WO2024206930A2 - Bifunctional molecules targeting lymph node progenitor exhausted t cells and methods of use - Google Patents

Abstract

Aspects of the present disclosure include bifunctional molecules. In certain embodiments, the bifunctional molecules comprise a first moiety that binds to a molecule on the surface of a lymph node (LN) progenitor exhausted T cell, and a second moiety that activates the LN progenitor exhausted T cell. The molecule on the surface of the LN progenitor exhausted T cell may be, e.g., FCRL3, LAMP1, PECAM1, IFITM1, CD2, or SIRPG. In certain embodiments, the second moiety is a cytokine, an agonist of a T cell co-stimulatory receptor, or an immune checkpoint inhibitor. Methods of using the bifunctional molecules of the present disclosure are also provided. For example, provided are methods of activating LN progenitor exhausted T cells in a subject in need thereof, the method comprising administering to the subject a bifunctional molecule of the present disclosure in an amount effective to activate LN progenitor exhausted T cells in the subject.

Description

BIFUNCTIONAL MOLECULES TARGETING LYMPH NODE PROGENITOR EXHAUSTED T CELLS AND METHODS OF USE

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Patent Application No. 63/455,766, filed March 30, 2023, which application is incorporated herein by reference in its entirety.

INTRODUCTION

Immune checkpoint blockade (ICB) has been a remarkable clinical advance in the treatment of cancer. Nonetheless, the majority of patients do not benefit from ICB therapy, and many of those who do eventually succumb to the disease. Recent data has demonstrated that ICB can operate via activation, expansion, and recruitment of CD8⁺ T cells from the peripheral circulation [1] [2], Unfortunately, isolated tumor biopsies at the time of resistance to ICB are limited in their ability to capture T cell dynamics at a systemic level.

The development of paired single-cell RNA and T cell receptor sequencing (scRNA/TCR- seq) has enabled deep profiling of T cells in the context of their clonal lineage, phenotypic heterogeneity, tissue distribution, and peripheral persistence [3]. However, compared to murine systems, the analysis of T cell responses to cancer in humans has been limited by the challenge of simultaneously achieving sufficient scale of scRNA/TCR-seq T cell profiles that is linked to multi-regional and longitudinal sampling within the same patient. For example, a scRNA/TCR- seq dataset was previously generated in basal cell carcinoma, which revealed that ICB can function to expand a new clonal repertoire of T cells; however, this dataset was limited by its lack of assessment of multiple tumor regions, healthy tissue, and longitudinal peripheral blood samples [4], Recent studies have analyzed either large patient cohorts [5] or regional tumor heterogeneity with scRNA-seq [6]; however, these studies were limited by the depth of per patient T cell clone sampling.

SUM ARY

Aspects of the present disclosure include bifunctional molecules. In certain embodiments, the bifunctional molecules comprise a first moiety that binds to a molecule on the surface of a lymph node (LN) progenitor exhausted T cell, and a second moiety that activates the LN progenitor exhausted T cell. The molecule on the surface of the LN progenitor exhausted T cell may be, e.g., FCRL3, LAMP1 , PECAM1 , IFITM1 , CD2, or SIRPG. In certain embodiments, the second moiety is a cytokine, an agonist of a T cell co-stimulatory receptor, or an immune checkpoint inhibitor. Methods of using the bifunctional molecules of the present disclosure are also provided. For example, provided are methods of activating LN progenitor exhausted T cells in a subject in need thereof, the method comprising administering to the subject a bifunctional molecule of the present disclosure in an amount effective to activate LN progenitor exhausted T cells in the subject.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1A to 1 B depict results demonstrating that exhausted CD8+, Treg, and TFH cells are enriched in proximity to viable cancer cells. (A) quantification of surface area of individuals lesions on radiographical studies over time in three patients. Red lines indicate lesions that were resected and analyzed in this study. (B) schematic of time interval from start of anti-PD-1 therapy to time of resections and clinical outcome across the three patients. Timeline of associated peripheral blood collections are indicated in red text below. Purple triangle indicates a change in systemic therapy from anti-PD-1 monotherapy. Cross indicates patient death. NED = no evidence of disease. AWD = alive with disease.

FIG. 2A to 2C depict results demonstrating that exhausted CD8+, Treg, and TFH cells are enriched in proximity to viable cancer cells. (A) UMAP of cell clusters obtained from scRNA/TCR-seq of sorted CD3⁺ T cells, which are further defined in (B). (B) heat map of differentially expressed genes found in each T cell cluster. (C) UMAP overlaid with TCRap clone size as assessed from scTCR-seq data.

FIG. 3A to 3C depict results demonstrating that exhausted CD8+, Treg, and TFH cells are enriched in proximity to viable cancer cells. (A-B) proportion of cells from each region type in each CD8⁺ (A) and CD4⁺ (B) T cell cluster. Heatmap colors show proportions scaled per cluster. (C) scatter plot of exhaustion scores among CD8⁺ T cells ordered along diffusion pseudotime (DPT), colored by anatomical region.

FIG. 4A to 4E depict results demonstrating that exhausted CD8+, Treg, and TFH cells are enriched in proximity to viable cancer cells. (A) box and whisker plot of exhaustion score per cell in the indicated region types. Statistical testing by two-sided t-test (**** <0.0001 ). (B-C) flow cytometric quantification of %CD39 (B) or PD-1 MFI (C) on CD8⁺ T cells across the indicated region types. Statistical testing by two-sided t-test (** <0.01 ). Error bars represent standard error of the mean. For A-C, only thoracic resection regions from MSK 1263 and 1302 were included in this analysis due to concomitant availability of adjacent normal, no viable tumor, viable tumor, and LN regions. (L) CD39 and PD-1 flow cytometry plots from two adrenal regions involved with tumor in MSK 1263 gated on CD8+ T cells. (M) paired box and whisker plots of average exhaustion score per clonotype that is matched between regions without viable tumor and regions with viable tumor. Statistical testing by paired two-sided t-test. Error bars represent standard error of the mean. Box and whisker plots are defined as: center line, median; box, interquartile range; upper whisker limit, maximum without outliers; lower whisker limit; minimum without outliers; points, outliers.

FIG. 5A to 5C depict results demonstrating that exhausted CD8+, Treg, and TFH cells are enriched in proximity to viable cancer cells. (A-B) scatter plot of exhaustion scores among Treg (A) or TFH (B) cells ordered along diffusion pseudotime. Points are colored by region type as in FIG. 3C. (C) comparison and overlap of top genes correlated with DC1 (top 20^th percentile) for CD8⁺, Treg, and TFH. Numbers indicate the number of genes in each set. Select genes in each category are shown.

FIG. 6A to 6D depict results demonstrating that intratumoral CD8+ T cells can be found in a TCF-1 + CD62L+ progenitor exhausted state in the regional LN. (A) UMAP of re-clustered cells from CD8⁺ T cell clones with high exhaustion scores (exhaustion*¹') that were expanded (>2 cells) and found in both LN and tumor regions. Cells are colored according to phenotype cluster. (B) UMAP of re-clustered cells from (A) split by region type: LN (left), tumor (right). Bar plots of phenotypic cluster distribution among cells from LN or tumor regions (bottom). (C) Heat map showing expression of select memory, exhaustion, and progenitor exhausted cluster markers among the clusters from (A). (D) Volcano plot of differentially expressed genes between clone- matched cells in the LN and tumor from exhaustion*¹' CD8⁺ T cell clones.

FIG. 7A to 7D depict results demonstrating that intratumoral CD8+ T cells can be found in a TCF-1 + CD62L+ progenitor exhausted state in the regional LN. (A) paired box and whisker plots of average progenitor score per CD8⁺ T cell clone in the CD8-EXH and CD8-PROLIF-EXH clusters in thoracic regions of MSK 1263 and 1302 (left) or adrenal regions of MSK 1263 (right) that is matched among the LN, regions without viable tumor, and regions with viable tumor. Statistical testing by paired two-sided t-test. Error bars represent standard error of the mean. (B) paired box and whisker plots of average progenitor score per exhaustion*¹' CD8⁺ T cell clone in thoracic regions of MSK 1263 and 1302 (left) or adrenal regions of MSK 1263 (right) that is matched among the LN, regions without viable tumor, and regions with viable tumor. Statistical testing by paired two-sided t-test. Error bars represent standard error of the mean. (C-D) pie charts of CD8+ T cell clones in the CD8-EXH and CD8-PROLIF-EXH clusters (C) or exhaustion*¹' clones (D) in the tumor that could be matched to a clonotype in the LN (medium blue and dark blue, “TCR match in LN”). Dark blue slice indicates that the matched clone could be found with a progenitor score >0 in the LN. Box and whisker plots are defined as: center line, median; box, interquartile range; upper whisker limit, maximum without outliers; lower whisker limit; minimum without outliers; points, outliers.

FIG. 8A to 8D depict results demonstrating that intratumoral CD8+ T cells can be found in a TCF-1 + CD62L+ progenitor exhausted state in the regional LN. (A-B) pie charts of exhaustion*¹* CD8⁺ T cell clones in two external datasets that could be matched to a clonotype in the LN (medium blue and dark blue, “TCR match in LN”). Dark blue slice indicates that the matched clone could be found with a progenitor score >0 in the LN. (C-D) paired box and whisker plot of average progenitor score per clone that is matched among the LN and tumor regions in five separate patients from two external datasets. Statistical testing by paired two-sided t-test. Error bars represent standard error of the mean. Box and whisker plots are defined as: center line, median; box, interquartile range; upper whisker limit, maximum without outliers; lower whisker limit; minimum without outliers; points, outliers.

FIG. 9A to 9E illustrate phenotypic and regional enrichment of tumor-specific CD8+ T cell clones. (A) bar plots of the proportion of cells in the indicated region type among the top 40 most expanded TR^hi (left) or TR¹⁰ (right) CD8⁺ clones. (B) bar plots of the proportion of cells in the indicated phenotype clusters among the top 40 most expanded TR^hi (left) or TR¹⁰ (right) CD8⁺ clones. (C) venn diagram of overlap between TCRp sequences from MSK 1263 identified by empirical tumor-specific methods and the tissue sorted CD3⁺ scRNA/TCR-seq dataset (yellow). Numbers indicate the number of TCRp sequences in each intersection. Numbers bolded in red represent TCRp clones identified by at least two empirical methods (designated as high- confidence neopeptide-specific clones). (D) box and whisker plot of tumor-reactivity scores [34] among CD8⁺ T cells with the indicated TCR specificity. (E) box and whisker plots of proportion of clones within each specificity belonging to the indicated CD8⁺ T cell clusters. Each point represents one TCR clone. Statistical testing by two-sided Wilcoxon-test (* <0.05, “ <0.01 , ***<0.001 , ****<0.0001 ). Box and whisker plots are defined as: center line, median; box, interquartile range; upper whisker limit, maximum without outliers; lower whisker limit; minimum without outliers; points, outliers.

FIG. 10A to 10E illustrate phenotypic and regional enrichment of tumor-specific CD8+ T cell clones. (A-B) bar plots of the proportion of cells in the indicated phenotype cluster (A) or region type (B) among the top most expanded high-confidence peptide-specific clones (left), viral- specific clones (middle), or clones with unknown specificity (right). (C) paired box and whisker plot of average progenitor score per high-confidence neopeptide-specific clone in MSK 1263 that is matched among the LN and tumor regions. Statistical testing by paired two-sided t-test. (D) box and whisker plots of gene signature scores for CD8⁺ tumor-reactivity [34] among clones with the indicated TCR regional pattern. (E) bar plots of the proportion of clones with each TCR specificity colored by TCR tumor regional pattern. Box and whisker plots are defined as: center line, median; box, interquartile range; upper whisker limit, maximum without outliers; lower whisker limit; minimum without outliers; points, outliers.

FIG. 11 A to 11 B illustrate peripheral persistence of tumor-specific CD8+ T cell clones. (A) circulating frequency of clonotypes with the indicated CD4⁺, CD8⁺, or MAIT phenotypes designated by tissue scRNA/TCR-seq in MSK 1263, 1302, and 1344. Each clonotype was counted once based on majority phenotype. Box and whisker plots are defined as: center line, median; box, interquartile range; upper whisker limit, maximum without outliers; lower whisker limit; minimum without outliers; points, outliers. (B) spearman correlation of mean tumor-reactivity score and peripheral blood frequency per CD8⁺ (left) or CD4⁺ (right) T cell cluster.

FIG. 12A to 12B illustrate peripheral persistence of tumor-specific CD8+ T cell clones. (A) circulating frequency over time of TR^hi (top) and TR^l0 (bottom) CD8⁺ clones from patients MSK 1263, MSK 1302, and MSK 1344. (B) circulating frequency over time of CD8⁺ T cell clones with the indicated empirical antigen specificity from patient MSK 1263.

FIG. 13A to 13C demonstrate regional bulk transcriptional heterogeneity in resections after ICB. (A) quantification of surface area of individuals lesions on radiographical studies over time normalized to baseline lesion size in three patients. Red lines indicate lesions that were resected and analyzed in this study. (B) principal component analysis of bulk RNA sequencing of regions from three patients undergoing oligometastatic resections. (C) heat map of CIBERSORT quantification of various immune populations (y axis) across the different regions from three patients (x axis).

FIG. 14A to 14B demonstrate regional bulk transcriptional heterogeneity in resections after ICB. (A) percentage of cells in various immune populations as quantified by CIBERSORT. Each point represents one region. Error bars represent standard error of the mean. (B) GSEA of pathways differentially expressed among viable vs. no viable tumor regions as measured by bulk RNA-seq.

FIG. 15 depicts a representative gating strategy for the isolation of CD3+ T cells by flow cytometry.

FIG. 16 provides box and whisker plots of number of genes detected per cell, number of unique molecular identifiers (UMIs) per cell, percent mitochondrial reads per cell, and number of cells captured per region undergoing scRNA/TCR-seq. Cutoffs used for quality filtering are shown as dotted red lines. Box and whisker plots are defined as: center line, median; box, interquartile range; upper whisker limit, maximum without outliers; lower whisker limit; minimum without outliers; points, outliers.

FIG. 17A to 17B quality control and comparison of cluster-defining genes to published scRNA-seq clusters. (A) bar plot of absolute number of cells passing (green) and failing (orange) QC per region undergoing scRNA/TCR-seq. Numbers indicate percentage of cells in library passing QC. (B) bar plot of absolute number of cells for which TCRa only (light blue), TCRp only (green), or both TCRa and TCRp chains (teal) were reconstructed per region undergoing scRNA/TCR-seq. T cells for which multiple TCRp chains were captured (light green, orange, red) were excluded from further analysis.

FIG. 18 provides a heat map comparing clusters designated in the dataset (x axis) and clusters designated in the indicated external scRNA-seq datasets (y axis). Color scale represents external cluster gene scores computed per cell in the dataset and normalized per row.

FIG. 19A to 19B quality control and comparison of cluster-defining genes to published scRNA-seq clusters. (A) PDCD1 expression of cells in each cluster from each patient. (B) ENTPD1 expression of cells in each cluster from each patient.

FIG. 20 provides bar plots of the proportion of cells in the indicated clusters among CD8+ T cells (top) or CD4+ T cells (bottom) per region undergoing scRNA-seq. FIG. 21 A to 21 B illustrate cluster and TOR clone representation across patients. (A) UMAP of cluster representation across the 31 regions undergoing scRNA/TCR-seq that passed QC. (B) UMAP of sorted CD3+ T cells among each region type colored by cell density.

FIG. 22A to 22C illustrate TCR repertoire similarity and diversity. (A) heat map of TCR clonal overlap between patients based on CDR3ap nucleotide (top) or amino acid (bottom) sequence. (B) scatterplot of percent CD4+ T cells in each clone versus clone size among clones in each clone designation (CD8+, CD4+, mixed, or MAIT). Each point represents one TCRap clone and is colored by the percentage of CD8+ cells in the clone. (C) bar plots of cells within each clone type colored by phenotype cluster.

FIG. 23A to 23B illustrate TCR repertoire similarity and diversity. (A) Morisita-Horn Index of TCRap repertoire similarity among different regions (minimum clone size = 10). (B) TCR repertoire diversity of each region type as measured by normalized Shannon index. Data is from the tissue sorted CD3+ scRNA/TCR-seq dataset and three external datasetsl 6,30,31 of samples from lung cancer patients. Box and whisker plots are defined as: center line, median; box, interquartile range; upper whisker limit, maximum without outliers; lower whisker limit; minimum without outliers; points, outliers.

FIG. 24A to 24B illustrate TCR repertoire similarity and diversity. (A) scatterplot of the number of cells in regions with no viable tumor vs. regions with viable tumor per clone. Each point represents one clone classified as enriched in viable tumor (dark orange) or no viable tumor (light orange) regions (Fisher’s exact test, p<0.05). (B) bar plot of clones enriched in no viable tumor regions or viable tumor regions, colored by majority phenotype within each clone. (* denotes significance as determined by Fisher’s exact test, p<0.05).

FIG. 25A to 25D provide diffusion analysis of CD8+ T cell clones. (A) diffusion map of cells from CD8+ T cell clones colored by phenotype cluster (left), region type (center), or diffusion pseudotime (DPT) (right). (B) diffusion map of cells from CD8+ T cell clones colored by DPT branch. (C-D) density plots of cell phenotypes (C) or region type (D) along DPT branches B1 , B2, and B3.

FIG. 26A to 26D provide diffusion analysis of CD8+ T cell clones. (A-B) scatter plot of exhaustion scores among CD8+ T cells ordered along DPT branches B1 , B2, and B3. Points are colored by CD8+ phenotype cluster (A) or region type (B). (C) scatter plot of exhaustion scores among CD8+ T cells ordered along DPT branches B1 , B2, and B3 calculated without genes in the exhaustion signature. Points are colored by region type. (D) correlation of % CD8+ T cells expressing CD39 and % viable tumor per region from MSK 1263 and 1302.

FIG. 27A to 27B provide diffusion analysis of Treg, TFH, and effector CD4+ T cell clones. (A-B) phenotype (A) and regional (B) composition of Treg and TFH clones expanded >10 cells from MSK 1263, 1302, and 1344. FIG. 28 depicts a scatterplot of phenotypic overlap between TFH and Treg clones. Each dot representing one clone is colored by the clone phenotype assigned by majority cluster and sized according to clone size.

FIG. 29A to 29B provide diffusion analysis of Treg, TFH, and effector CD4+ T cell clones. (A-B) diffusion map of cells from Treg (B) or TFH (A) clones with clone size >10 cells colored by region type (left) or DPT (center). Beeswarm plot of cells ordered by DPT grouped by region type (right).

FIG. 30A to 30B provide diffusion analysis of Treg, TFH, and effector CD4+ T cell clones. (A-B) expression changes of select top DC1 -varying genes among Treg (A) or TFH (B) cells ordered along DPT and colored by region type as in FIG. 29A and FIG. 29B.

FIG. 31 A to 31 H provide diffusion analysis of Treg, TFH, and effector CD4+ T cell clones. (A-D) flow cytometric quantification of %CD39 (A), PD-1 MFI (B), GITR MFI (C), and CXCR4 MFI (D) on Treg cells across the indicated region types. Error bars represent standard error of the mean. (E-H) flow cytometric quantification of %CD39 (E), PD-1 MFI (F), GITR MFI (G), and CXCR4 MFI (H) on TFH cells across the indicated region types. Error bars represent standard error of the mean.

FIG. 32A to 32B provide diffusion analysis of Treg, TFH, and effector CD4+ T cell clones. (A) heatmap of IL32 expression among Treg clones present in at least two region types. (B) heatmap of CXCL13 expression among TFH clones present in at least two region types.

FIG. 33A to 331 provide diffusion analysis of Treg, TFH, and effector CD4+ T cell clones. (A) diffusion map of cells from effector CD4+ T cell clones (CD4-EFF1 and CD4-EFF2 clusters) with clone size >10 cells colored by region type (left) or DPT (center). Beeswarm plot of cells ordered by DPT grouped by region type (right). (B) scatter plot of exhaustion scores among effector CD4+ cells ordered along the DPT. Points are colored by region type as in (A). (C) quantification of transcriptomic levels of CXCL13 by bulk RNA-seq of regions without and with viable tumor. Error bars represent standard error of the mean. (D) TLS count was performed by automated counting by Halo software. Statistical testing by student’s t-test. Error bars represent standard error of the mean. (E) TLS area was calculated by expressing the surface area of TLSs (automated annotation by Halo) as percent of tissue area. Statistical testing by student’s t-test. Error bars represent standard error of the mean. (F-l) linear correlation per region of TLS count and percent viable tumor (F), necrosis (G), stroma (H), and uninvolved (I).

FIG. 34A to 34F identification of LN progenitor states. (A) distribution of average exhaustion score among CD8+ T cell clones in tumor tissue regions. Clones with an average exhaustion score >0 were defined as CD8+ T cell clones with high exhaustion scores (exhaustion*¹'). (B) histograms showing the percentage of cells per clone in progenitor exhausted cluster 2 among the LN and tumor compartments. (C) proportion of expanded exhausted*¹' CD8+ T clones shared between the LN and tumor with LN cells in progenitor exhausted cluster 2. (D) distribution of average TCF7 expression among CD8+ T cell clones in LN regions. (E) pie chart of CD8+ T cell clones in the CD8-EXH and CD8-PROLIF-EXH clusters in the tumor that could be matched to a clonotype in the LN (medium blue and dark blue, “TCR match in LN”). Dark blue slice indicates that the matched clone could be found expressing TCF7 in the LN. (F) distribution of average progenitor score among CD8+ T cell clones in LN regions. Clones with an average progenitor score >0 were defined as CD8+ T cell clones with a LN progenitor.

FIG. 35 provides bar plots of phenotype composition within each region for top expanded exhaustion¹¹' CD8+ T cell clones that could be found in both the LN and tumor regions. Bars are colored by re-clustered (top) or original total T cell population (bottom) cluster.

FIG. 36A to 36C identification of LN progenitor states. (A) pie chart of exhaustion*¹¹ CD8+ T cell clones in the tumor that could be matched to a clonotype in the LN (medium blue and dark blue, “TCR match in LN”) in the scRNA/TCR-seq dataset. Dark blue slice indicates that the matched clone could be found expressing TCF7 in the LN, as in FIG. 34E. (B-C) pie charts of CD8+ T cell clones with high exhaustion scores in the tumor that could be matched to a clonotype in the LN (medium blue and dark blue, “TCR match in LN”) based on datasets generated by Caushi et al. [30] (B) and Nagasaki et al. [31] (C). Dark blue slice indicates that the matched clone could be found expressing TCF7 in the LN as in FIG. 34E.

FIG. 37A to 37D identification of LN progenitor states. (A-B) volcano plot of differentially expressed genes between clone-matched cells in the LN and tumor from TFH (A) or Treg (B) clones. (C-D) bar plots of phenotype composition within each region for top expanded TFH (C) or Treg (D) clones that could be found in both the LN and tumor regions. Bars are colored by original total T cell population clustering.

FIG. 38A to 38E characterization of TR^hi and TR^|0 CD8+ and CD4+ T cell clones. (A) heat map of Pearson correlation matrix between CD8+ tumor-reactivity score with ‘tumor-specific’[35], ‘MANA-specific’[30], 'NeoTCR-CD8’[36], ‘virus-specific’[35], and ‘influenza-specific’[30] scores computed on all cells. (B) box and whisker plots of ‘tumor-specific’[35], ‘MANA-specific’[30], ‘virus-specific’[35], and ‘influenza-specific’[30] scores among CD8+ T cells with high (>0, TR^hi) and low (<0, TR^l0) tumor-reactivity scores. Statistical testing by two-sided t-test (**" <0.0001 ). (C) scatterplot of clone size and tumor-reactivity score per CD8+, colored by tumor-reactivity category. ‘Top TR^hi’ and ‘top TR^l0’ represent the 40 most expanded TR^hi or TR^l0 clones, respectively. (D) box and whisker plot of tumor-reactivity scores34 among the indicated CD8+ T cell clusters in MSK 1263, 1302, and 1344. Statistical testing by two-sided t-test (”** <0.0001 ). (E) box and whisker plot of CD8+ tumor-reactivity scores across the indicated region types. Statistical testing by two-sided t-test (““ <0.0001 ). Box and whisker plots are defined as: center line, median; box, interquartile range; upper whisker limit, maximum without outliers; lower whisker limit; minimum without outliers.

FIG. 39A to 39B characterization of TR^hi and TR^l0 CD8+ and CD4+ T cell clones. (A) box and whisker plot of CD4+ tumor-reactivity scores36 among the indicated CD4+ T cell clusters in MSK 1263, 1302, and 1344. Statistical testing by two-sided t-test (****<0.0001 ). Box and whisker plots are defined as: center line, median; box, interquartile range; upper whisker limit, maximum without outliers; lower whisker limit; minimum without outliers. (B) scatterplot of clone size and tumor-reactivity score per CD4+ clone, colored by tumor-reactivity category. ‘Top TR^hi’ and ‘top TR¹⁰’ represent the 40 most expanded TR^hi or TR¹⁰ clones, respectively.

FIG. 40A to 40B characterization of TR^hi and TR¹⁰ CD8+ and CD4+ T cell clones. (A-B) bar plots of the proportion of cells in the indicated region type (A) or phenotypic cluster (B) among the top 40 most expanded TR^hi (left) or TR¹⁰ (right) CD4+ clones.

FIG. 41 A to 41 B depict empirical methods for identifying tumor specificity. (A) summary of empirical approaches for identifying tumor-specific TCR clones utilizing tumor-infiltrating lymphocytes (TIL) and peripheral blood. (B) bar plots of anti-b2M-PE fluorescence on streptavidin-coated beads that were incubated with HLA monomers and peptides utilized in the HLA-peptide binding assay. Control conditions (e.g. positive control and negative control) are indicated by the striped lines. MFI of 1000 was utilized as the cutoff for HLA binding and stabilization by candidate neopeptide. Conditions yielding a PE MFI >1000 are colored in red.

FIG. 42A to 42C depict empirical methods for identifying tumor specificity. (A) flow cytometry plots of multimer+ populations gated on CD8+ T cells from MSK 1263 tissue TILs cultured with multimer pool or control multimer. (B-C) flow cytometry plots of multimer+ populations gated on CD8+ T cells from MSK 1302 (B) and MSK 1344 (C) tissue TILs cultured with multimer pool.

FIG. 43 provides TCRs that elicited preferential reactivity to neoantigen (NeoAg) peptide pool. Heading indicates CDR3 sequence of TCRp chain.

FIG. 44 provides TCRs that elicited preferential reactivity to viral antigen (ViralAg) peptide pool. Heading indicates CDR3 sequence of TCRp chain.

FIG. 45A to 45C illustrate results of scRNA/TCR-seq of sorted neoantigen peptide multimer+ CD8+ T cells. (A) histograms of barcoded multimer tag counts in each sequenced multimer+ CD8+ T cell scRNA/TCR-seq library. (B) bar plot of absolute number of sorted multimer+ CD8+ T cells for which TCRp only (green) or both TCRa and TCRp chains (teal) were reconstructed per regional sample undergoing scRNA/TCR-seq. (C) UMAP of cell clusters obtained from scRNA/TCR-seq of sorted multimer+ CD8+ T cells from MSK 1263.

FIG. 46A to 46D illustrate results of scRNA/TCR-seq of sorted neoantigen peptide multimer+ CD8+ T cells. (A) phenotype cluster concordance of clusters from tissue multimer+ CD8+ T dataset (query dataset, columns) and clusters from the tissue CD3+ scRNA/TCR-seq dataset (reference dataset, rows) for cells after label transfer from the reference. Heatmap values are scaled per tissue multimer+ cluster. (B) projected phenotypes of cells in the tissue multimer+ scRNA/TCR-seq dataset. (C) venn diagram of overlap between TCRp sequences from MSK 1263 identified by empirical TCR specificity methods. Numbers indicate the number of TCRp sequences. Numbers in red represent TCRp clones identified as neoantigen-specific by at least two empirical methods (designated as high-confidence neopeptide-specific clones). (D) concordance of empirical antigen specificity and transcriptional tumor-reactivity category per cell in the tissue CD3+ scRNA/TCR-seq dataset. Tiles are colored by the proportion of cells within each TCR specificity.

FIG. 47A to 47B illustrate results of scRNA/TCR-seq of sorted neoantigen peptide multimer+ CD8+ T cells. (A) volcano plot of differentially expressed genes between clone- matched cells in the LN and tumor from high-confidence neopeptide-specific CD8+ T cell clones. (B) volcano plot of differentially expressed genes between clone-matched cells in regions with and without viable tumor from high-confidence neopeptide-specific CD8+ T cell clones.

FIG. 48A to 48C illustrate regional patterns, peripheral frequency, and persistence of TCR clones. (A-B) bar plot of the number of clones within each non-overlapping TCR regional pattern per patient (A) and per clone type among all patients (B). (C) bar plots of the proportion of clones with the indicated clone sizes per TCR regional pattern of CD4+ and CD8+ T cell clones among all patients.

FIG. 49A to 49B illustrate regional patterns, peripheral frequency, and persistence of TCR clones. (A-B) volcano plots of differentially expressed genes between clones in the tumor enriched - pan (left) or tumor enriched - oligo (right) categories compared to ubiquitous clones among CD8+ (A) or CD4+ (B) T cell clones.

FIG. 50 illustrates circulating frequency over time of TR^hi (top) and TR^l0 (bottom) CD4+ clones from patients MSK 1263, MSK 1302, and MSK 1344.

FIG. 51 A to 51 B show heat maps from computational gene expression analysis identifying genes that preferentially mark LN progenitor exhausted T cells. Such markers include FCRL3, LAMP1 , PECAM1 , IFITM1 , CD2, SIRPG, ZNF302, CMC1 , GZMM, PDLIM2, PDIA3, EOMES, IL32, RARRES3, CCL5 and CST7. The LN progenitor exhausted T cell markers shown in FIG. 51 B (FCRL3, LAMP1 , PECAM1 , IFITM1 , CD2, and SIRPG) are those expressed on the cell surface and therefore may be utilized for targeting the bifunctional molecules of the present disclosure to LN progenitor exhausted T cells as described herein.

DETAILED DESCRIPTION

Before the bifunctional molecules and methods of the present disclosure are described in greater detail, it is to be understood that the bifunctional molecules and methods are not limited to particular embodiments described, as such may, of course, vary. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only, and is not intended to be limiting, since the scope of the bifunctional molecules and methods will be limited only by the appended claims.

Where a range of values is provided, it is understood that each intervening value, to the tenth of the unit of the lower limit unless the context clearly dictates otherwise, between the upper and lower limit of that range and any other stated or intervening value in that stated range, is encompassed within the bifunctional molecules and methods. The upper and lower limits of these smaller ranges may independently be included in the smaller ranges and are also encompassed within the bifunctional molecules and methods, subject to any specifically excluded limit in the stated range. Where the stated range includes one or both of the limits, ranges excluding either or both of those included limits are also included in the bifunctional molecules and methods.

Certain ranges are presented herein with numerical values being preceded by the term “about.” The term “about” is used herein to provide literal support for the exact number that it precedes, as well as a number that is near to or approximately the number that the term precedes. In determining whether a number is near to or approximately a specifically recited number, the near or approximating unrecited number may be a number which, in the context in which it is presented, provides the substantial equivalent of the specifically recited number.

Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the bifunctional molecules and methods belong. Although any bifunctional molecules and methods similar or equivalent to those described herein can also be used in the practice or testing of the bifunctional molecules and methods, representative illustrative bifunctional molecules and methods are now described.

All publications and patents cited in this specification are herein incorporated by reference as if each individual publication or patent were specifically and individually indicated to be incorporated by reference and are incorporated herein by reference to disclose and describe the materials and/or methods in connection with which the publications are cited. The citation of any publication is for its disclosure prior to the filing date and should not be construed as an admission that the present bifunctional molecules and methods are not entitled to antedate such publication, as the date of publication provided may be different from the actual publication date which may need to be independently confirmed.

It is noted that, as used herein and in the appended claims, the singular forms “a”, “an”, and “the” include plural referents unless the context clearly dictates otherwise. It is further noted that the claims may be drafted to exclude any optional element. As such, this statement is intended to serve as antecedent basis for use of such exclusive terminology as “solely,” “only” and the like in connection with the recitation of claim elements, or use of a “negative” limitation.

It is appreciated that certain features of the bifunctional molecules and methods, which are, for clarity, described in the context of separate embodiments, may also be provided in combination in a single embodiment. Conversely, various features of the bifunctional molecules and methods, which are, for brevity, described in the context of a single embodiment, may also be provided separately or in any suitable sub-combination. All combinations of the embodiments are specifically embraced by the present disclosure and are disclosed herein just as if each and every combination was individually and explicitly disclosed, to the extent that such combinations embrace operable processes and/or compositions. In addition, all sub-combinations listed in the embodiments describing such variables are also specifically embraced by the present bifunctional molecules and methods and are disclosed herein just as if each and every such subcombination was individually and explicitly disclosed herein.

As will be apparent to those of skill in the art upon reading this disclosure, each of the individual embodiments described and illustrated herein has discrete components and features which may be readily separated from or combined with the features of any of the other several embodiments without departing from the scope or spirit of the present methods. Any recited method can be carried out in the order of events recited or in any other order that is logically possible.

BIFUNCTIONAL MOLECULES TARGETING LYMPH NODE (LN) PROGENITOR EXHAUSTED T CELLS

Aspects of the present disclosure include bifunctional molecules that target lymph node (LN) progenitor exhausted T cells. In certain embodiments, the bifunctional molecules comprise a first moiety that binds to a molecule on the surface of a LN progenitor exhausted T cell, and a second moiety that activates the LN progenitor exhausted T cell. The bifunctional molecules of the present disclosure are based in part on the unexpected identification in subjects with cancer of progenitor exhausted T cells in the lymph nodes (e.g., tumor draining lymph nodes) that were clonally linked to intratumoral exhausted T cell populations. As demonstrated herein, this previously unidentified population of LN progenitor exhausted T cells fuels the intratumoral T cell response in human cancers, and activation of such LN progenitor exhausted T cells using the bifunctional molecules of the present disclosure constitutes a new modality for enhancing anticancer T cell responses in subjects in need thereof. Details regarding the bifunctional molecules of the present disclosure will now be described.

The first and second moieties may be independently selected from a polypeptide (e.g., an antigen-binding domain of an antibody), a ligand, a small molecule, an aptamer, or any other useful moiety for binding to the cell surface molecule or activating the LN progenitor exhausted T cell.

The terms “polypeptide,” “peptide,” and “protein”, used interchangeably herein, refer to a polymeric form of amino acids of any length, which can include genetically coded and non- genetically coded amino acids, chemically or biochemically modified or derivatized amino acids, and polypeptides having modified peptide backbones. The term includes fusion proteins, including, but not limited to, fusion proteins with a heterologous amino acid sequence, fusions with heterologous and homologous leader sequences, with or without N-terminal methionine residues; and the like.

The term “antibody” may include an antibody or immunoglobulin of any isotype (e.g., IgG (e.g., lgG1 , lgG2, lgG3, or lgG4), IgE, IgD, IgA, IgM, etc.), whole antibodies (e.g., antibodies composed of a tetramer which in turn is composed of two dimers of a heavy and light chain polypeptide); single chain antibodies (e.g., scFv); fragments of antibodies (e.g., fragments of whole or single chain antibodies) which retain specific binding to the cell surface molecule of the target cell, including, but not limited to single chain Fv (scFv), Fab, (Fab’)₂, (scFv’)₂, and diabodies; chimeric antibodies; monoclonal antibodies, human antibodies, humanized antibodies (e.g., humanized whole antibodies, humanized half antibodies, or humanized antibody fragments, e.g., humanized scFv); and fusion proteins comprising an antigen-binding portion of an antibody and a non-antibody protein. According to some embodiments, the antibody is selected from an IgG, Fv, single chain antibody, scFv, Fab, F(ab')2, or Fab'. In certain embodiments, the antibody is a nanobody (an antibody fragment consisting of a single monomeric variable antibody domain - also known as a single-domain antibody (sdAb)), a monobody (a synthetic binding protein constructed using a fibronectin type III domain (FN3) as a molecular scaffold), or a Bi-specific T- cell engager (BiTE).

An immunoglobulin light or heavy chain variable region (V_L and V_H, respectively) is composed of a “framework” region (FR) interrupted by three hypervariable regions, also called “complementarity determining regions” or “CDRs”. The extent of the framework region and CDRs have been defined (see, E. Kabat et al., Sequences of proteins of immunological interest, 4th ed. U.S. Dept. Health and Human Services, Public Health Services, Bethesda, MD (1987); and Lefranc et al. IMGT, the international ImMunoGeneTics information system®. Nucl. Acids Res., 2005, 33, D593-D597)). The sequences of the framework regions of different light or heavy chains are relatively conserved within a species. The framework region of an antibody, that is the combined framework regions of the constituent light and heavy chains, serves to position and align the CDRs. The CDRs are primarily responsible for binding to an epitope of an antigen.

An “antibody” thus encompasses a protein having one or more polypeptides that can be genetically encodable, e.g., by immunoglobulin genes or fragments of immunoglobulin genes. The recognized immunoglobulin genes include the kappa, lambda, alpha, gamma, delta, epsilon and mu constant region genes, as well as myriad immunoglobulin variable region genes. Light chains are classified as either kappa or lambda. Heavy chains are classified as gamma, mu, alpha, delta, or epsilon, which in turn define the immunoglobulin classes, IgG, IgM, IgA, IgD and IgE, respectively. In some embodiments, an antibody employed in a bifunctional molecule is an IgG antibody, e.g., an lgG1 antibody, such as a human lgG1 antibody.

As used herein, a “ligand” is a substance that forms a complex with a biomolecule to serve a biological purpose. The ligand may be a substance selected from a circulating factor, a secreted factor, a cytokine, a growth factor, a hormone, a peptide, a polypeptide, a small molecule, and a nucleic acid, that forms a complex with a cell surface molecule. In certain embodiments, when the first and/or second moiety is a ligand, the ligand is modified in such a way that complex formation with the cell surface molecule occurs, but the normal biological result of such complex formation does not occur. According to some embodiments, the first and/or second moiety is a small molecule. By “small molecule” is meant a compound having a molecular weight of 1000 atomic mass units (amu) or less. In some embodiments, the small molecule is 900 amu or less, 750 amu or less, 500 amu or less, 400 amu or less, 300 amu or less, or 200 amu or less. In some instances, the small molecule is not made of repeating molecular units such as are present in a polymer.

In certain embodiments, the first and/or second moiety is an aptamer. By “aptamer” is meant a nucleic acid (e.g., an oligonucleotide) that has a specific binding affinity for the target cell surface molecule. Aptamers exhibit certain desirable properties for targeted delivery of the bifunctional molecule, such as ease of selection and synthesis, high binding affinity and specificity, low immunogenicity, and versatile synthetic accessibility. Aptamers that bind to cell surface molecules are known and include those described in Zhu et al. (2015) ChemMedChem 10(1 ):39-45; Sun et al. (2014) Mol. Ther. Nucleic Acids 3:e182; and Zhang et al. (201 1 ) Curr. Med. Chem. 18(27):4185-4194.

In certain embodiments, the molecule on the surface of the LN progenitor exhausted T cell to which the first moiety binds is Fc receptor-like protein 3 (FCRL3 - UniProt Accession No. Q96P31 (human)), lysosome-associated membrane glycoprotein 1 (LAMP1 - UniProt Accession No. P11279 (human)), platelet endothelial cell adhesion molecule (PECAM1 - UniProt Accession No. P16284 (human)), interferon-induced transmembrane protein 1 (IFITM1 - UniProt Accession No. P13164 (human)), T cell surface antigen CD2 (CD2 - UniProt Accession No. P06729 (human)), or signal-regulatory protein gamma (SIRPG - UniProt Accession No. Q9P1 W8 (human)). As demonstrated herein, these are cell surface molecules that preferentially mark LN progenitor exhausted T cells. In some instances, the molecule on the surface of the LN progenitor exhausted T cell to which the first moiety binds is FCRL3, LAMP1 , PECAM1 , IFITM1 , or SIRPG. According to some embodiments, the first moiety does not bind CD2.

Moieties that bind FCRL3, LAMP1 , PECAM1 , IFITM1 , CD2, and SIRPG are known and available. For example, antibodies that bind these cell surface markers are known and available and include those described in the non-patent literature and patent literature referenced for these markers in the Therapeutic Antibody Database (Tabs) available at tabs.craic.com.

A non-limiting example of an antibody that binds FCRL3 is that described in Polson et al. (2006) Int Immunol. 18:1363-1373, the disclosure of which is incorporated herein by reference in its entirety for all purposes.

Non-limiting examples of antibodies that bind LAMP1 include those described in US20180142032, US20160280793, US9809653, WO2014102299 and WO2023034571 , the disclosures of which are incorporated herein by reference in their entireties for all purposes.

Non-limiting examples of antibodies that bind PECAM1 include those described in WO201 1003996, W02020040523 and US20220033495, the disclosures of which are incorporated herein by reference in their entireties for all purposes. A non-limiting example of an antibody that binds I FITM1 is that described in Raposo et al. (2017) JCI Insight. 2(1 ):e85811 , the disclosure of which is incorporated herein by reference in its entirety for all purposes.

Non-limiting examples of antibodies that bind CD2 include those described in WO1999003502, W02004022097, US20220249683, US20210260212, WO2020247872, WO2019108860, US20200368363, WO2021259927, WO2021195513, WO2019104075, WO1999003502 and US5817311 , the disclosures of which are incorporated herein by reference in their entireties for all purposes.

Non-limiting examples of antibodies that bind SIRPG include those described in W02020039049, WO2018149938 and US20190382483, the disclosures of which are incorporated herein by reference in their entireties for all purposes.

In some instances, a bifunctional molecule of the present disclosure comprises a third moiety that binds to a molecule on the surface of the LN progenitor exhausted T cell, wherein the third moiety binds to a different cell surface molecule than the first moiety, i.e., the third moiety binds to a cell surface molecule expressed from a different gene than the gene that expresses the cell surface molecule bound by the first moiety. By way of example, in certain embodiments, the first moiety binds a cell surface molecule selected from FCRL3, LAMP1 , PECAM1 , IFITM1 , CD2, and SIRPG, and the third moiety binds to a cell surface molecule selected from FCRL3, LAMP1 , PECAM1 , IFITM1 , CD2, and SIRPG different from the cell surface molecule to which the first moiety binds.

The second moiety may be any moiety suitable for activating the LN progenitor exhausted T cell. Types of second moieties which may be employed in a bifunctional molecule of the present disclosure include, but are not limited to, cytokines, agonists of T cell co-stimulatory receptors, immune checkpoint inhibitors, and the like.

According to some embodiments, the second moiety is a cytokine. Cytokines are intercellular signaling molecules that aid cell to cell communication in immune responses and stimulate the movement of cells towards sites of inflammation, infection and trauma. The downstream effect of a particular cytokine occurs through its high-affinity binding of its receptor expressed on the surface of a target cell. This action may occur in an autocrine (acts on the same cell), paracrine (acts on nearby cells) or endocrine (acts on distant cells) manner. Receptor engagement triggers intracellular signaling cascades leading to altered gene expression in the target cell, leading to a biological effect. Cytokines can be divided into several categories including the interleukins (ILs), transforming growth factors (TGFs), interferons (IFNs), colony- stimulating-factors (CSFs), tumor necrosis factors (TNFs), and chemokines.

Interleukins (ILs) are a group of cytokines that are expressed and secreted by white blood cells (leukocytes) as well as some other body cells. Interleukins and associated cytokines serve as the means of communication for innate and adaptive immune cells as well as non-immune cells and tissues. All IL-1 family members share a conserved beta-trefoil structure and bind to members of the IL-1 receptor (IL-1 R) family. Members of the IL-1 R family contain extracellular Ig-like domains and mediate signaling through an intracellular Toll/IL-1 R (TIR) domain.

The four-helix bundle cytokine superfamily is subdivided into the class I and class II cytokine receptor families. Ligands for the class I cytokine receptor family include short-chain and long-chain helical cytokines. The short-chain helical cytokine family includes members of the common gamma-chain and common beta-chain families of cytokines. The common beta-chain and common gamma-chain cytokine families include cytokines such as IL-2, IL-3, IL-4, IL-5, IL- 7, IL-9, IL-15, IL-21 , and GM-CSF. Members of the common beta-chain family signal through heterodimeric receptor complexes that contain the common beta-chain subunit, while members of the common gamma-chain family signal through heterodimeric or heterotrimeric receptor complexes that contain the common gamma-chain subunit. The common y chain (yc) family consists of IL-2, IL-4, IL-7, IL-9, IL-15, and IL-21 and was named for binding of these factors to the yc receptor (CD132). They act mainly as growth and proliferation factors for progenitors and mature cells and also have roles in lineage-specific cell differentiation.

In certain embodiments, when the second moiety is a cytokine, the second moiety is an interleukin in the common y chain (yc) family. For example, the interleukin may be IL-2, IL-15 or IL-21. In some instances, the interleukin is IL-2.

According to some embodiments, the second moiety is a wild-type cytokine or functional fragment thereof. In other embodiments, the second moiety is an engineered cytokine or functional fragment thereof. By “engineered” in this context is meant the cytokine (e.g., an interleukin such as, e.g., IL-2) comprises one or more amino acid substitutions, deletions, insertions, is fused to a heterologous amino acid sequence, or any combination thereof, where the engineering confers upon the cytokine one or more new and/or improved properties (e.g., higher affinity, preferential specificity, increased receptor activation, and/or the like) as compared to the wild-type cytokine.

In some instances, the second moiety comprises an agonist of a T cell co-stimulatory receptor. For example, in certain embodiments, the second moiety comprises an agonist of CD28, ICOS, CD28, CD27, HVEM, LIGHT, CD40, 4-1 BB, 0X40, DR3, GITR, CD30, TIM1 , SLAM, CD2, or CD226. According to some embodiments, the second moiety comprises an agonist of a T cell co-stimulatory receptor of the immunoglobulin super-family. In some instances, the second moiety comprises an agonist of CD28. Agonists of CD28 are known and include, e.g, antibody TGN1412 (Brown (2018) Diseases 6(2):41 ), antibody D665 (Que et al. (2022) Science Advances 8(31 ):eabo4413), and many others - see, e.g., Poirier (2012) Am J Transplant 12(7):1682-90.

In certain embodiments, the second moiety comprises an immune checkpoint inhibitor. As used herein, an “immune checkpoint inhibitor” is any agent (e.g., small molecule, nucleic acid, protein (e.g., antibody)) that prevents the suppression of any component in the immune system such as MHC class presentation, T cell presentation and/or differentiation, any cytokine, chemokine or signaling for immune cell proliferation and/or differentiation. According to some embodiments, the second moiety is an immune checkpoint inhibitor selected from a programmed cell death-1 (PD-1 ) inhibitor, a cytotoxic T-lymphocyte-associated antigen 4 (CTLA-4) inhibitor, a lymphocyte activation gene-3 (LAG-3) inhibitor, a T-cell immunoglobulin domain and mucin domain 3 (TIM-3) inhibitor, an indoleamine (2,3)-dioxygenase (IDO) inhibitor, a T cell immunoreceptor with Ig and ITIM domains (TIGIT) inhibitor, a V-domain Ig suppressor of T cell activation (VISTA) inhibitor, or a B7-H3 inhibitor. In some instances, the second moiety is a PD- 1 inhibitor. A vast number of inhibitors of these checkpoint molecules are known and approved for therapeutic use, as described in, e.g., Shiravand et al. (2022) Curr Oncol. 29(5):3044-3060, the disclosure of which is incorporated herein by reference in its entirety for all purposes.

The bifunctional molecules of the present disclosure may take a variety of forms including conjugates, fusion proteins, heterodimeric molecules, etc. In some instances, the bifunctional molecule is a conjugate where the first moiety is conjugated to the second moiety. Non-limiting examples of linkers that may be employed for conjugation of the first moiety to the second moiety include ester linkers, amide linkers, maleimide or maleimide-based linkers; valine-citrulline linkers; hydrazone linkers; N-succinimidyl-4-(2-pyridyldithio)butyrate (SPDB) linkers; Succinimidyl-4-(/V-maleimidomethyl)cyclohexane-1 -carboxylate (SMCC) linkers; vinylsulfone- based linkers; linkers that include polyethylene glycol (PEG), such as, but not limited to tetraethylene glycol; linkers that include propanoic acid; linkers that include caproleic acid, and linkers including any combination thereof.

The first moiety may be conjugated to the second moiety using any convenient approach. For example, the conjugating may include site-specif ically conjugating the first moiety to a preselected amino acid of the second moiety (or vice versa). In certain aspects, the pre-selected amino acid is at the N-terminus or C-terminus of the second moiety. In other aspects, the preselected amino acid is internal to the second moiety - that is, between the N-terminal and C- terminal amino acid of the second moiety. In some embodiments, the pre-selected amino acid is a non-natural amino acid. Non-limiting examples of non-natural amino acids which may be provided to the second moiety (or first moiety) to facilitate conjugation include those having a functional group selected from an azide, alkyne, alkene, amino-oxy, hydrazine, aldehyde (e.g., formylglycine, e.g., SMARTag™ technology from Catalent Pharma Solutions), nitrone, nitrile oxide, cyclopropene, norbornene, iso-cyanide, aryl halide, and boronic acid functional group. Unnatural amino acids which may be incorporated and selected to provide a functional group of interest are known and described in, e.g., Maza et al. (2015) Bioconjug. Chem. 26(9):1884-9; Patterson et al. (2014) ACS Chem. Biol. 9:592-605; Adumeau et al. (2016) Mol. Imaging Biol. (2):153-65; and elsewhere. Numerous strategies are available for conjugating the first moiety and second moiety through a linker. For example, the first moiety may be derivatized by covalently attaching the linker to the first moiety, where the linker has a functional group capable of reacting with a “chemical handle” on the second moiety. Also by way of example, the second moiety may be derivatized by covalently attaching the linker to the second moiety, where the linker has a functional group capable of reacting with a “chemical handle” on the first moiety. The functional group on the linker may vary and may be selected based on compatibility with the chemical handle on the cell-targeting moiety or first moiety. According to one embodiment, the chemical handle is provided by incorporation of an unnatural amino acid having the chemical handle into the first moiety or the second moiety. In some embodiments, conjugating the first moiety and second moiety is by copper-free, strain-promoted cycloaddition, alkyne-azide cycloaddition, or the like.

According to some embodiments, when the first and second moieties are each polypeptides, a bifunctional molecule of the present disclosure is a fusion protein comprising the first moiety fused to the second moiety. When the bifunctional molecule is a fusion protein, the first moiety may be fused directly to the second moiety (e.g., at the N- or C-terminus of the second moiety), or the first moiety may be fused indirectly to the second moiety via a linker. Any useful linkers may be employed, including but not limited to, a serine-glycine linker, or the like. In particular embodiments, the length of a linker is about 1 to about 25 amino acids, about 5 to about 20 amino acids, or about 10 to about 20 amino acids, or any intervening length of amino acids. In some embodiments, the linker is 1 , 2, 3, 4, 5, 6, 7, 8, 9, 10, 1 1 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, 23, 24, 25, or more amino acids long. Also provided are nucleic acids that encode the fusion proteins of the present disclosure, as well as expression vectors comprising such nucleic acids, and host cells comprising such nucleic acids and/or expression vectors. Such host cells may express the fusion proteins, thereby producing the fusion proteins.

In some instances, when the first and second moieties are each polypeptides, the bifunctional molecule is a dimer comprising the first moiety dimerized with the second moiety. In certain embodiments, the first moiety is a polypeptide fused to a heterologous amino acid sequence, the second moiety is a polypeptide fused to a heterologous amino acid sequence, or both. According to some embodiments, one or both of the first and second moieties may be fused to an antibody heavy chain comprising a CH1 domain, a hinge region, a CH2 domain, a CH3 domain, or any combination thereof. In some instances, the bifunctional molecule is a dimer comprising the first moiety dimerized with the second moiety via the antibody heavy chain.

By way of example, the first and second moieties may each be fused to a fragment crystallizable (Fc) region, and the first and second moieties may be dimerized via the Fc regions. Such heterodimers may include substitutions at the heavy chain CH3 interface in each half molecule to favor heterodimer formation either in vitro in a cell-free environment or using coexpression. The “knob-in-hole” strategy (see, e.g., WO 2006/028936) may be used to promote heterodimerization, including but not limited to, when the bifunctional molecule is a bispecific antibody. Briefly, selected amino acids forming the interface of the CH3 domains in human IgG can be mutated at positions affecting CH3 domain interactions to promote heterodimer formation. An amino acid with a small side chain (hole) is introduced into a heavy chain fused to the first moiety and an amino acid with a large side chain (knob) is introduced into a heavy chain fused to the second moiety. After co-expression of the two monomers, a heterodimer is formed as a result of the preferential interaction of the heavy chain with a “hole” with the heavy chain with a “knob”. Exemplary CH3 substitution pairs forming a knob and a hole are (expressed as modified position in the first CH3 domain of the first heavy chain/modified position in the second CH3 domain of the second heavy chain): T366Y7F405A, T366W/F405W, F405W/Y407A, T394W/Y407T, T3945/Y407A, T366W/T394S, F405W/T394S and

T366W/T366S_L368A_Y407V.

Other strategies such as promoting heavy chain heterodimerization using electrostatic interactions by substituting positively charged residues at one CH3 surface and negatively charged residues at a second CH3 surface may be used, as described in US2010/0015133; US2009/0182127; US2010/028637 or US201 1/0123532. In other strategies, heterodimerization may be promoted by the following substitutions (expressed as modified position in the first CH3 domain of the first heavy chain/modified position in the second CH3 domain of the second heavy chain): L351 Y_F405A_Y407V T394W, T366l_K392M_T394W/F405A_Y407V,

T366L_K392M_T394W/F405A_Y407V, L351 Y_Y407A'T366A_K409F,

L351 Y_Y407A/T366V_K409F, Y407A/T366A_K409F, or

T350V_L351 Y_F405A_Y407V/T350V_T366L_K392L_T394W as described in US2012/0149876 or US2013/0195849.

Approaches that may be employed to produce heterodimers (e.g., bispecific antibodies) from the monomers described herein include, but are not limited to, Ellerman, D. (2019). "Bispecific T-cell engagers: Towards understanding variables influencing the in vitro potency and tumor selectivity and their modulation to enhance their efficacy and safety." Methods 154: 102- 117; Brinkmann, U. and R. E. Kontermann (2017). "The making of bispecific antibodies." mAbs 9(2): 182-212; and Suurs, F. V., et al. (2019). "A review of bispecific antibodies and antibody constructs in oncology and clinical challenges." Pharmacol Ther 201 : 103-1 19; the disclosures of which are incorporated herein by reference in their entireties for all purposes.

Any of the first and second moieties described herein may comprise an antigen-binding domain of an antibody. For example, the first moiety may comprise an antibody (e.g., a full-length antibody, an antibody fragment, a single chain antibody, etc.) comprising an antigen-binding domain of an antibody that specifically binds the molecule on the surface of the LN progenitor exhausted T cell. Non-limiting examples of such antibodies that bind to FCRL3, LAMP1 , PECAM1 , IFITM1 , CD2, and SIRPG are described hereinabove. Using the information provided herein, the bifunctional molecules of the present disclosure may be prepared using standard techniques known to those of skill in the art. For example, when the first and second moieties are each polypeptides, a nucleic acid sequence(s) encoding the amino acid sequences of the first and second moieties of the bifunctional molecules of the present disclosure can be used to express the first and second moieties. The nucleic acid sequence(s) can be optimized to reflect particular codon “preferences” for various expression systems according to standard methods known to those of skill in the art. Using the sequence information provided, the nucleic acids may be synthesized according to a number of standard methods known to those of skill in the art.

Once a nucleic acid(s) encoding a subject first and/or second moiety is synthesized, it can be amplified and/or cloned according to standard methods. Molecular cloning techniques to achieve these ends are known in the art. A wide variety of cloning and in vitro amplification methods suitable for the construction of recombinant nucleic acids are known to persons of skill in the art and are the subjects of numerous textbooks and laboratory manuals.

Expression of natural or synthetic nucleic acids encoding the first and/or second moieties can be achieved by operably linking a nucleic acid encoding the first and/or second moieties to a promoter (which may be either constitutive or inducible), and incorporating the construct into an expression vector to generate a recombinant expression vector. The vectors can be suitable for replication and integration in prokaryotes, eukaryotes, or both. Typical cloning vectors contain functionally appropriately oriented transcription and translation terminators, initiation sequences, and promoters useful for regulation of the expression of the nucleic acid encoding the first and/or second moieties. The vectors optionally contain generic expression cassettes containing at least one independent terminator sequence, sequences permitting replication of the cassette in both eukaryotes and prokaryotes, e.g., as found in shuttle vectors, and selection markers for both prokaryotic and eukaryotic systems.

To obtain high levels of expression of a cloned nucleic acid it is common to construct expression plasmids which typically contain a strong promoter to direct transcription, a ribosome binding site for translational initiation, and a transcription/translation terminator, each in functional orientation to each other and to the protein-encoding sequence. Examples of regulatory regions suitable for this purpose in E. coli are the promoter and operator region of the E. coli tryptophan biosynthetic pathway, the leftward promoter of phage lambda (PL), and the L-arabinose (araBAD) operon. The inclusion of selection markers in DNA vectors transformed in E. coli is also useful. Examples of such markers include genes specifying resistance to ampicillin, tetracycline, or chloramphenicol. Expression systems for expressing polypeptides (e.g., antibodies) are available using, for example, E. coli, Bacillus sp. and Salmonella. E. co// systems may also be used.

The nucleic acid(s) encoding the first and/or second moieties may also be subcloned into an expression vector that allows for the addition of a tag (e.g., FLAG, hexahistidine, and the like) at the C-terminal end or the N-terminal end of the first and/or second moiety to facilitate purification. Methods of transfecting and expressing genes in mammalian cells are known in the art. Transducing cells with nucleic acids can involve, for example, incubating lipidic microparticles containing nucleic acids with cells or incubating viral vectors containing nucleic acids with cells within the host range of the vector.

Once the nucleic acid encoding the first and/or second moiety is isolated and cloned, one can express the nucleic acid in a variety of recombinantly engineered cells known to those of skill in the art. Examples of such cells include bacteria, yeast, filamentous fungi, insect (e.g. those employing baculoviral vectors), and mammalian cells.

Isolation and purification of the first and/or second moiety can be accomplished according to methods known in the art. For example, a protein can be isolated from a lysate of cells genetically modified to express the protein constitutively and/or upon induction, or from a synthetic reaction mixture, by immunoaffinity purification (or precipitation using Protein L or A), washing to remove non-specifically bound material, and eluting the specifically bound first and/or second moiety. The isolated first and/or second moiety can be further purified by dialysis and other methods normally employed in protein purification methods. In one embodiment, the first and/or second moiety may be isolated using metal chelate chromatography methods. The first and/or second moiety may contain modifications to facilitate isolation, as discussed above.

The first and/or second moiety may be prepared in substantially pure or isolated form (e.g., free from other polypeptides). The protein can be present in a composition that is enriched for the polypeptide relative to other components that may be present (e.g., other polypeptides or other host cell components). Purified first and/or second moieties may be provided such that the cell- first and/or second moiety is present in a composition that is substantially free of other expressed proteins, e.g., less than 90%, usually less than 60% and more usually less than 50% of the composition is made up of other expressed proteins.

First and/or second moieties produced by prokaryotic cells may require exposure to chaotropic agents for proper folding. During purification from E. coH, for example, the expressed protein can be optionally denatured and then renatured. This can be accomplished, e.g., by solubilizing the bacterially produced first and/or second moiety in a chaotropic agent such as guanidine HCI. The first and/or second moiety is then renatured, either by slow dialysis or by gel filtration. Alternatively, nucleic acid encoding the first and/or second moiety may be operably linked to a secretion signal sequence such as pelB so that the first and/or second moiety are secreted into the periplasm in correctly-folded form.

According to some embodiments, the LN progenitor exhausted T cell is a tumor draining LN progenitor exhausted T cell. Tumor-draining lymph nodes (TDLNs) are primary sites, where anti-tumor lymphocytes are primed to tumor-specific antigens and play pivotal roles in immune responses against tumors. Nucleic Acids, Expression Vectors and Cells

In view of the section above regarding methods of producing the bifunctional molecules of the present disclosure, it will be appreciated that the present disclosure also provides nucleic acids, expression vectors and cells.

In certain embodiments, provided is a nucleic acid encoding the first moiety, the second moiety, or both, of any one of the bifunctional molecules of the present disclosure.

Also provided are expression vectors comprising any of the nucleic acids of the present disclosure. Expression of natural or synthetic nucleic acids encoding the first and/or second moieties can be achieved by operably linking a nucleic acid encoding the first and/or second moieties to a promoter (which is either constitutive or inducible) and incorporating the construct into an expression vector to generate a recombinant expression vector. The vectors can be suitable for replication and integration in prokaryotes, eukaryotes, or both. Typical cloning vectors contain functionally appropriately oriented transcription and translation terminators, initiation sequences, and promoters useful for regulation of the expression of the nucleic acid encoding the first and/or second moieties. The vectors optionally contain generic expression cassettes containing at least one independent terminator sequence, sequences permitting replication of the cassette in both eukaryotes and prokaryotes, e.g., as found in shuttle vectors, and selection markers for both prokaryotic and eukaryotic systems.

Cells that comprise any of the nucleic acids and/or expression vectors of the present disclosure are also provided. According to some embodiments, a cell of the present disclosure comprises a nucleic acid that encodes a first moiety and/or second moiety of any of the bifunctional molecules of the present disclosure. In certain such embodiments, the bifunctional molecule is a fusion protein (as described above) and the nucleic acid encodes the fusion protein. According to some embodiments, provided is a cell comprising a first nucleic acid encoding any of the first moieties of the bifunctional molecules of the present disclosure, and a second nucleic acid encoding any of the second moieties of the bifunctional molecule. In certain embodiments, such as cell comprises a first expression vector comprising the first nucleic acid, and a second expression vector comprising the second nucleic acid.

Also provided are methods of making the bifunctional molecules of the present disclosure, the methods comprising culturing a cell of the present disclosure under conditions suitable for the cell to express the first moiety and/or second moiety, wherein the first moiety and/or second moiety is produced. The conditions for culturing the cell such that the first moiety and/or second moiety is expressed may vary. Such conditions may include culturing the cell in a suitable container (e.g., a cell culture plate or well thereof), in suitable medium (e.g., cell culture medium, such as DMEM, RPMI, MEM, IMDM, DMEM/F-12, or the like) at a suitable temperature (e.g., 32°C - 42°C, such as 37°C) and pH (e.g., pH 7.0 - 7.7, such as pH 7.4) in an environment having a suitable percentage of CO₂, e.g., 3% to 10%, such as 5%). COMPOSITIONS

Aspects of the present disclosure further include compositions. According to some embodiments, a composition of the present disclosure comprises a bifunctional molecule of the present disclosure. For example, the bispecific molecule may be any of the bifunctional molecules described in the Bifunctional Molecule section hereinabove, which descriptions are incorporated but not reiterated herein for purposes of brevity.

In some instances, a composition of the present disclosure includes the bifunctional molecule present in a liquid medium. The liquid medium may be an aqueous liquid medium, such as water, a buffered solution, or the like. One or more additives such as a salt (e.g., NaCI, MgCh, KCI, MgSO₄), a buffering agent (a Tris buffer, N-(2-Hydroxyethyl)piperazine-N'-(2-ethanesulfonic acid) (HEPES), 2-(N-Morpholino)ethanesulfonic acid (MES), 2-(N-Morpholino)ethanesulfonic acid sodium salt (MES), 3-(N-Morpholino)propanesulfonic acid (MOPS), N- tris[Hydroxymethyl]methyl-3-aminopropanesulfonic acid (TAPS), etc.), a solubilizing agent, a detergent (e.g., a non-ionic detergent such as Tween-20, etc.), a nuclease inhibitor, a protease inhibitor, glycerol, a chelating agent, and the like may be present in such compositions.

According to some embodiments, a composition of the present disclosure is formulated for administration to a subject in need thereof. In certain embodiments, such compositions comprise the bifunctional molecule of the present disclosure, and a pharmaceutically acceptable carrier.

The compositions generally include a therapeutically effective amount of the bifunctional molecule. By “therapeutically effective amount” is meant an amount sufficient to produce a desired result, e.g., an amount sufficient to effect beneficial or desired therapeutic (including preventative) results, such as a reduction in a symptom of a disease (e.g., cancer), as compared to a control. An effective amount can be administered in one or more administrations.

A “therapeutically effective amount” of the bifunctional molecule may vary according to factors such as the disease state, age, sex, and weight of the subject, and the ability of the bifunctional molecule to elicit a desired response in the subject. A therapeutically effective amount is also one in which any toxic or detrimental effects of the bifunctional molecule are outweighed by the therapeutically beneficial effects. The term “therapeutically effective amount” includes an amount that is effective to “treat” a subject (e.g., a patient). When a therapeutic amount is indicated, the precise amount of the compositions contemplated in particular embodiments, to be administered, can be determined by a physician in view of the specification and with consideration of individual differences in age, weight, tumor size, extent of infection or metastasis, and condition of the patient (subject).

The bifunctional molecules can be incorporated into a variety of formulations for therapeutic administration. More particularly, the bispecific molecules can be formulated into pharmaceutical compositions by combination with appropriate, pharmaceutically acceptable excipients or diluents, and may be formulated into preparations in solid, semi-solid, liquid or gaseous forms, such as tablets, capsules, powders, granules, ointments, solutions, injections, inhalants and aerosols.

Formulations of the bifunctional molecules for administration to an individual (e.g., suitable for human administration) are generally sterile and may further be free of detectable pyrogens or other contaminants contraindicated for administration to a patient according to a selected route of administration.

In pharmaceutical dosage forms, the bifunctional molecules can be administered in the form of their pharmaceutically acceptable salts, or they may also be used alone or in appropriate association, as well as in combination, with other pharmaceutically active compounds. The following methods and carriers/excipients are merely examples and are in no way limiting.

For oral preparations, the bifunctional molecules can be used alone or in combination with appropriate additives to make tablets, powders, granules or capsules, for example, with conventional additives, such as lactose, mannitol, corn starch or potato starch; with binders, such as crystalline cellulose, cellulose derivatives, acacia, corn starch or gelatins; with disintegrators, such as corn starch, potato starch or sodium carboxymethylcellulose; with lubricants, such as talc or magnesium stearate; and if desired, with diluents, buffering agents, moistening agents, preservatives and flavoring agents.

The bifunctional molecules may be formulated for parenteral (e.g., intravenous, intraarterial, intraosseous, intramuscular, intracerebral, intracerebroventricular, intrathecal, subcutaneous, intralymph node (intra-LN) (e.g., intra-tumor draining lymph node (intra-TDLN), etc. administration. In some instances, the bifunctional molecules are formulated for injection by dissolving, suspending or emulsifying the bifunctional molecules in an aqueous or non-aqueous solvent, such as vegetable or other similar oils, synthetic aliphatic acid glycerides, esters of higher aliphatic acids or propylene glycol; and if desired, with conventional additives such as solubilizers, isotonic agents, suspending agents, emulsifying agents, stabilizers and preservatives.

Compositions that comprise the bifunctional molecules suitable for administration to a subject may be prepared by mixing the bifunctional molecules having the desired degree of purity with optional physiologically acceptable carriers, excipients, stabilizers, surfactants, buffers and/or tonicity agents. Acceptable carriers, excipients and/or stabilizers are nontoxic to recipients at the dosages and concentrations employed, and include buffers such as phosphate, citrate, and other organic acids; antioxidants including ascorbic acid, glutathione, cysteine, methionine and citric acid; preservatives (such as ethanol, benzyl alcohol, phenol, m-cresol, p-chlor-m- cresol, methyl or propyl parabens, benzalkonium chloride, or combinations thereof); amino acids such as arginine, glycine, ornithine, lysine, histidine, glutamic acid, aspartic acid, isoleucine, leucine, alanine, phenylalanine, tyrosine, tryptophan, methionine, serine, proline and combinations thereof; monosaccharides, disaccharides and other carbohydrates; low molecular weight (less than about 10 residues) polypeptides; proteins, such as gelatin or serum albumin; chelating agents such as EDTA; sugars such as trehalose, sucrose, lactose, glucose, mannose, maltose, galactose, fructose, sorbose, raffinose, glucosamine, N-methylglucosamine, galactosamine, and neuraminic acid; and/or non-ionic surfactants such as Tween, Brij Pluronics, Triton-X, or polyethylene glycol (PEG).

The pharmaceutical composition may be in a liquid form, a lyophilized form or a liquid form reconstituted from a lyophilized form, wherein the lyophilized preparation is to be reconstituted with a sterile solution prior to administration. The standard procedure for reconstituting a lyophilized composition is to add back a volume of pure water (typically equivalent to the volume removed during lyophilization); however solutions comprising antibacterial agents may be used for the production of pharmaceutical compositions for parenteral administration.

An aqueous formulation of the bifunctional molecules may be prepared in a pH-buffered solution, e.g., at pH ranging from about 4.0 to about 7.0, or from about 5.0 to about 6.0, or alternatively about 5.5. Examples of buffers that are suitable for a pH within this range include phosphate-, histidine-, citrate-, succinate-, acetate-buffers and other organic acid buffers. The buffer concentration can be from about 1 mM to about 100 mM, or from about 5 mM to about 50 mM, depending, e.g., on the buffer and the desired tonicity of the formulation.

A tonicity agent may be included to modulate the tonicity of the formulation. Example tonicity agents include sodium chloride, potassium chloride, glycerin and any component from the group of amino acids, sugars as well as combinations thereof. In some embodiments, the aqueous formulation is isotonic, although hypertonic or hypotonic solutions may be suitable. The term "isotonic" denotes a solution having the same tonicity as some other solution with which it is compared, such as physiological salt solution or serum. Tonicity agents may be used in an amount of about 5 mM to about 350 mM, e.g., in an amount of 100 mM to 350 mM.

A surfactant may also be added to the formulation to reduce aggregation and/or minimize the formation of particulates in the formulation and/or reduce adsorption. Example surfactants include polyoxyethylensorbitan fatty acid esters (Tween), polyoxyethylene alkyl ethers (Brij), alkylphenylpolyoxyethylene ethers (Triton-X), polyoxyethylene-polyoxypropylene copolymer (Poloxamer, Pluronic), and sodium dodecyl sulfate (SDS). Examples of suitable polyoxyethylenesorbitan-fatty acid esters are polysorbate 20, (sold under the trademark Tween 20™) and polysorbate 80 (sold under the trademark Tween 80™). Examples of suitable polyethylene-polypropylene copolymers are those sold under the names Pluronic® F68 or Poloxamer 188™. Examples of suitable Polyoxyethylene alkyl ethers are those sold under the trademark Brij™. Example concentrations of surfactant may range from about 0.001% to about 1% w/v.

A lyoprotectant may also be added in order to protect the bifunctional molecule against destabilizing conditions during a lyophilization process. For example, known lyoprotectants include sugars (including glucose and sucrose); polyols (including mannitol, sorbitol and glycerol); and amino acids (including alanine, glycine and glutamic acid). Lyoprotectants can be included, e.g., in an amount of about 10 mM to 500 nM.

In some embodiments, the composition includes the bifunctional molecule, and one or more of the above-identified components (e.g. , a surfactant, a buffer, a stabilizer, a tonicity agent) and is essentially free of one or more preservatives, such as ethanol, benzyl alcohol, phenol, tricresol, p-chlor-m-cresol, methyl or propyl parabens, benzalkonium chloride, and combinations thereof. In other embodiments, a preservative is included in the formulation, e.g., at concentrations ranging from about 0.001 to about 2% (w/v).

METHODS OF USE

Aspects of the present disclosure also include methods of using the bifunctional molecules of the present disclosure. For example, in certain embodiments, provided are methods comprising activating LN progenitor exhausted T cells in a subject in need thereof. In some instances, provided are methods of activating LN progenitor exhausted T cells in a subject in need thereof, such methods comprising administering to the subject a composition comprising a bifunctional molecule of the present disclosure in an amount effective to activate LN progenitor exhausted T cells in the subject.

The methods of the present disclosure may be performed to treat a variety of conditions in the subject. In certain embodiments, the subject has cancer, and the method stimulates a T cell response against the cancer, thereby treating the cancer. The methods may be employed to stimulate a T cell response against a large variety of cancers. “Tumor”, as used herein, refers to all neoplastic cell growth and proliferation, whether malignant or benign, and all pre-cancerous and cancerous cells and tissues. The terms “cancer” and “cancerous” refer to or describe the physiological condition in mammals that is typically characterized by unregulated cell growth/proliferation. Examples of cancers that may be treated using the subject methods include, but are not limited to, cancers comprising a solid tumor, e.g., a carcinoma, a sarcoma, a lymphoma, a blastoma, a melanoma, a germ cell tumor, or a carcinosarcoma. In certain embodiments, the cancer comprises a hematological malignancy, e.g., a leukemia, multiple myeloma, or the like. More particular examples of such cancers include renal cancer; kidney cancer; glioblastoma multiforme; metastatic breast cancer; breast carcinoma; breast sarcoma; neurofibroma; neurofibromatosis; pediatric tumors; neuroblastoma; malignant melanoma; carcinomas of the epidermis; leukemias such as but not limited to, acute leukemia, acute lymphocytic leukemia, acute myelocytic leukemias such as myeloblastic, promyelocytic, myelomonocytic, monocytic, erythroleukemia leukemias and myelodysplastic syndrome, chronic leukemias such as but not limited to, chronic myelocytic (granulocytic) leukemia, chronic lymphocytic leukemia, hairy cell leukemia; polycythemia vera; lymphomas such as but not limited to Hodgkin's disease, non-Hodgkin's disease; multiple myelomas such as but not limited to smoldering multiple myeloma, nonsecretory myeloma, osteosclerotic myeloma, plasma cell leukemia, solitary plasmacytoma and extramedullary plasmacytoma; Waldenstrom's macroglobulinemia; monoclonal gammopathy of undetermined significance; benign monoclonal gammopathy; heavy chain disease; bone cancer and connective tissue sarcomas such as but not limited to bone sarcoma, myeloma bone disease, multiple myeloma, cholesteatoma-induced bone osteosarcoma, Paget's disease of bone, osteosarcoma, chondrosarcoma, Ewing's sarcoma, malignant giant cell tumor, fibrosarcoma of bone, chordoma, periosteal sarcoma, soft- tissue sarcomas, angiosarcoma (hemangiosarcoma), fibrosarcoma, Kaposi's sarcoma, leiomyosarcoma, liposarcoma, lymphangio sarcoma, neurilemmoma, rhabdomyosarcoma, and synovial sarcoma; brain tumors such as but not limited to, glioma, astrocytoma, brain stem glioma, ependymoma, oligodendroglioma, nonglial tumor, acoustic neurinoma, craniopharyngioma, medulloblastoma, meningioma, pineocytoma, pineoblastoma, and primary brain lymphoma; breast cancer including but not limited to adenocarcinoma, lobular (small cell) carcinoma, intraductal carcinoma, medullary breast cancer, mucinous breast cancer, tubular breast cancer, papillary breast cancer, Paget's disease (including juvenile Paget's disease) and inflammatory breast cancer; adrenal cancer such as but not limited to pheochromocytom and adrenocortical carcinoma; thyroid cancer such as but not limited to papillary or follicular thyroid cancer, medullary thyroid cancer and anaplastic thyroid cancer; pancreatic cancer such as but not limited to, insulinoma, gastrinoma, glucagonoma, vipoma, somatostatin-secreting tumor, and carcinoid or islet cell tumor; pituitary cancers such as but limited to Cushing's disease, prolactinsecreting tumor, acromegaly, and diabetes insipius; eye cancers such as but not limited to ocular melanoma such as iris melanoma, choroidal melanoma, and ciliary body melanoma, and retinoblastoma; vaginal cancers such as squamous cell carcinoma, adenocarcinoma, and melanoma; vulvar cancer such as squamous cell carcinoma, melanoma, adenocarcinoma, basal cell carcinoma, sarcoma, and Paget's disease; cervical cancers such as but not limited to, squamous cell carcinoma, and adenocarcinoma; uterine cancers such as but not limited to endometrial carcinoma and uterine sarcoma; ovarian cancers such as but not limited to, ovarian epithelial carcinoma, borderline tumor, germ cell tumor, and stromal tumor; cervical carcinoma; esophageal cancers such as but not limited to, squamous cancer, adenocarcinoma, adenoid cyctic carcinoma, mucoepidermoid carcinoma, adenosquamous carcinoma, sarcoma, melanoma, plasmacytoma, verrucous carcinoma, and oat cell (small cell) carcinoma; stomach cancers such as but not limited to, adenocarcinoma, fungating (polypoid), ulcerating, superficial spreading, diffusely spreading, malignant lymphoma, liposarcoma, fibrosarcoma, and carcinosarcoma; colon cancers; colorectal cancer, KRAS mutated colorectal cancer; colon carcinoma; rectal cancers; liver cancers such as but not limited to hepatocellular carcinoma and hepatoblastoma, gallbladder cancers such as adenocarcinoma; cholangiocarcinomas such as but not limited to papillary, nodular, and diffuse; lung cancers such as KRAS-mutated non-small cell lung cancer, non-small cell lung cancer, squamous cell carcinoma (epidermoid carcinoma), adenocarcinoma, large-cell carcinoma and small-cell lung cancer; lung carcinoma; testicular cancers such as but not limited to germinal tumor, seminoma, anaplastic, classic (typical), spermatocytic, nonseminoma, embryonal carcinoma, teratoma carcinoma, choriocarcinoma (yolk-sac tumor), prostate cancers such as but not limited to, androgen-independent prostate cancer, androgendependent prostate cancer, adenocarcinoma, leiomyosarcoma, and rhabdomyosarcoma; penal cancers; oral cancers such as but not limited to squamous cell carcinoma; basal cancers; salivary gland cancers such as but not limited to adenocarcinoma, mucoepidermoid carcinoma, and adenoidcystic carcinoma; pharynx cancers such as but not limited to squamous cell cancer, and verrucous; skin cancers such as but not limited to, basal cell carcinoma, squamous cell carcinoma and melanoma, superficial spreading melanoma, nodular melanoma, lentigo malignant melanoma, acrallentiginous melanoma; kidney cancers such as but not limited to renal cell cancer, adenocarcinoma, hypernephroma, fibrosarcoma, transitional cell cancer (renal pelvis and/or uterer); renal carcinoma; Wilms' tumor; and bladder cancers such as but not limited to transitional cell carcinoma, squamous cell cancer, adenocarcinoma, carcinosarcoma. In some embodiments, the cancer is myxosarcoma, osteogenic sarcoma, endotheliosarcoma, lymphangioendotheliosarcoma, mesothelioma, synovioma, hemangioblastoma, epithelial carcinoma, cystadenocarcinoma, bronchogenic carcinoma, sweat gland carcinoma, sebaceous gland carcinoma, papillary carcinoma, or papillary adenocarcinomas.

By treatment is meant at least an amelioration of one or more symptoms associated with the condition of the subject (e.g., cancer), where amelioration is used in a broad sense to refer to at least a reduction in the magnitude of a parameter, e.g., symptom, associated with the condition being treated. As such, treatment also includes situations where the condition (e.g., cancer), or at least one or more symptoms associated therewith, are completely inhibited, e.g., prevented from happening, or stopped, e.g., terminated, such that the subject no longer suffers from the condition, or at least the symptoms that characterize the condition.

According to some embodiments, the methods comprise administering the composition to the subject as part of a combination therapy. In one non-limiting example, the composition may be administered to a subject receiving immune checkpoint blockade (ICB) therapy. In some embodiments, the subject is receiving an ICB therapy involving administration of a programmed cell death-1 (PD-1 ) inhibitor, a cytotoxic T-lymphocyte-associated antigen 4 (CTLA-4) inhibitor, a lymphocyte activation gene-3 (LAG-3) inhibitor, a T-cell immunoglobulin domain and mucin domain 3 (TIM-3) inhibitor, an indoleamine (2,3)-dioxygenase (IDO) inhibitor, a T cell immunoreceptor with Ig and ITIM domains (TIGIT) inhibitor, a V-domain Ig suppressor of T cell activation (VISTA) inhibitor, or a B7-H3 inhibitor. In some instances, the subject is receiving an ICB therapy involving administration of a PD-1 inhibitor.

Also by way of example, the composition may be administered to a subject receiving an adoptive cell therapy. A “cell based therapy” or “cell therapy” refers to the transfer of autologous or allogeneic cellular material into a subject for medical purposes. Non-limiting examples of cell therapies include CAR T cell therapy, engineered T cell therapy (e.g., T cells that express a recombinant T cell receptor (TCR)), a therapy comprising administering T cells which do not express a recombinant receptor (e.g., tumor infiltrating lymphocytes (TILs)), CAR NK cell therapy, a macrophage therapy, and the like.

According to some embodiments, the methods further comprise, prior to the administering, assessing a lymph node of the subject (e.g., a tumor draining lymph node (TDLN)) for the presence of LN progenitor exhausted T cells. Such methods may comprise contacting the LN progenitor exhausted T cells, if present, with a detection reagent that specifically binds to a LN progenitor exhausted T cell marker. In some instances, the LN progenitor exhausted T cell marker is FCRL3, LAMP1 , PECAM1 , I FITM1 , CD2, or SIRPG.

Aspects of the present disclosure further include assessing a lymph node of a subject (e.g., a tumor draining lymph node (TDLN)) for the presence of LN progenitor exhausted T cells. Such methods may comprise contacting the LN progenitor exhausted T cells, if present, with a detection reagent that specifically binds to a LN progenitor exhausted T cell marker. In some instances, the LN progenitor exhausted T cell marker is FCRL3, LAMP1 , PECAM1 , IFITM1 , CD2, or SIRPG.

In certain embodiments, the assessing is performed in vitro. For example the methods may comprise assessing a biopsy of the lymph node for the presence of LN progenitor exhausted T cells. Any convenient and appropriate technique for surgical biopsy may be utilized for collection of lymph node cells to be employed in the methods described herein including but not limited to, e.g., excisional biopsy, incisional biopsy, wire localization biopsy, and the like. In some instances, a surgical biopsy may be obtained as a part of a surgical procedure which has a primary purpose other than obtaining the sample, e.g., including but not limited to tumor resection, mastectomy, and the like.

Various other biopsy techniques may be employed to obtain lymph node biopsy tissue. As a non-limiting example, a lymph node biopsy sample may be obtained by a needle biopsy. Any convenient and appropriate technique for needle biopsy may be utilized for collection of a sample including but not limited to, e.g., fine needle aspiration (FNA), core needle biopsy, stereotactic core biopsy, vacuum assisted biopsy, and the like.

In certain embodiments, the assessing is performed in vivo. For example the assessing may comprise in vivo imaging of the lymph node. The phrase “in vivo imaging” as used herein refers to methods of detecting the LN progenitor exhausted T cells in a whole, live mammal. Optically detectable agents, such as fluorescent agents (e.g., indocyanine green (ICG)), bioluminescent agents (e.g., luciferases, such as nanoluciferases), and radioactively labeled agents may be detected by in vivo imaging. In vivo imaging may be used provide 2-D as well as 3-D images of the lymph node or cells therein. Charge-coupled device cameras, photodiodes, avalanche photodiodes, photomultiplier tubes, CMOS, or 3D tomographers may be used to carry out in vivo imaging. For example, Burdette JE (2008) Journal of Mol. Endocrin. 40: 253-261 reviews the uses of computed tomography, magnetic resonance imaging, ultrasonography, positron emission tomography, single-photon emission computed tomography, etc., for in vivo imaging. Methods for using a detectable label for real-time imaging of luciferase expression in live animals can be readily adapted for use in the subject methods disclosed herein (e.g., Greer LF et al. (2002) Luminescence 17: 43-74). In vivo imaging of fluorescent proteins in live animals is described in, e.g., Hoffman (2002) Cell Death and Differentiation 9:786-789. In some embodiments, in vivo imaging may be performed by detecting a label that emits light at a wavelength designed to penetrate living tissue. Such labels include long wavelength emitting fluorescent dyes or proteins such as infrared and near infrared dyes or proteins including but not limited to dyes or proteins that emit in the range of about 600nm to about 800nm, about 650 nm to about 800nm, or about 700nm to about 800 nm. Alternatively, labels designed to emit light that penetrates living tissue may include non-fluorescent reagents including but not limited to red- shifted luciferases.

In vivo imaging can also involve computed tomography, magnetic resonance imaging, ultrasonography, positron emission tomography, single-photon emission computed tomography (SPECT) (See Burdette JE (2008) Journal of Mol. Endocrin., 40:253-261 for details). SPECT can also be used with an integrated x-ray CAT (CT) scanner (SPECT/CT) in the subject methods.

According to some embodiments, the in vivo imaging comprises photoacoustic imaging. Photoacoustic imaging (PAI) bridges the traditional depth limits of ballistic optical imaging and the resolution limits of diffuse optical imaging. Using the acoustic waves generated in response to the absorption of pulsed laser light, it provides noninvasive images of absorbed optical energy density at depths of several centimeters with a resolution of ~100 pm. This versatile and scalable imaging modality has proven useful for molecular imaging, which enables visualization of biological processes with systemically introduced contrast agents. Agents that find use in photoacoustic imaging include those described in Weber et al. (2016) Nature Methods 13:639- 650. In certain embodiments, employed as a photoacoustic imaging agent is indocyanine green (ICG), a tricarbocyanine dye that is safe for intravenous administration.

According to some embodiments, the bifunctional molecules of the present disclosure comprise an in vivo imaging agent (e.g., any of the in vivo imaging agents described elsewhere herein), and upon administration of a composition of the present disclosure, the in vivo imaging agent associated with (e.g., conjugated to) the bifunctional molecule is utilized for in vivo imaging of LN progenitor exhausted T cells in the lymph node (e.g., TDLN) of the subject.

For purposes of completeness, the present disclosure is further defined in the following numbered clauses. 1. A bifunctional molecule comprising: a first moiety that binds to a molecule on the surface of a lymph node (LN) progenitor exhausted T cell; and a second moiety that activates the LN progenitor exhausted T cell.

2. The bifunctional molecule of clause 1 , wherein the molecule on the surface of the LN progenitor exhausted T cell is Fc receptor-like protein 3 (FCRL3), lysosome-associated membrane glycoprotein 1 (LAMP1 ), platelet endothelial cell adhesion molecule (PECAM1), interferon-induced transmembrane protein 1 (IFITM1 ), T cell surface antigen CD2 (CD2), or signal-regulatory protein gamma (SIRPG).

3. The bifunctional molecule of clause 1 or clause 2, wherein the bifunctional molecule comprises a third moiety that binds to a molecule on the surface of the LN progenitor exhausted T cell, wherein the third moiety binds to a different cell surface molecule than the first moiety.

4. The bifunctional molecule of clause 3, wherein the first and third moieties bind to different cell surface molecules selected from those recited in clause 2.

5. The bifunctional molecule of any one of clauses 1 to 4, wherein the second moiety comprises a cytokine.

6. The bifunctional molecule of clause 5, wherein the cytokine is an interleukin (IL).

7. The bifunctional molecule of clause 6, wherein the interleukin is an interleukin in the common y chain (yc) family.

8. The bifunctional molecule of clause 7, wherein the interleukin is IL-2, IL-15 or IL-21 .

9. The bifunctional molecule of clause 8, wherein the interleukin is IL-2.

10. The bifunctional molecule of any one of clauses 5 to 9, wherein the cytokine is a wildtype cytokine or functional fragment thereof.

11 . The bifunctional molecule of any one of clauses 5 to 9, wherein the cytokine is an engineered cytokine or functional fragment thereof.

12. The bifunctional molecule of clause 1 or clause 2, wherein the second moiety comprises an agonist of a T cell co-stimulatory receptor.

13. The bifunctional molecule of clause 12, wherein the second moiety comprises an agonist of a T cell co-stimulatory receptor of the immunoglobulin super-family.

14. The bifunctional molecule of clause 13, wherein the agonist is a CD28 agonist.

15. The bifunctional molecule of clause 1 or clause 2, wherein the second moiety comprises an immune checkpoint inhibitor.

16. The bifunctional molecule of clause 15, wherein the immune checkpoint inhibitor is a programmed cell death-1 (PD-1) inhibitor, a cytotoxic T-lymphocyte-associated antigen 4 (CTLA-4) inhibitor, a programmed cell death ligand-1 (PD-L1 ) inhibitor, a lymphocyte activation gene-3 (LAG-3) inhibitor, a T-cell immunoglobulin domain and mucin domain 3 (TIM-3) inhibitor, an indoleamine (2,3)-dioxygenase (IDO) inhibitor, a T cell immunoreceptor with Ig and ITIM domains (TIGIT) inhibitor, a V-domain Ig suppressor of T cell activation (VISTA) inhibitor, or a B7-H3 inhibitor

17. The bifunctional molecule of clause 16, wherein the immune checkpoint inhibitor is a PD-1 inhibitor.

18. The bifunctional molecule of any one of clauses 1 to 17, wherein the first moiety is conjugated to the second moiety.

19. The bifunctional molecule of any one of clauses 1 to 18, wherein the first moiety and the second moiety are each polypeptides.

20. The bifunctional molecule of clause 19, wherein the first moiety and the second moiety are comprised within a fusion protein.

21 . The bifunctional molecule of clause 19, wherein the bifunctional molecule is a dimer comprising the first moiety dimerized with the second moiety.

22. The bifunctional molecule of clause 19 or 21 , wherein the first moiety is fused to a heterologous amino acid sequence, the second moiety is fused to a heterologous amino acid sequence, or both.

23. The bifunctional molecule of clause 22, wherein: the first moiety is fused to an antibody heavy chain comprising a CH1 domain, a hinge region, a CH2 domain, a CH3 domain, or any combination thereof; the second moiety is fused to an antibody heavy chain comprising a CH1 domain, a hinge region, a CH2 domain, a CH3 domain, or any combination thereof; or both.

24. The bifunctional molecule of clause 23, wherein the bifunctional molecule is a dimer comprising the first moiety dimerized with the second moiety via the antibody heavy chain.

25. The bifunctional molecule of clause 23 or 24, wherein the antibody heavy chain comprises a fragment crystallizable (Fc) region.

26. The bifunctional molecule of any one of clauses 1 to 25, wherein the first moiety comprises an antigen-binding domain of an antibody that specifically binds the molecule on the surface of the LN progenitor exhausted T cell.

27. The bifunctional molecule of any one of clauses 1 to 26, wherein the LN progenitor exhausted T cell is a tumor draining LN progenitor exhausted T cell.

28. A nucleic acid encoding the first moiety, the second moiety, or both, of the bifunctional molecule of any one of clauses 19 to 27.

29. The nucleic acid of clause 28, wherein the nucleic acid is comprised within an expression vector and operably linked to a promoter.

30. A host cell comprising the nucleic acid of clause 28 or 29. 31 . The host cell of clause 30, comprising the nucleic acid of clause 29, wherein the host cell expresses the first moiety, the second moiety, or both.

32. A composition comprising the bifunctional molecule of any one of clauses 1 to 26.

33. The composition of clause 32, wherein the composition is formulated for administration to a subject in need thereof.

34. A method of activating LN progenitor exhausted T cells in a subject in need thereof, the method comprising administering to the subject the composition of clause 33 in an amount effective to activate LN progenitor exhausted T cells in the subject.

35. The method according to clause 34, wherein the subject has cancer, and wherein the method stimulates a T cell response against the cancer.

36. The method according to clause 35, wherein the cancer comprises a solid tumor.

37. The method according to clause 36, wherein the solid tumor is a carcinoma, a sarcoma, lymphoma, blastoma, melanoma, germ cell tumor, or carcinosarcoma.

38. The method according to clause 35, wherein the cancer comprises a hematological malignancy.

39. The method according to any one of clauses 34 to 38, wherein the composition is administered to the subject as part of a combination therapy.

40. The method according to clause 39, wherein the subject is receiving an immune checkpoint blockade (ICB) therapy.

41 . The method according to clause 40, wherein the subject is receiving an ICB therapy comprising administration of a programmed cell death-1 (PD-1) inhibitor, a cytotoxic T- lymphocyte-associated antigen 4 (CTLA-4) inhibitor, a lymphocyte activation gene-3 (LAG-3) inhibitor, a T-cell immunoglobulin domain and mucin domain 3 (TIM-3) inhibitor, an indoleamine (2,3)-dioxygenase (IDO) inhibitor, a T cell immunoreceptor with Ig and ITIM domains (TIGIT) inhibitor, a V-domain Ig suppressor of T cell activation (VISTA) inhibitor, or a B7-H3 inhibitor.

42. The method according to clause 39, wherein the subject is receiving an adoptive cell therapy.

43. The method according to clause 42, wherein the adoptive cell therapy is a CAR T cell therapy, an engineered T cell therapy, a cell therapy comprising administering T cells which do not express a recombinant receptor, a tumor infiltrating lymphocyte (TIL) therapy, or a CAR NK cell therapy.

44. The method according to any one of clauses 34 to 43, further comprising, prior to the administering, assessing a lymph node of the subject for the presence of LN progenitor exhausted T cells. 45. The method according to clause 44, wherein the assessing comprises contacting the LN progenitor exhausted T cells, if present, with a detection reagent that specifically binds to a LN progenitor exhausted T cell marker.

46. The method according to clause 45, wherein the detection reagent specifically binds FCRL3, LAMP1 , PECAM1 , IFITM1 , CD2, or SIRPG.

47. A method comprising assessing a lymph node of a subject for the presence of LN progenitor exhausted T cells.

48. The method according to clause 47, wherein the assessing comprises contacting the LN progenitor exhausted T cells, if present, with a detection reagent that specifically binds to a LN progenitor exhausted T cell marker.

49. The method according to clause 48, wherein the detection reagent specifically binds FCRL3, LAMP1 , PECAM1 , IFITM1 , CD2, or SIRPG.

50. The method according to any one of clauses 44 to 49, wherein the assessing is performed in vitro.

51 . The method according to clause 50, wherein the method comprises assessing a biopsy of the lymph node for the presence of LN progenitor exhausted T cells.

52. The method according to any one of clauses 44 to 49, wherein the assessing is performed in vivo.

53. The method according to clause 52, wherein the assessing comprises in vivo imaging of the lymph node.

54. The method according to any one of clauses 44 to 53, wherein the assessing detects the presence of LN progenitor exhausted T cells in the lymph node of the subject.

55. The method according to any one of clauses 44 to 54, wherein the lymph node is a tumor draining lymph node.

The following examples are offered by way of illustration and not by way of limitation.

EXPERIMENTAL

Example 1 - Clinical and pathological characteristics of lung cancer resections after ICB

Three patients (MSK 1263, 1302, and 1344) were profiled with metastatic non-small cell lung cancer (NSCLC) who were treated with anti-PD-1 monotherapy at Memorial Sloan Kettering Cancer Center. All three patients had mixed responses, with most metastatic sites demonstrating response but at least one site showing persistence or progression during treatment (FIG. 1 A, FIG. 13A). In these cases, the resistant site of disease was surgically resected, and multiple regions from each lesion were collected for analyses. Following resection, two patients (MSK 1302 and 1344) remain alive over two years afterwards, while one patient (MSK 1263) quickly developed systemic disease recurrence and died. From the three patients, four tumor resections were obtained that underwent sectioning into eight 1 -2cm² sections per tumor and were subjected to pathological evaluation, regional bulk RNA sequencing, flow cytometry, and scRNA/TCR-seq of sorted CD3⁺ T cells (FIG. 1 B). Adjacent normal tissue and regional LNs (not involved by tumor on pathological analysis) were also obtained from MSK 1263 and 1302. Serial peripheral blood samples were collected up to 216, 452, and 1 ,013 days after the start of anti-PD-1 therapy in MSK 1263, 1302, and 1344, respectively.

Pathological analysis revealed substantial tumor heterogeneity among the various tissue regions. MSK 1263 and 1302 each had four regions containing varying amounts of cancer cells and four regions without evident viable cancer cells (Table 1 ); MSK 1344 had viable cancer cells in all regions but with varying involvement (Table 1 ). Bulk RNA sequencing of tumor regions demonstrated inter-regional heterogeneity, particularly in MSK 1263 and 1302 (FIG. 13B-13C, FIG. 14A-14B). The tumor regions with viable cancer cells showed enrichment for pathways that indicated an ongoing immune response, including ‘inflammatory response’ and ‘interferon gamma response’ (FIG. 14B, Table 1 ). In line with this observation, CD8⁺ immunohistochemistry (IHC) revealed that areas with viable tumor cells consistently showed an ‘immune-infiltrated’ pattern, as opposed to ‘immune-desert’ or ‘immune-excluded’ patterns (Table 1 ) [15]. Since intra- and inter-patient heterogeneity can be obscured by bulk analysis, it was hypothesized that applying scRNA/TCR-seq to CD3⁺ T cells (FIG. 15) in these heterogeneous regions could yield important insights into the systemic anti-tumor T cell response during ICB.

Table 1 : Pathological Analysis

Example 2 - Multi-regional single-cell RNA/TCR sequencing of T cell response to NSCLC

Recent work has reported scRNA/TCR-seq datasets across a number of different cancer types at relatively low per-patient depth. In order to obtain greater resolution into the intra-patient T cell response during ICB, scRNA/TCR-seq was performed on sorted CD3⁺ T cells from 32 adjacent normal, tumor, and LN regions (31 passing quality control; FIG. 16, FIG. 17A-17B, Table 1 ). T lymphocytes were clustered into seven CD8⁺ T, six CD4⁺ T, and one mucosal-associated invariant T (MAIT) cell clusters (FIG. 2A). These clusters were annotated by comparing differentially expressed cluster markers to previously published cluster definitions in scRNA-seq datasets [4] [16] [17] (FIG. 2B, FIG. 18, FIG. 19A-19B, FIG. 20, FIG. 21 A-21 B). Next scTCR-seq data was utilized to identify and link T cell clones to their cellular phenotypes. As expected, there was minimal TCR overlap among the three patients (FIG. 22A). By pairing TCR information with phenotypes, it was observed that CD8⁺ T cell clusters contained clones with substantially larger clone sizes relative to CD4⁺ T cell clusters (FIG 2C, FIG. 22B-22C). It was observed that the TCR composition within the three adrenal regions from MSK 1263 were more similar to each other than to the primary tumor or adjacent normal regions (FIG. 23A). It was found that the TCR repertoire was most diverse in the LN regions; in contrast, there was greater clonal enrichment in the tumor regions (FIG. 23B). Given these findings, it was proposed that integration of T cell clonotypic features with cell state and pathological features would yield informative insights into the clonal T cell response during ICB.

Example 3 - Exhausted CD8⁺ T cells, Treg, and TFH are enriched in tumor regions with viable cancer cells

Next it was evaluated whether specific T cell phenotypes were enriched in regions containing viable tumor cells. 20 regions from MSK 1263 and 1302 resection samples were focused on that included all representative region types. Among CD8⁺ T cell clusters, it was observed that LNs were enriched for CD8-NAIVE and CD8-TCF1 cells, while adjacent normal regions were enriched in CD8-EFF cells (FIG. 3A). The two exhausted CD8⁺ clusters (TEX) were enriched in the tumor regions relative to adjacent normal regions, and this effect was more pronounced in the tumor bed regions with viable cancer cells, which is consistent with prior reports in lung cancer [16] [18]. Among CD4⁺ T cell clusters, an enrichment of CD4-NAI VE, CD4- TREG1 , and CD4-TFH cells was observed in LNs, while CD4-EFF1 and CD4-EFF2 were enriched in adjacent normal regions (FIG. 3B). CD4-TREG1 , CD4-TREG2, and CD4-TFH cells were enriched in tumor regions relative to adjacent normal regions, and these cells were further enriched in the regions with viable tumor. Furthermore, by tracing T cell clones across regions, it was observed that clones enriched among regions with viable tumor were over-represented by CD4-TREG1 and CD4-TFH phenotypes (FIG. 24A-24B). In summary, the regional distribution of T cell subtypes is non-random, and TEX, T reg, and TFH are coordinately enriched in regions with viable tumor. Example 4 - Transcriptional signatures of progressive CD8⁺ T cell exhaustion across organs

Gene expression changes of CD8⁺ T cells were systematically characterized with respect to their anatomic locations to identify cell fate transitions during the T cell response to ICB. First diffusion maps were used to reconstruct developmental relationships between CD8⁺ T cell subsets using pseudotime [19] (Table 1 ). It was found that cells were ordered along the diffusion pseudotime (DPT) according to phenotype cluster, with CD8-NAIVE, CD8-EFF, and CD8- PROLIF-EXH cells each at one of the three ends of the diffusion map (FIG. 25A). Using DPT to identify potential differentiation branch points, it was observed that CD8-NAIVE T cells transitioned through the CD8-GZMK and CD8-TRM populations in branch 1 (B1 ) before diverging into an exhaustion branch (B2) or effector branch (B3) (FIG. 25B-25C). Cells along the exhaustion branch showed preferential localization to the viable tumor and adrenal regions, whereas cells along the effector branch were preferentially found in the adjacent normal tissue and tumor regions without viable tumor (FIG. 25D).

Next a CD8⁺ T cell exhaustion score [20] was calculated along the DPT, which revealed that T cells in viable tumor areas displayed the highest level of exhaustion (FIG. 3C, FIG. 4A, FIG. 26A-26B). Notably, this increase in exhaustion was maintained even when genes in the exhaustion signature were removed from the DPT calculation (FIG. 26C), suggesting that the DPT is capturing a broader biological program independently associated with CD8⁺ T cell exhaustion. To verify this finding, flow cytometry was performed on CD8⁺ T cells and it was found that cells from viable tumor regions expressed higher levels of the exhaustion markers, CD39 and PD-1 , compared to T cells from other regions (FIG. 4B-4D, FIG. 26D). Finally, clone-matched analysis of 851 CD8⁺ T cell clones (612 from MSK 1263; 239 from MSK 1302) present in both non-viable and viable tumor regions demonstrated that CD8⁺ T cells exhibited a marginally greater exhaustion score in viable tumor regions than in non-viable tumor regions (FIG. 4E), indicating that cells within a clone may take on distinct cell states depending on positioning within the tumor.

Example 5 - Treq and TFH have distinct clonal repertoires and acquire exhaustion-associated transcriptional programs

Since Treg and TFH also showed similar regional enrichment as CD8⁺ TEX, their regional gene expression patterns were further interrogated. First 52 Treg- and 51 TFH-predominant clonotypes (clone size >10 cells) were identified based on the majority phenotype among cells within each clonotype (FIG. 27A-27B). Notably, Treg and TFH-predominant clonotypes were largely non-overlapping (FIG. 28). Next, DPT analysis was performed on the Treg- and TFH- predominant clonotypes to examine transitional states across anatomical regions. For both T reg- and TFH-predominant clonotypes, DPT correlated with the anatomic region of the tumor, similar to CD8⁺ T cells (FIG. 29D-29E). Since DPT was associated with anatomic region, the genes that correlated with DC1 were examined to discover region-dependent transcriptional patterns of T reg and TFH. Despite minimal clonal overlap, Treg and TFH cells shared region-associated gene expression changes, including ENTPD1, PDCD1, TNFRSF18 (GITR), TNFRSF4 (OX-40) as genes that positively correlated with DC1 and CXCR4, KLF6, and IL7R as genes that negatively correlated with DC1 (FIG. 30A-30B). To validate these findings, flow cytometry was performed on regional samples and it was found that the expression of CD39, PD-1 , and GITR was greater in the tumor regions with viable tumor (relative to the normal and LN regions), while CXCR4 expression was lower (FIG. 31 A-31 B).

IL32 and CXCL13 were observed to be the top positively correlated gene for DC1 in Treg and TFH, respectively (FIG. 30A-30B). Since gene variation from bulk diffusion component analysis could be explained by intra-clonotypic regional heterogeneity or differential regional prevalence of clonotypes with distinct gene expression programs, the regional variation was evaluated in /L32 and CXCL13 in Treg and TFH, respectively, at the clonal level. To this end, 40 Treg-predominant clones and 42 TFH-predominant clones were examined that were present across at least two region types and observed that the expression of I L32 and CXCL13 varied regionally even when controlling for clonotype (FIG. 32A-32B).

It was observed that the genes associated with CD8⁺ T cell exhaustion were also associated with region-correlated DPT for Treg and TFH (e.g. LAG3, CTLA4, PDCD1, TIGIT, HAVCR2, ENTPD1). Although T cell exhaustion is best described for CD8⁺ lymphocytes, it has been proposed that an analogous pathway may exist in CD4⁺ T cells [21 -23]. To assess whether CD4⁺ T cells also demonstrate region-dependent progressive exhaustion, the exhaustion signature score for Treg and TFH clones were evaluated and it was observed that the exhaustion signature increased in both CD4⁺ populations along DPT (FIG. 5A-5B). Notably, there was no rise in DPT-associated exhaustion score for effector CD4⁺ clones (FIG. 33A-33B). Common genes associated with region-associated exhaustion in TEX, Treg, and TFH included the activation/exhaustion markers TNFRSF18, CD38, HAVCR2, TIGIT, and HLA-DRA, the chemokine receptor CXCR3, and the proliferation marker TUBB (FIG. 5C).

CXCL13 was highly expressed in thoracic regions containing viable tumors relative to regions of no viable tumors (FIG. 33C). Since CXCL13 from CD8⁺ T cells has been associated with the recruitment of TFH and B cells to tertiary lymphoid structures (TLS) [24], it was assessed whether TLSs are also enriched in regions of viable tumor. CD3⁺ CD20⁺ TLSs were quantified by IHC across the various thoracic and adrenal regions. While there was a correlation between TLS number and the amount of stroma in a particular region, there was no correlation with the level of viable tumor in a region (FIG. 33D-33I). This suggests that the enrichment of exhausted CD8⁺ T cells and TFH in regions of viable tumor may occur independently of TLSs, which has been previously described for human tumor-infiltrating lymphocytes [25] [26]. Thus, the combined clonal, phenotypic, and regional analysis reveals that Treg and TFH undergo gene expression changes that resemble CD8⁺ T cell exhaustion, suggesting that tumor antigen-specific signaling may also drive Treg and TFH differentiation. Example 6 - Intratumoral exhausted CD8⁺ T cells are clonally linked to LN progenitors

Recent studies have demonstrated that exhausted CD8⁺ T cells in the tumor are derived from LN TCF-1 ⁺ progenitor exhausted CD8⁺ T cells [1 1 ] [13] [27], Taken with the above findings, it was mext asked whether human exhausted CD8⁺ T cells in the tumor have a clonally linked TCF-1 ⁺ counterpart in the draining LN, and whether the cell states among clonally linked CD8⁺ T cells in the two compartments are distinct. To improve the resolution of analyzing exhausted cell states, cells were re-clustered from 115 clones (comprising 7,1 16 cells) exhibiting high intratumoral exhaustion scores (>0, exhaustion^hi) that were expanded (>2 cells) and present in both the LN and tumor regions of MSK 1263 and 1302, the two patients from which uninvolved draining LN T cells (20.4% of 564 expanded exhaustion¹¹' clones) were sequenced (FIG. 6A, FIG. 34A). This yielded 7 clusters, ranging from central memory-like and progenitor exhausted clusters to 4 exhausted populations expressing varying levels of inhibitory receptors. It was observed that cells from the LN regions were enriched for progenitor exhausted cluster 2 (FIG. 6B) (41.2% of LN cells; 1 .0% of tumor compartment), which expressed TCF7 and other memory T cell markers such as SELL (encoding CD62L, L-selectin) and !L7R, albeit at slightly lower levels than the central memory cluster (FIG. 6D). Cells in this progenitor exhausted cluster also expressed high levels of TOX and moderate levels of PDCD1, HAVCR2, and LAG3 in comparison to the more terminally exhausted clusters 4-7. Differential expression analysis of this LN-dominant cluster identified several additional less well-described genes that mark these clonally linked LN cells, including GZMA, IL32, KLF12, and KLRG1 (FIG. 6D).

By comparing the phenotype of cells within the LN and tumor compartment at the clonal level, it was found that 63 of 1 15 clones (54.8%; 44.4% of MSK 1263 clones; 92.0% of MSK 1302 clones) contained LN cells present in the progenitor exhausted cluster (FIG. 34B). Furthermore, a higher proportion of the LN compartment per clone was found in the progenitor exhausted state (average % of clone in progenitor exhausted cluster 2: LN 7.9%, tumor 1 .1 %) (FIG. 34C, FIG. 35). Conversely, a higher fraction of tumor CD8⁺ T cells was present in the terminally exhausted clusters as compared to their clonally-linked LN counterparts. Analogous analysis using the original total T cell population-based clustering yielded similar results (FIG. 35). To probe for finer transcriptional differences between LN and tumor cells, clone-matched differential expression analysis was performed of these exhausted CD8⁺ T cell clones that could be found in both the LN and tumor. A higher level of TCF7 in the LN (FIG. 6C) was observed, as well as increased expression of SELL, CD27, GZMK, and heat shock proteins (HSPA1A, HSPA 1B, HSPA6\ Conversely, CD8⁺ T cells within the tumor regions overexpressed DUSP4, ZFP36, CCL4, CXCR4, and exhaustion-related markers such as CXCL13 and ZNF683.

To validate these findings through alternative transcriptional signature-based approaches, the frequency of LN progenitor exhausted CD8⁺ T cell clones were enumerated by surveying clones that could be found in the exhausted CD8⁺ T clusters (CD8-EXH or CD8- PROLIF-EXH) in the tumor tissue of MSK 1263 and 1302 and identifying clone-matched cells in the regional LN. The percentage of the matched clones that had a TCF7 transcript >0 was then assessed (FIG. 34D). It was observed that 16.7% and 21 .4% of intratumoral exhausted CD8⁺ T cell clones with paired representation in the LN of MSK 1263 and 1302, respectively, were TCF- 1⁺ (5.7% and 7.3% of total exhausted CD8⁺ T cell clones, FIG. 34E). Since TCF-1 expression may also mark naive CD8⁺ T cells rather than progenitor exhausted populations, and since gene dropout might result in undercounting of TCF-1 ⁺ progenitors, this analysis was repeated with a progenitor signature that was derived from antigen-specific TCF-1 ⁺ Tim-3 PD-1 ⁺ CD8⁺ T cells from a murine melanoma model [28] and validated in human lung cancer [29], which included TCF7, SLAMF6, IL7R, and XCL1. Using a progenitor score cutoff of >0 (FIG. 34F), it was noted that 24.3% and 35.7% of exhausted CD8⁺ clones that could be found in the LN of MSK 1263 and 1302, respectively, could be found in a progenitor exhausted state in the LN (8.4% and 12.2% of total exhausted CD8⁺ T cell clones, FIG. 7C). The same analysis was also performed using exhaustion¹¹' CD8⁺ T cell clones, which yielded a similar proportion of clones found in a LN progenitor state (FIG. 7D, FIG. 36A). To assess how clonal CD8⁺ T cell states vary across regions, the extent to which the progenitor phenotype of exhausted CD8⁺ clones could be found in LNs, regions of no viable tumor, and regions of viable tumor was evaluated. It was found that the progenitor score of CD8⁺ T cell clones was reduced in cells from the tumor relative to the LN (FIG. 7A-7B).

To confirm these findings in independent cohorts, scRNA/TCR-seq data from five patients across two cohorts was examined with resection of primary tumor and regional LNs after receiving anti-PD-1 treatment for lung cancer [31] [32]. 27.6-45.1% of CD8⁺ T cell clones with high exhaustion scores that were present in both the LN and tumor were found in a progenitor state in the LN (12.8-21 .6% of total exhausted CD8⁺ clones, FIG. 8A-8D, FIG. 36B-36C). In these patients, there was also a reduction in progenitor score when comparing clone-matched CD8⁺ T cells between the LN and tumor (FIG. 8C-8D).

TCF-1 ⁺ PD-1 ⁺ precursor of TFH was characterized in a murine LCMV model [32]. To evaluate for a putative human LN progenitor of CD4⁺ T cell populations, TFH and Treg clones were examined that were present in both the LN and tumor compartments. This clone-matched analysis revealed greater expression of TCF7 and PDCD1 in the LN for TFH but not Treg clones (FIG. 37A-37B). This transcriptional difference between LN and tumor cells from TFH clones was observed even though cells from both compartments were designated as TFH based on clustering (FIG. 37C-37D). Altogether, these results point to the presence of TCF-1 ⁺ LN progenitor populations that are clonally linked to exhausted CD8⁺ T cells and TFH in the tumor microenvironment as a feature of T cell responses in human lung cancer.

Example 7 - Tumor-specific CD8⁺ T cells are enriched in viable tumor regions

To identify tumor-specific T cells, a tumor-reactivity signature score was first utilized based on published features of tumor-specific CD8⁺ T cells [34], This tumor-reactivity signature had high concordance with three other recently published signatures derived from single-cell sequencing of neoantigen- and tumor antigen-specific tumor-infiltrating lymphocytes [30] [35] [36], and had minimal signature overlap with viral-specific CD8⁺ T cells (FIG. 38A-38B). Consistent with prior reports that exhausted T cells comprise the tumor-specific population and that they are enriched in tumor regions [24] [30] [34-35] [37-39], it was found that the CD8⁺ TEX clusters displayed the highest tumor-reactivity score (FIG. 38D), and that CD8⁺ T cells in viable tumor regions had the highest tumor-reactivity score (FIG. 38D). Additionally, the top 40 most expanded CD8⁺ T cell clones with high tumor-reactivity scores (TR^hi) (FIG. 38B) were preferentially found in viable tumor regions, whereas the top 40 most expanded CD8⁺ T cell clones with low tumor-reactivity scores (TR¹⁰) were more enriched in the LN, adjacent normal lung, and tumor regions without viable cancer (FIG. 9A). Furthermore, TR^hi CD8⁺ T cell clones were often found in the CD8-TRM and TEX clusters, whereas TR¹⁰ CD8⁺ clones were enriched in effector CD8⁺ clusters (FIG. 9B), suggesting that T cell clones with tumor-specific features are preferentially present in an exhausted state within regions with viable cancer.

A similar analysis was performed with a 40-parameter tumor-reactivity score for CD4⁺ T cells [36] and it was observed that the Treg and TFH clusters exhibited the highest CD4⁺ tumorreactivity score (FIG. 39A). Concordant with CD8⁺ T cells, the top 40 most expanded TR^hi CD4⁺ T clones were more enriched in viable tumor regions relative to TR¹⁰ CD4⁺ clones (FIG. 39B, FIG. 40A). Moreover, the top 40 most expanded TR^hi CD4⁺ T clones were enriched in the Treg and TFH cell states (FIG. 40B). Overall, these results show that clonally expanded CD8⁺ and CD4⁺ T cells with tumor-specific features are enriched in regions of viable tumor.

Example 8 - Validation of tumor-specific T cell responses with empirically defined TCR clones

Next, to confirm these features of tumor-specific clones, neoantigens were computationally predicted from tumor whole exome sequencing of each patient using NetMHC, a neural network-based algorithm trained on a large dataset of peptide binding to human leukocyte antigens (HLAs) [40] [41 ] (FIG. 41 A, Methods). Predicted candidate neoantigens were then tested for empiric HLA binding capacity by flow cytometry (FIG. 41 B, Methods). In total, 6, 6, and 8 neoantigen peptide candidates were identified that could bind the cognate HLA for MSK 1263, 1302, and 1344, respectively. Three parallel methods were then employed to identify T cell clones that recognize these neoantigens: 1 ) bulk TCR sequencing of multimer⁺ CD8⁺ TILs, 2) mutation-associated neoantigen functional expansion of specific T cells (MANAFEST) assay [42], and 3) scRNA/TCR-seq of multimer⁺ CD8⁺ TILs.

First, multimers were generated against predicted neoantigen peptides (neopeptides) and bulk TCR sequencing of sorted multimeC CD8⁺ TILs was performed (FIG. 42A-42C). For MSK 1263, it was observed that ~4-38% of CD8⁺ T cells were specific for neopeptides (FIG. 42A), which indicated that the neoantigen prediction pipeline could identify bona fide neopeptides. Since the multimer⁺ population was negligible for MSK 1302 and 1344 (FIG. 42B-42C), subsequent analyses were restricted to MSK 1263.

Second, a MANAFEST assay was performed on the peripheral blood of MSK 1263 to identify neoantigen- and viral antigen-specific clones (FIG. 43. FIG. 44). Briefly, CD8⁺ T cells were cultured with no peptide, a pool of neoantigen peptides, or a pool of viral peptides. Enrichment of TCRs in each culture condition was then assessed by bulk TCR sequencing to determine reactivity to neoantigen or viral peptides. 9 TCRs were found to be reactive to neoantigens, while 12 TCRs were reactive to viral antigens.

Third, scRNA/TCR-seq was performed on sorted multimeC CD8⁺ T cells from tumor and LN regions from MSK 1263 (FIG. 45A). From this approach, 25,588 multimer⁺ CD8⁺ T cells were obtained, of which 22,440 (87.7%) had paired TCRap chains captured (FIG. 45B). To examine the concordance of multimer⁺ CD8⁺ phenotypes with those in the original tissue regional dataset (FIG. 2A), the multimer⁺ cells were projected onto the original data using label transfer and it was observed that most of the multimer⁺ cells mapped to the CD8-GZMK, CD8-TRM, and TEX clusters (FIG. 45C, FIG. 46A-46B).

The extent of overlap between the three independent methods was examined. 54 TCR clones were identified as tumor-specific by at least two methods, of which 53 were present in the original tissue scRNA/TCR-seq dataset (FIG. 9C, FIG. 46C). These clones are referred to as tumor-specific high-confidence clones, while all other clones identified as tumor-specific by at least one method are categorized as low-confidence. Next, the concordance was assessed between empirically defined tumor-specific T cells and those inferred based on the tumorreactivity signature score. High concordance was observed between the two definitions, as 11 ,818 of 12,935 (91 .3%) high-confidence tumor-specific T cells were also categorized as TR^hi (FIG. 46D). Notably, all viral antigen-specific T cells identified by the MANAFEST assay were categorized as TR^l0. Additionally, high-confidence tumor-specific T cells displayed the highest CD8⁺ T cell tumor-reactivity score relative to low-confidence tumor-specific, viral-specific, or unknown-specificity clones (FIG. 9D). In comparison to viral-specific clones, it was observed that tumor-specific clones were composed mainly of CD8-TRM and TEX cells (FIG. 9E, FIG. 10A), which was in line with the phenotypes of TR^hi clones. Conversely, viral-specific clones and clones with unknown specificity were dominated by CD8-EFF and CD8-GZMK clusters, which mirrored TR^l0 clones.

The regional presence of tumor-specific clones was examined next. In line with the observations on regional skewing of TR^hi clones, it was observed that tumor-specific clones were preferentially present in viable tumor regions (FIG. 10B). It was also evaluated whether tumorspecific clones could be found in a progenitor exhausted state in the LN. Indeed, among tumorspecific T cell clones that could be found in both LN and tumor, it was found that the LN CD8⁺ T cells had a higher clonal progenitor score relative to their intratumoral counterparts (FIG. 10C). These tumor-specific LN cells expressed TCF7, CCR7, IL7R and GZMK, while their clone- matched counterparts in the tumor expressed DUSP4, CCL4, CD52, CXCR6, HLA-DRB1, HLA- DPA1, and GZMB (FIG. 47A). Compared tumor-specific CD8⁺ T cells was also compared within regions with or without viable tumor and observed that tumor-specific CD8⁺ T cells in regions with viable tumor expressed higher levels of GZMB, CD27, CD38, GZMK, as well as markers associated with tumor-reactivity such as ENTPD1 and TNFRSF9 (FIG. 47B). Altogether, these findings demonstrate that empirically defined tumor-specific T cells display region-dependent transcriptional states and are clonally linked to LN progenitors.

Example 9 - Tumor-specific clones display pan-tumor, but not ubiquitous, regional distribution

The regional distribution of the tumor-specific T cell clones was investigated next. By assigning TCR clones into mutually exclusive regional categories (FIG. 48A-48C, Methods), it was observed that tumor-specific clones were most frequently observed in the pan- and oligo- regional tumor enriched distribution (FIG. 10D), suggesting that they move throughout the tumor and are not restricted to a single region. Similar distributions were observed for empirically defined tumor-specific clones, as nearly all expanded high-confidence tumor-specific clones were present in multiple or all tumor regions (FIG. 10E).

Comparison of tumor enriched and ubiquitous CD8⁺ clones revealed higher expression of DUSP4, CXCL13, TIGIT, TOX, ENTPD1, and CTLA4 in tumor enriched clones (FIG. 49A), supporting the notion that these clones likely recognize tumor antigen. Conversely, ubiquitous CD8⁺ clones differentially expressed cytotoxic genes such as NKG7, PRF1, GZMA, GZMB, and other markers of activation, such as CCL4. Similarly, tumor enriched CD4⁺ T cell clones also overexpressed many of the same genes as tumor enriched CD8⁺ clones, including DUSP4, CXCL13, TIGIT, TOX, ENTPD1, and CTLA4 (FIG. 49C). Altogether, characterization of regional clonotype distribution patterns revealed that tumor-reactive CD8⁺ and CD4⁺ T cell clones are preferentially found in a tumor enriched distribution.

Example 10 - T umor-specific T cell clones persist throughout the course of ICB

Whether the different T cell clusters in resected tumors were differentially represented in the peripheral blood was assessed next. To do this, bulk TCRp sequencing was performed on the peripheral blood of each patient at multiple time points after ICB, which included the time period before, during, and after resection. The latest blood collection ranged from 216 to 1 ,013 days after the start of ICB. For both CD8⁺ and CD4⁺ T cell clusters, substantial heterogeneity was noted in the representation of each cluster in the peripheral blood (FIG. 1 1 A). Among CD8⁺ T cells, the variability was striking: TCR clones associated with the CD8-TCF1 cluster were the least prevalent in the peripheral blood, whereas clones associated with the CD8-EFF cluster were the most prevalent, with an almost 100-fold difference between the two (FIG. 11 A). Among CD4⁺ T cells, TFH clones in the tissue were the least prevalent in the peripheral blood, while CD4⁺ effector clones were the most prevalent (FIG. 1 1 A). Furthermore, the tumor-reactivity score of both CD8⁺ and CD4⁺ T cells from the tissue was inversely proportional to their frequency in the peripheral blood at the phenotypic cluster level (FIG. 1 1 B).

Due to the logistical difficulty of tracking a multitude of tumor-specific CD8⁺ T cell clones, the importance of the persistence of endogenous tumor-specific T cell clones is not well established. The persistence of tumor-specific clones defined by transcriptional features in the peripheral blood was evaluated during ICB utilizing bulk TCR sequencing. 287, 137, and 438 CD8⁺ T cell clones were tracked that could be found at all time points analyzed from MSK 1263, 1302, and 1344, respectively, and it was observed that both TR^hi and TR^l0 CD8⁺ T cell clones persist over time in the peripheral blood (FIG. 12A). For MSK 1344, these clones were tracked for nearly three years after the start of ICB. Additionally, empirically validated tumor-specific CD8⁺ T cell clones were traced in the peripheral blood of MSK 1263 and were found to persist over time (FIG. 12B). 427, 189, and 957 CD4⁺ T cell clones were also longitudinally tracked that could be found at all time points analyzed from MSK 1263, 1302, and 1344, respectively (FIG. 50) and persistence of these clones in the periphery was observed.

Example 11 - Identification of LN progenitor exhausted T cell markers

Computational gene expression analysis was performed on LN progenitor exhausted T cells and identified genes that preferentially mark this population of cells.

FIG. 51 A to 51 B show heat maps from the computational gene expression analysis identifying genes that preferentially mark LN progenitor exhausted T cells. Such markers include FCRL3, LAMP1 , PECAM1 , IFITM1 , CD2, SIRPG, ZNF302, CMC1 , GZMM, PDLIM2, PDIA3, EOMES, IL32, RARRES3, CCL5 and CST7. The LN progenitor exhausted T cell markers shown in FIG. 51 B are those expressed on the cell surface (FCRL3, LAMP1 , PECAM1 , IFITM1 , CD2, and SIRPG), one or more of which may be utilized to target bifunctional molecules of the present disclosure to the LN progenitor exhausted T cells.

Summary

In this study, paired scRNA/TCR-seq was performed of 187,650 T cells from 31 tissue regions, including matched tumor, adjacent normal tissues, and LNs from 3 patients. This dataset yielded several insights into the tissue distribution, persistence, and differentiation trajectories of T cells in patients receiving ICB therapy. It was first demonstrated that regions with viable cancer cells were enriched for exhausted CD8⁺ T cells, Treg, and TFH. These findings confirm prior reports demonstrating the correlation of Treg and exhausted CD8⁺ T cells in tumor regions (relative to adjacent normal) in lung cancer [16] [18] [43] and further strengthen the findings by including analyses of tumor bed regions that were not involved with viable cancer cells. These findings are also consistent with a recent pan-cancer analysis which revealed that exhausted CD8⁺ T, TNFRSF9⁺ Treg, and TFH are tumor-enriched metaclusters, indicating that this enrichment may not be specific to NSCLC [5]. Beyond enrichment, diffusion pseudotime analyses was used to demonstrate that CD8⁺ T cells, Treg, and TFH undergo progressive exhaustion in proximity to viable cancer cells, suggesting that tumor antigen recognition may drive similar transcriptional programs in each cell type. Concordantly, intratumoral ICOS⁺ PD-1⁺ CD4⁺ TFH cells preferentially recognize tumor-derived neoantigens compared to other CD4⁺ subsets [44],

The depth of paired scRNA/TCR-seq data across tissue regions enabled the identification of a population of LN progenitors that was clonally linked to intratumoral exhausted CD8⁺ T cells. Clonally linked cells in the LN were marked by higher expression of TCF7, SELL, EOMES, and KLRG1 in comparison to their intratumor counterparts, supporting recent reports of a 'stem-like' progenitor exhausted subpopulation that expresses higher levels of SELL in murine models of LCMV chronic infection [45] [46]. Functional tumor-reactivity assays to empirically define tumorspecific T cells confirmed neoepitope specificity of clones containing progenitor exhausted T cells. Moreover, by comparing TFH clones between the LN and tumor tissues, clonally linked LN counterparts were also observed for TFH that expressed higher levels of PD-1 and TCF-1 , which is consistent with a recent report of PD-1 ⁺ TCF-1 ⁺ progenitors of TFH cells in LCMV infection [32], Hence, the data substantiates the concept that progenitors in the draining LNs fuel the intratumoral T cell response in human cancers, and that similar progenitor cell states may underlie CD8⁺ and CD4⁺ T cell responses to persistent tumor antigens. A unidirectional movement from the LN to the tumor microenvironment is inferred using the above results, though murine models demonstrate it is possible T cells may egress from the tumor and seed the LN [47].

The regional localization of tumor-specific T cells was assessed, compared to bystander or virus-specific T cells. It was demonstrated that CD8⁺ T cell clones with a ‘tumor enriched - pan’ pattern score the highest for tumor-reactivity relative to other regional distributions, which suggests that tumor-specific T cells frequently distribute broadly throughout the tumor bed. This was confirmed by tracking empirically defined tumor-specific CD8⁺ T cell clones and showing that they are infrequently found in only a single tumor region. Thus, the regional resolution of the dataset revealed the anatomic distribution of tumor-specific T cells and highlights their ability to permeate various tumor regions simultaneously, likely through a combination of preferential migration and local expansion.

Finally, it was reported that tumor-specific T cells that were defined by published transcriptional signatures and empiric validation persist in the blood after ICB therapy. The persistence of TR^hi CD8⁺ T cell clones is consistent with prior reports longitudinally tracking individual tumor-specific T cells in metastatic melanoma [35]. For MSK 1263, evidence was not found that the overt loss of tumor-specific T cell clones coincided with this patient’s rapid clinical progression of cancer; however, the frequency of TR^hi CD8⁺ T clones did decline from the initial time point. Altogether, this work serves as a unique comprehensive single cell resource with regional, clonal, and longitudinal resolution and provides insights into human T cell responses during ICB therapy. Methods

Data and Code Availability

All scRNA/TCR-seq and bulk RNA-seq data have been deposited to NIH GEO under accession number GSE185206. Bulk TCR-seq data have been deposited into the ImmuneACCESS database at Adaptive Biotechnologies.

Human biosoecimens

Resection materials and blood were obtained with informed consent from patients under protocol #06-107 approved by MSKCC. Regional clonal analyses comparing T cells from lymph node and primary tumor were performed for the MSK 1263 and 1302 lung resection samples. In these two samples, the mediastinal lymph nodes level 7 and 9, respectively, were evaluated as draining lymph nodes based on expected drainage patterns. These lymph nodes were not involved by tumor.

Pathologic review

Histologic review for extent of tumor viability was performed on H&E slides following the IASLC multidisciplinary recommendations for pathologic assessment of lung cancer resection specimens after neoadjuvant therapy [48]. Briefly, the percent of the tissue section that was covered by the tumor bed (as opposed to uninvolved lung parenchyma, uninvolved adrenal tissue, or uninvolved lymph node) was noted. Within the tumor bed region, sub-regions were defined as ‘viable tumor’, ‘necrosis’, or ‘stroma’. Thus, the sum of ‘viable tumor’, ‘necrosis', ‘stroma’, and ‘uninvolved tissue’ is 100%. 'Non-viable tumor' regions refer to the sum of all of the regions that are not 'viable tumor' regions (e.g. ‘necrosis’, ‘stroma’, and ‘non-viable’).

Determination of tumor-infiltrating lymphocyte pattern

CD8⁺ IHC stain was performed by at the Precision Pathology Center at MSKCC. Tissue slides were stained with anti-human CD8 antibody (Clone C8/144B, Dako, catalog # M7103, 1 :1000 dilution). IHC was performed on BOND RX platform (Leica Biosystems) using standard Protocol with the following steps: Heat epitope retrieval with ER2 for 30 minutes, incubation of primary antibody for 30 minutes, and BOND Polymer Refine Detection system (Leica, catalog # DS9800).

Within the regions with viable tumor (annotated on a separate slide as described above), the areas with viable tumor were analyzed for the dominant CD8⁺ tumor-infiltrating lymphocyte pattern: inflamed, excluded, or desert [15] (Table 1 ).

Tertiary lymphoid structure immunohistochemistry

Histology was performed using a GLP ready Standard Operating Procedure and a fully automated workflow. Samples were processed and embedded in paraffin followed by sectioning at 4pm. Immunohistochemistry (IHC) was performed on a Bond Rx autostainer (Leica Biosystems) with Citric Acid based retrieval buffer with a PH of 6.0 and Heat Induced Epitope Retrieval (HIER) for 20 minutes. Polyclonal CD20 antibody was used at a 1 :2000 dilution and developed with DAB using the Leica Bond Refine Detection kit. CD3 antibody (clone SP7) was used at a 1 :100 Dilution and developed with AEC Red using the Leica Bond Refine Red Detection Kit. This was followed by a hematoxylin counterstain and the stained slides were coverslipped with tape and imaged on a Leica Aperio AT2 line scanner.

The TLS were identified and quantified by HistoWiz Inc. using Halo software version 3.3.2541 (Indica Labs, USA) from Indica Labs and using the random forest classifier algorithm. The RF classifier was trained on a few representative slides by selecting a small number of ROIs as examples of TLS, tissue and glass. A minimum TLS size threshold of 60,000mm² was set to exclude any TLS below this size threshold.

Bulk RNA sequencing

Approximately 200-500 ng of FFPE RNA extracted from FFPE slides with a DV200 range between 3-99 or 65-100 ng of fresh frozen RNA (DV200 98-99) per sample were used for RNA library construction using the KAPA RNA Hyper library prep kit (Roche, Switzerland). The number of pre-capture PCR cycles was adjusted based on the quality and quantity of RNA extracted from the samples. Customized adapters with 3bp unique molecular indexes (UMI) (Integrated DNA Technologies, USA) and sample-specific dual-index primers (Integrated DNA Technologies, USA) were added to each library. The quantity of libraries was measured with Qubit (Thermo Fisher Scientific, USA) and the quality was assessed by TapeStation Genomic DNA Assay (Agilent Technologies, USA). Approximately 500 ng of each RNA library were pooled for hybridization capture with IDT Whole Exome Panel V1 (Integrated DNA Technologies, USA) using a customized capture protocol modified from NimbleGen SeqCap Target Enrichment system (Roche, Switzerland). The captured DNA libraries were then sequenced on an Illumina HiSeq4000 in paired ends (2X100bp) to a target 50 million read pairs per sample. The demultiplexed FASTQ files were aligned to the human genome reference hg19/GRCh37 using STAR (v2.7.3a) and deduplicated from the combination of UMI sequence and alignment coordinate using UMI-tools (v1 .0.1 ). Rsubread (v2.6.4) was used to extract the feature count matrix from alignments. edgeR (V3.34.1 ) was used for normalization, multidimensional scaling, differential expression, and gene ontology (GO) enrichment analyses. For GSEA, fgsea (v1 .18.0) was used with MSigDB (v7.4) hallmark pathway gene set. Cell type deconvolution was performed using CIBERSORTx (https://cibersortx.stanford.edu) with reference matrix derived from one lung tumor sample (LUNG_T31 ) within previously published single-cell data [49].

Fresh tumor preparation

Gross resection specimens were promptly sectioned within 1 hour of the resection and tumor pieces from the various regions were placed into human complete medium (RPMI + 10% human serum albumin + 1% penicillin with streptomycin + 0.1 % amphotericin + 1 X sodium pyruvate + 1X GlutaMAX + 1 X minimum essential amino acids) on ice. Human tissue from the various regions were minced with a razor blade and digested in GentleMACS enzyme mix in individual tubes per region for 30-60 minutes according to manufacturer’s recommendations. After centrifugation of a filtered single cell mix, the cell pellet was resuspended in human complete medium and underwent one round of ACK lysis. A subset of this cell pellet was cryopreserved for future use in Bambanker media (Wako Chemicals).

Flow cytometry and cell sorting

Cells were incubated with TruFCX (for human cells) to block nonspecific binding, and then stained (15 min, 4 °C) with appropriate dilutions of CD45-BV510 (clone 2D1 ), CD3-BV650 (clone UCHT1 ), CD8-PerCP-Cy5.5 (clone SK1 ), CD8-PerCP-Cy5.5 (clone BV510), CD4-Alexa700 (clone A161 A1 ), CD39-APC (clone A1 ), CD39-PE-Cy7 (clone A1 ), PD-1 -APC-Fire 750 (clone EH12.2H7), FOXP3-FITC (clone PCH101 ), CXCR5-PE (clone QA18A64), GITR-APC (clone I OS- 17), and CXCR4-PerCP-Cy5.5 (clone 12G5). TruFCX and all antibodies were purchased from BioLegend. DAPI CD45⁺ CD3⁺ cells analyzed by a BD LSRII or were sorted by FACS Aria. Debris, doublets and dead cells were excluded on the basis of forward and side scatter and 4', 6- diamidino-2-phenylindole (DAPI, 1 mg/ml). Flow cytometry data was analyzed with FlowJo V10.8.1 (TreeStar). Representative gating strategy is depicted in FIG. 15.

Single-cell RNA sequencing

Sorted T cells were stained with Trypan blue and Countess II Automated Cell Counter (ThermoFisher) was used to assess both cell number and viability. Following QC, the single cell suspension was loaded onto Chromium Chip A (10X Genomics PN 230027) and GEM generation, cDNA synthesis, cDNA amplification, and library preparation of 2,700-1 1 ,000 cells proceeded using the Chromium Single Cell 5' Reagent Kit (10X Genomics PN 1000006) according to the manufacturer’s protocol. cDNA amplification included 13-14 cycles and 1 1 -50ng of the material was used to prepare sequencing libraries with 14-16 cycles of PCR. Indexed libraries were pooled equimolar and sequenced on a NovaSeq 6000 or NextSeq 500 in a PE26/92, PE28/91 or PE100 run using the NovaSeq 6000 SP, S1 , or S2 Reagent Kit (100, 200, or 500 cycles) or TG NextSeq 500/550 High Output Kit v2.5 (150 cycles) (Illumina). An average of 179 million reads was generated per sample.

Single-cell TCR seguencing

An aliquot of cDNA generated using the methods described above was used to enrich for V(D)J regions using the Chromium Single Cell V(D)J Enrichment Kit Human T Cell (10X Genomics PN 1000005) according to the manufacturer’s protocol with 10 cycles of PCR during enrichment and 9 cycles during library preparation. Indexed libraries were pooled equimolar and sequenced on a NovaSeq 6000 in a PE150 run using the NovaSeq 6000 SP, S1 , or S4 Reagent Kit (300 cycles) (Illumina). An average of 129 million paired reads was generated per sample. Pre-processing of scRNA/TCR-seg libraries

Reads from 10x scRNA expression libraries were aligned to human genome assembly GRCh38 (hg19) and quantified using cellranger count (10x Genomics, v3.1.0). The filtered feature-barcode matrices containing only cellular barcodes were used for further analysis. Single cell gene expression matrices were imported into R (v3.6.1 ) and analyzed using Seurat (v3.1.4) [50]. Cells with >450 genes captured and <15,000 UMIs were included in downstream analyses. Additionally, cells with >15% mitochondrial RNA reads were excluded from subsequent analyses. Sequencing data from 31 of 32 regional samples passed initial quality control (FIG. 16, Methods). After removing the single region that did not pass QC, 63.5-89.9% of the individual cells per region (FIG. 17A, Table 1 ) passed QC filtering, retaining 162,062 high-quality T cells for downstream analyses.

Single cell TCR reads were aligned to human genome assembly GRCh38 (hg19) and assembled into reconstructed TCR consensus sequences using cellranger vdj (10x Genomics, v3.1 .0). Only productive TCRa and TCRp sequences were considered for further analysis. At least one chain of the TCR was captured in 141 ,110 cells (87% of the cells that passed QC, 76.0- 92.7% per region, Fig 17B), and paired TCRap chains were captured in 103,181 cells in total. Cells with multiple TCRp chains captured (pp, app, aapp) were excluded from further analysis. Only cells with conventional paired TCR chain combinations ap or aap were included in downstream TCR clonal analyses. Cells sharing the same CDR3ap nucleotide sequences were defined as belonging to the same TCR clone. scRNA-seg data integration and clustering scRNA-seq libraries from each region were Iog10-normalized individually and integrated with Seurat by identifying anchors between datasets using reciprocal PCA with 30 dimensions. TCR genes were excluded from the selection of integration anchors to prevent TCR chain driven biases. Dimensionality reduction of the integrated matrix was performed using Uniform Manifold Approximation and Projection (UMAP) with the first 30 principal components. Phenotypic clusters were defined by constructing a k-nearest neighbors graph and identifying groups of cells using the Louvain algorithm with resolution of 0.6.

Naive CD8⁺ T cells highly expressed SELL, CCR7, and IL7R. There were two effector CD8⁺ clusters: CD8-EFF highly expressed GNLY, NKG7, PRF1, and KLRG1, whereas CD8- GZMK highly expressed GZMK, CCL4, NKG7, GZMA, GZMH, PRF1, LAG3, and PDCD1. A CD8⁺ cluster that highly expressed GMZK, LAG3, NKG7, ENTPD1, HAVCR2, CD38, CD274, and TCF7 was annotated as CD8-TCF1 . A CD8⁺ tissue resident memory (TRM) cluster highly expressed ITGAE, CD69, PDCD1, ZNF683, CXCR3, GZMA, and GZMB. Finally, two exhausted CD8⁺ T cells clusters distinguishable by their proliferative status were identified: CD8-EXH highly expressed TOX, GZMB, LAG3, NKG7, ENTPD1, HAVCR2, CXCL13, TNFRSF9, and IFNG, while CD8-PROLIF-EXH expressed high levels of these genes in addition to GZMA, CD38, and proliferation genes (TUBB, TUBA1, MKI67, AURKB). Similar to naive CD8⁺ T cells, naive CD4⁺ T cells expressed CCR7, SELL, IL7R, and LEF1. Among the two CD4⁺ T effector clusters, CD4- EFF1 highly expressed IL7R and CD69, while CD4-EFF2 highly expressed GZMA, PRDM1, and CXCR6. Two clusters expressing FOXP3 were annotated as Treg clusters; CD4-TREG1 and TREG2 were distinguished by lower and higher expression of FOXP3, ENTPD1, TNFRSF4, TNFRSF9, TNFRSF18, CD274, ICOS, CTLA4, and TIGIT, respectively. CD4-TFH highly expressed TOX, ICOS, PDCD1, BCL6, CXCR5, and CXCL13.

Categorization of CD4/CD8⁺ TCR clones

Clones with >75% cells within CD4⁺ clusters were categorized as CD4⁺ clones (subcategorized into ‘CD4⁺ only’ clones with 100% CD4⁺ cells, or ‘CD4⁺ majority’ clones with 75- 99% CD4⁺ cells). CD8⁺ clones were similarly defined. Clones that were present in the MAIT cluster but none of the CD4⁺ or CD8⁺ clusters were categorized as MAIT clones. Clones that did not meet any of the above criteria were categorized as ‘mixed’ clones.

TCR clone regional pattern categorization

TCR clones were categorized into mutually exclusive regional patterns for each patient by assessing the combination of region types (i.e. LN, adjacent normal, or tumor regions) in which cells with shared CDR3ap nucleotide sequences could be found. ‘Ubiquitous’ TCR clones were defined as those found in all LN, adjacent normal, and tumor regions sampled. ‘LN enriched’ and ‘normal enriched’ TCR clones were those found only in LN or adjacent normal regions, respectively. ‘Tumor enriched’ clones were found only in tumor regions, but not in LN nor adjacent normal regions, and were further sub-classified as ‘single region’ (found in only one tumor region), ‘oligo-regional’ (found in >1 but not all tumor regions), or ‘pan-regional’ (found in tumor regions).

TCR clone enrichment in viable/non-viable tumor

TCR clones were categorized as enriched in viable tumor regions or no viable tumor regions based on CDR3ap nucleotide sequence. For each clone, the number of cells found in viable tumor or no viable tumor regions was calculated and constructed into a 2x2 contingency table to test for enrichment by Fisher’s exact test. Clones with p-value < 0.05 were considered enriched in viable or no viable tumor regions.

Gene signature scoring

To characterize cells according to previously reported gene signatures of tumor-reactivity, CD8⁺ T cell exhaustion, progenitor exhausted T cells, tumor- and viral-specificity, and expanded clones, gene scores were calculated per cell using the AddModuleScore function from Seurat.

Diffusion pseudotime analysis

To investigate expression dynamics within CD8⁺ subsets, cells belonging to the CD8⁺ phenotype clusters were taken for diffusion component analysis. Diffusion maps were constructed with 40 principal components using destiny (v3.0.1 ) [19]. Diffusion pseudotime ordering was calculated with the DPT() function using a window width of 0.1 and specifying the top eigenvector-ranked cell as the root cell. Analogous diffusion component analyses were performed with Treg- and TFH-predominant clones expanded >10 cells to probe for gene expression dynamics within CD4⁺ T cell subsets across anatomical regions. Top genes that correlated with the primary diffusion component were analyzed further at the clonal level.

Pathway enrichment analysis

Gene ontology enrichment analysis was performed with enrichGO() from clusterProfiler (v3.14.3) [51] using a p-value cut-off of 0.01 and a Benjamini-Hochberg adjusted q-value of 0.05. Molecular Function, Cellular Component, and Biological Process gene sets were tested for overrepresentation.

Clone-matched analysis

To compare cell state differences between CD8⁺ T cells in regions with no viable tumor vs. viable tumor, clone-matched analysis of CD8⁺ clones was performed with at least one cell present in both no viable tumor and viable tumor regions. Clonal scores were calculated per region by averaging the scores of cells within each clone in each region.

To characterize T cell state transitions of CD8⁺ clones between LN and tumor regions, CD8⁺ clones in an exhausted state were defined in two ways: (1 ) clones with tumor cells belonging to the CD8-EXH or CD8-PROLIF-EXH phenotype cluster, or (2) clones displaying an average exhaustion score >0 among tumor cells (exhaustion*¹'). Clonal progenitor scores were calculated per region by averaging the scores of cells within each clone in each region.

External scRNA/TCR-seg dataset analysis

Single cell data from Caushi et al. [30] were obtained from GEO (GSE176021 ) and analyzed as described above. Only samples from patients with matched LN and tumor samples (MD01 -004, MD01 -005, MD043-01 1 ) were analyzed. Data from a second scRNA/TCR-seq dataset³¹ (DNA Data Bank of Japan: JGAS000480), which included data from two lung cancer patients with matched LN and tumor samples (LC01 and LC03), were similarly analyzed.

Bulk TCR sequencing gDNA was extracted from the peripheral blood utilizing the AllPrep DNA/RNA Kit (Qiagen) and was sent to Adaptive Biotechnologies for bulk TCR|3 sequencing. Data was processed using the ImmunoSEQ Analyzer (Adaptive Biotechnologies, v3.0).

Multimer assays

Neoantigens were predicted from whole exome sequencing data and bulk RNA sequencing data from the three patients. For neoantigen candidates that were expressed in the bulk RNA sequencing data (counts per million >0), the neopeptides were sorted by the difference between wild-type peptide binding rank and mutant peptide binding rank as predicted by NetMHC v4.0 [40] [52]. For HLA alleles for which multimers were commercially available (e.g. HLA- A*01 :01 , A*02:01 , A*03:01 , C*07:01 ), the neoantigen candidates with the top 6 ‘Rank Diff EL’ scores were selected for empiric testing. In total, the top 10-12 neoantigen candidates per patient were custom synthesized by Genscript to >95% purity. Each candidate neopeptide was tested for stabilization of cognate MHC monomers (Immudex, Denmark) using a flow cytometry-based anti-human b2M-PE assay, per manufacturer’s recommendations. A mean fluorescence intensity >1000 was utilized as the cutoff for monomer stabilization. The 6-8 neopeptide candidates per patient that empirically stabilized the cognate MHC monomers were utilized for multimer assays and MANAFEST assay (below).

The initial multimer assays to identify tumor-specific TCRs were performed using U-Load monomers and PE-dextramers and APC-dextramers (Immudex), according to manufacturer’s instructions. Prepared dextramers specific for each patient were pooled prior to staining of thawed single cell suspensions from tissue regions. PE⁺ and APC⁺ CD8⁺ T cells were sorted on an Aria Sorter and the pellet was frozen. DNA was extracted from the frozen pellet and submitted for bulk TCR|3 sequencing.

As a parallel method to identify tumor-specific TCRs for MSK 1263, pooled multimers were prepared from UV-cleavable Flex-T monomers (Biolegend). The monomers were linked in a peptide-specific manner by separated reactions with barcoded Streptavidin-PE reagents (Totalseq-C0951 -C0955 and C0961 , Biolegend). For MSK 1263, previously cryopreserved single cell suspensions were thawed and stained with TotalSeq C anti-human hashtag reagent with a unique barcode (Biolegend) to subsequently permit deconvolution of region from which the cell originated. After washing, these cells were then stained with pooled barcoded multimers, followed by staining for CD45, CD3, and CD8. The cells were then sorted on PE⁺ CD8⁺ T cells and submitted for single cell RNA/TCR/multimer sequencing. Subsequent analyses revealed that the barcoding by peptide could not deconvolve peptide specificity with the conditions utilized in this experiment. Although the specific neoantigen specificity could not be determined, the multimer⁺ cells, as assessed by presence of a barcode, could be inferred to be tumor-specific.

MANAFEST Assay

This assay was carried out as previously described [30] [42], Neoantigen peptide pools for MSK 1263 were prepared by mixing 1 mg/ml of the six neopeptides confirmed to stabilize the cognate HLA (as described above). The viral antigen peptide pool utilized was 1 mg/ml of the CEF (CMV, EBV, Flu) pool (jpt Peptide Technologies). In brief, on day 0, T cells were isolated from patient-specific thawed previously cryopreserved PBMC by EasySep Human T cell Isolation negative selection kit (STEMCELL Technologies). The T cell-negative fraction was irradiated in a Cesium source gamma irradiator at 30 Gy. 2x10⁵ cells from this fraction were then co-cultured with an equal number of T cells in a 96 well plate in AIM V media with 50 pg/ml gentamicin with a neoantigen peptide pool, viral peptide pool, or no peptides. On day 3, half the medium was replaced with fresh medium containing cytokines for a final concentration of 50 IU ml-1 IL-2 (Peprotech), 25 ng ml-1 IL-7 (Peprotech) and 25 ng/ml IL-15 (Peprotech). On day 7, half the medium was replaced with fresh culture medium containing cytokines for a final concentration of 100 lU/ml IL-2 and 25 ng/ml IL-7 and IL-15. On day 10, cells were harvested, and the CD8⁺ fraction was isolated using a CD8⁺ EasySep negative enrichment kit (STEMCELL Technologies). Adaptive files were uploaded onto the publicly available MANAFEST analysis web app (www.stat-apps.onc.jhmi.edu/FEST) to bioinformatically identify tumor-specific T cell clonotypes.

Processing of multimer sorted single-cell sequencing data

Single-cell RNA, TOR, and antibody capture libraries from multimer sorted tissue CD8⁺ T cells were processed using cellranger multi (10x Genomics, v7.0.0). The dataset was filtered to only include cells with <10% mitochondrial content, number of genes captured within 2 standard deviations of the mean, <1 ,000 multimer tag counts. Additionally, only cells with TCRp, TCRap, or TCRaap were kept for further analysis. The 25,588 cells that passed these filter criteria were subsequently processed as describe above. To assess the correspondence of phenotypes between the multimer tissue T cell dataset and the total CD3⁺ tissue T cell dataset, multimer sorted cells (query) were mapped onto the total CD3⁺ tissue (reference) dataset by identifying anchors between the two datasets using Seurat’s FindTransferAnchors() function with 30 dimensions and projected onto the reference UMAP structure using MapQuery() [50].

Quantification and Statistical Analysis

Statistical analysis of bulk and single-cell sequencing data was performed in R (v3.6.1 ). Statistical analysis of flow cytometry data was performed in GraphPad Prism (v9.0).

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Accordingly, the preceding merely illustrates the principles of the present disclosure. It will be appreciated that those skilled in the art will be able to devise various arrangements which, although not explicitly described or shown herein, embody the principles of the invention and are included within its spirit and scope. Furthermore, all examples and conditional language recited herein are principally intended to aid the reader in understanding the principles of the invention and the concepts contributed by the inventors to furthering the art, and are to be construed as being without limitation to such specifically recited examples and conditions. Moreover, all statements herein reciting principles, aspects, and embodiments of the invention as well as specific examples thereof, are intended to encompass both structural and functional equivalents thereof. Additionally, it is intended that such equivalents include both currently known equivalents and equivalents developed in the future, i.e., any elements developed that perform the same function, regardless of structure. The scope of the present invention, therefore, is not intended to be limited to the exemplary embodiments shown and described herein.

Claims

WHAT IS CLAIMED IS:

1. A bifunctional molecule comprising: a first moiety that binds to a molecule on the surface of a lymph node (LN) progenitor exhausted T cell; and a second moiety that activates the LN progenitor exhausted T cell.

2. The bifunctional molecule of claim 1 , wherein the molecule on the surface of the LN progenitor exhausted T cell is Fc receptor-like protein 3 (FCRL3), lysosome-associated membrane glycoprotein 1 (LAMP1 ), platelet endothelial cell adhesion molecule (PECAM1), interferon-induced transmembrane protein 1 (IFITM1 ), T cell surface antigen CD2 (CD2), or signal-regulatory protein gamma (SIRPG).

3. The bifunctional molecule of claim 1 or claim 2, wherein the bifunctional molecule comprises a third moiety that binds to a molecule on the surface of the LN progenitor exhausted T cell, wherein the third moiety binds to a different cell surface molecule than the first moiety.

4. The bifunctional molecule of claim 3, wherein the first and third moieties bind to different cell surface molecules selected from those recited in claim 2.

5. The bifunctional molecule of any one of claims 1 to 4, wherein the second moiety comprises a cytokine.

6. The bifunctional molecule of claim 5, wherein the cytokine is an interleukin (IL).

7. The bifunctional molecule of claim 6, wherein the interleukin is an interleukin in the common y chain (yc) family.

8. The bifunctional molecule of claim 7, wherein the interleukin is IL-2, IL-15 or IL-21 .

9. The bifunctional molecule of claim 8, wherein the interleukin is IL-2.

10. The bifunctional molecule of any one of claims 5 to 9, wherein the cytokine is a wild-type cytokine or functional fragment thereof.

11 . The bifunctional molecule of any one of claims 5 to 9, wherein the cytokine is an engineered cytokine or functional fragment thereof.

12. The bifunctional molecule of claim 1 or claim 2, wherein the second moiety comprises an agonist of a T cell co-stimulatory receptor.

13. The bifunctional molecule of claim 12, wherein the second moiety comprises an agonist of a T cell co-stimulatory receptor of the immunoglobulin super-family.

14. The bifunctional molecule of claim 13, wherein the agonist is a CD28 agonist.

15. The bifunctional molecule of claim 1 or claim 2, wherein the second moiety comprises an immune checkpoint inhibitor.

16. The bifunctional molecule of claim 15, wherein the immune checkpoint inhibitor is a programmed cell death-1 (PD-1) inhibitor, a cytotoxic T-lymphocyte-associated antigen 4 (CTLA-4) inhibitor, a programmed cell death ligand-1 (PD-L1) inhibitor, a lymphocyte activation gene-3 (LAG-3) inhibitor, a T-cell immunoglobulin domain and mucin domain 3 (TIM-3) inhibitor, an indoleamine (2,3)-dioxygenase (IDO) inhibitor, a T cell immunoreceptor with Ig and ITIM domains (TIGIT) inhibitor, a V-domain Ig suppressor of T cell activation (VISTA) inhibitor, or a B7-H3 inhibitor

17. The bifunctional molecule of claim 16, wherein the immune checkpoint inhibitor is a PD- 1 inhibitor.

18. The bifunctional molecule of any one of claims 1 to 17, wherein the first moiety is conjugated to the second moiety.

19. The bifunctional molecule of any one of claims 1 to 18, wherein the first moiety and the second moiety are each polypeptides.

20. The bifunctional molecule of claim 19, wherein the first moiety and the second moiety are comprised within a fusion protein.

21 . The bifunctional molecule of claim 19, wherein the bifunctional molecule is a dimer comprising the first moiety dimerized with the second moiety.

22. The bifunctional molecule of claim 19 or 21 , wherein the first moiety is fused to a heterologous amino acid sequence, the second moiety is fused to a heterologous amino acid sequence, or both.

23. The bifunctional molecule of claim 22, wherein: the first moiety is fused to an antibody heavy chain comprising a CH1 domain, a hinge region, a CH2 domain, a CH3 domain, or any combination thereof; the second moiety is fused to an antibody heavy chain comprising a CH1 domain, a hinge region, a CH2 domain, a CH3 domain, or any combination thereof; or both.

24. The bifunctional molecule of claim 23, wherein the bifunctional molecule is a dimer comprising the first moiety dimerized with the second moiety via the antibody heavy chain.

25. The bifunctional molecule of claim 23 or 24, wherein the antibody heavy chain comprises a fragment crystallizable (Fc) region.

26. The bifunctional molecule of any one of claims 1 to 25, wherein the first moiety comprises an antigen-binding domain of an antibody that specifically binds the molecule on the surface of the LN progenitor exhausted T cell.

27. The bifunctional molecule of any one of claims 1 to 26, wherein the LN progenitor exhausted T cell is a tumor draining LN progenitor exhausted T cell.

28. A nucleic acid encoding the first moiety, the second moiety, or both, of the bifunctional molecule of any one of claims 19 to 27.

29. The nucleic acid of claim 28, wherein the nucleic acid is comprised within an expression vector and operably linked to a promoter.

30. A host cell comprising the nucleic acid of claim 28 or 29.

31 . The host cell of claim 30, comprising the nucleic acid of claim 29, wherein the host cell expresses the first moiety, the second moiety, or both.

32. A composition comprising the bifunctional molecule of any one of claims 1 to 26.

33. The composition of claim 32, wherein the composition is formulated for administration to a subject in need thereof.

34. A method of activating LN progenitor exhausted T cells in a subject in need thereof, the method comprising administering to the subject the composition of claim 33 in an amount effective to activate LN progenitor exhausted T cells in the subject.

35. The method according to claim 34, wherein the subject has cancer, and wherein the method stimulates a T cell response against the cancer.

36. The method according to claim 35, wherein the cancer comprises a solid tumor.

37. The method according to claim 36, wherein the solid tumor is a carcinoma, a sarcoma, lymphoma, blastoma, melanoma, germ cell tumor, or carcinosarcoma.

38. The method according to claim 35, wherein the cancer comprises a hematological malignancy.

39. The method according to any one of claims 34 to 38, wherein the composition is administered to the subject as part of a combination therapy.

40. The method according to claim 39, wherein the subject is receiving an immune checkpoint blockade (ICB) therapy.

41 . The method according to claim 40, wherein the subject is receiving an ICB therapy comprising administration of a programmed cell death-1 (PD-1) inhibitor, a cytotoxic T- lymphocyte-associated antigen 4 (CTLA-4) inhibitor, a lymphocyte activation gene-3 (LAG-3) inhibitor, a T-cell immunoglobulin domain and mucin domain 3 (TIM-3) inhibitor, an indoleamine (2,3)-dioxygenase (IDO) inhibitor, a T cell immunoreceptor with Ig and ITIM domains (TIGIT) inhibitor, a V-domain Ig suppressor of T cell activation (VISTA) inhibitor, or a B7-H3 inhibitor.

42. The method according to claim 39, wherein the subject is receiving an adoptive cell therapy.

43. The method according to claim 42, wherein the adoptive cell therapy is a CAR T cell therapy, an engineered T cell therapy, a cell therapy comprising administering T cells which do not express a recombinant receptor, a tumor infiltrating lymphocyte (TIL) therapy, or a CAR NK cell therapy.

44. The method according to any one of claims 34 to 43, further comprising, prior to the administering, assessing a lymph node of the subject for the presence of LN progenitor exhausted T cells.

45. The method according to claim 44, wherein the assessing comprises contacting the LN progenitor exhausted T cells, if present, with a detection reagent that specifically binds to a LN progenitor exhausted T cell marker.

46. The method according to claim 45, wherein the detection reagent specifically binds FCRL3, LAMP1 , PECAM1 , IFITM1 , CD2, or SIRPG.

47. A method comprising assessing a lymph node of a subject for the presence of LN progenitor exhausted T cells.

48. The method according to claim 47, wherein the assessing comprises contacting the LN progenitor exhausted T cells, if present, with a detection reagent that specifically binds to a LN progenitor exhausted T cell marker.

49. The method according to claim 48, wherein the detection reagent specifically binds FCRL3, LAMP1 , PECAM1 , IFITM1 , CD2, or SIRPG.

50. The method according to any one of claims 44 to 49, wherein the assessing is performed in vitro.

51 . The method according to claim 50, wherein the method comprises assessing a biopsy of the lymph node for the presence of LN progenitor exhausted T cells.

52. The method according to any one of claims 44 to 49, wherein the assessing is performed in vivo.

53. The method according to claim 52, wherein the assessing comprises in vivo imaging of the lymph node.

54. The method according to any one of claims 44 to 53, wherein the assessing detects the presence of LN progenitor exhausted T cells in the lymph node of the subject.

55. The method according to any one of claims 44 to 54, wherein the lymph node is a tumor draining lymph node.