Paper - Review
10.1016/j.cell.2020.09.048
DOI: 10.1016/j.cell.2020.09.048
Summary
1⃣ Re-activation 2⃣ Clonal expansion ← of TSA-reactive T cells
← TSA: tumor-specific antigen
→ to 1⃣ the success of checkpoint blockade 2⃣ Adoptive transfer of TIL-based therapies
← TIL: tumor-infiltrating lymphocyte
NO ❌ reliable markers
→ to specifically identify (the repertoire ← of TSA-reactive T cells)
∵ their heterogeneous composition
FucoID
→ to detect endogenous antigen-specific T cells
1⃣ Intra-tumoral TSA-reactive CD4+ & CD8+ T cells 2⃣ TSA-suppressive CD4+ T cells
→ can 1⃣ be detected 2⃣ be separated
← from by stander T cells
← based on their cell-surface enzymatic fucosyl-biotinylation
TSA-reactive CD8+ TILs
→ possess 1⃣ substantial capabilities ← of proliferation 2⃣ tumor-specific killing
Introduction
Development of 1⃣ immune checkpoint inhibitors 2⃣ adoptive cell transfer (ACT) -based therapies
→ revolutionized cancer treatment
❗: Success is limited → a small subset (← of 1⃣ patients 2⃣ cancer types)
Successful anti-tumor immune responses
← following checkpoint blockade immunotherapy
→ to require 1⃣ re-activation 2⃣ clonal expansion
← of tumor-specific antigen (TSA)-reactive T cells
TILs
→ 1⃣ consist those T cells specific for TSAs
2⃣ recognize epitopes unrelated → to the tumor
∴ Identifying TSA-reactive T cells
← from cancer patients
→ critical implications → for 1⃣ prediction 2⃣ therapeutic applications
Tumor-reactive TIL candidates
→ can be roughly enriched
← from tumor digests ← through the detection ← of 1⃣ cell-surface markers 2⃣ bystander T cell expressing
The precise therapeutic effect ←of immuno-therapies
← that boost immunity of endogenous T cells
→ is governed ← by the T-cells' capability → to recognize TSAs
Interaction of unique T-cell receptors
← with cognate peptide-major histone compatibility complexes (pMHCs)
pMHC multi-mers
→ are widely used
→ 1⃣ to profile TCR specificity of a known antigen 2⃣ to identify TILs specific to a particular TSA
Computational tools
→ are used → to generate peptides harboring epitopes
←which encoded by these non-synonymous mutations
A large portion ←of identified TSA candidates
→ is NOT ❌ immunogenic
∵ Available computational tools cannot ❌ predict
→ T-cell reactivity ←with full accuracy
Requiring 1⃣ deep sequencing 2⃣ bioinformatic analysis 3⃣ machine learning
→ heavily rely ← on expertise in computational biology
∴ As a result, turnover times are long
❗A method → for the identification of antigen-specific T-cells
← 1⃣ based on an interaction-dependent fucosyl-biotinylation
← 2⃣ the use of this methods → to isolated endogenous tumor antigen-specific T-cells
← without the previous knowledge of the TSA identities
FucoID
← tumor-lysate primed DCs presenting TSAs
→ are equipped with an enzyme → that induced proximity-based transfer of fucosylated biotin (Fuc-Bio) tags → to the surface of T-cells that interact with the DCs
The tagged T-cells
→ are bona-fide antigen-specific T-cells
→ are based on their cell-surface fucosyl-biotinylation
Isolate
→ intra-tumoral 1⃣ TSA-reactive CD4+ 2⃣ CD8+ T-cells 3⃣ TSA-suppressive CD4+ T-cells
Nearly all TSA-reactive CD8+ cells
→ co-express PD-1
← bystander T-cells ← consist of 1⃣ PD-1+ 2⃣ PD-1- subsets
TSA-reactive CD8+ TILs
→ possess → 1⃣ a distinct TCR repertoire 2⃣ unique gene features
→ that are characterized ← by a dysfunction/activation 1⃣ transcript profiles 2⃣ genes
← which up-regulated in 1⃣ steroid biosynthesis 2⃣ related metabolic pathways
1⃣ Tumor-specific antigen-suppressive 2⃣ Tumor-specific antigen-reactive CD4+ T-cells
→ co-exist in the tumor micro-environment
→ execute (opposite roles) ← in the regulation of (anti-tumor immunity of CD8+ T-cells)
FucoID features
→ much simpler procedures
→ a quicker turnover cycle
❗: FucoID
→ creates an avenue
→ for 1⃣ the characterization 2⃣ the manipulation
← of 1⃣ the entire repertoire ←of endogenous 2⃣ tumor antigen-specific TILs
Results
Install H. pylori α FT onto the Cell Surface for Probing Cell-Cell Interactions
Enzyme-based proximity-labelling systems
→ have been developed → to profile protein-protein interactions
→ ❗ very few can be applied → to probe cell-cell interactions
To design ← an enzymatic approach
→ for probing cell-cell interactions of primary cells
→ requires (an enzyme) ← which can be installed onto (the cell surface ← of bait cells)
❗: The challenge
→ to identify an enzyme → to achieve inter-cellular labelling with hight sensitivity
← when 1⃣ two cells interact 2⃣ low background in the absence of an interactions
A chemo-enzymatic method
→ to conjugate recombinant proteins ← onto the cell surface
H. pylori α FT
→ a glyco-syltransferase → possessing remarkable donor substrate tolerance
→ enables (rapid & quantitative) transfer of proteins
← conjugated to the enzyme's natural donor substrate
∴ Devised → a genetic-engineering free strategy
→ to probe cell-cell interactions
← using the membrane anchored FT
GDP-Fuc-FT
← GDP-fucose-conjugated FT
→ a unique small molecule-protein
← conjugate bearing both 1⃣ the donor substrate 2⃣ the glycosyltransferase itself
The donor substrate-modified enzyme
→ served as a self-catalyst
→ to transfer Fuc-FT onto the cell-surface LacNA-cylated glycans
1⃣ Primary mouse CD8+ T-cells 2⃣ bone marrow-derived DCs 3⃣ human lymphocytes & DCs
→ 1⃣ were conjugated with FT 2⃣ the robust modifications were achieved
The membrane-anchored FT
→ may serve as an ideal tools → to enable proximity-dependent labeling of prey cells ← which interact with bait cells harboring the enzyme
∵ is hight Km & Kcat
The local concentration of LacNAc
→ is hight
∴ the pseudo-zero-order reaction rate → is determined ← by Kcat
Incubated FT-functionalized wild-type CHO cells
← with adherent CHO cells
→ to form cell-cell contacts
→ to assess if FT-functionalized cells → could mediate intercellular labeling
← by transferring a probe molecule
Probe DC-T Cell Interactions via FucoID
Examined FT-modified iDCs
← immature DCs
→ for the selective labeling of CD8+ T-cells
← which express a transgenic TCR specific ← fot the SIINFEKL peptide
→ to determine if FucoID could be applied → to probe antigen-specific DC-T-cell interactions
Robust Fuc-Bio labeling
→ was found ← on the interacting CD8+ T-cells
← with a singal-to-background ratio of 36% vs. 1%
The iDC-FT loaded with the lymphocytic choriomeningtis virus Gp peptide
→ only induced the background level labeling of OT-I CD8+ T-cells
← indicating that the labeling is antigen-specific
Mixed naïve (OT-I & P14 CD8+ T-cells)
←which recognized LCMV GP33-41 presented by MHC-I
→ to further assess 1⃣ the sensitivity 2⃣ the specificity ← of FucoID
❓: determine if antigen-specific labeling via FucoID
→ could be achieved ← in cell mixtures of 1⃣ natural components 2⃣ complexity
The OVA-specific fucosyl-biotinylation of OT-I CD8+ T-cell
→ was detected
Spiked → OT-I T-cells → into C57BL/6J splenocytes
→ to form a cell-mixture ←with a low frequency of OT-I
→ to assess if FucoID could be applied → to label antigen-specific T-cells
Repeated the OT-I labeling experiment
← using iDC-FT primed ← with altered peptide ligands (APLs)
← derived from the original OT-I ligand SIINFEKL (N4)
→ to determine if FucoID can distinguish ← strong cell-cell interactions
Surface molecules ←on APCs
→ could be transferred to T-cell ← by trogocytosis
Trogocytosis ← of TCR proteins occurring
← during Jurkat-K562 interactions
→ could be used → to identify tumor neoantigens
Fuco ID
→ could also be applied → to probe iDC-CD4+ interactions
← despite significantly weaker binding ← betwenen 1⃣ MHC-II bound peptides 2⃣ CD4+ TCRs
Rapid Detection and Enrichment of TSA-Reactive TILs Based on FucoID in a B16-OVA Tumor model
Assessed → the feasibility of using this strategy
→ to detect TSA-reactive TILs ← from tumor digest
←with the validation of FucoID ←as (a reliable technique)
A harvest solid tumor
→ is dissociated → to prepare (a single suspension & tumor lysates)
← in which (the tumor lysates) → are used to prime autologous iDCs
Both 1⃣ non-mutant peptide 2⃣ TSAs
→ are loaded onto MHCs ← on the iDC surface
The prime iDC
→ are then 1⃣ subjected to FT conjugation 2⃣ added to the single cell suspension
A majority of self-antigen-reactive T-cells
→ would have already been eliminated ← in the thymus
← via "negative selection"
The labeled T-cells
→ are highly enriched → for tumor-specific T-cells
→ their specificities → are directed toward TSAs
Well-established B16-OVA melanoma
← which expresses chicken ovalbumin as TSAs
→ was used as → the first model system → to test this hypothesis
The labeled CD8+ TILs
→ were fluorscence-activated cell sorting (FACS) isolated & expanded
← using a reported rapid expansion protocol
Bona fide TSA-reactive T-cells
→ were enriched ← in PD-1+ Bio+ TILs
← which is a sub-population of them → should be OVA-specific
PD-1+ 1⃣ BIO- 2⃣ BIO+ TILs
→ were cultured
→ for allowing the complete decay of the biotinylated molecules
←from the cell surface
1⃣ PD-1 Bio- 2⃣ PD-1- subset
→ can undergo 1⃣ re-expansion 2⃣ memory formation
→ the isolated TILs → were transferred → into antigen-free WT hosts
→ to determine → ❓ the isolated PD-1+ Bio+
TILs Labeled via FucoID in Multiple Syngeneic Murine Tumor Models Are Bona Fide TSA-Reactive TILs
Explore → its (scope & limitation)
∵ FucoID ← a highly effective approach ← to (detect & enrich)
Tumor cell suspensions prepared
← from 1⃣ B16 melanoma 2⃣ E0771 TNBC (Triple negative breast cancer) 3⃣ MC38 colon tumors
→ were subjected → to the FucoID-based labeling
these TILS
→ have encountered ← their congnate antigens
← All 3 tumor model
The isolated 1⃣ PD-1- 2⃣ PD-1+ Bio- 3⃣ PD-1+ Bio+ CD8+ TILs
→ were cultured & expanded
← using (a rapid expansion protocol) ← in the presence of feeder cells
← 1⃣ anto-mCD3 2⃣ rhIL-2
→ to characterize (the function ← of these subsets)
The differences ← in TSA-reactivities
← of 1⃣ PD-1+ Bio+ 2⃣ PD-1+ Bio- CD8+ TILs
→ the differences of their TCR repertories
TCR β deep sequencing
→ was employed
→ for quantifying the frequency of individual T-cell clonotype ← in each subset
A productive CDR3 sequence
← that does not contain (stop codons & frameshifts)
→ represents → a unique TCR clonotype
Total number (← of unique sequences)
→ determines → the clonal diversity
TCR βs
← in the PD-1+ Bio+ population
→ were significantly more oligoclonal
← than their counter-parts ← in the PD-1+ Bio- subset
∴ The cells (← in the PD-1+ Bio+ subset) → have undergone ← substantial TSA-driven clonal expansion
1⃣ PD-1+ Bio+ 2⃣ PD-1+ Bio- TILs
→ represent 1⃣ functionally 2⃣ clonaltypically distinct T-cell subsets
← which 1⃣ co-exist in the same tumors 2⃣ share a certain degree of phenotypical similarities
1⃣ PD-1+ Bio- 2⃣ total PD-1+ TILs
→ exhibits very similar expansion rate
→ was significantly faster ← than that of PD-1+ Bio+ TILs
← during the entire course of expansion
The expanded PD-1+ Bio+ CD8+ TILs Exhibit Significantly Higher Anti-Tumor Activities Than the Entire PD-1+ TILs In Vivo
The use of the TILs isolated
← from the B16 melanoma model
→ to control tumor growth in mice
← with established pulmonary micro-metastases
→ to compare anti-tumor activities ← of 1⃣ the expanded PD-1+ Bio+ CD8+ TIL 2⃣ the total PD-1+ subset
The total PD-1+ TILs
→ showed moderate therapeutic potency
→ for preventing tumor proliferations
Use the TILs
← isolated from the subcutaneous MC38 tumors
→ to assess capabilities of the expanded PD-1+ Bio+ CD8+ TILs
→ for suppressing solid tumor growth
PD-1+Bio+ CD8+ TILs Are Distinct from PD-1+Bio– TILs and Display Activation/Dysfunction Gene Signatures
Characterized → transcriptional profiles
← of 1⃣ PD-1+ Bio+ 2⃣ PD-1+ Bio- 3⃣ PD-1- CD8+ T-cells
← which isolated from MC38 subcutaneous tumors
→ to gain an understanding of the genetic
The transcript profiles ( ←of these 3 subset of TILs)
→ shared substantial divergence
❗ The less pronounced transcriptional differences
← of 1⃣ PD-1+ Bio+ 2⃣ PD-1+ Bio- CD8+ TILs
Several up-regulated genes ( ←of PD-1+ Bio+ TILs)
→ were significantly enriched ← in steroid bio-synthesis
→ related metabolic pathways ← 1⃣ MSMO1 2⃣ DHCR7
An increase
← in the plasma membrane cholesterol level
← of CD8+ T-cells augments T-cell receptor 1⃣ clustering 2⃣ signaling 3⃣ the more efficient formation ← of the immunological synapse
← which are essential ←for the effector function (← of CD8+ T-cells)
Down-regulated genes
→ were enriched
← in more diverse biological process networks
← including 1⃣ lymphocyte differentiation 2⃣ T-cell migration & activation 3⃣ viral response 4⃣ calcium homeostasis
Performed GSEA (gene set enrichment analysis)
← using 1⃣ the gene signatures 2⃣ the gene modules
← established in 1⃣ chronic virus infection induced T-cell exhaustion 2⃣ tumor associated T-cell activation & dysfunction models
→ to further characterize genetic differences
Assessed → the 3 subsets TILs
→ for the enrichment ← of the exhaustion signature
← that derived from (exhausted T-cell) ← which isolated from (chronic LCMV infection TILs)
Compared ← the TSA-reactive PD-1+ Bio+ TILs
← with the bystander 1⃣ PD-1+ Bio- 2⃣ PD-1- TILs
→ for the enrichment of the gene modules shared by T-cells infiltrating human & murine tumors
An enrichment of the up-regulated cell-cycle gene signature
← that was validated ← for human melanoma TILs
→ was found in PD-1+ Bio+ TILs
← to both 1⃣ PD-1+ Bio- 2⃣ PD-1- bystander TILs
This finding
→ provided (strong evidence)
→ for ongoing proliferation
← ⭕ within this dysfunctional ❌ TSA-reactive T-cell subset
Analyzed → the transcript level of the gene
← that were previously reported ← as tumor-reactive TILs selection markers
← ing 1⃣ PD-1+ Bio+ 2⃣ PD-1+ Bio- TIL subsets ← isolated from the MC38 colon cancer model
Analyzed TILs
← that isolated from 1⃣ B16 2⃣ E0771 tumor model
← that found similar varying expression levels of these markers
∴ FucoID
→ may be more generally applicable
← than previously reported (functional markers-based selection approaches)
→ to identify TSA-reactive TILs
Intra-tumoral antigen-specific CD4+ T-cells play bi-directional roles in the regulation of anti-tumor immunity of TSA-reactive CD8+ TILs
Intra-tumoral antigen-specific CD4+ T-cells
→ have been identified
← in both 1⃣ patient 2⃣ murine tumor models
❓: their (biological function) → be elucidated partially
∵ the lag of research-tool development
Prospect antigen-specific 1⃣ CD8+ 2⃣ CD4+ TILs
→ were successfully labeled ← via FucoID
→ were isolated by FACS
Analyzed 1⃣ Bio+ 2⃣ Bio- CD4+ TILs subsets
← using functional markers
→ to characterized their phenotypes
1⃣ CD137+ 2⃣ CD137- 3⃣ CD134+ 4⃣ CD134-
→ were found ← in the bio+ CD4+ TILs
❗Bio- CD4+ TILs
→ were mainly 1⃣ CD137- 2⃣ CD134-
One-third were CD25-
← Among the FoxP3+ cells
∴ CD25- Bio+ TILs
→ contained → a relatively smaller portion of FoxP3+
→ contained → a larger potion of FoxP3- cells
❗: All CD25+ Bio+ CD4+ TILs → were FoxP3+
Performed → and IFN-γ ELISpot assay
← using 5 isolated TIL subsets
→ to verify → the isolated Bio+ TILS → were tumor antigen-specific
→ to further characterize ← their biological functions
Discussion
TILs
← within individual tumors ← consist of hetero-geneous population
← including 1⃣ the T-cells specific → for tumor antigens 2⃣ those recognizing (a wide range of epitopes) ← unrelated to cancer
These bystander CD8+ TILs
→ have (diverse phenotypes)
← which overlap with those of the tumor antigen-specific T-cells
→ are NOT ❌ tumor-reactive
TSA-reactive TILs
← in 1⃣ less abundant 2⃣ rare populations
→ could be missed
← using such (indirect selection methods)
∵ These TILs → may not share 1⃣ the same exhaustion status 2⃣ phenotypes
← with the most abundant TSA-reactive TILs
This subset of TILs
→ could NOT ❌ → be easily identified
← by conventional methods
e.g. 1⃣ tetramer staining 2⃣ functional markers