Single-cell RNA sequencing reveals distinct tumor microenvironmental patterns in lung adenocarcinoma

Abstract

Recent developments in immuno-oncology demonstrate that not only cancer cells, but also the tumor microenvironment can guide precision medicine. A comprehensive and in-depth characterization of the tumor microenvironment is challenging since its cell populations are diverse and can be important even if scarce. To identify clinically relevant microenvironmental and cancer features, we applied single-cell RNA sequencing to ten human lung adenocarcinomas and ten normal control tissues. Our analyses revealed heterogeneous carcinoma cell transcriptomes reflecting histological grade and oncogenic pathway activities, and two distinct microenvironmental patterns. The immune-activated CP²E microenvironment was composed of cancer-associated myofibroblasts, proinflammatory monocyte-derived macrophages, plasmacytoid dendritic cells and exhausted CD8+ T cells, and was prognostically unfavorable. In contrast, the inert N³MC microenvironment was characterized by normal-like myofibroblasts, non-inflammatory monocyte-derived macrophages, NK cells, myeloid dendritic cells and conventional T cells, and was associated with a favorable prognosis. Microenvironmental marker genes and signatures identified in single-cell profiles had progonostic value in bulk tumor profiles. In summary, single-cell RNA profiling of lung adenocarcinoma provides additional prognostic information based on the microenvironment, and may help to predict therapy response and to reveal possible target cell populations for future therapeutic approaches.

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ARTICLE OPEN
Single-cell RNA sequencing reveals distinct tumor
microenvironmental patterns in lung adenocarcinoma
Philip Bischoff
1,2
✉, Alexandra Trinks
1,2
, Benedikt Obermayer
3
, Jan Patrick Pett
3
, Jennifer Wiederspahn
1,4
, Florian Uhlitz
4,5
,
Xizi Liang
1
, Annika Lehmann
1
, Philipp Jurmeister
1,2,5
, Aron Elsner
6
, Tomasz Dziodzio
2,6
, Jens-Carsten Rückert
6
, Jens Neudecker
6
,
Christine Falk
7,8
, Dieter Beule
3
, Christine Sers
1,2,5
, Markus Morkel
1,2,5
, David Horst
1,2,5
, Nils Blüthgen
1,2,4,5,9
and
Frederick Klauschen
1,5,9
© The Author(s) 2021
Recent developments in immuno-oncology demonstrate that not only cancer cells, but also the tumor microenvironment can guide
precision medicine. A comprehensive and in-depth characterization of the tumor microenvironment is challenging since its cell
populations are diverse and can be important even if scarce. To identify clinically relevant microenvironmental and cancer features, we
applied single-cell RNA sequencing to ten human lung adenocarcinomas and ten normal control tissues. Our analyses revealed
heterogeneous carcinoma cell transcriptomes reflecting histological grade and oncogenic pathway activities, and two distinct
microenvironmental patterns. The immune-activated CP²E microenvironment was composed of cancer-associated myofibroblasts,
proinflammatory monocyte-derived macrophages, plasmacytoid dendritic cells and exhausted CD8+T cells, and was prognostically
unfavorable. In contrast, the inert N³MC microenvironment was characterized by normal-like myofibroblasts, non-inflammatory
monocyte-derived macrophages, NK cells, myeloid dendritic cells and conventional T cells, and was associated with a favorable prognosis.
Microenvironmental marker genes and signatures identified in single-cell profiles had progonostic value in bulk tumor profiles. In
summary, single-cell RNA profiling of lung adenocarcinoma provides additional prognostic information based on the microenvironment,
and may help to predict therapy response and to reveal possible target cell populations for future therapeutic approaches.
Oncogene (2021) 40:6748–6758; https://doi.org/10.1038/s41388-021-02054-3
INTRODUCTION
Lung cancer has a poor prognosis and accounts for the majority of
new cases and deaths of cancer worldwide [1]. The most common
subtype of lung cancer, in particular in nonsmokers, is lung
adenocarcinoma [2]. Presently, molecular profiling of driver
mutations is guiding treatment with targeted therapies [3,4]
and expression of PD-L1 in tumor cells is used to predict response
to immune checkpoint inhibitors [5]. Comprehensive characteriza-
tion including features of the tumor microenvironment could
provide more precise patient stratifications.
Cancers are multicellular communities comprising malignant
epithelial cells and different types of nonmalignant immune and
stromal cells which exhibit dynamic and reciprocal interactions.
Modulation of immune responses, remodeling of the extracellular
matrix and neoangionesis essentially determine the aggressive-
ness of cancer [6]. Current bulk omics analyses do not allow high-
resolution characterization of cellular diversity of the tumor
microenvironment. However, comprehensive single-cell profiling
of patient tissue is emerging as an essential tool to estimate the
clinical relevance of individual cell types in the tumor.
In this study, we analyzed tumor epithelial cells and associated
nonmalignant cells of the tumor microenvironment of lung
adenocarcinomas by single-cell RNA sequencing. Extending
previous single-cell studies [7–10], we found that the hetero-
geneous cellular composition of the tumor microenvironment
across patients follows specific patterns that were associated with
the differentiation grade of carcinoma cells. Translation of our
findings to an retrospective cohort characterized by bulk gene
expression revealed potential prognostic relevance of microenvir-
onmental patterns. We conclude that a comprehensive profiling of
the lung adenocarcinoma microenvironment may help to reveal
novel clinically relevant tumor subtypes based on carcinoma cells
and microenvironmental features.
RESULTS
Single-cell RNA sequencing uncovers the cellular diversity of
lung adenocarcinomas
To study the cellular composition of lung adenocarcinoma, ten
normal lung and ten lung adenocarcinoma fresh tissue samples
Received: 26 April 2021 Revised: 16 September 2021 Accepted: 30 September 2021
Published online: 18 October 2021
1
Institute of Pathology, Charité—Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.
2
Berlin Institute of
Health, Charité—Universitätsmedizin Berlin, Berlin, Germany.
3
Core Unit Bioinformatics (CUBI), Berlin Institute of Health at Charité—Universitätsmedizin Berlin, Berlin, Germany.
4
IRI Life Sciences, Humboldt University of Berlin, Berlin, Germany.
5
German Cancer Consortium (DKTK) Partner Site Berlin, German Cancer Research Center (DKFZ), Heidelberg,
Germany.
6
Department of Surgery, Campus Charité Mitte and Campus Virchow-Klinikum, Charité—Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and
Humboldt-Universität zu Berlin, Berlin, Germany.
7
Institute of Transplant Immunology, Hannover Medical School, Hannover, Germany.
8
TTU-IICH Hannover-Braunschweig Site, German
Center for Infectious Diseases (DZIF), Braunschweig, Germany.
9
These authors contributed equally: Nils Blüthgen, Frederick Klauschen. ✉email: philip.bischoff@charite.de
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were collected during surgery and subjected to unsorted single-
cell RNA sequencing, yielding 114,489 high-quality transcriptomes
after quality control and filtering (Fig. 1A, Supplementary Fig. 1A,
B). Evaluation of consecutive H&E stained tissue sections showed
tumor morphology ranging from well differentiated lepidic to
poorly differentiated sarcomatoid growth patterns (Supplemen-
tary Fig. 2).
Analysis and visualization by Uniform Manifold Approximation
and Projection (UMAP) [11] showed that single-cell transcriptomes
of different tissue types or patients intermingled in many clusters,
excluding general batch effects, and partly formed tumor- or
patient-specific clusters, indicating underlying biological differ-
ences (Fig. 1B, C, Supplementary Fig. 1C). To uncover which
cellular compartments account most for interpatient variability, we
analyzed single-cell transcriptomes for the expression of epithelial,
immune, and stromal marker genes. In total, 20,450 epithelial,
89,766 immune, and 4273 stromal single-cell transcriptomes were
covered, suggesting an overrepresentation of immune cell
transcriptomes as observed in other studies [9,12]. Yield of
epithelial transcriptomes varied by histological subtype, as we
observed a frequency <10% in solid/sarcomatoid, but up to >40%
in lepidic/acinar carcinomas (Fig. 1D). Epithelial cells showed the
highest degrees of interpatient heterogeneity (Fig. 1C, D).
Intertumoral heterogeneity of tumor epithelial cells reflects
differentiation gradients
To further dissect interpatient variability within the epithelial cell
compartment, epithelial transcriptomes were subset and reclus-
tered (Supplementary Fig. 3A). Clusters were defined as normal or
tumor cell clusters, based on tissue origin (Supplementary Fig. 3B),
which was largely congruent with the copy-number status of cells
(Supplementary Fig. 4A, B), and demonstrated a tumor purity
>90%. Within the normal cell clusters, we found alveolar type 1
and 2, club, ciliated, and even a small cluster of neuroendocrine
cells (Fig. 2A), which were characterized by expression of typical
individual marker genes (Fig. 2B) and gene signatures (Supple-
mentary Fig. 5A, B) [13,14]. The club cell cluster also expressed
basal cell marker genes such as NGFR and KRT5 indicating an
admixture of small amounts of basal cells (Fig. 2B). Tumor cell
clusters segregated from normal cell clusters and were mainly
patient-specific, indicating intertumoral heterogeneity (Fig. 2A).
This was underlined by a variety of genes differentially expressed
across tumors such as EGFR, TFF3, CDKN2A, and SFTPA2 (Fig. 2C,
black arrowheads), correlating with protein expression as shown
by immunostaining (Fig. 2D). We quantified oncogenic signal
strengths by pathway target gene signature expression and found
highly variable activities for EGFR, TGFβ, JAK/STAT, Hypoxia, and
PI3K signaling across the different patients (Fig. 2E). These signal
strengths were largely unrelated to the mitotic activity of tumor
epithelial cells (Supplementary Fig. 3C). p53 signaling was
significantly reduced in tumors harboring TP53 mutations,
whereas pathway activity scores for EGFR and MAPK signaling
were not significantly higher in KRAS-mutated compared to
KRAS-wildtype tumors (Supplementary Fig. 3D).
Despite obvious intertumoral heterogeneity, we also noted
shared features of epithelial tumor cell transcriptomes across
patient subgroups. In order to emphasize similarities between
tumors, epithelial transcriptomes were embedded in low-
dimensional UMAPs (4, 6, 8 instead of 20 dimensions). Here,
tumor cells clustered by histological subtype rather than by
Fig. 1 Single-cell RNA sequencing of lung adenocarcinomas. A Schematic representation of the workflow, ten normal (blue) and ten tumor
(red) tissue samples were obtained from 12 patients. B,CUMAPs based on the top 15 principal components of all single-cell transcriptomes
after filtering, color-coded by (B) tissue type, or (C) patient. DOverview of clinical features, clinically relevant oncogenic mutations and gene
fusions; quantification of main cell types per patient and UMAP of all single-cell transcriptomes color-coded by main cell type.
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Fig. 2 Intertumoral heterogeneity of tumor epithelial cells in lung adenocarcinomas. A UMAPs based on the top 20 principal components
of all epithelial single-cell transcriptomes color-coded by tissue type, cell type and patient, and quantification of epithelial cell types per tissue
type, AT1, alveolar type 1 cells, AT2, alveolar type 2 cells. BAverage gene expression of selected marker genes for normal epithelial cell types.
CDifferentially expressed genes in tumor epithelial cells grouped by patients, maximum top ten genes showed per patient, for patient color
code see (A). DImmunohistochemical staining of proteins encoded by selected differentially expressed genes indicated by black arrowheads
in (C). EMean pathway activity scores of tumor epithelial cells grouped by patient. FDistribution of histological subtypes, (G) mean module
scores of normal epithelial cell type gene signatures, and (H) mean pathway activity scores of tumor epithelial cells sorted along principal
component 1 (PC1). F,G,HPrincipal component analysis based on gene expression of all tumor epithelial single-cell transcriptomes;
schematic depiction of tumor cell signature module scores along PC1.
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patient (Supplementary Fig. 3E), and the first principal component
(PC1) displayed a gradient along histological grades (Fig. 2F,
Supplementary Fig. 3E, F). Interestingly, SCGB3A1 and SCGB3A2
(Fig. 2C, white arrowheads), two genes that were previously
associated with lung development [15], were positively correlated
with PC1 (Supplementary Fig. 3G, arrowheads). Moreover, gene
signature scores of normal lung cell types [13] along PC1 showed
a strong positive correlation with gene expression profiles of
alveolar type 1 and 2 as well as club cells (Fig. 2G). Together, this
indicates that PC1 reflects the degree of differentiation of tumor
epithelial cells. Hence, the top 30 genes positively and negatively
correlated with PC1 were defined as an “alveolar/club-like”and
“undifferentiated”tumor cell signature, respectively (Fig. 2F–H,
Supplementary Fig. 3G, H). While tumor cells with different
degrees of differentiation exhibited no clear differences in mitotic
activity (Supplementary Fig. 3I), we found high pathway activity
scores for JAK/STAT, Hypoxia, EGFR and TGFβsignaling in
“undifferentiated”, and high scores for PI3K signaling in “alveo-
lar/club cell-like”tumor epithelial cells, respectively (Fig. 2H). We
conclude that tumor epithelial cells of different lung adenocarci-
noma patients exhibit transcriptional patterns along a spectrum
ranging from undifferentiated to alveolar/club cell-like pheno-
types correlating with distinct oncogenic pathway activity.
Two subtypes of myofibroblasts constitute the tumor stromal
microenvironment
We subset and analyzed stromal cells from both normal
and tumor tissue samples. Different clusters of endothelial and
lymphatic endothelial cells, fibroblasts, myofibroblasts and
smooth muscle cells and mesothelial cells (Fig. 3A, Supplementary
Fig. 6A) were identified by marker genes (Fig. 3B) and gene
signatures (Supplementary Fig. 5A, B) [13,14]. Tumor endothelial
cells were mainly represented by clusters 2 and 4 (Fig. 3A), and
showed high expression of angiogenesis markers such as VWA1
and HSPG2, as well as INSR, encoding an endothelial marker
protein and possible therapeutic target [9] (Supplementary Fig. 6B,
arrowheads).
Among the fibroblastic/muscle cell clusters, we detected a shift
from fibroblast to myofibroblast cell clusters in tumor tissues
(Fig. 3A), which we also observed in an independent dataset
(Supplementary Fig. 7A). Myofibroblast clusters were characterized
by expression of both fibroblastic marker genes, such as PDGFRA
and LUM, and smooth muscle marker genes, such as MYLK and
ACTA2 (Fig. 3B). Notably, myofibroblast cluster 2 was almost
exclusively found in tumor tissues while myofibroblast cluster 1
encompassed normal and tumor tissues. Myofibroblast cluster 2
displayed high expression of collagens such as COL3A1,COL5A1,
Fig. 3 Composition of the stromal microenvironment of lung adenocarcinomas. A UMAPs based on the top 20 principal components of
all stromal single-cell transcriptomes split by tissue type, color-coded by cell cluster; and relative quantification of endothelial and
fibroblastic/muscle cell clusters per tissue type and, for tumor samples, per patient. BAverage gene expression of selected marker genes
for stromal cell clusters, for cell cluster color code see (A). CDifferentially expressed genes of fibroblastic/muscle cell clusters, maximum top
ten genes showed per cell cluster, for cell cluster color code see (A), black arrowheads indicate relevant marker genes of myofibroblast
cluster 2 mentioned in the main text. DMean pathway activity scores of different fibroblastic/muscle cell clusters, mesothelial cells
excluded, black arrowheads indicate relevant pathways of myofibroblast clusters 1 and 2 mentioned in the main text. ECorrelation of the
relative quantity of myofibroblast clusters 1 and 2, color-coded by patient; Spearman’s correlation statistics, linear regression line.
FImmunohistochemical staining of CTHRC1 as marker for myofibroblast cluster 2 (see also (C)), quantification of proportion of stromal
areal covered by CTHRC1+cells, mean ± s.d., n=10 per patient, for patient color code see (E); Pearson’s correlation statistics and linear
regression line using mean values per patient.
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COL5A2 and COL6A3, other matrix proteins such as VCAN,aswellas
matrix-degrading enzymes such as SULF1 and MMP11, suggesting
roles in extracellular matrix remodeling (Fig. 3C, arrowheads).
Myofibroblast cluster 2 was also characterized by high activity of
TGFβand JAK/STAT signaling as well as hypoxia-induced pathways
(Fig. 3D), which are known features of cancer-associated myofibro-
blasts [16,17]. In contrast, myofibroblast cluster 1 exhibited low
activities of these pathways. Relative proportions of myofibroblast
clusters 1 and 2 within the fibroblastic/muscle cell compartment
correlated inversely across patients (Fig. 3E). The distribution of
myofibroblast cluster 2 cells could be validated by immunostaining
for the cluster-specificmarkerCTHRC1(Fig.3F, see also Fig. 3C). We
conclude that myofibroblasts cluster 1 and 2 represent “normal-like”
and “cancer-associated”phenotypes of myofibroblasts, respectively,
and either of them can predominate the stromal microenvironment.
The tumor immune microenvironment exhibits pro- and non-
inflammatory traits
We next subset and analyzed immune cells of the tumor
microenvironment. We identified different clusters of tissue-
resident and monocyte-derived macrophages, monocytes, mye-
loid and plasmacytoid dendritic cells, mast cells, and T, NK, B,
and plasma cells (Fig. 4A, Supplementary Fig. 8A–C) based on
typical marker genes (Fig. 4B) and gene signatures (Supplemen-
tary Fig. 5A, B) [13,14].
Although immune cell clusters usually encompassed both normal
and tumor tissue-derived transcriptomes, we noted quantitative
shifts in the cellular composition of the tumor immune microenvir-
onment (Fig. 4A). Among myeloid cell types in the tumor,
monocyte-derived macrophages, defined by markers such as
CD14, CSF1R and LGMN (Fig. 4B) [18,19] and dendritic cells were
increased, while tissue-resident macrophages and monocytes were
decreased, which we also observed in an independent dataset
(Supplementary Fig. 7B). Tumor myeloid cell compartments were
particularly rich in monocyte-derived macrophage clusters 1 and 2.
Of these, cluster 1 showed high expression of SELENOP (Supple-
mentary Fig. 8B, arrowhead), which has been related to M2
polarization [20], and low scores of immune response-related
signatures (Fig. 4C, white arrowheads), while cluster 2 expressed
high levels of proinflammatory chemokines, such as CXCL9 and
CXCL10, the proinflammatory cytokine IL1B (Supplementary Fig. 8B,
arrowheads), and gene signatures related to immune response and
M1 polarization [21,22](Fig.4C, black arrowheads). This indicates
that monocyte-derived macrophage clusters 1 and 2 represent non-
and proinflammatory functional states, respectively, which were
inversely correlated across patients (Fig. 4D). In addition, the
proportion of proinflammatory monocyte-derived macrophages
cluster 2 correlated with other myeloid cell types such as
plasmacytoid dendritic cells (Fig. 4D). The distribution of proin-
flammatory monocyte-derived macrophages cluster 2 and plasma-
cytoid dendritic cells was validated by immunostaining for CXCL9
and CD123, respectively (Fig. 4E, Supplementary Fig. 8D).
Within the tumor lymphoid cell compartment, CD8+T, B, and
plasma cells were increased, while NK and conventional T cells
were decreased compared to normal tissue controls (Fig. 4A).
Regulatory T cells were almost exclusively found in tumor tissue
samples and expressed inhibitory molecules such as CTLA4 and
TIGIT (Supplementary Fig. 8C, arrowheads) corresponding to their
immunosuppressive role [23]. We identified in total four clusters of
CD8+T cells (Fig. 4A). The tumor-enriched CD8+T cell clusters 1
and 2 and proliferating CD8+T cells were characterized by an
exhaustion signature [10] (Fig. 4F, black arrowheads), while this
score was low in the normal-enriched CD8+T cell cluster 3
(Fig. 4F, white arrowhead). Exhausted CD8+T cells were
significantly associated with proinflammatory monocyte-derived
macrophages (cluster 2) and plasmacytoid dendritic cell numbers
(Fig. 4G), and a similar trend, albeit not significant, was seen for
regulatory T cells.
Taken together, we identify patient-overarching changes of the
immune cell composition from normal lung tissue to adenocarci-
noma, and distinct tumor immune microenvironment patterns
contributing to interpatient heterogeneity.
The tumor microenvironment of lung adenocarcinoma
features two major patterns
To integrate our analyses of variable cell prevalences in the tumor
microenvironment, we calculated proportions of cells of the
myeloid, lymphoid, endothelial and fibroblastic/muscle cell
compartments across patients (for cell counts see Supplementary
Tables 1–4). Principal component analysis showed that tumors
formed subgroups based on the cellular composition of the tumor
microenvironment (Fig. 5A). One group of tumors (P018, P019,
P024, P031, P032, and P033) was marked by normal-like
myofibroblasts, non-inflammatory monocyte-derived macro-
phages, NK cells, myeloid dendritic cells and conventional
T cells, referred to as N³MC pattern (Fig. 5B, Supplementary Figs.
7C, 9A). A second group of tumors (P023, P027, P030 and P034)
was characterized by cancer-associated myofibroblasts, proin-
flammatory monocyte-derived macrophages, plasmacytoid den-
dritic cells and exhausted CD8+T cells, referred to as CP²E pattern
(Fig. 5B, Supplementary Figs. 7C, 9A). Comparable patterns were
also found in an independent single-cell dataset of primary lung
adenocarcinoma (Supplementary Fig. 7D–F). The two microenvir-
onmental patterns roughly separated tumors by histological grade
(Fig. 5A, Supplementary Fig. 7C), by expression scores of “alveolar/
club-like”versus “undifferentiated”tumor cell signatures (Fig. 5B),
and by prevalence of microenvironmental cell clusters (Fig. 5B, C,
Supplementary Fig. 9B).
We quantified potential receptor-ligand interactions within the
N³MC and CP²E tumor microenvironments, which were most
frequent between tumor cells and cancer-associated myofibro-
blast cluster 2 of the CP²E pattern (Supplementary Table 5).
Focusing on the most relevant oncogenic pathways (Supplemen-
tary Table 6), we found that tumor cells in the CP²E environment
receive potential paracrine signals from cancer-associated myofi-
broblast cluster 2 activating Ephrin, FGF, WNT, TGFβ, and BMP
signaling, and from proinflammatory monocyte-derived macro-
phages cluster 2 potentially activating JAK/STAT signaling (Fig. 5D,
Supplementary Fig. 10).
To test if tumor microenvironmental patterns can be recapitu-
lated by bulk gene expression patterns in larger patient cohorts,
we analyzed the expression of cell cluster marker genes in The
Cancer Genome Atlas (TCGA) lung adenocarcinoma cohort
encompassing 533 patients (for marker genes see Supplementary
Tables 7–10). We found a specific positive correlation of the
alveolar/club-like tumor cell signature with non-inflammatory
monocyte-derived macrophages and normal-like myofibroblasts,
recapitulating the N³MC pattern, and of the undifferentiated
tumor cell signature with proinflammatory monocyte-derived
macrophages and cancer-associated myofibroblasts, recapitulat-
ing the CP²E pattern (Fig. 5E, Supplementary Fig. 9C).
To investigate the biological and clinical relevance of these
patient subgroups, we analyzed the overall survival of the TCGA
lung adenocarcinoma cohort contigent on expression of the
different gene signatures. N³MC-related gene signatures were
associated with a better overall survival compared to signatures of
the CP²E pattern (Fig. 5F). In multivariate analyses, this association
was significant for the tumor cell signatures (HR 0.50, 95% CI 0.35-
0.71, p=0.0001). Although individual cell cluster signatures had
different prognostic relevance, a combination of all cell cluster
signatures as well as a simplified signature of 20 genes separated
patients into prognostic subgroups (Fig. 5F, Supplementary Fig.
11A–C, Supplementary Table 11). The group with worse prognosis
and higher CP²E scores was characterized by more frequent
mutations in KEAP,NTRK3,ROS1, and TP53 as well as higher
mutational burden (Fig. 5G).
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In summary, our results show that lung adenocarcinomas can
be stratified into clinically relevant subgroups which are not only
characterized by the grade of tumor epithelial cells, but also by
the cellular composition of their associated tumor microenviron-
ment (Fig. 5H). We therefore propose two major microenviron-
mental patterns in lung adenocarcinoma which we term N³MC
and CP²E as akronyms of the respective characteristic cell types.
DISCUSSION
By applying single-cell RNA sequencing to lung adenocarcinomas,
we identified two major microenvironmental patterns, referred to
as N³MC and CP²E (Fig. 5H). We characterized the N³MC tumor
microenvironment as rather inert and normal-like, as it was
defined by normal-like myofibroblasts, conventional T cells, NK
cells, non-inflammatory monocyte-derived macrophages and
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myeloid dendritic cells. The N³MC pattern was associated with an
alveolar/club-like gene expression pattern of carcinoma cells,
lower histological grade and better prognosis. In contrast, the CP²E
tumor microenvironment was proinflammatory and characterized
by proinflammatory monocyte-derived macrophages, plasmacy-
toid dendritic cells, exhausted CD8+T cells and cancer-associated
myofibroblasts. The CP²E pattern was associated with an
undifferentiated gene expression pattern of carcinoma cells,
higher histological grade, and worse prognosis.
In the clinic, tumor stage and histological grading are used to
predict prognosis in lung adenocarcinoma. Grading is based on
the predominant growth pattern but other histological features
have been proposed in order to improve risk stratification [24,25].
However, these concepts focus on the carcinoma cells and do not
include features of the complex composition of the tumor
microenvironment. Most available studies on prognostic micro-
environmental features focus on single-cell types identified by
immunostaining. Cancer-associated fibroblasts were found to
correlate with worse prognosis using different biomarkers
[26–28], while tumor-infiltrating T cells indicated better prognosis
in multiple studies [29–31]. The prognostic value of proinflamma-
tory tumor-associated macrophages (M1) is inconclusive across
the available studies, likely because differentiation of macrophage
subtypes by immunostaining is limited [32]. In order to account for
the complexity of the tumor microenvironment, patterns based on
tumor-infiltrating T cells and PD-L1 expression have been
proposed [33,34]. Beyond this, the cellular complexity has
recently been studied by multiplexed immunostaining [35,36].
Gene expression profiling of immune-related genes provide
further functional insights, but have yet been limited to the use
of bulk methods [37–39]. Single-cell RNA sequencing combines
both a comprehensive census of microenvironmental cell types
and an in-depth functional characterization of their transcriptional
profiles. In our study, the microenvironmental CP²E pattern was
associated with worse prognosis, which is in line with existing
evidence for cancer-associated myofibroblasts [40] and plasma-
cytoid dendritic cells [41], but partly contradicts previous works
with regard to proinflammatory tumor-associated macrophages
[32]. The absence of cancer-associated myofibroblasts, and the
presence of NK cells [42] and conventional T cells [31] support the
association of the N³MC pattern with better prognosis.
Characterization of PD-L1 expression on tumor-infiltrating
immune cells is already used to predict response to immune
checkpoint inhibitors [43], but more comprehensive character-
ization of the microenvironment will likely provide even deeper
insights for patient stratification and drug development.
Recently, expression of the proinflammatory cytokines CXCL9
and CXCL10 by tumor-associated macrophages has been shown
to be essential for tumor response to anti-PD-L1 therapy [44,45].
High expression of these cytokines in proinflammatory
monocyte-derived macrophages, high numbers of exhausted
CD8+T cells [46] and high mutational burden [47] indicates a
predictive relevance of the CP²E microenvironment. In addition,
the CP²E pattern was characterized by cancer-associated
myofibroblasts expressing potential novel therapeutic targets,
such as PSTN and MMP11 [48,49]. Despite an overall depletion of
NK cells in tumors in agreement with other studies [7,9], the
N³MC microenvironmental pattern still contained small popula-
tions of NK cells which could potentially be targeted by
immunostimulatory agents [50]. Furthermore, we confirmed
enrichment of B cells in some tumors [8,9]withpotential
importance in development of novel immunotherapies [51].
We envisage that single-cell approaches will also contribute to a
better understanding of tumor cells with regard to their high
mutational burden [52] and genomic intertumoral heterogeneity
[53]. Squamous and adenocarcinomas of the lung can exhibit
distinct transcriptional differences [54,55], and interestingly,
despite intertumoral heterogeneity, we found that tumor epithe-
lial transcriptomes retained histology-related patterns that we
termed “alveolar/club-like”and “undifferentiated”across patients
which are reminiscent of recently defined signatures [9]. The
“undifferentiated”tumor cell signature was characterized by high
activity of TGFβsignaling and hypoxia-induced pathways, which
are both known activators of epithelial-mesenchymal transition
[56,57], corresponding to poor histological differentiation of these
tumors. The “alveolar/club-like”signature was characterized by
PI3K signaling, an important oncogenic pathway promoting tumor
growth and potentially affecting the tumor microenvironment by
upregulating PD-L1 expression in carcinoma cells [58–60]. EGFR
and JAK/STAT signaling, associated with the “undifferentiated”
signature, and PI3K signaling, associated with the “alveolar/club-
like”signature, are potential therapeutic targets [4,60,61]. We
identified cancer-associated myofibroblasts as potential sources of
TGFβligands in the CP²E pattern, while EGFR interactions were
equally present in both patterns, underlining that signaling
pathways in tumor cells can be activated extrinsically via paracrine
signals from the microenvironment but also via other mechan-
isms, e.g., intrinsically via oncogenic mutations.
Single-cell approaches provide high-resolution information on
the cellular complexity of tumor tissues but are limited in cohort
size. We validated some findings from single-cell RNA sequencing
by immunostainings and identified a prognostic set of 20 genes
recapitulating microenvironmental patterns. Alternative methods,
such as multiplex immunostaining or bulk gene expression
profiling, will facilitate validation of findings in large cohorts and
translation into clinical application. So far, our findings suggest a
prognostic relevance of tumor microenvironmental patterns
which could aid in therapeutical decision making. Moreover,
preclinical findings from other groups suggest that tumor
microenvironmental patterns could be associated with differential
responses to immune checkpoint inhibition, thus distinct micro-
environmental features such as tumor-infiltrating CD8+T cells or
CXCL9+macrophages could complement current patient strati-
fication by PD-L1 expression. Finally, characterization of the tumor
microenvironment on single-cell resolution can provide insight on
possible novel therapeutic targets. It remains an open question to
which extent cancer cells shape their microenvironment and to
which extent the microenvironment affects cancer cells. Preclinical
efforts need to be complemented by translational studies to
identify critical mechanisms in this complex network that
Fig. 4 Composition of the immune microenvironment of lung adenocarcinomas. A UMAPs based on the top 20 principal components of all
immune single-cell transcriptomes split by tissue type, color-coded by cell cluster; and relative quantification of myeloid and lymphoid cell
clusters per tissue type and, for tumor samples, per patient. BAverage gene expression of selected marker genes for immune cell clusters, for
cell cluster color code see (A). CModule scores of gene signatures related to inflammation and M1/M2 polarization of different macrophage
clusters, white and black arrowheads indicate monocyte-derived macrophage clusters 1 and 2, respectively, for cell cluster color code see
(A). DCorrelation of the relative quantity of selected myeloid immune cell clusters, for patient color code see (G); Spearman’s correlation
statistics, linear regression line. EImmunohistochemical staining of CXCL9 and CD123 as markers for monocyte-derived macrophage cluster 2
and plasmacytoid dendritic cells, respectively, quantification of CXCL9+or CD123+cells per 0.48 mm², mean ± s.d., n=10 per patient, for
patient color code see (G); Pearson’s correlation statistics and linear regression line using mean values per patient. FModule scores of gene
signatures related to cytotoxicity and exhaustion of different CD8+T cell clusters, white and black arrowheads indicate cell clusters enriched
in normal or tumor tissue, respectively, for cell cluster color code see (A). GCorrelation of the relative quantity of selected lymphoid and
myeloid immune cell clusters, color-coded by patient; Spearman’s correlation statistics, linear regression line.
P. Bischoff et al.
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determine tumor response to targeted or immune therapies in the
clinical context. While our study demonstrates how single-cell
gene expression profiling of clinical samples can contribute to this
task, in the future, other single-cell approaches comprising spatial
information [62], surface protein expression [63], and epigenetic
characterization [64] will help to complete the picture [65].
METHODS
Collection of tissue specimens
Fresh normal lung parenchyma and tumor tissues were obtained during
intraoperative pathologist consultation from previously untreated lung
adenocarcinoma patients undergoing primary surgery. Patients were
aware of the planned research and agreed to the use of tissue. Research
was approved by vote EA4/164/19 of the ethics committee of Charité—
Universitätsmedizin Berlin.
Tissue dissociation and single-cell isolation
Tissue specimens of ~0.1–0.5 cm³ were stored on ice in Tissue Storage
Solution (Miltenyi, Bergisch Gladbach, Germany) for transport. Tissues were
minced, dissociated using the Tumor Dissociation Kit, human (Miltenyi) and a
gentleMACS Octo Dissociator with heaters (Miltenyi), using program
37C_h_TDK_1 for 30–45 min. Subsequent steps were performed at 4 °C. Cell
suspensions were filtered using 100 μm filters, pelleted by centrifugation at
300 × gin BSA-coated low-binding tubes, treated with 1 ml ACK erythrocyte
lysis buffer for 1 min, washed with DMEM, pelleted, resuspended in PBS,
P. Bischoff et al.
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filtered using 20 μm filters, debris was removed using the Debris Removal
Solution (Miltenyi), and cells were counted using a Neubauer chamber.
Single-cell RNA sequencing
10,000 single cells were used for single-cell library production, using the
Chromium Single Cell 3′Reagent Kit v3 and the Chromium Controller (10x
Genomics, Pleasanton, California, USA) according to the manufacturer’s
protocol. Libraries were sequenced on a HiSeq 4000 Sequencer (Illumina,
San Diego, California, USA) at in average 54,000 reads per cell.
H&E and immunostaining
3–5 μm tissue sections of formalin-fixed and paraffin-embedded (FFPE) tissue
were prepared. For H&E staining, sections were stained for 8 min in acidic
haemalum staining solution (Waldeck, Münster, Germany) and for 2.5 min in
eosin staining solution (Sigma-Aldrich, St. Louis, Missouri, USA) using a Tissue-
Tek Prisma Plus slide stainer (Sakura, Staufen im Breisgau, Germany).
Immunohistochemical stainings were performed on the BenchMark XT
immunostainer (Ventana, Oro Valley, Arizona, USA). Tissue sections were
incubated in CC1 or CC2 buffer (Ventana) for 30 min at 100 °C, and
subsequently with primary antibodies for 60 min and with secondary
antibodies for 30 min at room temperature. Multiplex immunofluorescence
stainings were performed using t-CyCIF as described in [66]. Primary
antibodies are listed in Supplementary Table 12. H&E and immunohisto-
chemical images were taken using a Pannoramic SCAN 150 scanner
(3DHISTECH, Budapest, Hungary), immunofluorescence images were taken
using a CQ1 microscope (Yokogawa, Musashino, Japan). For quantification,
10 random areas of 0.48 mm² each were analyzed by a pathologist. CD123-
and CXCL9-positve cells were counted per area. For CTHRC1 quantification,
using the ImageJ software, stromal area was manually marked as region of
interest, color channels were split by “Color deconvolution”, the DAB
channel was binarized using an intensity threshold of 150, and the
proportion of area covered by positive signal was measured per image.
Panel sequencing
For panel sequencing, tumor-enriched areas were macrodissected from
FFPE tissue sections.
DNA was isolated using the Maxwell RSC DNA FFPE Kit (Promega,
Madison, Wisconsin, USA) on a Maxwell RSC 48 Instrument (Promega) and
analyzed using the nNGM panel v1 (ThermoFisher, Waltham, Massachu-
setts, USA), an Ion 530 chip (ThermoFisher) and the Ion Chef/Ion S5 XL
System (ThermoFisher).
RNA was isolated using the Maxwell RSC RNA FFPE Kit (Promega) on a
Maxwell RSC 48 Instrument (Promega) and analyzed using the Oncomine
Focus RNA Assay (ThermoFisher), an Ion 530 chip and the Ion Chef/Ion S5
XL System (ThermoFisher).
The Sequence Pilot Software (Version 4.4.0, JSI Medical Systems) and the Ion
Reporter Software (Version 5.12, ThermoFisher) were used for variant calling.
Single-cell RNA sequencing data analysis
Preprocessing, filtering, and normalization. UMIs were quantified using
Cellranger 3.0.2 (10x Genomics) with reference transcriptome GRCh38.
Subsequent analyses were performed using “Seurat v3”[67], if not stated
otherwise. Single-cell gene expression data of all patients were merged,
and transcriptomes were filtered for cells with 500–10,000 genes detected,
1000–100,000 UMIs counted, fraction of mitochondrial reads <30%, and
fraction of hemoglobin reads <5%. After filtering, UMI counts were
variance-stabilized using scTransform with 3000 variable features [68],
while regressing out number of UMIs and fraction of mitochondrial reads.
Clustering and cell type annotation. Top 15 principal components were
used to construct SNN graph and UMAP embedding. Main cell types were
identified by scoring canonical cell type markers across clusters. PCA, SNN
graph construction and UMAP embedding was rerun on main cell type
subsets. Cell type markers were adapted from Habermann et al. [69]. and
Tata and Rajagopal [70] (Supplementary Table 13). Cell type signatures
from Vieira Braga et al. [13] and Travaglini et al. [14] were used to validate
manual cell type annotation. Epithelial or immune contaminated clusters
were identified by EPCAM or PTPRC, respectively, and removed before
further analyses. DoubletFinder v2.0 was used to estimate cell doublet
distribution (Supplementary Fig. 1D). Epithelial cell clusters overrepre-
sented in tumor tissue samples were annotated as tumor cells. For copy-
number assignment, InferCNV v1.3.3 was used with default parameters.
Differential gene expression analysis. Marker genes for each cell cluster
versus all cells of the respective subset were computed using the
FindAllMarkers function with the following parameters: only positive
markers, fraction of expressing cells inside the cluster ≥0.25, difference
between fraction of expressing cells inside and outside the cluster ≥0.25,
log fold change between cells inside and outside the cluster ≥0.25.
Functional analysis. Cell cycle phases were scored as implemented in
“Seurat v3”. Expression of gene sets of the Hallmark signature collection of
the Broad Institute [21], and M1 vs. M2 up- and downregulated genes [22]
were scored using the AddModuleScore function. Oncogenic signaling
pathway activity scores were computed using the R package “progeny”
[71,72]. Potential paracrine interactions were computed using the
“CellPhoneDB”toolkit with default parameters [73].
Analysis of the TCGA lung adenocarcinoma cohort
FPKM-normalized gene expression values of 533 lung adenocarcinoma
(LUAD) cases were downloaded using the R package “TCGAbiolinks”[74]and
log2 transformed. Marker genes from myeloid, lymphoid, endothelial, stromal
cell subsets, and the “alveolar/club-like”and “undifferentiated”tumor cell
signatures were used as gene sets to perform single-sample gene set
enrichment analysis (ssGSEA) [75]onTCGALUADgeneexpressiondatausing
the R package “GSVA”[76]. Associations between enrichment scores (ES)
from ssGSEA were calculated using the R package “corrplot”.
Next, patients were grouped by dichotomization of ssGSEA ES (> or
≤median). Survival data of the TCGA LUAD cohort was available for 524
cases and downloaded using the R package “TCGAbiolinks”[74]. Survival
curves, log-rank statistics and Cox regression were calculated using the R
packages “survival”and “survminer”. Data on mutations and mutational
burden was available for 525 and 242 cases, respectively.
Statistical analysis
Patient groups were compared using the Welch’sttest or Mann–Whitney
Utest (both two-sided, unequal variances), as indicated, after testing for
normal distribution using the Shapiro–Wilk’s test. Distribution of mutations
across patient groups was analyzed using the Chi-squared test. For
correlation analysis, we calculated the Pearson or Spearman correlation
coefficient, as indicated. Pvalues < 0.05 were considered significant.
Fig. 5 Tumor microenvironmental patterns in lung adenocarcinomas. A Principal component analysis based on the proportion of stromal
and immune cell clusters, color-coded by histological subtype, patients indicated. BNormalized proportion of stromal and immune cell
clusters, mean module scores of tumor cell signatures, histological subtypes and mutation status per patient, patients sorted along the first
principal component from principal component analysis in (A), cell clusters included in the model in (H) in bold. CCorrelation of the
proportion of stromal and immune cell clusters, most connected section of correlation network plot shown; Spearman’s correlation statistics,
only correlations with rho > 0.7 and p< 0.05 shown. A,B,CCell clusters occurring in <3 patients were excluded from analyses. DNumber of
potential paracrine interactions from microenvironmental cell clusters to tumor cells of the N³MC or CP²E pattern, computed using
CellPhoneDB, grouped by interaction families, color-coded by number of interactions (see also Supplementary Fig. 10). E–GAnalysis of the
TCGA lung adenocarcinoma cohort. ECorrelation of ssGSEA enrichment scores based on marker genes of selected microenvironmental cell
clusters and tumor cell signatures; n=533, Spearman’s correlation statistics, linear regression line. FKaplan–Meier overall survival curves,
cases grouped by the ratio of ssGSEA enrichment scores of indicated microenvironmental cell clusters or tumor cell signatures or a combined
signature encompassing all cell clusters of the N³MC or CP²E pattern, respectively; n=524, log-rank statistics. GProportion of patients with
oncogenic mutations and tumor mutational burden (TMB), patients grouped by ratio of ssGSEA enrichment scores of the combined signature
in (F); n=525 for mutations, Chi-squared test, n=242 for TMB, two-sided Welch’sttest. HSchematic representation of subtypes of lung
adenocarcinoma characterized by different grades of tumor epithelial cell differentiation and different composition of the corresponding
tumor microenvironment.
P. Bischoff et al.
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Oncogene (2021) 40:6748 – 6758
Content courtesy of Springer Nature, terms of use apply. Rights reserved

DATA AVAILABILITY
Processed count data used for analyses is available as a Code Ocean capsule from
https://doi.org/10.24433/CO.0121060.v1.
CODE AVAILABILITY
Code used for analyses is available as a Code Ocean capsule from https://doi.org/
10.24433/CO.0121060.v1. Complementary code is available from https://github.com/
bischofp/single_cell_lung_adenocarcinoma.
REFERENCES
1. Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer
statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36
cancers in 185 countries. CA Cancer J Clin. 2018;68:394–424.
2. Wakelee HA, Chang ET, Gomez SL, Keegan TH, Feskanich D, Clarke CA, et al. Lung
cancer incidence in never smokers. J Clin Oncol. 2007;25:472–8.
3. Cancer Genome Atlas Research N. Comprehensive molecular profiling of lung
adenocarcinoma. Nature. 2014;511:543–50.
4. Hirsch FR, ScagliottiGV, Mulshine JL, Kwon R, Curran WJ Jr, Wu YL, et al. Lung cancer:
current therapies and new targeted treatments. Lancet. 2017;389:299–311.
5. Memon H, Patel BM. Immune checkpoint inhibitors in non-small cell lung cancer:
a bird’s eye view. Life Sci. 2019;233:116713.
6. Altorki NK, Markowitz GJ, Gao D, Port JL, Saxena A, Stiles B, et al. The lung
microenvironment: an important regulator of tumour growth and metastasis. Nat
Rev Cancer. 2019;19:9–31.
7. Lavin Y, Kobayashi S, Leader A, Amir ED, Elefant N, Bigenwald C, et al. Innate
immune landscape in early lung adenocarcinoma by paired single-cell analyses.
Cell. 2017;169:750–65.e17.
8. Lambrechts D, Wauters E, Boeckx B, Aibar S, Nittner D, Burton O, et al. Phenotype
molding of stromal cells in the lung tumor microenvironment. Nat Med.
2018;24:1277–89.
9. Kim N, Kim HK, Lee K, Hong Y, Cho JH, Choi JW, et al. Single-cell RNA sequencing
demonstrates the molecular and cellular reprogramming of metastatic lung
adenocarcinoma. Nat Commun. 2020;11:2285.
10. Guo X, Zhang Y, Zheng L, Zheng C, Song J, Zhang Q, et al. Global characterization
of T cells in non-small-cell lung cancer by single-cell sequencing. Nat Med.
2018;24:978–85.
11. Becht E, McInnes L, Healy J, Dutertre CA, Kwok IWH, Ng LG, et al. Dimensionality
reduction for visualizing single-cell data using UMAP. Nat Biotechnol. 2019;37:38–44.
12. Lee HO, Hong Y, Etlioglu HE, Cho YB, Pomella V, Van den Bosch B, et al. Lineage-
dependent gene expression programs influence the immune landscape of col-
orectal cancer. Nat Genet. 2020;52:594–603.
13. Vieira Braga FA, Kar G, Berg M, Carpaij OA, Polanski K, Simon LM, et al. A cellular
census of human lungs identifies novel cell states in health and in asthma. Nat
Med. 2019;25:1153–63.
14. Travaglini KJ, Nabhan AN, Penland L, Sinha R, Gillich A, Sit RV, et al. A molecular cell
atlas of the human lung from single-cell RNA sequencing. Nature. 2020;587:619–625.
15. Naizhen X, Kido T, Yokoyama S, Linnoila RI, Kimura S. Spatiotemporal expression
of three secretoglobin proteins, SCGB1A1, SCGB3A1, and SCGB3A2, in mouse
airway epithelia. J Histochem Cytochem. 2019;67:453–63.
16. Yoshida GJ. Regulation of heterogeneous cancer-associated fibroblasts: the mole-
cular pathology of activated signaling pathways. J Exp Clin Cancer Res. 2020;39:112.
17. Petrova V, Annicchiarico-Petruzzelli M, Melino G, Amelio I. The hypoxic tumour
microenvironment. Oncogenesis. 2018;7:10.
18. Franklin RA, Li MO. Ontogeny of tumor-associated macrophages and its impli-
cation in cancer regulation. Trends Cancer 2016;2:20–34.
19. Solberg R, Smith R, Almlof M, Tewolde E, Nilsen H, Johansen HT. Legumain
expression, activity and secretion are increased during monocyte-to-macrophage
differentiation and inhibited by atorvastatin. Biol Chem. 2015;396:71–80.
20. Solinas G, Schiarea S, Liguori M, Fabbri M, Pesce S, Zammataro L, et al. Tumor-
conditioned macrophages secrete migration-stimulating factor: a new marker for
M2-polarization, influencing tumor cell motility. J Immunol. 2010;185:642–52.
21. Liberzon A, Birger C, Thorvaldsdottir H, Ghandi M, Mesirov JP, Tamayo P. The
Molecular Signatures Database (MSigDB) hallmark gene set collection. Cell Syst.
2015;1:417–25.
22. Martinez FO, Gordon S, Locati M, Mantovani A. Transcriptional profiling of the
human monocyte-to-macrophage differentiation and polarization: new mole-
cules and patterns of gene expression. J Immunol. 2006;177:7303–11.
23. Jiang Y, Li Y, Zhu B. T-cell exhaustion in the tumor microenvironment. Cell Death
Dis. 2015;6:e1792.
24. Travis WD, Brambilla E, Nicholson AG, Yatabe Y, Austin JHM, Beasley MB, et al. The
2015 World Health Organization classification of lung tumors: impact of genetic,
clinical and radiologic advances since the 2004 classification. J Thorac Oncol.
2015;10:1243–60.
25. Moreira AL, Ocampo PSS, Xia Y, Zhong H, Russell PA, Minami Y, et al. A grading
system for invasive pulmonary adenocarcinoma: a proposal from the Interna-
tional Association for the Study of Lung Cancer Pathology Committee. J Thorac
Oncol. 2020;15:1599–610.
26. Kubouchi Y, Yurugi Y, Wakahara M, Sakabe T, Haruki T, Nosaka K, et al. Podo-
planin expression in cancer-associated fibroblasts predicts unfavourable prog-
nosis in patients with pathological stage IA lung adenocarcinoma.
Histopathology. 2018;72:490–9.
27. Shi J, Hou Z, Yan J, Qiu W, Liang L, Meng M, et al. The prognostic significance of
fibroblast activation protein-alpha in human lung adenocarcinoma. Ann Transl
Med. 2020;8:224.
28. Inoue C, Tamatsuki D, Miki Y, Saito R, Okada Y, Sasano H. Prognostic significance
of combining immunohistochemical markers for cancer-associated fibroblasts in
lung adenocarcinoma tissue. Virchows Arch. 2019;475:181–9.
29. Schalper KA, Brown J, Carvajal-Hausdorf D, McLaughlin J, Velcheti V, Syrigos KN,
et al. Objective measurement and clinical significance of TILs in non-small cell
lung cancer. J Natl Cancer Inst. 2015;107:dju435.
30. Donnem T, Hald SM, Paulsen EE, Richardsen E, Al-Saad S, Kilvaer TK, et al. Stromal
CD8+T-cell density—a promising supplement to TNM staging in non-small cell
lung cancer. Clin Cancer Res. 2015;21:2635–43.
31. Geng Y, Shao Y, He W, Hu W, Xu Y, Chen J, et al. Prognostic role of tumor-
infiltrating lymphocytes in lung cancer: a meta-analysis. Cell Physiol Biochem.
2015;37:1560–71.
32. Conway EM, Pikor LA, Kung SH, Hamilton MJ, Lam S, Lam WL, et al. Macrophages,
inflammation, and lung cancer. Am J Respir Crit Care Med. 2016;193:116–30.
33. Teng MW, Ngiow SF, Ribas A, Smyth MJ. Classifying cancers based on T-cell
Infiltration and PD-L1. Cancer Res. 2015;75:2139–45.
34. Lin Z, Gu J, Cui X, Huang L, Li S, Feng J, et al. Deciphering microenvironment of
NSCLC based on CD8+TIL density and PD-1/PD-L1 expression. J Cancer.
2019;10:211–22.
35. Parra ER, Villalobos P, Behrens C, Jiang M, Pataer A, Swisher SG, et al. Effect of
neoadjuvant chemotherapy on the immune microenvironment in non-small cell
lung carcinomas as determined by multiplex immunofluorescence and image
analysis approaches. J Immunother Cancer. 2018;6:48.
36. Peng H, Wu X, Zhong R, Yu T, Cai X, Liu J, et al. Profiling tumor immune
microenvironment of non-small cell lung cancer using multiplex immuno-
fluorescence. bioRxiv. 2021. https://doi.org/10.1101/2021.05.28.446005.
37. Tao Y, Li Y, Liang B. Comprehensive analysis of microenvironment-related genes
in lung adenocarcinoma. Future Oncol. 2020;16:1825–37.
38. Ojlert AK, Halvorsen AR, Nebdal D, Lund-Iversen M, Solberg S, Brustugun OT, et al.
The immune microenvironment in non-small cell lung cancer is predictive of
prognosis after surgery. Mol Oncol. 2019;13:1166–79.
39. Huang J, Li J, Zheng S, Lu Z, Che Y, Mao S, et al. Tumor microenvironment
characterization identifies two lung adenocarcinoma subtypes with specific
immune and metabolic state. Cancer Sci. 2020;111:1876–86.
40. Ito M, Ishii G, Nagai K, Maeda R, Nakano Y, Ochiai A. Prognostic impact of cancer-
associated stromal cells in patients with stage I lung adenocarcinoma. Chest.
2012;142:151–8.
41. Koucky V, Boucek J, Fialova A. Immunology of plasmacytoid dendritic cells in
solid tumors: a brief review. Cancers. 2019;11:470
42. Zhang S, Liu W, Hu B, Wang P, Lv X, Chen S, et al. Prognostic significance of
tumor-infiltrating natural killer cells in solid tumors: a systematic review and
meta-analysis. Front Immunol. 2020;11:1242.
43. Herbst RS, Giaccone G, de Marinis F, Reinmuth N, Vergnenegre A, Barrios CH, et al.
Atezolizumab for first-line treatment of PD-L1-selected patients with NSCLC. N
Engl J Med. 2020;383:1328–39.
44. Qu Y, Wen J, Thomas G, Yang W, Prior W, He W, et al. Baseline frequency of
inflammatory Cxcl9-expressing tumor-associated macrophages predicts response
to avelumab treatment. Cell Rep. 2020;32:107873.
45. House IG, Savas P, Lai J, Chen AXY, Oliver AJ, Teo ZL, et al. Macrophage-derived
CXCL9 and CXCL10 are required for antitumor immune responses following
immune checkpoint blockade. Clin Cancer Res. 2020;26:487–504.
46. Jiang W, He Y, He W, Wu G, Zhou X, Sheng Q, et al. Exhausted CD8+T cells in the
tumor immune microenvironment: new pathways to therapy. Front Immunol.
2020;11:622509.
47. Sholl LM, Hirsch FR, Hwang D, Botling J, Lopez-Rios F, Bubendorf L, et al. The
promises and challenges of tumor mutation burden as an immunotherapy bio-
marker: a perspective from the International Association for the Study of Lung
Cancer Pathology Committee. J Thorac Oncol. 2020;15:1409–24.
48. Okazaki T, Tamai K, Shibuya R, Nakamura M, Mochizuki M, Yamaguchi K, et al.
Periostin is a negative prognostic factor and promotes cancer cell proliferation in
non-small cell lung cancer. Oncotarget. 2018;9:31187–99.
P. Bischoff et al.
6757
Oncogene (2021) 40:6748 – 6758
Content courtesy of Springer Nature, terms of use apply. Rights reserved

49. Yang H, Jiang P, Liu D, Wang HQ, Deng Q, Niu X, et al. Matrix metalloproteinase
11 is a potential therapeutic target in lung adenocarcinoma. Mol Ther Oncolytics.
2019;14:82–93.
50. Wrangle JM, Velcheti V, Patel MR, Garrett-Mayer E, Hill EG, Ravenel JG, et al. ALT-
803, an IL-15 superagonist, in combination with nivolumab in patients with
metastatic non-small cell lung cancer: a non-randomised, open-label, phase 1b
trial. Lancet Oncol. 2018;19:694–704.
51. Patel AJ, Richter A, Drayson MT, Middleton GW. The role of B lymphocytes in the
immuno-biology of non-small-cell lung cancer. Cancer Immunol Immunother.
2020;69:325–42.
52. Alexandrov LB, Nik-Zainal S, Wedge DC, Aparicio SA, Behjati S, Biankin AV, et al.
Signatures of mutational processes in human cancer. Nature. 2013;500:415–21.
53. Liu Y, Zhang J, Li L, Yin G, Zhang J, Zheng S, et al. Genomic heterogeneity of
multiple synchronous lung cancer. Nat Commun. 2016;7:13200.
54. Niemira M, Collin F, Szalkowska A, Bielska A, Chwialkowska K, Reszec J, et al.
Molecular signature of subtypes of non-small-cell lung cancer by large-scale
transcriptional profiling: identification of key modules and genes by weighted
gene co-expression network analysis (WGCNA). Cancers. 2019;12:37.
55. Molina-Romero C, Rangel-Escareno C, Ortega-Gomez A, Alanis-Funes GJ, Aviles-
Salas A, Avila-Moreno F, et al. Differential gene expression profiles according to
the Association for the Study of Lung Cancer/American Thoracic Society/Eur-
opean Respiratory Society histopathological classification in lung adenocarci-
noma subtypes. Hum Pathol. 2017;66:188–99.
56. Hao Y, Baker D, Ten Dijke P. TGF-beta-mediated epithelial-mesenchymal transi-
tion and cancer metastasis. Int J Mol Sci. 2019;20:2767.
57. Tam SY, Wu VWC, Law HKW. Hypoxia-induced epithelial-mesenchymal transition
in cancers: HIF-1alpha and beyond. Front Oncol. 2020;10:486.
58. Lastwika KJ, Wilson W 3rd, Li QK, Norris J, Xu H, Ghazarian SR, et al. Control of PD-
L1 expression by oncogenic activation of the AKT-mTOR pathway in non-small
cell lung cancer. Cancer Res. 2016;76:227–38.
59. Gao Y, Yang J, Cai Y, Fu S, Zhang N, Fu X, et al. IFN-gamma-mediated inhibition of
lung cancer correlates with PD-L1 expression and is regulated by PI3K-AKT sig-
naling. Int J Cancer. 2018;143:931–43.
60. Tan AC. Targeting the PI3K/Akt/mTOR pathway in non-small cell lung cancer
(NSCLC). Thorac Cancer. 2020;11:511–8.
61. Mohrherr J, Haber M, Breitenecker K, Aigner P, Moritsch S, Voronin V, et al. JAK-
STAT inhibition impairs K-RAS-driven lung adenocarcinoma progression. Int J
Cancer. 2019;145:3376–88.
62. Stahl PL, Salmen F, Vickovic S, Lundmark A, Navarro JF, Magnusson J, et al.
Visualization and analysis of gene expression in tissue sections by spatial tran-
scriptomics. Science. 2016;353:78–82.
63. Stoeckius M, Hafemeister C, Stephenson W, Houck-Loomis B, Chattopadhyay PK,
Swerdlow H, et al. Simultaneous epitope and transcriptome measurement in
single cells. Nat Methods. 2017;14:865–8.
64. Buenrostro JD, Wu B, Litzenburger UM, Ruff D, Gonzales ML, Snyder MP, et al.
Single-cell chromatin accessibility reveals principles of regulatory variation. Nat-
ure. 2015;523:486–90.
65. Rajewsky N, Almouzni G, Gorski SA, Aerts S, Amit I, Bertero MG, et al. LifeTime and
improving European healthcare through cell-based interceptive medicine. Nat-
ure. 2020;587:377–386.
66. Lin JR, Izar B, Wang S, Yapp C, Mei S, Shah PM, et al. Highly multiplexed
immunofluorescence imaging of human tissues and tumors using t-CyCIF and
conventional optical microscopes. Elife. 2018;7:e31657.
67. Stuart T, Butler A, Hoffman P, Hafemeister C, Papalexi E, Mauck WM 3rd, et al.
Comprehensive integration of single-cell data. Cell. 2019;177:1888–902.e21.
68. Hafemeister C, Satija R. Normalization and variance stabilization of single-cell
RNA-seq data using regularized negative binomial regression. Genome Biol.
2019;20:296.
69. Habermann AC, Gutierrez AJ, Bui LT, Yahn SL, Winters NI, Calvi CL, et al. Single-cell
RNA sequencing reveals profibrotic roles of distinct epithelial and mesenchymal
lineages in pulmonary fibrosis. Sci Adv. 2020;6:eaba1972.
70. Tata PR, Rajagopal J. Plasticity in the lung: making and breaking cell identity.
Development. 2017;144:755–66.
71. Schubert M, Klinger B, Klunemann M, Sieber A, Uhlitz F, Sauer S, et al.
Perturbation-response genes reveal signaling footprints in cancer gene expres-
sion. Nat Commun. 2018;9:20.
72. Holland CH, Tanevski J, Perales-Paton J, Gleixner J, Kumar MP, Mereu E, et al.
Robustness and applicability of transcription factor and pathway analysis tools on
single-cell RNA-seq data. Genome Biol. 2020;21:36.
73. Efremova M, Vento-Tormo M, Teichmann SA, Vento-Tormo R. CellPhoneDB:
inferring cell-cell communication from combined expression of multi-subunit
ligand-receptor complexes. Nat Protoc. 2020;15:1484–506.
74. Colaprico A, Silva TC, Olsen C, Garofano L, Cava C, Garolini D, et al. TCGAbiolinks:
an R/Bioconductor package for integrative analysis of TCGA data. Nucleic Acids
Res. 2016;44:e71.
75. Barbie DA, Tamayo P, Boehm JS, Kim SY, Moody SE, Dunn IF, et al. Systematic RNA
interference reveals that oncogenic KRAS-driven cancers require TBK1. Nature.
2009;462:108–12.
76. Hanzelmann S, Castelo R, Guinney J. GSVA: gene set variation analysis for
microarray and RNA-seq data. BMC Bioinform. 2013;14:7.
ACKNOWLEDGEMENTS
We thank Manuela Pacyna-Gengelbach and Barabara Meyer-Bartell for excellent
technical assistance. The results of this study are in part based upon data generated
by the TCGA Research Network: https://www.cancer.gov/tcga.
AUTHOR CONTRIBUTIONS
NB, FK and PB conceived and designed the study; PB, AT, FK, DH, AE, TD, JN and JR
contributed to clinical sample acquisition and preparation; AT, PB and XL conducted
experiments; PB, BO, JP, DB, NB, JW and FU performed bioinformatic analyses; PB, FK,
NB, PJ, CF, DH, MM and CS analyzed and interpreted data and/or supervised parts of
the study; PB wrote the paper; MM, FK and NB revised the paper; all authors provided
critical feedback and helped shaping the research, analysis and paper.
FUNDING
The work was in part funded by the Berlin Institute of Health (to PB, PJ, DH, MM, CS,
and NB), the German Cancer Consortium DKTK (to MM and NB), the German Research
Foundation DFG (RTG2424 CompCancer to NB) and the Einstein Foundation/Stiftung
Charite (to NB). PB is participant in the BIH-Charité Junior Clinician Scientist Program
funded by the Charité—Universitätsmedizin Berlin and the Berlin Institute of Health.
PJ is participant in the BIH-Charité Digital Clinician Scientist Program funded by the
Charité—Universitätsmedizin Berlin and the Berlin Institute of Health and the
German Research Foundation (DFG). Open Access funding enabled and organized by
Projekt DEAL.
COMPETING INTERESTS
The authors declare no competing interests.
ADDITIONAL INFORMATION
Supplementary information The online version contains supplementary material
available at https://doi.org/10.1038/s41388-021-02054-3.
Correspondence and requests for materials should be addressed to Philip Bischoff.
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© The Author(s) 2021
P. Bischoff et al.
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