and blue, respectively; predicted
regulators are marked in grey. Source data are provided as a Source
Data file.
We next set to analyze some of the DE genes at the protein level. We
chose to focus on MUC5B and SERPINA1, two of the most significantly
upregulated genes in BRCA-mut CAFs. These genes encode secreted
proteins, which were previously proposed to serve as prognostic
biomarkers of pancreatic neoplasms based on proteomic analysis of
pancreatic fluids. SERPINA1 levels were elevated in PanIN3
lesions^[205]58 and correlated to CLU expression in two lung cancer
cell lines^[206]59, and MUC5B was identified in pancreatic main duct
fluid collected at the time of surgical resection^[207]60 but no known
association with CLU was reported. IHC staining of tumor sections from
PDAC patients showed that MUC5B and SERPINA1 are expressed by PDAC
stromal cells (Fig. [208]2b, c; as well as by cancer cells; see
Fig. [209]2e below). To test whether these proteins are secreted by
PDAC human tumors, we assessed the exosomal content of 21 PDAC
specimens and 16 normal adjacent controls in an independent patient
cohort^[210]61 (see Methods and Supplementary Data [211]6). We detected
multiple mucin and serpin proteins that were highly expressed in tumor
exosomes compared to normal adjacent tissue-derived exosomes
(Fig. [212]2d and Supplementary Data [213]6). Specifically, MUC5B was
detectable in 71% of PDAC-derived exosomes, compared to 19% of adjacent
pancreatic tissue-derived exosomes. SERPINA1 was found in 100% of
PDAC-derived exosomes, however, it was also found in 50% of the control
tissues (Fig. [214]2d and Supplementary Data [215]6).
The exosome cohort did not include the BRCA status, therefore we could
not compare the exosomal levels of MUC5B and SERPINA1 in BRCA-mut vs.
BRCA-WT tumors. Instead, we performed MxIF staining of SERPINA1 and
MUC5B in BRCA-mut vs. BRCA-WT tumors. The total protein levels of
SERPINA1 and MUC5B (when analyzing all stromal cells together) were
similar in BRCA-mut vs. BRCA-WT. However, further analysis of MUC5B
expression within the different CAF subtypes revealed significantly
higher levels of MUC5B in CLU^+ CAFs than in SMA^+ CAFs. Moreover, the
localization of MUC5B within CLU^+ CAFs was significantly higher in
BRCA-mut patients compared to BRCA-WT patients, suggesting a possible
association between MUC5B and CLU^+ CAFs (Fig. [216]2e, f and
Supplementary Figure [217]2e).
To identify potential upstream regulators of the BRCA-associated CAF
transcriptional program we analyzed our DE gene dataset using the
Causal Network tool in the Ingenuity Pathway Analysis (IPA) software
(see Methods)^[218]62. This analysis highlighted heat shock protein 90α
gene, HSP90AA1, as a potential upstream regulator of multiple genes in
our network (Fig. [219]2g). HSP90α is a stress-induced chaperone.
Previous studies have reported a role for HSP90 in PDAC
progression^[220]63, and synergistic effects of CLU and HSP90α in
promoting epithelial-to-mesenchymal transition and metastasis in breast
cancer^[221]64. As both CLU and HSP90AA1 are regulated by
HSF1^[222]65,[223]66, the master transcriptional regulator of the heat
shock response, we hypothesized that HSF1 may be orchestrating these
BRCA-mut-induced transcriptional changes in the stroma.
Activation of stromal HSF1 is elevated in BRCA-mut PDAC tumors
Work by us and others has shown indispensable roles for HSF1 in
transcriptional rewiring of fibroblasts into CAFs in various cancer
types^[224]32–[225]36. To test whether HSF1 is differentially activated
in BRCA-mut vs. BRCA-WT CAFs, we performed MxIF staining. HSF1
translocates to the nucleus upon activation, and thus its nuclear
localization serves as a proxy for its activation (Fig. [226]3a, b;
Supplementary Figure [227]3a). Comparing 14 BRCA-mut tumors with 20
BRCA-WT tumors from our patient cohort, we found significantly higher
activation of HSF1 in BRCA-mut stroma compared to BRCA-WT stroma
(Fig. [228]3c).
Fig. 3. A network of stress responses is activated in BRCA-mut stroma.
[229]Fig. 3
[230]Open in a new tab
FFPE tumor sections from BRCA-mut and BRCA-WT PDAC patients were
stained by MxIF using antibodies for the indicated proteins. (a, b)
Representative images are shown. DAPI was used to stain nuclei. Scale
bar, 50 μm. (c) Quantification of HSF1 mean intensity within all
stromal cells in BRCA-mut (n = 14) and BRCA-WT samples (n = 20). 3-5
images per patient were analyzed using ImageJ software, HSF1 staining
intensity was averaged within patients, and is presented as mean
(across patients) ± SEM. Statistical analysis was conducted with a
two-tailed unpaired t-test. (d, e) Pearson correlation matrices of
stress TF coactivation in BRCA-mut (n = 8 for d; n = 13 for e) vs.
BRCA-WT (n = 11 for d; n = 17 for e) patients. Source data are provided
as a Source Data file.
We were next curious to see whether other stress responses are also
activated in BRCA-mut stroma, possibly due to DNA-damage-induced
stress, or whether this phenomenon was specific to HSF1. To portray the
stress network in PDAC, we stained for five additional stress-induced
transcription factors (TFs): X-box binding protein 1 (XBP1)^[231]67 and
Activating Transcription Factor 6 (ATF6^[232]68; ER-stress response);
Hypoxia-inducible factor 1-α (HIF1α; Hypoxia)^[233]30, Nuclear factor
erythroid-2-related factor 2 (NRF2; oxidative stress)^[234]69, and
Activating Transcription Factor 4 (ATF4^[235]70; the integrated stress
response; Fig. [236]3a, b). While none of these additional
stress-activated TFs showed significant differential activation
(Supplementary Figure [237]3a–f), a significant crosstalk between all
these stress pathways was evident. All pairs of stress-TFs exhibited
higher co-activation (per-patient) in BRCA-mut tumors compared to
BRCA-WT tumors (Fig. [238]3d, e), suggesting that the stress inflicted
by BRCA mutations is different than that found in a BRCA-WT PDAC
microenvironment, leading to coordinated activation of a network of
stress responses in the stroma of BRCA-mutated PDAC.
HSF1 upregulates CLU/αSMA ratio in BRCA-mut tumors
CLU is an extracellular chaperone transcriptionally regulated by HSF1
in various contexts^[239]65,[240]71,[241]72 and upregulated in response
to DNA damage^[242]73,[243]74. CLU was shown to play a critical role in
promoting pancreas regeneration and tumorigenesis^[244]75,[245]76.
Supported by our findings of higher HSF1 activation and CLU/αSMA ratios
in BRCA-mut tumors, we hypothesized that HSF1 may affect
BRCA-associated CAF compositions through transcriptional regulation of
stromal gene expression and specifically the regulation of CLU
expression. To test this hypothesis, we first assessed the correlation
between HSF1 activation and CLU/αSMA ratio in our clinical cohort. We
found that HSF1 activation is correlated with CLU/αSMA ratio only in
BRCA-mut patients and not in BRCA-WT patients (Fig. [246]4a, b). Next,
we asked whether CLU expression is HSF1-dependent. To this end, we
measured mRNA expression of Clu in primary PSCs isolated from WT and
Hsf1 null mice (Fig. [247]4c). We found that the expression of Clu was
significantly lower in Hsf1 null PSCs compared to WT PSCs, while the
expression of other CAF markers, such as Acta2 and Il6, was not altered
(Fig. [248]4e, f). Muc5B showed somewhat reduced expression but this
result was not significant (Fig. [249]4d).
Fig. 4. HSF1 directly regulates Clu expression in BRCA-mut tumors.
[250]Fig. 4
[251]Open in a new tab
(a–b) FFPE tumor sections from 14 BRCA-WT and 11 BRCA-mut PDAC patients
were stained by MxIF for HSF1, CLU and αSMA. Images were analyzed by
ImageJ. Pearson correlation between HSF1 mean intensity and CLU/αSMA
ratio in the stroma of (a) BRCA-WT patients, and (b) BRCA-mut patients
was calculated. (c–f) Expression levels of Clu (c), Muc5b (d) Acta2 (e)
and Il6 (f) in primary PSCs freshly isolated from WT and Hsf1-null
mice. n = 6 WT and 7 Hsf1-null mice, combined from 3 independent
experiments. Statistical analysis was conducted via unpaired two-tailed
t-test (g–j) Immortalized PSCs were seeded in Matrigel for 4 days.
Conditioned media (CM) from KPC cells in which Brca2 was silenced by
shRNA or non-targeting shControl (KPC) was then added for an additional
4 days, or cells were left in growth medium as control. 3 nM of the
HSF1 inhibitor, CMLD011866 (aglaroxin C), or PBS control was added to
the conditioned media (CM) every 2 days, for 4 days, after which the
expression levels of Clu (g), Muc5b (h) Acta2 (i), and Il6 (j), were
measured by qRT-PCR. For each condition n = 5-14 biologically
independent culture domes combined from 3 independent experiments.
Statistical analyses were conducted using one way ANOVA and Tukey’ test
for multiple comparisons. (k–m) Immortalized PSCs were cultured with or
without KPC-shControl-CM for 24 h. ChIP-PCR was performed for putative
heat-shock elements of Hsp1a1 (k), Clu (l), and Hsp90aa (m), and for an
intergenic region for normalization, following pulldown with anti-HSF1
antibody compared to IgG control. One-way ANOVA was performed on log2
values to compare between the group ratios of expression and Tukey’s
test was performed to adjust for multiple comparisons. Data are
presented as mean
[MATH: ± :MATH]
SEM for each primer normalized to the intergenic control in (c–m)
(n = 6 biologically independent culture wells combined from 3
independent experiments). (n) PSCs were cultured in the presence of
boiled or unboiled CM from KPC-shControl (unboiled n = 4, boiled n = 6
biologically independent samples) and KPC-shBrca2 organoids (unboiled
n = 6, boiled n = 5 biologically independent samples) as described in
(g–j). Expression of Clu in PSCs were subsequently measured by qRT-PCR.
Statistical analysis was conducted with two-way ANOVA. Data are
presented as mean
[MATH: ± :MATH]
SEM (o) Top 10 differentially expressed proteins (fold change of
protein abundance based on mass-spectometry label-free quantification,
shBrca2/shControl) from CM of KPC-shControl and KPC-shBrca2 organoids
as measured by mass-spectrometry. Source data are provided as a Source
Data file.
To characterize the effect of BRCA mutations on HSF1-dependent Clu
upregulation, we employed shRNA for Brca2 in KPC cells (mimicking BRCA2
loss-of-function) or non-targeting control (shControl; Supplementary
Figure [252]4a). We chose to target Brca2 rather than Brca1 since
mutations in BRCA2 are more prevalent than in BRCA1 in PDAC, and were
found in 73% of our BRCA-mut cohort. Immortalized PSCs were cultured in
3D matrigel domes for four days in growth medium and four additional
days in the presence of conditioned medium (CM) from KPC pancreatic
cancer cell-organoids transduced with shBrca2 or shControl
(Supplementary Figure [253]4b). Normal growth medium served as control
for both conditions (Fig. [254]4g–j). These growth conditions were
previously shown to suppress the myCAF phenotype and induce an
inflammatory CAF phenotype (Supplementary Figure [255]4c–e
and^[256]13). Indeed, we found that Acta2 expression was abolished by
the addition of KPC CM (Fig. [257]4i). In stark contrast, the
expression of Clu and Muc5b was induced by addition of KPC CM, and
silencing of Brca2 in the KPCs led to a further, significant induction
of both Clu and Muc5b expression (as compared to CM from KPC-shControl
or from PSCs; Fig. [258]4g, h). Il6 expression was also induced by KPC
CM, though this induction was not statistically significant
(Fig. [259]4j). To test whether the cancer-induced upregulation of Clu
and Muc5b is HSF1-dependent, we added to these cultures the synthetic
small molecule CMLD011866 ((-)-aglaroxin C)^[260]77–[261]79. This
compound is a pyrimidinone variant of the rocaglate/flavagline natural
product class, recently shown by us to inhibit HSF1
activity^[262]33,[263]79. Aglaroxin C was added to the CM every two
days and the expression of Clu, Muc5B, Acta2, and Il6 was measured
(Fig. [264]4g–j). Treatment with aglaroxin C abolished the induction of
Clu expression, suggesting that this induction is HSF1 dependent, and
that HSF1 regulates the expression of this gene.
To examine if Clu and Hsp90aa are direct target genes of HSF1 in our
system, we exposed PSCs to KPC-CM and performed chromatin
immunoprecipitation (ChIP) with anti-HSF1 antibodies followed by qPCR
with primers flanking heat-shock elements on the DNA of Clu and
Hsp90aa. Hspa1a, a well-known HSF1-target gene, served as control
(Fig. [265]4k–m). Both Clu and Hsp90aa were significantly enriched in
the HSF1-bound fraction compared to IgG control, demonstrating direct
regulation of these genes by HSF1 in cancer-conditioned PSCs
(Fig. [266]4k–m). Together, these findings suggest that BRCA-deficient
cancer cells induce an HSF1-dependent transcriptional program in PSCs.
In search for factors that may mediate this effect, we next analyzed
the medium conditioned by KPC-shBrca2 cells. First, to test whether
HSF1 and Clu upregulation is mediated by secretion of proteins, we
boiled CM from KPC-shControl and KPC-shBrca2 organoids and treated PSCs
with either unboiled or boiled CM. Boiling of KPC-shBrca2 CM
significantly reduced expression of Clu by PSCs (Fig. [267]4n),
suggesting that this effect is mediated by a secreted protein(s). Next,
we preformed mass-spectrometry analysis of the organoid CM. The two
most differentially secreted proteins from KPC-shBrca2 relative to
KPC-shControl organoids were the Regenerating islet-derived (Reg)
proteins, REG3B/G (Fig. [268]4o, Supplementary Data [269]7). REG3B/G
are C-type secreted lectins that play active roles in pancreatitis and
in the transition from pancreatitis to pancreatic cancer through
different mechanisms, including induction of STAT3, RAF-MEK-ERK
signaling, and immune cell modulation^[270]80–[271]83. Of note, we have
previously demonstrated an association between REG3B/G and HSF1
signaling, by showing that REG3B/G are upregulated during inflammation
in the colon in an HSF1-dependent manner^[272]33. In fact, six of the
ten most differentially secreted proteins from KPC-shBrca2 vs
KPC-shControl organoids were previously shown by us to be upregulated
during colon inflammation in an HSF1-dependent manner (REG3G, REG3B,
GC, SERPINH1, FN1, and PXDN)^[273]33. We also found CLU itself in this
list. These findings suggest that loss of BRCA2 in cancer cells leads
to differential secretion of proteins resulting activation of HSF1 in
stromal fibroblasts, and, potentially, also in the cancer cells
themselves.
BRCA-deficient cancer cells induce a distinct transcriptional program in PSCs
To further dissect the transcriptional shift induced by BRCA-deficient
cancer cells in PSCs we performed RNA-seq of PSCs following 3D Matrigel
cultures in the presence of KPC-shBrca2-CM, KPC-shControl-CM, or normal
growth medium (DMEM), as control (Fig. [274]5a, Supplementary
Data [275]8 and Supplementary Figure [276]5a–c). In parallel, we
sequenced KPC-shBrca2 and KPC-shControl cells (Supplementary
Figure [277]5d–f and Supplementary Data [278]9), which confirmed that
Brca2 is among the top 20 differentially downregulated genes in
KPC-shBrca2 vs KPC-shControl (Supplementary Figure [279]5f). In
addition to the shared response to cancer CM, we detected distinct
transcriptional changes in PSCs exposed to KPC-shBrca2 CM vs
KPC-shControl CM (Supplementary Data [280]8). 31 genes were
differentially upregulated only by KPC-shBrca2 CM, and not by
KPC-shControl CM. Notably, Clu ranked 6th on this list, highlighting
its prominence in Brca2-mut reprogramming of PSCs (Fig. [281]5a and
Supplementary Data [282]8).
Fig. 5. BRCA2 expression in the cancer cells affects the transcriptional
profile of PSCs and CAFs.
[283]Fig. 5
[284]Open in a new tab
(a) A heatmap representing the 20 most upregulated genes in
KPC-shBrca2-CM-treated PSCs compared to shControl, (see Methods and
Supplementary Data [285]8, tab 7), and the 20 most upregulated genes in
KPC-shControl-CM-treated PSCs (compared to shBrca2). Clu is marked by
an arrow. n = 2 biologically independent domes for DMEM treated PSCs
and n = 3 biologically independent domes for shControl-CM-treated and
KPC-shBrca2-CM-treated PSCs. (b) Schematic representation of the
workflow. 2×10^4 KPC-shBrca2 or KPC-shControl cells were inoculated
into the pancreata of syngeneic C57BL/6 J mice. Three weeks later the
mice were sacrificed, tumors were dissected and CAFs were isolated by
FACS sorting (see Methods). The scheme was generated with
biorender.com. (c, d)RNA-seq analysis of CAFs from KPC tumors. n = 3
mice for KPC-shControl tumors and n = 4 mice for KPC-shBrca2 tumors (c)
Heatmap representing hierarchical clustering of the DE genes between
CAFs from KPC-shBrca2 or KPC-shControl tumors (right), and pathways
enriched in each cluster with their corresponding p-values (left).
Pathway analysis was performed using Metascape, selected pathways are
shown, see Supplementary Data [286]10 for the full list. (d) A heatmap
representing the 20 most upregulated genes in KPC-shBrca2-tumor CAFs,
and the 20 most upregulated genes in KPC-shControl-tumor CAFs. (e, f)
DE genes between KPC-shBrca2-CM treated- and KPC-shControl-CM
treated-PSCs (e) and CAFs (f) were matched to a database of murine HSF1
targets genes. The statistical significance of the dependency between
treatment (shBrca2/ shControl) and HSF1 targets was tested using a
Chi-square test. Source data are provided as a Source Data file.
PSCs are highly plastic and assume distinct transcriptional and
functional properties depending on culture conditions (2D vs 3D, cancer
CM etc)^[287]11. To explore the transcriptional changes of CAFs in an
in-vivo setting, we inoculated KPC-shBrca2 and KPC-shControl cells
orthotopically into the pancreata of C57BL/6 J mice. Three weeks later,
tumors were harvested, digested into single-cell suspensions, and CAFs
were isolated by fluorescence-activated cell sorting (FACS;
Fig. [288]5b and Supplementary Figure [289]5g). The cells were lysed
immediately after sorting and processed for RNA-seq. Differential
expression analysis revealed 482 genes significantly upregulated and
666 genes significantly downregulated in CAFs from KPC-shBrca2 tumors
compared to CAFs from KPC-shControl tumors (Fig. [290]5c, d,
Supplementary Figure [291]5h, and Supplementary Data [292]10). Pathway
analysis highlighted cell adhesion, MAPK-cascade regulation, and
positive regulation of cell death among the most differentially
upregulated pathways in CAFs from KPC-shBrca2 tumors compared to those
from KPC-shControl tumors (Supplementary Data [293]10). ECM
organization, IGF signaling and TGFβ signaling were differentially
downregulated in these CAFs compared to CAFs from KPC-shControl tumors
(Fig. [294]5c, d and Supplementary Data [295]10). Clu was among the
significantly upregulated in CAFs from KPC- shBrca2 tumors compared to
KPC-shControl tumors (Supplementary Data [296]10), consistently
supporting its activation in BRCA-mut human tumors and
Brca2-deficientcancer-conditioned PSCs.
Our finding that HSF1 is preferentially activated in CAFs of
BRCA-mutated tumors, and preferentially induces the expression of Clu
in shBrca2-conditioned-PSCs and CAFs, suggested that HSF1 may serve as
a master regulator of the BRCA-CM-mediated transcriptional shift. To
test this, we systematically queried a publicly-available dataset of
HSF1 target genes ([297]https://hsf1base.org/)^[298]84 for the DE genes
between PSCs conditioned by KPC-shBrca2-CM and KPC-shControl-CM
(Fig. [299]5e). We performed a similar search also for the DE genes
between CAFs from KPC-shBrca2 tumors vs CAFs from KPC-shControl tumors
(Fig. [300]5f). We found that HSF1 targets were significantly enriched
in genes upregulated in Brca2-associated PSCs and CAFs (Fig. [301]5e, f
and Supplementary Data [302]11), supporting the notion that HSF1 plays
a role in regulating the BRCA-deficiency-mediated stromal
transcriptional shift.
PSCs reprogrammed by Brca2-deficient cancer cells shift from myofibroblastic
into immune-regulatory CAFs
The shift in CLU^+/SMA^+ CAF ratios in human patients, and the shift in
Clu vs. Acta2 expression in PSCs conditioned by Brca2-deficient cancer
cells, suggest that CAFs of BRCA-mutated tumors may undergo a shift
from myofibroblastic to immune-regulatory functions. Supporting this
notion, Pdl1, whose upregulation in PDAC CAFs was suggested to mediate
T-cell immune suppression^[303]19,[304]85,[305]86, was induced in PSCs
by KPC-shBrca2 CM, but not by KPC-shControl CM (Fig. [306]6a). Similar
to Clu, the induction of Pdl1 was inhibited by aglaroxin C, suggesting
an increased immune-regulatory function for PSCs induced by
BRCA-deficient cancer cells, in an HSF1-dependent manner
(Fig. [307]6a). To functionally test the ability of CAFs isolated from
Brca2-deficient tumors to regulate immune cell function, we isolated
CD8^+ T-cells from spleens of naïve C57BL/6 J mice, and activated them
in the presence of CAFs from KPC-shControl or KPC-shBrca2 tumors
(Fig. [308]6b, c and Supplementary Figure [309]6a, b). T-cells
activated in the presence of CAFs from Brca2-deficient tumors were
significantly more repressed in their ability to upregulate the
activation markers CD25 and CD69 compared to T-cells activated in the
presence of CAFs from shControl tumors.
Fig. 6. Brca2-deficient cancer cells shift CAF functions.
[310]Fig. 6
[311]Open in a new tab
(a) Immortalized PSCs were seeded in Matrigel for 4 days. CM from PSCs
or from KPC cells in which Brca2 was silenced by shRNA or nontargeting
shControl (KPC) was then added for an additional 4 days. 3 nM of
aglaroxin C or PBS control was added to the conditioned media. Pdl1 was
measured by qRT-PCR. Statistical analysis was conducted via two way
ANOVA. Data are presented as mean
[MATH: ± :MATH]
SEM. For each condition n = 3-9 biologically independent culture domes
combined from 3 independent experiments. (b–c) CD8^+ T-cells were
isolated from spleens of naïve C57BL/6 J mice and activated in the
presence of CAFs isolated from either KPC-shControl or KPC-shBrca2
tumors. Subsequently, T-cells were subjected to FACS analysis for
surface expression of CD69 and CD25. Statistical analysis was conducted
via unpaired two tailed t test. Data quantified are presented as mean
[MATH: ± :MATH]
SEM of 6 KPC-shControl and 6 KPC-shBrca2 mice. (d) Scores of
ligand-receptor binding were calculated using the ICELLNET R package
(see Methods) to predict potential differential interactions between
ligands of CAFs derived from shControl vs shBrca2 tumors with immune
checkpoint receptors on CD8^+ T-cells. (e–g) CAFs were isolated from
KPC-shControl (n = 3 mice) and shBrca2 (n = 3 mice) and stained with
anti-CD155 (e), anti-Nectin2 (f), and anti-PD-L1 (g). Statistical
significance was assessed by two tailed t-test. Data are presented as
mean ± SEM (h-i) PSCs were treated with CM derived from KPC-shBrca2 or
KPC-shControl organoids, or with growth medium as control for 4 days.
Then, cultures were stained with Sirius red (SR) to assess collagen
deposition (see Methods). Each point represents the average of shBrca2
or shControl normalized to the average of the growth medium control in
each experiment. n = 3 independent experiments, each representing an
average of 5 independent culture wells. Two tailed t-test was performed
on normalized values. (i) Representative images of PSCs stained with SR
following 4 days treatment with CM derived from KPC-shBrca2 or
KPC-shControl organoids. Scale bar, 300 μm. (j–m) FFPE tumor sections
from BRCA-mut and BRCA-WT PDAC patients were stained by double staining
for αSMA and CLU and imaged using Second harmonic generation (SHG)
imaging. (j) Representative images are shown. DRAQ5 was used to stain
nuclei. Scale bar, 100 μm, or 25 μm (inset). (k–m) Quantification of
matrix pattern using the TWOMBLI plug-in (see Methods) (n = 3 BRCA-WT
and n = 3 BRCA-mut patients). The following parameters were analyzed:
(k) Alignment—the extent to which fibers within the field of view are
oriented in a similar direction; (l) Curvature- the mean change in
angle moving incrementally along 40 µm mask fibers; and (m)
Branchpoint—the number of intersections of mask fibers in the image.
Statistical analysis was conducted via unpaired two tailed t test. Data
are presented as mean ± SEM (n) Schematic representation of the
proposed model. Secreted factors from BRCA-mutated cancer cells induce
HSF1 activation in a subset of adjacent PSCs leading to their
transcriptional rewiring into immune-regulatory CLU^+ CAFs. Source data
are provided as a Source Data file.
To search for potential factors that could be mediating the inhibitory
effect of CAFs from shBrca2 tumors on T-cells, we mined our
tumor-derived CAF RNA-seq data from KPC tumors using the ICELLNET
receptor-ligand analysis tool employing a CD8^+ T-cell-receptor
dataset^[312]87. ICELLNET is a computational tool that calculates a
communication score for ligand-receptor interactions based on
transcriptomic data and a database of potential ligand-receptor pairs.
We applied ICELLNET to screen for immune modulatory surface ligands in
CAFs that may inhibit T-cell activity, and found that CAFs from
KPC-shBrca2 tumors scored higher than KPC-shControl CAFs in the
checkpoint signaling axis involving the TIGIT and CD96 T-cell
inhibitory receptors, and their cognate ligands CD155 and Nectin1-3
(Fig. [313]6d). Notably, Nectin2 (PVRL2) is also differentially
upregulated in CLU^high CAFs in human PDAC as shown by our analysis of
scRNA-seq data from Peng et al. (Supplementary Data [314]2). To
experimentally validate the cell-surface expression of inhibitory
checkpoint markers on CAFs, we isolated CAFs from KPC-shControl or
KPC-shBrca2 tumors, and performed FACS analysis using antibodies for
PD-L1, CD155, and Nectin2. CAFs from KPC-shBrca2 tumors demonstrated
higher cell surface expression of CD155 and Nectin2 (as compared to
KPC-shControl), and a similar trend was observed for the expression of
PD-L1 (Fig. [315]6e–g and supplementary Fig. [316]6c), confirming the
ICELLNET receptor-ligand results and further supporting their role in
suppressing T-cell function. These findings also further strengthen the
connection between Clu upregulation and induction of immune regulatory
pathways in CAFs.
Next, we assessed myofibroblastic functions. To that end, we measured
the ability of PSCs to secrete collagen, in-vitro, using Sirius Red
staining. We found that PSCs conditioned by KPC-shBrca2 medium secreted
significantly less collagen than PSCs conditioned by KPC-shControl
medium (Fig. [317]6h, i). To test the relevance of these findings in
patients we assessed ECM organization in the vicinity of CLU^+ or
αSMA^+ CAFs by second harmonic generation (SHG) imaging combined with
MxIF staining in BRCA-WT vs BRCA-mut patients (Fig. [318]6j–m).
CLU-rich stromal regions that are abundant in BRCA-mut tumors
demonstrated an altered ECM architecture, characterized by
significantly reduced parallel alignment, and increased curvature and
branching of collagen streaks compared to αSMA-rich regions
(Fig. [319]6j–m).
Overall, our findings suggest that BRCA-mut cancer cells promote a
stressful TME that leads to the activation of HSF1 in a subset of PSCs.
These PSCs are reprogrammed into immune regulatory CLU^+ CAFs,
resulting in a different stromal landscape in BRCA-mut compared to
BRCA-WT PDAC tumors (Fig. [320]6n).
Discussion
Accumulating evidence over the past few years unraveled vast
heterogeneity of CAFs in the TME^[321]7,[322]8,[323]88,[324]89. This
heterogeneity was proposed to stem from different cells of origin
giving rise to CAFs^[325]37–[326]41,[327]90, and from transcriptional
rewiring driven by different external cues received from neighboring
cells and local environmental conditions^[328]28,[329]29. The
contribution of germline mutations in the cancer cells to stromal
rewiring is largely uncharted. Here we show that BRCA mutations in the
cancer cells elicit stromal reprogramming in the microenvironment
resulting in distinct stromal landscapes of BRCA-mut PDAC compared to
BRCA-WT PDAC. Specifically, we show that human BRCA-mut tumors express
higher levels of CLU^+ CAFs, and, consequently, higher CLU^+/αSMA^+ and
CLU^+/HLA-DR^+ CAF ratios. We portray the transcriptional landscapes
and potential upstream regulators of CAFs in BRCA-mut and BRCA-WT
tumors from patients, and reveal a network of stress responses
activated in BRCA-mut-associated stroma. Within this network, we find a
specific role for HSF1 as the transcriptional regulator of Clu. Using
cancer organoids, co-cultures, and in-vivo models we show that HSF1
mediates a transcriptional shift of PSCs into CLU^+ CAFs which exert
immune regulatory characteristics (Fig. [330]6n).
Recent studies by us and others have utilized single-cell
RNA-sequencing and imaging technologies to classify CAFs into
functional subtypes based on differential expression of cell surface
markers and genes. Here we find three CAF subtypes, distinctively
marked by αSMA, CLU and HLA-DR (i.e. MHC-II). αSMA is a classic marker
for myofibroblasts. MHC-II was only recently discovered to mark a
subtype of CAFs^[331]8,[332]12, referred to as antigen-presenting CAFs,
though their actual antigen-presenting activities remain to be
elucidated. CLU marks SMA^low CAFs in mouse models of breast and
pancreatic cancer^[333]8,[334]13,[335]40. Our MxIF analysis showing
that CLU marks a discrete population from HLA-DR^+ CAFs, together with
the analysis of human scRNA-seq datasets highlighting immune-regulatory
pathways in CLU^+ CAFs, suggest that in human PDAC, CLU marks
immune-regulatory CAFs. These CLU^+ CAFs are significantly upregulated
in BRCA-mut PDAC compared to BRCA-WT. Our co-culture and mouse studies
confirm these findings from human patients and suggest that in mice
too, immune-regulatory CLU^+ CAFs are significantly upregulated in
BRCA-deficient PDAC compared to BRCA-WT. BRCA-mut PDAC immune
microenvironments in other cancer types are characterized by increased
infiltration of T cells^[336]49–[337]52. Our IF analysis of CD3
staining in patients did not show increased T cell infiltration,
however it does not exclude the possibility that the T cell composition
is altered. Moreover, CLU^+ CAFs may act to modulate the activity of
additional cells in the immune microenvironment such as macrophages.
Clu is transcriptionally regulated by the stress-induced master
regulator, HSF1^[338]65. HSF1 has been shown by us and others to play
key roles in the transcriptional rewiring of fibroblasts into CAFs in
various cancers^[339]32–[340]36. Here we describe a preferential
activation in a specific subtype of cancer, BRCA-mut PDAC, and expose a
new facet of HSF1’s stromal activities, affecting CAF composition.
Preferentially activated in the stroma of BRCA-mut PDAC, HSF1 activates
Clu, and leads to induction of immune-regulatory CLU^+ CAFs.
CLU is a molecular chaperone, harboring two isoforms—a nuclear isoform
(nCLU) and a secreted one (sCLU). These isoforms were shown to have
opposing activities; nCLU is a pro-apoptotic factor, while sCLU is a
stress-induced, pro-survival factor. In epithelial cells, sCLU is
upregulated by DNA damage^[341]91, it is overexpressed in various
cancers^[342]92, and the shift of nCLU to sCLU expression is associated
with progression towards high-grade and metastatic carcinoma in
different cancers^[343]93–[344]96. The nuclear-to-secreted transition
of CLU has not been extensively characterized in fibroblasts. Here, we
detect upregulation of the secreted form of CLU in CAFs of BRCA-mut
PDAC. Moreover, silencing of Brca2 in cancer cells is sufficient to
induce Clu expression in CAFs of mouse tumors and in WT PSCs in
culture, confirming not only that Clu is induced by BRCA deficiency but
also that this is a non-cell-autonomous pathway induced by BRCA
deficiency in the cancer cells. These CAFs have immune regulatory
effects and modulate the adjacent ECM organization. Previous studies
have implicated TGFβ in promoting the transition of CAFs from
inflammatory to myofibroblast-like^[345]14. TGFβ was also shown to
negatively regulate the expression of sCLU in fibroblasts during
fibrosis^[346]97,[347]98. Our RNA-seq analysis of LCM stroma from
patients shows upregulation of the TGFβ inhibitor, Gremlin1 (GREM1), in
BRCA-mut PDAC, and our RNA-seq analysis of mouse KPC and CAFs shows
differential downregulation of TGFβ signaling both in KPC-shBrca2 cells
and in CAFs from KPC-shBrca2 tumors compared to CAFs from KPC-shControl
tumors (Fig. [348]5b and Supplementary Figure [349]5d). In line with
our findings, GREM1^+ fibroblasts were previously shown to be
upregulated during chronic pancreatitis and PDAC^[350]99, possibly
promoting disease progression through M2 macrophage polarization.
Another recent study demonstrated that GREM1 orchestrates cellular
heterogeneity in PDAC by maintaining the epithelial
compartment^[351]100. Since cellular heterogeneity in PDAC is a
prominent characteristic of PDAC subtype designation^[352]101, GREM1
expression by CAFs and paracrine signaling with epithelial
subpopulations may play an important role in maintenance of epithelial
heterogeneity in BRCA2 mutated tumors. Taken together these findings
further support the notion that BRCA mutations in the cancer cells lead
to a stromal shift from TGFβ induced SMA^+ myofibroblasts to
HSF1-induced CLU^+ fibroblasts.
Clinical studies using inhibition of CLU by single-stranded antisense
oligonucleotides showed elevated sensitivity to chemotherapy and
radiotherapy in cancer patients^[353]102. In addition, CLU was shown to
regulate DNA repair pathways, including the BRCA1 pathway^[354]103.
These findings suggest that BRCA-mut patients may show improved
response to CLU inhibition. Moreover, highlighted by our RNA-seq data,
HSP90 is suggested to partially regulate the BRCA-driven
transcriptional changes we identified. Notably, CLU was shown to form a
complex with HSP90 proteins^[355]104 to synergistically promote EMT and
metastasis^[356]64, and their combined inhibition showed improved
response in prostate cancer^[357]105. Nevertheless, inhibiting the
expression of CLU^+ CAFs may shift the balance and enhance the relative
expression of SMA^+ CAFs, resulting in a stiff, myofibroblast-rich
stroma that may be more protumorigenic than the CLU-rich
stroma^[358]19. Thus, future studies should test the outcome of CLU
inhibition and/or of HSP90 inhibition in combination with
platinum-based chemotherapy or PARP inhibitors on BRCA-mut cancer.
Future studies should also assess the efficacy of combining
immune-regulatory CAF inhibition with immunotherapies—while early
phases of clinical trials show promising results in combining
PARP-inhibitors with immunotherapy in BRCA mutated tumors, the role of
BRCA1/2 mutations in immunotherapy is still
controversial^[359]106,[360]107. How CAFs play into the response to
immunotherapy in this context is still unknown.
This work identifies a unique stress-response network that is activated
in BRCA-mut stroma. Tumors are stressful environments and stress
responses are well known to play important roles in supporting survival
of cancer cells. For example, activation of Nrf2 in cancer cells leads
to elevated mRNA translation and mitogenic signaling^[361]69, the
endoplasmic reticulum (ER) stress response was shown to mediate
chemoresistance in PDAC cells^[362]108, and expression of ATF4 in
fibroblasts was suggested to promote disease progression and resistance
to chemotherapy in PDAC^[363]70. Other studies reported regulation of
stress responses by BRCA1. BRCA1 was shown to actively regulate
reactive-oxygen-species (ROS) in response to oxidative stress^[364]109,
and to regulate the unfolded protein response (UPR)/ER stress response
by regulating glucose-regulated protein (GRP)78, CHOP and
GRP94^[365]110. Furthermore, BRCA1 induction led to downregulation of
HSF1^[366]111. Our results indicate that while only HSF1 was
significantly higher in BRCA-mut tumors compared to BRCA-WT tumors, a
broader stress network is activated in the BRCA-mut TME. This may
reflect a mechanism by which DNA repair deficiencies in the cancer
cells impose unique stress conditions on the TME that reshape the
stress-response network in the stroma. Nevertheless, HSF1 appears to
play a dominant role in this network, perhaps also through activation
in the cancer cells themselves, as reflected by our mass-spectrometry
analysis of proteins secreted from KPC-shBrca2 cells. Indeed, HSF1 is
well-known to be activated in cancer cells of various tumor
types^[367]112, and may be mediating a pro-tumorigenic feedback loop
between cancer cells and CAFs in BRCA2-deficient tumors.
CAFs are highly plastic and dynamically shift between myofibroblastic
and immune-regulatory functions when exposed to different
microenvironments, including different culture conditions, making their
functional characterization challenging^[368]7. Nevertheless, by
combining organoid cultures and mouse injections of cancer cells with
3D cultures of PSCs and CAFs we could define a functional shift induced
by BRCA-deficient cancer cells in a subset of CAFs. We find that PSCs
conditioned by BRCA-deficient cancer cells exert reduced collagen
production activity and increased immune-regulatory activity than PSCs
conditioned by BRCA-proficient cancer cells. In patients, CLU^+ CAFs
associate with distinct ECM structures compared to SMA^+ CAFs. Several
recent single-cell studies performed by us and others on human tumors
and mouse models identified diverse CAF subtypes in breast, pancreatic,
ovarian and prostate
cancers^[369]8,[370]10,[371]22,[372]37,[373]113,[374]114. To which
extent these subtypes are cancer-type specific or represent pan-cancer
markers is a burning question in our field. Even more pressing is the
question of whether the mutation dependencies identified in our study
are PDAC-specific or Pan-BRCA, or perhaps even represent a general
characteristic of homologous recombination deficiency (HRD) cancers.
These questions bear important implications on future therapeutic
strategies. Defining common and segregating design principles of CAFs
between tissues and organs sharing similar BRCA/HRD mutations will be
an important step towards advancing therapy directed at these
poor-prognosis cancers.
Methods
Ethics statement
All clinical samples and data were collected following approval by
Memorial Sloan Kettering Cancer Center (MSKCC; IRB, protocols #06-107,
#15-015 and 13-217), Shaare Zedek Medical Center (IRB protocol #101/13;
Ministry of Health no. 920130134), Sheba Medical Center at Tel-Hashomer
(IRB protocol #0967-14-SMC), and the Weizmann Institute of Science
(IRB, protocols # 186-1) Institutional Review Boards. All animal
studies were conducted in accordance with the regulations formulated by
the Weizmann Institute of Science Institutional Animal Care and Use
Committee (IACUC; protocol #02040220-2, #33870217-2, #32520117-2,
#06920921-2).
Human patient samples
Tumor samples from surgically resected primary pancreas ductal
adenocarcinomas were from patients treated at Memorial Sloan Kettering
Cancer Center (MSKCC), at Shaare Zedek Medical Center, and at Sheba
Medical Center; informed consent to study the tissue was obtained via
MSK IRB protocols #06-107 and 13-217 (Cohort 1; Supplementary
Data [375]1), and #15-015 for the exosome analysis (Cohort 2;
Supplementary Data [376]6), and via Shaare Zedek Medical Center (IRB
protocol #101/13; Ministry of Health no. 920130134), Sheba Medical
Center at Tel-Hashomer (IRB protocol #0967-14-SMC), and the Weizmann
Institute of Science (IRB, protocols # 186-1) Institutional Review
Boards. Cohort 1 included a total of 27 BRCA-WT PDAC patients and 15
BRCA-mut PDAC patients (Supplementary Data [377]1). Cohort 2 included
fresh samples from 26 patients from which tumor tissues and/or normal
adjacent controls were collected (Supplementary Data [378]6). Of the 15
BRCA-mut patients, 4 are BRCA1-mut carriers and 11 are BRCA2 carriers,
which is consistent with the reported prevalence of BRCA1 and BRCA2
mutations in PDAC^[379]115. FFPE whole tumor sections and deeply
annotated demographic, clinical, pathologic and genomic (MSK-IMPACT^TM)
data were collected for all MSKCC patients in the study. In addition,
fresh-frozen tumor tissue was collected for a subset of 12 patients.
Mice
C57BL/6 J 8-week males were purchased from Envigo (Jerusalem Israel).
8-week male Hsf1 null mice and their WT littermates (BALB/c × 129SvEV,
by Ivor J. Benjamin^[380]116) were maintained under
specific-pathogen-free conditions at the Weizmann Institute’s animal
facility. Mice were sacrificed by CO[2] for pancreata harvesting. All
animal studies were conducted in accordance with the regulations
formulated by the Institutional Animal Care and Use Committee of the
Weizmann Institute of Science (WIS) (IACUC; protocol #02040220-2,
#33870217-2, #32520117-2, #06920921-2).
Cell lines and primary cell cultures
Mouse-immortalized PSCs, the KPC cell line and KPC organoids were
provided by David Tuveson’s laboratory^[381]13. Immortalized mouse PSCs
and the KPC cell line were cultured in growth medium containing
Dulbecco’s modified Eagle’s medium (DMEM; Biological industries,
01-052-1 A) supplemented with 10% fetal bovine serum (FBS) and
pen/strep. Silencing of Brca2 in KPC cell lines was achieved by
lentiviral infection, using mouse Brca2 shRNA in pLKO.1 vector
(Horizon, RMM4534) and pLKO.1 non-targeting control vector (Sigma
Aldrich, SHC002), and puromycin selection.
Primary PSC isolation
Pancreata were collected postmortem from Hsf1 null mice or WT
littermates into HBSS (Sigma-Aldrich, H6648), then minced into Roswell
Park Memorial Institute 1640 (RPMI) (Biological industries,
01-100-1 A), supplemented with 0.5 mg/mL Collagenase D (Merck,
11088866001), 0.1 mg/mL Deoxyribonuclease I (Worthington,
[382]LS002007) and 1 mM HEPES (Biological Industries, 03-025-1B).
Pancreata were incubated at 37 °C for 40 min with mechanical disruption
every 5 min. Cells were then filtered with 100 μm filters, centrifuged
and isolated by Histodenz gradient (Sigma-Aldrich, D2158) dissolved in
HBSS. Cells were resuspended in HBSS with 0.3% BSA and 43.75%
Histodenz, HBSS with 0.3% BSA was layered on top of the cell
suspension, and centrifuged for 20 min at 1,400 RCF. The cell band
above the interface between the Histodenz and HBSS was harvested,
washed in PBS, and plated in growth medium. One week after seeding,
immune and epithelial cells were depleted by anti-EpCAM (Miltenyi,
130-105-958) and anti-CD45 (Miltenyi, 130-052-301) magnetic beads, and
transferred to LS columns (Miltenyi, 130-042-401). For gene expression
measurements, PSCs were then cultured for 3 days and mRNA was isolated.
Organoid lines derived from primary pancreatic KPC tumors
KPC organoids provided by the laboratory of David Tuveson^[383]13 were
cultured in Corning® Matrigel® Growth Factor Reduced (GFR) Basement
Membrane Matrix, Phenol Red-free, LDEV-free, (Corning, 365231) with
complete organoid medium^[384]117. Silencing of Brca2 in KPC organoid
lines was achieved by lentiviral infection as described above for the
KPC cell-lines. Conditioned medium was collected following 3-4 days of
culture with 5% FBS DMEM.
PSC 3D cultures
For 3D culture, 4×10^4 cells were seeded in Matrigel® GFR in growth
medium for 4 days. Medium was changed to KPC-shBrca2 or KPC-shControl
conditioned medium or to their own conditioned medium as control for 4
additional days and cells were either harvested for RT-PCR or RNA-seq.
For HSF1 inhibition, 3 nM CMLD011866 (aglaroxin C)^[385]78,[386]79 was
added every 2 days.
In vivo tumor model
KPC orthotopic PDAC tumors were established as previously
described^[387]118. Briefly, C57BL/6 J 8-week males were anaesthetized,
and a small incision was made in the left part of the abdomen. Cancer
cells (2×10^4 KPC-shBrca2 or KPC-shControl per mouse) were suspended in
Matrigel (Becton Dickinson), diluted 1:1 with cold PBS (total volume of
40 μL), and injected into the pancreas using a 26-gauge needle. The
abdominal wall and then the skin were closed with Surgibond (Vetmarket,
Israel). Buprenorphine was administered 30 min prior to the injection,
and on the following day. Mice were sacrificed and tumors were
collected for further analysis at 2-3 weeks postinjection. The maximum
tumor volume of 2000 (mm)^3 was not reached in any experiment.
Sirius red assay
2×10^4 PSCs were seeded in 96-wells and cultured overnight with growth
medium. The medium was then replaced with conditioned medium from
KPC-shBrca2 organoids, KPC-shControl organoids, or with control medium
(DMEM 5% FBS, P/S) and the cells were incubated at 37 °C for 4 days.
The medium was then aspirated and Sirius red/ fast green staining
(Chondrex, cat. #9046) was performed according to the manufacturer
instructions. Briefly, cells were washed with PBS and incubated in
100 μL Kale fixative for 10 min, after which the fixative was
aspirated, the cells were washed with PBS and 100 μL of Sirius red/fast
green dye was added for 30 min. Samples were imaged with an inverted
Leica DMI8 wide-field (Leica Microsystems, Mannheim, Germany), Leica
DFC7000GT monochromatic camera, 20x/0.8 Air. The dye was then
aspirated, the cells were washed, extraction buffer was added, and OD
values at 540 nm and 605 nm were read with a spectrophotometer. The
amount of collagen per sample was calculated using the following
formula:
[MATH: OD540value−(OD605value*0.291)0.0378 :MATH]
Immunohistochemistry of human tissues
4-μm FFPE sections from PDAC tumors were deparaffinized and treated
with 1% H[2]O[2]. Antigen retrieval was performed using citrate buffer
(pH 6.0) for all antibodies, except for MUC5B and HLA-DR (for which
Tris-EDTA buffer (pH 9.0) was used). Slides were blocked with 10%
normal horse serum (Vector Labs, S-2000) and the antibodies listed in
Supplementary Data [388]12 were used. Visualization was achieved with
3,3’-diaminobenzidine as a chromogen (Vector Labs, SK4100).
Counterstaining was performed with Mayer hematoxylin (Sigma Aldrich,
MHS16). Images were taken with a Nikon Eclipse Ci microscope
(Fig. [389]1a) or scanned by the Pannoramic SCAN II scanner, with
20×/0.8 objective (3DHISTECH, Budapest, Hungary) (Fig. [390]2b, c).
Multiplexed Immunofluorescent (MxIF) staining and imaging of human tissues
MxIF staining
FFPE sections from 22 BRCA-WT and 14 BRCA-mut PDAC patients were
deparaffinized, and fixed with 10% neutral buffered formalin. Antigen
retrieval was performed using citrate buffer (pH 6.0) for all
antibodies, except for MUC5B and HLA-DR (for which Tris-EDTA buffer (pH
9.0) was used). Slides were then blocked with 10% BSA + 0.05% Tween20
and the antibodies listed in Supplementary Data [391]11 were diluted in
2% BSA in 0.05% PBST and used in a multiplexed manner with OPAL
reagents (AKOYA BIOSCIENCES). All primary antibodies were incubated
overnight at 4 °C, except for αSMA which was incubated for 1.5 hrs at
RT. Briefly, following primary antibody incubation, slides were washed
with 0.05% PBST, incubated with secondary antibodies conjugated to HRP,
washed again and incubated with OPAL reagents. Slides were then washed
and antigen retrieval was performed as described above. Then, slides
were washed with PBS and stained with the next primary antibody or with
DAPI at the end of the cycle. Finally, slides were mounted using
Immu-mount (#9990402, Thermo Scientific).
MxIF imaging
FFPE samples were imaged with a LeicaSP8 confocal laser-scanning
microscope (Leica Microsystems, Mannheim, Germany), equipped with a
pulsed white-light and 405 nm lasers using a HC PL APO ×40/1.3
oil-immersion objective and HyD SP GaAsP detectors. The following
fluorophores and parameters were used: DAPI (Ex. 405 nm Em.
424–457 nm); Opal 520 (Ex. 494 nm Em. 510–525 nm); Opal 570 (Ex. 568 nm
Em. 575–585 nm); Opal 620 (Ex. 588 nm Em. 601–616 nm); Opal 650 (Ex.
638 nm Em. 647–664 nm); Opal 690 (Ex. 670 nm Em. 725–794 nm); and
pinhole of 1 AU. Samples were acquired with a pixel size of
0.142 µm/pixel.
MxIF analysis
Images were analyzed using Fiji image processing platform^[392]119. For
all panels of MxIF staining 3-5 images were obtained per patient. For
each image, five slices were Z projected (max intensity) and linear
spectral unmixing was performed. We used two main methods for image
analysis—object-based analysis and pixel-based analysis. Object-based
analysis was applied for co-expression studies, such as in
Supplementary Figure [393]1c–e, in which we aimed to determine whether
our CAF markers mark different cells. Since these proteins might be
expressed in the same cell, but not necessarily at the same pixel, we
used object-based analysis, in which cells’ borders are inferred based
on the nuclei shape by DAPI staining. When assessing the abundance of
each marker on its own, we used pixel-based analysis, as some of these
proteins are secreted and thus may be underrepresented if analyzed by
cell structure inference rather than by analyzing the whole image. To
assess the CAF composition (Fig. [394]1) each channel was thresholded
to create a mask of its area. The area of each CAF marker within the
CD45^- CK^- area was calculated by pixel-based analysis and divided by
the total region of interest (ROI), as defined by the CD45^- CK^- area.
For the assessment of CAF subtype ratios, values were logged and
averaged per patient. To study the discrete expression of the different
CAF markers (Supplementary Figure [395]1b–d) we performed an
object-based analysis using the QuPath software^[396]120. Briefly,
using a training image, each cell marker classifier was trained
independently of all the other markers. CD45^+ and CK^+ cells were
excluded. Then, the number of positive cells for each marker was
calculated. The ratio of positive cells for each marker (αSMA, CLU,
HLA-DR) was defined as N/A if there were less than 10 positive cells of
that marker in that image. If there were more than 10 positive cells
within the image, all 1^st and 2^nd order overlap ratios (relative to
the chosen marker) were calculated. All images per patient were
averaged. To analyze TF activation (Fig. [397]3) we used the Fiji image
processing platform. First, we defined ROIs to exclude all cancer
cells. Then, we detected nuclei of all stromal cells using the DAPI
channel. Then, the mean intensity of each TF in all stromal cell nuclei
per image was calculated. For each patient, the average intensity of
all images was calculated. To analyze the expression of MUC5B and
SERPINA1 within CLU^+ and αSMA^+ CAFs we used the Fiji image processing
platform. First, we excluded all CK^+ area. Then, the area of each of
the CAF markers and secreted proteins was thresholded to create a mask
of its area. Then, the area of CLU or αSMA out of MUC5B or SERPINA1 was
calculated.
Single-cell validation
Two human PDAC single-cell datasets^[398]12,[399]16 were analyzed using
the Seurat (V4.0) R toolkit^[400]56,[401]121. Pathway analysis was
performed using Metascape^[402]122. UMAP images displaying gene
expression were plotted using a minimum cutoff of the 10’th quantile.
Peng et al. dataset^[403]16—All cells that were defined as
‘Fibroblast_cell’ or ‘Stellate_cell’ in the original dataset were
analyzed. Non-tumor samples and two samples with less than 50
Fibroblast or Stellate cells were filtered out. All other functions
were run with default parameters. This yielded a large and
comprehensive dataset of 11,010 cells from 22 patients. Harmony
integration^[404]123 with default parameters was used to minimize the
patient batch effect, and shared nearest neighbor (SNN) modularity
optimization-based clustering was then used with a resolution parameter
of 0.14^[405]124. Two clusters were excluded from further analysis, one
had less than 10 cells and the other is a cluster that expresses the
pericyte marker, MCAM.
Elyada et al. dataset^[406]12—All cells that were originally defined as
iCAF or myCAF were analyzed, yielding 972 cells from 10 patients. All
other functions were run with default parameters. Harmony
integration^[407]123 with default parameters was used to minimize the
patient batch effect, and shared nearest neighbor (SNN) modularity
optimization-based clustering was then used with a resolution parameter
of 0.1, which resulted in 4 clusters, following the exclusion of two
clusters that had less than 10 cells, each.
Pathway enrichment analysis
Pathway enrichment analysis was performed using Metascape^[408]122 to
analyze the DE genes in the LCM RNA-seq results, as well as the
different clusters of the single-cell and bulk data.
Pixel classification of H&E stained slides from PDAC patient samples
H&E slides were scanned by the Pannoramic SCAN II scanner, with 20×/0.8
objective (3DHISTECH, Budapest, Hungary). Quantification of CAF-rich,
cancer-rich, and immune-rich regions within the tumor area of each
section was done by QuPath (version 0.2.3)^[409]120 using pixel
classification. The classifier method used was Artificial neural
network (ANN_MLP) with high resolution. The same classification
parameters were used for all images.
Laser capture microdissection of human PDAC samples
Fresh frozen blocks of BRCA-WT and BRCA-mut PDAC tumors were obtained
from MSKCC (Supplementary Data [410]1). 7 mm sections were sliced in a
cryostat and placed on PEN Membrane Glass Slides (Thermo Fisher
Scientific, LCM0522). Then, sections were stained using the Histogene™
LCM Frozen Section Staining Kit (Thermo Fisher Scientific, KIT0401) and
stromal regions were dissected. Immune islands, cancer cells and blood
vessels were excluded from microdissection. Slides were left to dry for
5 min at RT followed by microdissection using the Arcturus (XT) laser
microdissection instrument (Thermo Fisher Scientific, #010013097).
Infrared capture was used to minimize RNA damage. CapSure Macro LCM
caps (Thermo Fisher Scientific, #LCM0211) were used to capture
microdissected tissue. Microdissected tissue from each sample was
incubated for 1 h in 65 °C in the lysis buffer of the RNA extraction
kit and frozen at −80 °C. RNA extraction was performed using the
PicoPure™ RNA Isolation Kit (Thermo Fisher Scientific, KIT0204)
according to the manufacturer’s instructions.
Library preparation and RNA-sequencing of LCM samples
Libraries were prepared using the SMARTer Stranded Total RNA-Seq
v2-Pico Input Mammalian Kit (Takara Bio USA, #634415) according to the
instructions of the manufacturer. Libraries were sequenced on Illumina
NextSeq 500, at 25 M reads per sample.
Differential expression analysis of LCM samples
The DeSeq2 package^[411]125 was used to identify DE genes between
BRCA-mut and BRCA-WT samples, and the FDRtool^[412]126 was used to
compute local FDR values from the p-values calculated using DeSeq2.
Genes with a fold change (FC) > = 1.5 and false discovery rate
(FDR) < 0.05 were considered significantly differentially expressed
between groups. For comparison of shBrca2 and shControl samples, genes
were significant at FC > = 0.5, FDR < 0.1 and basemean value > 5. Batch
biases were corrected using Deseq2 package as RNA extraction and
library preparation were performed in two batches.
Isolation of CAFs from KPC tumors
C57BL/6 J mice were orthotopically injected with 2×10^4 KPC-shBrca2 or
KPC-shControl cells. At 2-3 weeks following injection animals were
sacrificed, and tumors were excised, dissociated, minced and incubated
with enzymatic digestion solution containing 2.5 mg/mL collagenase D
(Sigma Aldrich, 11088866001), 70 U/mL Dnase, and 1 mM HEPES (Biological
Industries) in RPMI 1640 (Biological Industries, 01-100-1 A) for 40 min
at 37 °C. To enrich for stromal cells, single-cell suspensions were
incubated with anti-EpCAM (Miltenyi, 130-105-958) and anti-CD45
(Miltenyi, 130-052-301) magnetic beads, transferred to LS columns
(Miltenyi, 130-042-401) and the stromal enriched (CD45, EpCAM depleted)
flow-through was collected and pelleted.
Bulk RNA sequencing of CAFs, PSCs and KPC cells
CAFs
Two to three weeks following KPC shControl or KPC shBrca2 orthotropic
injection into the pancreas the mice were sacrificed, PDAC tumors were
excised, digested into single cells and suspended in MACS buffer. The
cell suspension was depleted of CD45^+ and EpCAM^+ cells using manganic
beads (Miltenyi, 130-105-958 and 130-052-301) and LS columns (Miltenyi,
130-042-401). The CAF-enriched flow through was then stained for FACS
sorting with the following antibodies: CD45-FITC, CD31-FITC,
EpCAM-FITC, PDPN-APC, and Ghost Dye-violet 450 for detection of live
cells. To collect CAFs, the following gating strategy was applied:
CD45^−/CD31^−/EpCAM^−/PDPN^+ (as PDPN was previously shown to be a
global CAF marker, including Clu^+ CAFs, in mice^[413]12,[414]14).
~10000 CAFs were sorted into 40 μL of lysis/binding buffer (Life
Technologies) and mRNA was isolated using Dynabeads oligo (dT) (Life
Technologies).
PSCs
PSCs were cultured in 3D as described above, and mRNA was extracted
from the culture using Dynabeads® mRNA DIRECT™ Purification Kit
(Thermo-Fisher scientific cat# 61012).
KPC
200,000 KPC-shBrca2 or KPC-shControl cells were seeded in 6-wells and
cultured in growth medium. After two days the cells were stained with
propidium iodide (PI, 1:1000) and then 10,000 PI^- cells were FACS
sorted into 40 μL of lysis binding buffer (Thermo-Fisher scientific
cat# 61012), and mRNA was isolated using Dynabeads oligo (dT).
RNA libraries of CAFs, PSCs and KPCs were prepared and sequenced on
Illumina NextSeq 500, using the MARS-seq protocol, as previously
described^[415]90.
Clustering and differential expression analysis for bulk RNA-seq of CAFs,
PSCs, and KPCs
Raw read counts were processed and normalized utilizing the Deseq2
pipeline, using Likelihood ratio test for the paired comparisons. For
CAF and KPC samples, genes were considered DE between shBrca2 and
shControl and used for downstream analysis if their Padj < 0.1,
absolute log2 FC > 0.5, and basemean value greater than 5. Then,
pathway analysis was performed on the genes in each cluster using
Metascape^[416]122, and prediction of upstream regulators was performed
using IPA^[417]62.
For PSCs, two steps of differential expression and pathway analysis
were performed: First, we extracted genes that were differentially
expressed between any two of the three groups (shBrca2-CM, shControl CM
and Growth medium), and the resulting p-values were adjusted using FDR.
Genes with any Padj < 0.1 and absolute log2 FC > 0.5 (for the same
comparison), and a basemean value greater than 5 (n = 1320;
Fig. [418]5a and Supplementary Data [419]8), were used for pathway
analysis on each of the 4 clusters in Fig. [420]5a. Next, to extract
genes upregulated only in shBrca2-CM treated PSC, the DE genes in the
PSC dataset (n = 1320) were filtered for Padj < 0.1 and log2FC < −0.5
for shBrca2 vs. shControl, and for Padj <0.1 and log2FC < −0.5 for
shBrca vs growth medium. To extract genes upregulated only in
shControl-CM treated PSC, the DE genes in the PSC dataset (n = 1320)
were filtered for Padj < 0.1 and log2F C > 0.5 for shControl vs
shBrca2, and for Padj < 0.1 and log2F C > 0.5 for shControl vs growth
medium (See Supplementary Data [421]8 tab 6).
CIBERSORTx
To estimate the fraction of fibroblasts in the LCM-dissected samples,
we used the computational deconvolution tool, CIBERSORTx, that
estimates the relative abundance of individual cell types in a mixed
cell population based on single cell RNA-seq profiles^[422]57. The
single-cell human PDAC dataset by Peng et al.^[423]16 was used as a
reference to the cell type signatures. Then, we applied CIBERSORTx to
estimate the distribution of immune cell populations within these
samples using LM22, a validated leukocyte gene signature
matrix^[424]127. The quantile normalization was disabled as recommended
by the CIBERSORTx web interface
([425]https://cibersortx.stanford.edu/). Permutations were set to 500.
Proteomic analysis of human exosomes
Fresh pancreatic cancer tissue and peritumoral non-involved pancreas
tissue were cut into small pieces and cultured for 24 h in serum-free
RPMI, supplemented with penicillin (100 U/mL) and streptomycin
(100 μg/mL). Conditioned medium was processed for exosome isolation.
Exosomes were purified by sequential ultracentrifugation as previously
described^[426]61,[427]128. Briefly, cell contamination was removed
from resected tissue culture supernatant by centrifugation at 500x g
for 10 min. To remove apoptotic bodies and large cell debris, the
supernatants were then spun at 3000x g for 20 min, followed by
centrifugation at 12,000x g for 20 min to remove large microvesicles.
Finally, exosomes were collected by ultracentrifugation twice at
100,000x g for 70 min. Five micrograms of exosomal protein were used
for mass spectrometry analysis^[428]61. High resolution/high mass
accuracy nano-LC-MS/MS data was processed using Proteome Discoverer
1.4.1.14/Mascot 2.5. Human data was queried against the UniProt’s
Complete HUMAN proteome.
CM boiling experiment
CM was collected from KPC-shControl and KPC-shBrca2 organoid cultures
following 4 days in culture. CM was boiled for 20 min and added to PSCs
grown in 3D cultures for 72 h. Clu expression in PSCs was analyzed by
RT-PCR.
Mass spectrometry of organoid CM
Serum-depleted CM was collected from 8 KPC-shControl and 8 KPC-shBrca2
organoid cultures following 4 days in culture. Mass spectrometry was
carried out at the De Botton Protein Profiling institute of the Nancy
and Stephen Grand Israel National Center for Personalized Medicine,
Weizmann Institute of Science. Samples were concentrated and denatured
using urea and subjected to tryptic digestion. The resulting peptides
were analyzed using nanoflow liquid chromatography (nanoAcquity)
coupled to high resolution, high mass accuracy mass spectrometry (Q
Executive HF). Each sample was analyzed on the instrument separately in
a random order in discovery mode. Raw data was processed using MaxQuant
version 2.0.1. Data was searched against the mouse protein sequences
from UniprotKB. Quantification was based on the LFQ method, based on
all peptides. The mass spectrometry proteomics data have been deposited
to the ProteomeXchange Consortium via the PRIDE^[429]129 partner
repository with the dataset identifier PXD036629.
Ingenuity pathway analysis
This tool generates multi-level regulatory networks by suggesting
upstream regulators that may lead to the transcriptional changes
evident in a dataset. To identify predicted upstream regulators of the
Brca2-associated CAF transcriptional program we analyzed our DE gene
dataset using the Causal Network tool in the Ingenuity Pathway Analysis
(IPA) software^[430]62.
Real-time PCR
RNA was isolated using TRIzol reagent based on the TRI reagent user
manual (Bio-Lab, 959758027100). Reverse transcription was done by
High-Capacity cDNA reverse transcription kit (Cat 4368814, Thermo
Fischer Scientific) according to the manufacturer instructions.
Quantitative RT–PCR analysis was performed using Fast SYBR Green Master
mix (Applied Biosystems, 4385610) or Taqman Fast Advanced Master Mix
(Applied Biosystems, 4444556), and data was normalized to the
house-keeping gene HPRT. The primer sequences for qPCR used in this
study are provided in Supplementary Data [431]13.
Aglaroxin C
((-)-Aglaroxin C (CMLD011866) was synthesized according to published
protocols^[432]78,[433]79 and used at a concentration of 3 nM.
Chromatin immunoprecipitation (ChIP) followed by qRT-PCR
Immortalized PSCs were treated with KPC-conditioned medium. 24 hrs
later, PSCs were harvested for CHIP assay as described in^[434]130.
Anti-HSF1 Ab (Cell Signaling, 4356 S) was used to immunoprecipitate
HSF1, and normal rat IgG (Cell Signaling, 2729 S) was used as control.
qRT-PCR was performed using the primers listed in Supplementary
Data [435]13 to assess the binding of HSF1 to Clu, Hsp90aa, and Hspa1a.
These genomic primers were designed to flank an HSE site homologues to
that reported to bind HSF1 in human cells^[436]65. (Clu HSE sequence:
TTCCAGAAAGCTC, Mus musculus strain C57BL/6 J chromosome 14, GRCm39,
66205967- 66205980). Primers targeting an intergenic region (to which
HSF1 is not expected to bind) were used as control.
Conditioned medium for Chromatin IP
KPC cells were plated at a density of 15×10^4/cm^2 in DMEM supplemented
with 5% FBS and L-glu and Pen/Strep. 24 h later, the medium was
replaced and cells were left to grow for an additional 48 h. The medium
was then collected and filtered through 0.22 μm filters, and placed on
top of PSC cultures.
Analysis of HSF1 target genes
The HSF1base.org database of HSF1 targets^[437]84 was queried for
murine HSF1 target genes. For PSCs, this list was then matched with the
filtered list of DE genes between shBrca2-CM treated- and shControl-CM
treated-PSCs from Supplementary Data [438]8, tab 6. For CAFs, the list
of murine HSF1 targets was matched with DE genes from Supplementary
Data [439]10 with p.adj < 0.05 and absolute log2FC > 1.5. The
statistical significance of the dependency between treatment (shBrca2/
shControl) and HSF1 targets was tested using a Chi-square test.
Ligand-receptor analysis
ICELLNET R package^[440]87 was used to analyze normalized gene counts
of CAFs derived from either shControl or shBrca2 tumors. Normalized
gene counts that were above the median expression in each population
were used. The ICELLNET ligand-receptor dataset of classifications was
set to “Checkpoint” on the CD8^+ T- cells that are provided by the
package. The scores were calculated based on the expression of CAF
ligands and CD8^+ T-cell receptors^[441]87.
Flow cytometry for CAF ligands
KPC shControl and shBrca2 tumors were dissociated into single-cell
suspensions and treated with red blood cell lysis buffer (Biolegend,
420301). Subsequently, cells were depleted of CD45 + and EpCAM+ cells
as described above. For CAF enrichment, the CD45- and EpCAM-depleted
fraction was incubated with PDPN–biotin antibody and the PDPN-enriched
cell suspension was isolated with anti-biotin magnetic beads (Miltenyi,
130-090-485). Cells were stained for anti-CD45 and anti-EPCAM FITC
(Miltenyi, 130-110-658, 130-117-752), anti-PDPN BV421 (Biolegend
127423), anti-PD-L1 APC (Biolegend 124312), anti-CD155 PE (Biolegend
132205), and anti-Nectin2 BV711 (BD Biosciences, 748049). Dead cells
were excluded using Draq7 staining (Biolegend, 424001). FACS analysis
was performed using flowjo software v.10.7.1 and CytExpert version
2.5.0.77
T-cell activation assay
2 × 10^4 CAFs from either shControl or shBrca2 KPC tumors were plated
in 96 wells in RPMI 1640 supplemented with 10% FBS. After 24 h,
2 × 10^4 CD8 + T cells were isolated from normal spleens by a
positive-selection kit (CD8a (Ly-2) Microbeads, mouse, Miltenyi
130-117-044), in the presence or absence of CD3/CD28 Dynabeads. For
positive and negative controls, T-cells were activated without CAFs or
cultured in the absence of CD3/CD28 beads. After 24 h of co-culture,
magnetic beads were removed, and cells were analyzed by flow cytometry.
CD25-BV711 and CD69-APC antibodies were used to determine CD8 + T-cell
activation status, Ghost-Dye-Violet 450 (TONBO) was used to exclude
dead cells. FACS analysis was performed using flowjo software v.10.7.1
and CytExpert version 2.5.0.77
ECM structure analysis
IF staining
FFPE tumor sections from BRCA-WT and BRCA-mut PDAC patients (n = 3
each) were double stained with antibodies against αSMA (1:350) and CLU
(1:100), using citrate buffer (pH = 6) for antigen retrieval. This was
followed by the secondary antibodies AF488 anti mouse for αSMA and
AF568 antirabbit for CLU. To detect nuclei staining, we incubated the
slides for 10 min with DRAQ5 (ab108410). Slides were kept in PBS until
imaging.
Second harmonic generation (SHG) imaging
FFPE slides were taken for SHG imaging using an upright Leica TCS SP8
MP microscope, equipped with external nondescanned detectors (NDD) HyD
and acusto optical tunable filter (Leica microsystems CMS GmbH,
Germany). The SHG signal was excited by a 885 nm laser line of a
tunable femtosecond laser 680–1080 Coherent vision II (Coherent GmbH
USA). The emission signal was collected using an external NDD HyD
detector through a long pass filter of 440 nm. The transmitted signal
was collected using a PMT detector in transmission position for general
morphology. In addition, αSMA, CLU and DRAQ5 were imaged using
excitation of Argon, DDPSS 561 and HeNe 633 lasers, with emission
collection at 585-620 nm, 505-535 nm and 670–760 nm (respectively).
Images were acquired using a format of 2048 × 2048 (XY) with an HC
FLUOTAR L 25X/0.95 W VIS objective, and the following parameters: scan
speed 600 Hz; Zoom 1; Line average- 4; bit depth- 16 FOV- X 442.86 µm,
Y 442.86 µm; pixel size- 216 nm; Z step- 0.568 µm; Z stacks were
acquired using the galvo stage, with 0.568 µm intervals. The acquired
images were visualized using LASX software (Leica Application Suite
XLeica microsystems CMS GmbH).
SHG analysis
For each image a ROI (100 µm X 100 µm) expressing high levels of either
αSMA (in BRCA-WT) or CLU (in BRCA-mut) was chosen. ECM structures were
analyzed according to a plug-in adapted from Wershof et al.^[442]131-
TWOMBLI. Briefly, we started with Max projection (3 slices), and the
desired ROI were cropped for further analysis. We ran the TWOMBLI with
the following parameters: Contrast Saturation- 0.35; Min Line Width-10;
Max Line Width- 15; Min Curvature Window- 30; Max Curvature Window- 50;
Minimum Branch Length- 10; Maximum Display HDM- 50; Minimum Gap
Diameter- 10.
Statistical analysis
Statistical analysis and visualization were performed using R (Versions
3.6.0 and 4.0.0, R Foundation for Statistical Computing Vienna,
Austria) and Prism 9.1.1 (Graphpad, USA). Statistical tests were
performed as described in each Figure legend. Mann-Whitney test was
used to analyze data that is not normally distributed. Student’s t-test
or ANOVA were used to analyze normally distributed data. Pearson’s
correlation coefficient was used to assess the association between two
continuous variables. RNA-seq analysis of mouse cells was performed as
described above. All other statistical tests were defined as
significant if p value < 0.05 or FDR < 0.05 for multiple comparisons.
“ns” in all Figures marks p-values greater than 0.05. Values that were
more than 2 standard deviations of their group-mean were defined as
outliers and excluded.
Reporting summary
Further information on research design is available in the [443]Nature
Research Reporting Summary linked to this article.
Supplementary information
[444]Supplementary Information^ (2.4MB, pdf)
[445]Peer Review File^ (9.3MB, pdf)
[446]41467_2022_34081_MOESM3_ESM.pdf^ (2.4KB, pdf)
Description of Additional Supplementary Files
[447]Supplementary Data 1^ (19.9KB, xlsx)
[448]Supplementary Data 2^ (680.1KB, xlsx)
[449]Supplementary Data 3^ (147.5KB, xlsx)
[450]Supplementary Data 4^ (24.8KB, xlsx)
[451]Supplementary Data 5^ (19.8KB, xlsx)
[452]Supplementary Data 6^ (9.9KB, xlsx)
[453]Supplementary Data 7^ (18.8KB, xlsx)
[454]Supplementary Data 8^ (594.5KB, xlsx)
[455]Supplementary Data 9^ (75.5KB, xlsx)
[456]Supplementary Data 10^ (320.5KB, xlsx)
[457]Supplementary Data 11^ (16.2KB, xlsx)
[458]Supplementary Data 12^ (11KB, xlsx)
[459]Supplementary Data 13^ (10.1KB, xlsx)
[460]Reporting Summary^ (1.5MB, pdf)
Acknowledgements