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CANCER IMMUNOLOGY RESEARCH | RESEARCH ARTICLE
Molecular Subgroups of Intrahepatic
Cholangiocarcinoma Discovered by Single-Cell RNA
Sequencing–Assisted Multiomics Analysis
Xuanwen Bao
1
, Qiong Li
1
, Jinzhang Chen
2
, Diyu Chen
3
, Chanqi Ye
1
, Xiaomeng Dai
1
, Yanfang Wang
4
, Xin Li
5
,
Xiaoxiang Rong
2
, Fei Cheng
6
, Ming Jiang
7
, Zheng Zhu
8
, Yongfeng Ding
1
, Rui Sun
9,10
, Chuan Liu
11
,
Lingling Huang
12
, Yuzhi Jin
1
, Bin Li
1
,JuanLu
13
, Wei Wu
1
, Yixuan Guo
1
, Wenguang Fu
14
,
Sarah Raye Langley
15
, Vincent Tano
15
, Weijia Fang
1
, Tiannan Guo
9,10
, Jianpeng Sheng
16
, Peng Zhao
1
, and
Jian Ruan
1
ABSTRACT
◥
Intrahepatic cholangiocarcinoma (ICC) is a relatively rare
but highly aggressive tumor type that responds poorly to chemo-
therapy and immunotherapy. Comprehensive molecular char-
acterization of ICC is essential for the development of novel
therapeutics. Here, we constructed two independent cohorts from
two clinic centers. A comprehensive multiomics analysis of ICC
via proteomic, whole-exome sequencing (WES), and single-cell
RNA sequencing (scRNA-seq) was performed. Novel ICC tumor
subtypes were derived in the training cohort (n¼110) using
proteomic signatures and their associated activated pathways,
which were further validated in a validation cohort (n¼41).
Three molecular subtypes, chromatin remodeling, metabolism,
and chronic inflammation, with distinct prognoses in ICC were
identified. The chronic inflammation subtype was associated with
a poor prognosis. Our random forest algorithm revealed that
mutation of lysine methyltransferase 2D (KMT2D) frequently
occurred in the metabolism subtype and was associated with
lower inflammatory activity. scRNA-seq further identified an
APOE
þ
C1QB
þ
macrophage subtype, which showed the capacity
to reshape the chronic inflammation subtype and contribute to a
poor prognosis in ICC. Altogether, with single-cell transcriptome-
assisted multiomics analysis, we identified novel molecular sub-
types of ICC and validated APOE
þ
C1QB
þ
tumor-associated
macrophages as potential immunotherapy targets against ICC.
Introduction
Intrahepatic cholangiocarcinoma (ICC) is a relatively rare but
highly aggressive form of primary liver tumors, following hepatocel-
lular carcinoma, and accounts for 5% to 10% of all primary liver
malignancies (1). ICC is a heterogeneous group of malignancies that
can be found at any position of the intrahepatic biliary tree, has distinct
genetic and genomic features, and therefore causes differences in
survival outcomes (1). Surgical resection is an effective approach for
ICC. Nevertheless, ICC patients are often diagnosed at a late stage
when resection is not possible and only palliative chemotherapy is
applicable (2).
1
Department of Medical Oncology, The First Affiliated Hospital, School of
Medicine, Zhejiang University and Key Laboratory of Cancer Prevention and
Intervention, Ministry of Education, Hangzhou, Zhejiang Province, People’s
Republic of China.
2
Departmentof Oncology,Nanfang Hospital, Southern Medical
University, Guangzhou, Guangdong Province, People’s Republic of China.
3
Division of Hepatobiliary and Pancreatic Surgery, Department of Surgery, First
Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang,
People’s Republic of China.
4
Ludwig-Maximilians-Universit€
at M€
unchen (LMU),
Munich, Germany.
5
Department Chronic Inflammation and Cancer, German
Cancer Research Center (DKFZ), Heidelberg, Germany.
6
Pathology Department,
The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou,
Zhejiang Province, People’s Republic of China.
7
The Children’s Hospital, Zhejiang
University School of Medicine and National Clinical Research Center for Child
Health, Hangzhou, ZhejiangProvince, People’s Republicof China.
8
Department of
Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston,
Massachusetts.
9
Westlake Laboratory of Life Sciences and Biomedicine, Key
Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences,
Westlake University, Hangzhou, Zhejiang Province, China.
10
Institute of Basic
Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, Zhejiang
Province, China.
11
China Medical University, Shenyang, Liaoning Province,
People’s Republic of China.
12
Westlake Omics (Hangzhou) Biotechnology Co.,
Ltd., Hangzhou, Zhejiang Province, China.
13
State Key Laboratory for Diagnosis
and Treatment of Infectious Diseases, National Clinical Research Center for
InfectiousDiseases, Collaborative Innovation Center for Diagnosisand Treatment
of InfectiousDiseases,The First Affiliated Hospital, Collegeof Medicine, Hangzhou,
Zhejiang University.
14
Department of Hepatobiliary Surgery, The Affiliated
Hospital of Southwest Medical University, Luzhou, Sichuan Province, People’s
Republic of China.
15
Lee Kong Chian School of Medicine, Nanyang Technological
University, Singapore, Republic of Singapore.
16
Zhejiang Provincial Key Labora-
tory of Pancreatic Disease, The First Affiliated Hospital, School of Medicine,
Zhejiang University, Hangzhou, Zhejiang Province, People’s Republic of China.
Note: Supplementary data for this article are available at Cancer Immunology
Research Online (http://cancerimmunolres.aacrjournals.org/).
X. Bao, Q. Li, J. Chen, D. Chen, and C. Ye contributed equally to this work.
Corresponding Authors: Jian Ruan, The First Affiliated Hospital, Zhejiang
University School of Medicine, 79 Qingchun Road, Hangzhou, 310000
China. Phone: 86-13828422476; E-mail: software233@zju.edu.cn; Peng Zhao,
The First Affiliated Hospital, Zhejiang University School of Medicine,
79 Qingchun Road, Hangzhou 310000, China. Phone: 86-13958124783; E-mail:
zhaop@zju.edu.cn; Jianpeng Sheng, The First Affiliated Hospital, Zhejiang
University School of Medicine, 79 Qingchun Road, Hangzhou 310000, China.
Phone: 86-15205811395; E-mail: shengjp@zju.edu.cn; and Tiannan Guo,
Westlake Laboratory of Life Sciences and Biomedicine, Key Laboratory of
Structural Biology of Zhejiang Province, School of Life Sciences, Westlake
University, 18 Shilongshan Road, Hangzhou 310024, Zhejiang Province, China.
E-mail: guotiannan@westlake.edu.cn
Cancer Immunol Res 2022;10:811–28
doi: 10.1158/2326-6066.CIR-21-1101
2022 American Association for Cancer Research
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The inflammatory environment near the biliary tree may facilitate
the development and progression of ICC. Specifically, inflammation in
the tumor microenvironment (TME) is promoted by the secretion of
various cytokines and chemokines, further aiding in tumor progres-
sion and distant metastasis formation. TNFa, IL1b, IL6, TGFb, and
other cytokines have been proven to take part in a multistep process of
ICC transformation (3–6). Several studies using bulk tumor tran-
scriptome data have developed different ICC subtype classification
systems, which correspond to specific pathway alterations and corre-
late with different prognoses (7–12). Dysregulated pathways (e.g.,
erythroblastic leukemia viral oncogene homolog 2 or fibroblast growth
factor signaling) were identified as therapeutic targets (9, 13). One
study using a cohort of 78 patients characterized four subtypes based
on the cellular composition of the TME, which related to distinct
prognosis and immune escape mechanisms (14). Nevertheless, the
above studies were all based on transcriptome data. One study, thus far,
has applied proteomics to identify molecular subtypes in ICC (15).
Most of the studies on the ICC TME are based on computational
methods to infer the abundance and populations of cellular compo-
nents in the TME. The detailed comprehensive molecular features of
the ICC TME are, however, still not clear, especially at the single-cell
level.
In this study, we performed a multiomics analysis on a large series of
ICC patient cohorts (n¼110). Three molecular subtypes, “chronic
inflammation,”“metabolism,”and “chromatin remodeling,”were
identified through the proteomics data. The chronic inflammation
subtype was associated with the poorest prognosis in ICC, which was
further validated in an independent validation cohort (n¼41). A
random forest algorithm identified the key featured gene mutations
within each molecular subtype using whole-exome sequencing (WES).
Single-cell RNA sequencing (scRNA-seq) was used to generate a
detailed characterization of the TME for the “chronic inflammation”
ICC subtype. Through this analysis, an APOE
þ
C1QB
þ
tumor-
associated macrophage (TAM) subtype was identified and shown to
affect T-cell inflammation, thus reshaping the TME in ICC into an
inflammatory state. In vitro and in vivo experiments further validated
the potential of APOE
þ
C1QB
þ
TAMs as targets of immunotherapy
against ICC.
Patients and Methods
Patient sample collection
The sample collection of this study (primary tumor tissue and blood
samples) was approved by the Ethics Committee of Nanfang Hospital
and the affiliated hospital of Southwest Medical University. The
samples were collected during 2014 to 2021. Tumor tissues were
formalin-fixed paraffin-embedded, and the blood samples were stored
in lipid nitrogen. The patient studies were conducted in accordance
with International Ethical Guidelines for Biomedical Research Involv-
ing Human Subjects (CIOMS). All participants provided written
informed consent to participate in the study. The detailed patient
information is available in Supplementary Table S1 (training cohort,
n¼110) and Supplementary Table S2 (validation cohort, n¼41). A
total of 84.5% of patients in our ICC cohort were HBV infected, which
is different from a previous study (16). The two cohorts were con-
structed from Nanfang Hospital and the affiliated hospital of South-
west Medical University, and the clinicopathologic features are
shown in Supplementary Tables S1 and S2. Overall survival (OS)
refers to the time that begins at diagnosis and up to the time of death.
All samples were assessed by pathologic examinations to evaluate
the proportion of cancer cells and degree of fibrosis/necrosis. The
presence or absence of histologic necrosis of each slide was identified
according to established criteria (17) and recorded as negative (necrot-
ic area ≤5%) or positive (necrotic area >5%). If tumor tissues were
estimated to be greater than 90% on the slides after a thorough
pathologic review, the samples were used for proteomics analysis. No
perihilar, distal, and combined hepato-cholangiocarcinoma were
included in the study. Pan-cancer MSK clinical data and genomic
data (patients, n¼7,315; ref. 18) were downloaded from cBioPortal
(https://www.cbioportal.org/) for validation.
Tandem mass tag-based proteomic analysis
A total of formalin-fixed paraffin-embedded (FFPE) tumor samples
from the ICC training cohort (n¼110) and the ICC validation cohort
(n¼41; 0.5–1 mg) were dewaxed and rehydrated and then subjected to
acidic hydrolysis with formic acid (FA; Thermo Fisher Scientific).
Proteins were denatured with 6 M urea (Sigma–Aldrich) and 2 M
thiourea (Sigma–Aldrich) with the assistance of pressure-cycling
technology (PCT; Pressure BioSciences Inc; 30 seconds 45,000 psi
and 10 seconds ambient pressure, 90 cycles). Proteins were then
digested into peptides with trypsin (1:20; Hualishi) and Lys-C
(1:80; Hualishi) with the assistance of PCT (50 seconds 20,000 psi
and 10 seconds ambient pressure, 120 cycles). The detailed settings
have been described previously (19, 20). Peptides were labeled with
TMTpro 16 plex (Thermo Fisher Scientific; ref. 21). Each batch
contained 15 experimental samples and one pooled sample in the
TMT126 channel for normalization.
The fractions (60 per batch) were separated using offline high-pH,
reversed-phase fractionation with a Thermo Dionex Ultimate 3000
RSLCnano System (Thermo Fisher Scientific) and then combined to a
total of 30 fractions per batch. Subsequently, the fractionated samples
were separated with a Thermo Dionex Ultimate 3000 RSLCnano
System (Thermo Fisher Scientific) with a reversed C18 column
(1.9 mm, 120 Å, 150 mm 75 mm i.d.; National Center for Protein
Sciences). The LC system was operated with buffer A (2% acetonitrile
and 0.1% FA in HPLC water) and buffer B (98% acetonitrile and 0.1%
FA in HPLC water). The peptides were eluted with a 60-minute LC
gradient of 5% to 28% buffer B at a flow rate of 300 nL/minute. The
samples were then analyzed with a Q Exactive HF mass spectrometer
(Thermo Fisher Scientific) with positive ion mode using the data-
dependent acquisition mode. The database search included all
reviewed human entries from UniProt (downloaded on April 14,
2020, containing 20,365 proteins) using Proteome Discoverer (version
2.4, Thermo Fisher Scientific). The detailed parameters have been
described previously without modification (22, 23). Among the 10,888
proteins, 6,311 proteins were detected in samples with less than 10%
missing values and were subjected to further analysis. To evaluate the
batch effect, the principal component analysis (PCA) was performed
with the protein matrix using R.
Molecular subtype identification
The “ConsensusClusterPlus”package from R software was applied to
the proteomic data to identify potential molecular subtypes (“chronic
inflammation,”“metabolism,”and “chromatin remodeling”)ofICC
with the default parameters(24). Differentially expressed protein (DEP)
analysis was performed with the “Limma”package using R software
to assess the proteomic data using the default parameters (25). An
empirical Bayesian method was applied to estimate the fold change
between every two molecular subtypes using moderated ttests. The
adjusted Pvalues for multiple tests were calculated using the
Benjamin–Hochberg correction. Feature protein in each subtype
was defined as the highly expressed proteins, with adjusted P<0.01.
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Single-sample gene set enrichment analysis and gene ontology
analysis
Single-sample gene set enrichment analysis (ssGSEA) was applied
on proteomics from the training cohort (n¼110) to evaluate the
enrichment scores (“chronic inflammation,”“metabolism,”and
“chromatin remodeling”) of each sample using the gene sets down-
loaded from The Broad Institute (https://www.gsea-msigdb.org/gsea/
msigdb/). The “GSVA”(gene set variation analysis) package using R
software was used for this analysis with the default paramters (26). The
hallmark gene sets were downloaded from The Broad Institute (https://
www.gsea-msigdb.org/gsea/msigdb/). All the gene sets of immune cell
populations [aDCs, B cells, CD8 T cells, cytotoxic cells, DCs, eosino-
phils, iDCs, macrophages, mast cells, neutrophils, NK CD56
bright
cells,
NK CD56
dim
cells, NK cells, pDCs, T cells, T helper cells, Tcm (cental
memory), Tem (effector memory), TFH cells (T follicular helper), gdT
cells, Th1 cells, Th17 cells, Th2 cells, and Tregs (regulatory T cells)]
were obtained from Bindea G and colleagues (27). The score using for
survival analysis was defined as high and low using the best-cutoff
method. Gene ontology (GO) analysis was performed with the “clus-
terProfiler”package using R software (28).
DNA extraction, library preparation, and targeted enrichment
Next-generation sequencing analyses were performed according to
protocols reviewed and approved by the Ethics Committee of Nanfang
Hospital. DNA extraction, library preparation, and target capture
enrichment were performed using previously described protocols with
minor modifications (29). Briefly, genomic DNA from FFPE tumor
samples and matched paired blood samples from patients were isolated
using the DNeasy Blood and Tissue Kit (Qiagen) following the
manufacturer’s protocol. After quantification of DNA with Qubit
3.0 using the dsDNA HS Assay Kit (Life Technologies), a NanoDrop
2000 (Thermo Fisher) was used to evaluate the DNA quality, and
samples with A
260
/A
280
ratio of 1.8–2.1 were used for downstream
application.
The KAPA Hyper Prep kit (KAPA Biosystems) was used to prepare
libraries according to previously described protocols (30). Briefly, 1 to
2mg of genomic DNA was sheared by a Covaris M220 instrument into
350-bp fragments. End repair, A-tailing, and adaptor ligation of
fragmented DNA were performed with the KAPA Hyper DNA Library
Prep kit (Roche Diagnostics). Agencourt AMPure XP beads (Beckman
Coulter) were used for size selection. The Adaptor-Ligated Library was
amplified in a thermal cycler (SimpliAmp Thermal Cycler, Thermo
Fisher) using the following program: 98C for 2 minutes, then 6 cycles
of 98C for 30 seconds, 65C for 30 seconds, 72C for 1 minute,
followed by one cycle of 72C for 10 minutes. All primers used during
library construction were from IDT (xGen Library Amplification
Primer Mix, 96rxns). 500 ng of the amplified library DNA was added
into a 1.5-mL LoBind tube. WES was performed with Agilent Sur-
eSelect Human All Exon V6 (Agilent Technologies) according to the
manufacturer’s instruction manual. PCR amplification was performed
on captured libraries with KAPA HiFi HotStart ReadyMix (KAPA
Biosystems). The KAPA Library Quantification kit (KAPA Biosys-
tems) was used to quantify the purified library. A Bioanalyzer 2100 was
used to calculate the fragment size distribution (350 bp).
Sequencing and bioinformatics analysis
The Illumina NovaSeq 6000 platform (Illumina Inc) was utilized for
genomic DNA sequencing in Acornmed Biotechnology Co., Ltd to
generate 150-bp paired-end reads. bcl2fastq (v2.19; Illumina) was used
to demultiplex the sequencing data. Trimmomatic (31) was used to
remove low-quality (quality<15) or N bases. The alignment of the data
to the hg19 reference human genome was then performed by the
Burrows–Wheeler Aligner (bwa-mem; ref. 32), followed by further
processing using the Picard suite (available at https://broadinstitute.
github.io/picard/) and the Genome Analysis Toolkit (GATK; ref. 33).
GATK-HaplotypeCaller was used to call germline single-nucleotide
polymorphisms (SNP), and the GATK-Mutect2 was used to call
somatic single-nucleotide variants and indels. Here, Picard software
was used to deduplicate the sequence. The mutant allele frequency
cutoff was set as 0.5%. Common variants were removed according to
dbSNP (https://www.ncbi.nlm.nih.gov/SNP/) and the 1000 Genomes
Project (http://www.1000genomes.org). Gene fusions and copy-
number variations were identified by FACTERA (34) and ADTEx (35),
respectively. For the tumor tissue samples, the log
2
ratio cutoffs for the
copy-number gain and the copy-number loss were defined as 2.0 and
0.6, respectively. Tumor mutational burden was defined as the number
of somatic, coding, base substitution, and indel mutations per mega-
base of the examined genome and was calculated as previously
described (36). Briefly, all base substitutions were considered, includ-
ing nonsynonymous and synonymous alterations and indels in the
coding region of targeted genes, except for known hotspot mutations in
oncogenic driver genes and truncations in tumor suppressors (https://
www.oncokb.org).
Random forest algorithm for mutation importance ranking
A random forest algorithm (37) was applied to the WES data to
identify the most important mutations associated with the inflamma-
tory response in ICC. Briefly, the in-house WES data set (n¼78) and
inflammatory response score from the GSVA method were applied as
input. The “ranger”package in R software was used to find the best
hyperparameter in the regression process for the random forest
model (38). The “e1071”package in R software was then used to
generate the random forest model (39).
Single-cell RNA-seq data processing
RPMI-1640 (Gibco; cat. no. 11875-093) with 1 mmol/L protease
inhibitor (Solarbio; cat. no. P6730) was used to transport human ICC
tumor tissues (n¼3) from the training cohort. Tissues were digested
with a dissociation enzyme cocktail prepared by dissolving 2 mg/mL
Dispase II (Sigma–Aldrich; cat. 42613-33-2), 1 mg/mL Type VIII
collagenase (Sigma–Aldrich; cat. no. C2139), and 1 unit/mL DNase I
(NEB, cat. no. M0303S) in phosphate-buffered saline (PBS) with 5%
FBS (Gibco; cat. no. 16000-044) for 40 minutes at 37C. The cells were
dissociated by manual vortexing and pipetting and collected every 20
minutes and then filtered using a 40-mm nylon cell strainer (Falcon;
cat. no. 352340). Red blood cell lysis buffer (Invitrogen) with 1 unit/mL
DNase I was used to remove red blood cells. Finally, the cells were
washed in PBS with 0.04% BSA (Sigma–Aldrich; cat. no. B2064). The
concentrations of the single-cell suspensions were computed with
Countess (Thermo) and adjusted to 1,000 cells/mL. Cells were loaded
according to the Chromium single-cell 30kit standard protocol to
capture 5,000 to 10,000 cells/chip position (V2 chemistry). Library
construction, and all the other processes were performed according to
the standard manufacturer’s protocol.
Processing for RNA isolation and cDNA library preparation from
the 3 tumor tissue samples were carried out according to the man-
ufacturer’s instructions. A limited dilution approach was applied to
capture more than 10,000 cells. The surface of supersaturated beads
was filled with oligonucleotide barcodes; thus, a bead could be paired
with a cell in a microwell. Once cells were lysed in the cell lysis buffer,
the beads were hybridized by polyadenylated RNA molecules. The
RNA molecules were then used for reverse transcription to obtain
Molecular Subgroups of ICC Discovered by Multiomics
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cDNA. Each cDNA was tagged with a special cell label fragment during
cDNA synthesis to carry information about the cell of origin. Libraries
for scRNA-seq were generated using the BD Rhapsody platform and
sequenced on the Illumina Novaseq 6000. STAR software was used to
align FASTQs to the human reference genome (GRCh38) using the
default parameters (40). For each sample, gene-barcode matrices were
generated by counting the unique molecular identifiers and filtering
noncell-associated barcodes. Finally, a gene–barcode matrix contain-
ing the barcoded cells and the gene-expression counts was generated.
Single-cell RNA-seq data analysis
Single-cell transcriptome analysis on the 3 tumor tissue samples was
performed with Seurat package from R software with the default
parameters (41). Nonlinear dimensional reduction was performed
with the t-distributed stochastic neighbor embedding (t-SNE) method
using the R software. The featured genes of each cluster were found by
“Seurat”(highly expressed genes adjusted P<0.001). The “GSVA”
package from the R software was used to compute the score of each
gene set as previously described above. NicheNET analysis was
performed with the “nichenetr”package from R software and was
used to analyze cell–cell interactions (CCI) by exploring the ligand and
target gene pairs (42). Briefly, macrophages and T cells were selected
from total cells and CCI analysis was performed using the nichenetr
package. The gene-of-interest was selected as the feature gene of CD4
þ
T cells. The regulatory potential score was calculated using network
propagation methods (42). Therefore, a total of 19 regulatory pairs
were identified with the previously defined network.
IHC staining of tissue sections
Human ICC tumor tissues from the training cohort were fixed with
4% paraformaldehyde (Solarbio, P1110) before embedding them in
paraffin (Solarbio, YA0012). Tissue sections (4 mm) were later depar-
affinized with xylene (Aladdin, X112054) and rehydrated with graded
alcohol (Aladdin, E111991). Antigen epitope retrieval was induced by
microwave heating. Tissue sections were then blocked for 1 hour at
room temperature in 100 mL per slide goat serum blocking solution
(Proteintech, B900780) and PBS (Solarbio, P1020). The sections were
incubated with a primary antibody against CD68 (1:200; 76437S, CST)
overnight at 4C. The next day, ICC tissue samples were stained using
an anti-mouse/rabbit universal IHC detection kit (pk10006, Protein-
tech) following the manufacturer’s instructions. Mounted sections
were examined by light microscopy (Leica), images were analyzed with
Image-Pro Plus (version 6.0), and high/low expression was used for
staining thresholds.
Multiplex immunofluorescence staining of tissue sections
Resected tumor tissues from ICC patients were fixed with 4%
paraformaldehyde before embedding in paraffin. First, the prepared
tissue sections (4 mm) were baked in an oven at 65C for 1 hour,
dewaxed with xylene, and rehydrated with graded alcohol. After
rehydration, the sections were fixed in 10% neutral buffered formalin
(Solarbio, G2161) at room temperature for 20 minutes, placed in an
appropriate AR buffer (AKOYA Biosciences, NEL820001KT), and
then placed in a microwave for 1 minute at 100% power and then for an
additional 15 minutes at 20% power. The slides were blocked with
Blocking/Ab Diluent (AKOYA Biosciences, NEL820001KT) after the
slides cooled at room temperature and incubated with primary
antibody at room temperature for 10 minutes. TBST (Solarbio,
T1082) was used to wash the slides three times, and then the slides
were incubated with Opal Polymer HRP MsþRb (AKOYA Bio-
sciences, NEL820001KT) to cover tissue sections at room temperature
for 10 minutes, generally 100 to 300 mL per slide. The slides were
rinsed with TBST three times before incubation with Opal Signal
Generation (AKOYA Biosciences, NEL820001KT). The microwave
treatment, blocking, primary antibody incubation, introduction of
Opal Polymer HRP, and signal amplification were repeated. Primary
antibodies in this assay including APOE (1:100, 13366S, CST), CD68
(1:100, #76437, CST), and CD4 (1:100, #48274, CST). Once three
targets were labeled, DAPI working solution (AKOYA Biosciences,
NEL820001KT) was applied in the dark for 5 minutes at room tempe-
rature, and the slides were washed with distilled water and TBST
before mounting. Finally, a confocal microscope (Nikon) was used to
take images of those tissue samples, and the acquired images were
analyzed using ImageJ (version 4.0), positive/negative expression was
used for staining thresholds.
Cell lines and culture
The following cell lines were purchased from the ATCC recently
and maintained in a humidified atmosphere in a 5% CO
2
incubator
(Thermo, 3111) at 37C. HCCC9810, THP1, and HuCCT1 cells were
maintained with RPMI-1640 medium (11875093, Gibco), whereas
HIBEC, CCLP-1, LICCF, and KMCH1 cells were cultured with
Dulbecco’s modified Eagle’s medium (DMEM; 11965092, Gibco). All
media for cell culture were supplemented with both FBS (10%;
10099141, Gibco) and penicillin/streptomycin (1%; 10378016, Gibco).
All cell lines used in this study were tested for mycoplasma every two
months.
Western blotting
Cells were lysed with RIPA buffer (R0020, Solarbio) supplemented
with a proteasome inhibitor (P6731, Solarbio) on ice. Protein con-
centrations were measured using the BCA protein assay kit (P0012S,
Beyotime). Protein samples were separated by sodium dodecyl sulfate–
polyacrylamide gel electrophoresis (SDS-PAGE) and were transferred
to nitrocellulose membranes (Merck Millipore, HATF00010). The
membranes were then blocked using 4% fat-free milk (LP0031B,
Solarbio) in PBS (Solarbio, P1020) for 1 hour at room temperature.
The membranes were incubated with the primary antibody (KMT2D,
1:1,000, Proteintech; GAPDH, 1:5,000, Proteintech) overnight at 4C.
PBST (P1031, Solarbio) was used for washing membranes three times.
The membranes were then incubated with the secondary antibody
(Goat Anti-Rabbit IgG H&L, ab205718, Abcam) for 1 hour at room
temperature the following day. The membranes were washed three
times with PBST before scanning on a Bio-Rad gel imager according to
the manufacturer’s instructions. Acquired data were quantitated using
Image Lab software, and high/low expression was used as staining
thresholds. Three biologically repeated experiments (n¼3) were
carried out.
Plasmid generation
Plasmids (pT3-EF1a-NICD, #46047; PT3-myr-AKT-HA, #31789;
and pCMV-CAT-T7-SB100, #34879) were purchased from Addgene.
Plasmid (1 mL) was cloned into E. coli DH5afor 30 minutes on ice.
Heat shock was performed with water for 1 minute at 42C. The E. coli
were then cultured at 37C with shaking at 250 RPM for 1 hour and
then incubated on ice again for another 5 minutes. A single bacteria
colony was obtained by growing the E. coli on the agarose. LB media
(200 mL) were used to culture the bacterial colonies in an incubator
overnight with shaking at 250 RPM. The medium was centrifugated at
3,000 gfor 5 minutes to obtain the precipitate. 10 mL Buffer S1
(AP-MX-EP-25, Axyprep), 10 mL Buffer S2 (AP-MX-EP-25, Axyprep),
and 10 mL Buffer S3K were added sequentially to resuspend the
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precipitate. The mixed buffer was transferred into the preparation tube
followed by centrifugation at 6,000 gfor 5 minutes. Lastly, 500 mL
Eluent A was used to dissolve the plasmid.
In vivo tumor model construction
The animal experiment was approved by the First Affiliated Hos-
pital, Zhejiang University School of Medicine. C57BL/6J mice were
purchased from the Model Animal Research Center of Nanjing
University (China). All mice were housed in the specific pathogen-
free facility of the First Affiliated Hospital, Zhejiang University School
of Medicine, with approval from the Institutional Animal Care and
Use Committee. To establish a spontaneous ICC mouse model, a
plasmid mixture carrying 25 mg pT3-EF1a-NICD, 25 mg pT3-myr-
AKT-HA, and 25 mg pCMV-CAT-T7-SB100 per mice was injected at
high pressure into the tail vein of the mice, injected with 2 mL plasmid
mixture in 7–8 seconds. One week later, the mice were treated with
10 mg/g CSF1R antibody (BE0213, Bio X Cell) via intraperitoneal in-
jection, and the same injections were performed every 3 days there-
after. On day 28, tumors were collected and further analyzed.
IHC staining for the in vivo tumor model
The spontaneous ICC tumor tissue sections from mice (4 mm) were
deparaffinized with xylene and rehydrated with graded alcohol. Anti-
gen epitope retrieval was induced by microwave heating before
blocking in blocking solution (Proteintech, B900780) in PBS for 1 hour
at room temperature. Ki67 (1:200; 550609, BD Biosciences) primary
antibodies were incubated overnight at 4C. The following day, tissue
sections were washed three times with PBS before staining using an
anti-mouse/rabbit universal IHC detection kit (pk10006, Proteintech)
according to the manufacturer’s instructions. Mounted sections were
examined by light microscopy (Leica); images were analyzed with
Image-Pro Plus software (version 6.0), and positive/negative expres-
sion was used for staining thresholds.
IF staining of tissue sections
Mouse spontaneous ICC tissue samples were deparaffinized with
xylene and rehydrated with graded alcohol as stated above. Antigen
epitope retrieval was induced by microwave heating. Tissue sections
were permeabilized with 0.2% Triton X-100 (Aladdin, T109027),
blocked with 5% BSA for 1 hour at room temperature, and then
incubated with the primary antibody (APOE, 1:100, #49285, CST;
C1QB, 1:100, abs136795, Absin; F4/80, 1:100, sc-365340, Santa Cruz)
overnight at 4C. The following day, tissue sections were washed with
PBS three times. Samples were then stained with fluorescent secondary
antibodies (Alexa Fluor 488, 1:400, A-11008, Invitrogen; Alexa Fluor
594, 1:400, A-11032, Invitrogen) in the dark for 1 hour at room
temperature and washed with PBS three times before mounting onto
slides using Mowoil (81381, Sigma) supplemented with DAPI (Beyo-
time, C1002) to stain the nucleus. Finally, a confocal microscope
(Nikon) was used to take images of ICCA tissue samples. Acquired
images were analyzed using ImageJ (version 4.0), and high/low
expression was used as staining thresholds.
IF staining for cell culture
To induce THP1 cells to differentiate into M0 macrophages, 110
5
THP-1 cells were treated with 80 nmol/L phorbol 12-myristate
13-acetate (PMA; ab120297, Abcam) in 12-well plates. After 48 hours,
210
5
ICCA tumor cells HCCC9810 or CCLP1 were cocultured with
M0 macrophages on glass coverslips in 12-well plates. Immunofluo-
rescence was performed. First, the cells were washed with PBS and
fixed with 4% paraformaldehyde before permeabilization with 0.2%
Triton X-100 at room temperature. Samples were next blocked with
2% BSA for 1 hour at room temperature and then incubated with the
primary antibodies (APOE, 1:100, 13366S, CST; CD68, 1:100, #76437,
CST; C1QB, 1:100, abs136795, Absin) for 1 hour at room tempera-
ture. To examine the colocalization of proteins of interest, cells were
stained with Alexa Fluor 488- and 594-labeled fluorescent secondary
antibodies (Invitrogen) for 1 hour in the dark at room temperature.
Coverslips were washed three times with PBS before mounting onto
slides using Mowoil supplemented with DAPI to stain the nucleus.
Finally, the cells were examined with a confocal microscope (Nikon).
The acquired images were analyzed using ImageJ (version 4.0), and
high/low expression was used as staining thresholds.
H&E staining
Mouse spontaneous ICC tumor tissue sections (4 mm) were
deparaffinized with xylene and then rehydrated with graded alco-
hol. Tissue sections were washed three times using PBS before
nuclear staining with hematoxylin (Solarbio, G1080) for 30 minutes
at room temperature before being washed with PBS three times. The
sections were then exposed to 1% ammonia water (Solarbio, G1822)
to change the hematoxylin-stained nuclei from a reddish to blue–
purple appearance. Subsequently, 75% alcohol was used to rinse the
tissue sections for 2 minutes at room temperature, and then the
cytoplasm was stained with eosin (Solarbio, G1100) for 1 hour at
room temperature. The sections were then directly rinsed with
graded alcohol. Finally, xylene was used to displace the anhydrous
alcohol before mounting with slides. Sections were examined by
light microscopy (Leica), images were analyzed with Image-Pro Plus
software (version 6.0), and normal/abnormal structures of the liver
were used as staining thresholds.
Flow cytometry analysis
THP1 cells were treated with 80 nmol/L PMA for 48 hours to induce
THP1 to M0 macrophages. Differentiated M0 macrophages were then
cocultured with ICC tumor cells, and the cell number was the same as
in the previous section. Before the cells were incubated with fluoro-
chrome-labeled antibodies, they were stimulated with 1Cell Stim-
ulation Cocktail (plus protein transport inhibitors; eBioscience,
00-4975-03) at 37C for 6 hours. Human peripheral blood mononu-
clear cells (PBMC) from patients were first isolated by Ficoll density
gradient (10771, Sigma). Briefly, isotonic Ficoll solution was added
into the blood slowly and centrifuged at 600 gfor 30 minutes. Buffy
layers containing cells were collected and washed for PBMC recovery.
Human T cells were isolated from PBMCs through negative selection
to avoid T-cell activation, with the Pan T-cell isolation kit (130096535,
Miltenyi Biotec), LS column (130042401, Miltenyi Biotec), and
MidiMACS separator (130042302, Miltenyi Biotec) following the
manufacture’s protocol, and the purity is more than 90%. Directly
before analysis, T cells were first incubated with a Golgi inhibitor
cocktail (2 mL of the cocktail was added for every 1 mL of cell culture)
for 2 to 4 hours following the manufacturer’s protocol (BD, 550583).
For surface marker analysis, live cells were resuspended in 1PBS
supplemented with 2% FBS and stained with live/dead staining
(BD Bioscience, 564407), BUV395 mouse anti-human CD3 (clone:
UCHT1, BD Bioscience, 563546), FITC anti-human CD4 (clone:
OKT4, BioLegend, 317408), or APC/Cyanine7 anti-human CD8
(clone: SK1, BioLegend, 344714) at 4C for 30 minutes. Subsequently,
the cells were fixed and permeabilized with Fixation and Permeabi-
lization Solution (554722, BD Bioscience) for 30 minutes and then
incubated with PE anti-human TNFa(clone: MAb11, BioLegend,
502909), PE mouse anti-human IFNg(clone: B27, BD Bioscience,
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559327), or PE isotype control (981804, BioLegend) at 4C for 30
minutes. Data were acquired with an ACEA NovoCyte flow cytometer
and analyzed with FlowJo software (version 10).
Mouse ICCA tissue dissociation assays were performed by mecha-
nical and enzymatic digestion in the culture medium containing
0.6 mg/mL collagenase IV (17104019, Gibco), 0.01 mg/mL DNase I
(11284932001, Merck), and 2% FBS at 37C for 1 hour. Single-cell
suspensions were resuspended in 36% Percoll (P4937, Sigma) and
centrifugated at 300 gfor 5 minutes, followed by red blood cell lysis
by using blood lysis buffer (555899, BD) for 10 minutes at room
temperature. Cells were resuspended in DMEM supplemented with
2% FBS, and stimulated with Cell Stimulation Cocktail as described
above. Cells were then centrifuged at 500 gfor 5 minutes and then
incubated with 2.5 mg/mL Fc blocker (BioLegend, 15660) on ice for
20 minutes. For surface marker staining, suspensions were incubated
with fluorochrome-labeled antibodies (CD45, CD3, CD4, and CD8) at
4C for 30 minutes, washed with 1PBS supplemented with 2% FBS,
and centrifuged at 500 gfor 5 minutes. The antibodies included
CD45-BV785 (103149; clone: 30-F11; 1:600; BioLegend), CD3-FITC
(clone: 17A2; 100203; 1:600; BioLegend), CD4-PerCP-Cy5.5 (clone:
RM4-5; 561115; 1:600, BD Biosciences), and CD8-BV605 (clone:
5H10-1; 752632; 1:600; BD Biosciences). For intracellular marker
staining, cells were fixed and permeabilized with Fixation and
Permeabilization Solution (554722, BD Bioscience) at room
temperature for 20 minutes in the dark before incubation with
anti-mouse TNFa(clone: MP6-XT22, 506305 BioLegend) or PE
isotype control (400408, BioLegend). For the analysis assay, the
samples were washed and resuspended in 1PBS supplemented
with2%FBS.Thedatawereacquiredwithafive-laser flow
cytometer (BD Bioscience) and analyzed with FlowJo software
(version 10).
Real-time reverse transcription-PCR (qRT-PCR)
THP1 cells were treated with 80 nmol/L PMA to differentiate the
cells into M0 macrophages. The differentiated M0 macrophages were
then cocultured with CCLP1 cells, and the cell number was the same as
in the previous section. After 48 hours of coculture, cells were isolated
and stained with FITC anti-human CD14 (325604, BioLegend) to label
macrophages, following the FACS staining methods described previ-
ously. After staining, macrophage sorting was performed by Beckman
moflo Astrios EQ, and the purity is more than 90%. TRIzol reagent
(Invitrogen) was added to extract the total RNA from the cells, accord-
ing to the manufacturer’s instructions. Reverse transcription was per-
formed using PrimeScript reagent (RR047A, TaKaRa) to synthesize
cDNA. Quantitative real-time (qRT)-PCR was performed with TB
Green Premix Ex Taq (RR820A, TaKaRa) and quantified by a CFX
Real-Time PCR Detection System (Bio-Rad), and the template is less
than 100 ng. Primers were used as follows: APOE (forward: GCGGC-
TTGGTAAATGTGCTG, reverse: AATCCCAAAAGCGACCCAGT),
C1QB (forward: CAGGGATAAAAGGAGAGAAAGGG, reverse: GG-
CCGACTTTTCCTGGATTC), and GAPDH (forward: CGGATTTG-
GTCGTATTGGG, reverse: CTGGAAGATGGTGATGGGATT) was
used for normalization, and the 2
–DDCt
method was used to determine
the relative gene-expression. Three biologically repeated experiments
(n¼3) were carried out.
Statistical analysis
Survival analysis for the training and validation cohorts was per-
formed with the “survival”package from R software (43). The hazard
ratio (HR) was determined via univariate Cox regression analysis.
Statistical tests were selected based on the specific assumptions relative
to the data distribution and its variability. T tests and Kruskal–Wallis
tests were performed with the basic function of R software. Sample data
were analyzed by a two-tailed Student’ttest to identify statistically
significant differences between two groups; the Kruskal–Wallis test
was used to identify differences between three groups. The data are
represented as the means standard error of the mean (SEM). A P
value <0.05 indicated statistical significance.
Data availability
The data generated in this study have been deposited in iProX
(Project number: IPX0003037000).
Results
Three molecular subtypes of human ICC discovered by
proteomic analysis
To comprehensively characterize the molecular features of the ICC
tumors, ICC tissues derived from 110 patients (Supplementary
Table S1) were procured for proteomics analysis (Fig. 1A). A total
of 10,888 proteins were identified and quantified, with low batch
variance (Supplementary Fig. S1A). Among the 10,888 proteins, 6,311
proteins were detected in samples with less than 10% missing values
and were subjected to further analysis (Supplementary Fig. S1B).
Based on the proteomic matrix of 6,311 proteins, the ICC cohort was
divided into three distinct clusters (C1, C2, and C3), with an optimal
consensusk-value of 3 (Fig. 1B; SupplementaryFig. S1C and S1D). Each
of these clusters was distinguishable in the PCA plot of the patients
(Supplementary Fig. S1E). A DEP analysis was performed for each pair
of molecular subtypes (Supplementary Fig. S1F–S1H). A Venn dia-
gram was applied to obtain the key DEPs for the three molecular
subtypes (Fig. 1C). A total of 85 proteins were identified to be differ-
entially expressed in all comparisons between the three molecular
subtypes (Fig. 1C; Supplementary Table S3). Among the 85 proteins,
32 proteins were upregulated in subtype 1, 24 proteins were upregulated
in subtype 2, and 29 proteins were upregulated in subtype 3 (Supple-
mentary Table S3). We performed GO analysis for the three protein
sets (Supplementary Fig. S1I–S1K). Based on the results from GO
analysis, we annotated the three molecular subtypes as “chronic
Figure 1.
Molecular subtypes of ICC with distinct prognoses. A, Schematic diagram of this study. Three molecular subtypes were identified through proteomic analysis, and the
genomic landscape of each subtype was revealed. scRNA-seq revealed a subset of macrophages promoted inflammation in the chronic inflammation subtype. B,
Consensus clustering to identify molecular subtypes was performed using the proteomic data (n¼110, training cohort). C, Venn diagram showing the overlapping
proteins for the three molecular subtypes identified (n¼110, training cohort). D, Inflammatory response ssGSEA score, (E) metabolism ssGSEA score, and (F)
chromatin remodeling ssGSEA for the three molecular subtypes (n¼110, training cohort) were determine d and are shown in violin plots. Significance was evaluated
by Kruskal–Wallis analysis, Pvalues indicated. D–F, The inflammatory response score, chromatin remodeling score, and metabolis m score were calculated using each
gene set on the normalized proteomic data. The box portion is defined by two lines at the 75th percentile and the 25th percentile of the values. The middle line
indicates 50th percentile (median). G, Heatmap showing the expression of the 85 overlapping proteins in each molecular subtype (n¼110; Supplemen tary Table S1)
and correlation with disease characteristics. The color key from blue to red indicates the relative expression of genes from low to high. H, Kaplan–Meier estimates of
OS for patients in different molecular subtypes (n¼110, metabolism n¼40, chromatin remodeling n¼33, and chronic inflammation n¼37).
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inflammation”(subtype 1), “metabolism”(subtype 2), and “chromatin
remodeling”(subtype 3; Fig. 1B and C). Gene set variation analysis
(GSVA) was performed on the proteomic data using the related gene
sets (ref. 44; Supplementary Table S4), which showed that the inflam-
matory response score was greatest in the chronic inflammation subtype
compared with the other two subtypes (Fig. 1D). Similarly, the metab-
olism score was greatest in the metabolism subtype, and the chromatin
remodeling score was greatest in the chromatin remodeling subtype
(Fig. 1E and F). The density distribution of the three ssGSEA scores is
shown in Supplementary Fig. S1L. The differentially expressed proteins
and pathway alterations are shown in Fig. 1G, indicating the significant
different pathway activation in the three molecular subtypes.
Figure 2.
Machine learning methods identifying the association of KMT2D mutation and the inflammatory response in ICC. A, Diagram elucidating the genomic
differences of patients from different molecular subtypes (n¼78, training cohort). The genomic landscape for each molecular subtype was detected. A
random forest algorithm was applied to investigate the gene mutations that mostly associated with a chronic inflammatory score. B, Genomic landscape of
the three molecular subtypes of ICC (n¼78, training cohort). C–E, Featureimportancerankingbytherandomforestalgorithmforthe(C)chromatin
remodeling subtype, (D) the chronic inflammation subtype, and (E) the metabolism subtype. The feature with the greatest ranking importance score
indicates the most association (either positive or negative) with the subtype. The box portion is definedbytwolinesatthe75thpercentileandthe25th
percentile of the values. The middle line indicates 50th percentile (median). F, OS of patients with KMT2D mutation (Mut; n¼8) or wild-type (WT, n¼70)
KMT2D (n¼78). G, KMT2D status (Mut or WT) in the three molecular subtypes (metabolism n¼40, chromatin remodeling n¼33, and chronic inflammation
n¼37). H, Metabolism scores in tissues based on KMT2D status. Significance was evaluated by a two-tailed ttest. I, Inflammatory response scores in
tissues based on KMT2D status. Significance was evaluated by a two-tailed ttest. The inflammatory response score and metabolism score were calculated
with the ssGSEA method using each gene set on the normalized proteomic data.
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Figure 3.
The immune landscape of the three molecular subtypes. A, Heatmap showing the immune cell populations of patients belonging to different molecular
subtypes (n¼110, metabolism n¼40, chromatin remodeling n¼33, and chronic inflammation n¼37). The immune cell populations were inferred with the
ssGSEA method on a proteomic matrix. The color key from blue to red indicates the relative expression of genes from low to high. B, Boxplot showing immune
cell abundance stratified by molecular subtype. Significance was evaluated by Kruskal–Wallis with ,P<0.05; ,P<0.01; ,P<0.001; ,P<0.001.
The immune cell abundance was calculated using each cell signature on the normalized proteomic data. The box portion is defined by two lines at the
75th percentile and the 25th percentile of the values. The middle line indicates 50th percentile (median). C, Representative IHC for CD68 within the tumor for
the three molecular subtypes (n¼10 for each molecular subtype); scale bar ¼50 mm. D, Cumulative data for CD68
þ
macrophages in the three molecular
subtypes. Five fields per well were randomly selected to take images, followed by ImageJ to analyze the number of target cells in each well. Significance was
evaluated by Kruskal–Wallis analysis with ,P<0.05; ,P<0.01; ,P<0.001; ,P<0.001. E, Boxplot showing immune cell abundance stratified by KMT2D
status. Significance was evaluated by the two-tailed ttest with ns: not significant and ,P<0.05. The box portion is defined by two lines at the 75th percentile
and the 25th percentile of the values. The middle line indicates 50th percentile (median). F, Representative IHC staining for CD68 in KMT2D
mut
and KMT2D
wt
tissues; n¼8forKMT2D
mut
tissues and n¼10 for KMT2D
wt
tissues; scale bar, 50 mm. G, Cumulative data for CD68
þ
macrophages in KMT2D
mut
and KMT2D
wt
tissues. Five fields per well were randomly selected to take images, followed by ImageJ to analyze the number of target cells in each well. Significance
was evaluated by Kruskal–Wallis analysis with ,P<0.05; ,P<0.01; ,P<0.001; ,P<0.001.
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When we stratified the ICC patients based on the three subtypes, we
observed that ICC patients had significantly different survival rates
(Fig. 1H). Specifically, patients belonging to the chronic inflammation
subtype exhibited the shortest survival, whereas those with chromatin
remodeling subtype survived the longest. No significance was detected
between clinicopathologic features and molecular subtypes in the
training cohort (Supplementary Table S5). We further validated the
molecular subtype in an independent ICC cohort (Supplementary
Fig. S2). The proteomics data from the validation cohort were inte-
grated with the training cohort and were subjected to consensus
clustering, and the molecular subtypes of samples in the validation
cohort were identified. PCA showed distribution of tumor tissues from
the validation cohort into the three molecular subtypes (Supplemen-
tary Fig. S2A). The clinicopathologic features and molecular subtypes
in the validation cohort are shown in Supplementary Table S6. The
Kaplan–Meier estimates of OS also suggested the worst prognosis
for patients within the chronic inflammation molecular subtype in
the independent validation ICC cohort (Supplementary Fig. S2B).
Taken together, through proteomic analysis, we were able to stratify
ICC into groups with distinct molecular characteristics and patient
survival rates.
KMT2D
mut
associates with the chronic inflammation and
metabolism ICC subtypes
To further explore the genomic characteristics of the three
molecular subtypes, WES was performed using human ICC tumors
in the training cohort, and the genomic landscape was depicted for
78 samples (Fig. 2A). ARID1A, KRAS, PBRM1, BRCA2, CREBBP,
TP53, KMT2D, ATM, and ATRX were the most frequently mutated
genes in ICC tumor tissues (Fig. 2B). A detailed summary of the
mutation landscape showed that missense mutations were the
most frequent variant classification, and that SNPs were the most
frequent variant type (Supplementary Fig. S3A). As expected,
RTK-RAS was the most significantly altered pathway in ICC
(Supplementary Fig. S3B; ref. 45), and KRAS and BRAF were the
most frequently mutated genes in the RTK—RAS pathway (Sup-
plementary Fig. S3C). Nevertheless, the Kaplan–Meier curve and
univariate Cox regression indicated an insignificant association
between OS and KRAS mutation or RTK—RAS pathway alteration
(Supplementary Fig. S3D and S3E).
We then used a random forest–based machine learning method to
identify the key gene mutations in each molecular subtype. The GSEA
score from Fig. 1D–Fwas used as the output, and the gene mutations
were applied as the input. The feature importance ranking is shown
in Fig. 2C–E.KMT2D mutations were found in both chronic inflam-
mation and metabolism subtypes. Among all identified mutations,
only mutations in KMT2D were significantly associated with a better
prognosis for ICC patients (Fig. 2F). MSK pan-cancer genomic data
also verified that patients with KMT2D mutations (KMT2D
mut
) had a
better prognosis than patients with wild-type KMT2D (KMT2D
wt
;
Supplementary Fig. S3F). A significantly high proportion of KMT2D
mutations was also found in the metabolism subtype (Chi-squared test
P<0.01; Fig. 2G). KMT2D
mut
tissue had a greater metabolism ssGSEA
score and lower inflammatory ssGSEA score than KMT2D
wt
tissues
(Fig. 2H and I). We also found that most KMT2D
mut
had a missense
mutation in exon 39 (Supplementary Table S7). Overall, we identified
KMT2D
mut
as being associated with the chronic inflammation and
metabolism of ICC subtypes.
Immune landscape of three molecular subtypes of human ICC
Next, we dissected the immune landscape of different subtypes of
ICC. The abundance of 21 immune cell populations was estimated
using the ssGSEA method by assessing the gene signature for each cell
type on the proteomic data (Fig. 3A). A significantly greater abun-
dance of macrophages was found in the chronic inflammation subtype
(Fig. 3B). IHC staining was performed to validate the results from the
proteomic analysis. As expected, more macrophages were found in
samples with the chronic inflammation subtype than in the other two
subtypes (Fig. 3C and D). We also observed that more macrophages
were found in patients in the KMT2D
WT
group than in the KMT2D
mut
group (Fig. 3E), and IHC staining validated the greater number of
macrophages in KMT2D
WT
than in KMT2D
mut
tissues (Fig. 3F
and G).
APOE
þ
C1QB
þ
TAMs contribute to the chronic inflammation
subtype of human ICC
To further characterize the immune features of the chronic
inflammation subtype, we performed scRNA-seq on tumor tissues
within the chronic inflammation subtype. A total of 10 cell types,
including malignant cells, fibroblasts, endothelial cells and various
immune cells, were identified in the ICC tumor microenvironment
(TME; Fig. 4A). The featured genes in each cell type were then
determined (Supplementary Fig. S4). For instance, SPP1,S100A6,
FGG,GDF15,andFGB had high expression in malignant cells. We
nextfocusedonTAMsinthechronicinflammation TME because
TAMs were found to be a major difference between the three
subtypes. Macrophages were further split into two clusters: type
1andtype2(Fig. 4B). Several M2 macrophage markers (IL10,IL1B,
HLADRA,andCD163) were expressed in both type 1 and type 2
clusters (Supplementary Fig. S5A). The ssGSEA signature scores of
Figure 4.
Identification of APOE
þ
C1QB
þ
TAMs by scRNA-seq. A, Color-coded tSNE plot of cell types in the three ICC tumor tissues in the training cohort. B, Uniform
manifold approximation and projection (UMAP) plot of macrophages color-coded by two macrophage subtypes. C, Violin plot showing the difference in the
inflammatory response scores between type 1 and type 2 TAMs. Significance was evaluated by Wilcoxon analysis. D, UMAP plot of macrophages showing
inflammatory response scores. E, Volcano plot showing the DEGs between type 1 and type 2 TAMs. F, GO analysis based on the DEGs from the comparison of
type 1 and type 2 TAMs. G, UMAP plot of macrophages and C1QB expression. H, UMAP plot of macrophages and APOE expression. I, Representative costaining
of C1QB/CD68 and APOE/CD68; scale bar, 10 mm. J, Representative costaining of C1QB/CD68 and APOE/CD68 in the three molecular subtypes; scale bar:
10 mm. K, Quantification of APOE
þ
macrophage numbers in each molecular subtype. Five fields per well were randomly selected to take images, followed by
ImageJ to analyze the number of target cells in each well. Significance was evaluated by Kruskal–Wallis analysis. L, Representative costaining of C1QB/CD68
and APOE/CD68 in KMT2D
mut
and WT tissues; scale bar, 10 mm. M, Quantification of APOE
þ
macrophage numbers in KMT2D
mut
and WT tissues. Five fields per
well were randomly selected to take images, followed by ImageJ to analyze the number of target cells in each well. Significance was evaluated by a two-tailed
ttest. The box portion is defined by two lines at the 75th percentile and the 25th percentile of the values. The middle line indicates 50th percentile (median).
N, Violin plots showing APOE
þ
C1QB
þ
TAM signature scores in the three molecular subtypes. The score was calculated using the APOE
þ
C1QB
þ
TAM signature
on the normalized proteomic data. Significance was evaluated by Kruskal–Wallis analysis. O, Violin plots showing the APOE
þ
C1QB
þ
TAM signature scores
in KMT2D
mut
and WT tissues. Significance was evaluated by a two-tailed ttest. The box portion is defined by two lines at the 75th percentile and the
25th percentile of the values. The middle line indicates 50th percentile (median).
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M2 and M1 were both greater in type 2 macrophages than in type 1
macrophages (Supplementary Fig. S5B and S5C), suggesting that
the traditional M1/M2 classification for macrophages in ICC may
not be suitable.
We next characterized the two types of macrophages in several
steps. The inflammatory response score was calculated for the two
subtypes of TAMs using the GSVA method. Type 2 TAMs showed a
significantly greater inflammatory response score than type 1 TAMs
(Fig. 4C and D). Differentially expressed genes (DEG) analysis was
then performed between the two subgroups of TAMs. APOE,LIPA,
CTSD,C1QB, and several other genes were significantly upregulated
in type 2 TAMs (Fig. 4E). The upregulated genes in type 2 TAMs
were used to perform GO analysis, and we found enrichment for the
positive regulation of the inflammatory response and negative regu-
lation of the immune system, suggesting the potential involvement
of type 2 macrophages in regulating the inflammatory response in
ICC (Fig. 4F). The coexpression of APOE and C1QB in type 2 TAMs
was verified by the costaining of APOE/CD68 and C1QB/CD68
(Fig. 4G–I). A higher number of APOE
þ
TAMs was found in the
chronic inflammation subtype than in the other two subtypes
(Fig. 4J and K). Tissues with wild-type KMT2D exhibited a greater
number of APOE
þ
TAMs than tissues with KMT2D mutation
(Fig. 4L and M). Kaplan–Meier curve and univariate Cox regression
analysis suggested that patients with high expression of APOE and
C1QB had significantly worse OS than those in the low expression
group (Supplementary Fig. S5D and S5E).
The top 20 upregulated type 2 TAM DEGs from the comparison
between type 1 and type 2 TAMs were used to define a type 2
APOE
þ
C1QB
þ
TAM signature (Supplementary Table S8), and we
performed ssGSEA based on the proteomic data in the training
cohort. The chronic inflammation subtype exhibited the highest
APOE
þ
C1QB
þ
TAM score (Fig. 4N), suggesting that type 2
APOE
þ
C1QB
þ
TAMs are the main determinant of the chronic
inflammation subtype of ICC. The type 2 APOE
þ
C1QB
þ
TAM
score was higher in KMT2D
WT
patients than in KMT2D
Mut
patients
(Fig. 4O), and patients with a high type 2 APOE
þ
C1QB
þ
TAM
signature score exhibited significantly worse OS than those with a
low signature score (Supplementary Fig. S5F).
APOE
þ
C1QB
þ
TAMs promote the secretion of TNFafrom human
CD4
þ
T cells
We next asked how KMT2D mutation reshaped TAMs in the
ICC TME. First, we screened a series of human ICC cell lines and
found that KMT2D was highly expressed by CCLP1 cells, whereas
it had low expression by HCCC9810 cells (Fig. 5A; Supplementary
Fig. S6A). We thus chose both cell lines and cocultured them with
THP1-derived M0 macrophages induced by PMA (46). The
expression of APOE and C1QB was determined by IF staining
and qRT-PCR (Fig. 5B). IF showed that M0 macrophages cocul-
turedwithCCLP1cellsexhibitedsignificantly higher APOE and
C1QB expression than M0 macrophages cocultured with
HCCC9810 cells (Fig. 5C and D). Macrophages were sorted by
FACS, followed by qRT-PCR. M0 macrophages cocultured with
ICC cells (HCCC9810 or CCLP1) resulted in a higher expression of
APOE and C1QB in cocultivated M0 cells than in M0 cells cultured
alone, which confirmed the induction of APOE
þ
C1QB
þ
TAMs by
ICC malignant cells (Fig. 5E), with M0 macrophages cocultured
with CCLP1 cells showing significantly higher APOE and C1QB
expression than M0 macrophages cocultured with HCCC9810 cells
(Fig. 5D and E).
Next, we investigated how APOE
þ
C1QB
þ
TAMs promoted inflam-
mation in the ICC TME. APOE
þ
C1QB
þ
TAM and CD4
þ
T-cell
ssGSEA scores were calculated using their gene signatures on the
proteomic matrix; we found that patients with high APOE
þ
C1QB
þ
TAM abundance exhibited higher CD4
þ
T-cell infiltration (Fig. 5F).
Multiplex (m)IF on human ICC tissues from the training cohort
indicated that ICC tissues with more APOE
þ
TAMs also had greater
CD4
þ
T-cell numbers (Fig. 5G and H). We also found many CD4
þ
T
cells in the ICC TME-produced TNFa(Fig. 5I).
To investigate whether APOE
þ
C1QB
þ
TAMs regulated the inflam-
matory activity of T cells, we performed a NicheNET analysis on
APOE
þ
C1QB
þ
TAMs and T cells, modeling their intercellular com-
munication by linking ligands of sender cells to target genes of receiver
cells (42). In APOE
þ
C1QB
þ
TAMs, IL6,CD40,CXCL16, and several
other genes were ranked as the top potential ligands, according to their
activity in regulating the target genes of T cells (Fig. 5J). The
downstream genes in T cells included AHR,CCL20,CCL5,CSF1,
IL7R,IRF1,TNF,TNFRSF1B, and other key important genes involved
in the regulation of the inflammatory response of T cells. Because TNF
is the target for most ligands from APOE
þ
C1QB
þ
TAMs, we thus
focused on the effect of APOE
þ
C1QB
þ
TAMs on T cells in coculture
experiments. KMT2D
high
CCLP1 cells and KMT2D
low
HCCC9810
cells were cocultured with M0 macrophages and T cells (Fig. 5K).
FACS was performed to elucidate the effect of APOE
þ
C1QB
þ
macro-
phages on T cells (gating strategy in Fig. 5L). Our results indicated
the upregulation of TNFain CD4
þ
T cells when cells were cocultured
Figure 5.
APOE
þ
C1QB
þ
TAMs affect the inflammatory activities of CD4
þ
T cells. A, Western blot showing the expression of KMT2D in ICC cell lines. B, Scheme showing the
workflow of immunofluorescence (IF) staining. The M0 macrophage was cocultured with ICC tumor cell lines. C, Representative costaining of C1QB/CD68 and APOE/
CD68; scale bar, 10 mm. D, The comparison of IF intensity in each group. Five fields per well were randomly selected to take images, followed by ImageJ to analyze
the fluorescence intensity and calculate the mean intensity of each well. Significance was evaluated by paired two-tailed ttest with ,P<0.05; ,P<0.01. E, The
relative expression of C1QB and APOE in each group by qRT-PCR. Relative mRNA expression is shown as the mean SD of n¼3 technical replicates of three
independent experimental repetitions. Significance was evaluated by paired two-tailed ttest with ,P<0.05; ,P<0.01. F, Box plot showing the CD4
þ
T-cell
abundance in tumor tissues with low and high APOE
þ
C1QB
þ
TAM abundance (n¼110). The APOE
þ
C1QB
þ
TAM and CD4
þ
T-cell abundance was calculated using the
normalized proteomic data with each cell protein signature. Significance was evaluated by the Wilcoxon test with ,P<0.05; ,P<0.01; ,P<0.001; ,P<0.001.
n¼3 technical replicates of three independent experimental repetitions were performed for mIF staining and cell counting. G, Representative mIF imaging for ICC
tissues with APOE (green), CD68 (red), CD4 (purple), and DAPI (blue); scale bar, 10 mm. H, Box plot showing the CD4
þ
T-cell number in tumor tissues with low and
high APOE
þ
C1QB
þ
TAM abundance (n¼110). Significance was evaluated by the Wilcoxon test with ,P<0.05. I, tSNE plot of cells with TNF expression. J, Outcome
of NicheNet’s ligand activity prediction on target genes (left). The color key from gray to yellow indicates the prediction ability from low to high; the color key from
gray to red indicates the relative expression of ligands on ICC tissu es from low to high. Ligand–target gene interactions between APOE
þ
C1QB
þ
TAMs and T cells. The
color key from gray to purple indicates the regulatory potential from low to high. Scaled expression of target genes in T cells of three ICC tissues. The color key
from blue to red indicates the scaled expression from low to high. K, Scheme showing the workflow of flow cytometry analysis. The CD4
þ
T cells were cultured
with M0 macrophages and ICC tumor cells. L, The gating strategy of flow cytometry. CD4
þ
and CD8
þ
T cells were identified. M, FACS analysis of CD4 and TNFa
in the three groups. N, The quantification of TNFaamong the five groups according to our FACS analysis. Error bars, SEM (n¼5). Significance was evaluated
by Kruskal–Wallis analysis.
Molecular Subgroups of ICC Discovered by Multiomics
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Bao et al.
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with APOE
þ
C1QB
þ
macrophages (induction by the ICC cell lines;
Fig. 5M). The coculture of KMT2D
high
CCLP1 cells, M0 macrophages,
and CD4
þ
T cells resulted in the highest TNFaexpression in CD4
þ
T cells (Fig. 5N). Nevertheless, APOE
þ
C1QB
þ
macrophages did not
improve the percentage of IFNg
þ
CD8
þ
T cells (Supplementary
Fig. S6B and S6E). We further checked the effect of APOE
þ
C1QB
þ
macrophages on CD8
þ
T cells. APOE
þ
C1QB
þ
macrophages did not
drive the accumulation of TNFa-secreting CD8
þ
T cells or IFNg
þ
CD8
þ
T cells (Supplementary Fig. S6C and S6D; S6F and S6G). There
was a trend for an increased percentage of TNFa-secreting CD8
þ
T cells and IFNg
þ
CD8
þ
T cells, but this did not reach significance
(Kruskal–Wallis).
APOE
þ
C1QB
þ
TAMs may be a potential immunotherapy target
Because APOE
þ
C1QB
þ
TAMs were the most prominent player
driving the chronic inflammation subtype of ICC with the worst
prognosis, we tried to target APOE
þ
C1QB
þ
TAMs in a mouse ICC
tumor model using a blocking CSF1R antibody to deplete TAMs
(Fig. 6A). Tumor tissues were harvested, and we found a signif-
icantly lower tumor burden in the CSF1R antibody–treated group
(Fig. 6B and C). H&E staining was used to assess the properties of
the ICC tumor model (Fig. 6D). Ki67 staining suggested lower
proliferation in the CSF1R antibody–treated group than in the
control group (Fig. 6E). IF staining revealed a lower number of
APOE
þ
C1QB
þ
TAMs in the CSF1R antibody–treated group than
in controls (Fig. 6F and G). The proportion of APOE
þ
C1QB
þ
TAMs in all F4/80
þ
cells was quantified. A lower proportion of
APOE
þ
C1QB
þ
TAMs was found in the CSF1R antibody–treated
group than in controls (Fig. 6H). FACS analysis was performed to
assess TNFaproduction by T cells. After CSF1R antibody treat-
ment, APOE
þ
C1QB
þ
TAMs were depleted, and less T-cell inflam-
mation was observed, as quantified by the TNF signal (Fig. 6I
and J). These results indicated that APOE
þ
C1QB
þ
TAMs may be a
potential target for immunotherapy against ICC.
Discussion
ICC is a relatively rare but highly aggressive cancer type that has a
low 5-year survival rate. The clinical features and survival outcomes of
ICC vary among cohorts (8, 47), thus requiring a novel molecular
classification-based prognostic model. However, the relatively rare
cases of ICC hinder the construction and validation of a molecular
model. In this study, we, therefore, collected a relatively high number
of ICC tissues and provided a comprehensive molecular characteri-
zation of ICC through system-level multiomics analyses. Three novel
molecular subtypes were identified, and the potential underlying
molecular mechanisms of the poor prognosis were also explored.
Inflammation has long been related to ICC development and
metastasis formation through the modulation of the components of
the TME (48, 49). However, the mechanism and effect of inflammation
in ICC are still controversial. Daniela Sia and colleagues identified
inflammation and proliferation classes as two distinct subtypes in
ICC (8). The inflammatory class, defined by the humoral response and
imbalance of cytokines, is associated with a more favorable progno-
sis (8). Nevertheless, several other studies have provided contradictory
data and opinions (47, 50, 51). Andersen and colleagues reported two
categories of ICC tissues, each containing two subgroups (SGI-SGIV)
characterized by distinct gene-expression profiles. Genes differen-
tially expressed between SGI and SGII were mainly involved in the
immune response, whereas overexpression of genes involved in the
proteasomal activation pathway distinguished SGIII from SGIV (7).
Therefore, it is considered that proteasome and anti-inflammatory
inhibitors represent a novel therapeutic option for a defined subgroup.
Sylvie Job and colleagues constructed another classification system
based on the TME of ICC (14). With the help of computational
methods, four distinct molecular subtypes, including immune desert,
immunogenic (inflamed), myeloid (enriched with M2 macrophages),
and mesenchymal, were identified in an ICC cohort (14), which
highlighted the importance of macrophages and other immune cell
populations in reshaping the TME of ICC. Nevertheless, most of the
previous reports focus on bulk transcriptome-based molecular clas-
sification systems in ICC, and the underlying mechanisms of the
reshaping function of tumor inflammation in ICC warrant further
exploration. Consistent with previous studies, we showed that inflam-
mation was the key regulator in ICC. Patients with the chronic
inflammation subtype exhibited the worst survival outcome compared
with patients in the other two subtypes in our training and validation
cohorts. These findings were confirmed by one study using proteomics
for molecular subtyping, where four ICC patient subgroups (S1–S4)
with subgroup-specific biomarkers were identified (15). The inflam-
matory and mesenchymal subgroups exhibit a more aggressive phe-
notype and have a relatively worse prognosis than the other two
subgroups (15). Distinct genetic alterations, microenvironment dys-
regulation, tumor microbiota composition, and potential therapeutics
were found in the four subgroups. Taken together, previous studies
have mainly focused on molecular subtyping and have opposing
opinions on the relationship between inflammatory activity and
prognosis in ICC. Therefore, we tried to explore the detailed molecular
mechanism of inflammation in ICC at the genomic and single-cell
transcription levels.
Through assessment of the genomic landscape, as expected, several
key genes, such as ARID1A,KRAS, and PBRM1, were found to be
mutated in ICC. The results showed molecular similarities with
HCC (52). ARID1A, which only accounts for a 7% mutation rate in
the TCGA-HCC cohort, is the most mutated gene in ICC (53).
ARID1A performs context-dependent oncogenic and tumor-
suppressing functions in liver cancer (54). The detailed mechanism
of ARID1A mutation still requires further exploration in ICC. KRAS
Figure 6.
In vivo experiments showing the roles of APOE
þ
C1QB
þ
TAMs in regulating inflammation. A, Schematic diagram of our in vivo experiments. CSF1R antibody
was applied to treat mice with spontaneous ICC tumors. B, Tumor sizes from the control and anti-CSF1R–treated groups (n¼6 for each). C, Tumor burden
comparison between the CSF1R antibody–treated (n¼6) and control groups (n¼6). Significance was evaluated by a two-tailed ttest with ,P<0.05; ,P<0.01,
and ,P<0.001. D, H&E staining of tumors from the CSF1R antibody–treated and control groups; scale bar, 100 mm. E, Representative IHC staining for Ki67 in
tumors from the CSF1R and control groups; scale bar, 40 mm. F, Representative IF staining of APOE, C1QB, and CD68 in tumors from CSF1R antibody–treated
and control groups; scale bar:, 10 mm. G, Quantification of APOE
þ
C1QB
þ
TAMs between the CSF1R antibody–treated and control groups. Five fields per well
were randomly selected to take images, followed by ImageJ to analyze the fluorescence intensity and calculate the mean intensity of each well. Significance was
evaluated by a two-tailed ttest with ,P<0.05; ,P<0.01. H, Proportion of APOE
þ
C1QB
þ
TAMs in F4/80
þ
cells. Five fields per well were randomly selected to
take images, followed by Chi-squared test. I, Flow cytometry analysis for the proportion of TNFa
þ
in total CD4
þ
T cells. J, The histogram showing the difference
between the control and anti-CSF1R–treated groups. Error bars, SEM (n¼6), with ns, not significant; ,P<0.05; ,P<0.01; ,P<0.001.
Molecular Subgroups of ICC Discovered by Multiomics
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mutations are associated with poor prognosis in ICC in the Ander-
sen and colleagues cohort (7). However, KRAS mutations did not
reach significance in our cohort (P¼0.13). The Chi-squared test
also suggested that no significance existed for the association of
KRAS mutations and the three molecular subtypes in our cohort.
KMT2D is a histone methyltransferase that plays multiple roles in
development, cell fate transition, and tumor suppression (55, 56).
KMT2D was associated with lower inflammatory activity and
mainly occurred within the metabolism subtype by our random
forest algorithm. Genes involved in antigen presentation (e.g.,
PSMA7) and ubiquitination (e.g., PSMA7,PSMA1)werealso
enriched in the metabolism subtype. Data by Gu and colleagues
highlight the correlation between KMT2D and the ubiquitination
proteasome system (57). Hence, we considered that KMT2D muta-
tion may be a key event in the metabolism subtype. Our data
indicated that KMT2D mutation is associated with a better prog-
nosis than wild-type KMT2D in our ICC cohort, which is supported
by an independent study based on Cas9 mutagenic screening (58).
One study also indicates that ICC patients harboring KMT2D
mutations have higher immunotherapy response rates and pro-
longed survival outcomes than patients with wild-type KMT2D (59).
In vitro experiments showed that the coculture of the KMT2D
high
cell line, macrophages, and T cells resulted in the highest TNFa
expression in T cells. Hence, we considered that KMT2D mutation
is associated with the regulation of inflammatory activities in ICC.
Patients harboring KMT2D mutations or bearing ICC tumors that
belong to the metabolism subtype may obtain more immunother-
apy benefits.
Through ssGSEA of the proteomic data, we found that macrophages
were mostly associated with the chronic inflammation subtype of ICC.
We thus focused on macrophage populations at the single-cell tran-
scriptome level. scRNA-seq analysis was performed with tumor tissues
from the chronic inflammation subtype, and the macrophage popula-
tions were further identified. M1 and M2 macrophages have been
found in various tumor types with distinct properties (60, 61). M1
macrophages are highly active in inflammatory responses and are
commonly considered antitumor macrophages, whereas M2 macro-
phages can promote tumor cell growth and evasion by dysregulating
several key pathways in tumor cells (61). Unlike the traditional M1 and
M2 classification, in our study, ICC TAMs were divided into two
subtypes with distinct molecular features. Type 2 TAMs showed both
higher M1 and M2 scores than type 1 TAMs, suggesting that a new
classification method should be used in ICC macrophages. Type 2
TAMs were characterized by high expression of APOE and C1QB,
along with a high inflammatory response score, implying the impor-
tance of type 2 APOE
þ
C1QB
þ
TAMs in reshaping the inflammatory
response of the TME. A high abundance of APOE
þ
C1QB
þ
TAMs was
also found in the chronic inflammation subtype, which once again
implied the importance of APOE
þ
C1QB
þ
TAMs in the inflammatory
response of ICC.
Rheumatoid arthritis (RA) is an autoimmune disease characterized
by chronic inflammation in the synovium of the joint tissue, which
leads to joint destruction, disability, and shortened life span (62).
Interestingly, a study by Zhan and colleagues reveals that 4 subsets of
monocytes with distinct molecular signatures exist in RA (63) and that
one subset, M3, is enriched with C1QA, C1QB, and other proin-
flammatory genes. They conclude that the transcriptional profiles of
M3 monocytes do not align with known activation states, possibly
indicating that M3 represents cell phenotypes tailored to the unique
homeostatic needs of the synovium (63). TREM2
þ
APOE
þ
macro-
phages, which are involved in neurodegenerative disorders (64), have
been found in tumors, where they display a potentially immuno-
suppressive role (65). Anti–PD-1–based immunotherapy is more
efficient when TREM2
þ
APOE
þ
macrophages are targeted by mono-
clonal antibodies (66). The previous findings and the results of our
research confirmed the importance of some subsets of monocyte/
macrophage populations (usually characterized by C1QA, C1QB,
APOE, and other molecules) in reshaping the inflammatory land-
scape among different diseases and tumor types.
To further explore the mechanism by which type 2 APOE
þ
C1QB
þ
TAMs activated inflammatory activity in the TME of ICC, we explored
the interactions of APOE
þ
C1QB
þ
TAMs by single-cell transcriptome
analysis. We found that APOE
þ
C1QB
þ
TAMs promoted TNFa
secretion in CD4
þ
T cells through several ligands. To confirm the
results of the bioinformatics analysis, we cocultured M0 macrophages,
CD4
þ
T cells, and ICC tumor cell lines. FACS revealed that
APOE
þ
C1QB
þ
TAMs promoted CD4
þ
T-cell inflammation by
increasing TNFasecretion. To confirm the results from bioinformatic
analysis and in vitro data, we used an in vivo spontaneous ICC murine
model. Among target-TAM therapeutics, CSF1R is a promising mol-
ecule, and blocking CSF1R leads to significant TAM depletion (67).
Pexidartinib, which exhibits selective activity against CSF1R, has been
approved for the treatment of adult patients with symptomatic teno-
synovial giant cell tumors not amenable to improvement with sur-
gery (68). Nevertheless, the significant toxicity and insufficient effect
have limited the application of pexidartinib in several solid tumors.
Thus, considering the significant toxicity and the existing compensa-
tory mechanisms, targeting a specific TAM subset may be more
advantageous to overcome the drawback of general TAM blockade
therapy. We administered anti-CSF1R in the in vivo tumor model,
which led to the depletion of APOE
þ
C1QB
þ
TAMs and a decrease in
T-cell inflammation. Nevertheless, other macrophages/monocytes
beyond the APOE
þ
C1QB
þ
TAM population were also affected by
anti-CSF1R treatment. Developing a novel antibody for targeting the
APOE
þ
C1QB
þ
TAM population would, therefore, be a promising way
to hinder the progression of ICC and improve the immunotherapy
response.
Toourknowledge,thisisthefirst report to apply scRNA-seq-
assisted multiomics analysis to two independent ICC cohorts. Most
of the ICC studies are either based on genomic or transcriptomic
data in a single center (69, 70). We combined genomic and
proteomic information from the in-house ICC cohort to identify
key mutations associated with the molecular subtypes of ICC. Our
two-center proteomic analysis further confirmed the robustness of
the molecular subtyping. Compared with previous studies (14,
71, 72) based on transcriptome data, our scRNA-seq–assisted
proteomic analysis might represent substantial progress in the
in-depth analysis of the ICC TME at the single-cell level. Never-
theless, there are still some limitations to our study. We illustrated
the association between KMT2D mutation and inflammatory
activities in the ICC TME. The underlying molecular mechanism
is worthy of further exploration. We recruited 151 cases of fresh
tumor tissues, but we were not able to perform WES in all of the
samples, which may have reduced statistical power in our analyses
and, thus, could affect our conclusions. Tissues from two institu-
tions from one country were used to construct the cohort. More
centers from different countries could help reduce geographical
polymorphisms. Last, the patient number for scRNA-seq was
relatively low. It will also be important to show the single-cell
landscape for the other two molecular subtypes.
In conclusion, we introduced a novel workflow for the
scRNA-seq–assisted molecular subtyping process for ICC. Three
Bao et al.
Cancer Immunol Res; 10(7) July 2022 CANCER IMMUNOLOGY RESEARCH
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molecular subtypes were identified for stratifying ICC patients
with different prognoses. In particular, the chronic inflammation
subtype associated with the poorest outcome. Mutations in KMT2D
were found to be significantly involved in the inflammatory re-
sponse and metabolic activity of ICC, and an APOE
þ
C1QB
þ
TAM
subtype showed the capacity to affect T cells, therefore reshaping
the inflammatory landscape of the TME in the chronic inflamma-
tion subtype of ICC.
Authors’Disclosures
T. Guo reports other support from Westlake Omics Inc, nonfinancial support from
Pressure Biosciences Inc, Thermo Fisher, Sciex, and Bruker outside the submitted
work. No disclosures were reported by the other authors.
Authors’Contributions
X. Bao: Conceptualization, supervision, funding acquisition, validation, investi-
gation, visualization, methodology, writing–original draft, project administration,
writing–review and editing. Q. Li: Validation, investigation, visualization, method-
ology. J. Chen: Investigation, visualization, methodology. D. Chen: Data curation.
C. Ye: Data curation, methodology. X. Dai: Validation, investigation, methodology.
Y. Wang: Validation, investigation, visualization, writing–original draft. X. Li: Data
curation, validation, investigation. X. Rong: Resources, data curation, investigation.
F. Cheng: Methodology. M. Jiang: Methodology. Z. Zhu: Methodology. Y. Ding:
Methodology. R. Sun: Validation, visualization, methodology. C. Liu: Visualization,
methodology. L. Huang: Methodology. Y. Jin: Methodology. B. Li: Formal analysis.
J. Lu: Formal analysis. W. Wu: Formal analysis. Y. Guo: Formal analysis. W. Fu:
Resources, data curation. S. Langley: Methodology. V. Tano: Methodology. W. Fang:
Supervision, investigation, writing–review and editing. T. Guo: Data curation,
supervision. J. Sheng: Formal analysis, supervision, project administration.
P. Zhao: Supervision, project administration. J. Ruan: Supervision, funding acqui-
sition, investigation.
Acknowledgments
This work was supported in part by the National Natural Science Foundation
of China 82101830 (X. Bao), 82102817 (X. Dai), 81874173 (J. Ruan), 81472346
(P. Zhao), 82074208 (P. Zhao), 81972492 (T. Guo), by the National Key R&D
Program of China grant 2019YFA0803000 (J. Sheng), 2020YFE0202200 (T. Guo),
by the Young Investigator Research Program 588020-D01907 (M. Jiang), and by
the Natural Science Foundation of Zhejiang Province LY20H160033 (P. Zhao)
and LQ22H160041 (X. Dai). We thank OE Biotech Co., Ltd (Shanghai, China) for
providing single-cell RNA-seq.
The costs of publication of this article were defrayed in part by the payment of page
charges. This article must therefore be hereby marked advertisement in accordance
with 18 U.S.C. Section 1734 solely to indicate this fact.
Received January 3, 2022; revised March 7, 2022; accepted May 19, 2022;
published first May 20, 2022.
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