Transcriptomics profiling of the non-small cell lung cancer microenvironment across disease stages reveals dual immune cell-type behaviors

Abstract

Background
Lung cancer is the leading cause of cancer death worldwide, with poor survival despite recent therapeutic advances. A better understanding of the complexity of the tumor microenvironment is needed to improve patients’ outcome.

Methods
We applied a computational immunology approach (involving immune cell proportion estimation by deconvolution, transcription factor activity inference, pathways and immune scores estimations) in order to characterize bulk transcriptomics of 62 primary lung adenocarcinoma (LUAD) samples from patients across disease stages. Focusing specifically on early stage samples, we validated our findings using an independent LUAD cohort with 70 bulk RNAseq and 15 scRNAseq datasets and on TCGA datasets.

Results
Through our methodology and feature integration pipeline, we identified groups of immune cells related to disease stage as well as potential immune response or evasion and survival. More specifically, we reported a duality in the behavior of immune cells, notably natural killer (NK) cells, which was shown to be associated with survival and could be relevant for immune response or evasion. These distinct NK cell populations were further characterized using scRNAseq data, showing potential differences in their cytotoxic activity.

Conclusion
The dual profile of several immune cells, most notably T-cell populations, have been discussed in the context of diseases such as cancer. Here, we report the duality of NK cells which should be taken into account in conjunction with other immune cell populations and behaviors in predicting prognosis, immune response or evasion.

Full text

Transcriptomics profiling of the
non-small cell lung cancer
microenvironment across
disease stages reveals dual
immune cell-type behaviors
Marcelo Hurtado
1
*
‡§
, Leila Khajavi
1
†‡
, Abdelmounim Essabbar
1
,
Michael Kammer
1,2
, Ting Xie
1
†
, Alexis Coullomb
1
†
,
Anne Pradines
1,3
, Anne Casanova
1,3
, Anna Kruczynski
4
,
Sandrine Gouin
5
, Estelle Clermont
1,3
,Le
´a Boutillet
1
†
,
Maria Fernanda Senosain
6,7
, Yong Zou
6,7
, Shillin Zhao
8
‡
,
Prosper Burq
9
, Abderrahim Mahfoudi
4
†
, Jerome Besse
4
,
Pierre Launay
4
, Alexandre Passioukov
4
, Eric Chetaille
4
†
,
Gilles Favre
1
, Fabien Maldonado
6
, Francisco Cruzalegui
4
†
,
Olivier Delfour
4
, Julien Mazières
1,5
and Vera Pancaldi
1,10
*
1
CRCT, Universite
´de Toulouse, Institut national de la sante
´et de la recherche me
´dicale (Inserm),
Centre national de la recherche scientifique (CNRS), Universite
´Toulouse III-Paul Sabatier, Centre de
Recherches en cance
´rologie de Toulouse, Toulouse, France,
2
Department of Medicine, Vanderbilt
University Medical Center, Nashville, TN, United States,
3
Laboratory Medicine, Oncopole Claudius
Regaud, Toulouse, France,
4
Institut de Recherche Pierre Fabre, Toulouse, France,
5
Pulmonology
Department, Larrey Hospital, University Hospital of Toulouse, Toulouse, France,
6
Division of Allergy,
Pulmonary, and Critical Care Medicine, Department of Medicine, Vanderbilt University Medical
Center, Nashville, TN, United States,
7
Cancer Early Detection and Prevention Initiative, Vanderbilt-
Ingram Cancer Center, Vanderbilt University Medical. Center, Nashville, TN, United States,
8
Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, United States,
9
Data Science, Centre Hospitalier Universitaire de Toulouse, Toulouse, France,
10
Life Sciences
Department, Barcelona Supercomputing Center, Barcelona, Spain
Background: Lung cancer is the leading cause of cancer death worldwide, with
poor survival despite recent therapeutic advances. A better understanding of the
complexity of the tumor microenvironment is needed to improve
patients’outcome.
Methods: We applied a computational immunology approach (involving immune
cell proportion estimation by deconvolution, transcription factor activity
inference, pathways and immune scores estimations) in order to characterize
bulk transcriptomics of 62 primary lung adenocarcinoma (LUAD) samples from
patients across disease stages. Focusing specifically on early stage samples, we
validated our findings using an independent LUAD cohort with 70 bulk RNAseq
and 15 scRNAseq datasets and on TCGA datasets.
Results: Through our methodology and feature integration pipeline, we
identified groups of immune cells related to disease stage as well as potential
immune response or evasion and survival. More specifically, we reported a duality
in the behavior of immune cells, notably natural killer (NK) cells, which was shown
to be associated with survival and could be relevant for immune response or
Frontiers in Immunology frontiersin.org01
OPEN ACCESS
EDITED BY
Chiara Porta,
University of Eastern Piedmont, Italy
REVIEWED BY
Xinjun Wang,
Memorial Sloan Kettering Cancer Center,
United States
Davide Cora,
University of Eastern Piedmont, Italy
*CORRESPONDENCE
Marcelo Hurtado
marcelo.hurtado@inserm.fr
Vera Pancaldi
vera.pancaldi@inserm.fr
†
PRESENT ADDRESSES
Leila Khajavi,
Bioinformatics Department, Evotec, Toulouse,
France
Ting Xie,
Institut national de la santé et de la recherche
médicale (INSERM) U981, Gustave Roussy
Institute, Université Paris-Saclay, Paris, France
Alexis Coullomb,
RESTORE Research Center, Université de
Toulouse, INSERM 1301, Centre national de la
recherche scientifique (CNRS) 5070,
E
´tablissement franc¸ais du sang (EFS), E
´cole
nationale ve
´te
´rinaire de Toulouse (ENVT),
Toulouse, France
Le
´a Boutillet,
Faculté de Médecine et de Pharmacie,
Université de Poitiers, France
Abderrahim Mahfoudi,
Abdul Latif Jameel Health, Dubai,
United Arab Emirates
Eric Chetaille,
EC Medical Consulting, Paris, France
Francisco Cruzalegui,
In Vitro Pharmacology Department, Evotec,
Toulouse, France
‡
These authors have contributed equally to
this work
§
Equipe Labellisée LIGUE Contre le Cancer
RECEIVED 02 March 2024
ACCEPTED 24 September 2024
PUBLISHED 31 October 2024
TYPE Original Research
PUBLISHED 31 October 2024
DOI 10.3389/fimmu.2024.13949 65

evasion. These distinct NK cell populations were further characterized using
scRNAseq data, showing potential differences in their cytotoxic activity.
Conclusion: The dual profile of several immune cells, most notably T-cell
populations, have been discussed in the context of diseases such as cancer.
Here, we report the duality of NK cells which should be taken into account in
conjunction with other immune cell populations and behaviors in predicting
prognosis, immune response or evasion.
KEYWORDS
lung adenocarcinoma, natural killer cells, immune landscape, cell deconvolution,
transcription factor activity
Background
Lung adenocarcinoma exhibits diverse clinical behaviors, ranging
from indolent to aggressive metastatic disease. However, the biological
underpinnings of this heterogeneity remain poorly understood. Non
Small Cell Lung Cancer (NSCLC) is often diagnosed at an advanced
stage and its management is currently undergoing significant
transformation. Molecular testing, targeted therapies, and
immunotherapy are now part of routine clinical care (1). However,
despite major progress in the therapeutic management of NSCLC
cancer, many patients are still refractory to the initial treatment or
develop resistance leading to tumor recurrence. Furthermore, the
clinical and pathological diversity of NSCLC is associated with a
highly complex genomic landscape and heterogenous immune tumor
microenvironment. Interactions between tumor cells and the immune
microenvironment are known to profoundly impact cancer
pathogenesis and progression (2).
Lung cancer tumor biopsies contain a heterogeneous mix of
cancer cells, healthy cells, immune cells, and extracellular factors that
constitute the tumor microenvironment (TME). The specific
composition and functional profiles of immune cells within the
TME can profoundly influence tumor pathogenesis. Detailed
characterization of immune cell diversity in the TME has therefore
become a major goal in cancer research. However, dissecting the
immune landscape from bulk tumor profiling remains challenging
(3–5). Single cell RNA sequencing enables high-resolution dissection
of tumor-immune interactions, but remains prohibitively costly for
large-scale or clinical applications (6). Additionally, each single cell
isolation approach introduces distinct technical biases that can skew
rare cell detection. Computational deconvolution approaches can
leverage unique gene expression signatures to estimate immune cell
subsets from bulk transcriptomics in a more accessible and
standardized way (7). However, numerous deconvolution
algorithms exist with little consensus on best practices. In this
study, we performed an integrated analysis using bulk RNAseq and
validating our results with single cell RNA sequencing data. We
applied this multi-omics pipeline to understand heterogeneity
specifically in the microenvironment of early-stage lung
adenocarcinomas, for which could validate our results on an
independent cohort and on early stage lung adenocarcinoma
(LUAD) samples from TCGA. We further correlated immune
deconvolution features with clinical outcomes, highlighting the
potential value of our approaches to reveal clinically relevant
cellular populations and potentially implicating distinct NK cell
phenotypes in survival. By correlating the deconvolution immune
cell estimates and inferred transcription factor activities, we aimed to
overcome limitations of individual methods. This study provides a
framework for robust characterization of tumor immune landscapes
from bulk transcriptomics.
Methods
Patient summary
The primary analysis cohort was derived from a pilot study
stemming from a collaborative effort between l’Institut Universitaire
du Cancer de Toulouse (IUCT) and Institut de Recherche Pierre
Fabre (IRPF) aimed at assessing the technical feasibility of developing
molecular characterization of lung tumors in order to enrich the
activities already initiated by the IUCT. Patients were enrolled in the
study if they were diagnosed with non-small cell lung cancer
(NSCLC). Patients were excluded from this study if they were
treated for any NSCLC prior to study enrollment. All individuals
involved signed a non-objection form to part-take in the research
program under the LUNG PREDICT protocol. Blood samples were
gathered as part of a collection declared to the Ministry of Research
under the number DC-2011-1382. Tissue samples are the remaining
parts of the whole tissue belonging to the patient coming from the
tumor library of CHU Biological Resource Center (IUCT-O) declared
to the Ministry of Research under the number DC-2008-463. All
clinical, pathological and molecular data were prospectively collected.
Patients’therapeutics and outcome were collected overtime with a 33
months median follow-up.
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Sample selection and extraction
Acertified pathologist made the selection of slides with
haematoxylin eosin slide coloration. The paraffin embedded block
was cut, 1 HE to control the extraction and 4 to 16 sections of 10 µm
for the RNA extraction, which was performed with High Pure FFPE
RNA extraction kit from Roche (Ref 0665077500). The purified
RNA samples were analyzed with Fragment Analyser (Advanced
Analytical Technologies Inc., Agilent Technologies, US) and High
Sensitivity RNA Kit (DNF-472-0500, Agilent Technologies, US) to
determine the RIN and the DV200 (percentage of RNA ≥200 bp).
RNA sequencing
The libraries were prepared with the KAPA RNA HyperPrep
Kit with RiboErase (HMR) (Kapa/Roche KK8560) for whole
transcriptome sequencing as recommended by the supplier using
1µg input of RNA. Briefly, rRNA was hybridized with DNA probes
to 5S, 8.8S, 18S, 28S, 12S and 16S rRNA, then the hybrids were
depleted by enzymatic depletion using RNAse H. After, DNA
digestion and fragmentation with high temperature were done.
First strand, second strand synthesis and A-tailing were performed.
Next, adapters from 1.5-7 µM depending on the DV200 were ligated
and the library was amplified. Library size and quality were
confirmed on Fragment Analyzer (Advanced Analytical
Technologies Inc., Agilent Technologies, US) and High Sensitivity
NGS Fragment Analysis Kit (DNF-474-0500, Agilent Technologies,
US). Qubit (ThermoFisher Scientific, US) was used to quantify
libraries. Samples were pooled in equimolar fashion (10nM), then
denatured and 1.8 pM was sequenced on NextSeq 550 (Illumina,
US) in pair-end sequencing (76 bp reads) and double index 8 bp
with NextSeq 500/550 High Output kit v2.5, 150 cycles (20024907,
Illumina, US) and 1% PhiX (FC-110-3001, Illumina, US).
Bulk RNAseq sample processing
Raw sequences were quality checked using FastQC (8(v0.11.2))
and FastqScreen (9(v0.15.2)) prior to aligning to the Homo sapiens
primary genome sequence (Gencode: GRCh38, v27) using STAR
(10 (v2.7.10a)) with encode options. FastQC was again used to
assess the mapping quality. RSEM (11 (v1.3.1)) was used to generate
the expression matrix (featureCounts from Rsubread R package (12
(v1.22.2)) was used for validation data).
Differential expression analysis
Expression matrices from bulk RNAseq were analyzed with
DESeq2 (13 (v1.42.1)) in the R environment (14); R Core Team (15)
(version v4.2.3, BioConductor version v3.9 (16,17) to identify
differentially expressed genes (DEGs) between samples groups.
ClusterProfiler (18 (v4.4.4)) was used to classify the DEGs into
KEGG pathways. Heatmaps were generated using both pheatmap
(v1.0.12) and ComplexHeatmap (19 (v2.0.0)) R packages. Volcano
plots were generated using the EnhancedVolcano (20 (v1.2.0)) R
package. Counts were normalized by Log2(TPM + 1) using the R
package ADImpute (21 (v1.12.0)).
Pathway activity calculation
Log
2
(TPM + 1) counts were used to calculate pathway activities
using the PROGENy database (22), a compendium of publicly
available signaling perturbation experiments based on footprint
genes to yield a common core of 14 signaling pathways. Pathways
regulatory activities were calculated using the Multivariate Linear
Model (MLM) from the package decoupleR (23 (v2.9.7)).
Immune cell-type deconvolution
In computational biology, deconvolution is an approach to
quantitatively estimate the proportions of cell types in a mixed
sample (e.g. bulk RNAseq) based on the observed gene expression
profiles for separate cell types. Log
2
(TPM + 1) (transcript per
million) normalized raw counts were used to estimate immune
cell-type proportions for lymphocytes (B, T and NK cells), myeloid
cells(monocytes,macrophagesanddendriticcells)aswellas
cancer, endothelial, eosinophils, plasma, myocytes, mast cells and
cancer-associated fibroblasts (CAFs). These cell-type proportion
estimates were obtained by applying different reference-based
deconvolution methods and several cell type signatures (see
Supplementary Table 1). These methods can provide absolute cell
abundance quantification using signatures derived from single cell
and bulk RNA seq data.
Transcription factor activity inference
Log
2
(TPM + 1) counts were used to infer transcription factor
(TF) activity. We use prior knowledge networks (PKN) to infer the
activity of different TFs from the gene expression of its direct target
genes quantified in the gene count matrix. We used CollecTRI (24)
from the package decoupleR (23 (v2.9.7)), a collection of
transcriptional regulatory interactions, which provides regulons
containing signed transcription factor (TF) - target gene
interactions compiled from 12 different resources as database and
VIPER (25 (v1.30.0)) as the inference algorithm. Depending on the
level of the counts and considering that one TF can have many
targets and one target can be regulated by more than one TF, the
algorithm can estimate the level of activity of the regulator based on
correlation between gene expression values.
Estimation of immune response
scores estimation
Immune-scores were estimated on the TPM normalized raw
counts using the EasieR package (26 (v1.4.0)) to generate immune
profiles on a per sample basis. Briefly, immune-scores are calculated
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using gene sets that have been validated in different publications
(see Supplementary Table 2) as signatures to estimate certain
hallmarks of the immune response.
Feature selection
The Boruta algorithm was applied using the R package Boruta
(27 (v8.0.0)) using a bootstrapping approach to ensure consistency
in the selection of features. Briefly, the algorithm performs feature
selection and it was applied 100 times using different seeds, each
time labeling features as ‘Confirmed’,‘Tentative’or ‘Rejected’.
Features labeled as ‘Confirmed’more than 90% of the times are
finally selected.
Processing of deconvolution features
Applying several combinations of deconvolution methods and
signatures leads to several hundreds of features describing the TME
landscapes in the samples. We applied specifically 6 methods
(quanTIseq, XCell, MCPcounter, DeconRNASeq, EpidISH and
CibersortX) and 9 signatures (BPRNACan, BPRNACanPro,
BPRNACan3DProMet, TIL10, LM22, CCLE.TIL10, CBSX.
HNSCC.scRNAseq, CBSX.Melanoma.scRNAseq and CBSX.
NSCLC.PBMCs.scRNAseq), see Supplementary Table 1
generating 351 features related to 13 cell types (and 30 subtypes).
To reduce the dimensionality and eliminate redundancies we then
applied a combination of unsupervised filtering techniques and
iterative linear and proportionality based correlations within each
cell type to form deconvolution feature subgroups. Applying an
unsupervised approach, we removed features with a high
proportion of zeros or low variance across samples. We then set
out to eliminate redundant features calculating pairwise
correlations of these filtered features to identify highly correlated
(≥0.7) feature pairs. We interpret these high correlations as
evidence that those features are estimating the presence of the
same cell-type despite potential differences in signature
nomenclature and hence combine these features into a single
feature subgroup. This procedure is carried out until no
correlations above the specified threshold remain.
Processing of TF activity features
The other set of descriptors of our samples stem from TF
activity analysis, which returns a score of TF activity for each TF in
each sample, amounting to 769 features. Adapting the Weighted
correlation network analysis (WGCNA) approach (28 (v1.72-5)),
we performed dimensionality reduction on these features by
constructing what we defined as Weighted TFs co-activity
networks (WTCNA) to detect highly correlated modules of TFs
based on pairwise correlation of their inferred activity. Modules are
defined as densely connected groups of nodes in the TF network,
where connections represent correlation of activities, and they are
arbitrarily named using colors. These TF modules were functionally
characterized using pathway activities estimated for each sample
(see Pathway Activity calculation above) and calculating the
Pearson correlation between these TF module scores and the
pathways activity scores. A PCA using the correlation matrix
between the TFs module scores and the pathways activities
allowed us to identify clusters of TF modules with correlated
pathway activities, further grouping TF modules into broader
functional groups. These combined TF module groups are named
by combining the names of TF modules included, thus generating
names that include multiple colors.
TF modules functional enrichment analysis
TFs module enrichment was done by identifying the hub TFs
from each module, these are genes which play a central role in the
network’s module structure and function due to their high
connectivity and influence on other genes. Thus, they often
represent key regulators or drivers of important biological
processes. We considered as hub TFs those which exhibit high
module membership, meaning their activity is strongly correlated
with the module’s score, indicating that they are highly
representative of the module’s overall behavior. Also, since these
genes are typically connected to many other genes within the
network, we also considered the level of connectivity for the hub
selection. We selected TFs with a high “degree”, a measure of the
number of direct connections or edges a TF has with other TFs in
the network. Overall, TFs with high module membership (r>0.8)
and belonging to the top 10% of genes with high degree were
selected as hub TFs. From the hub TFs, we identified their
corresponding target genes using the CollecTRI database (24). We
considered only the top 20% most variable (based on gene
expression) and unique target genes per TF module. Using these
lists, we performed an over representation analysis (ORA) using the
R package ReactomePA (29 (v1.46.0)) and the Reactome database
(30 (v1.86.2)) to provide functional interpretation of the modules.
Integration of deconvolution and
TF features
Using both deconvolution and TF activity features across
samples we set out to define combined features as groups of cells
that share TF activity profiles, potentially describing their
phenotypic states. We performed hierarchical clustering using
ward.D2 as the agglomeration method of the matrix of
correlation between grouped deconvolution features and TF
module scores. This leads to clustering of grouped deconvolution
features that each refer to specific cell types, producing further
grouping of different cell types. We refer to these as Cell type groups.
The existence of these cell type groups suggests that several cell
types could be activating specific biological processes, as reflected by
similar activities of the TF modules, potentially revealing different
cell states (e.g. cell growth profiles could be observed in cancer cells
or fibroblasts by detecting similar TF activities across patients).
These new cell type groups are new features composed of different
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grouped deconvolution features referring to different cell types,
named using a specific nomenclature (e.g Dendrogram_red_
turquoise.group_2) where “dendrogram”indicates that the feature
came from a hierarchical clustering, colors indicate which TFs
module were merged to produce the dendrogram and “group_x”
refers to the actual cluster in this dendrogram. This definition of cell
type groups implies cell types cluster together because they share
similar biological activity as measured by the TFs activity profiles.
Finally, the Boruta feature selection algorithm (see above) was
applied to measure the importance of these integrated features in
the classification of samples.
Validation cohort
An independent cohort consisting of 77 surgically resected
adenocarcinomas (1 = stage 0, 44 = stage I, 26 = stage II and 6 =
stage III) (31), from which only 70 early stage (I, II) samples were
used to validate the findings from the primary analysis cohort. The
validation cohort samples were collected at Vanderbilt University
Medical Center, Nashville, TN, from treatment-naive patients
undergoing surgical resection. The dataset included both bulk and
scRNAseq samples, with 15 patients for this last one. From this,
only 9 patients have both bulk and scRNAseq information.
Single-cell RNAseq analysis
Preprocessed single-cell RNAseq data was obtained from (31).
The Seurat package (6,32–34 (v4.3.0.1)) was used for downstream
analysis of the data in the R environment. We computed a principal
component analysis for dimensionality reduction followed by the
neighborhood graph on the first 20 principal components, using the
elbow plot, obtaining 24 clusters. Cell annotation was done with the
following references: Human Primary Cell Atlas, Immune Cell
Expression, Monaco and Blueprint Encode using the celldex R
package (35 (v1.10.1)). A consensus of all three annotations was
taken for identification of NK clusters. Reference-based
deconvolution was done using the scRNAseq object and the
BayesPrism method (36 (v.2.0.0)), obtained from the
Omnideconv R package (37 (v.0.1.0)).
Survival analysis
Patients from the validation cohort with early stage disease
(Stage I and II) were included in a survival analysis. “Time”is
measured in days and “event”was definedaseitherdeath,
recurrence or progression. Cox proportional hazards modeling
was performed using the R packages rms (v.6.8.0) and survival
(38,39 (v3.5.5)), and Kaplan-Meier curves were prepared using
ggplot2 (40 (v3.4.3)) and Survminer (41 (v0.4.9)). Univariate and
multivariate cox proportional hazards (coxPH) models were
evaluated across selected cell type groups to investigate whether
the effect of a single or multiple cell type groups on the hazard of an
event (death/progression/recurrence) was significant for the
survival outcomes. After fitting the CoxPH models to different
cell type groups combinations, we stratified our patients based on
the linear predictors of the model (risk scores) from which we
define as ‘high’the patients with risk scores above the median value
of the cox model’s linear predictors and as ‘low’the patients below
it. We then performed a Kaplan Meier analysis and plotted the
survival curves for each risk group. Finally both survival curves were
assessed via a log rank test to see if there was a statistically
significant difference between risk groups (p value < 0.01).
TCGA analysis
Samples counts from TCGA were retrieved using TCGAbiolinks
R package (42–44 (v2.30.4)). We selected open-access cases from the
project TCGA-LUAD, using transcriptome profiling as data category,
RNA-seq as experimental assay and STAR-counts as analysis
workflow type. Applying these filters, 600 cases were retrieved.
Since our focus was only on early stage samples (I, II) we selected
the corresponding 399 cases. Survival analysis was done following the
same pipeline described above; for this, the variables “vital_status”,
“days_to_last_follow_up”,“days_to_death”were considered to
determine the overall_survival (PFS) and whether the event (death)
occurred. Six patients were removed from this analysis due to the
presence of missing values in these variables.
Results
The Lung Predict cohort
Bulk RNAseq was performed on surgically resected tumor
tissue or tumor biopsies from 82 patients with NSCLC, 62 of
which were diagnosed as lung adenocarcinoma (LUAD) and were
considered further (the “Primary Analysis Cohort”). Of these 62
adenocarcinomas, 30 are female and 32 are male; 21 were enrolled
at Stage I, 10 at Stage II, 11 at Stage III and 20 at Stage IV. A full
breakdown of this cohort is presented in Table 1 and a patient
inclusion flow chart is included in Figure 1, together with details of
the validation cohort (see the following sections).
We applied a computational immunology approach integrating
several features derived from transcriptomics data to better
characterize and profile the TME of LUAD tumor samples in our
cohort. The features extracted included cell-type proportions, level
of activity of specific Transcription Factors (TFs) and scores of
immunogenicity commonly used in the literature.
Briefly, reference-based deconvolution involves applying
statistical methods to infer cell type proportions in biological
tissue samples starting from transcriptomic profiles of specific cell
types (signatures) and bulk transcriptomics from the samples, such
as tumoural tissues in this case. We applied several deconvolution
methods to the transcriptomes from our LUAD samples and used
different cell type signatures to generate estimates of cell type
proportions (see Methods, see Supplementary Table 1).
Normally, the application of dimensionality reduction methods,
such as pathway activity analysis or the calculation of immune cell
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type proportions allows better interpretation of the signal from bulk
gene expression data but at the cost of introducing artificial noise or
removing potentially interesting data features. Selecting
deconvolution methods is not trivial and the different results
obtained with different methods and signatures suggest that they
capture different aspects of the samples. In this study, we aimed to
use a variety of different methods and signatures instead of choosing
a single one. However, using several deconvolution methods and
signatures, each covering a range of cell types, produced over 300
different deconvolution-related features, which led us to an increase
in dimensionality, exposed high variability between features related
to the same cell types, ultimately hindering interpretation and
imposing the need for much larger sample sizes to achieve
statistical power. This pushed us to address these issues by
engineering novel approaches to produce meaningful cell
deconvolution features integrated with TF activity profiles.
We therefore performed TF activity estimation, which is an
approach to quantify the strength of activity of specificTFs
(essentially an estimated combination of their abundance as proteins
and their post-translational modifications if required for their activity)
based on the expression level of their targets. These approaches involve
a prior-knowledge network of TF-target interactions in combination
with gene expression levels from bulk transcriptomics data and they
allow to identify activation of specificregulons(TFsandtheirtargets)
despite the fact that TF activities are rarely regulated at the
transcriptional level (see Methods). Complementary to this analysis,
we have calculated scores of activation of specificpathwaysusing
PROGENy, which help us to define the processes that dominate the
transcriptome of our patient samples (see Methods).
Finally, several scores have been proposed in the literature to
estimate the level of immunogenicity in tumor samples from bulk
transcriptomics data and we have calculated these immuno-scores
across our cohort (see Methods).
FIGURE 1
An overview of the Lung Predict cohort. A description of the cohort is presented on the left with some summary graphics on the right specifically
detailing tumor stages in male and female patients, RNAseq batches, presence of the most frequent somatic mutations as detected by a gene panel
assay (STK11, EGFR, KRAS), smoking status, metastasis occurrence, age category, type of sample (primary or metastatic sample), location of
the sample.
TABLE 1 Summary of the total number of patients included in the Lung
Predict and the Vanderbilt validation cohort (VUMC) (percentages of
totals in brackets).
Lung Predict VUMC
Total 62 77
Sex (Female) 30 (48) 42 (55)
Age (<70) 46 (74) 47 (61)
Smoking Status: Never 10 (16) 14 (18)
Smoking status: Former 9(15) 53 (69)
Smoking status: Current 43 (69) 10 (13)
Stage
0–-1 (1)
I21(34) 44 (57)
II 10 (16) 26 (34)
III 11 (18) 6 (8)
IV 20 (32) -
Metastatic (non-primary) 20 (32) 0–
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Deconvolution features along with inferred
TF activity profiles reveal different immune
profiles describing the tumor
microenvironment across patients of
different stages
As a result of applying cell type deconvolution, we considered
351 features for each sample. Multiple signatures were used for each
cell type, leading to several estimates of proportions of the same cell
type (e.g. monocytes). This multiplication of features referred to the
same cell type is due either to the fact that they capture different cell
subtypes (e.g. classical or non-classical monocytes), or simply to
differences in the generation of the signatures from the literature
(from in-vitro co-cultures, from tumor samples, etc.). To reduce the
redundancy and dimensionality in our data we first grouped
deconvolution features quantifying the same cell types based on
their correlation across patients and generated deconvolution
feature subgroups (see Methods). Briefly, if multiple features
estimating the same cell type have high correlation it suggests
that they do not differ biologically and do not capture distinct
subtypes, so we merge the corresponding features) (see
Supplementary Table 3 for details).
We then performed unsupervised hierarchical clustering of
patient samples based on the grouped deconvolution features, to
identify patient clusters with correlated immune cell proportion.
We identified three patient groups based on the grouped
deconvolution features and interpreted them based on the
immuno-scores in each sample. Patient cluster 1 contained
mostly “intermediate”tumors, patient cluster 2 contained mainly
“hot”tumors and patient cluster 3 was constituted by a mixture of
“cold/intermediate”tumors (Figure 2). We also visually notice more
early stage samples (I, II) in patient cluster 1, a high presence of late
stage samples (IV) in patient cluster 3 and a high presence of
intermediate stages (III) in patient cluster 2.
To estimate the main processes driving the transcriptomic
profiles of our samples, we calculated TF activities and
constructed weighted co-activity TFs networks, identifying TF
modules (named with colors), which are groups of TFs showing
correlated activity profiles across samples (see Methods and
Supplementary Table 4 for TF module composition). We then
applied a Boruta feature selection approach to select the most
important deconvolution features driving the patient classification
into these three patient clusters, identifying 27 deconvolution
features to be the most influential.
To further investigate the mechanistic processes that might
underlie the 3 patient clusters, we observed how different features
(cell type composition, TF module scores, and pathway scores)
correlated with each other across patients (c.f. Figure 2). We note
that several cell types appear in multiple rows as separated features,
possibly indicating that the different signatures capture distinct cell
subtypes. For example we observe multiple features related to NK
cells and B cells. The name of the feature reflects the name of the
public signature that generated this feature and often suggests
which subtype is captured (activated/naive etc.), while the names
including ‘subgroup’denote several features that were combined in
the earlier step of deconvolution feature grouping since they
displayed strong correlations across patients. We characterized
the 3 patient subgroups as follows (Figure 3) according to the
values of different cell type features:
Patient cluster 1 (“intermediate”tumors): associated to low
presence of cancer cells, several CAF signatures, some myeloid
cells (M1 macrophages and monocytes), some lymphocytes (CD4 T
helper), some type of unspecified NK cells and higher abundance of
B cells, resting CD4 and dendritic cells with NK cells denoted as
activated. TF activity analysis showed an involvement of TFs
modules yellow, brown, red and blue, involved in biological
pathway activities related to Androgen, Trail and p53, suggesting
a relation with apoptosis and tumor suppression.
Patient cluster 2 (“hot”tumors): associated to intermediate
presence of cancer cells, several CAF signatures, some myeloid
cells (M1 macrophages and monocytes), some lymphocytes (CD4 T
helper) and some type of unspecified NK cells and low abundance
of naive B cells, resting CD4 and dendritic cells and NK cells
denoted as activated with varying levels of a group of non-naive B
cells. TF activity analysis showed high scores in modules black,
green, red and brown, which seems to be related to high scores of
JAK/STAT, VEGF, MAPK and hypoxia pathways, as well as low
levels of modules blue, and yellow, with particularly low scores for
Trail and p53, suggesting activation of immunity, stress response
and proliferation.
Patient cluster 3 (“cold/intermediate”tumors): mostly late stage,
showing particularly high proportions of cancer cells, CAF cells and
some macrophages. TF activity showed particularly high scores for
module black and low scores for Trail, p53 but also NFkB, VEGF
and JAK/STAT, MAPK, suggesting a highly aggressive,
immunosuppressive and proliferative phenotype.
To better interpret the duality of specific cell type features we
consider how they correlate with each other (row feature groups 1
to 3 on Figure 3).
Interestingly, we identified a complex behavior profile for the
different features estimating the presence of the same cell types. In
some cases, signature names can suggest which cell subtype we are
considering but there are known issues with signatures for myeloid
cell subtypes, for example, despite the importance of these details to
understand whether the TME is immunosuppressive or not. Here
we focus on NK cells, for which several signatures appear to give
conflicting results. The first NK profile (exemplified by the NK
CBSX_melanoma …feature from feature group 1) is associated with
a presence of cancer cells and CAFs and is found in samples with
lower immune scores (subset of Patient cluster 3). This profile may
imply the presence of dysfunctional NK cells, which are
characterized by reduced proliferation and cytotoxic capabilities.
The other profile (NK from EpiDISH_CCLE_NK …from feature
group 2) is associated with endothelial cells and the presence of
certain B and CD4 T-cells found in samples with intermediate
immune-scores, perhaps signifying the presence of tertiary
lymphoid structures (TLS), organized immune cell aggregates that
can be good prognosis markers when identified through spatial
omics. Another group of NK cells (NK from feature group 3) more
associated with the presence of neutrophils, dendritic and M2
macrophages does not seem to be associated to the 3 patient
clusters identified, showing variable values in all sample clusters,
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Taken together, these findings suggest that NK cells of different
kinds, associated with other immune cells, can be found in either
immune-desert tumors, where they are likely to be dysfunctional,
typically in late stage samples, but also in intermediate tumors and
early stage samples, where they can be associated with different
kinds of immune landscapes, depending on their partners.
Data integration to uncover associations
between cell-type deconvolution features
and TF activity profiles
Having observed interesting associations between TF activity
and pathway scores and TME landscapes, and the duality of certain
cell types (NK for example) we decided to investigate whether a
combination of these features could reveal connections between cell
states across the different cell types in the TME. As an example, we
reasoned that the presence of specific cytokines in the tumor could
have an impact on the state of specific immune cells (say
cytotoxicity of NK cells) in specific patients. We therefore set out
to develop a computational method to integrate cell type proportion
estimates and TF activity scores to evaluate the state of the different
cell populations present in the samples.
Briefly, we start by considering the grouped deconvolution
features and TF module activity scores as descriptors of our
samples. Since TF module scores and grouped deconvolution
features can both be calculated in each sample, we can visualize
the association between each TF module and the different grouped
FIGURE 2
Overview of patient sample clustering based on immune deconvolution subgroups. Our immune deconvolution features after being processed
identified three clusters of patients corresponding to “cold/intermediate”,“hot”and intermediate tumors. Heatmap showing patients within the 3
clusters identified by hierarchical clustering. The gray scale at the top corresponds to the stage of the disease: the darker the color, the later the
stage. The orange to brown scale corresponds to the immune-scores (see Methods): the darker the color, the higher the immune scores.
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deconvolution features as a heatmap. Hierarchical clustering of this
matrix (deconvolution feature by TF module) allows us to cluster
deconvolution features, even grouping those that estimate
proportions of different cell types, allowing us to define Cell Type
Groups (see Methods). The appearance of clusters of deconvolution
features referring to different cell types but having similar TF
activity profiles suggests some commonality of biological
processes ongoing in the distinct cell types present in specific
patients (Figure 4).
Integrated analysis of cell type
composition and TF activity profiles in
early stage LUAD samples uncovers two
distinct patient groups
Our results highlighted a possible difference in the immune profile
of samples according to stage, with most late stage samples (stage IV)
being in ‘cold’patient groups. To avoid any confounding effect of stage
and sample type (primary vs. metastasis biopsy) and reduce the
heterogeneity of processes likely to take place in our samples, we
decided to focus on the early stage samples (stages I and II).
To assess the immune landscape in the stage I and II samples of
the Lung Predict cohort, we performed immune cell-type
deconvolution and inferred TF activities across these samples
(see Methods).
Focusing specifically on stage I and II from the Lung Predict
cohort (31 samples), we applied our integrative approach to
combine grouped deconvolution features and TF module activity
scores. (99 deconvolution features including 46 cell subgroups
(Supplementary Table 5) and 53 non-grouped features, 7 TF
modules (Supplementary Table 6), each containing different
groups of TFs (Supplementary Figure 1A) correlating with
different biological activities (Supplementary Figure 1B).
In order to further study the composition of these modules, we
identified the most central (hub) TFs in each module (see Methods),
which highlighted 20 hub TFs in total (6 for module red, 6 for module
brown, 3 for module black, 2 for module green and 3 for module blue).
No hub TFs were found for module turquoise and yellow
(Supplementary Figure 1C). Further enrichment of these TFs
modules was done by identifying the corresponding target genes of
the hub TFs (see Methods). Using only target genes that belong to only
one module to avoid overlapping ones, we performed an over-
representation analysis (ORA) and identified enriched pathways
using the Reactome database. Results showed an enrichment for
neutrophil degranulation and chemokines binding for TFs module
black, suggesting a potential role of this module in the interaction of
neutrophils with other cells. The Brown module is mostly enriched in
pathways related to EGFR signaling, suggesting a potential role of these
TFs in regulation of cell growth. Module blue showed enrichment for
toll-like receptor pathway components, suggesting an association with
immune suppression factors and tumor progression. Module green
FIGURE 3
Annotation of the three patient clusters from (Figure 2) using TFs module scores, pathways scores, and values of Boruta selected immune
deconvolution features. (A) Three feature groups (in rows) are identified from the deconvolution features (values shown on scale red to blue from
high to low). The panel also shows as column annotations of each sample the immuno-score (brown to white from high to low), the TF module
scores (red to green from high to low, see composition of each module in (Supplementary Table 4) and the pathway scores (yellow to blue from
high to low). (B) Heatmap showing significant Pearson correlation between pathway activities and TF modules scores shown in panel A (denoted by
the colors at the top of the columns: blue, cyan, yellow, brown, red, black, green, pink from left to right). Heatmap colors represent levels of
correlation (darker red implies high positive correlation, darker blue implies high negative correlation). Statistics are shown as text only for significant
correlations (p value < 0.05).
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showed an enrichment in transmembrane transporters, this might
suggest the metabolic uptake and efflux of nutrients and the metabolic
crosstalk between cells in the TME. Finally, module red is enriched in
cell cycle checkpoint terms, confirming its role in regulation of cell
proliferation (Supplementary Figure 1D).
In order to reduce the dimensionality of these TF modules, we
identified different module categories by using information of
signaling pathways from PROGENy (see Methods). From this we
performed a PCA analysis to see which TF modules cluster together
based on their association with these pathways. TF modules blue,
green and yellow clustered together and showed a common activation
of p53 and apoptosis pathways while TF modules black, brown,
turquoise and red grouped together by showing a similar association
to VEGF, NFkB and TGFb (Supplementary Figure 2).
Taken together and considering both enrichment and PCA
analysis, our results showed that modules blue, green and yellow are
more associated with cancer-related pathways, including tumor
suppression and progression; while brown, black, turquoise and
red have an association with cell growth.
Associations between these two categories of TFs modules and
deconvolution features were investigated defining several cell type
groups with correlated TF module scores (see Methods). As a
reminder, cell type groups consist of subsets of grouped
deconvolution features that share similar TF profiles, for example,
Dendrogram_red_turquoise_black_brown.group_1, which contains
several deconvolution features related to B cells, cancer cells and
dendritic cells (Supplementary Table 7). With this approach, 14 cell
type groups containing deconvolution features with significant Pearson
correlations with TF module scores (p-value < 0.05, cut.height = 5)
were identified. These cell type groups naturally divide patients into
two groups (Figure 5A). A feature selection algorithm (see Methods)
was applied to estimate the importance of different cell type groups in
the classification of patients in the two patient clusters identified by
unsupervised hierarchical clustering. After 100 repeats, 10 cell type
group features were selected as important to classify Lung Predict early
stage samples into the two patient clusters shown in (Figures 5A,B).
These cell type group features can themselves be grouped into two
main broader categories (Figure 5C).
Performing a PCA using these cell type groups as features across
the samples, we observed that two cell type groups (PCA variance
explained >20%) were mostly driving this separation of the two
patient groups (Figure 5D). The first, namely “Dendrogram_red_
turquoise_black_brown.group_3”.
is composed mainly by resting NK cells, cancer cells, fibroblasts,
CAF, NKT cells, T helper cells, dendritic cells and M1/M0
macrophages (Supplementary Table 7)andissignificantly
associated with pathways related to cell growth and angiogenesis
based on the TF modules involved (red, turquoise, black and brown,
c.f. Supplementary Figures 1B,C).
The second cell type group, namely “Dendrogram_yellow_
blue_green.group_2”, is highly present in patients with intermediate
immune-scores and is composed mostly by CD4 T cells, dendritic cells,
M2 macrophages, neutrophils, monocytes, mast cells, endothelial cells
and NK cells, while being associated to pathways related to immune
response activation and tumor suppression based on TF modules
involved (yellow, blue, green, c.f. Supplementary Figures 1B,C).
The two patient subgroups identified in the
LungPredict early stage samples are
validated in an external early stage
LUAD cohort
Senosain et al. have recently published an in-depth
characterisation of an early stage clinically annotated LUAD
cohort (31). This cohort, to which we will refer as Vanderbilt,
FIGURE 4
Overview of the deconvolution and TF activity integration pipeline. Immune cell deconvolution features and modules of TFs sharing inferred activity
profiles are integrated together using a combination of clustering methods in order to reduce the dimensionality of the results (see Methods). The
output are groups of immune cells characterized by the same TF activities.
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including 70 early-stage (stage I and II) lung adenocarcinomas, for
which bulk RNAseq as well as 15 scRNAseq samples are available
(with an overlap of 9 patients), was used as external validation.
Before using the validation cohort, we verified that these
two datasets Lung Predict and Vanderbilt were comparable
(Supplementary Text 1,Supplementary Figures 3,4).
To validate our newly identified patient clusters, we considered
the same 10 most important cell type groups identified via the
feature selection algorithm using data from our validation cohort to
see if the identified groups can also classify an independent cohort,
namely the stage I and II samples from the Vanderbilt cohort. We
performed a cell group projection analysis, which consists of
identifying the same TFs modules based on the gene expression
from the independent cohort. We then projected the same
deconvolution subgroups into the unprocessed deconvolution
features from the Vanderbilt samples and recreated the same cell
type groups identified in the Lung Predict cohort. The independent
validation cohort samples also display a separation into two patient
groups based on the values of the selected cell type groups
(Figure 6A). A PCA analysis suggests that the feature with the
highest contribution (>40%) is the same as in the Lung Predict
analysis (Figure 6B). This important cell type group is composed
mainly by resting NK cells and M1 cells and associated with cell
growth and angiogenesis. This cell type group is present mostly in
patients with intermediate and high immune-scores and lacking in
patients with low immune-scores (Figure 6C).
Taken together, these results suggest that the two patient groups
we identified in the LP cohort are also identified in the validation
cohort. Our findings suggest the importance of resting NK and M1
cells and activation of cell growth and angiogenesis in the
separation of the two patient clusters observed similarly in the
two cohorts.
Differential expression analysis between
patients with alternative profiles of NK cells
hints at differing cytotoxicity of these cells
In order to understand the difference between two clusters of
patients defined by the selected cell type groups, we performed a
differential expression analysis between Vanderbilt patients from
cluster 1 (green) and Cluster 2 (red) in Figure 6A. We obtained 665
differential expressed genes (p.adj < 0.05, abs(log2FoldChange) > 1)
between the two patient subgroups (Figure 7A). We then summarized
these DEGs into KEGG pathways identifying enrichment of
deregulated genes in several immunologically and oncologically
relevant pathways (p value < 0.01) (Figure 7B), including the NK
cell-mediated cytotoxicity pathway. A network plot was generated
linking enriched pathways and the genes contained in them in order
to interrogate the genes present in this pathway and understand the
overlap with other immunologically relevant pathways (Figure 7C). In
this network plot, we see the downregulation of CD3 (epsilon and
FIGURE 5
Selected cell type group features reveal two profiles in the Lung Predict early stage cohort. (A) Hierarchical clustering dendrogram of early stage
patient samples using the 14 cell type group scores. (B) Feature selection based on importance for predicting the two patient clusters in 5A using a
Boruta algorithm, showing confirmed features (green) and rejected features (red) after 100 repeats (see Methods). (C) Heatmap showing the 10 cell
groups selected after feature selection. The panel also shows as column annotations of each sample the immuno-score (brown to white from high
to low), the TF module scores (red to green from high to low, see composition of each module in Supplementary Table 5) and the pathway scores
(yellow to blue from high to low). (D) Contribution of cell type groups to the PCA variation in the classification of patient clusters.
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delta) as well as CD8 alpha suggesting a reduction in the activation -
and, perhaps, no involvement - of CD8 T-cells in the functional profile
of NK cells from cluster 1. Many KIR genes, important for NK
cytotoxicity) appear downregulated in this cluster, confirming the
potential presence of dysfunctional or resting NK cells in this first
cluster of patients. In a deeper analysis of the NK-cell mediated
cytotoxicity pathway (Supplementary Figure 5), we observe a
downregulation in inhibitory receptors KLRC1 (NKG2A) and
KIR3DL2. The inhibitory potential of NKG2A is dependent on its
dimerization with CD94, which is not differentially expressed in our
analysis (45). Anfossi et al. reported that KIR+NKG2A+ NK cells were
responsive upon stimulation with tumor targets whereas NK cells
lacking these inhibitory markers are hyporesponsive (46). Furthermore,
the observed downregulation in protein kinase C (PKC) can have a
direct effect on the granulization and cytotoxic effect of these NK cells
(47). Taken together, these results suggest that these two patient
clusters might be defined by the presence of either functional or
dysfunctional (resting) NK cells.
Single-cell analysis in the validation cohort
confirms multiple subgroups of NK cells
In an effort to better characterize the dual behavior associated with
NK cells detected at the bulk RNAseq level, we analyzed single cell
transcriptomics data from 15 patients from our validation cohort.
Following standard procedures for scRNAseq analysis, we performed
graph-based clustering of cells to identify cell groups sharing similar
gene expression (Figure 8A) using annotations already provided in the
scRNAseq object from the validation cohort (31) and didn’tidentify
any batch effect (Supplementary Figure 6). Since our focus was to
identify different NK subclusters, we then re-annotated these cells. We
performed annotation using reference expression datasets with curated
cell type labels for automatic annotation in order to establish a
consensus for the NK cell annotation (see Methods) (Supplementary
Figure 7). We extracted the cell clusters identified as NK (cluster 8) and
performed an additional clustering step to identify subclusters within
this population. We obtained 3 subclusters of NK cells (Figure 8B)that
we investigated based on specific NK markers. All three subclusters
showed a high expression of KLRK1, which is expressed on all NK cells
as well as on a small subset of cytotoxic CD8 T-cells. Interestingly,
when profiling the expression of GNLY (cytolytic compound expressed
by cytotoxic cells) and KLRC2 (activation receptor, expressed on NK
cells), cluster 0 did not show any detectable expression. Cluster 1 also
lacks expression of KLRC2 while Cluster 2 shows expression of both
markers, with higher expression of GNLY. Further analysis revealed
that cluster 1 had the lowest expression of perforin (PRF1), granzyme B
(GZMB) and interferon-g(IFNG), suggesting that this cluster may
include resting or dysfunctional NK cells, with reduced cytotoxic
potential. Clusters 0 and 2 display high expression of PRF1, GZMB
and IFNG suggesting that they are functionally competent sub-types of
NK cells. Cluster 0 is the only NK cluster expressing FCGR3A (Fc-
gamma receptor III, also known as CD16), which suggests that it may
contain cytotoxic, peripheral blood NK cells (48).Cluster2hashigh
expression of ITGAE (CD103) and ZNF683 (HOBIT - regulates
immune cell development (49) without any expression of S1PR5
FIGURE 6
Selected cell type groups features found in LP cohort projected in early stage samples of validation cohort. (A) Dendrogram obtained by hierarchical
clustering revealed two groups of patients based on the cell type groups values. (B) Cell type groups feature contribution to the PCA variation in the
classification of patient clusters. (C) Early stage patient samples from the validation cohort are divided in two main clusters based on the values of
the selected cell type groups from LP analysis.
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(plays a role in migration of immune cells) and low expression of KLF2
(plays a role in the regulation of NK cell maturation), which suggest
that this cluster may include cytotoxic, tissue-resident NK cells (50)
(Figure 8C). For details about the differential expression markers
between the NK clusters refer to Supplementary Tables 8,–10.We
observe varying proportions of NK cell subtypes across our patient
cohort, but unfortunately only 9 patients had both scRNAseq and bulk
RNAseq, from which only 7 correspond to early stage samples (I, II)
(Figure 8D), so we could not confidently estimate whether our
grouping of bulk RNAseq samples into two patient groups according
to NK subtype (indicated by numbers on each barplot) could be
associated to the dominance of dysfunctional NK cells in the
scRNAseq data.
Reference-based bulk RNA-seq
deconvolution using the scRNAseq from
the validation cohort to estimate cell type
proportions in our primary cohort reveals
the different annotated NK profiles in the
LP early stage patients
To strengthen and validate our findings regarding cell type
composition in the bulk data from our LungPredict cohort, we
performed single-cell reference-based bulk RNAseq deconvolution
using the scRNAseq data from Vanderbilt as our reference for
extracting signatures. We used BayesPrism as implemented in the
Omnideconv R package (see Methods) to deconvolve our early stage
LungPredict samples. We identified the three different annotated NK
subtypes across our samples and found the peripheral cytotoxic NK cell
subtype to be the most predominant and the dysfunctional NK subtype
to be the least abundant (Figure 9).
Cell type groups are associated with
recurrence-free-survival in the
validation cohort
Focusing on early stage disease, we can evaluate the potential
association of the immune landscape and disease recurrence. The
association of the immune profiles determined through the integration
of shared inferred TF activity and the deconvolution features with
recurrence was assessed using the mature follow-up available for
patients from the validation cohort. CoxPH models were evaluated
across all the 10 selected cell type groups and then used to stratify
samples based on the linear predictors of the model. Kaplan Meier
analysis and log rank tests were used to assess the difference between
FIGURE 7
Supervised analysis of the patient groups identified in Figure 6A.(A) Volcano plot summarizing the 665 differentially expressed genes (DEGs) (padj < 0.05,
abs(log2FoldChange) > 1) identified by comparing the green and red clusters from Figure 6A (B) Top results from the KEGG pathway enrichment analysis
(p value < 0.01) on the DEGs summarized in the volcano plot. (C) Network plot of immunological pathways showing the genes involved in each pathway
and overlapping among pathways. Node colors communicate the log2FoldChange of the genes between the two patient groups.
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risk groups (see Methods). Two multivariate models were found as
significant after log rank test (p value = 0.007 and p value = 0.0068)
(Figure 10). In model 1, the variables (covariates) that are most
associated to recurrence free survival were Dendrogram_red_
turquoise_black_brown.group_3, including resting NK cells,
Dendrogram_red_turquoise_black_brown.group_9 and
Dendrogram_red_turquoise_black_brown.group_combined_1,
including more active/cytotoxic NK cells with other immune cells like
neutrophils, T cells and activated dendritic cells.
Model 2 also contains as covariate the Dendrogram_red_
turquoise_black_brown.group_3 feature, and additionally two other
cell type groups: Dendrogram_yellow_blue_green.group_2, containing
the NK resting subgroup as well as otherrestingimmunecells(CD4,
dendritic, Mast), and Dendrogram_yellow_blue_green.group_3,
containing the more active NK subgroup in combination with T cells
(CD4 and CD8) and dendritic cells in their active state (see
Supplementary Table 7 for detailed composition of the cell groups).
This result is limited by small sample size (n=70) and a low event rate
FIGURE 8
Single-cell RNAseq characterization of natural killer (NK)-cell clusters in LUAD samples from the Vanderbilt cohort. (A) Graph-based UMAP clustering. (B)
UMAP of cluster 8 identified as NK cells after automatic annotation showing the 3 NK subclusters. (C) Characterization of the three NK subclusters using
several cell surface markers. (D) Proportions of each NK cluster, labeled according to the marker analysis. The numbers at the bottom correspond to the
patient cluster to which the corresponding bulk RNAseq sample belongs (Cluster 1= green, Cluster 2 = red) according to (Figure 6A).
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(n=11), however the results serve as preliminary evidence for the
applicability of transcriptomically defined immune patient profiles in
real world outcomes among early stage lung adenocarcinoma patients.
TCGA LUAD cohort analysis confirms
similar immune infiltration profiles across
early stage patients
To further test the validity of our findings, we selected the 399
early stage (I,II) lung adenocarcinoma (LUAD) from TCGA. We
performed immune cell type deconvolution and inferred TF activity
across these samples as described above. We then projected and
recreated the 10 selected cell type groups (see above) using the same
TF modules found in the analysis mentioned above using early stage
samples in the primary and in the validation cohorts. Our results
showed three patient clusters related to distinct immune infiltration
profiles. Two of the three patient clusters revealed similar expression
patterns as the ones found in the LP and Vanderbilt cohorts
(Figure 11A) and we identified patient clusters 1 (red) and 3
(green) as the clusters defined by two opposite NK profiles
(Figure 11B). We then performed a differential expression analysis
and a functional enrichment analysis using the KEGG database,
identifying 1518 differentially expressed genes (padj <.00001, abs
(log
2
FoldChange) > 1.5) revealing an enrichment in immunological
and cytotoxic related pathways (p value < 0.05) (Figure 11C).
Survival analysis in TCGA revealed that
both resting and activated NK subtypes are
significant predictors of survival
Linear predictors from univariate cox proportional hazards
(coxPH) models across all the 10 selected cell groups were
evaluated to stratify patients based on their risk-scores,
subsequently computing the survival curves through Kaplan
Meier analysis and testing whether the survival between the two
groups is significantly different (p value < 0.01). In this dataset we
applied stricter filtering due to the high number of patients (n=393),
stratifying as high-risk only the top 34% of patients (based on their
risk scores) and the remaining 66% as “low-risk”. Two models were
found to be significantly associated with the survival time of
FIGURE 9
Reference-based deconvolution of primary cohort using BayesPrism method. (A) Deconvolution proportions from early stage samples from the
LungPredict cohort. NK cells are subdivided into the three subgroups considered above: dysfunctional, peripheral and tissue resident. (B) NK
subtypes proportions in early stage samples using the cell-type annotations from the scRNAseq object of the validation cohort.
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patients (Figure 12). Cell type groups dendrogram_red_
turquoise_black_brown.group_3 and dendrogram_red_turquoise_
black_brown.group_4 with p value = 0.0063 and p value = 0.0027,
respectively. The first cell type group corresponds to the subgroup
of resting NK cells with macrophages M1 and the second one
corresponds to the NK subgroup in combination with cancer,
fibroblasts, dendritic, and Thelper cells (see Supplementary
Table 7). Both these features were predictors of survival in the
univariate models. These results suggest an important association
between these NK subtypes and patient survival.
Patient subgroups identified are related to
oncogene and tumor suppressor
TF modules
To further investigate the functional mechanisms leading to the
subgrouping of patients into 2 categories according to their TME
landscapes, we further explored the association between TF
modules and deconvolution features. In particular we highlight
the modules that are associated with abundance of cancer cells as
potentially capturing oncogenic processes while other modules
negatively correlated with cancer cells could be considered as
tumor suppressor processes (Figure 13). The module that is more
strongly positively correlated with cancer cell estimates is red, which
shows strong repression of Trail and p53 pathways and activation of
MAPK, VEGF and Hypoxia and is strongly positively correlated to
the presence of resting NK cells and negatively to the presence of
active NK cells. The black and brown modules are negatively
correlated with the same features and show instead strong
activation of immune processes (NFkB and TFGb). The
repression of module red clearly sets patients in cluster 1 apart
(c.f. Figure 5C). The TF activity profiles across early stage Lung
Predict samples of TFs contained in each module are shown in
Supplementary Figure 8.
Since TF activities are estimated based on bulk RNAseq, we
cannot be sure of whether these pathways are activated mainly in
the cancer cells or the correlation directly reflects the tumor sample
purity. However, combining these two types of features we have
demonstrated that discordance between deconvolution signatures
FIGURE 10
Multivariate cox proportional hazards (Cox PH) models were developed across all selected 10 cell type groups (Figure 5B). (A) Survival curves based
on high and low risk groups using linear predictors after fitting Cox PH model using as covariates cell type groups corresponding to Dendrogram_
red_turquoise_black_brown.group_3, Dendrogram_red_turquoise_black_brown.group_9 and Dendrogram_red_turquoise_black_brown.group_
combined_1 (p value = 0.007). (B) Survival curves based on high and low risk groups using linear predictors after fitting Cox PH model using as
covariates cell type groups corresponding to Dendrogram_red_turquoise_black_brown.group_3, Dendrogram_yellow_blue_green.group_2 and
Dendrogram_yellow_blue_green.group_3 (p value = 0.0068).
Hurtado et al. 10.3389/fimmu.2024.1394965
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might simply reflect substantial differences in the subtypes of cells
they refer to.
Discussion
This study leveraged integrative computational approaches to
dissect immune heterogeneity in the tumor microenvironment of
lung adenocarcinomas. Integrating bulk transcriptomics with
bioinformatic analyses for cell type deconvolution and TF activity
inference, we identified profiles associated with dual immune cell
phenotypes (51).
Specifically, our combined analysis suggested the presence of
two subgroups of natural killer (NK) cells. One subgroup is
associated with a high proportion of cancer cells and CAFs and
could be potentially associated with a “resting”or “dysfunctional”
behavior. Dysfunctional NK cells are characterized by reduced
proliferation and cytotoxic capabilities. In contrast, we inferred a
high presence of B-cells, T-cells and NK cells in early stage samples
with high immune-scores. This different group of NK cells may
display cytotoxic capabilities and might even be subdivided into two
NK profiles, depending on co-occurrence of other cell types, namely
endothelial cells. Focusing on early stage (stage I and II) patient
samples, we confirmed these dual NK subgroups in an independent
LUAD cohort and in the 399 stage I and II LUAD samples from
TCGA after further characterizing them in an scRNAseq dataset.
Interestingly, in the scRNAseq data analysis, we identified three
major NK clusters. We characterized these three clusters as resting/
dysfunctional, circulating cytotoxic and tissue-resident cytotoxic
NK cells. The single-cell analysis provided independent validation
of the computationally defined NK cell subtypes/states, and
provided further resolution into tissue-resident versus circulating
NK cell subsets. Finally, we were able to show that our engineered
features based on cell type groups, which take into account TF
activity profiles to estimate presence of groups of different cell types,
have predictive value on recurrence free survival (in our validation
cohort) and on overall survival (in the TCGA cohort).
To summarize, we revealed a striking duality in NK cell
phenotypes across three independent cohorts, with NK subsets
displaying signatures of dysfunctional exhaustion versus cytotoxic
competence. Dysfunctional NK cells have reduced proliferative and
functional capacity, resulting from constant exposure to immune
suppressive signals in the tumor microenvironment. Our findings
align with other recent studies showing phenotypic heterogeneity in
NK cells and other immune cell types in the context of cancer (52,
53) and with reports that NK cell states might be essential for
response to PD-1/PD-L1 blockers (54)andkeyplayersin
immunotherapy (55,56). Beyond those results, our approach is a
first step towards delineating the type of inter-cellular interactions
that could be established in the TME in connection to the presence
of these two NK cell subtypes.
Overall, our study sheds light on the significant diversity of
immune cells in the lung cancer microenvironment. The integrated
computational frameworks provide an accessible, robust and
general methodology for immune profiling of tumor samples via
bulk RNAseq.
FIGURE 11
TCGA analysis using selected cell type groups from Figure 9A.(A) Heatmap showing cell type groups scores after projection using the computed
deconvolution and the inferred TF activity. (B)Samples dendrogram using hierarchical clustering based on the cell type groups scores (C). Dotplot
showing KEGG pathways (p value < 0.05) related to the enrichment of DEG (padj < 0.05, abs(log
2
FoldChange) > 1) after comparison between patient
Cluster 1 and 3 (red and blue in panel B, respectively).
Hurtado et al. 10.3389/fimmu.2024.1394965
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Immune cell dysfunction arises from continuous stimulation in a
persistent inflammatory environment. In the tumor microenvironment
(TME), the presence of various immune suppressive signals
exacerbates immune cell dysfunction leading to tumor progression
and metastasis (57). The ability to resolve immune cell dysfunction
versus activation states could significantly improve prognostic models
and prediction of immunotherapy response (58). Whether these
dysfunctional characteristics are a result of exhaustion or senescence
will need to be determined (59).Ourapproachisaverysteptowards
delineating the type of inter-cellular interactions that could be
established in the TME in connection to the presence of these two
NK cell subtypes.
Our exploration of the single cell data further strengthens the
hypothesis that there are two major subgroups of NK cells,
dysfunctional/resting and functional, associated with immune
cells presence and that patients might be characterized based on
the dominance of either of these two NK cell subgroups. It could be
speculated that the profile of NK cell subtypes present could be
related to response to immune checkpoint blockers. However, early
stage LUAD patients are still rarely treated with this type of therapy,
while only a few patients in Lung Predict received it, requiring
alternative cohorts to validate this hypothesis. However, we note
that in any non-pharmacologically treated tumor a strong immune
response is likely to improve survival, potentially explaining why
the active NK subtype, which associates with M1-like macrophages,
could also improve survival in cases that are treated by surgery
alone, as those included in our primary and validation cohorts.
We note that our initial analysis on the Lung Predict cohort
across stages suggests that the duality in NK cells populations is not
limited to early stage disease. Looking forward, extension of these
analyses across lung cancer stages and histological subtypes could
provide valuable insights into reprogramming of the immune
microenvironment during progression. Incorporating spatial and
proteomic data could help further resolve the tissue localization
and functional capacities of distinct immune cell subsets in
lung tumors. Ultimately, comprehensive mapping of immune
heterogeneity in lung cancer provides a path towards more precise
immunotherapeutic strategies (53,60).
Nevertheless, this study has several limitations to be considered.
First, the sample size was relatively small, with only 62 lung
adenocarcinomas in the primary analysis cohort and 70 in the
validation cohort. The number of samples included in our analysis
FIGURE 12
Survival curves corresponding to the analysis done for TCGA-LUAD (393 early stage patients). (A) Survival curves showed a significant difference
(p value = 0.0063) of survival using formula 1 (Surv(time, status) ~ dendrogram_red_turquoise_black_brown.group_3) when comparing high-risk
patients (yellow) and low-risk (blue) patients defined based on the risk scores. (B) Survival curves showed a significant difference (p value = 0.0027)
of survival using formula 2 (Surv(time, status) ~ dendrogram_red_turquoise_black_brown.group_4) when comparing high-risk patients (yellow) and
low-risk (blue) patients defined based on the risk scores.
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from TCGA is considerable (399) and helped us confirm our
findings, but the cohort is likely to be less homogeneous. Larger
studies on deeply clinically characterized samples will be needed to
further validate the findings. Second, we utilized only transcriptomic
data, which provides an incomplete picture of cellular states
compared to integrating proteomics and adding spatial resolution.
Third, our study lacked longitudinal samples, with which we could
assess how immune profiles change over time and with therapy.
Fourth, bulk transcriptomics may underestimate certain rare cell
populations that are better captured by single-cell sequencing. Our
in-depth analysis of 15 samples for which scRNAseq was available
and using NK populations identified therein helped us confirm the
presence of the NK subtypes in our bulk RNAseq datasets. Fifth, the
specific deconvolution algorithms used can impact results, and
incorporating additional methods could provide further validation.
Finally, functional validations to directly test immune cell cytotoxicity
or dysfunctional profiles in NK cells were not performed. This would
require either in-vitro experiments or very deep characterisation of
clinical samples that are beyond the scope of this study.
Overall, this proof-of-concept study demonstrated the potential
of integrated computational immunology techniques to identify
signatures of immune cell dysfunction from bulk tumor profiling.
However, further experimental and clinical validations are needed
to fully characterize the phenotypic diversity of anti-tumor immune
responses in lung adenocarcinoma patients.
Conclusion
In summary, our multi-omics computational framework
elucidated heterogeneous immune microenvironments in lung
adenocarcinoma. Deconvolution and TF activity analysis
identified groups of immune cells with coordinated regulation/
states. The ability to resolve dysfunctional/resting versus activated
immune cell states from bulk tumor profiling could have important
implications for prognosis and prediction of response to
immunotherapy, as suggested by our preliminary evidence of an
association to survival in 3 early LUAD cohorts. Further
characterization of dynamic immune reprogramming during
cancer progression and therapy response represents an important
future direction. We make the RNAseq datasets from our Lung
Predict cohort and all the code available to the research community,
hoping to contribute to reproducibility and open-research practices
for the ultimate benefit of patients.
FIGURE 13
TF module characterisation based on association with grouped deconvolution features in early stage Lung Predict samples. The heatmap shows
Pearson correlation between TF module scores and deconvolution features, highlighting cancer-related features. Colors represent levels of
correlation (darker red implies high positive correlation, darker blue implies high negative correlation). Statistics are shown only for significantly
correlated pairs (p value < 0.05).
Hurtado et al. 10.3389/fimmu.2024.1394965
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Data availability statement
The primary LUAD cohort (Lung Predict) transcriptomics data
is available on NCBI GEO with study number GSE251840. The
validation LUAD cohort (Vanderbilt) data is available on Zenodo
under accession number 7878082. The code to reproduce the
analysis and figures is available on github at https://github.com/
VeraPancaldiLab/LungPredict1_paper.
Ethics statement
The studies involving human participants were reviewed and
approved by the Ministry of Research under the number DC-2008-
463. The patients/participants signed a non-opposition form to
participate in this study under the LUNG PREDICT protocol. 2018.
For the validation dataset, tumor tissue samples were collected from
patients undergoing lung resection surgery following an
Institutional Review Board–approved protocol 000616 at the
Vanderbilt University Medical Center (Nashville, TN). Written
informed consent was obtained from all subjects.
Author contributions
MH: Conceptualization, Investigation, Methodology, Software,
Visualization, Writing –original draft. LK: Formal analysis,
Investigation, Methodology, Software, Visualization, Writing –
original draft. AE: Methodology, Software, Writing –review &
editing. MK: Investigation, Software, Supervision, Validation,
Writing –review & editing. TX: Investigation, Methodology,
Software, Writing –review & editing. AC: Software, Writing –
review & editing. AP: Investigation, Methodology, Writing –
review & editing. AnC: Investigation, Writing –review & editing.
AK: Funding acquisition, Project administration, Writing –review &
editing. SG: Project administration, Writing –review & editing. EC:
Investigation, Writing –review & editing. LB: Data curation, Writing
–review & editing. MFS: Data curation, Formal analysis,
Investigation, Methodology, Software, Writing –review & editing.
YZ: Formal analysis, Investigation, Writing –review & editing. SZ:
Software, Writing –review & editing. PB: Data curation, Writing –
review & editing. AM: Funding acquisition, Writing –review &
editing. JB: Data curation, Writing –review & editing. PL:
Methodology, Writing –review & editing. AlP: Project
administration, Funding acquisition, Writing –review & editing.
ErC: Funding acquisition, Writing –review & editing. GF: Funding
acquisition, Writing –review & editing. FM: Resources, Supervision,
Writing –review & editing. FC: Supervision, Writing –review &
editing. OD: Supervision, Writing –review & editing. JM: Resources,
Supervision, Writing –review & editing. VP: Conceptualization,
Funding acquisition, Investigation, Methodology, Project
administration, Supervision, Writing –original draft, Writing –
review & editing.
Funding
The author(s) declare financial support was received for the
research, authorship, and/or publication of this article. This work
was supported by the Lung Predict pilot project as part of an
alliance between the Pierre Fabre Research Institute and the IUCT.
Work in the Pancaldi lab was funded by the Chair of Bioinformatics
in Oncology of the CRCT (INSERM; Fondation Toulouse Cancer
Santé and Pierre Fabre Research Institute) and Ligue Nationale
Contre le Cancer. while the work on the Vanderbilt cohort was
funded by the National Institutes of Health of the USA
(U01CA196405 & U01CA152662). This study has been partially
supported through the grant EUR CARe N°ANR-18-EURE-0003 in
the framework of the Programme des Investissements d’Avenir and
an Eiffel Excellence doctoral fellowship to M. H.
Acknowledgments
We would like to express our sincerest gratitude for all the
patients who took part in this and other studies. Without their
consent and contributions, there would be no progress and
advancements in this field of research.
Conflict of interest
The authors declare that the research was conducted in the
absence of any commercial or financial relationships that could be
construed as a potential conflict of interest.
The author(s) declared that they were an editorial board
member of Frontiers, at the time of submission. This had no
impact on the peer review process and the final decision.
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.2024.1394965/
full#supplementary-material
SUPPLEMENTARY FIGURE 1
TFs modules characterization from early stage Lung predict samples. (A)
Number of TFs across each of the 7 modules. (B) Module association between
TFs modules scores and pathway values (only showing significant correlations
considering p value < 0.05). (C) Heatmap of the TF activity of the 20 hub TFs
across samples, showing their related module as the color annotation on the
Hurtado et al. 10.3389/fimmu.2024.1394965
Frontiers in Immunology frontiersin.org20

right. (D) Reactome enrichment results from unique target genes from hub
TFs of each module.
SUPPLEMENTARY FIGURE 2
TFs modules classification and characterization from analysis on early stage
samples from Lung Predict cohort. (A) Construction of weighted TFs modules
based on inferred co-activity. (B) Hierarchical clustering based on association
values between TFs modules and pathway activities. (C) Biplot representing
the contribution of the top 6 pathways classifying the TFs modules.
(D) Contribution of each pathway on the TFs module classification.
SUPPLEMENTARY FIGURE 3
Analysis of combined LungPredict and Vanderbilt validation cohort A.
(A) difference between the LP and Vanderbilt cohorts on normalized counts
was evident and treated as a batch effect (B) Heatmap showing the Pearson
correlation between the principal components and the metadata variables
(the darker the green the higher the correlation). p values 0, 0.0001, 0.001,
0.01, 0.05, 1 correspond to ‘****’,‘***’,‘**’,‘*’,‘‘ respectively. (C) PCA plot
using TFs activity values after calculating it independently in each cohort,
shows the difference between cohorts was removed. (D) Heatmap showing
no significant correlation between cohorts (treated here as batches) and the
principal components (PCs) using TFs activity.
SUPPLEMENTARY FIGURE 4
PCA analysis to assess batch effect within the validation cohort. (A) PCA of
validation cohort (Vanderbilt) normalized counts before batch correction (B).
PCA of validation cohort normalized counts after batch effect removal by
Combat_seq from the sva R package (61 (v3.50.0)) to maintain the integrity of
the raw counts.
SUPPLEMENTARY FIGURE 5
KEGG pathway diagram of differentially expressed genes between two patient
clusters identified in the Vanderbilt cohort early stage samples (c.f. Figure 9A).
The diagram shows the “Natural Killer Cell mediated cytotoxicity pathway”
produced using the pathview R package (62 (v1.42.0)) components and
interactions, highlighting downregulation of inhibitory (KIR3DL1/2)
receptors as well as protein kinase C (PKC).
SUPPLEMENTARY FIGURE 6
UMAP of scRNAseq data from 15 Vanderbilt cohort patients (31). UMAP shows
no batch effect influence in the cell based clustering.
SUPPLEMENTARY FIGURE 7
Automatic cluster annotation from Vanderbilt scRNA cohort using reference
expression datasets with curated cell type labels. (A) Cluster automation using
Human Primary Cell Atlas. (B) Cluster annotation using Database Immune Cell
Expression Data. (C) Cluster annotation using Monaco database. (D) Cluster
annotation using Blueprint Encode Data.
SUPPLEMENTARY FIGURE 8
TFs activity of module composition from TF modules. Modules black, red, blue,
brown, green, turquoise and yellow correspond to Figures (A–G) respectively.
SUPPLEMENTARY TEXT 1
related to Supplementary Figure 3,Supplementary Figure 4 Evaluation of
batch effects within and between cohorts: To assess comparability between
the Lung Predict and Vanderbilt early stage cohorts, we performed a PCA
analysis using the R package PCAtools (63 (v2.14.0)) where we joined the two
datasets and tested whether they separated or not. As expected, there is a big
difference between the two cohorts based on normalized counts
(Supplementary Figure 3A)withapearsoncorrelationof1(pvalue<
0.0001) between cohort (here batch) and the first principal component
(Supplementary Figure 3B). Instead of removing the batch effect, which
potentially can also eliminate some important biological differences, and
since our analysis does not directly use normalized counts, we decided to
calculate TFs activity independently for each cohort and assess again for
batch effects. As expected, calculation of the inferred TFs activity removed
the batch effects between the two cohorts (Supplementary Figure 3 C)
showing no correlation (r = 0.01) between the cohorts and the PC1
(Supplementary Figure 3D). Once we confirmed that the two datasets can
be comparable when looking at the TF activity profiles, we performed the
previously described analysis only on the validation cohort to assess for
within-dataset batch effects. A PCA analysis identified two main groups
confounded by batches (Supplementary Figure 4A). For this reason, we
performed both our TFs inference analysis and immune cell type
deconvolution calculation independently for each batch. We then
concatenated our results and saw that even though the TFs analysis was
not affected by the batch effect, this was still present in the deconvolution
results. We then used Combat_seq from the sva R package (61 (v3.50.0)) to
remove batch effects from our counts and maintain the integrity of the raw
counts (Supplementary Figure 4B). Finally, after log
2
(TPM + 1) normalization
we calculated deconvolution features from batch corrected datasets.
SUPPLEMENTARY TABLE 1
Deconvolution methods and signatures.
SUPPLEMENTARY TABLE 2
Immune-scores hallmarks.
SUPPLEMENTARY TABLE 3
Composition of deconvolution features subgroups on all samples from Lung
Predict cohort.
SUPPLEMENTARY TABLE 4
Composition of TF modules obtained from all samples from Lung
Predict cohort.
SUPPLEMENTARY TABLE 5
Composition of deconvolution features subgroups on early stage samples
from Lung Predict cohort.
SUPPLEMENTARY TABLE 6
Composition of TF modules obtained from early stage samples from Lung
Predict cohort.
SUPPLEMENTARY TABLE 7
Composition of cell groups obtained from early stage samples from Lung
Predict cohort.
SUPPLEMENTARY TABLE 8
Differential expression markers between NK peripheral (pct.1) and NK
dysfunctional (pct.2) (p_val_adj <0.05 and abs(avg_log2FC) > 1).
SUPPLEMENTARY TABLE 9
Differential expression markers between NK peripheral (pct.1) and NK Tissue
resident (pct.2) (p_val_adj <0.05 and abs(avg_log2FC) > 1).
SUPPLEMENTARY TABLE 10
Differential expression markers between NK Dysfunctional (pct.1) and NK
Tissue resident (pct.2) (p_val_adj <0.05 and abs(avg_log2FC) > 1).
References
1. Mazieres J, Drilon A, Lusque A, Mhanna L, Cortot A, Mezquita L, et al. Immune
check- point inhibitors for patients with advanced lung cancer and oncogenic driver
alterations: results from the IMMUNOTARGET registry. Ann Oncol. (2019) 30:1321–
8. doi: 10.1093/annonc/mdz167
2. Zhang C, Zhang Z, Zhang G, Zhang Z, Luo Y, Wang F, et al. Clinical significance
and inflammatory landscapes of a novel recurrence-associated immune signature in
early-stage lung adenocarcinoma. Cancer Lett. (2020) 479:31–41. doi: 10.1016/
j.canlet.2020.03.016
3. Sturm G, Finotello F, Petitprez F, Zhang JD, Baumbach J, Fridman WH, et al.
Comprehensive evaluation of transcriptome-based cell-type quantification methods for
immuno-oncology. Bioinformatics. (2019) 35:i436–45. doi: 10.1093/bioinformatics/
btz363
Hurtado et al. 10.3389/fimmu.2024.1394965
Frontiers in Immunology frontiersin.org21

4. Avila Cobos F, Alquicira-Hernandez J, Powell JE, Powell JE, Mestdagh P, Preter
De K. Benchmarking of cell type deconvolution pipelines for transcriptomics data. Nat
Commun. (2020) 11:5650. doi: 10.1038/s41467-020-19015-1
5. Merotto L, Zopoglou M, Zackl C, Finotello F. Next-generation deconvolution of
transcriptomic data to investigate the tumor microenvironment. In: International
review of cell and molecular biology. Academic Press (2023).
6. Stuart T, Butler A, Hoffman P, Hafemeister C, Papalexi E, Mauck WM 3rd, et al.
Comprehensive integration of single-cell data. Cell. (2019) 177. doi: 10.1016/
j.cell.2019.05.031
7. Ruan X, Ye Y, Cheng W, Xu L, Huang M, Chen Y, et al. Multi-omics integrative
analysis of lung adenocarcinoma: An in silico profiling for precise medicine. Front Med.
(2022) 9:894338. doi: 10.3389/fmed.2022.894338
8. Andrews S. FastQC: a quality control tool for high throughput sequence data
(2010). Available online at: http://www.bioinformatics.babraham.ac.uk/projects/fastqc/
. (accessed March 10, 2022)
9. Wingett S, Andrews S. FastQ Screen: A tool for multi-genome mapping and
quality control. F1000Res. (2018) 7:1338. doi: 10.12688/f1000research
10. Dobin A, Davis C, Schlesinger F, Drenkow J, Zaleski C, Jha S, et al. STAR:
ultrafast universal RNA-seq aligner. Bioinformatics. (2013) 29:15–21. doi: 10.1093/
bioinformatics/bts635
11. Li B, Dewey C. RSEM: accurate transcript quantification from RNA-Seq data
with or without a reference genome. BMC Bioinf. (2011) 12:323. doi: 10.1186/1471-
2105-12-323
12. Liao Y, Smyth GK, Shi W. featureCounts: an efficient general-purpose program
for assigning sequence reads to genomic features. Bioinformatics. (2014) 30:923–30.
doi: 10.1093/bioinformatics/btt656
13. Love M, Huber W, Anders S. Moderated estimation of fold change and
dispersion for RNA-seq data with DESeq2. Genome Biol. (2014) 15:550.
doi: 10.1186/s13059-014-0550-8
14. Morandat F, Hill B, Osvald L, Vitek J. Evaluating the design of the R language. In:
Noble J, editor. ECOOP 2012 –object-oriented programming, vol. 7313 (2012) (Berlin,
Heidelberg: Springer).
15. R Core Team. R: A language and environment for statistical computing. Vienna,
Austria: R Foundation for Statistical Computing (2020). Available at: https://www.R-
project.org/.
16. Gentleman R, Carey V, Bates D, Bolstad B, Dettling M, Dudoit S, et al.
Bioconductor: open software development for computational biology and
bioinformatics. Genome Biol. (2004) 5:R80. doi: 10.1186/gb-2004-5-10-r80
17. Huber W, Carey V, Gentleman R, Anders S, Carlson M, Carvalho BS, et al.
Orchestrating high-throughput genomic analysis with bioconductor. Nat Methods.
(2015) 12:115–21. doi: 10.1038/nmeth.3252
18. Yu G, Wang L, Han Y, He Q-Y. clusterProfiler: an R package for comparing
biological themes among gene clusters. A J Integr Biol. (2012) 16:284–7. doi: 10.1089/
omi.2011.0118
19. Gu Z, Eils R, Schlesner M. Complex heatmaps reveal patterns and correlations in
multi- dimensional genomic data. Bioinformatics. (2016) 32:2847–9. doi: 10.1093/
bioinformatics/btw313
20. Blighe K, Rana S, Lewis M. EnhancedVolcano: Publication- ready volcano plots
with enhanced colouring and labeling. (2018). doi: 10.18129/B9.bioc.EnhancedVolcano
21. Leote AC, Wu X, Beyer A. Regulatory network-based imputation of dropouts in
single-cell RNA sequencing data. PLOS Comput Biology. (2024) 18(2):1009849.
doi: 10.1371/journal.pcbi.1009849
22. Schubert M, Klinger B, Klünemann M, Sieber A, Uhlitz F, Sauer S, et al.
Perturbation-response genes reveal signaling footprints in cancer gene expression.
Nat Commun. (2018) 9:20. doi: 10.1038/s41467-017-02391-6
23. Badia-i-Mompel P, Velez Santiago J, Braunger J, Geiss C, Dimitrov D, Müller-Dott
S, et al. decoupleR: ensemble of computational methods to infer biological activities from
omics data. Bioinf Adv. (2022) 2(1):vbac016. doi: 10.1093/bioadv/vbac016
24. Müller-Dott S, Tsirvouli E, Vazquez M, Ramirez Flores RO, Badia-I-Mompel P,
Fallegger R, et al. Expanding the coverage of regulons from high-confidence prior
knowledge for accurate estimation of transcription factor activities. Nucleic Acids Res.
(2023) 51(20):10934–49. doi: 10.1093/nar/gkad841
25. Alvarez M, Shen Y, Giorgi F, Lachmann A, Ding B, Ye B, et al. Functional
characterization of somatic mutations in cancer using network-based inference of
protein activity. Nat Genet. (2016) 48:838–47. doi: 10.1038/ng.3593
26. Lapuente-Santana O, van Genderen M, Hilbers P, Hilbers PAJ, Finotello F,
Eduati F. Interpretable systems biomarkers predict response to immune-checkpoint
inhibitors. Cell. (2021) 2. doi: 10.1101/2021.02.05.429977
27. Kursa MB, Rudnicki WR. Feature selection with the boruta package. J Stat
Software. (2010) 36:1–13. doi: 10.18637/jss.v036.i11
28. Langfelder P, Horvath S. WGCNA: an R package for weighted correlation
network analysis. BMC Bioinf. (2008) 9:559. doi: 10.1186/1471-2105-9-559
29. Yu G, He Q. ReactomePA: an R/Bioconductor package for reactome pathway
analysis and visualization. Mol Biosyst. (2016) 12:477–9. doi: 10.1039/C5MB00663E
30. Ligtenberg W. reactome.db: A set of annotation maps for reactome. R package
version 1.68.0.Bioconductor (2019).
31. Senosain M, Zou Y, Patel K, Zhao S, Coullomb A, Rowe DJ, et al. Integrated
multi-omics analysis of early lung adenocarcinoma links tumor biological features with
predicted indolence or aggressiveness. Cancer Res Commun. (2023) 7:1350–65.
doi: 10.1158/2767-9764.CRC-22-0373
32. Satija R, Farrell J, Gennert D, Schier AF, Regev A. Spatial reconstruction of
single-cell gene expression data. Nat Biotechnol. (2015) 33. doi: 10.1038/nbt.3192
33. Butler A, Hoffman P, Smibert P, Papalexi E, Satija R. Integrating single-cell
transcriptomic data across different conditions, technologies, and species. Nat
Biotechnol. (2018) 36. doi: 10.1038/nbt.4096
34. Hao Y, Hao S, Andersen-Nissen E, Mauck WM 3rd, Zheng S, Butler A, et al.
Integrated analysis of multimodal single-cell data. Cell. (2021) 184(13):p3573–87.
doi: 10.1101/2020.10.12.335331
35. Aran D, Looney A, Liu L, Wu E, Fong V, Hsu A, et al. Reference-based analysis
of lung single-cell sequencing reveals a transitional profibrotic macrophage. Nat
Immunol. (2019) 20:163–72. doi: 10.1038/s41590-018-0276-y
36. ChuT,WangZ,Pe’er D, Danko CG. Cell type and gene expression deconvolution
with BayesPrism enables Bayesian integrative analysis across bulk and single-cell RNA
sequencing in oncology. Nat Cancer. (2022) 3:505–17. doi: 10.1038/s43018-022-00356-3
37. Dietrich A, Merotto L, Pelz K, Eder B, Zackl C, Reinisch K, et al. Benchmarking
second-generation methods for cell-type deconvolution of transcriptomic data. biorxiv.
(2024). doi: 10.1101/2024.06.10.598226
38. Therneau T. A Package for Survival Analysis in R. R package version 3.7-0 (2024).
Available online at: https://CRAN.R-project.org/package=survival. (accessed August
05, 2024)
39. Therneau TM, Grambsch PM. Modeling survival data: extending the cox model.
New York: Springer (2000), ISBN: .
40. Wickham H. ggplot2: elegant graphics for data analysis. New York: Springer-
Verlag (2016). Available at: https://ggplot2.tidyverse.org, ISBN: .
41. Kassambara A, Kosinski M, Biecek P. survminer: Drawing Survival Curves using
‘gg- plot2 (2021). Available online at: https://CRAN.R-project.org/package=survminer.
(accessed August 05, 2024)
42. 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. (2015) 44(8):e71. doi: 10.1093/nar/gkv1507
43. Silva CT, Colaprico A, Olsen C, D'Angelo F, Bontempi G, Ceccarelli M, et al.
TCGA Workflow: Analyze cancer genomics and epigenomics data using Bioconductor
packages. F1000Research. (2016) 5. doi: 10.12688/f1000research
44. Mounir M, Lucchetta M, Silva CT, Olsen C, Bontempi G, Chen X, et al. New
functionalities in the TCGAbiolinks package for the study and integration of cancer
data from GDC and GTEx. PloS Comput Biol. (2019) 15:e1006701. doi: 10.1371/
journal.pcbi.1006701
45. Wang H, Xiong, Ning Z. Implications of NKG2A in immunity and immune-
mediated diseases. Front Immunol. (2022) 13. doi: 10.3389/fimmu.2022.960852
46. Anfossi N, Andre P, Guia S, Falk CS, Roetynck S, Stewart CA, et al. Human NK
cell education by inhibitory receptors for MHC class I. Immunity. (2006) 25:331–42.
doi: 10.1016/j.immuni.2006.06.013
47. Comet N, Aguilo J, Rathore M, Catalan E, Garaude J, UzeG, et al. IFNasignaling
through PKC-qis essential for antitumor NK cell function. OncoImmunol. (2014) 3.
doi: 10.4161/21624011.2014.948705
48. Poli A, Michel T, The´re´sine M, Andrès E, Hentges F, Zimmer J. CD56bright
natural killer (NK) cells: an important NK cell subset. Immunology. (2009) 4:458–65.
doi: 10.1111/j.1365-2567.2008.03027.x
49. Post M, Cuapio A, Osl M, Lehmann D, Resch U, Davies DM, et al. The
transcription factor ZNF683/HOBIT regulates human NK-cell development. Front
Immunol. (2017) 8:535. doi: 10.3389/fimmu.2017.00535
50. Marquardt N, Kekalainen E, Chen P, Lourda M, Wilson JN, Scharenberg M,
et al. Unique transcriptional and protein-expression signature in human lung tissue-
resident NK cells. Nat Commun. (2019) 10. doi: 10.1038/s41467-019-11632-9
51. Satija R, Shalek AK. Heterogeneity in immune responses: from populations to
single cells. Trends Immunol. (2014) 35:219–29. doi: 10.1016/j.it.2014.03.004
52. Cong J, Wei H. Natural killer cells in the lungs. Front Immunol. (2019) 10:1416.
doi: 10.3389/fimmu.2019.01416
53.SchmidtL,EskiocakB,KohnR,DangC,JoshiNS,DuPageM,etal.Enhanced
adaptive immune responses in lung adenocarcinoma through natural killer cell stimulation.
Proc Natl Acad Sci U S A. (2019) 116:17460–9. doi: 10.1073/pnas.1904253116
54. Hsu J, Hodgins JJ, Marathe M, Nicolai CJ, Bourgeois-Daigneault M-C, Trevino
TN, et al. Contribution of NK cells to immunotherapy mediated by PD-1/PD-L1
blockade. J Clin Invest. (2018) 128:4654–68. doi: 10.1172/JCI99317
55. Huntington ND, Cursons J. amp]]amp; J. Rautela. The cancer–natural killer cell
immunity cycle. Nat Rev Cancer. (2020) 20:437–45. doi: 10.1038/s41568-020-0272-z
56. Davis-Marcisak EF, Fitzgerald AA, Kessler MD, Kessler MD, Danilova L, Jaffee
EM, Zaidi N, et al. Transfer learning between preclinical models and human tumors
Hurtado et al. 10.3389/fimmu.2024.1394965
Frontiers in Immunology frontiersin.org22

identifies a conserved NK cell activation signature in anti-CTLA-4 responsive tumors.
Genome Med. (2021) 13:129. doi: 10.1186/s13073-021-00944-5
57. Zhang W, Zhao Z, Li F. Natural killer cell dysfunction in cancer and new
strategies to utilize NK cell potential for cancer immunotherapy. Mol Immunol. (2022)
144:58–70. doi: 10.1016/j.molimm.2022.02.015
58. Danaher P, Kim Y, Nelson B, Griswold M, Yang Z, Piazza E, et al. Advances in
mixed cell deconvolution enable quantification of cell types in spatial transcriptomic
data. Nat Commun. (2022) 13:385. doi: 10.1038/s41467-022-28020-5
59. Judge S, Murphy W, RJ C. Characterizing the dysfunctional NK cell: assessing
the clinical relevance of exhaustion, anergy, and senescence. Front Cell Infect Microbiol.
(2020) 10. doi: 10.3389/fcimb.2020.00049
60. Isaacson B, Mandelboim O. Sweet killers: NK cells need glycolysis to kill tumors.
Cell Metab. (2018) 28:183–4. doi: 10.1016/j.cmet.2018.07.008
61. Leek JT, Johnson WE, Parker HS, Jaffe AE, Storey JD. The sva package for
removing batch effects and other unwanted variation in high-throughput experiments.
Bioinformatics. (2024) 28:882–3. doi: 10.1093/bioinformatics/bts034
62. Luo W, Brouwer C. Pathview: an R/Bioconductor package for pathway-based
data integration and visualization. Bioinformatics. (2013) 29:1830–1. doi: 10.1093/
bioinformatics/btt285
63. Blighe K, Lun A. PCAtools: PCAtools: Everything Principal Components Analysis.
R package version 2.16.0 (2024). Available online at: https://github.com/kevinblighe/
PCAtools. (accessed August 10, 2024)
CITATION
Hurtado M, Khajavi L, Essabbar A, Kammer M, Xie T, Coullomb A,
Pradines A, Casanova A, Kruczynski A, Gouin S, Clermont E, Boutillet L,
Senosain MF, Zou Y, Zhao S, Burq P, Mahfoudi A, Besse J, Launay P,
Passioukov A, Chetaille E, Favre G, Maldonado F, Cruzalegui F, Delfour O,
Mazières J and Pancaldi V (2024) Transcriptomics profiling of the non-
small cell lung cancer microenvironment across disease stages reveals dual
immune
cell-type behaviors.
Front. Immunol. 15:1394965.
doi: 10.3389/fimmu.2024.1394965
COPYRIGHT
© 2024 Hurtado, Khajavi, Essabbar, Kammer, Xie, Coullomb, Pradines,
Casanova, Kruczynski, Gouin, Clermont, Boutillet, Senosain, Zou, Zhao, Burq,
Mahfoudi, Besse, Launay, Passioukov, Chetaille, Favre, Maldonado, Cruzalegui,
Delfour, Mazières and Pancaldi. 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.
Hurtado et al. 10.3389/fimmu.2024.1394965
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