Paper - Review

DOI: 10.1038/s41467-020-16164-1

Abstract

Advanced metastatic cancer
→ poses utmost clinical challenges
→ may present 1⃣ molecular 2⃣ cellular features
← which distinct from an early-stage cancer

❗: present → single-cell transcriptome profiling
← of metastatic lung adenocarcinoma
← the most prevalent histological lung cancer type → diagnosed at stage IV

Identify → a cancer cell subtype
← deviating from the normal differentiation trajectory
← dominating the metastatic stage
∵ From 200K cells ← populating (1⃣ the normal tissues 2⃣ early to metastatic stage cancer)

(The stromal & immune cell dynamics)
→ reveal 1⃣ ontological 2⃣ functional changes
← that create 1⃣ a pro-tumoral 2⃣ immuno-suppressive micro-environment

Normal resident myeloid cell population
→ are gradually replaced
← with 1⃣ monocyte-derived macrophages 2⃣ dendritic cells
→ along with T-cell exhaustion

This extensive single-cell analysis
→ enhances → our understanding of (1⃣ molecular 2⃣ cellular dynamics)
← in metastatic lung cancer
→ reveals → 1⃣ potential diagnostic 2⃣ therapeutic targets
← in cancer-micro-environment interactions

Introduction

NSCLC
→ non-small cell lung cancer
→ is histologically divided → into 1⃣ adenocarcinoma 2⃣ squamous cell carcinoma 3⃣ large-cell carcinoma

LUAD
← lung adenocarcinoma
→ is the most common type → for 40% of all lung cancers

LUAD
→ is often detected ← at the metastatic stage
← with prevalence ← in 1⃣ brain 2⃣ bones 3⃣ respiratory system

❗: Distant metastasis
→ is the major cause of mortality in lung cancer
❓: specific aspects of 1⃣ metastatic lung cancer 2⃣ its associated micro-environment
→ remain poorly understood

❗: Efforts
→ made → for the understanding of 1⃣ lung cancer progression 2⃣ metastasis
→ have largely focused
← on profiling of cancer cell ← with genetic aberrations

❓: 1⃣ progression 2⃣ metastasis
→ are also influenced
← by 1⃣ complex 2⃣ dynamic features ← in tumor surroundings

The parsing of (unique classes) ← of tumor micro-environment
← in advanced tumor
→ can reveal the key elements
← involved in the pre-disposition to tumor-induced immunological changes

∴ These elements
→ can be exploited → for novel immunotherapeutic strategies

scRNA-seq
→ has been used
→ for the profiling of tumor micro-environments

This technology
→ allows massively parallel characterization of (thousands of cells)
← at the transcriptome level

❓: Previous scRNA-seq studies
→ have been limited
→ to 1⃣ early stage primary tumors 2⃣ normal tissues resected
← from a small number of samples of mixed histological type

❗: report → the comprehensive single-cell transcriptome profiling of LUAD
← from early to advanced stages of 1⃣ primary cancer 2⃣ distant metastases
❗: unveil → 1⃣ cellular dynamics 2⃣ molecular features
→ associated with the tumor progression

Results

Cellular dynamics in early, advanced, and metastatic LUAD

Tumor
← from 1⃣ primary lung tissues 2⃣ pleural fluids 3⃣ lymph node & brain metastases
→ were obtained ← from 44 patients ← with treatment-naïve LUAD
← during 1⃣ endobronchial ultrasound 2⃣ bronchoscopy biopsy 3⃣ surgical resection

1⃣ distant normal tissues 2⃣ lymph nodes
→ were also collected
→ for comparative analysis

Cataloged 208,506 cells
→ into nine distinct cell lineages
← annotated with canonical marker gene expression
∴ Identifying 1⃣ epithelial 2⃣ stromal 3⃣ immune cells 3⃣ oligodendrocytes

scRNA-seq data
→ over-estimated the immune cell proportions
← in comparison to the (stromal & epithelia cell types)
∵ the bias introduce ← during tissue dissociations

The recovery rate of (tumor cells)
→ was affected ← by the histological types of LUAD

Assessed → the compositions of (immune cell subsets)
← after removing 1⃣ the epithelial 2⃣ stromal populations

The results faithfully
→ reproduced the immune cell profiles
← detected by mass cytometry by time of-flight

❗: the most abundant immune cells
← at primary tumor sites
→ were 1⃣ T-lymphocytes 2⃣ myeloid cells

Confirmed
→ T&B lymphocyte enrichment
→ the decline of NK & myeloid cells
← in 1⃣ early 2⃣ advanced-stage lung cancers
∴ The activation of (adaptive immune responses)

Metastatic lymph nodes (mLN)
→ harbored → a significant number of myeloid cells
← unlike normal lymph nodes (nLN)
∴ An association of (myeloid infiltration) ← with metastasis

mBrain samples
→ contained immune cells
← e.g. 1⃣ T-cell 2⃣ B-cell 3⃣ NK-cells
→ at detectable levels ← as well as resident cells
← e.g. 1⃣ oligodendrocytes 2⃣ myeloid cells

These cellular compositions
→ demonstrated differences ← in tissue-specific resident populations
∴ Gross alterations ← inflicted by 1⃣ tumor growth 2⃣ tumor invasion

Tumor intrinsic signatures associated with LUAD progression

Explored → intrinsic characteristics of (adenocarcinoma cells)
→ through (comparative analysis) ← between 1⃣ normal epithelial 2⃣ tumor cells

Normal epithelial cells
→ mainly comprised → 4 distinct sub-populations
← i.e. 1⃣ alveolar types I 2⃣ AT2 3⃣ club cells 4⃣ ciliated cells

Epithelial cell types
→ may contain → residual non-malignant cells
← along with malignant tumor cells

The inferred CNV patterns
→ confirmed patient-specific perturbations
← in 1⃣ malignant tLung 2⃣ tL/B 3⃣ mLN 4⃣ mBrain cells

❗: excluded → epithelial cells
← without CNV present in tumor tissues
∵ their ambiguous identity

Constructed → a transscriptional trajectory
→ to adjust the inter-patient genomic hetero-geneity
→ to find (key gene expression programs) ← governing the tumor progression
← using 1⃣ the definitive tumor 2⃣ normal epithelial cells

transcriptional states
← in the trajectory
→ revealed 1⃣ normal differentiation paths 2⃣ progression-associated changes in tumors

❗: 1⃣ ciliated epithelial 2⃣ alveolar cells
→ were located ← in separated trajectory branches
∴ Their distinct differentiation states

❗: club cells
→ were located ← between 1⃣ the ciliate 2⃣ alveolar branches
∴ the intermediate differentiation state

❗: tumor cells
→ formed → a branched structure
1⃣ two transcriptional states → along the normal epithelial cells
2⃣ one was observed → to be distinctly positioned ← at the opposite ends

The separation of tS2
← from the normal epithelial cells
→ was repeatedly observed ← despite the different trajectory structure ← in each patient
∵ The limited representation of (cellular components)

Selected → differentially expressed genes specific
→ to each (tumor & normal) cell state
→ to identify transcriptional signatures defining cellular states

Most of (1⃣ S1- 2⃣ S3-) associated genes
→ were shared
→ differentially regulated ← between (tumor & normal) cells
→ related → to (normal epithelial functions) ← maintaining 1⃣ the surfactant homeostasis 2⃣ lung alveolus development 3⃣ cilium movement

S2-associated genes
→ showed definitive tumor-oriented characteristics
← e.g. 1⃣ aggressive cell movement 2⃣ abnormal proliferation 3⃣ apoptosis

∴ 1⃣ (tS1 & tS3) states → represented a de-regulation of the normal differentiation programs
2⃣ the tS2 tumor cell state → deviated completely ← from the normal transcriptional programs

LUAD patients
→ contained → 1⃣ tS1 2⃣ tS2 tumor sub-populations
← with minor number of tS3

The tS2-specific gene expression
→ was increased ↑ → for tumor cells
← that were isolated from 1⃣ the late-stage biopsies 2⃣ metastases (e.g. tL/B, mLN, mBrain)
∴ An association ← with 1⃣ tumor progression 2⃣ metastasis

An increase ↑ ← in tS2-specific gene expression
→ was supported ← at the protein level ← through immuno-histochemical staining of LUAD samples

⁉: the clinical impact ← of the tS2 signature
← using an independent LUAD cohort
← from the TCGA

∴ Patients
← with high tS2 signature gene expression
→ showed → worse overall survival

❗: No survival differences
→ for lung squamous cell carcinoma (LUSC)
∴ An explicit involvement of the tS2 signature
← with LUAD progression

⁉: to identify genes
← related to 1⃣ LUAD progression 2⃣ LUAD metastasis

Compared tumor cells
← in early 🆚 advanced-stage primary
← in primary 🆚 metastasis samples

∴ the gene sets
→ to be differentially regulated
← during 1⃣ tumor progression 2⃣ metastasis

Stromal cells orchestrate tissue remodeling and angiogenesis

⁉: to investigate → stromal cell dynamics
← in the tumor micro-environment

Obtain → 6K presumed 1⃣ fibroblasts 2⃣ endothelial cells
Performed → a principal component analysis

∴ The first principal component
→ was observed → to split the cells → into 1⃣ 2K endothelial cells 2⃣ 4K fibroblasts
∴ A concordant expression ← of representative marker genes

Sub-clustering of endothelial cells (ECs)
→ revealed → 8 eight clusters

Most EC clusters
→ were observed
→ to belong to the normal tissues
→ to assigned to known (vascular cell types)
← e.g. 1⃣ tip-like cells 2⃣ stalk-like cells 3⃣ lymphatic ECs 4⃣ endothelial progenitor cells

One distinct cluster
→ was identified ← as tumor-derived ECs (EC-C1)
← present in 1⃣ tLung 2⃣ mBrain samples

Tumor ECs
→ demonstrated → a strong activation of 1⃣ VEGF 2⃣ Notch signaling
← with regulates 1⃣ the development 2⃣ cell fate determination ← of endothelial cells

Gene expression network analysis
← of tumor ECs
→ highlighted 1⃣ angiogenesis 2⃣ the up-regulated genes' functional category

∴ 1⃣ brain metastases 2⃣ primary tumors
→ induced → similar vascular changes
→ to accommodate (extensive neovascularization)

Insulin receptor (INSR) over-expression
← in th tumor vasculature
→ was suggested ← as an attractive therapeutic target

∴ These data
→ supported → the therapeutic strategies
← targeting pro-angiogenic pathways
← in 1⃣ lung cancer 2⃣ brain metastases

Significantly down-regulated genes
← in tumor ECs
→ were related → to immune activation
∴ Tumor ECs → suppress the immune responses

Sub-clustering of fibroblasts
→ revealed 12 distinct clusters
← assigned to 7 known cell types
← e.g. 1⃣ COL13A1+ & COL14A1+ matrix fibroblasts 2⃣ myofibroblasts 3⃣ smooth muscle cells 4⃣ mesothelial cells 5⃣ fibroblast-like cells

The 1⃣ COL13A1+ 2⃣ COL14A1+ matrix fibroblasts
→ comprised → the main fibroblast types
← in 1⃣ normal lung 2⃣ early stage tumor tissues

Myofibroblasts ← in FB-C3
→ exclusively originated ← from tumor tissues
← e.g. 1⃣ tLung 2⃣ tL/B 3⃣ mLN

Myofibroblasts
→ have been described
← as 1⃣ cacer-associated fibroblasts ← promoting extensive (tissue remodeling) 2⃣ angiogenesis 3⃣ tumor progression

The myofibroblasts ← in mLN
→ might be → fibroblastic reticular cells
← which is immunologically specialized myofibroblasts
← using (encapsulated mesenchymal sponges) → to gather immune cells → into the lymph node

The fibroblast-like cells ← in mBrain
→ might represent cells ← within the perivascular space ← of (central nervous system (CNV))
← that expanded after CNS injury

The infiltration of myofibroblasts ← in LUAD
→ was confirmed
← by the expression of the marker protein (alpha smooth muscle actin (α-SMA))

Partial protein expression ← of α-SMA
→ was observed
← in the vascular smooth muscle cells ← in normal tissues

Suppressive immune micro-environment primed by myeloid cells

Myeloid cells
→ play → a critical role ← in maintaining tissue homeostasis
→ regulate inflammation ← in the lung

Sub-clustering of 42K myeloid cells
→ revealed them → to be 1⃣ monocytes 2⃣ macrophages 3⃣ dendritic cells

Two macrophages types
→ are known → to populate (the normal adult lung)
→ 1⃣ the alveolar (AM) type ← which highly expressing the 1⃣ MARCO 2⃣ FABP4 3⃣ MCEMP1
→ 2⃣ the interstitial type ← derived from (circulating monocytes)

Mo-Macs
← which are functionally different ← from tissue-resident macrophages
→ are (recruited & induced) → to express pro-fibrotic genes
← during lung fibrosis

Detected → the AM type ← in normal lung tissues
← e.g. 1⃣ anti-inflammatory AM 2⃣ pro-inflammatory AM 3⃣ actively cycling AM ← expressing anti-inflammatory markers

❗: 1⃣ lung tumor 2⃣ distant metastasis tissue
→ were strongly enriched ← in mo-Macs

❗: 1⃣ normal tissues 2⃣ tumor tissues
→ contained ← clusters of 1⃣ S100A9+ 2⃣ dendritic cells

The remaining clusters
→ displayed → 1⃣ origin-specific heterogeneity 2⃣ diverse macrophage characteristics
← including 1⃣ pleural macrophages ← from PE 2⃣ microglia & macrophages ← derived from mBrain samples

The pleural macrophages
→ lacked → the expression of pro-inflammatory cytokine genes ← e.g. 1⃣ IL1B 2⃣ CXCL8
→ expressed CD163 transcripts
← which are associated ← with a non-inflammatory phenotype

∴ Tumor-associated macrophages
← in 1⃣ primary lung tumors 2⃣ distant metastases
→ propagated ← from mo-Macs
← that were ontologically different ← from tissue-resident macrophages

⁉: to understand → the transcriptional transition
← from monocytes to TAMs

∴ Performed → an unsupervised trajectory analysis
→ to infer changes ← in the status of macrophages
← from 1⃣ lung 2⃣ lymph node samples

Macrophages
→ can manifest diverse functional phenotypes ← in 1⃣ health 2⃣ disease conditions
← e.g. 1⃣ pro-inflammatory 2⃣ anti-inflammatory sub-populations

Detected → a serial transformation of (pro-inflammatory monocytes)
→ into macrophages ← along (the pseudo-time axis)
← with cells 1⃣ losing their pro-inflammatory nature 2⃣ gaining anti-inflammatory signatures

∴ Reached → a branching points
→ 1⃣ the two macrophage sub-populations → retained part of their pro-inflammatory signatures
→ 2⃣ the two macrophage sub-populations → were skewed → to an anti-inflammatory gene expression phenotype

❗: Normal lung → pro-inflammatory macrophages
❗: tumor tissue → anti-inflammatory macrophages

MIF-expressing macrophages
→ express 1⃣ IL1B 2⃣ TNF
← at level comparable → to those in pro-inflammatory monocytes

∴ Unique macrophage profiles ← in mLN

Dendritic cell clusters
→ manifested → a variegated marker gene expression
∴ The presence of heterogeneous DC sub-populations

❗: The minor DC populations
← within the total myeloid cell clusters

❗: pDCs
→ were rarely found ← in normal lung tissues
→ recovered ← in 1⃣ selected tumor tissues 2⃣ metastatic lymph nodes

The pDCs
→ demonstrated → an immuno-suppressive phenotype
← by the up-regulation ← of 1⃣ leukocyte immunoglobulin-like receptor (LILR) family gene 2⃣ granzyme B (GZMB) production
← by loss of 1⃣ CD86 2⃣ CD83 3⃣ CD80 4⃣ LAMP3 activation marker expression

∴ 1⃣ mo-Macs 2⃣ pDCs
→ could create → an immuno-suppressive micro-environment
← that possibly caused → a sub-optimal tumor antigen presentation
← in 1⃣ LUAD 2⃣ distant matastases

Activation and perturbation of adaptive immunity

Tumor-infiltrating B-cells
→ have been identified
← in tertiary lymphoid structures ← within NSCLC

Tumor-infiltrating B-cells
→ are associated ← with prolonged patient survival
∵ Mediating → an anti-tumor immune response

Relative proportion ← of B-cells
→ was observed → to be increased ← in primary tumors
∵ Comparing nLung samples

Sub-clustering of 27K B-cells
→ revealed → 14 clusters
← converging → to 5 differentiation states

❗: GC B-cells
→ were separated → into either (1⃣ dark 2⃣ light zone cells)
← with distinct transcriptional programs → for 1⃣ proliferation 2⃣ activation

Follicular B-cells
→ were observed
→ to be the most abundant ← in all samples

Observed
→ tissue-specific enrichment
→ for other subsets

Normal lung tissues
→ were enriched ↑ ← in granzyme B-secreting cytotoxic cells
→ whose differentiation was modulated ← by T-cell-derived IL-12

❗: Granzyme B secretion ← from these cells
→ could play → a significant role
← in mediating cellular cytotoxicity ← as an alternative to T-cells

❗: More GC B-cells
← in 1⃣ primary tumors 2⃣ LN metastases
← than in 1⃣ normal lung 2⃣ lymph nodes

∴ Highly activated (humoral immune responses)
← in some LUAD patients

Each B-cells sub-types
→ displayed → a slightly different 1⃣ B-cell receptor 2⃣ Ig light chain variable gene expression profile
∴ 1⃣ The generation 2⃣ clonal expansion ← of tumor antigen-specific B-cells

T-lymphocytes
→ are the central players ← mediating anti-tumor immunity
→ are the targets of immune-checkpoint therapies

❗: Collected 90K cells
← from 1⃣ T-cells 2⃣ NK-cells clusters
→ sharing common (transcriptome characteristics)
→ defined 64K T/NK-cells ← with a secondary cell filtration ← using marker gene expression

The T/NK-cell sub-clusters
→ reflected → 1⃣ heterogeneous cell lineages 2⃣ functional states

∴ 1⃣ The depletion of NK-cells
2⃣ The emergence of (regulatory T-cells (Tregs)
← in the primary tumor tissues ← compared to normal tissues

Treg cells
→ persisted ← in 1⃣ tL/B 2⃣ mLN 3⃣ mBrain
∴ Delivering → a suppressive mechanism of (anti-tumor immunity)
← during 1⃣ tumor progression 2⃣ metastasis

CD8+ T-cells
→ demonstrated → a dynamic functional spectrum
← as that in 1⃣ naïve 2⃣ cytotoxic 3⃣ exhausted states
← from the transcriptional trajectory

1⃣ Exhausted CD8+ T-cells → were mainly collected ← from tumor tissues
2⃣ Cytotoxic effector CD8+ T-cells → were collected ← from nLung

Differences
← in the T/NK-cell subset dynamics
← between 1⃣ primary tumor 2⃣ normal lung tissues
→ were further supported ← by conventional flow cytometry analysis

∴ The changes
← in 1⃣ cellular composition 2⃣ gene expression phenotype of T-cells
→ confirmed the direction of (tumor immunity)
→ towards (immune suppression) ← in LUAD

Inference of inter-cellular and molecular interactions

Cellular dynamics → during LUAD progression
→ was further confirmed
→ through (chi-square tests) → for different tissue distributions
← of 40 1⃣ immune 2⃣ stromal cell subsets

Tumor-specific populations
← e.g. 1⃣ mo-Macs 2⃣ pDCs 3⃣ Tregs 4⃣ myofibroblasts 5⃣ tumor ECs
→ were spread out ← in 1⃣ primary tumors 2⃣ distant metastases

Origin-specific immune & stromal cell subsets
← e.g. 1⃣ alveolar mac 2⃣ pleural mac 3⃣ microglia/mac 4⃣ FB-like cells
→ were specifically associated ← with their corresponding tissue sites

The proportion of 1⃣ exhausted CD8+ T-cells 2⃣ mo-Macs
→ were markedly increased ↑
← during 1⃣ LUAD progression 2⃣ metastases

Increase ← in these two sub-populations
← in the tumor micro-environment
→ was associated ← with the high TMB

The results
→ support → the role of 1⃣ mo-Macs 2⃣ exhausted CD8+ T-cells
← in the successful application of (immune checkpoint therapies) ← in advanced LUAD
∵ High TMB → is the principal predictor
← of successful immune checkpoint therapy

Constructed → a cellular communication network
← using potential receptor-ligand pair interaction
⁉: To delineate the molecular associations
← underlying inter-cellular relationships

❗: Interactions
← between 1⃣ the tS2 cells 2⃣ mo-Macs
→ were predicted → to be most significant
❗: Interactions
← between 1⃣ mo-Macs 2⃣ exhausted CD8+ T-cells
→ were observed → to be the most prominent ← within the immune cell network

❗: the proportion ← of 1⃣ mo-Macs 2⃣ exhausted CD8+ T-cells
→ demonstrated → positive correlations
← with an increase ←in tS2 cancer cells

Found → potential interactions
← between 1⃣ tS2/Malignant cells 2⃣ tumor ECs
→ through (angiogenesis signaling molecules)

Tumor ECs
→ receive → angiogenic stimulatory signals
← from mo-Mac/malignant cells
→ through VEGF & its receptor ← as a key mediator of angiogenesis in cancer

Predicted → the molecular interactions
← between 1⃣ mo-Macs 2⃣ exhausted CD8+ T-cells 3⃣ cancer cells
← in 1⃣ primary tumors ← tLung & tL/B 2⃣ distant metastases ← mLN & mBrain

Most ligand-receptor pairs
← between 1⃣ mo-Macs 2⃣ tS2/Malignant cells
→ were involved ← in signaling of growth factors
← e.g. 1⃣ VEGFA 2⃣ VEGFB → for all stage samples

Malignant cells
→ would receive → activation signals from mo-Macs
→ through 1⃣ TNFR 2⃣ TGFBR 3⃣ EGFR ← in mLN

Potential signal transductions → to the exhausted CD8+ T-cells
→ was mostly inhibitory
∴ Delivered ← by tS2/malignant cells
→ 1⃣ for samples of all tumor stages 2⃣ for metastatic lymph nodes

mo-Macs → were predicted
→ to deliver both 1⃣ activating (TNF- TNFRSF1B/ICOS) 2⃣ inhibitory (LGALS9-HAVCR2) signals
→ to exhausted CD8+ T-cells

∴ The complex nature of (mo-Macs ← in LUAD)
← which greatly influences T-cells functionalities
→ to balance → immune activation 🆚 exhaustion

∴ A tight relationship
← between 1⃣ immune cell dynamics 2⃣ molecular features of cancer cells
→ that may determine → the prognostic & therapeutic responses ← in LUAD

Discussion

Depicted → the cellular landscape of LUAD
← from the early to the advanced stages
∴ Encompassing → the primary & metastatic sites

This LUAD atlas
→ has revealed → the characteristics of tumor cells
→ has associated → micro-environments
→ has illuminated changes ← in 1⃣ cellular 2⃣ molecular networks ← during tumor progression

∴ The most comprehensive (cellular interaction map) ← of LUAD
∴ A framework → for 1⃣ future discoveries of molecular 2⃣ cellular therapeutic targets

Uncovered → malignant molecular features ← of cancer cells
← previously masked ← by 1⃣ inter-patient 2⃣ intra-patient genomic heterogeneity

The projections of (1⃣ cancer 2⃣ normal epithelial cells)
→ into a joint transcriptional trajectory
→ has revealed → their similarities & disparities
∴ Implicating → the paths of malignant transformation

Club cells
→ were located ← at the root of all three branches
∴ Versatile progenitor properties

Normal samples
→ demonstrated → a variable branch distribution
← indicating regional heterogeneity ← with differential 1⃣ proximal 2⃣ distal cells

Club cells ← in the S1 & S3 branches
→ expressed a touch ← of 1⃣ distal alveolar cell marker 2⃣ micro-tubule assembly genes

Club cells
← at the S2 branching point highly expressed genes
→ involved ← in 1⃣ innate immune response 2⃣ detoxification
∴ Their original protective function

Most alterations ← in the tumor micro-environment
← from normal lung tissues
→ were inflicted ← at an early stage
→ sustained ← in later stages

Found → most alterations ← in the tumor micro-environment
← from normal lung tissues
→ were inflicted ← at an early stage
→ were sustained ← in later stages

1⃣ tumor ECs → acquired highly anigogenic
2⃣ myofibroblast → replaced matrix fibroblasts
3⃣ mo-Macs & dendritic cells → expanded & differentiated toward → an overall anti-inflammatory phenotype →
4⃣ overpowered alveolar macrophages ← in lung tissues
5⃣ conventional DCs ← in the LNs
6⃣ B-cells → were activated & expanded ← in tumor tissues
7⃣ cytotoxic NK-cells → were diminished

An exhausted T-cell phenotype
→ has expanded → throughout cytotoxic effector populations

∴ These alterations
← in stromal & immune populations
→ cooperatively transformed → immune-competent tissues
→ into an immune-suppressive tumor micro-environment

Aberrant anti-tumor immune responses
← involving 1⃣ antibodies 2⃣ regulatory & exhausted T-cells
→ provide → therapeutic opportunities
→ to direct the immune reaction → into productive directions
← using 1⃣ immune checkpoint inhibitors 2⃣ other immune modulators

Demonstrated → 1⃣ vibrant cell-population dynamics 2⃣ molecular interactions
← between 1⃣ tumor 2⃣ stromal 3⃣ immune compartments

Numerous highly significant interactions
→ were inferred
← between 1⃣ mo-Macs 2⃣ aggressive/metastatic tumor cells
← involving the activation of 1⃣ TNF 2⃣ TGF-β 3⃣ EGFR signaling pathways

Malignant cells
→ differentially induced → the activation of 1⃣ cellular 2⃣ humoral immune responses
→ provided inhibitory signals → to induce immune exhaustion

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