Mukai et al. BMC Veterinary Research(2020) 16:206
https://doi.org/10.1186/s12917-020-02395-3

RESEARCH ARTICLE

Open Access

Chromatin run-on sequencing analysis
finds that ECM remodeling plays animportant role in canine hemangiosarcoma
pathogenesis
Chinatsu Mukai1* , Eunju Choi2, Kelly L. Sams1, Elena Zu Klampen1, Lynne Anguish1, Brooke A. Marks1,
Edward J. Rice1, Zhong Wang1, Lauren A. Choate1, Shao-Pei Chou1, Yukinari Kato3,4, Andrew D. Miller2,
Charles G. Danko1 and Scott A. Coonrod1

Abstract
Background: Canine visceral hemangiosarcoma (HSA) is a highly aggressive cancer of endothelial origin thatclosely resembles visceral angiosarcoma in humans, both clinically and histopathologically. Currently there is anunmet need for new diagnostics and therapies for both forms of this disease. The goal of this study was to utilize
Chromatin run-on sequencing (ChRO-seq) and immunohistochemistry (IHC) to identify gene and protein expression
signatures that may be important drivers of HSA progression.
Results: ChRO-seq was performed on tissue isolated from 17 HSA samples and 4 normal splenic samples.
Computational analysis was then used to identify differentially expressed genes and these factors were subjected togene ontology analysis. ChRO-seq analysis revealed over a thousand differentially expressed genes in HSA tissuecompared with normal splenic tissue (FDR < 0.005). Interestingly, the majority of genes overexpressed in HSA tumor
tissue were associated with extracellular matrix (ECM) remodeling. This observation correlated well with ourhistological analysis, which found that HSA tumors contain a rich and complex collagen network. Additionally, wecharacterized the protein expression patterns of two highly overexpressed molecules identified in ChRO-seq
analysis, podoplanin (PDPN) and laminin alpha 4 (LAMA4). We found that the expression of these two ECMassociated factors appeared to be largely limited to transformed endothelial cells within the HSA lesions.
Conclusion: Outcomes from this study suggest that ECM remodeling plays an important role in HSA progression.
Additionally, our study identified two potential novel biomarkers of HSA, PDPN and LAMA4. Interestingly, given thatfunction-blocking anti-PDPN antibodies have shown anti-tumor effects in mouse models of canine melanoma, ourstudies raise the possibility that these types of therapeutic strategies could potentially be developed for treating
canine HSA.
Keywords: Hemangiosarcoma, ChRO-seq, ECM, Collagen, LAMA4, PDPN

* Correspondence: cmk53@cornell.edu
1
Baker Institute for Animal Health, College of Veterinary Medicine, Cornell
University, Ithaca, NY, USA
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Mukai et al. BMC Veterinary Research(2020) 16:206

Background
Angiosarcomas (AS) are highly aggressive malignant tumors originating from endothelial cells. They accountfor approximately 2% of all soft tissue sarcomas inhumans with the number of cases increasing significantly over the past 30 years [1–5]. For patients presenting with non-metastatic AS, the reported five-year
survival rate is ~ 35%. These patients also have a 75%
chance of local recurrence within 24 months and a 50%
likelihood of metastases developing despite local treatment [4, 6]. When metastases are already present at theinitial presentation, the five-year survival rate is poor,
with a median survival time of just 3 months [4]. Although published research on this rare tumor is increasing, we still know very little about the pathogenesis of
this disease.
Canine hemangiosarcoma (HSA) is histopathologicallysimilar to angiosarcoma, with both forms of this disease
following a similar clinical course [7]. Canine HSA is
most commonly observed in the spleen, but also occurs
in other organs such as the heart, liver, and dermis withthe later form often being associated with ultraviolet
radiation-associated oncogenesis. Prognosis for the visceral forms of HSA is poor, with most dogs dying fromthis disease within months of their diagnosis [8, 9]. For
dogs diagnosed with splenic HSA, surgical removal of
the spleen can increase the life expectancy up to 6
months, and when combined with chemotherapy, canprolong their life for up to 12 months [8, 9].
While all breeds are susceptible to hemangiosarcoma,
German Shepherds, Golden Retrievers, and Labrador Retrievers, are particularly predisposed to developing thisdisease [10], with the estimated lifetime risk of Golden
Retrievers developing HSA being 20%. This breed predisposition suggests a genetic component for HSA [11].
Genome-wide association studies have been carried out
to identify risk loci [12, 13]. SNP array analysis of genomic DNA from Golden Retrievers with HSA identified
a risk locus on chromosome 5 that was shared by ~ 20%of the cases [12]. In another study, microarray analysis
of genomic DNA from dogs with HSA revealed gained
copy number aberrations on several genes such as
PDGFRA, KDR and VEGFA [13]. A recent whole exome
sequencing study of dogs with HSA across a range ofbreeds revealed somatic mutations in tumor suppressor
genes, including TP53, and two genes (PIK3CA andPIK3R1) in the Phosphoinositide 3-kinase (PI3K) pathway [14]. Comparison of somatic copy number aberration profiles in human angiosarcoma and canine
hemangiosarcoma identified recurrent copy number
gains in KDR (31% in human, 22% in canine) and KIT(17% in both) [15]. These genome-wide studies reveal
that, while specific genetic aberrations are associatedwith HSA in some populations, these alterations are notPage 2 of 14

sufficient to explain the majority of HSA cases. Theseobservations support the hypothesis that HSA pathogenesis is heterogeneous in nature.
Transcriptome analysis has been previously performedon cell lines and tumor tissues to identify molecular features that define canine HSA. Gene expression profilingof HSA cell lines and non-malignant proliferating endothelial cells revealed that HSA cell lines appeared tooverexpress genes associated with inflammation, angiogenesis, adhesion, invasion, metabolism, cell cycle progression, and patterning [16]. Microarray and RNA-seq
analysis of visceral HSA tumors identified three distinct
molecular subtypes; angiogenesis, inflammation andlipogenesis. These molecular subtypes did not appear to
be associated with a specific breed or tumor morphologic subtype [17].
A variety of analytic tools exist for assaying the transcriptome and molecular alterations in tissue. One ofthese tools, chromatin run-on and sequencing (ChROseq), uses RNA polymerase activity to measure transcription and, as such, provides for a highly-sensitive,
base-pair level readout of gene expression [18, 19].
Given the lack of clarity regarding the molecular underpinnings of HSA, the major goal of this study was toutilize ChRO-seq to document changes in gene expression between normal splenic tissue and HSA tumor tissue. Our results show that the majority of genes that are
upregulated in HSA appear to be associated with extracellular matrix (ECM) remodeling. Additionally, we further characterize two ECM-associated molecules thatwere highly overexpressed in HSA tumor tissue, podoplanin (PDPN) and laminin alpha 4 (LAMA4). We showby immunohistochemistry (IHC) that the expression ofthese two cancer-associated factors appears to be primarily limited to HSA tumor cells.

Results
ChRO-Seq analysis of transcription in HSA and normal
splenic tissue

For our genome-wide study, we performed ChRO-seq
analysis on tumor tissue from dogs histopathologicallydiagnosed with HSA (20) and on normal splenic tissue(4) (Table 1). We quantified the similarity of transcription between these tissue samples using Pol II abundance in annotated gene bodies. Three HSA samples(B297, B675, B788) were excluded due to a low numberof mappable reads (< 2 million reads). Sequencing datafrom the remaining samples was then analyzed to createa Spearman’s rank correlation matrix (Fig. 1a). Seven
thousand seven genes (> 20 rpkm) were used to calculate
the correlation coefficients by GENE-E. Four normal tissues and 3 HSA tissues (B307, B829 and C349) were
found to form one cluster, with 2 HSA samples (C001
and C034) also being similar to the Normal/HSA cluster.

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Table 1 Sample demographics for ChRO-seq, RT-PCR, IHC, and Masson’s trichrome staining
Sample ID

breedage/sex

mappable reads

B775

Std Poodle

9M

2,896,317

B297NM

Boxer

8M

11,511,843

8M

767,118

Pit Bull

10 F

16,262,534

10 F

7,465,452

B675a

Lab Ret10 F

1,445,922

B788a

mixed

unknown F

289,530

B743

GS

10 F

12,893,170

B554

mixed

12 M

14,950,537

B180

GR

11 F

2,398,038

B280

Lab Cross

11 F

4,095,959

B829

Flat Coated Ret

9M

15,142,541

C073

Lab Ret12 M

13,668,541

C085

Bloodhound

12 F

8,272,496

yC340

mixed breed

9M

13,105,709

y

C349

Ches Bay ret

13 F

6,443,615

C356

Rhodesian ridge

11 F

12,787,355

C369

mixed breed

11 M

17,736,413

C001NM

German Shepherd Dog

a

B297

B307NM
B307

C001
C034NM

German Shepherd Dog

C034

11 M

38,197,939

11 M

27,098,934

11 M

39,192,419

11 M

19,416,386

RT-PCR

IHC

TC

y

y

y

y

C253

Golden Retriever

11 M

20,874,160

C442

Golden Retriever

10 M

34,231,915

B001NM

BeagleF2

N/A

y

B004NM

Beagle

F2

N/A

y

y

y

B006NM

Beagle

F2

N/A

y

y

y

B648

Golden Retriever

FS11

N/A

y

y

B172

Boxer

F9

N/A

y

y

B176

Lab Ret

FS13

N/A

y

y

B783

Jack Russell

M14

N/A

y

y

B848

Bulldog

FS12

N/A

y

y

Sample ID (B:CUHA sample, C: CCOGC sample), breed and age/sex are in the table
NM normalasample data were excluded from further analysis due to the low mapped reads

DESeq2 analysis was then performed on the samples to
identify differentially expressed genes. When compared tonormal splenic tissue, a total of 906 genes were upregulated in the HSA samples (FDR < 0.005) while 358 genes
were downregulated (FDR < 0.005). An MA plot showing
the differentially expressed gene set is shown in Fig. 1b. In
this plot, the X axis represents the average expression overall samples and the Y axis represents the log2 fold change
between the tumor samples and normal spleen. Highly
upregulated extracellular matrix (ECM)-associated genes

and downregulated heme synthesis-associated genes arelabelled with blue dots (Fig. 1b). Differentially expressed
genes (FDR < 0.001, 659 genes) were then assessed toevaluate expression levels in individual samples (Fig. 1c).
Two hundred and ninety-one genes (rpkm > 20) are
shown in the resulting heatmap which also contains an associated hierarchical clustering dendrogram (one minus
spearman rank correlation with complete linkage). Results
show that the expression profiles of HSA samples appear
to be distinct from normal splenic tissue.

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Fig. 1 Correlation matrix and MA-plot from ChRO-seq analysis. a Spearman’s rank correlation of 17 HSA (orange bar) and 4 Normal samples(green bar). The sample order is based on single-linked hierarchical clustering of the matrix, shown by the dendrogram. 7007 genes (> 20 rpkm)
were used to calculate the correlation coefficients by GENE-E. b MA-plot of DESeq2 analysis. Results show that ECM-associated gene expression
tends to be upregulated in HSA while genes involved in normal splenic function tend to be downregulated. X-axis represents average expression
over all samples. Y-axis represents log2 fold change between HSA and normal. Genes with an adjusted p-value below 0.01 are shown in red. c
Heatmap of differentially expressed genes with hierarchical clustering of samples. Differentially expressed genes from DEseq analysis (FDR < 0.001)
were examined to evaluate gene expression levels in individual samples

Extracellular matrix remodeling appears to be a major
feature of HSA

When evaluating the top 50 most highly upregulated
genes in the HSA group, we observed that many of thesemolecules are associated with the ECM, examples ofwhich include podoplanin (PDPN), laminin alpha 4(LAMA4), ADAM12, and fibronectin 1 (Fig. 1b). To test
the prevalence of ECM-associated molecules in ourMukai et al. BMC Veterinary Research(2020) 16:206

dataset, we carried out gene ontology (GO) analysis
using PANTHER (protein analysis through evolutionary
relationships) (Fig. 2). We utilized a total of 369 of themost highly upregulated genes (FDR < 0.0005) as an input list, with 282 of these genes then being identified as
uniquely mapped genes in a Canis lupus familiaris reference database. A total of 20 annotation categories weredetected (FDR < 0.05) from the reactome pathway database, with the top 10 annotation categories from thisdataset being found to be related to ECM regulation. A
more detailed list of the ECM-associated factors thatwere upregulated in HSA tumor tissue is shown in
Table 2, along with their fold change (log2) and FDR. A
full list of Reactome pathways terms and the list of genes
is shown in the supplemental table.
Interestingly, when we queried genes in the downregulated dataset (124 genes with an FDR cut-off of 0.0005),
we did not observe statistically significant functional associations using GO analysis. Therefore, we adjusted the
FDR cut-off to 0.005 and this adjustment identified 358
genes as being downregulated in the HSA samples with
301 of these genes being uniquely mapped in the reference database. As shown in Fig. 2, five annotation

Page 5 of 14

categories were detected in this dataset, with the mostenriched reactome pathway term being heme biosynthesis. Examples of specific genes that are associatedwith heme biosynthesis include, UROS, FECH, UROD,
JCHAIN, and ALAD (Fig. 1b). This result suggests thatthe expression of genes involved in normal splenic function is suppressed in HSA tumor tissue.
Characterization of ECM-associated proteins in HSA tumor
tissueTo more directly test the hypothesis that ECMassociated factors are overexpressed in HSA tumor tissue, we first stained paraffin sections of tumor and normal tissue with Masson’s trichrome to label collagenfibers (Fig. 3). Staining of the more vascular regions of
the HSA tumor tissue show that collagen fibrils areabundant throughout these regions. This observation fits
well with our finding that multiple collagen isoforms
were highly overrepresented in our HSA-overexpression
ChRO-Seq dataset. Interestingly, in these vascular regions, the malignant tumor cells appear to surround thecollagen bundles, forming what resembles inverted bloodvessels. To further support these findings, images andFig. 2 Gene ontology analysis of differentially expressed genes. Upper panel: Top 10 annotation categories identified in upregulated HSA geneset. Lower panel: Annotation categories identified in downregulated HSA gene set. X axis represents fold enrichment, FDR values are labelled
under each bar

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Table 2 List of upregulated genes related to ECM
ECM proteinslog2 fold change

FDR

4.1598

6.87E-06

COL5A3Collagen type V alpha 3 chain

COL18A1

Collagen type XVIII alpha 1 chain

3.5531

5.90E-06

COL15A1

Collagen type XV alpha 1 chain

3.4661

3.79E-06

COL1A1

Collagen alpha-1(I) chain

3.2225

7.54E-05

COL3A1

Collagen type III alpha 1 chain

3.18392.31E-05

COL16A1

Collagen type XVI alpha 1 chain

2.8378

1.37E-05

COL27A1

Collagen type XXVII alpha 1 chain

2.60610.0001001

COL5A2

Collagen type V alpha 2 chain

2.5905

1.21E-05

COL6A3Collagen type VI alpha 3 chain

2.40470.00016717

COL6A2

Collagen type VI alpha 2 chain

2.35390.00022812

COL24A1

Collagen type XXIV alpha 1 chain

2.1612

9.72E-05

COL6A1

Collagen type VI alpha 1 chain

2.12720.0003894

COL4A2

Collagen type IV alpha 2 chain

1.70880.00033908

POSTN

Periostin

6.74281.01E-07

SPP1

Secreted phosphoprotein 1;Osteopontin

4.62412.73E-05

LAMA4

Laminin subunit alpha 43.7745

1.58E-09

LAMB4

laminin subunit beta-4

2.12444.93E-05

FN1

Fibronectin

2.73272.75E-06

LAMC2

Laminin subunit gamma 22.40190.00041187

ECM binding protein

log2 fold change

FDR

LUM

Lumican

4.94970.00013751

ITGA2

Integrin subunit alpha 2

3.11840.00031867

ITGA6

Integrin subunit alpha 62.3456

4.69E-05

DDR2

Discoidin domain receptor tyrosine kinase 22.18740.00011485

BGN

Biglycan

2.17030.00022031

ITGA5

Integrin subunit alpha 51.9946

1.61E-06

PDPN*

Podoplanin

3.6171

6.43E-05

log2 fold change

FDR

ECM related enzyme
ADAMTS14ADAM metallopeptidase with thrombospondin type 1 motif 14

3.5027

1.30E-08

ADAM12

ADAM metallopeptidase domain 12

3.0033

1.90E-10

TLL1

Metalloendopeptidase

3.21160.00023572

ADAMTS4ADAM metallopeptidase with thrombospondin type 1 motif 42.75260.00018318

TIMP1

Metalloproteinase inhibitor 1

2.43160.00021979

P4HA2

Prolyl 4-hydroxylase subunit alpha 2

1.7259

2.73E-06

PLOD1

Procollagen-lysine,2-oxoglutarate 5-dioxtgenase 1

1.68270.0001534

MMP14

Matrix metalloproteinase

1.6784

6.43E-07

quantitative data from six additional HSA and two normal
splenic samples are shown in supplemental figure, S1.
We next further characterized two ECM-associatedfactors that were highly overexpressed in HSA tumor tissue. For this analysis we focused on PDPN (FDR 6.43E05, fold change 3.62) and LAMA4 (FDR 1.58E-09, foldchange 3.77) because these molecules have beenpreviously associated with cancer progression in othertumor types [20–25]. For further characterization, we
first investigated PDPN and LAMA4 expression levels in
our ChRO-seq dataset. A genome browser view of transcription levels from one normal and three HSA samples
is shown in Fig. 4a. Expression levels from all ChRO-seq
samples are shown in supplemental figure S2. Results

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Fig. 3 Histology of H&E (top) and Masson’s trichrome (bottom)-stained HSA tumor sections. H&E staining shows that collagen bundles are
surrounded by neoplastic endothelial cells and appear to form “inside out” blood vessels (a and b). Trichrome-stained HSA sections (c and d)show collagen bundles (blue staining) in vascular regions of the tumor are surrounded by endothelial cells. a and c: low magnification, (b and d):high magnification. Scale bars represent 50 μm in a-d, and 10 μm in insets in b and d. HSA (B783) FFPE sections were stained

show that, at the PDPN locus, two of the HSA cases(C085 and C356) showed increased RNA polymerase activity compared to normal tissue, while expression levelsof the third HSA sample (C340) was comparable withnormal tissue. LAMA4 was transcribed from the minus
strand and all HSA samples showed higher transcriptional activity than normal tissue. We next investigatedthe expression of mature PDPN and LAMA4 transcripts
in tumor tissue and normal spleen using RT-PCR. Results show that, while PDPN or LAMA4 transcripts were
either absent or weakly expressed in normal spleen,
LAMA4 was significantly expressed in all three tested
HSA tumor samples while PDPN was robustly expressedin one (C356) of the three HSA samples (Fig. 4b andsupplemental Figure S7).
Next, we performed IHC analysis of PDPN and
LAMA4 on HSA tumor sections to confirm that thesemolecules are expressed in tumor tissue at the protein
level and to investigate their localization. HSA samples
B297, B783 and B848 are shown in Figs. 5 and 6 withimages and quantitative data from additional samples
being shown in supplemental Figure S3 and S4. IHC
analysis found that anti-PDPN staining in normal splenic
tissue was low to absent. In HSA tumor tissue, however,anti-PDPN staining was robust in the cytoplasm oftumor cells encircling the collagen fibrils (Fig. 5c and d,arrow). In addition to tumor cells, anti-PDPN staining
was also evident in the cytoplasm of endothelial cells intumor-adjacent blood vessels (Fig. 5b, arrowhead). The
localization pattern of anti-LAMA4 staining was similar
to that of PDPN, with little staining being seen in normal splenic tissue and strong anti-LAMA4 staining being observed in the cytoplasm of HSA tumor cells
encircling collagen fibrils. Again, as with PDPN, antiLAMA4 staining was also observed in the cytoplasm in
the endothelial cells from tumor-adjacent blood vessels(Fig. 6c and d).
Serial sections of additional HSA samples were thenstained with Masson’s trichrome and immunohistochemistry was performed on these additional tissues
using anti-PDPN and LAMA4 antibodies (Supplemental
figure S5 and S6). Sections from one sample (B176, Figure S5) showed solid/cavernous features with blood vessels (Figure S5 a). Masson’s trichrome staining
highlighted the presence of collagen fibrils filling spaces
between regions of neoplastic cells (Figure S5 a and b).
Anti-PDPN staining appeared to be primarily cytosolicin these neoplastic cells and in endothelial cells lining
adjacent blood vessels (Figure S5 c). Anti-LAMA4 staining was strong in extracellular regions of the tumor andMukai et al. BMC Veterinary Research(2020) 16:206

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Fig. 4 PDPN and LAMA4 transcription and mRNA expression in HSA and normal splenic samples. a Genome browser views (UCSC browser) of
the ChRO-seq dataset showing PDPN and LAMA4 gene loci. ChRO-seq reads on sense and anti-sense strands are shown in red and blue,
respectively. Red lines indicate the encoding gene in the Ensemble database. b RT-PCR analysis documenting PDPN and LAMA4 transcript
expression in normal (1: B001NM, 2: B004NM, 3: B006NM) and HSA samples corresponding to ChRO-seq data (C085, C340 and C356). GAPDH
primers were used as a positive control. Negative control (NC) lacks cDNA template. Results show that PDPN and LAMA4 mRNA do not appear to
be expressed in normal spleen. LAMA4 transcripts were observed in all three HSA samples while PDPN showed strong expression in one of thethree HSA samples

in endothelial cells lining blood vessels (Figure S5 d).
Sections from sample B554 primarily showed capillary/cavernous features (Figure S6). Masson’s trichrome
staining of this sample revealed a core collagen region
surrounded by malignant endothelial cells with spindle
shaped nuclei (Figure S6 a and b). Anti-PDPN and
LAMA4 staining appeared to be primarily cytosolic intumor cells from this sample.

Discussion
In this study, we first utilized ChRO-seq to identify RNA
polymerase activity in hemangiosarcoma tumor tissue
and normal splenic tissue. Analysis of the correlation
matrix for our ChRO-seq dataset finds that 14 of the
HSA tumor samples clustered together, while 3 of the
HSA samples appeared to be more similar to normal
splenic tissue (Fig. 1). While speculative, it is possible

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Fig. 5 Immunohistochemical localization of PDPN in HSA and normal splenic samples. a PDPN detection in normal splenic tissue was low toabsent (B297, normal white pulp). b anti-PDPN staining appears weak in endothelial cells of blood vessels that are adjacent to tumor masses(B783). c and d anti-PDPN staining is strong in cells within the tumor’s vascular regions (B848). In these regions the PDPN-positive tumor cells
were seen to surround what appears to be collagen bundles. PDPN was also detected in tumor cells from the more avascular regions of thetumor (Supplemental materials). c = low magnification, d = high magnification. Scale bar represents 50 μmthat these 3 samples represent a different subtype of
HSA and, as such, displayed a gene expression profilethat is more similar to normal tissue. Alternatively, it isalso possible that these 3 presumptive HSA tissues wereactually tumor-adjacent normal tissue or a mix of normal and tumor tissue. While grading of canine hemangiosarcoma is not often utilized due to the aggressive
nature of the neoplasm, a grading scheme does exist anddifferences in gene expression between the samples may
correlate with progression and eventual outcome [26].
Previous HSA transcriptomic profiling studies have
implicated a number of signaling pathways in HSA
pathogenesis. Tamburini et al., for example, found that,when compared to cell lines derived from splenic hematomas, HSA cell lines exhibited several different distinct
gene expression profiles, including signatures for angiogenesis, inflammation, adhesion, invasion metabolism,
cell cycle and signaling. In another study, Gorden et al.
performed microarray and RNA-seq analyses on visceral
HSA tumor tissues (12 spleen, 7 heart, 4 liver, 1 lung)and identified three distinct tumor subtypes that wereassociated with either angiogenesis, inflammation, andadipogenesis. Our finding that ECM remodeling appears
to be a major gene expression signature in HSA differsfrom these previous studies. One possible reason for thedifference in outcomes between our study and theTamburini study is that our report compared gene expression patterns in HSA splenic tissue with normal
splenic tissue while the Tamburini study was comparing
the gene expression profiles of HSA cell lines withsplenic hematoma cell lines [16]. Thus, the differences
may be due to the fact that the expression profile of normal splenic samples differs significantly from hematoma
samples. Additionally, these differences may also be because the expression profile of cell lines may differ significantly from primary tissue samples due to decreased
cellular complexity and prolonged cell culture. One potential reason why outcomes from our study differedfrom the Gorden study is that we evaluated differencesin gene expression patterns between HSA and normal
splenic tissue while the other study analyzed gene expression patterns within HSA tissues. Lastly, anotherreason why outcomes from our study may have differedfrom both of these previous studies is that ChRO-seq
analysis detects nascently synthesized RNA as opposed
to mature transcripts, whose levels can be affected by avariety of factors including transcript stability.
Regarding the specific types of ECM factors that wereupregulated in our dataset (Table 2), we identified genes
encoding fibrous components of the ECM, including 12
collagen genes, 3 laminin genes, and fibronectin. Several
ECM binding proteins were also identified in this dataset

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Fig. 6 Immunohistochemical localization of LAMA4 protein in HSA and normal splenic samples. a LAMA4 detection was low to absent in normal
splenic tissue (B297, normal white pulp). b anti-LAMA4 staining appears weak in endothelial cells of blood vessels that are adjacent to tumor
masses (B783). c and d anti-LAMA4 staining is strong in tumor cells within the tumor’s vascular regions (B848). In these regions the LAMA4positive tumor cells are seen to surround what appears to be collagen bundles. LAMA4 was also detected in tumor cells from the more avascularregions of the tumor (Supplemental materials). c = low magnification, d = high magnification. Scale bar represents 50 μm

including, Lumican and Biglycan, which are small leucinerich proteoglycans that regulate collagen fibril and matrix
assembly [27–30]. Integrin alpha 2 is directly associatedwith fibril-forming collagens (1,2,3,5,6,14, 18) [31] while
DDR2 functions as a cell surface receptor for fibrillar collagen and regulates cell differentiation by remodeling
ECM [32, 33]. Additionally, integrin alpha 5 binds directly
to fibronectin while integrin alpha 6 binds to laminin [34,35]. Several ECM-related enzymes were also found in thisdataset including P4HA2 and PLOD1 which catalyze collagen biosynthesis [36, 37] along with ADAMTS14,
ADAMTS4, ADAM12 and TLL1 which process procollagen to collagen by cleaving N-propeptide and C- propeptide [38, 39]. Lastly, two molecules directly involved in
ECM turnover, tissue inhibitor of metalloproteinase 1(TIMP1) and matrix metalloproteinase 14 (MMP14), werealso found in this dataset [40–42].
The extent to which ECM genes were upregulated in
HSA tumors in our study is highlighted by our GO analysis which found that 9 of the top 10 biological process
categories were ECM-related (Fig. 2). Interestingly, 5 ofthese 9 categories related to collagen function. We more
directly tested for the abundance of collagen fibers in
the tumor samples using Masson’s trichrome stain andfound extensive collagen deposition throughout thetumor tissue (Fig. 3). Collagen deposition was observed

both in the more solid areas of the tumor and, in particular, in the tumor regions filled with malformed vascular channels. In these vascular regions, neoplastic
endothelial cells are often found to encircle the collagenbundles forming “gumball”-shaped structures that look
like inverted blood vessels. While stromal fibroblasts arepresumably primarily responsible for the synthesis ofthese collagen fibers, it is also possible that the tumor
cells may also be partly responsible for synthesis of thesecollagen fibers.
In addition to collagen, we also found that PDPN and
LAMA4 were highly overexpressed in HSA tumor tissue
when compared to normal tissue. These molecules wereof particular interest to us given their close association
with cancer progression in other types of cancer. Podoplanin is a mucin-type protein that contains an extracellularregion, transmembrane domain, and intracellular tail. It is
widely known as a marker for lymphatic endothelial cells
and also to play a critical role in heart and lung development and in development of the lymphatic endothelial
system [43–45]. PDPN appears to play several roles incancer progression. A number of studies have shown that
PDPN expression in cancer cells promotes tumor cell proliferation and invasion [20–22]. In addition to cellintrinsic effects, PDPN is also believed to promote tumor
metastasis by interacting with its receptor, CLEC2, on theMukai et al. BMC Veterinary Research(2020) 16:206

platelets. This interaction then promotes the coating oftumor cells by platelets, thereby protecting tumor cells
from the immune system [46]. In addition to its role inhuman cancers, PDPN is also overexpressed in canine
squamous cell carcinomas and melanomas [47] and PDPN
mAbs were recently found to have potent anti-tumor activity in mouse xenograft models of canine melanoma[48]. Interestingly, overexpression of PDPN in mice leads
to disseminated intravascular coagulation [49], a condition
that is strikingly similar to that seen in many dogs with
HSA [50]. Our ChRO-seq data demonstrated that PDPN
expression varies significantly between HSA samples(Figs. 1 and 4). IHC analysis found that PDPN was robustly expressed in transformed endothelial cells in certain
HSA tumor samples. However, similar to our ChRO-seq
finding, we did find that PDPN appears to only be
expressed in a subset of HSA tumors. In future studies, weplan to test whether PDPN expression in HSA tumors
correlates with disease severity.
Laminins form large heterotrimeric αβγ protein complexes and are a prominent component of basement
membranes. LAMA4 is distinct from other laminin isoforms in that it lacks a polymerization domain with theloss of this domain potentially facilitating tumor cell migration [23]. Interestingly, LAMA4 was recently described as an “oncolaminin” due to its strong association
with cancer cell migration and tumor progression in arange of cancers [24]. These links to cancer include recent studies which found that LAMA4 and MCAM
(melanoma cell adhesion molecule) are highly enrichedin tumor blood vessels in renal cell carcinoma and colorectal carcinoma. Additionally another study found that
expression of these molecules is enhanced in locally invasive and metastatic clear cell renal cell carcinoma [25].
Further, antisense oligonucleotides against laminin-8(LAMA4 and LAMB1) were found to block the invasionof glioma cells and neovascularization in vitro [51].
Taken together, these published studies indicate that
LAMA4 plays a key role for vascular development,
tumor progression and metastasis. In our study, deseq2
analysis found that LAMA4 is highly transcribed in HSA
tumor tissue. This observation was supported by PCR
analysis of mRNA isolated from HSA tumor tissue (Fig. 4
and supplemental Figure S7). Additionally, our IHC analysis found that LAMA4 protein expression appears tobe primarily limited to malignant endothelial cells(Fig. 5). Given the previously defined roles for LAMA4
in cancer progression and tumor metastasis, our findings
raise the possibility that LAMA4 is an important mediator of canine HSA pathobiology.

Conclusions
Outcomes from our studies found that the majority of
upregulated genes in HSA tumor tissue appear to be

Page 11 of 14

associated with ECM remodeling. This finding was supported by IHC analysis which found a robust collagennetwork throughout HSA tumor tissue. Additionally, we
further characterized two ECM-associated factors, PDPN
and LAMA4, and found that their expression was largely
limited to tumor cells in HSA tissue. Both of these molecules have been previously identified as potential biomarkers in other types of cancer. Given the recent
previous preclinical mouse studies showing that anticanine PDPN antibodies can block the growth of melanomas, our findings raise the possibility that similartypes of therapies may have utility for treating canine
HSA.

Methods
SamplesCanine tissue samples were obtained from the Canine
Comparative Oncology & Genomics Consortium andCornell University Hospital for Animals. Pathology was
independently confirmed, and patient demographics aredescribed in the supplemental material (Table 1). ChROseq libraries were made from splenic hemangiosarcoma(n = 20) and normal splenic tissues from HSA dogs (n =4). Normal splenic samples from three beagles were either snap-frozen or fixed in PFA and Paraffin-embedded(FFPE) for sectioning. Additional FFPE sections of HSA
cases were obtained from Cornell University Hospitalfor Animals.
Chromatin- run-on and sequencing

Chromatin was extracted from each tissue sample andchromatin run-on was performed as described previously[18, 19, 52]. ChRO-seq library preparations were executed
according to the Illumina protocol and were sequenced
using Illumina NextSeq500 sequencing. Raw sequence
FASTQ files were assessed by FastQC for quality control[53]. Single-read sequencing data were preprocessed andmapped to the canine genome assembly using the proseq
2.0 pipeline (https://github.com/Danko-Lab/proseq2.0)[54]. Briefly, Single-read sequencing data were preprocessed to remove the adapter sequences and trimmed
based on base quality, and deduplicating the reads basedon unique molecular identifiers in RNA adapters. Sequencing reads were mapped into a canine genome assembly(CanFam 3.1) using the Burrows-Wheeler Aligner software package [55]. The number of mappable reads arelisted in Table 1. Aligned BAM files were converted into
bigWig format, which were used to create the matrix ofread counts mapping to each annotated gene (CanFam3
ensGene). Seven thousand seven genes (> 20 rpkm) wereused to calculate the Spearman’s rank correlation coefficients to create the matrix and dendrogram by GENE-E(https://software.broadinstitute.org/GENE-E/). The list ofdifferentially expressed genes and an MA-plot wereMukai et al. BMC Veterinary Research(2020) 16:206

produced by running DESeq2 (false discovery rate < 0.01)[56]. Upregulated genes (369 genes, FDR < 0.0005) anddown regulated genes (358 genes, FDR < 0.005) in HSA
were subjected to PANTHER (Protein Analysis Through
Evolutionary Relationships) overrepresentation gene ontogeny analysis (GO) on Reactome pathway dataset (version 65, released 2019-12-22) [57]. Differentially expressed
genes (FDR < 0.001, 659 genes) were assessed to show theexpression level in individual samples. Heatmaps and dendrograms were created by GENE-E.

Page 12 of 14(Applied Biosystems) following the manufacture’s protocol (25 °C for 10 min, 37 °C for 120 min, 85 °C for 5 min).
All cDNA reactions were diluted 5-fold with water prior
to use in PCR. 1 μl of diluted cDNA was used for PCR
using Go-Taq master mix (Promega Corporation) withcanine specific primers (0.4 μM each, sequences areshown in supplemental material), products were amplified using the following parameters: 95 °C for 5 min, 30
cycles of 3 steps (95 °C for 30 s, 58 °C for 30 s, and 72 °C
for 20 s) and 72 °C for 5 min. The amplified products
were analyzed using agarose gel electrophoresis (1.2%).

Immunohistochemistry and histology staining

Prior to all staining protocols, paraffin section slides
were rehydrated in xylene followed by sequential washes
in 100, 90, 80, and 70% EtOH. H&E and Masson’s trichrome staining were performed on 10 HSA samples
and 4 normal spleen sections. Gill’s hematoxylin and
Eosin Y solutions were used for H&E. Masson’s trichrome staining was performed following the manufacture’s protocol (Masson’s Trichrome Stain Kit,
Polysciences, Inc. #25088–100). Immunohistochemistry(IHC) experiments using anti-canine PDPN (clone
PMab-38 [58], now commercially available at FUJIFILM
Wako Pure Chemical Corporation, Osaka, Japan; 017–
27,091, 1:200) and anti-LAMA4 (Sigma-Aldrich Corp.
St. Louis, MO, HPA015693, Human Atlas antibodies for
IHC, Rabbit polyclonal, epitope homology 85%, 1:300),
were performed on 8 HSA samples and 2 normal spleens
following a standard protocol [59]. Briefly, slides wereboiled for 40 min in 0.01 M sodium citrate to retrieve
antigens. Slides were cooled and washed in running tap
water for 20 min, then incubated for 15 min in 3%hydrogen peroxide to quench endogenous peroxidases.
Slides were blocked in 2.5% normal goat serum for 30
min at room temperature. Primary antibodies were diluted in 1% BSA/TBST, and incubated overnight at 4C.
Slides were washed three times with TBS-T, then incubated with a secondary antibody for 1 h at room
temperature (Goat anti-mouse-HRP, Vector laboratories,
Inc., Burlingame, CA; #MP7452 or Goat anti-rabbitHRP, Vector laboratories, Inc., #MP7451). ImmPACT
NovaRED peroxidase substrate (Vector laboratories, Inc.,
SK-4805) with hematoxylin counterstain was used forsignal detection. Slides were dehydrated through 80, 95
and 100% EtOH to Xylene and coverslipped. Blue staining and NovaRED signals were quantified using image J(see supplemental method and figures S1-S7).
RT-PCR

Snap frozen tissues (3 normal spleens and 3 HSA tissues,see Table 1 for demographics) were pulverized in a tissue crusher and total RNA was extracted with Trizol reagent. 1 μg of RNA was reverse-transcribed to cDNA
using High-capacity cDNA reverse transcription kit

Supplementary information
Supplementary information accompanies this paper at https://doi.org/10.
1186/s12917-020-02395-3.
Additional file 1. Supplemental methods and figure legends.
Additional file 2. Supplemental tables for gene ontology analysis.
Additional file 3. FS1. Trichrome staining quantification.
Additional file 4. FS2. ChRO-seq counts on PDPN and LAMA4 gene
bodies.
Additional file 5. FS3. PDPN immunohistochemistry quantification.
Additional file 6. FS4. LAMA4 immunohistochemistry quantification.
Additional file 7. FS5. Masson’s trichrome staining and IHC on serial
sections from case B176 (HSA).
Additional file 8. FS6. Masson’s trichrome staining and IHC on serial
sections from case B554 (HSA).
Additional file 9. FS7. Original gel images for Fig. 4b.

Abbreviations
HSA: Hemangiosarcoma; ChRO-seq: Chromatin run-on sequencing;
IHC: Immunohistochemistry; ECM: Extracellular matrix; PDPN: Podoplanin;
LAMA4: Laminin alpha 4; AS: Angiosarcoma; PI3K: Phosphoinositide 3-kinase;
GO: Gene ontology; PANTHER: Protein analysis through evolutionary
relationships; FFPE: Fixed in PFA and Paraffin-embedded; RT-PCR: Reverse
transcription – polymerase chain reaction
Acknowledgements
We would like to acknowledge Dr. Marta G. Castelhano, Ms. Susan J. Garrison(Cornell Veterinary BioBank), Dr. Daniel Fletcher (Emergency and Critical Care,
Cornell University Companion Animal Hospital), and Ms. Luce E. Guanzini(Center for Animal Resources and Education, Cornell University) for collecting
samples that were used for this manuscript. The authors also acknowledge
generous resources provided by Canine Comparative Oncology and
Genomics Consortium (CCOGC).
Authors’ contributions
CM, CD and SAC were involved in conception and design of this study. CM,
ER, ZW, LC, SPC, BAM, CD, SAC prepared the library and analyzed
bioinformatic data. CM, EC, LA, KS, EK, YK, AM, SAC acquired and interpreted
histology data. CM and SAC wrote the manuscript. All authors have been
involved in revising the manuscript and approved the manuscript.
Funding
This research was supported by the Judy and Fred Wilpon Family
Foundation, Baker Institute internal grant (dog project #3), Cornell Research
Animal Health grant (SAC) in the design of the study and collection, analysis,
and interpretation of data and in writing the manuscript. We would also liketo thank Kyle’s Legacy Inc., Mr. Jake Holshuh, Mrs. Alice C. Sinclair, andClancysCure. The production of podoplanin antibody was supported by thefollowing grants, JP19am0401013, JP19am0101078, JP19ae0101028 from
AMED (YK).

Mukai et al. BMC Veterinary Research(2020) 16:206

Availability of data and materials
The raw files (FASTQ) and processed files (BigWig) analysed during thecurrent study are available in the Gene Expression Omnibus GSE150705(https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE150705).
Ethics approval and consent to participate
Not applicable. All samples in this study were purchased from the Canine
Comparative Oncology & Genomics Consortium and Cornell Veterinary
Biobank.
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Author details
1
Baker Institute for Animal Health, College of Veterinary Medicine, Cornell
University, Ithaca, NY, USA. 2Department of Biomedical Sciences, Section of
Anatomic Pathology, College of Veterinary Medicine, Cornell University,
Ithaca, NY, USA. 3Department of Antibody Drug Development, Tohoku
University Graduate School of Medicine, Sendai, Japan. 4New Industry
Creation Hatchery Center, Tohoku University, Sendai, Japan.
Received: 22 November 2019 Accepted: 29 May 2020

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