US-12618832-B2 - Identification of cognate pairs of ligands and receptors

US12618832B2US 12618832 B2US12618832 B2US 12618832B2US-12618832-B2

Abstract

A method for identifying cognate pairs of a ligand species and a receptor species includes co-compartmentalizing ligand species and receptor species, forming a set of microreactors, each microreactor including a ligand species and preferably a receptor species; assaying the recognition between ligands and receptors in each microreactor and based on this assay, classifying each microreactor as positive when a ligand species and receptor species in the microreactor recognize one with the other or negative when no ligand species and no receptor species recognize in the microreactor; identifying ligand species and receptor species contained in each positive microreactor; establishing a subset of positive microreactors containing the same receptor species; determining the probability that the ligand species recognizing the receptor species corresponds to the most frequent co-compartmentalized ligand species. If the determined probability exceeds a threshold, identifying as a cognate pair the receptor species and the most frequent co-compartmentalized ligand species.

Inventors

Andrew Griffiths
Sebastien AMIGORENA
Olivier Lantz
David Weitz
Philippe NGHE
Annabelle Gerard

Assignees

PARIS SCIENCES ET LETTRES
CENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE
INSTITUT CURIE
THE PRESIDENT AND FELLOWS OF HARVARD COLLEGE
HIFIBIO (HK) LIMITED
ECOLE SUPERIEURE DE PHYSIQUE ET DE CHIMIE INDUSTRIELLES DE LA VILLE DE PARIS
INSTITUT NATIONAL DE LA SANTE ET DE LA RECHERCHE MEDICALE (INSERM)

Dates

Publication Date: 20260505
Application Date: 20191219
Priority Date: 20181219

Claims (20)

1 . A method for identifying cognate pairs of a ligand species and a receptor species, comprising the following steps: providing a set of ligands comprising a plurality of ligand species, in which each ligand species is present more than one time; providing a set of receptors comprising at least one receptor species; compartmentalizing ligands of the set of ligands and receptors of the set of receptors to form a set of microreactors, wherein a plurality of the microreactors comprise at least one ligand species and at least one receptor species; assaying recognition between ligands and receptors within microreactors of the set of microreactors and, based on an assay readout within the microreactors, classifying microreactors of the set of microreactors as positive or negative, wherein a microreactor is classified as positive when at least one ligand and receptor in the microreactor recognize one with the other or as negative when no ligand and receptor recognize one with the other in the microreactor; identifying ligand species and receptor species contained in one or more positive microreactors; establishing a subset of positive microreactors each containing a first receptor species; determining a probability that in the subset of positive microreactors containing the first receptor species, the ligand species recognized by the first receptor species corresponds to the most frequent co-compartmentalized ligand species in the subset of positive microreactors; if the determined probability is greater than a predetermined threshold, identifying as a cognate pair the first receptor species and the most frequent co-compartmentalized ligand species in the subset of positive microreactors.
2 . The method according to claim 1 , wherein the probability is determined in function of the diversity of ligand species, the average number of true positive microreactors for the subset of positive microreactors containing the first receptor species, and the average number of non-cognate ligand species contained in positive microreactors containing the first receptor species.
3 . The method according to claim 2 , wherein the average number of true positive microreactors for a given subset of positive microreactors containing the same receptor species, is determined according to the following expression: l=fn, wherein: l is the average number of true positive microreactors in the given subset of positive microreactors containing the same receptor species; n is the number of microreactors containing said receptor species; and f is the average frequency of each ligand species per microreactor which is determined as the ratio between a number of ligand species per microreactor and the total diversity of ligands species.
4 . The method according to claim 3 , wherein the average number of non-cognate ligand species contained in positive microreactors is determined according to the following expression: b=f ( l+e ), wherein: b is the average number of non-cognate ligand species contained in positive microreactors; and e is an average number of measurement errors within the subset of positive microreactors which is determined as a product of a rate of technical false positives due to the assaying and the number of microreactors containing said receptor species.
5 . The method according to claim 1 , wherein said probability is determined according to the following expression: σ = ∑ k = 1 + ∞ ( ∫ b + ∞ t k - 1 ⁢ e - t ⁢ d ⁢ t ( k - 1 ) ! ) d - 1 ⁢ e - l ⁢ l k k ! , wherein: σ is said probability for a given subset of positive microreactors containing the same receptor species; d is the diversity of ligand species; l is the average number of true positive microreactors in the given subset of positive microreactors containing the same receptor; and b is the average number of non-cognate ligand species contained in positive microreactors containing the same receptor species.
6 . The method according to claim 1 , wherein (a) receptors of the set of receptors are expressed by one or more cells, displayed on the surface of one or more cells or one or more beads, or are in vitro encoded; or (b) ligands of the set of ligands are expressed by one or more cells or displayed on the surface of one or more cells or one or more beads, or are in vitro encoded; or both (a) and (b).
7 . The method according to claim 6 , wherein the recognition between ligands and receptors in each microreactor is assayed by determining if a cellular response is induced in said microreactor, wherein a microreactor is classified as positive when an induced cellular response is determined in said microreactor or as negative when no induced cellular response is determined in said microreactor.
8 . The method according to claim 1 , wherein the set of receptors is a set of T cell receptors (TCR) and the set of ligands is a set of T cell antigens.
9 . The method according to claim 6 , wherein additional reagents are added to the positive microreactors, said additional reagents comprising one or more of a reverse transcriptase (RT), a cell lysis buffer, deoxynucleoside triphospates (dNTPs), a plurality of barcoded primers specific for a nucleic acid sequence encoding ligands of the set of ligands, and a plurality of barcoded primers specific for a nucleic acid sequence encoding receptors of the set of receptors, wherein the barcoded primers specific for the ligand-encoding nucleic acid sequence comprise a primer sequence specific for the ligand-encoding nucleic acid sequence and a barcode sequence or barcode set of sequences, wherein the barcoded primers specific for the receptor-encoding nucleic acid sequence comprise a primer sequence specific for the receptor-encoding nucleic acid sequence and a barcode sequence or barcode set of sequences, and wherein the barcode sequence or barcode set of sequences contained in a microreactor is distinguishable from the barcode sequence or barcode set of sequences contained in other microreactors, but the barcoded primers specific for the ligand-encoding nucleic acid sequence and for the receptor-encoding nucleic acid sequence contained in a given microreactor carry a common barcode sequence or barcode set of sequences.
10 . The method according to claim 9 , wherein said barcoded primers are delivered on particles, wherein each particle carries a barcode sequence or barcode set of sequences distinguishable from barcode sequences or barcode sets of sequences carried by other particles, and each microreactor contains a single particle or between 2 to 10 particles.
11 . The method according to claim 9 , wherein in the positive microreactors, barcoded cDNAs are prepared by: lysing the cells expressing or displaying receptors and/or the cells expressing or displaying ligands, to release mRNA from the cells, hybridizing at least some of the released mRNA coding for the receptor to the receptor-encoding nucleic acid sequence specific barcoded primer, and at least some of the released mRNA coding for the ligand to the ligand-encoding nucleic acid sequence specific barcoded primer, in at least some of the microreactors, reverse transcribing the released mRNA hybridized to the barcoded primers, thereby obtaining barcoded cDNAs.
12 . The method according to claim 8 , wherein the set of TCR and the set of T cell antigens are from a subject of interest suffering from cancer, autoimmune disease, inflammatory disease, infectious disease, or metabolic disease.
13 . The method of claim 1 , wherein each microreactor comprises no more than one receptor species.
14 . The method according to claim 2 , wherein said probability is determined according to the following expression: σ = ∑ k = 1 + ∞ ( ∫ b + ∞ t k - 1 ⁢ e - t ⁢ d ⁢ t ( k - 1 ) ! ) d - 1 ⁢ e - l ⁢ l k k ! , wherein: σ is said probability for the given subset of positive microreactors containing the same receptor species; d is the diversity of ligand species; l is the average number of true positive microreactors in the given subset of positive microreactors containing the same receptor; and b is the average number of each non-cognate ligand species contained in positive microreactors containing the same receptor species.
15 . The method according to claim 3 , wherein said probability is determined according to the following expression: σ = ∑ k = 1 + ∞ ( ∫ b + ∞ t k - 1 ⁢ e - t ⁢ d ⁢ t ( k - 1 ) ! ) d - 1 ⁢ e - l ⁢ l k k ! , wherein: σ is said probability for the given subset of positive microreactors containing the same receptor species; d is the diversity of ligand species; l is the average number of true positive microreactors in the given subset of positive microreactors containing the same receptor; and b is the average number of each non-cognate ligand species contained in positive microreactors containing the same receptor species.
16 . The method according to claim 4 , wherein said probability is determined according to the following expression: σ = ∑ k = 1 + ∞ ( ∫ b + ∞ t k - 1 ⁢ e - t ⁢ d ⁢ t ( k - 1 ) ! ) d - 1 ⁢ e - l ⁢ l k k ! , where: σ is said probability for the given subset of positive microreactors containing the same receptor species; d is the diversity of ligand species; l is the average number of true positive microreactors in the given subset of positive microreactors containing the same receptor; and b is the average number of each non-cognate ligand species contained in positive microreactors containing the same receptor species.
17 . The method according to claim 1 , wherein the set of receptors is a set of T cell receptors and the set of ligands is a set of T cell antigens bound to major histocompatibility complex (MHC) displayed on the surface of antigen-presenting cells (APCs).
18 . The method of claim 17 , further comprising obtaining the APCs by introducing a library of nucleic acids encoding T cell antigens into APCs.
19 . The method of claim 17 , further comprising obtaining the APCs by introducing into APCs a library of synthetic mRNAs encoding antigens, optionally wherein said mRNAs are identified by sequencing the genome, exome or transcriptome of a tumor.
20 . The method according to claim 17 , wherein the set of T cell receptors is displayed on the surface of T cells.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS This application is the U.S. national phase of International Application No. PCT/EP2019/086334 filed Dec. 19, 2019 which designated the U.S. and claims priority to EP 18306741.2 filed Dec. 19, 2018, the entire contents of each of which are hereby incorporated by reference. BACKGROUND OF THE INVENTION Field of the Invention The present invention concerns methods for identifying cognate pairs of ligands and receptors, in particular cognate pairs of T cell receptors and T cell antigens or cognate pairs of B cell receptors and B cell antigens. Description of the Related Art Immunotherapy has become an epoch-making and attractive therapeutic modality for cancer, which offers potentially targeted therapy with fewer adverse-effects compared with conventional therapy. One type of immunotherapy is a checkpoint blockade therapy using humanized mAbs specific to CTL antigen 4 (CTLA-4), programmed cell death-1 (PD-1), or its ligand PD-L1. This therapy induced remarkable and durable clinical responses in patients with melanoma, lung, renal, and bladder cancers. However, only a subset of patients (between 20 and 30%) responds to such immune checkpoint therapies and only patients suffering from certain types of cancer. Accordingly, immunotherapy of cancers using vaccine approaches may be relevant in patients that do not respond to such therapies. However, very few tumor antigens, which can elicit effective and safe T-cell-mediated antitumor immunity in cancer patients are known. Indeed, such effective tumor antigens need to be overexpressed in cancer tissues, not expressed in normal tissues and be capable of inducing a tumor-antigen specific T-cell response. Reliable identification of T cell antigens would thus address an unmet need in the field of cancer immunology. Furthermore, immunotherapy approaches can also potentially be applied to treat autoimmune disease, inflammatory and autoimmune disease, infectious disease and metabolic disease, where efficient and reliable identification of T cell antigens is likewise of great importance. Autoimmune diseases may also be treated through cell based therapy or by tolerization approach, which also necessitate reliable identification of antigens of interest. Unfortunately, despite this potential utility, the discovery and characterization of T cell antigen has moved forward very slowly, in particular because of both the vastness of the T cell repertoire and the large number of potential T cell antigens. Current methods to identify T cell epitopes generally involve isolating T cells, making individual T cell clones and screening a panel of tumor cell lines or expression libraries from the autologous tumor cells (either fresh or from established cell lines) (Boon et al. (1994) Annu Rev Immunol. 12:337-65). This process is labor intensive and inefficient as both T cell clones and tumor cell lines should be established, which is long and is not possible for all tumor types. More recently, deep sequencing of the tumor DNA together with RNA analysis has allowed the definition and ranking of candidate epitopes using peptide binding prediction algorithms to the specific MHC alleles avoiding the need of establishing tumor cell lines (Gubin et al. (2015) J. Clin. Invest. 125:3413-3421). However, for MHC class II restricted epitopes recognized by CD4 T cells, the prediction algorithms are not very reliable. Epitopes can also be identified by proteomic analysis of the acid eluate from immuno-precipitate of MHC class I molecules obtained from the tumor cells, further refining the predictive capacity of the process. In both cases, MHC tetramers loaded with the most likely antigen candidate are then synthesized and used to fluorescently label and isolate potential reactive T cells (Yadav et al. (2014) Nature 515:572-576 and Andersen et al. (2012) Nat. Protoc. 7:891-902). However, making MHC class II tetramers is still challenging for many epitopes. Alternatively, T cell clones or cell lines expressing cloned TCR are functionally tested against antigen-presenting cells (APC) loaded with synthetic peptides, expression libraries (Gaugler et al. (1994) J. Exp. Med. 179:921-930) or transduced with mRNA coding for the candidate epitopes (Holtkamp et al. (2006) Blood 108:4009-4017). DNA tagged MHC oligomers technique (Bentzen et al. (2016) Nat. Biotechnol. 34:1037-1045) requires prior knowledge of the candidate antigens and is only applicable at the moment for MHC-I restricted epitopes. However, each method has disadvantages: making T cell clones is extremely labor intensive as is the screening of the resulting clones for antigen specificity; identifying recurrent TCR and/or tumor reactive TCR without cell expansion from bulk population by deconvolution methods is applicable only if only a few TCRs of interest are increased in frequency and enough cells are available; elution of peptides from tumor MHC molecules requires many tumor cells; and bio-informatic analy