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Integrated immunogenomic
analyses of high-grade
serous ovarian cancer
reveal vulnerability to
combination immunotherapy
Raphael Gronauer
1
, Leonie Madersbacher
1
,
Pablo Monfort-Lanzas
1,2
, Gabriel Floriani
1
, Susanne Sprung
3
,
Alain Gustave Zeimet
4
, Christian Marth
4
, Heidelinde Fiegl
4
and Hubert Hackl
1
*
1
Institute of Bioinformatics, Biocenter, Medical University of Innsbruck, Innsbruck, Austria,
2
Institute of
Medical Biochemistry, Biocenter, Medical University of Innsbruck, Innsbruck, Austria,
3
Institute of
Pathology, Innpath GmbH, Innsbruck, Austria,
4
Department of Obstetrics and Gynecology, Medical
University of Innsbruck, Innsbruck, Austria
Background: The efficacy of immunotherapies in high-grade serous ovarian
cancer (HGSOC) is limited, but clinical trials investigating the potential of
combination immunotherapy including poly-ADP-ribose polymerase inhibitors
(PARPis) are ongoing. Homologous recombination repair deficiency or
BRCAness and the composition of the tumor microenvironment appear to play
a critical role in determining the therapeutic response.
Methods: We conducted comprehensive immunogenomic analyses of HGSOC
using data from several patient cohorts. Machine learning methods were used to
develop a classification model for BRCAness from gene expression data.
Integrated analysis of bulk and single-cell RNA sequencing data was used to
delineate the tumor immune microenvironment and was validated by
immunohistochemistry. The impact of PARPi and BRCA1 mutations on the
activation of immune-related pathways was studied using ovarian cancer cell
lines, RNA sequencing, and immunofluorescence analysis.
Results: We identified a 24-gene signature that predicts BRCAness. Comprehensive
immunogenomic analyses across patient cohorts identified samples with BRCAness
andhighimmuneinfiltration. Further characterization of these samples revealed
increased infiltration of immunosuppressive cells, including tumor-associated
macrophages expressing TREM2,C1QA,andLILRB4,asspecified by single-cell
RNA sequencing data and gene expression analysis of samples from patients
receiving combination therapy with PARPi and anti-PD-1. Our findings show also
that genomic instability and PARPi activated the cGAS-STING signaling pathway in
vitro and the downstream innate immune response in a similar manner to HGSOC
patients with BRCAness status. Finally, we have developed a web application (https://
ovrseq.icbi.at) and an associated R package OvRSeq, which allow for comprehensive
characterization of ovarian cancer patient samples and assessment of a vulnerability
score that enables stratification of patients to predict response to the
combination immunotherapy.
Frontiers in Immunology frontiersin.org01
OPEN ACCESS
EDITED BY
Nikolaos Gavalas,
National and Kapodistrian University of
Athens, Greece
REVIEWED BY
Silvia Pesce,
University of Genoa, Italy
Deniz Cizmeci,
Ragon Institute, United States
*CORRESPONDENCE
Hubert Hackl
hubert.hackl@i-med.ac.at
RECEIVED 31 August 2024
ACCEPTED 11 November 2024
PUBLISHED 28 November 2024
CITATION
Gronauer R, Madersbacher L,
Monfort-Lanzas P, Floriani G, Sprung S,
Zeimet AG, Marth C, Fiegl H and Hackl H
(2024) Integrated immunogenomic analyses
of high-grade serous ovarian cancer reveal
vulnerability to combination immunotherapy.
Front. Immunol. 15:1489235.
doi: 10.3389/fimmu.2024.1489235
COPYRIGHT
© 2024 Gronauer, Madersbacher,
Monfort-Lanzas, Floriani, Sprung, Zeimet,
Marth, Fiegl and Hackl. This is an open-access
article distributed under the terms of the
Creative Commons Attribution License (CC BY).
The use, distribution or reproduction in other
forums is permitted, provided the original
author(s) and the copyright owner(s) are
credited and that the original publication in
this journal is cited, in accordance with
accepted academic practice. No use,
distribution or reproduction is permitted
which does not comply with these terms.
TYPE Original Research
PUBLISHED 28 November 2024
DOI 10.3389/fimmu.2024.1489235
Conclusions: Genomic instability in HGSOC affects the tumor immune
environment, and TAMs play a crucial role in modulating the immune
response. Based on various datasets, we have developed a diagnostic
application that uses RNA sequencing data not only to comprehensively
characterize HGSOC but also to predict vulnerability and response to
combination immunotherapy.
KEYWORDS
high-grade serous ovarian cancer, BRCAness, PARP inhibitor, immunotherapy, tumor-
associated macrophages, precision oncology, tumor immune microenvironment
1 Introduction
Despite newer therapeutic concepts, ovarian cancer, particularly
high-grade serous ovarian cancer, is still the deadliest gynecologic
malignancy, with 13,270 expected deaths in 2023 in the U.S (1).
While immunotherapy, such as immune checkpoint inhibition
monotherapy (e.g., antibodies against PD-1 or PD-L1), has
dramatically changed the therapeutic concepts of different cancer
types, especially those with mismatch repair deficiency (2), the
benefit for ovarian cancer patients with an objective response rate of
approximately 10% was found to be rather modest (3–6). However,
poly-ADP-ribose polymerase inhibitors (PARPis) and
antiangiogenic therapy have improved the survival outcomes of
ovarian cancer patients beyond standard care, namely, debulking
surgery and platinum-based therapy (7). Furthermore, a number of
clinical trials of combination therapies, including immune
checkpoint blockade, are underway (8–12). Whereas the recent
primary analysis of the double-blind placebo-controlled ENGOT-
Ov41/GEICO 69-O/ANITA phase III trial showed that the addition
of the anti-PD-L1 antibody (atezolizumab) did not significantly
improve the clinical outcome (12), early analysis of the MEDIOLA
phase II study adding the PD-L1 inhibitor (durvalumab) and the
angiogenesis inhibitor (bevacizumab) to a PARPi (olaparib) was
promising, with an objective response rate >90% for a specific
patient group with platinum-sensitive relapsed ovarian cancer
harboring germline BRCA mutations (11).
PARP is involved in DNA damage and repair, binds to single-
strand DNA breaks, and performs posttranslational modifications of
histones and DNA-associated proteins by poly-ADP-ribosylation,
also known as parylation. PARP inhibitors trap PARP and stall the
replication fork, which can subsequently cause DSBs. PARP
inhibition is synthetic lethal with deleterious BRCA1 and BRCA2
mutations because homologous recombination repair (HRR) cannot
restore these double-strand breaks, introducing genome instability by
nonhomologous end joining or leading to tumor cell death (13). In
high-grade serous ovarian cancer, approximately 14% harbor a
germline and 6% a somatic mutation in the BRCA1 or BRCA2
gene, and approximately 50% are HRR deficient (HRD), indicating
that they are favorable for PARPi therapy (14,15). Sequencing
approaches enable researchers to detect mutations in other genes
involved in HRR. However, the concept of HRD or BRCAness goes
beyond, as it encompasses instabilities and genomic scars, including
large-scale transitions, loss of heterozygosity, telomeric allelic
imbalance and specific mutational processes with uneven base
substitution patterns (mutational signature 3). Several diagnostic
assays from commercial providers for the detection of HRD have
already been approved (16). However, further efforts are undertaken
to identify various biomarkers based on different modalities, such as
gene expression or methylation, in the context of different cancer
types (17–21). Deleterious BRCA1 mutations and/or PARP inhibition
can trigger an immune response at least in part through the cGAS-
STING pathway (22–26), suggesting advantages for combined
immunotherapies. However, biomarkers or phenotypes to predict
the response to therapies, including PARPis and immune checkpoint
blockers, are lacking.
In this study, we conducted comprehensive immunogenomic
analyses of HGSOC using data from multiple patient cohorts.
Integrated gene expression analysis and machine learning on bulk
and single-cell RNA sequencing data enabled the 1) development of
a 24-gene expression classification model for BRCAness, 2)
stratification of patient samples with BRCAness and high
immune infiltration, whereby tumor-associated macrophages
(TAMs) proved to be an important suppressive component, 3)
identification of the activation of immune-related pathways such as
the cGAS-STING or JAK-STAT pathway and downstream
signaling by PARPi and BRCA1 mutation (BRCAness), and 4)
the development of a diagnostic application from RNA sequencing
data to comprehensively characterize HGSOC samples and predict
vulnerability and response to combination immunotherapy.
2 Methods
2.1 Patient cohorts and datasets
The analysis workflow and used datasets from various cohorts
are summarized in Supplementary Figure S1. Patient characteristics
for the TCGA-OV cohort (n=226) (15) and the new HGSOC cohort
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from the Medical University in Innsbruck (MUI) (n=60) are listed
in Supplementary Tables S1 and S2. RNA sequencing data and
clinical data for the validation cohort (Medical University of
Innsbruck; MUI) were deposited at https://doi.org/10.5281/
zenodo.10251467. Controlled access data for whole exome
sequencing and RNA sequencing data for the TCGA-OV cohort
were obtained through dbGaP access permission (phs000178).
Processed data (including methylation beta values) and clinical
data were downloaded from Firebrowse (firebrowse.org, BROAD
Institute). Additional clinical data were retrieved from the
supplementary data of another resource (27). Raw RNA
sequencing data and clinical annotations for the ICON7 cohort
(28) were downloaded from the EGA archive (EGAS00001003487).
Single-cell RNA-seq data (29) were downloaded from the Gene
Expression Omnibus (GEO) (GSE180661) as an annotated count
matrix (anndata-object) in h5ad-format. Data files from the
TOPACIO clinical trial (9) were retrieved from Synapse (https://
doi.org/10.7303/syn21569629). Data from the Clinical Proteomic
Tumor Analysis Consortium ovarian cancer cohort (CPTAC-OV)
(30) were downloaded from (https://proteomics.cancer.gov)
(n=71). Data from RNA sequencing analysis of OVCAR 3 and
UWB1.289 cancer cell lines performed in this study were deposited
in GEO (GSE237361). Only complete data sets were used, and
observations (rows) with missing values were deleted before specific
analyses were performed.
2.2 Cell line experiments
Two epithelial ovarian carcinoma cell lines, UWB1.289
harboring a deleterious BRCA1 and OVCAR3 with intact
BRCA1, were obtained from ATCC. OVCAR3 cells were grown
in RPMI 1640 with 0.01 mg/ml bovine insulin and 20% FBS,
whereas UWB1.289 cells were grown in a mixture of 48.5%
MEGM Bullet Kit medium (Lonza) and 48.5% RPMI 1640 with
3% FBS. Viability assays were used to determine the IC50 for
olaparib. Both cell lines were treated with olaparib or DMSO for
96 hours in four replicates. Treated and untreated UWB1.289 and
OVCAR3 cells were stained with indirect immunofluorescent
antibodies to detect gH2AX as an indicator of double-strand
breaks. To determine activated STING signaling, double-stranded
DNA and its presence in the cytosol, cGAS, STING, and
phosphorylated STING were detected. The antibodies used are
listed in Supplementary Table S3.
2.3 Immunohistochemistry analyses
Slices of 10 selected tumor blocks were subjected to
immunohistochemistry analyses performed on the BenchMark
ULTRA automated staining device (Ventana, Oro Valley, AZ/
Roche, Vienna, Austria). The examined markers were CD163 for
macrophages and CD8, PD-1, CD4, and FOXP3 for T cells.
Furthermore, the markers gH2AX and STING were analyzed. All
antibodies used are listed in Supplementary Table S4.
2.4 RNA sequencing analyses
RNA from cancer cell line samples was isolated from 2x10
6
cells
each using the RNeasy Mini Kit (Qiagen) according to the
manufacturer’s protocols. RNA quantity and quality were
assessed using NanoDrop™2000c and Bioanalyzer 2100 with
Agilent 6000 Nano Kit and cDNA libraries were generated using
the QuantSeq 3’mRNA-Seq Library Prep Kit (Lexogen) according
to the manufacturer’s instructions. Paired-end sequencing (150 bp)
was performed on a NovaSeq 6000 sequencing device at
GENEWIZ/Azenta. RNA isolation from 60 fresh frozen tumor
samples from the MUI HGSOC validation cohort was conducted
in a similar manner resulting in sufficient quality (RIN factors from
6.4 to 9.9), and sequencing was performed at Novogene
(Cambridge, UK) for paired end sequencing (PE150) on an
Illumina NovaSeq 6000 sequencing device using TrueSeq
(Illumina) strand-specific total RNA libraries.
2.5 RNA sequencing data analyses
Raw reads were quality checked using FastQC. Reads were mapped
tothehumanreferencegenomeversionhg38(GRch38)usingSTAR
(version 2.7.1) in 2pass mode (31). Gene level expression quantification
was performed with featureCounts (version 2.0.0) using GENCODE
annotations (v36). Raw counts were normalized using TPM
(transcripts per million). RNA sequencing raw data from the MUI
HGSOC cohort and the ICON7 cohort were analyzed in the same way.
For sequencing data of the cell lines, single-end reads were processed by
trimming adapter and low-quality sequences using BBDuk with the
parameters specified by Lexogen. The trimmed reads were mapped to
the human reference genome version hg38 (GRch38) using STAR
(version 2.7.9a) in 2-pass mode. Gene level expression quantification
was performed with featureCounts (version 2.0.0) and GENCODE
annotations (v38).
2.6 Whole exome sequencing analyses and
variant calling
Raw exome sequencing reads in fastq format were quality
checked using FastQC. Reads of paired tumor and normal
samples were mapped against the human reference genome
version hg38 (GRch38) using BWA. For the detection of germline
variants the HaplotypeCaller was used. To assess somatic variants in
the tumor samples, four different variant callers, Mutect2 (32),
SomaticSniper (33), Varscan2 (34), and Strelka2 (35) were used. If a
variant was called by two of four variant callers and the variant allele
frequency was ≥0.05 in the tumor sample and <0.05 in the normal
sample, the variant passed filtering. Variants were annotated using
VEP (36) with the ClinVar extension. Only pathogenic (class V)
and likely pathogenic (class IV) variants were considered to affect
the function of HRR genes such as BRCA1 or BRCA2. Tumor
mutational burden was calculated based on the number of
nonsynonymous single nucleotide variants per megabase for each
Gronauer et al. 10.3389/fimmu.2024.1489235
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tumor sample. For neoantigen prediction, from somatic mutation
derived peptide sequences with lengths between 8-11 amino acids -
taking phasing into account - were generated and tested for the
respective HLA alleles with NetMHCpan-4.0 (37), whereby %
rank<2 was considered a weak binder and %rank<0.5 was
considered a strong binder. Dissimilarity to the normal human
proteome (hg38) was identified by the antigen.garnish package.
Neoantigen load was calculated for each tumor based on predicted
weak and strong binding neoantigens –irrespective of their peptide
length and taking all HLA alleles into account –per megabase.
2.7 Functional analysis of gene expression
and the tumor immune environment
Differential gene expression analysis was conducted using the R
packageDESeq2(38). P values were adjusted for multiple testing based
on the false discovery rate (FDR) according to the Benjamini
−Hochberg method. Genes with more than a twofold change at an
FDR<0.1 and average expression across all samples (baseMean>10)
were considered significantly differentially expressed. To identify
functional annotation and affected biological processes, log2-fold
change preranked gene set enrichment analyses (GSEA) (39)using
hallmark and selected immune-related gene sets from MSigDB were
performed. ClueGO was used to build a network and group
significantly overrepresented pathways, which share genes (40). The
STRING database (https://string-db.org/) was used to identify an
interaction network within the differentially expressed genes, and
subnetworks were found by MCL clustering with inflation
parameter=3. Footprint analyses of response genes of perturbed
cancer signaling pathways were performed using PROGENy (41).
To assess tumor infiltration of immune cells quanTIseq (42) using
the immunedeconv R package was applied to bulk RNA sequencing
data. To characterize the immune-related processes, well-described
immune signatures, such as T-cell inflammation, IFN gamma
signature, cytolytic activity, cytotoxic T lymphocyte function, and
T-cell exhaustion (Supplementary Table S5), were analyzed. Based
on log2(TPM+1) normalized expression data, single sample gene
set enrichment using GSVA (43) was performed for signatures with
more than 10 genes or otherwise average expression was calculated.
The tumor-immune phenotype (infiltrated, excluded, desert) was
determined based on a previously developed classification model
based on 157 genes using digital pathology describing the presence
and position of CD8+ T cells relative to the center or margin of the
tumor (28) and a random forest model was used to characterize
samples from the TCGA and the MUI cohorts. To classify ovarian
cancer samples into molecular subtypes, the consensusOV R
package (44) was used. The immunophenoscore (IPS) was
determined as described previously (45).
2.8 Determination and classification
of BRCAness
BRCAness was determined based on HRD scores (46),
mutational signature 3 (47), mutations in homologous
recombination repair pathway genes and methylation of promoter
regions of BRCA1. All HGSOC samples of the TCGA cohort for
which paired tumor and normal exome sequencing and matched
RNA sequencing data were available (n=226) were used. Samples
were classified with a BRCAness phenotype when they had either a
deleterious mutation in the homologous recombination pathway,
an ovarian cancer-specific HRD score of ≥63 (48), a mutational
signature 3 ratio > 0.25 or a methylation level beta value >0.7 of the
BRCA1 promoter. HRD scores were calculated as the unweighted
sum of the three genomic scar values, loss of heterozygosity (LOH)
(49), telomeric allelic imbalance (TAI) (50), and large-scale state
transitions (LST) (51). To compute the genomic scar values,
scarHRD (52) was used on genome segmentation files generated
with Sequenza (53). The mutational signature 3 score was
computed using MutationalPatterns (54) and we calculated the
ratio between mutational signature 3 supporting mutations and all
detected mutations. To classify BRCAness, genes expressed in at
least one ovarian cancer cell were identified using single-cell
RNAseq data. Normalized gene expression values (log2 (TPM+1))
of these genes in the TCGA dataset were then subjected to recursive
feature elimination in a balanced design with three different
machine learning models (random forest, AdaBoost and gradient
boosting) to identify the 50 most important features for each model.
Since ensemble methods and random subsampling (bootstrap) were
included, we did not use nested cross-validation to avoid overfitting.
Genes that were among the top 50 in at least two of the three models
(24 genes) were then used subsequently to train a random forest
classification model to discriminate between BRCAness and
noBRCAness samples based on gene expression data. The
performance of the classifier was evaluated by analysis of the
receiver operating characteristic curve with 10-fold cross-
validation. The area under curve (AUC) was used as a
performance measure. A cutoff for BRCAness (P>0.5266) was
selected using the Youden index. Furthermore, the classifier was
tested in 29 patients of the independent validation cohort (MUI)
with HRD information based on SNP arrays and further validation
using Myriad MyChoice CDx. Samplewise BRCA classification in
single-cell RNA sequencing data from 29 patients was performed
with an optimized cutoff (P>0.45) and based on the majority of
classified tumor cells. A method to detect mutational signature 3
(Sig3), termed SigMA, from clinical panel sequencing data have
been previously developed and associated with HRD (55). To
further compare our BRCAness classifier, which is based on a
BRCAness definition more completed as compared to the other
methods described in literature such as the SigMA score, using
available RNA sequencing and mutation data from the Clinical
Proteomic Tumor Analysis Consortium ovarian cancer (CPTAC-
OV) cohort (30).
2.9 Single-cell RNA sequencing analysis
All analyses of single-cell data were performed in Python using
scanpy (56) and scvi-tools (57). Since the samples were sequenced
separately for sorted CD45+ and CD45- cells, the raw read counts
were integrated using scvi-tools with a batch effect correction.
Counts were normalized to counts per million (CPM) and log2
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transformed, adding a pseudocount of 1. Quality metrics were
determined using scanpy and filtered for genes that are expressed
in at least one cell. The dataset was filtered for samples from the
primary tumor (adnexal tumor tissue). Principal component
analysis and nearest neighbor analyses were calculated with
default settings, and clustering was performed with the Leiden
algorithm. Super cell types were annotated as previously defined.
Subtypes of T-cell and myeloid cell clusters were assigned based on
the expression of marker genes using published marker genes for
different cell types and the PanglaoDB (58). Differentially expressed
genes between clusters were calculated using the Wilcoxon ranked
sum test. For visualization, we used uniform manifold
approximation and projection (UMAP) dimensional reduction.
Gene expression between cell types was compared by heatmaps,
violin plots, and bubble plots. Distribution of cell fractions for each
sample or combined for BRCAness and noBRCAness group were
compared by stacked barplots and two-sided Wilcoxon rank sum
test. Pseudobulk analyses and DESeq2 analyses was performed for
selected immune response markers to test effect of BRCAness versus
noBRCA samples on the immune response. To assess ligand
−receptor interactions between cancer cells and cells from the
TME, CellPhoneDB (59) analysis was used.
2.10 Gene expression analysis
Gene expression analysis in the TOPACIO cohort was
performed on the NanoString platform. We used the nSolver
software from NanoString (Seattle, US) to obtain normalized
data. Differential expression analysis was performed using the R
package limma (60), and genes with p<0.05 were considered
differentially expressed.
2.11 Vulnerability score and maps
Vulnerability maps consist of three variables: the vulnerability
score, the BRCAness score and the cytolytic activity (CYT) to C1QA
ratio. For the BRCAness score, the prediction probability from the
random forest classifier was used. The CYT to C1QA ratio was
calculated from the log2 (TPM+1) values of GZMB,PRF1, and
C1QA (Equation 1).
CYT to C1QA ratio =0:5(GZMB +PRF1)=C1QA (1)
The CYT to C1QA ratio was transformed to values between 0
and 1 using a sigmoid function with softmax transformation and
parameters derived from the TCGA cohort and termed C2C
(Equation 2).
C2C=1=1+e−CYT to C1QA ratio−0:301
0:0433
(2)
The vulnerability score was defined as the weighted sum of
BRCAness probability and C2C (Equation 3), whereby the weights
were identified using a logistic regression model on the CYT to
C1QA ratio using log2 intensity expression values and SigMA status
(mutational signature 3) as proxy for BRCAness from the
TOPACIO cohort and the treatment response as a binary
dependent variable.
Vulnerability score
=2:597 *BRCAness probability +1:166 *C2C(3)
For visualization of the vulnerability map, a two-dimensional
map was created with C2C as one coordinate, BRCA probability as
the other coordinate, and the color-coded vulnerability score.
2.12 Statistical analysis
Survival analyses were performed for both HGSOC cohorts
(TCGA, MUI) for selected genes, immune parameters, or immune
cell fractions by dichotomization of patients based on the median or
maximum log-rank statistics using the R package survival. For the
TCGA cohort, overall survival (OS) and progression free survival
(PFS) survival status were derived from a clinical data resource for
TCGA (27) and for the cohort from Medical University Innsbruck
from the clinical data as provided by the Department of Obstetrics
and Gynecology. Univariate and multivariable Cox regression
analyses taking clinical parameters into account (age, FIGO stage,
residual tumor) were performed, Kaplan-Meier survival curves were
generated, and compared by log rank test. To determine the
association between continuous or binary variables, point biserial
correlation analysis was used. For the correlation between binary
variables, the Phi coefficient and chi-square test were used, and for
the correlation between continuous variables, Pearson’s correlation
coefficient was used. To compare parameters between two groups,
the Wilcoxon rank-sum test was used. For multiple group
comparisons the non-parametric Kruskal-Wallis test followed by
pairwise two-tailed Dunn posthoc tests with p-value adjustment
based on the false discovery rate (FDR) were used. Where indicated,
p values were adjusted for multiple testing based on the FDR
according to the Benjamini−Hochberg method. P<0.05 or
FDR<0.1 were considered significant.
3 Results
We performed immunogenomic characterization and
multimodal integrative analyses of data from several patient
cohorts, including the TCGA cohort (n=226), the MSK cohort
(n=29), the ICON7 cohort (n=327), the CPTAC cohort (n=71),
patients from the TOPACIO study receiving combination
immunotherapy (n=22), and a new MUI cohort for validation
(n=60). The used data modalities and complete analysis workflow
is outlined in Supplementary Figure S1.
3.1 A 24-gene signature predicts BRCAness
in HGSOC patients
Because the response to platinum-based chemotherapies or
therapy with PARP inhibitors in ovarian cancer is not limited to
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patients with tumors harboring BRCA1 or BRCA2 mutations, we
expanded the group of patients by using a genomic characterization
termed BRCAness, which has very much in common with
homologous recombination repair deficiency (HRD) status (61).
BRCAness status includes mutations of genes in the homologous
recombination DNA repair pathway (HRR), genomic scars, loss of
heterozygosity, telomeric allelic imbalance, or large-scale
transitions, mutational signature 3, or promoter methylation of
the BRCA1 or BRCA2 gene. We assessed these parameters based on
whole exome sequencing data and methylation data from the
TCGA OV cohort (Figure 1A). Very few patients harboring HRR
mutations or BRCA1 promoter methylation fell below the
combination of the HRD cutoff (HRD>63) and the MutSig3 ratio
cutoff (0.25), indicating a reasonable selection of the cutoff values
(Figure 1B). To identify BRCAness solely based on gene expression
data, we developed a machine learning classifier that can
discriminate between BRCAness and non-BRCAness samples
using bulk and single-cell RNA sequencing data (Figure 1A).
Recursive feature elimination based on multiple models resulted
in a BRCAness gene expression signature with 24 genes, which was
used to train a random forest model discriminating between
BRCAness and noBRCAness. The receiver operating
characteristics with 10-fold cross-validation on the training
dataset showed an area under the curve (AUC) of 0.91 ± 0.04
(Figure 1C). To validate our BRCAness classifier we tested its
performance in two independent ovarian cancer cohorts. We
could classify BRCAness in a new HGSOC cohort from the
Medical University Innsbruck (MUI) (n=60) based on RNA
FIGURE 1
BRCAness classification based on the expression of 24 genes. (A) Determination of BRCAness in the TCGA-OV cohort and the development of a
gene expression-based BRCAness classifier. (B) Different BRCAness parameters in the TCGA cohort compared between the HRD score and the
mutation signature 3 ratio. Samples with mutated homologous recombination repair pathway genes are marked in red, BRCA1/2 promoter
methylation in blue and samples with an HRD score > 63 and/or a signature 3 ratio > 0.25 but no mutation or BRCA1/2 promoter methylation are
marked in yellow. Samples without BRCAness are marked in white. (C) Mean ROC curve with 10-fold cross-validation of the classifier tested on the
TCGA dataset. (D) Confusion matrices with correctly and incorrectly classified instances when the classifier was tested in independent test cohorts
of single-cell RNA sequencing and bulk RNA sequencing data. (E) Z scores of log2(TPM+1) normalized expression of the 24 genes of the BRCAness
signature in the TCGA cohort as a heatmap clustered by BRCAness and non-BRCAness samples.
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sequencing data with an accuracy of 0.79, an F1-score of 0.86, and a
positive prediction value of 0.86 (Figure 1D). Furthermore, we
demonstrated that in addition to classifying bulk RNAseq samples
the classifier is also capable of classifying samples from single cell
RNA-seq data at the sample level in the HGSOC MSK cohort
(n=29) with an accuracy of 0.86, an F1 score of 0.87 and a positive
prediction value of 0.87 (Figure 1D).
There was also good agreement with a recently defined gene
expression-based HRDness signature including 173 up- and 76
downregulated genes (62) using a single sample gene set enrichment
derived score in the TCGA cohort as well as the MUI validation
cohort with Spearman’s rank correlation of r=0.72 (P<0.001) and
r=0.63 (P<0.001), respectively (Supplementary Figures S2,S3).
Interestingly, six genes from our 24-gene signature (Figure 1E)to
classify BRCAness (CCDC90B,CRABP2,FZD4,GPAA1,PRCP,
SNRP1) were also among the upregulated and two genes (RAD17,
LTA4H) among the downregulated genes. The 24-gene BRCAness
signature was further compared to the SigMA score (mutational
signature 3) of the CPTAC-OV cohort (n=71). Although our
BRCAness signature is based on a more complete BRCAness
definition than the mutational signature, a significant correlation
was observed with a Spearman’s rank correlation of r=0.43
(P<0.001) (Supplementary Figure S4). We used this BRCAness
classification for all remaining analyses.
In summary, we developed a 24-gene-based BRCAness model
validated in several single-cell and bulk RNAseq datasets with
reasonable classification performance.
3.2 Genome instability is associated with
immune-related processes
To identify the relationship between genomic instability and
the activation of the immune system, we performed correlation
analyses between the BRCAness status and various immune-related
signatures in the TCGA cohort (n=226). BRCAness could
be significantly positively associated with the enrichment
of immune-related signatures, such as those for IFNG response
(rho=0.38, p=0.004) and T-cell inflamed tumor microenvironment
(rho=0.46, p=0.0014), even to a larger extent with high tumor
mutational burden (p<0.001) and high neoantigen load (p<0.001)
(Figures 2A,B). However, compared to other cancer types with
defective DNA mismatch repair the TMB or neoantigen load in
ovarian cancer is rather low. Thus, increased immune activity is
more indicative of deficient HRR. It is known that BRCAness is
associated with longer overall survival, indicating that those patients
are more responsive to platinum-based chemotherapy. In order to
determine to which extend this could be explained by higher
immune cell infiltration we estimated CD8+ T-cell infiltration
from RNA sequencing data using quanTIseq and divided patients
into 4 groups: BRCAness patients with high CD8 T cell fraction,
BRCAness patients with low CD8 T cell fraction, noBRCAness
patients with high CD8 T cell fraction, and noBRCAness patients
with low CD8 T cell fraction. We observed a significant association
with overall survival (p<0.0001; log rank test) (Figure 2C) and
progression-free survival (p=0.0016; log rank test) (Figure 2D), with
the BRCAness group with a high proportion of positive CD8 T cells
being associated with the longest survival. Multivariable Cox
regression analysis showed a significant effect of BRCAness vs. no
BRCAness on overall survival (p=0.00062, HR=0.51with 95%-CI
0.35-0.75), while the impact of CD8 T cell was not significant
(Supplementary Table S6) indicating a more pronounced role of the
BRCAness status. Nevertheless, analyses of signaling pathways by
downstream target expression using PROGENy indicated for the
TCGA cohort (n=226) as well as the MUI validation cohort (n=60)
that immune-related pathways, including TNFa, NFkB, and JAK-
STAT, were activated in the BRCAness samples (Figures 2E,F).
Using STRING analyses in the MUI cohort (n=60), we also
identified a highly connected network including various
chemokines and interleukins and their respective receptors
(CCL7, CCL11, CXCL5, CXCL9, CXCL13, CCR2, CCR3, CCR4,
CCR8, CXCR3, and IL6), which were significantly higher expressed
in BRCAness tumors than in non-BRCAness tumors, indicating
attraction and interaction with various immune cells
(Supplementary Figure S5).
Essentially, we observed a correlation of BRCAness with various
immune-related processes and, in particular, a group of patients
with BRCAness and high CD8 T-cell proportion associated with
longer survival times in HGSOC patients.
3.3 PARP inhibition activates the cGAS-
STING pathway in vitro
To study the effect of PARPis on immune activation, we
performed in vitro analyses. As tumor models, an ovarian cancer
cell line with a proficient BRCA1 gene (OVCAR3) and a cell line
with a mutation in the BRCA1 gene (UWB1.289) were used. We
performed RNA sequencing analyses to identify differentially
expressed genes between olaparib (PARPi)-treated and control
(DMSO)-treated cell lines. Significantly upregulated genes
(Figures 3A,B) indicate activation of various processes
(Figures 3C,D), including pattern recognition receptor activation,
response to cytokine signaling, interferon alpha response (type I),
NFkB pathway, and cGAS-STING signaling. To further validate the
results at the protein level, we performed immunofluorescence
analyses indicating effects on gH2AX –a surrogate marker of
DNA damage - by mutation in the BRCA1 gene and an even
stronger effect by olaparib (PARPi) treatment (Figure 3E). Similarly,
we observed a different activation of cGAS and STING –and
indicating activation of the (innate) immune system –in the
BRCA1-deficient versus the BRCA1-proficient cell model
(Figure 3F). Furthermore, using gene set enrichment analyses, a
significant interferon alpha response was also observed in
BRCAness samples of both the TCGA cohort and the MUI
validation cohort (Figure 3C).
In summary, we observed cGAS-STING activation by olaparib
treatment in vitro and an interferon type I response as well as
chemokine expression in HGSOC patient cohorts with
BRCAness status.
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3.4 BRCAness and immune subtype
stratifies HGSOC patients
We next focused on characterizing the presence of cytotoxic T
lymphocytes and their spatial distribution in the tumor, following a
recent approach in which digital pathology could be linked to gene
expression (28). With the reported list of 157 genes and using
random forest analysis, we were able to divide the patients of the
TCGA cohort into a group with infiltrated, excluded, or desert
tumor-immune phenotypes. Interestingly, the excluded phenotype
was associated with upregulation of TGFband high expression of
markers for cancer-associated fibroblasts, such as FAP or PDPN,
FIGURE 2
Association between BRCAness and immune parameters. (A) Results of correlation analysis of selected immune signatures and BRCAness
parameters in the TCGA-HGSOC cohort (CYT, cytolytic activity; CTL, cytotoxic T lymphocytes; IFNG, interferon gamma signature; HRR mutations,
mutations in the homologous recombination repair pathway; NeoAG load, neoantigen load; TMB, tumor mutational burden); white dots indicate
significance (FDR<0.1). (B) Direct comparison of selected immune parameters between BRCAness and noBRCAness samples with significant
differences, Wilcoxon rank-sum test (FDR<0.1) in the TCGA cohort (n=226). (C) Kaplan−Meier curves according to overall survival (OS) and for 4
patient groups of the TCGA dataset (n=226) based on BRCAness information and median dichotomized estimated CD8 T cell fraction (quanTIseq):
patients with 1) BRCAness and high estimated CD8 T cell fraction, 2) BRCAness and low estimated CD8 T cell fraction, 3) noBRCAness and high
estimated CD8 T cell fraction, and 4) noBRCAness and high estimated CD8 T cell fraction (p-value is from logrank test). (D) Kaplan−Meier curves
according to progression free survival (PFS) for the same groups of patients from the TCGA cohort (n=226) as in (C).(E, F) Waterfall plot of
normalized enrichment scores (NES) for the footprint analysis of immune-related pathways with PROGENy between BRCAness and non-BRCAness
samples in the MUI (n=60) and TCGA (n=226) cohort.
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which could form a barrier to prevent T-cell infiltration (Figure 4A).
The immunoreactive molecular subtype (IMR) is very relevant to
delineate immunoreactivity because many of the immunity genes,
including cytotoxic effectors, factors involved in antigen processing
and presentation, or immune checkpoints, are highly expressed in
this condition (Figure 4A,Supplementary Table S7). We have
selected a group of patients with tumor BRCAness, an infiltrated
tumor immune phenotype and an immunoreactive molecular
subtype called BRCAness immune type (BRIT), which we expect
to respond well to combination immunotherapy. When comparing
the estimated immune cell infiltrates in these cancer samples with
BRCAness cancers without immune type (noBRIT), we found that
FIGURE 3
Results from cell line experiments with olaparib treatment (A) Top up- and downregulated genes for the cell lines OVCAR3 and UWB1.289 under
olaparib treatment when compared to DMSO control. (B) General distribution of up- and downregulated genes after olaparib treatment compared
to the DMSO control in both cell lines as volcano plots. Red indicates significantly upregulated genes (FDR<0.1, log2-fold change>1), and blue
indicates significantly downregulated genes (FDR<0.1, log2-fold change<-1). (C) Normalized enrichment score of pathways associated with
activation of the cGAS STING pathway in BRCA1 mutated (cell lines) and BRCAness samples (cohorts) as well as olaparib-treated cell lines.
(D) ClueGO network indicating overrepresented biological processes in the olaparib-treated UWB1.289 cell line. (E) Immunofluorescence staining of
the DNA damage marker gH2AX in OVCAR3 and UWB1.289 cell lines with and without olaparib treatment. Comparing the different response to
PARPi treatment between BRCA1 mutation and wild type BRCA1 (F) Immunofluorescence staining of cGAS, double stranded DNA (dsDNA) and
STING in the OVCAR3 and UWB1.289 cell line comparing the difference between BRCA1 mutation and wild type BRCA1.
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not only cytotoxic T lymphocytes such as CD8+ T cells were
significantly more abundant (p=0.001) but also a number of
suppressive immune cells (M2 macrophages (p<0.001), regulatory
T cells (p=0.005), myeloid-derived suppressor cells; MDSCs
(p<0.001) (Figure 4B,Supplementary Figure S6). This is in line
with previous observations (63) and in order to identify an effect by
BRCAness we additionally defined an immune type (IMT) with
noBRCAness, infiltrated tumor immune phenotype (INF), and an
immunoreactive molecular subtype (IMR). However no significant
difference between BRIT and IMT as well as noBRIT and noIMT
could be observed for the analyzed cell types (Figure 4B,
Supplementary Figure S6) indicating a more pronounced effect by
FIGURE 4
Profiles of immune parameters in the TCGA HGSOC cohort (n=226) (A) Heatmap of z scores of log2(TPM+1) expression of immune-related genes
and fraction of tumor infiltrating immune cells assessed with quanTIseq and patient samples categorized by BRCAness, tumor-immune phenotype,
molecular subtype and BRCA1/2 mutation. Furthermore, samples are stratified into four different tumor subtypes 1) BRCAness immune type samples
(BRIT), which show an immunoreactive molecular subtype and an infiltrated tumor-immune phenotype, 2) noBRIT samples, which only have
BRCAness but do not fulfill the other two requirements, 3) samples with an immune type (IMT) including an immunoreactive molecular subtype and
an infiltrated tumor-immune phenotype but noBRCAness, and 4) remaining noIMT samples. (B) Distribution of estimated CD8 T cell fractions and
estimated M2 macrophage fraction (from quanTIseq analyses) in the four different tumor subtypes. Benjamini-Hochberg adjusted p-values from
pair-wise two-sided Dunn’s posthoc test are indicated.
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immune infiltration than by BRCAness. Furthermore, we did not
observe a significant difference in overall survival between BRIT
versus noBRIT patients (p=0.56, HR =0.81, 95% CI 0.42-1.60).
These observations underscore the importance of the
suppressive immune environment and suggest that suppressive
immune cells may be an important factor, which is why ovarian
cancer patients have a limited response to immunotherapy.
3.5 Tumor-associated macrophages inform
therapy response
Single-cell RNA sequencing analyses allow a more
comprehensive characterization of the tumor environment and
evaluation of the cell interplay. Analyses of more than 300
thousand cells of adnexal ovarian tumor tissue from 29 patients
of the MSK cohort (29) allowed a clear separation between major
cell type populations by clustering and nonlinear projection
(UMAP) (Figure 5A,Supplementary Figure S7). In contrast to
cell types from the tumor microenvironment, tumor cells showed a
clear separation between BRCAness and noBRCness samples
(Figure 5A). To further investigate the effect of BRCAness on the
tumor microenvironment we compared the distribution of major
cell types between BRCAness and noBRCAness samples. We did
not observe any significant differences for the major cell types
between these patient groups (Supplementary Figure S8) but in a
summarized analysis (Figure 5A) the proportion of myeloid cell was
slightly higher and the proportion T/NK cells was slightly lower in
BRCAness compared to noBRCAness. Furthermore, we
investigated the differential expression of immune response
marker genes (Supplementary Table S7) in the BRCAness versus
the noBRCAness group by pseudobulk analysis with DESeq2.
Interestingly, 21 genes have been found significantly upregulated
in BRCAness compared to noBRCAness including immune
checkpoints such as CD274 and a number of antigen processing
and presentation genes (Supplementary Figure S9). Because cells
from the suppressive environment have a major impact, we focused
on the myeloid cell compartment and demonstrated that the
majority of these cells were macrophages, and we identified
subpopulations based on most dominant marker genes, including
CD169 (SIGLEC1) macrophages, CX3CR1 macrophages, and
MARCO macrophages (Figure 5B). One described hallmark
marker of TAMs is TREM2, which has been identified as an
attractive target for cell depletion therapy and is being tested in
an ongoing clinical trial (64). Notably, the expression patterns of
TREM2 and BRCAness are very similar, showing high expression in
all macrophage subtypes and, to a lesser extent, in monocytes
(Figure 5B). To search for further genes with similar expression
patterns in myeloid subpopulations, we analyzed known tumor-
associated macrophage and monocyte marker genes (65). As
indicated by this analysis, C1QA showed a similar but even more
pronounced expression pattern than TREM2 (Figures 5B,C). C1QA
was also recently described as a surrogate marker for the CD68
+CD163+ macrophage subset (66). We found that several genes are
highly expressed in macrophages (Supplementary Figure S10) and
that, based on marker genes, a polarization towards M2
macrophages occurs (Supplementary Figure S11). The immune
suppressive effect of tumor associated macrophages were
underscored by a number of myeloid immune checkpoint genes,
which show a worse effect on overall survival (hazard ratio>1) in the
TCGA cohort (Supplementary Figure S12).
Since our main goal was to predict vulnerability to combination
immunotherapy we took advantage of the availability of gene
expression data from a clinical trial (TOPACIO) and could identify
7 upregulated genes and 22 down regulated genes between responder
and non-responder to PARPi-immune checkpoint inhibition
combination therapy (niraparib and pembrolizumab) (Figure 5D).
We next analyzed in which cell types these genes are generally
expressed using the results from our single cell RNA-sequencing
data analyses from the MSK cohort and found a number of
downregulated genes in responders, such as LYZ,LILRB4, and
ITGB2, were most highly expressed in myeloid cells (macrophages),
LILRB4 in dendritic cells, and integrin subunit beta 2 (ITGB2)in
other cell types, such as T/NK cells (Figures 5E,F). ITGB2 was also
found correlated in the TCGA cohort with estimated M2
macrophages and CD8 T-cell infiltration (Supplementary
Figure S13).
Interestingly, we identified various ligand−receptor interactions
with expressed ligands in tumor cells and respective receptors
expressed in tumor-associated macrophage subsets using
CellPhoneDB (59)(Supplementary Figure S14). The growth
arrest-specific protein 6 (GAS6) –AXL tyrosine kinase (AXL)
interaction, for example, which are both associated with poor
outcome, have already been evaluated in clinical trials in ovarian
cancer by inhibiting their interaction (67). LILRB1 and LILRB2
expressed in macrophage subsets were found to interact with the
nonclassical human leukocyte antigen HLA-F expressed in cancer
cells. Blocking macrophage colony-stimulating factor CSF1 and its
receptor CSF1R axis and several drugs that target these factors have
been under investigation (68).
These observations summarized together suggest that TAMs
may not only play a role in immunotherapy alone but are also
essential in informing about therapy response when combined with
PARP inhibitors.
3.6 Analyses of an independent cohort
indicate vulnerability to
combination immunotherapy
To validate the results, we performed RNA sequencing analyses
of an HGSOC cohort of patients from Medical University
Innsbruck (n=60). Stratification of these patients resulted in very
similar expression patterns evident from a number of immune
marker genes, which were highly expressed in the BRCAness
immune type patient group (BRIT) (Figure 6A). To further
characterize immune infiltrates in different patient groups, we
performed immunohistochemistry analyses on ten selected
samples for various markers and highlight the results from three
patient samples. One BRIT tumor sample showed high gH2AX
activity, STING activation, CD8+ T-cell infiltration, CD4+ T-cell
infiltration, and strong CD163+ tumor-associated macrophage
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FIGURE 5
Single cell analysis of ovarian cancer adnexal samples from the MSK dataset (n=29) (A) UMAP showing the different cell types in of the ovarian
cancer samples and which cells and cell types are associated with BRCAness samples. Distribution of major cell types in the BRCAness and
noBRCAness are summarized as stacked bar plots. (B) UMAP plots of the myeloid cell compartment showing the association of macrophages with
BRCAness cells and the expression of the macrophage marker gene C1QA and the TAM marker gene TREM2 especially in cell clusters associated
with BRCAness. (C) Heatmap of expression of macrophage associated marker genes in the different cell types in the myeloid cell compartment.
(D) Log
2
fold changes of differentially expressed genes between responder [R] and non-responder [NR] to PARPi-immune checkpoint inhibition
combination therapy (niraparib and pembrolizumab) from the TOPACIO clinical trial (n=22) (p<0.05) (E) Distribution of expression visualized by
UMAP and violin plots indicating in which (myeloid) cell types LYZ,LILRB,orITGB2 are expressed (F) Dotplot indicating the distribution of expression
and fraction of cells in various cell type for genes up-regulated (red) or down-regulated (blue) in responders vs non-responders to combination
therapy as indicated in (D).
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populations (Figure 6B, left panel). These effects were even more
pronounced in one sample with no detected BRCA1 or BRCA2
mutation, underscoring the importance and validity of predicted
BRCAness (Figure 6B, middle panel). Another tumor sample with
no BRCAness, a desert tumor-immune phenotype, and a
differentiated molecular subtype was used as a negative control,
and in fact, no activity for any of the tested markers was observed
(Figure 6B, right panel). To better address the potential for
combination immunotherapy response, we again took advantage
of data from the TOPACIO trial and, based on the clinical response,
trained a logistic regression model and learned weights for three
surrogate variables: MutSig3 as an indicator for BRCAness, average
expression of PRF1 and GZMB as indicators for cytolytic activity,
and expression of C1QA as an indicator for tumor-associated
suppressive macrophages. Based on the HGSOC samples from
TCGA, we developed a two-dimensional vulnerability map, with
the ratio of cytolytic activity and C1QA expression as one variable
(C2C) and the BRCAness prediction probability as the other
variable. The vulnerability score is indicated by color (Figure 6C).
When applied to the selected examples from the MUI validation
cohort, these differed significantly for areas with high vulnerability
scores (indicating response to combination immunotherapy)
compared to the negative control with low vulnerability scores
(Figure 6C). Furthermore, we observed a significant difference in
FIGURE 6
Expression profiles in the MUI cohort, immunohistochemistry validation, and vulnerability map (A) Heatmap of z-scores log2(TPM+1) expression of
immune related genes and fraction of tumor infiltrating immune cells assessed with quanTIseq in all samples (n=60) from the MUI cohort categorized by
BRCAness, tumor-immune phenotype, molecular subtype and BRCA1/2 mutation. (B) Immunohistochemistry images stained for CD8, CD4, CD163,
gH2AX, and STING for three selected patients from the MUI cohort. Two BRIT samples one with a BRCA1 mutation and one without and one other
sample without BRCAness, a deserted tumor-immune phenotype and a differentiated molecular subtype. (C) Vulnerability map showing the ratio
between cytolytic activity CYT and C1QA (C2C) on the x-axis and the BRCAness score on the y-axis colored by the vulnerability score. The three
selected samples were mapped to the vulnerability map based on their CYT to C1QA ratio (C2C) and BRCAness score.
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overall survival between patients with high and low vulnerability
scores (p<0.001, HR = 0.47, 95% CI 0.33-0.66) in the TCGA
HGSOC cohort (Supplementary Figure S15), indicating a positive
association of a high vulnerability score with longer overall survival.
For patients in the MUI validation cohort, no significant difference
in overall survival (p=0.368, HR = 0.78, 95% CI 0.45-1.34) could be
revealed (Supplementary Figure S16). To enable the
characterization of newly diagnosed HGSOC samples based on
RNA sequencing data, we developed an easy-to-use R package
(OvRSeq), which allows us to not only estimate the parameters to
determine the vulnerability score (and generate the vulnerability
maps) but also comprehensively annotate the sample for
BRCAness, tumor-immune phenotype, molecular subtype,
estimate immune infiltrates, enrichment of immune-related
signatures, and individual marker genes. This also includes other
clinically relevant parameters, such as the angiogenesis score we
previously defined, which might be useful for the prediction of anti-
VEGF therapy (69). The web application (https://ovrseq.icbi.at)
allows the generation of summary information as a report of
individual samples (Supplementary Figure S17).
The developed application should ultimately be useful to
identify vulnerabilities and support clinical therapy decisions for
HGSOC patients.
4 Discussion
Here, we described how genomic instability in HGSOC affects
the tumor immune environment and the consequences and
vulnerabilities of combination immunotherapy combining PARP
inhibitors with immune checkpoint inhibitors. A particular status in
which patients respond well to PARP inhibitors and platinum-
based chemotherapy is given when genes of the HRR pathway such
as BRCA1 or BRCA2 are mutated. Genomic scars are consequences
of HRD and are used to define an HRD score, often measured by
established commercial assays, which allows the assignment of a
responsive status beyond BRCA1 and BRCA2 mutations. The
applicability and associated cutoff values for different assays and
cancer types are under discussion, as the HRD algorithm has been
used in clinical studies including different cancer types, such as
breast cancer and ovarian cancer (46,70,71). Genomic scars are
predictive but do not allow direct functional interpretation, whereas
gene expression signatures could be an alternative in this regard.
Very few approaches have associated gene expression with HRD
status (18,19,62). Whereas a sixty-gene signature (18) and a two-
gene signature (CXCL1,LY9)(19) have focused on microarray data,
a recent approach using RNA sequencing data identified a 249-gene
signature to predict HRD (62). We observed a number of overlaps
with our 24-gene BRCA signature and a high concordance of
signature scores in our training (TCGA) and validation (MUI)
cohorts, indicating the reliability of our approach. This was
underscored by comparison with mutational signature 3 (SigMA)
in an independent cohort. The performance of the BRCAness
classifier is reasonable, with AUC=0.91 (10-fold cross-validation)
and positive predictive value for validation on both bulk RNA
sequencing in the validation cohort (MUI) (PPV=0.86) and sample-
wise single-cell RNA sequencing data (PPV=0.87).
There is evidence that BRCA1/2-mutated tumors exhibit
significantly increased CD8+ TILs (22), although in breast cancer,
differential modulation between BRCA1 and BRCA2 mutations in
the tumor immune microenvironment has been found (72). We
found a significant association between BRCAness and several
immune-regulated signatures and evidence that several signaling
pathways and processes known to modulate the immune system are
activated by BRCA1 mutations or a BRCAness-related phenotype,
such as JAK-STAT signaling or an interferon type I response, which
are activated by free double-stranded DNA in the cytoplasm of
tumor cells via the cGAS-STING pathway and affect dendritic cells
(23–25). By expression and immunofluorescence analyses of
ovarian cancer cell lines and by treatment with PARPi, we
demonstrated that this axis is actually activated. Notably, the
STAT3 pathway, which is activated by PARP inhibition, may,
however, mediate treatment resistance by promoting the
polarization of protumor TAMs, which could be overcome by
STING agonism (26). STING, CSF1R, SREBP-1, and VEGFA
might also be targets to overcome resistance to PARPi-
immunotherapy combinations (73). The upregulation of many
chemokines and chemokine receptors indicates that BRCAness
tumors are actively involved in immune cell attraction and
interaction. For example, CCL5 produced by tumor cells or
CXCL9 and CXCL10 also expressed by tumor-resident myeloid
cells determine effector T-cell recruitment to the tumor
microenvironment (74). We detected significant upregulation of
CCL5 and CXCL10 by PARP inhibition, which was also identified as
a downstream target of STING (24). Another interesting chemokine
that is strongly upregulated in cancer cell lines, particularly by
olaparib treatment, is CCL20. CCL20 could be associated with
cancer metastasis and progression by interacting with its cognate
receptor CCR6 in an ovarian cancer mouse model. However, the
higher expression in the myeloid cell compartment, as evident from
single-cell analyses, overlies the intrinsic tumor effect.
One of our basic hypotheses was that samples with BRCAness
respond better to PARPi therapy and that hot tumors with an
activated immune milieu respond better to immune checkpoint
inhibition, as has been shown, for example, in melanoma for the
activated IFNG pathway (75). However, when we compared the
BRCAness immune type (BRIT) with other samples, we observed
by using deconvolution approaches that suppressive cell types such
as M2 macrophages, MDSCs, and Tregs were more abundant. In
particular, TAMs could be a major factor together with low
mutational burden, abnormal neovascularization, altered
metabolism, and failure to reverse T-cell exhaustion for the
limited immunotherapy response in ovarian cancer (76). By using
single-cell RNA sequencing data analyses of adnexal cancer tissue,
we demonstrated that myeloid cells are the most abundant immune
cells, and the majority were characterized as TAMs and rather
polarized towards M2-like macrophages compared to classical (M1-
like) macrophages, although these better described as continuum of
different stages than isolated cell types. A majority of these TAMs
are immune suppressive, as indicated by TREM2 expression.
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TREM2 is a promising therapeutic target for TAM depletion (68).
Inhibition of TREM2 has been shown to improve the anti-PD1
response in various mouse models and is currently being
investigated in a clinical trial (64,77). Another recent study
underscored the role of TAMs and demonstrated that specifically,
the Siglec-9-positive TAM subset is associated with an immune-
suppressive phenotype and adverse prognosis in HGSOC
patients (78).
Interestingly, a previous work using cyclic immunofluorescence
highlighted the role of exhausted T cells in the response to niraparib/
pembrolizumab. In responders, particularly in extreme responders,
frequent proximity between exhausted T cells and PD-L1+ (CD163+,
IBA1+, CD11b+) TAMs was observed (9). Noticeably, based on the
selected marker expression, we observed an overlap with the CD169/
SIGLEC-1 macrophage cluster (Supplementary Figure S10). In
addition, in patients who responded to this combination therapy,
we identified a number of downregulated genes that were also highly
expressed in TAMs, such as LYZ,LILRB4,andITGB2. Whereas
lysozyme (LYZ) is an antimicrobial ligand and is involved in central
macrophage function and is therefore nonspecifically and highly
expressed, LILRB4 is an immune checkpoint on myeloid cells,
indicating a more regulatory role. High expression of the integrin
ITGB2 was previously shown to be associated with poor survival
outcome (79), underscoring that high expression in TAMs is crucial.
In contrast, ITGB2 is also associated with CD8+ T cells, as it encodes
the beta chain of the LFA-1 protein, which has been shown to be
essential in the assembly of the immune synapse or to influence
lymphocyte extravasation and T-cell recruitment to the tumor and is
regulated by GDF-15 (80).
Because stratification of patients based on gene expression in our
validation cohort was very similar to the analysis on the TCGA cohort,
we set out to adapt our hypothesis and also include elements of the
suppressive environment. Already, it was shown that regulatory T cells
(Tregs) are an important component of the suppressive milieu and are
associated with unfavorable survival outcomes in ovarian cancer (81,
82). We performed immunohistochemistry analyses using FOXP3 and
CD163 antibodies in the validation cohort and found very pronounced
macrophage infiltration (CD163) but hardly Treg infiltration (FOXP3)
into the tumor site in some samples. The results of the single-cell RNA
sequencing analyses and the fact that various TAM marker genes were
associated with poorer overall survival also suggest that TAMs play a
more dominant role in ovarian cancer.
While infiltration of various cell types from the adaptive
immune system (83) and other markers, such as tumor
mutational burden (TMB) (84) or IFNG signature (75), have been
associated with good prognosis and immunotherapy response in
various cancer types, the suppressive immune environment with
tumor-supportive CD68+CD163+ macrophages is becoming more
important (66). Accordingly, a signature of the immune activation
ratio of CD8A/C1QA has been found to be prognostic and
predictive for immunotherapy response (66). We considered the
mean PRF1 and GZMB expression as a proxy for cytolytic activity
(45) as predominantly exerted by cytotoxic T lymphocytes. The
specific expression pattern of C1QA on TAMs was comparable to
that of TREM2 but at a much higher level. Therefore, we also used
the member of the complement system C1QA as a surrogate for
TAMs and the suppressive tumor immune environment and finally
built a ratio of cytolytic activity (CYT) to the expression of C1QA
(C2C), indicating the pro- and antitumoral balance of the immune
environment. Finally, to build a predictive algorithm for
combination therapy response, we included both C2C on the one
hand and BRCAness on the other hand into one model. Since HRD
measured with companion diagnostic tests is not able to predict all
PARPi responders, as shown in several clinical trials, and since
PARPi treatment can activate a number of immune-related
pathways even in situations with proficient HRR, which is also
underlined by our in vitro analyses, this model is considered to be
relevant for combination immunotherapy.
Our studies have some limitations in that the training and
validation patient cohorts were retrospective studies, and RNA
sequencing was performed at a later time point. Additionally, only a
limited number of patients who received combination therapy could be
included; therefore, the conclusion about the predictive power for the
treatment is limited and requires further validation in larger cohorts.
One component that was not considered in this study is malignant
ascites, which has been shown to contain various cell types, such as
macrophages, many soluble factors and cytokines, that influence the
protumorigenic phenotype and promote metastatic spread of HGSOC
through transcoelomic dissemination (85).
In conclusion, our approach using RNA sequencing data to
comprehensively characterize both genome instability and the
tumor immune environment enabled us to stratify HGSOC
patients. Further analyses indicate that suppressive TAMs in the
tumor immune microenvironment may play an essential role in
understanding why receiving (combination) immunotherapy shows
limited efficacy in ovarian cancer. Based on multiple datasets, we have
developed a methodology and corresponding easy-to-use diagnostic
application (https://ovrseq.icbi.at) and an R package OvRSeq that
uses RNA sequencing data not only to comprehensively
characterize newly diagnosed HGSOC patients but also to inform
therapy response. Ultimately, this approach will be very useful to
obtain comprehensive information about the phenotype of a tumor
sample, support clinical decisions, and stimulate further research.
Data availability statement
The original contributions presented in the study are included
in the article/supplementary material. RNA sequencing data can be
found at Gene Expression Omnibus (GEO) (https://
www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE237361). The
raw RNA sequencing data from the MUI cohort cannot be made
publicly available due to data protection restrictions and are
available on request from the corresponding author.
Ethics statement
The studies involving humans were approved by Ethics
Committee of the Medical University of Innsbruck (reference
number: 1189/2019). The studies were conducted in accordance
with the local legislation and institutional requirements. The
Gronauer et al. 10.3389/fimmu.2024.1489235
Frontiers in Immunology frontiersin.org15
participants provided their written informed consent to participate
in this study.
Author contributions
RG: Data curation, Formal Analysis, Methodology,
Visualization, Writing –original draft, Writing –review &
editing. LM: Investigation, Writing –original draft, Writing –
review & editing. PM: Software, Writing –original draft, Writing
–review & editing. GF: Formal Analysis, Writing –original draft,
Writing –review & editing. SS: Investigation, Validation, Writing –
original draft, Writing –review & editing. AZ: Writing –original
draft, Writing –review & editing. CM: Resources, Supervision,
Writing –original draft, Writing –review & editing. HF:
Investigation, Resources, Validation, Writing –original draft,
Writing –review & editing. HH: Conceptualization, Funding
acquisition, Project administration, Supervision, Writing –
original draft, Writing –review & editing.
Funding
The author(s) declare that financial support was received for the
research, authorship, and/or publication of this article. This research
was funded in whole, or in part, by the Anniversary Fund of the
National Bank of Austria (OeNB) (grant number 18279 to HH).
Acknowledgments
A version of the manuscript was deposited at the medRxiv
preprint server (86). Open Access Funding provided by the Medical
University of Innsbruck.
Conflict of interest
SS was employed by Innpath GmbH. AZ reports consulting fees
from Amgen, Astra Zeneca, GSK, MSD, Novartis, PharmaMar,
Roche, Seagen; honoraria from Amgen, Astra Zeneca, GSK, MSD,
Novartis, PharmaMar, Roche, Seagen; travel expenses from Astra
Zeneca, Gilead, Roche; participation on advisory boards from
Amgen, Astra Zeneca, GSK, MSD, Novartis, Pfizer, PharmaMar,
Roche, Seagen. CM reports consulting fees and honoraria from
Roche, Novartis, Amgen, MSD, PharmaMar, Astra Zeneca, GSK,
Seagen; travel expenses from Roche, Astra Zeneca; participation on
advisory boards from Roche, Novartis, Amgen, MSD, Astra Zeneca,
Pfizer, PharmaMar, GSK, Seagen. HH has received research funding
via Catalym and Secarna.
The remaining authors declare that they have no known
competing financial interests or personal relationships that could
have appeared to influence the work reported in this paper.
Publisher’s note
All claims expressed in this article are solely those of the authors
and do not necessarily represent those of their affiliated
organizations, or those of the publisher, the editors and the
reviewers. Any product that may be evaluated in this article, or
claim that may be made by its manufacturer, is not guaranteed or
endorsed by the publisher.
Supplementary material
The Supplementary Material for this article can be found online
at: https://www.frontiersin.org/articles/10.3389/fimmu.2024.
1489235/full#supplementary-material
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