Kim et al. BMC Medical Genetics 2018, 19(Suppl 1):0
https://doi.org/10.1186/s12881-018-0722-6

RESEARCH

Open AccessIntegrated molecular characterization of
adult soft tissue sarcoma for therapeutictargets
Jihyun Kim1†, June Hyuk Kim2†, Hyun Guy Kang2,3, Seog Yun Park4, Jung Yeon Yu5, Eun Young Lee5,
Sung Eun Oh2, Young Ho Kim6, Tak Yun6, Charny Park1, Soo Young Cho1* and Hye Jin You3,5*
From 29th International Conference on Genome Informatics
Yunnan, China. 3-5 December 2018

Abstract
Background: Several studies have investigated the molecular drivers and therapeutic targets in adult soft tissue
sarcomas. However, such studies are limited by the genomic heterogeneity and rarity of sarcomas, particularly inthose with complex and unbalanced karyotypes. Additional biomarkers are needed across sarcoma types to improvetherapeutic strategies. To investigate the molecular characteristics of complex karyotype sarcomas (CKSs) for therapeutictargets, we performed genomic profiling.
Results: The mutational landscape showed that TP53, ATRX, and PTEN genes were highly mutated. CKS samples werecategorized into three groups based on copy number variations that were associated with CDK4 and RB1 signatures.
Integrated analysis of genomic and transcriptomic data revealed several pathways related to PDGFR, which could be a
strategic target for anti-sarcoma therapy.
Conclusions: This study provides a detailed molecular classification of CKSs and proposes several therapeutic targets.
Targeted or combinational therapies for treating CKS should be considered before chemotherapy.
Keywords: Complex karyotype sarcoma, Molecular characterization, PDGFRA, CDK4 and RB1

Background
Soft tissue sarcomas (STSs) are rare cancers, comprisingless than ~ 1% of all cancers, and can arise anywhere in
the body. STSs are classified according to the morphology of the tissue they resemble. According to theWorld Health Organization classification reported in2013, STSs include more than 50 histologic subtypes
with diverse clinical behaviors, responses to chemotherapy, and overall outcomes. Among them, liposarcoma(LPS), undifferentiated sarcoma (US), leiomyosarcoma(LMS), myxofibrosarcoma (MFS), and synovial sarcoma* Correspondence: sooycho@ncc.re.kr; hjyou@ncc.re.kr†
Jihyun Kim and June Hyuk Kim contributed equally to this work.
1
Clinical Genomic Analysis Branch, Research Institute, National Cancer Center,
323 Ilsan-ro, Ilsandong-gu, Goyang, Gyeonggi 10408, South Korea
3
Department of Cancer Biomedical Science, NCC-GCSP, National Cancer
Center, Goyang, Gyeonggi 10408, South Korea
Full list of author information is available at the end of the article(SS) are the most common types in adults. The outcomes vary considerably by sarcoma subtype, distinct
molecular characteristics, and behavioral features [1].
STSs, grouped broadly according to molecular complexity, are classified into two groups: simple karyotype sarcoma with balanced translocation and complex
karyotype sarcoma (CKS) without aberrant dislocation[2]. Translocation-associated sarcomas, most represented by LPS and SS, account for 20–30% of all sarcomas [3]. Although targeted therapies against fusion
genes have not been successful, it has been postulated
that translocation variants in sarcoma are predictive of
patient outcome. CKSs comprise the largest category ofSTSs, which includes undifferentiated pleomorphic sarcoma (UPS; previously known as malignant fibrous histiocytoma), LMS, and MFS, in order of frequency [4].
CKS subtypes are heterogeneous, unstable, and profoundly altered in their genomic copy number [2].

© The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
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Kim et al. BMC Medical Genetics 2018, 19(Suppl 1):0

Several additional genes have been identified and characterized since Barretina et al. first defined genes involved in STSs based on subtype-specific genomic
alterations [5]. For example, NF1 tumor suppressor ismutated or deleted in several sarcoma subtypes. CDK4
and YEATS4 are related to cell proliferation. PI3KCA
mutations are associated with poor clinical outcome. Recently, our understanding of STS has been strengthened
by TCGA reports [6], and copy-number alterations inadult STSs show clear clustering. However, previous
studies discussed tumor type specific molecular featuresand did not provide molecular reclassification for therapeutic target. The rarity of these cancers hinders progress in developing potential drug therapies. To develop
anticancer strategies against STSs, further information isrequired, particularly in those with complex karyotypes
and pathways for tumor progression.
Here we provide molecular reclassification by characterizing the alterations of copy number and mRNA expression, and present potential therapeutic target forclinical application irrespective of histological types. We
used 14 fresh frozen samples for whole exome sequencing (WXS) and whole transcriptome sequencing. Comparing the copy number alterations and mRNA profiles,
we found that three molecular types were easily distinguishable from CKS patients. CDK4 amplification andRB1 deletion were seen in patterns of genomic damage
and PDGFRA profiles could be used to clearly separate
samples. We identified PDGFRA target drug disruption
in CDK4 amplification group. In conclusion, clusters
were closely related to potential therapeutic targets as
for anti-cancer drug strategy in STS.

Methods
Patient samples

This study was approved by the Ethics Review Board of
the National Cancer Center, Korea (IRB No.: NCC2017–0062). Written informed consent was obtained from all
patients before tissue acquisition, and all samples werestored according to the principles of the Declaration of
Helsinki. Tumor materials were obtained from the National Cancer Center Biobank. Frozen specimens from
14 adult patients with STS were obtained from the Biobank; 13 patients were Korean, and one patient was Russian. Table 1 lists the clinicopathologic features of thepatients and tumors. Frozen tissues were sliced andstained with hematoxylin and eosin (H&E). To ensure
adequate tumor cell density, a pathologist specializing insarcomas reviewed the stained slides. Microdissection
was performed to obtain the tumor and adjacent nontumor cells from H&E-stained frozen sections. Genomic
DNA and RNA were extracted using DNeasy and
RNeasy Blood and Tissue Kits (Qiagen, Hilden, Germany),
respectively.

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Table 1 Summary of clinical and pathologic information in soft
tissue sarcoma.
Total No. affected individual14

Age (mean ± S.D. (range))

53 ± 30 (23–75)

GenderFemale5 (35.7%)Male

9 (64.3%)

Tumor size
0-5 cm5 (35.7%)

5-10 cm

6 (42.9%)

10-15 cm

3 (21.4%)

Grade (FNCLCC)
I

1 (7.1%)II

4 (28.6%)III

9 (64.3%)

Primary site
Upper extremity

2 (14.3%)

Lower leg

2 (14.3%)Thigh

9 (64.3%)

Pelvis1 (7.1%)

Stage at time of sample procurement
Primary

9 (64.3%)

Local recurrence

3 (21.4%)

Distant recurrence

2 (14.3%)

Histology
Myxofibrosarcoma

3 (21.4%)

Leiomyosarcoma

5 (35.7%)

Undifferentiated sarcoma

6 (42.9%)

Undifferentiated pleomorphic sarcoma

4 (28.6%)

Undifferentiated round cell sarcoma

1 (7.1%)

Undifferentiated spindle cell sarcoma

1 (7.1%)Pre-operative TherapyNone

11 (78.6%)

Pre-operative Chemotherapy

2 (14.3%)

Pre-operative Radiation-Therapy

0 (0.0%)Both

1 (7.1%)

Median follow-up

42.29 months

Time to relapse
Local recurrence

2.0 months

Distant recurrence

6.1 months

Exome sequencing and mutation calling

WXS was captured using the SureSelectXT library kit
and performed on Illumina’s Hiseq2500 platform forpaired-ends. Before alignment, poor quality read bases
were cut off using Trimmomatic v0.36 [7]. Exome sequencing data from paired tumor and normal samples

Kim et al. BMC Medical Genetics 2018, 19(Suppl 1):0

were aligned independently with the hg19 version of thehuman reference using BWA v0.7.13, and duplicatedreads were removed using Picard v2.1.1. Mapped BAM
files were processed according to GATK [8] best practices, including insertion/deletion (indel) realigning,mate fixing, and recalibration. Somatic mutations werecalled using MuTect [9]. To improve accuracy, datafrom dbSNP Build 138 and COSMIC v76 were supplied
as parameters to MuTect. Somatic indels were identified
using Strelka v1.0.14 [10]. We applied Oncotator [11] toannotate the mutation impact of somatic variants thatcalled the above processes. Raw DNA sequencing data
have been deposited in the European Nucleotide Archive(ENA) under primary accession number PRJEB23898.

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Network and gene set enrichment analysis (GSEA)

For human PPI network analyses, we integrated information from available public databases, including HPRD,
BioGRID, IntAct, MINT, and Reactome. A total of
136,489 interactions among 14,215 human proteins wereprepared. All gene networks were visualized using
Cytoscape [18]. Overrepresented pathways and module
analysis of custom networks were identified using Cytoscape ReactomeFI and MCODE plug-in software, respectively [18]. GSEA was performed in R package GSVA,
with the KEGG pathway used as the background pathway database [19]. Thresholds for significant pathways
were determined as: FDR < 0.05; GSEA P < 0.05.
Drug response analysis

Mutation signature analysis and microsatellite instability(MSI) clustering

To investigate the mutational process, we selected
single-nucleotide alterations and converted them into
the six classes of base substitution (C > A, C > G, C > T,
T > A, T > C, and T > G) [12]. Non-negative matrix
factorization (NMF) clustering was applied to detectmutation signatures from called somatic mutation using
R package SomaticSignatures.
To identify the MSI status, we collected 42 MSI-highsignatures across 18 cancer types from a previous report[13]. Next, we performed unsupervised consensus clustering from 42 gene expression profiles using R package
NMF.
Copy number analysis

Copy number variants from WXS were estimated using
EXCAVATOR2 [14]. We ran each sample of BAM files
through the EXCAVATOR2 pipeline and identified theaberrant regions that were highly enriched with copy
number gains/losses in all 14 samples using GISTIC2.0[15]. The X and Y chromosomes were excluded fromanalysis. The copy number data from 206 patients were
downloaded from Level 4 available data archives on the
TCGA Data Portal website.

Using the Cancer Therapeutics Response Portal v2, wefound that the PDGFRA gene was targeted by six drugs,
including axitinib, Ki8751, lenvatinib, masitinib, nintedanib, and sunitinib. To investigate the drug response insarcoma cell lines, drug response data were downloaded
from a study by Iorio et al. [20], and genomic alteration
data were downloaded from the Cancer Cell Line
Encyclopedia (CCLE) [21]. We investigated drug concentrations that reduced viability by 50% (IC50) and the area
under the dose-response curve values to determine drug
target sensitivity. We detected four cell lines (G402,
A204, TE441T, and G401) that had no amplification of
CDK4 (copy number score < 0.1) and displayed positive
gene expression of PDGFRA, and seven cell lines(SKUT1, MESSA, GCT, SKLMS1, RH41, RH18, andRH30) that had CDK4 amplification (amp) (> 0.1) andpositive expression of PDGFRA. To determine the applicability of cancer therapies, we analyzed the following
subgroups: 1) cell lines of the RB1 del type (SKUT1,MESSA, TE617T, S117, and RKN), which showed low
gene expression of CDK4, PDGFRA, and RB1; and 2)
cell lines of the CDK4 amp type (RH41 and G401),
which showed high expression of CDK4, PDGFRA, andRB genes.

Results
RNA sequencing alignment and quantification

Patient characteristics

RNA sequencing was performed on Illumina’s Hiseq2500
sequencing platform using TruSeq libraries forpaired-end sequencing. All data were processed using
the International Cancer Genome Consortium pipelines.
After cutting bases from poor quality reads using Trimmomatic v0.36 [7], a two-pass method was employed,with STAR [16] used for read alignment and RSEM
v1.3.0 [17] used for gene expression quantification.
RSEM was used to estimate the gene expression values
as transcripts per million. Raw RNA sequencing data
have been deposited in the ENA (primary accession
number PRJEB24352).

Tumor and germline DNA/RNA samples were obtained
from 14 patients with a median age of 53 years (range
23–75 years). There were 9 male and 5 female patients.
Thirteen patients were Korean and one patient was Russian. Tumor samples consisted of six Undifferentiatedsarcomas (US; 4 UPS, 1 undifferentiated round cell sarcoma, 1 undifferentiated spindle sarcoma), five LMS,
and three MFS. Tumors were mostly high-grade (grade
II and III, 93%), and one patient had low-grade (grade I,
according to the FNCLCC grading system) disease.
Three samples were obtained from recurred tumor andtwo were from metastatic tumor. All metastatic samples

Kim et al. BMC Medical Genetics 2018, 19(Suppl 1):0

were obtained from the soft tissue extremities. 3 of the
14 patients had chemotherapy or radiotherapy before
tissue acquisition. One patient with recurred leiomyosarcoma had chemotherapy and the patient with recurredundifferentiated spindle cell sarcoma had chemotherapy
and radiation therapy, which performed 6 years before
surgery for recurrence in both patients. The other patient with recurred leiomyosarcoma had chemotherapy
for double malignancy (acute myeloblastic leukemia),
which performed 3 years before surgery. Table 1 lists allclinical characteristics.
Molecular landscape of three CKSs

We investigated the somatic molecular landscape acrossthree CKS types. In total, we identified 1819 somatic
truncation variants (1394 missenses, 220 frameshiftindels, 33 in-frame indels, 62 nonsenses, and 110 splicesites). Median coverage was 62.11× in normal andPage 14 of 31

116.03× in tumor samples. We found that FRG1B,
CDC27, TP53, ATRX, and PTEN were recurrent acrossthree CKS types. Fig. 1 lists the most frequently mutated(top 20) genes. FRG1B and CDC27 were the most highly
mutated genes in CKSs (57 and 42%, respectively). TP53,
ATRX, and PTEN were recurrently mutated (21.4%),
consistent with a previous report on STSs [6]. The median mutation frequency was 2.38 per Mb (range 0.51–
8.76 per Mb). The hypermutation samples were classified using mutation rates; three samples (UT05, FT13,
and LT02; Fig. 1) had mutation rates exceeding 1.5 times
the length of the interquartile range from the 75th percentile [22]. PTEN mutations were enriched in hypermutators (100%; P = 0.003, Fisher’s exact test). There wasno co-occurrence or mutual exclusiveness when comparing the clinical characteristics (age, disease subtype,tumor degree) in those with or without identified genetic
aberrations.

Fig. 1 Molecular landscape of complex karyotype sarcomas (CKSs). Data plots of the clinical and molecular features in CKS samples from 14 patients.
Top panel: frequency of mutations per Mb; middle panel: mutation plot of 20 genes ordered by mutation frequency; right panel: mutation frequencyof selected 20 genes; lower panel: sarcoma grade, patient sex, patient age, number of genomic segments, and sarcoma type. Color keys at right andbottom. FT, Myxofibrosarcoma; LT, Leiomyosarcoma; UT, Undifferentiated sarcoma

Kim et al. BMC Medical Genetics 2018, 19(Suppl 1):0

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MSI signatures in CKS

CKS characterization by somatic copy number alteration

Mutation signatures are a catalogue of somatic mutations that can be used to find hidden patterns for somatic mutations in human cancer. We observed a highmutational burden of increased C > T and/or C > A mutations at NpCpG (Fig. 2a). Alexandrov et al. designatedthis substitution pattern as mutational signature 6 [12].
Mutation signature 6, attributed to DNA mismatch repair, is found in microsatellite unstable tumors. Two distinct clusters with mutational frequency were revealed
using hierarchical clustering (Fig. 2b). One cluster was
dominated by three samples, annotated as hypermutators (UT05, FT13, and LT02). We examined the mRNA
expression of 42 high-level MSI (MSI-h) signature genes
to predict the MSI status using mRNA expression; three
sub-clusters were identified by non-negative matrix
factorization (NMF) algorithm. Of these sub-clusters,
MSI-h signature genes were highly expressed in cluster
2, comprising hypermutators (P = 0.19, F-test; Additional
file 1: Figure S1). Additionally, the hypermutators contained somatic mutations in DNA mismatch repair
genes such as MSH2, MSH3, and MSH6, and showed aslight association with the downregulation of these genes(Fig. 3a).

To identify the molecular characteristics of CKSs, WXS
was used to predict the somatic copy number alteration(SCNA). DNA copy number alteration is the primarymechanism in sarcomagenesis [2]. Significantly enrichedalterations in CKSs were identified using GISTIC [15]. In
this study, we found amplifications of 1q21.2, 7p22.3, and
12q14.1, which occur frequently in sarcomas [23–25], andrepresent a prognostic factor for adverse outcomes in avariety of cancer types [26]. Also, deletions were found in
11q24.2, 12p13.31, and 13q14.2, reported previously insarcoma [27] (Additional file 1: Figure S2). We classified
focal SCNAs: 63 regions were amplified (copy number
value > 2.0) and 25 regions were deleted (copy number
value < − 1.2). Unsupervised hierarchical clustering was
performed according to 88 focal SCNA regions; threeclusters were identified (Fig. 3a). All sarcoma types were
scattered among the clusters. The first cluster contained
four patients with LMS and US. The second cluster contained seven patients with MFS and US. The third cluster
had three patients with LMS. The first cluster wasenriched with CDK4 amplification (75%; P = 0.032, Fisher’s
exact test), which has an oncogenic role in sarcomagenesis[5]. Additionally, patients who were diagnosed with a

Fig. 2 Mutation signature and microsatellite instability (MSI) clustering. a Contribution of the different types of substitutions in all patients. b The
dendrogram was determined using the frequency of substitutions motif by hierarchical clustering. Red lines indicate clusters of hypermutators.
Consensus plot for three non-negative matrix factorization (NMF) clusters (10 runs) by mRNA expression across the 42 MSI signature genes. Red
color indicates hypermutators

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Fig. 3 Somatic copy number alteration (SCNA) clustering and molecular subtypes. a Hierarchical clustering using the copy number profiles of
focal regions from GISTIC2. Right bars indicate significant peak regions (7p22.3, 1q21.2, 13q14.2, and 12q14.1) from GISTIC2. Bottom panel:significantly mutated genes mentioned previously (TP53, ATRX, PTEN, and MSH family); copy number alterations in CDK4 (red) or RB1 (blue);gene expression profiles with DNA mismatch repair genes (MSH2, MSH3, MSH6, and PMS2) and PDGFRA; patient tumor status; cluster with
SCNA status. P-values were calculated by Fisher’s exact test. b Comparison with copy number values of CDK4/RB1 in focal regions from CKSs
and TCGA data. Red dots indicate patients with CDK4 amplification (copy number values > 2) and blue dots indicate patients with RB1
deletion (copy number values < − 1.2). P-values were calculated by t-testrecurrence had tumors that were enriched with CDK4
amplification (66.7%; Fig. 3a). According to the TCGA sarcoma data, CDK4 amplification and recurrence showed
co-occurrence (P = 1.60e-05, Chi-square test). The second
cluster was enriched with the RB1 deletion (100%; P =0.002, Fisher’s exact test), which accelerates additional
cancer gene mutations in sarcomagenesis [28]. Patientswith metastasis had tumors that were enriched in the RB1

deletion (66%; Fig. 3a). Although RB1 loss and metastasis
showed no correlation in the TCGA sarcoma data, astrong association of RB1 deletion with metastasis was reported previously in human and mice [29, 30]. The third
cluster showed chromosomal stability. UT05 and FT13 in
the third cluster were classified as MSH-h samples by mutation signature and MSI gene expression patterns. We
found three molecular groups in the CKSs based on

Kim et al. BMC Medical Genetics 2018, 19(Suppl 1):0

SCNA, including the CDK4 amplification enriched group(CDK4 amp group), RB1 deletion enriched group (RB1 delgroup), and stable chromosome group (CS group) except
for patients who observed MSI-h and high mutation rates.
As expected, these clusters based on SCNA similarly
identified in 206 TCGA sarcoma data (Additional file 1:
Figure S3).
PDGFRA pathway disarrangement in CKS

Correlation analysis of the three groups revealed a relationship between gene copy number and expression profiles. Analysis of genes in six significant peaks fromGISTIC revealed that 26 genes, including CDK4 andRB1, had a positive correlation with copy number values
and gene expression. Using the human protein-protein
interaction (PPI) network, we constructed sub-networks
with 558 genes and 11,643 interactions that included the26 genes and their first neighbors (Additional file 1: Figure S4 a,b). Then, the biological pathways were examined by using Cytoscape ReactomeFI plug-in. Cell
cycle:G1/S check point (FDR = 7.19e-03), PDGF receptor
signaling network (FDR = 9.93e-03), RB tumor suppressor/checkpoint signaling in response to DNA damage(FDR = 0.02), and signaling by the TGF-beta receptor
complex (FDR = 0.03) were significantly enriched inthese genes but no amplification of CDK4.
To investigate the differences between CDK4 amp andCS groups, we identified the PDGFRA target drug responses in nine sarcoma cell lines. We assumed that thecells, which have both CDK4 amplification and PDGFRA
expression, would nb1ot show a good performance for
PDGFRA target drugs, as CDK4 has a role downstreamof the PDGFRA signaling pathway. Six drugs showed
IC50 sensitivity using the Cancer Therapeutics Response
Portal v2 [20]. Four cell lines with no CDK4 amplification and positive expression of PDGFRA had lower IC50
values than seven cell lines that had CDK4 amplificationand positive expression of PDGFRA (P = 0.028, Wilcoxon rank-sum test; Fig. 4b). We found that the
PDGFRA signaling pathway in sarcoma cells, which have
CDK4 amplification and PDGFRA expression, was disarranged by CDK4 activation, with decreased effects ofdrugs targeting PDGFRA. Finally, the molecular characteristics of the CS group were defined as a PDGFRA putative target group.

Discussion
Given the wide genetic heterogeneity of STS types, defining the specific subtype is crucial for improving outcomes. According to a study that analyzed TCGA
sarcoma data involving 206 sarcomas of six types, thetype-specific copy-number alterations and histologic nuclear pleomorphisms correlate with aneuploidy estimates. In this study, we identified therapeutic groups

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across three CKS types and investigated the cancer
drivers targeted by recently proposed drugs. A network
analysis of the CKS abnormalities revealed PDGFRA as aputative target group and suggested that multiple-drugtherapy in CKS could function via CDK4 inhibition.
Molecular subtyping of the CKS samples showed theclusters of CDK4 amp, RB1 del, and PDGFRA putative
target groups. Although our sample size was small (n =14), the proportion of CDK4 amp and RB1 del showedno significant differences between the CKS and TCGA
sarcoma populations. CDK4 was amplified in 21.4% of
CKS patients and in 18.7% of TCGA sarcoma patients(P = 0.83, t-test; Fig. 3b). The RB1 deletion occurred in
21.4% of CKS patients and 9.7% of TCGA sarcoma patients (P = 0.76, t-test; Fig. 3b). Furthermore, the mutation landscape, mutation frequency, and MSI-hphenotype observations were analogous between the
CKS and TCGA sarcoma populations. These results suggest that the molecular characterization of CKS in oursmall patient population is informative regarding thespecific genomic landscape of CKS.
We found that the well-established somatic drivers ofgene mutation – FRG1B, CDC27, TP53, ATRX, and
PTEN – were altered in 57, 43, 21, 21, and 21% of CKSs.
FRG1B and CDC27 were highly mutated in CKS samples, but in only 3 and 0.4%, respectively, of TCGA sarcoma samples. FRG1B is in the FSHD region genefamily, which was reported as a fusion gene in acutelymphoblastic leukemia [31]. CDC27 is a tumor suppressor and a core component of cell cycle progression anddegradation of G1/mitotic checkpoint regulators [32]. In
the TCGA study, TP53 and ATRX were high-recurrence
mutations [6]. TP53 is a tumor suppressor that regulates
cell division by keeping cells from growing and proliferating. Missense mutations in TP53 may influence cancer
phenotype and survival [33]. ATRX plays an essential
role in normal development, helping to regulate the expression of other genes through a process known as
chromatin remodeling [34]. Additionally, long telomeresare associated with ATRX deletion or mutations in UPS
and MFS [6, 35]. Somatic mutations in PTEN are known
to cause tumorigenesis in human. A previous study suggested that the PTEN mutation might be a useful biomarker of cell proliferation in sarcomas [36].
The role of MSI-h in patients with CKS was suggested
using mutation signature and expression values of MSI
signature genes; MSI-h is found in 0–14% sarcoma patients [37]. Two studies suggested that patients with
MSI-h sarcoma have inferior clinical outcomes [38, 39],but one study found no clinical correlation [40]. We
identified two patients with MSI-h that had chromosomal stability, PTEN mutation, and low PDGFRA expression; one of these patients was classified as US.
These results suggest that MSI-h is not a major

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Fig. 4 PDGFRA signaling pathway and drug sensitivity. a The sub-network that interacts with PDGFRA of the PDGF signaling pathway from REACTOME.
Red or green nodes indicate up- or down-regulated genes in the PDGFRA-positive group compared with the CDK4 amplification group. Yellow bordercolors of nodes indicate significant differential gene expression between the PDGFRA-positive group and the CDK4 amplification group (P < 0.05; t-test).
Edges between the nodes indicate the signaling interactions. Box plot of the average gene expression values (y-axis; PDGFRA sub-network, PDGFRA and
CDK4, respectively) across samples within the sub-group (x-axis). P-values were calculated using the F-test. b Box plot of drug sensitivity (y-axis; -logIC50) inseven cell lines with CDK4 amplification (pink) and four cell lines with CDK4 normal copy number (green) across PDGFRA target drugs. P-values were
calculated by the Wilcoxon rank-sum test. c Box plot of scores (y-axis) of pathways that enriched RB1 deletion across samples by sub-group (x-axis).
P-values were calculated by the Kruskal-Wallis test

phenotype in CKS, but could respond to immunotherapy. A phase II study for ipilimumab has been
evaluated in sarcoma [41]. The FDA approved pembrolizumab (Keytruda) treatment for adult andpediatric patients with MSI-h, and the phase 2 trial
showed encouraging activity in patients with UPS or
dedifferentiated LPS [42]. Although the MSI-hphenotype in CKS remains controversial, the MSI-h
state in CKS could be a therapeutic target forpatient therapy.

To identify the molecular characteristics, a co-expression network was constructed using the relationship between gene copy number and gene expression profiles.
Thirteen sub-network modules were identified, andthe RB tumor suppressor-related pathway and PDGF
receptor signaling network pathways were enriched(Additional file 1: Figure S5 c,d). The CS group wasfunctionally classified into the PDGFRA putative target group. Recently, LARTRUVO (olaratumab), a
PDGFRA-blocking antibody for anti-tumor activity

Kim et al. BMC Medical Genetics 2018, 19(Suppl 1):0

against the PDGFRA signaling pathway, received FDA
approval for STS therapy [43]. The median overall survival (OS) was improved by 11.8 months in patients
who received LARTRUVO-doxorubicin compared tothose who received doxorubicin alone. CDK4 was significantly amplified in the CKSs and TCGA sarcoma
patients. CDK4, a key molecule in the cell cycle, is associated with favorable prognosis in sarcoma [44].
Two LMS samples (LT01 and LT03) and two US samples (UT02 and UT03) were clustered in the CDK4
amplification group, characterized by high expressionof PDGFRA, CDK4, and RB1. The CDK4 amp and CS
groups showed high PDGFRA expression. Of note,
CDK4 amplification in sarcoma cells was not susceptible
to the PDGFRA target drug. Although PDGFRA inhibition can occur in CKS, tumorigenesis is enhanced by
CDK4 amplification, which is a role downstream of the
PDGFRA signaling pathway. High-level CDK4 amplification is associated with poor recurrence-free survival compared to low-level CDK4 amplification [45]. Palbociclib
treatment was associated with a good progression-freerate in patients with CDK4-amplification and RB expression [46]. Furthermore, the CDK4 amp group wasenriched in both the CKSs and TCGA sarcomas in patients with recurrence. These indications show that
CDK4-targeted therapies in sarcomas can be used across
CKS types; continuous monitoring for disease recurrence
is required. The RB1 del group was identified in three
CKS samples (LT02, LT04, and LT06). In the network
module and gene set enrichment analysis, the RB tumor
suppressor-related pathway and DNA damage repair system related pathways such as DNA replication and homologous recombination was enriched in the RB1 del group(Additional file 1: Figure S4). RB1 loss leads to tumorigenesis, with regulation of the DNA damage repair system. In
particular, the loss of RB1 occurs in angiosarcomas andosteosarcomas, supporting a role for tumor suppressor inpathogenesis [47]. Recently, taxane chemotherapy (cabazitaxel) has been suggested in RB1-depleted tumor therapy[48], and used as an FDA-approved prostate cancer treatment. Phase II trial for cabazitaxel antitumor activity in
LPS is in progress (NCT01913652). Our findings suggestthat CKSs can be classified into three therapeutic subgroups; cancer drivers for each group should be targeted
in CKS therapies.
We investigated 74 drug responses in nine sarcoma
cell lines with available data to verify the applicability ofcancer therapies with subgroups. We identified the responses of three drugs (sunitinib, JQ1, and UNC0638) infive RB1 del type cell lines (Additional file 1: Figure S6).
Sunitinib is a multi-targeted receptor tyrosine kinase(e.g. PDGFR) inhibitor that was approved by the FDA
for the treatment of renal cell carcinoma and gastrointestinal stroma tumors. JQ1 is a small molecule that

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inhibits the BET family [49]. Although this molecule
awaits approval for marketing, it may be considered as apotential drug for patients with RB1 deletion. UNC0638,
a type of small molecule that can regulate the activity ofhistone methyltransferase [50], has been reported as apromising drug for cancer therapy [51].

Conclusions
This study had several limitations, such as the small
number of patients with CKS. Additionally, this retrospective study included patients having one of three
types of CKS, so the results are not generalizable to abroader population of patients with CKS. However, thefrequency of actionable mutations and SCNVs did not
differ from the TCGA reports in large CKS population
studies, which proposed three therapeutic subgroups.
The patterns of molecular characterization revealed inour study may provide clues to the target medications inpatients with CKS. Further subgroup classification protocols and targeted therapy in CKS will be required toanalyze long-term clinical trials that may translate into
therapeutic benefits for CKS patients with poor
prognosis.
Additional file
Additional file 1: Integrated molecular characterization of adult soft
tissue sarcoma for therapeutic targets. Figure S1. Expression with subclusters of MSI status. Box plots for the average expression values with subclusters by NMF clustering. P-values were calculated using the t-test. Figure
S2. SCNAs. Recurrent focal copy number alterations in 14 CKS samples by GISTIC2. Red and blue lines indicate the significant amplified and deleted regions,
respectively. Figure S3. Somatic copy number alteration (SCNA) clustering in
206 TCGA sarcoma data. Hierarchical clustering using the copy number profiles of focal regions (7p22.3; CDK4, 1q21.2, 13q14., and 12q14.1; RB1). Figure
S4. Pathway analysis using gene expression. A heat map indicates the scoresfrom the GSEA analysis. Euclidean clustering was performed on the KEGG
pathway. FT, Myxofibrosarcoma; LT, Leiomyosarcoma; UT, Undifferentiated sarcoma. Figure S5. Network analysis of significant genes correlated with expression and copy number. (a) Heat map of the 26
genes showing a significant relationship between copy number andgene expression profiles. (b) Whole network of 556 genes and 11,643
interactions, including the 26 genes and their first neighbors. Red or
blue nodes indicate amplified or deleted, respectively. Border colorsof nodes indicate expression values. (c,d) Sub-modules of RB tumor
suppressor-related and PDGFRA receptor signaling pathway, respectively.
Figure S6. Drug responses in cell lines with RB1 del type. Box plotof drug sensitivity (y-axis; area under the dose-response curve) in fivecell lines with RB1 del (dark blue) and 11 other sarcoma cell lines.
P-values were calculated by t-test. (DOCX 923 kb)

Abbreviations
CDK4: Cyclin-dependent kinase 4; CKS: Complex karyotype sarcoma;
LMS: Leiomyosarcoma; LPS: Liposarcoma; MFS: Myxofibrosarcoma;
MSI: Microsatellite instability; NMF: Non-negative matrix factorization;
PDGFRA: Platelet-derived growth factor receptor A; RB1: Retinoblastoma 1;
SCNA: Somatic copy number alteration; US: Undifferentiated sarcoma
Acknowledgements
We thank Hee Chul Yang, Yang-Gun Suh and Mi Sun Park of National Cancer
Center for their helpful suggestions.

Kim et al. BMC Medical Genetics 2018, 19(Suppl 1):0

Funding
This work was supported in part by National Cancer Center Grant NCC1710252 (to HJ YOU), NCC-1710251(to JH KIM) and NCC-1711290 (to SY
CHO). Publication of this article was sponsored by National Cancer Center
Grant NCC-1710252 (to HJYOU).
Availability of data and materials
Data are available in the European Nucleotide Archive (ENA) under primary
accession number PRJEB23898 and PRJEB24352.
About this supplement
This article has been published as part of BMC Medical Genetics Volume 19
Supplement 1, 2018: Proceedings of the 29th International Conference on
Genome Informatics (GIW 2018): medical genetics. The full contents of the
supplement are available online at https://
bmcmedgenet.biomedcentral.com/articles/supplements/volume-19supplement-1.

Page 20 of 31

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Authors’ contributions
JHK, HK, and HY conceived and planned the project. CP, SC and HYdeveloped methodology. JK, CP and SC analyzed and interpreted data suchas statistical analysis, biostatistics, and computational analysis. EL, SO, JY, SP,
TY, YK, and HY were responsible for acquiring samples from biobank,managing tissues for WXS and RNA seq analyses and collecting clinicalinformation of tumor patients for analysis and so on. JK, JHK, SC and HY
wrote, reviewed and revised the manuscript. CP, JK, SC and HY were
responsible for reporting or organizing data and constructing database. HY
and SC supervised this study. All authors read and approved the final
manuscript.
Ethics approval and consent to participate
This study was approved by the Ethics Review Board of the National Cancer
Center, Korea (IRB No.: NCC2017–0062). Written informed consent was
obtained from all patients before tissue acquisition, and all samples werestored according to the principles of the Declaration of Helsinki.
Consent for publication
Not applicable.

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15.

Competing interests
The authors declare that they have no competing interests.

16.

Publisher’s Note17.

Springer Nature remains neutral with regard to jurisdictional claims inpublished maps and institutional affiliations.

18.

Author details
1
Clinical Genomic Analysis Branch, Research Institute, National Cancer Center,
323 Ilsan-ro, Ilsandong-gu, Goyang, Gyeonggi 10408, South Korea.
2
Orthopaedic Oncology Clinic, Hospital, National Cancer Center, Goyang,
Gyeonggi 10408, South Korea. 3Department of Cancer Biomedical Science,
NCC-GCSP, National Cancer Center, Goyang, Gyeonggi 10408, South Korea.
4
Division of Pathology, Hospital, National Cancer Center, Goyang, Gyeonggi
10408, South Korea. 5Translational Research Branch, Research Institute,
National Cancer Center, Goyang, Gyeonggi 10408, South Korea. 6Rare Cancer
Branch, Research Institute, National Cancer Center, Goyang, Gyeonggi 10408,
South Korea.

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Published: 31 December 2018
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