Paper - Review

DOI: 10.1038/s41467-020-20019-0

Abstract

Cancer immunotherapy
→ as revolutionized (cancer treatment)

Cancer immunotherapy
→ relies ← on understanding of (the immune landscape) ← of the (tumor micro-environment (TME))

❗: obtain
→ a detailed immune cell atlas ← of (esophageal squamous cell carcinoma (ESCC))
← at single-cell resolution

1⃣ exhausted T-cells 2⃣ exhausted NK-cells 3⃣ regulatory T-cell (Tregs) 4⃣ alternatively activated macrophages 5⃣ tolerogenic dendritic cells
→ are dominant ← in the TME

Transcription profiling
← coupled with T-cell receptor (TCG) sequencing
→ reveal → lineage connections ← in T-cell populations

CD8 T-cells
→ show → continuous progression
→ from pre-exhausted → to exhausted T-cells

1⃣ CD4 T-cells 2⃣ CD8 T-cells 3⃣ NK-cells
→ are major proliferative cell components ← in the TME
The crosstalk
← between 1⃣ macrophage 2⃣ Tregs
→ contributes → to potential immuno-suppression ← in the TME

❗: indicate → several immuno-suppressive mechanisms
← which may be simultaneously responsible
→ for the failure of immuno-suveillance

Introduction

Esophageal cancer
→ is significantly understudied
← compared ← with other common tumor types

Esophageal cancer
→ can be classified → into two subtypes
→ 1⃣ EAC: adenocarcinoma 2⃣ ESCC: squamous cell carcinoma

ESCC
→ is the dominant subtype
→ accounts → for 90% of esophageal cancer cases

Esophageal cancer
→ is the tumor types
← with the highest median mutation burden

PD-1 antibodies
← e.g. 1⃣ pembrolizumab 2⃣ nivolumab
→ have been used ← in clinical trials
→ for subset of patients ← with advanced ESCC ← for whom first-line chemotherapy failed

A systematic interrogation ← of infiltrating immune cells
→ will help
→ to profile → the immune status of ESCC
→ to evaluate → the application of current (checkpoint blockades)
→ to lead → to innovative immunotherapies

Single-cell transcriptome analysis ← of (immune cells ← in tumors)
→ provides → a way → to comprehensively study these cells
← in a highly complex tumor micro-environment (TME)

Single-cell RNA sequencing (scRNA-seq)
→ has been applied → to tumor infiltrating immune cells
← which isolated from limited types of cancers
← e.g. 1⃣ cutaneous melanoma 2⃣ non-small cell lung cancer 2⃣ hepatocellular carcinoma 3⃣ basal cell carcinoma 4⃣ colorectal cancer 5⃣ breast cancer

❗: this studies → uncover
→ 1⃣ significant inter-tumoral & intra-tumoral hetero-geneity ← in tumor immune profiles 2⃣ diverse immuno-suppressive populations 3⃣ less-defined immune cell subsets 4⃣ signal transduction networks ← in related cancer types

Used high-demensional scRNA-seq
→ to total immune cells
← which isolated from 1⃣ seven surgically removed ESCC tumors 2⃣ their matched adjacent tissues

T-cell receptor (TCR) sequencing
→ was conducted → retrieve information ← on T-cell clonality

❗: Inter-turmal hetero-geneity
← among individual ESCC patients

A sub-group of ESCC tumors
→ significantly increased ← infiltration and clonal expansion of T-cells
← compared with → their matched adjacent tissue

Identified
→ 1⃣ exhausted T-cells 2⃣ exhausted NK cells 3⃣ regulatory T (Treg) cells 4⃣ alternatively activated macrophages 5⃣ tolerogenic dendritic cells
∴ An inflamed but immune-supressed TME ← in ESCC

Transcriptional profile
← coupled TCR-sequencing
→ revealed lineage connections
← 1⃣ CD4 2⃣ CD8 T-cells populations

Discovered → exhausted CD8 T-cells
→ showing continuous progression
← from a pre-exhausted state → to an exhausted state

Exhausted 1⃣ CD4 2⃣ CD8 T-cells 3⃣ NK cells
→ were major proliferative cell components
← in the TME

Identified → crosstalk
← among 1⃣ macrophages 2⃣ Tregs
← through ligand-receptor interactions
← that may contribute → to 1⃣ the immune suppressive state 2⃣ disease progression

Identified → a gene structure
← which associated ← with the survival of patients with ESCC

Results

scRNA-seq of immune cells isolated from ESCC

Profiled → 1⃣ single-cell gene expression programs 2⃣ TCR-sequencing
← from CD45+ cells infiltrating immune cells
← which isolated ← from 1⃣ seven pairs of fresh & surgically removed tumors 2⃣ matched adjacent tissues of ESCC

A total of 80K cells
→ were retained → for further analysis
→ after removing low-quality cells

Normalized & pooled
→ single-cell data ← from all samples
Conducted → unsupervised clustering
→ to identify distinguishable populations

Normalized & Pooled → single-cell data ← from all samples
Conducted → un-supervised clustering
→ to identify distinguishable populations
→ to enable (a systematic analysis) ← of immune cell populations

Annotated → these populations
← using their canonical markers
Identified → the major types of (tumor-infiltrating immune cells)
← including 1⃣ T-cell 2⃣ NK cells 3⃣ monocytes & macrophages 4⃣ dendritic cells (DCs) 5⃣ B-cells 6⃣ plasma cells 7⃣ mast cells

The expression of (classic markers) ← of these cell types
→ was consistent ← with the annotation

Most cells → had (copy number variations) (CNVs)
← including both 1⃣ amplifications 2⃣ deletions
∵ Analyzing other cluster ← which form tumors
∴ This cluster → included tumor cells

Found → an increase of (1⃣ T-cells 2⃣ monocyte/macrophages) ← in tumors
∵ by comparing ← each cell type ← in CD45+ cells

B-cell & NK-cells
→ were decrease
← in tumors

A large degree of variation
← in (the immune composition) ← among tumors

T-lineage cells
→ were the most abundant immune cell types
← in most tumors
∴ Making up 30-70% of (the total CD45+ cells)

There was → a high variation
← between 1⃣ matched tumor 2⃣ adjacent tissues
∵ Analyzed by (flow cytometry) ← during CD45+ cell isolation

There were only (minor differences)
← between 1⃣ the matched adjacent 2⃣ tumor tissues
← in three tumor-adjacent tissue pairs

T-cell made up to 2% of (total cells)
← in these tumors

Found → inter-patient variations
← in biologic signatures
← e.g. 1⃣ hypoxia 2⃣ inflammation response 3⃣ TNFA-vai NFKB pathways

Validated → our results
→ for the major (immune cell types) ←with additional samples
← by flow cytometry & IHC

Found
→ an increase ← in 1⃣ T-cells 2⃣ macrophages
→ a decrease ← in 1⃣ NK-cells 2⃣ B-cells
← in tumors
∴ Consistent ← with the scRNA-seq data

Neutrophils → were not identified
← in scRNA-seq

❗: Neutrophils → were not identified
← in scRNA-seq ← as a population
❗: Neutrophils → were detected ← in low abundance
← by 1⃣ flow cytometry 2⃣ IHC
∵ 1⃣ the combination of (the low abundance of neutrophils)
2⃣ the limitation of (the current scRNA-seq techniques)

Neutrophils' 1⃣ low RNA content 2⃣ abundance of RNase
→ may lead → to increased sensitivity → to prolonged (processes of scRNA-seq)
∴ Fewer transcripts being detected
∴ These cells NOT passing (quality control)

Compared → the major compartment of (infiltrating immune cells)
→ to other cancer types ← with available data

ESCC
→ was among → the tumor types
← with a higher number of 1⃣ infiltrating T-cell 2⃣ monocytes/macrophages
← with a lower number of (infiltrated B-cells)

∴ ESCC had →
1⃣ increased (T-cells & monocytes/macrophages)
2⃣ decreased B-cell ratio
← compared to their adjacent tissues

❗: Positive effects ← of (tumor-infiltrating B-cells)
← e.g. those in tertiary (lymphoid structures)
← on increasing response → to immuno-therapies

Clustering and subtype analyses of T and NK cells

Conducted (un-supervised clustering) ← of (1⃣ T-cells 2⃣ NK-cells)
← that were pooled from all samples
∵ (1⃣ T-cells 2⃣ NK-cells) → are the major (cytotoxic immune cells) ← in the TME

Identified
→ 1⃣ six CD4-T clusters 2⃣ seven CD8-T clusters 3⃣ one CD4 & CD8 double negative T-cells clusters 4⃣ three NK clusters

Used → (known functional markers) → to suggest CD4 T-cell populations
← 1⃣ naïve T-cells 2⃣ memory T-cells 3⃣ effector T-cells 4⃣ exhausted T-cells 5⃣ Tregs

The markers
→ identified CD8 T-cell populations
← including 1⃣ memory 2⃣ effector 3⃣ cytotoxic 4⃣ exhausted T-cells

Three clusters
→ expressed → variable levels of (checkpoint molecule genes)
← e.g. 1⃣ PDCD1 2⃣ TIGIT 3⃣ CTLA-4 4⃣ HAVCR2 5⃣ LAG-3
∴ representing (the phenotypes of exhausted cells)

These cells
→ hight expressed ← 1⃣ CD38 2⃣ CD39 3⃣ CD103
← which displayed ← an exhausted tissue-resident memory phenotype

Most cytotoxic markers
→ were highly expressed ←in exhausted CD8 T-cells
∴ Which is consistent ← with other observations

CD8-C1-NKG7
→ expressed (high levels) ← of 1⃣ granzyme genes 2⃣ NKG7
TCF7
← the lowest level of (checkpoint molecules genes) & SELL
→ was likely the recently activated (effector T-cells) (TEMRA)

Identified CD8-C3-GZMK
← as a transitional population
← which presented (a distinct expression pattern) ← of (transcription factors)

This cluster → was the only one to have cells
← which expressing (a high level) of GZMK
∴ A relationship ← between 1⃣ EOMES 2⃣ GZMK

Used → 1⃣ the public naïve 2⃣ Treg 3⃣ exhaustion 4⃣ cytotoxic signatures
Applied → these signatures → to 1⃣ CD4 2⃣ CD8 clusters
Computed → a transcriptional score
→ to investigate (gene networks)
← in 1⃣ cytotoxic 2⃣ exhausted CD8 T-cells & Treg cells

CD4-C6-FOXP3
→ had → the strongest (Treg signature)
CD4-C5-STMN1
→ had → the strongest (exhaustion signature)
CD4-C4-IFIT3
→ was enriched ← in cytotoxic signature

CD8-C1- NKG7
→ was → the most (active cytotoxic CD8 T-cells)
1⃣ CD8- C5-CCL5 2⃣ CD8-C6-STMN1
→ had lower (exhaustion scores)

The naïve score
→ was very low
← in CD8 T-cell clusters
∴ No naïve clusters ← of (CD8 T-cells)
→ were identified

∴ Most of (the tumor-infiltrating CD8 T-cells)
→ were ← in the 1⃣ active 2⃣ memory 3⃣ exhausted states ← in ESCC

Analyzed → the gene (← whose expression was highly correlated)
← with the expression of 1⃣ FGFBP2 2⃣ LAG3 3⃣ FOXP3

Used → these signatures
→ 1⃣ to analyzed T-cells clusters 2⃣ to found ← that the enrichment scores
→ were consistent
← with the published signatures

❗: Lineage connections
← with CD4 populations

The genes
← activated in Tregs
→ overlapped ← with genes
← characteristic of the exhaustion program ← in CD4 T-cells

Compared → the gene expression
← of both clusters ← with naïve-like cell population

Genes
→ enriched ← in both 1⃣ exhausted 2⃣ Treg cells
← included 1⃣ regulatory molecules 2⃣ many co-inhibitory 3⃣ co-stimulatory receptors
← e.g. 1⃣ TNFRSF9 2⃣ CSF1 3⃣ TIGIT

CD4-C6- FOXP3
→ expressed much higher levels ← of 1⃣ FOXP3 2⃣ IL2RA 3⃣ CTLA4
CD4-C5-STMN1
→ expressed higher levels ← of 1⃣ CCL5 2⃣ CCL4 3⃣ IFI6 4⃣ TOX 5⃣ PDCD1 6⃣ CXCL13 7⃣ IFNG 8⃣ ID2

Visualization
← of 1⃣ the exhaustion 2⃣ Treg scores
→ confirmed → the overlap ← between these two clusters

1⃣ Cytotoxic 2⃣ exhausted CD8 T-cells
→ expressed → many effector molecules
← e.g. 1⃣ GNLY 2⃣ GZMH
Exhausted CD8 T-cells
→ expressed a higher level of IFNG
← than cytotoxic cells
∴ Exhausted T-cells
→ expressed → high level of (some effector molecules)
→ tried → to respond → to tumor cells

Both genes
→ participate ← in establishing (epigenetic programs)
→ to install (permanent exhaustion status)

∴ 1⃣ CD8-C5-CCL5 ← at (an early stage of exhaustion)
2⃣ CD8- C7-TIGIT ← in the exhaustion stage
3⃣ CD8-C6-STMN1 ← in a transition stage

Large fractions ← of tumor-infiltrating CD8 T-cells
→ are bystanders ← that recognize (cancer un-related epitopes)

These cells
→ lack CD39
→ are (phenotypically distinct) ← from tumor antigen-specific CD8 T-cells

Analyzed → CD39 expression
← in CD8 T-cells

CD39 expression
→ was significantly higher
← in (pre-exhausted & exhausted) CD8 T-cells

Most CD8 T-cells
→ expressed higher CD39
← in tumors ← than adjacent tissues

Altered status of T and NK cells in tumors

Compared T-cell clusters
← between 1⃣ tumors 2⃣ adjacent tissues

1⃣ Treg cluster CD4-C6-FOXP3 2⃣ exhausted CD4 T-cells CD4- C5-STMN1
← in CD45+ cells
→ were significantly increased
← in tumors ← compared with (matched adjacent tissues)

∴ 1⃣ Tregs 2⃣ exhausted CD4 T-cells
→ were more than 50% of the total CD4 T-cells ← in tumors
→ were only 25% ← in adjacent tissues

Flow cytometry
→ demonstrated → the enrichment of Tregs
← in ESCC tumors

Exhausted CD8 T-cells
→ were enriched ← in tumors

The total percentage of (exhausted CD8 T-cells)
→ was < 20% ← in adjacent tissues
→ was 57% ← in tumors

PD1 expression
← in CD8 T-cells
→ was higher ← in ESCC

The most active cytotoxic CD8 T-cell groups
→ significantly decreased

The significant increase
← in 1⃣ Tregs 2⃣ exhausted CD4 2⃣ exhausted T-cells
← in tumor tissues
∴ An immune suppressive environment

Observed → a substantial decrease
← in NK-cells ← in tumor tissues

The major cluster ← NK-cells
→ switched → 1⃣ from NK- C1-NCR3 ← in adjacent tissues 2⃣ to NK-C3-KLRC1 ← in tumors

NK-C2-STMN1
→ increased ← in tumors

1⃣ NK-C3-KLRC1 2⃣ NK-C2-STMN1
→ had extremely low-cytotoxic scores
→ had elevated the exhaustion scores
∴ NK-cells → were (insufficient & function impaired) ← in ESCC

Analyzed → the cell cycle ← in 1⃣ T-cell clusters 2⃣ NK-cell clusters
→ to determine → the proliferating ability ← of cells

Generated → a proliferation score ← of (cell cycle gens)
→ that were previously shown → to denote 1⃣ G1/S 2⃣ G2/M phases
→ that were used to infer (the proliferation status) ← of 1⃣ T-cell clusters 2⃣ NK-cell clusters

∴ Exhausted T-cells
→ are the major intra-tumoral proliferating immune cell compartment

Clonality of CD4 and CD8 T cells

Analyzed → the results ← from the coupled TCR sequencing
← from the same samples
→ to determine → whether 1⃣ the clonal selection 2⃣ amplification of T-cells
→ contributed to the observed phenotypic diversity

Recovered → 1⃣ TCRα 2⃣ TCRβ sequences

Clonal expansion → was observed
📓: Clonal size range → [2, 2600]

The majority of TCRS → were unique
∵ consistent with other studies

66% of T-cell ← with TCRs
→ shared ← by more than two cells
∵ S149 & S150 tumors
∴ The high (clonal expansion) ← of T-cells

Each cluster
→ was composed ← of (different combinatorial subsets of the clonotypes)

❗: CD8 T-cells
→ had significantly more clonal cells
❗: the naïve cluster CD4-C1-CCR
→ displayed very limited (clonal expansion)

CD8-C1-NKG7
← the cytotoxic cluster ← in CD8 T-cells
→ had a higher frequency ← in adjacent tissues
→ showed increase (clonal expansion) ← in adjacent tissues

The Tregs ← in tumors
→ had an increased number of clones
← compared to match-adjacent tissues
∴ The expansion of (specific clone cells)
→ may be responsible
→ for the higher percentage of Tregs in tumors

Clonal amplification
→ was observed
→ to varying degrees ← in different clusters
← while (most cells) contained ← (unique TCRs)

Sharing of (TCR sequences)
← among (all clusters) ← in CD4 cells
All clusters ← within CD8 cells
← with the exception of C2

The number of clones
← shared between CD8-C7-TIGIT and CD8-C5- CCL5 and CD8-C6-STMN1
→ was 9.0% and 8.4%

CD8-C7-TIGIT cells
← in the adjacent tissues
→ shared more clonotypes
← with other CD8 clusters

CD4-C6-FOXP3
← the Treg cluster
→ had the same trend in tumors
∵ displaying 1⃣ 14.4% of shared clonotypes ← with CD4-C1-CCR7 2⃣ 40.7% ← in the adjacent tissue

Clonal T-cells
← in 1⃣ cytototxic 2⃣ exhausted 3⃣ Treg cells
→ shared limited TCR
← between the tumor and adjacent tissues
∴ (Potential common origins) ← of 1⃣ some Tregs 2⃣ naïve CD4 T-cells

Distinct functional composition of myeloid cells in ESCC

Conducted
→ (unsupervised clustering) ← of myeloid cells
∴ 14 clusters → were identified

Monocles
← an unsupervised inference method
→ to construct → the potential developmental trajectories of (cell conversion)
→ to further understand (the cell transitions)

1⃣ Monocyte 2⃣ M1 3⃣ M2
→ were consistent
← with the immune-suppressive function of (tumor-associated macrophages (TAMs))

WGCNA
→ to conduct → weighted-correlation network analysis
← in 1⃣ monocyte 2⃣ macrophages

The Turquoise module
→ was positively correlated ← with the monocyte clusters
→ was negatively correlated ← with 1⃣ the M2 cluster 2⃣ MDSC clusters

The gene ← in this module
→ were associated ← with 1⃣ myeloid leukocyte activation 2⃣ activation of immune response

Analyzed
→ 1⃣ the genes ← in this module
→ 2⃣ their association ← with Mono-C1-VCAN
→ to select (the top 50 genes) ← that most correlated to form (a signature set)

This signature
→ was strongly associated ← with a high probability of (progression-free survival)
← in 1⃣ ESCC 2⃣ cervical squamous cell carcinoma 2⃣ lung squamous cell carcinoma

∴ This signature → may serve
← as a prognostic biomarker
← in 1⃣ ESCC 2⃣ squamous cell carcinoma ← in other tissues

Applied → (single-cell regulatory network inference & clustering (SCENIC) method)
→ to explore the transcription factors
← that may regulate 1⃣ monocyte 2⃣ M1 3⃣ M2 4⃣ development

1⃣ MITF 2⃣ BHLHE40 3⃣ ATF3 4⃣ USF2
→ were up-regulated ← in M2

IRF transcription factors
← including 1⃣ IRF1 2⃣ IRF7 3⃣ IRF2 4⃣ IRF5 5⃣ PRDM1
→ were up-regulated ← in M1

1⃣ RARA 2⃣ FOSB 3⃣ NFKB2
→ were greatly increased
← in the monocyte clusters

A dichotomy
← between 1⃣ most tumor 2⃣ adjacent tissue pairs
∵ SCENIC

The transcriptional factor "BHLHE40"
→ was specifically expressed ← in M2

Conducted → the network analyses
→ to identify → the BHLHE40 downstream genes
→ to analyze the functions through Metascape

BHLHE40 downstream genes
→ were associated ← with myeloid cell differentiations
→ were negatively regulated → the cellular respnose

BHLHE40
→ has reported → to mediate
→ 1⃣ tissue-specific control ← of macrophage self-renewal 2⃣ proliferation

BHELHE40
→ was associated ← with (poor prognosis) of ESCC

BHLHE40
→ may play (a critical role)
← in inducing TAMs → toward the M2 phenotype
∴ To explore → the detailed mechanisms

An enrichment of (suppressive TAMs)
← in the ESCC micro-environment
→ may contributed → to the progression of disease

5 DC clusters
→ featured → high expression levels ← of 1⃣ CLEC9A 2⃣ CD1C 3⃣ FCER1A 4⃣ LAMP3 5⃣ CLEC4C

DC-C3- LAMP3
→ was enriched ← in tumors
→ compared to adjacent tissues

LAMP3 + DCs
→ were the most activated DC subset
← with potential (migration capacity) ← in tumors
→ originate ← from both 1⃣ CDC1 2⃣ cDC2

LAMP3 + DCs
→ had the highest 1⃣ activity 2⃣ migration ability
← compared to other DC subsets
∵ Comparing → the activation & migration scores of DCs

LAMP3 + DCs
→ enriched → the tolerogenic signature

LAMP3 + DCs
→ expressed → many regulatory molecules
← e.g. 1⃣ IDO1 2⃣ EBI3 3⃣ CD274 4⃣ IL10

Genes
← which up-regulated in LAMP3 + DCs
→ were enriched ← in the pathways
← e.g. 1⃣ cytokine-mediated signaling transduction 2⃣ DC cell differentiation 3⃣ leukocyte activation 4⃣ membrane trafficking 5⃣ antigen processing 6⃣ presentation

LAMP3 + DCs
→ expressed significantly higher 1⃣ CD83 2⃣ CCR7 3⃣ PDL1
∴ 1⃣ the maturation 2⃣ migration 3⃣ regulation ability ← of LAMP3 + DCs

The existent of 1⃣ CD11C 2⃣ LAMP3 3⃣ PDL1 4⃣ IDO 5⃣ DCs
← in tumor tissues
∵ multi-color IHC staining

1⃣ IFNγ 2⃣ LPS stimulation-induced DCs
→ expressing 1⃣ PDL1 2⃣ IDO
→ had an increased ability
→ to induce FOXP3 expression ← when co-cultured with 1⃣ CD4 2⃣ CD45RA 3⃣ naïve T-cells
∴ 1⃣ IFNγ 2⃣ LPS → may induce
→ the tolerogenic DCs ← in vitro

DC subsets
→ could be distinguished
← by different groups of (transcription factors)
∵ SCENIC analysis

LAMP3 + DCs
→ showed higher levels
← of 1⃣ RELB 2⃣ IRF1 3⃣ FOXO1 4⃣ ETS1

1⃣ CEBPD 2⃣ ETS2 3⃣ CDBPB 4⃣ CREB5
→ were up-regulated ← in cDC2
1⃣ BCL6 2⃣ BACH1 3⃣ FLI1 4⃣ RUNX1
→ were highly expressed ← in cDC1
High levels of 1⃣ SPIB 2⃣ IRF7 3⃣ NR3C1
→ were associated ← with pDCs

RELB
→ regulated cDC development
← by hematopoietic extrinsic mechanisms

RELB-dependent 1⃣ CD117 2⃣ CD172a 3⃣ murine DC subsets
→ induced → Th2 differentiation
→ supports → airway hyper-response

∴ Conducted (network analyses)
→ to identify → the RELB downstream genes

∴ These genes
→ were associated
← with 1⃣ cell migration 2⃣ DC differentiation 3⃣ negative regulation ← of cellular processes

Cell-cell interaction between immune cells in ESCC

Cell-cell communications
← mainly through (ligand-ligand interactions)
→ play (key roles)
← in 1⃣ determining the TME 2⃣ responding → to therapeutics

∴ Perform (systematic analyses)
→ on the potential (cell-cell interactions)
← based on co-expression of (known ligand-receptor pairs)
∴ Compared them
← between 1⃣ tumor 2⃣ adjacent color IHC
∴ Validated the IL1R2 expression ← in Tregs

IL1R2
→ was required → for Tregs
→ to inhibit IL-1β-dependent activation of (effector T-cells)
∵ in vitro 1⃣ co-culture 2⃣ antibody-blocking assays

Predicted an interactions
← between 1⃣ MHC in Tregs 2⃣ LILRB1 in macrophages

The MHC receptor "LILRB1"
→ is a negative regulator ← of myeloid cell activation
→ is a promotor ← of the M2 suppressive state

The MHC-LILRB1 interaction
→ suppressed macrophages
→ is a target ← of (cancer immuno-therapy)

Analyzed LILRB1 expression
← in macrophage ← by scRNA-seq
Validated it
← by FACS

The expression of LILRB1
← in macrophages
→ increased ← in ESCC
→ compared to adjacent tissues

Potential physical interaction
← between 1⃣ LILRB1 expression macrophages 2⃣ Tregs

Tregs
→ promoted → macrophages ← expressing M2 markers
→ decreased TNFα expression

Discussion

A comprehensive characterization of (immune cells)
← in seven pairs 1⃣ ESCC tumors 2⃣ matched adjacent tissues

1⃣ immune signature profiling 2⃣ TCR β-chain repertoire analysis
→ have been studied ← in ESCC
← using 1⃣ mRNA micro-array 2⃣ bulk RNA-seq

A mouse model
→ mimicking → 1⃣ human ESCC development 2⃣ construction of a single-cell ESCC developmental atlas

Combined → 1⃣ deep scRNA-seq 2⃣ TCR-seq
Illustrated → the whole immune landscape
← including 1⃣ the innate 2⃣ the adaptive immune cell atlas

ESCC
→ was enriched ← in immune-suppressive cell populations
← including 1⃣ Tregs 2⃣ exhausted CD8 T 3⃣ CD4 T 4⃣ NK-cells 5⃣ M2 macrophages 6⃣ tDCs

All these immune-inhibitory cells
→ contribute → to 1⃣ immune escape 2⃣ promote tumor progression

Exhausted 1⃣ CD4 2⃣ CD8 T-cells 3⃣ NK cells
→ were the major intra-tumoral proliferating immune cell compartments

Pre-exhuasted clusters
→ may serve ← as better targets → for immuno-therapies
→ are in a permanent & less reversible (exhausted stage)
→ making them more resistant → to checkpoint inhibition
∵ their epigenetic changes

Tumor-infiltrating NK-cells
→ were not only commonly reduced← in ESCC
→ expressed (high levels) ← of (checkpoint molecules)
∴ An exhausted state

1⃣ Anti-NKG2A 2⃣ anti-CD49d
→ are checkpoint inhibitors
← that promote anti-tumor immunity

Alternative pathways
→ to re-active anti-tumor immunity
← in ESCC

Clonal amplification
← of cells ← carrying identical TCR-seq → across clusters
→ was a strong evidence
→ to support the connection ← among these clusters
∴ The transition of cell status

Clonal amplification
→ was observed → to varying degrees ← in different clusters

Sharing of (TCR sequences)
← in almost all CD4 & CD8 clusters
∴ A broad differentiation → after T-cell priming

Exhausted CD8 T-cells
→ carried a much higher percentage of (shared clones)
← with other CD8 clusters
→ especially with the pre-exhaustion clusters
← which was consistent with the related status of (1⃣ these clusters 2⃣ the multi-step exhaustion hypothesis)

The inhibition of (cellular proliferation)
← of cytotoxic CD8 T-cells
→ contribute 1⃣ to the decreased number of (tumor-infiltrating effector CD8 T-cells) 2⃣ to the immune-suppressive micro-environment

The clonal amplification of (esophageal cytotoxic cells)
∵ Occasional exposure → to non-tumor antigens
← in the esophagus

Did NOT find
→ a significant population of (naïve CD8 T-cells)
Identified → a limited number of (naïve CD8 T-cells)

Naïve CD8 T-cells
→ less frequently infiltrate → into the esophagus
→ are activated ← by the local environment

The critical role ← of 1⃣ monocytes 2⃣ macrophages
← in tumors
→ has been described ← in 1⃣ liver 2⃣ breast 3⃣ lung cancers

Macrophage activation
→ is classified
→ into 1⃣ a pro-inflammatory M1 state 2⃣ an M2 state
← associated with the resolution of inflammation

1⃣ monocyte 2⃣ macrophages
→ reside ← along a spectrum of 1⃣ monocyte 2⃣ M1 3⃣ M2 states
Both 1⃣ M1-associated 2⃣ M2-associated genes
→ were frequently co-expressed

6 macrophage clusters
→ identified ← in hepato-cellular carcinoma
A macrophage clusters
→ was enriched → for signatures of MDSC

cDC2
→ is specially involved
← in 1⃣ MHC class II-mediate antigen presentation 2⃣ the activation & expansion of CD4 T-cells

cDC1
→ is also necessary
→ for anti-tumor immunity

A variation in DC states
← between 1⃣ different tumor tissues 2⃣ the settings of analysis
∴ Some differences

LAMP3 + DC
→ had multiple functions
← e.g. 1⃣ activation activity 2⃣ migration activity 3⃣ tolerogenic ability
∴ The conserved myeloid cells exist ← in many tumors

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