EP4731790A1

EP4731790A1 - Method for discriminating tex and tpex and uses thereof

Info

Publication number: EP4731790A1
Application number: EP24737404.4A
Authority: EP
Inventors: Sebastian Amigorena; Pierre-Emmanuel BONTE; Christel GOUDOT
Original assignee: Centre National de la Recherche Scientifique CNRS; Institut National de la Sante et de la Recherche Medicale INSERM; Institut Curie; Universite Paris Cite
Current assignee: Centre National de la Recherche Scientifique CNRS; Institut National de la Sante et de la Recherche Medicale INSERM; Institut Curie; Universite Paris Cite
Priority date: 2023-06-26
Filing date: 2024-06-26
Publication date: 2026-04-29
Also published as: WO2025003189A1

Abstract

The present application provides means for associating Transposal Elements (TEs) expression in T cells with a stage of T cell differentiation. The present application also relates to methods for differentiating progenitor exhausted T cells (Tpex) from Terminally exhausted T cells (Tex). The present invention is also related to methods and means for identifying Tpex from Tex by analysing Transcription Factor Fli1 expression. The present invention is also related to the use of TEs expression and/or Fli1 expression in T cells, in particular in Tpex and Tex, as biomarker(s) of the likeliness to positively respond to immunotherapy, in particular in a patient having a cancer or a chronic viral infection, in particular in response to an anti-PD-1 or anti-PD-L1 immunotherapy.

Description

Method for discriminating Tex and Tpex and uses thereof

Technical field of the invention

The invention is in the field of immunotherapy. The present application provides means for associating Transposal Elements (TEs) expression in T cells with a stage of T cell differentiation. The present application also relates to methods for differentiating progenitor exhausted T cells (Tpex) from Terminally exhausted T cells (Tex). The present invention is also related to methods and means for identifying Tpex from Tex by analysing Transcription Factor Fli1 expression. The present invention is also related to the use of TEs expression and/or Fli1 expression in T cells, in particular in Tpex and Tex, as biomarker(s) of the likeliness to positively respond to immunotherapy, in particular in a patient having a cancer or a chronic viral infection, in particular in response to an anti-PD-1 or anti-PD-L1 immunotherapy.

Background of the invention

Immunotherapy is a type of treatment that helps the immune system of a patient to fight a disease, like a cancer or an infection caused by a pathogen. As part of its normal function, the immune system detects and destroys abnormal cells like tumor cells or pathogens or cells infected by a pathogen and most likely prevents or curbs the growth of many diseases, including cancers and infections. T cells are for instance found in and around tumors. These T cells, called tumorinfiltrating lymphocytes or TILs, are a response of the immune system to the disease.

Even though the immune system can prevent or slow diseases, for example by reducing cancer growth or shrinking tumors, cancer and infected cells have ways to avoid destruction by the immune system. For example, cancer cells may have genetic alterations that make them less visible to the immune system, express proteins on their surface that turn off immune cells or modify the microenvironment around the tumor to interfere with how the immune system responds to the cancer cells. To overcome these issues, treatments by immunotherapy are developed to help the immune system of a patient having a disease.

Several immunotherapies are currently used or under development, including immune checkpoint inhibitors or activators, that block or activate immune checkpoints allowing immune cells to respond more effectively, T cell transfer therapy, which is a treatment that enhances the natural ability of T cells to fight disease (e.g. immune cells are taken from the patient, and those that are most active against the disease are selected or changed in vitro to better recognize or destroy tumor cells or pathogens). T cell therapy includes adoptive cell therapy, T cell modification, for example into CAR-T cells, T cell Receptors (TCRs) therapy.

T cell, also called T lymphocyte or white blood cell, is an essential part of the immune system. T cells are one of two primary types of lymphocytes — B cells being the second type — that determine the specificity of immune response to antigens. The T cells are involved in the acquired or antigen-specific immune response given that they are able to recognize and respond specifically to antigenic epitope. Tumor-infiltrating lymphocytes (TIL) are white blood cells that have left the bloodstream and migrated towards a tumor. They include T cells and B cells and are part of the larger category of ‘tumor-infiltrating immune cells’ which consist of both mononuclear and polymorphonuclear immune cells, (i.e., T cells, B cells, natural killer cells, macrophages, neutrophils, dendritic cells, mast cells, eosinophils, basophils, etc.) in variable proportions. Their abundance varies with tumor type and stage and in some cases relates to disease prognosis. TILs are implicated in killing tumor cells. The presence of lymphocytes in tumors is often associated with better clinical outcomes. Nonetheless, efficacy of immunotherapies is hindered by the nature of cells, in particular T cells, that are present in response to the disease. There is thus a need for more effectively determine in which cases immunotherapies are likely to efficiently cure or treat a patient having a disease from cases where immunotherapy is likely to have low effect due to a lack of immune cells able to positively respond to the immunotherapy.

In chronic infections and cancer, T cells are exposed to chronic stimulation, resulting in loss of function (or exhaustion) and impairment of effective protection. Exhausted T cells are heterogenous, and include early progenitors and terminally exhausted cells, referred to as Tpex and Tex (sometimes Ttex in the literature), respectively. Thus, there is a need for better characterizing T cells present within the environment of a disease.

After recognizing antigens, lymphocytes differentiate into short-lived effectors or long-lived memory cells that can be re-activated upon a second challenge with the same antigen. Short- lived effectors most often eliminate the threat, and long- lived memory cells ensure that the organism remains fully protected from a second encounter with the same agent. Chronically stimulated T cells, in the suppressive microenvironment of chronic inflammation, follow a distinct differentiation path, often referred to as “exhaustion”. Exhaustion has recently being analyzed transcriptomically in both chronic viral infections and tumors, including at the single cell level (1 , 2). T cell exhaustion represents a continuum of differentiation states ranging from progenitor/early dysfunctional (Tpex) to terminally dysfunctional T cells (Tex or Ttex) (3). Terminally exhausted T cells express high levels of multiple negative regulators of T cell activation (including TIM3, CD39) and fail to produce IFNg upon stimulation. Blocking the inhibitory PD- 1/PD-L1 axis (using anti-PD-1 or anti-PD-L1 antibodies) overcomes differentiation to terminal exhaustion and promotes tumor rejection (4). In mice, in both tumors and chronic infections, early and late exhausted T cells can be distinguished by specific transcription factors (including Tcf7, Eomes and Tbx21 ) and cell surface markers (such as SLAMF6 and TIM3) (5-7). No phenotypic markers, however, definitively distinguish effectors from Tex, or memory T cells from Tpex across experimental systems. Recent studies indicate a critical role for epigenetic programing in the establishment and development of exhaustion (8). Regulators of chromatin state, such as DNMT3A (a DNA methyltransferase) or SUV39H1 (a histone methyltransferase) play critical roles in the establishment and maintenance of terminally exhausted T cell phenotypes (9, 10).

Because the epigenetic control of chromatin dynamics affects the transcription of both coding and non-coding genomes, we decided to analyze how progression through the exhaustion path affects expression of non-coding transcripts, focusing on transposable elements (TEs). TEs are extremely abundant in mammalian genomes, representing up to 50% of the genomic DNA. TEs originate from ancient viruses or other DNA sequences that inserted in genomes during evolution. Autonomous and non-autonomous replication and re-insertion, followed by mutational degeneration, caused accumulation of millions of individual elements in mammalian genomes. TEs are classified into classes, families and sub-families. The broadest division is between DNA transposons and retrotransposons, the latter being by far the most numerous. Retrotransposons include LTR (long terminal repeat), such as endogenous retroviruses (ERVs), and non-LTR, which split into long interspersed (LINEs) and short interspersed (SINEs) nuclear elements (1 1 -13). TE families sub-divide into subfamilies, originating from one precursor copy. Members of the same sub-family share sequence similarity and consensus motifs from the founding ancestors but diverged over evolution by accumulation of mutations (which can be used to estimate the timelines of the initial insertion events). The huge diversity of TEs opens unique possibilities to define cellular identities and functional states, in health and disease, with possible applications in the field of biomarkers.

As a mechanism of protection against genome instability caused by transposition, transcription of TEs, the first step of the replication cycle, is actively repressed through multiple epigenetic mechanisms, including DNA and histone methylation. In most cells, TE loci are heavily decorated with different repressive histone marks, including H3K9me3, H4K20me3, and H3K64me3 (14-17). Histone modifiers, such as Setdbl , Suv39h1 , G9a and Lsd1 (18-21 ), and other factors, such as Trim28 (22), are involved in TE repression. TE transcription, however, can be de-repressed in adult tissues. Stem cells and certain tumors, for example, express detectable levels of TE mRNAs (23) and TE over-expression can stimulate innate sensors and induce IFN-I production (2, 24, 25). TE expression is also modulated in response to activation in myeloid cells and in B lymphocytes (26-28). TEs also carry regulatory cues and contribute to cis- regulatory regions by providing transcription factor motifs and architectural sequences (25, 29- 31 ). In mice, a few hundred TEs are still competent for transposition, most of them part of an ERV1 family. “VL30” are a family of 372 retroviral-like (5'LTR-gag-pol- env-3'LTR) retrotransposons that are individually mapped and characterized (32). 86 of these retroelements are full-length and the others are truncated or solo LTRs. VL30 have several biological roles, including in the control of gene expression (as IncRNAs) and in oncogenesis and cell proliferation (33). In some cases, TEs are translated into functional proteins, such as env of active ERV proteins, that can have immunomodulatory properties, including suppression of lymphocyte functions (15). Whether transposition and translation of VL30 also happen within lymphocytes is unknown, as are the functions of the vast majority of TEs. To date, very little is known about TE expression in T lymphocyte subpopulations.

Summary of the invention

The inventors analysed the expression of transposable elements (TEs) in subpopulations of CD8+ tumor infiltrating T lymphocytes (TILs). They show that members of the virus-like murine VL30 TE family (mostly intact, evolutionary young ERV1 s), are strongly repressed in terminally exhausted CD8+ T cells in both tumor and viral models of exhaustion. The transcription of these TEs is thus repressed in Tex, in both tumors and chronic infection. Silencing of TEs affects certain TE subfamilies more drastically, including a very well described family of autonomous, intergenic and young TEs, VL30 (some of which still bear intact ORFs). The transcription of VL30 is active in all T cell populations in tumors and chronically infected mice but is repressed in Tex.

They also shown that expression of these VL30 (which are mainly intergenic and transcribed independently of their closest gene neighbours) in Tpex is driven by Fli1 , a transcription factor (TF) involved in progression from Tpex to Tex. VL30 transcription in Tpex is driven by Fli1 , a transcription factor that controls progression to exhaustion (40). Thus, exhausted T cell reprograming by Immune Checkpoint Blocker (ICB) de-represses VL30 expression, indicating that TE expression is part of the re-programing induced by ICB treatmentsFurther, the expression of TEs is tightly regulated in TILs during establishment of exhaustion and re-programming by Immune Checkpoint Blocker therapy. Analyses of TE expression and/or Fli1 expression in T cells allow the identification of Tex and Tpex in the T cell population, allowing to ease determination of the effectiveness of immunotherapies depending on these T cell populations. These biomarkers belonging to the distal TEs family and TF Fli 1 may be used to prognose diseases, or to select therapies that are likely to strengthen the immune response against a disease.

In one aspect, the invention provides a method for determining if a human subject having a disease is likely to benefit from an immunotherapy, for example by administration of an immune checkpoint blocker or activator. To this end, the method comprises the steps of:

- Providing a biological sample previously obtained from the human subject wherein the sample is from the microenvironment of the disease and comprises T cells, in particular the sample is a biopsy of the microenvironment of the disease,

- Measuring the expression of distal Transposable Elements (TEs), in particular distal TEs belonging to the LTR retrotransposons (LTRs) and Long interspersed nuclear elements (LINEs) subclasses, and/or Transcription Factor Fli1 , in T cells,

- Identifying T cells as early progenitor T cells (Tpex) when distal TEs belonging to the MER31 -int subfamily are overexpressed as compared to the genomic MER31 -int subfamily expression,

- Identifying T cells as terminally exhausted T cells (Tex) when distal TEs belonging to the L1 MDb subfamily are overexpressed as compared to the genomic L1 MDb subfamily expression,

- Classifying the human subject as being likely to benefit from a treatment with the immune checkpoint blocker or immune checkpoint activator when Tex cells are not the most common type of T cells in the biological sample and/or when Tpex are more common compared to Tex in the biological sample.

In another aspect, it is provided to prognose a disease in a human subject, in particular a cancer or a viral infectious disease or a chronic viral infection, by characterizing the population of Tpex and Tex present in the environment of the disease. To this end, the method comprises the steps of:

- Providing a biological sample previously obtained from the human subject wherein the sample is from the microenvironment of the disease and comprises T cells, in particular the sample is a biopsy of the microenvironment of the disease, - Determining the presence in the biological sample of exhausted T cells (Tex) or progenitor exhausted T cells (Tpex) by measuring in T cells the expression of at least one distal Transposable Element selected from the group consisting of MER31 -int subfamily and

L1 MDb subfamily, and/or a Transcription Factor Fli1 ,

- Identifying the presence of Tex in the biological sample as T cells over-expressing the distal TEs belonging to the L1 MDb subfamily and/or under-expressing the Transcription Factor Fli1 ,

- Identifying the presence of Tpex in the biological sample as T cells over-expressing the TEs belonging to the MER31 -int subfamily and/or the Transcription Factor Fli1 ,

In another aspect, the invention provides a method to predict the response of a human subject to a treatment by immunotherapy, in particular by administrating an immune checkpoint blocker or immune checkpoint activator. Such a method comprises the steps of:

- Determining the presence in the biological sample of exhausted T cells (Tex) or progenitor exhausted T cells (Tpex) by measuring in T cells the expression of at least one distal Transposable Element selected from the group consisting of MER31 -int subfamily and

L1 MDb subfamily, and/or a Transcription Factor Fli1 ,

- Classifying the human subject as being likely to benefit from the immunotherapy when Tpex cells are more common compared to Tex, and/or classifying the human subject as being unlikely to benefit from the immunotherapy when Tex cells are more common compared to Tpex in the biological sample.

In another aspect, it is also provided a Transcription factor FLi1 or a nucleic acid molecule encoding a Transcription factor FLi1 or a compound enhancing the expression of Fli1 in T cells, for use in the treatment of a cancer or an infectious disease.

In another aspect, it is also provided a method for treating a human subject having a disease, in particular a cancer or an infectious disease. Such a method comprises the steps of:

L1 MDb subfamily, and/or a Transcription Factor Fli1 ,

- Administering to the human subject a checkpoint blocker or checkpoint activator, in particular an anti-PD-1 or an anti-PD-L1 antagonist compound, when Tex cells are not the most common type of T cells in the biological sample and/or when Tpex are more common compared to Tex in the biological sample, or

- Administering to the human subject a Transcription factor FLi1 or a nucleic acid molecule encoding a Transcription factor FLi1 or a compound enhancing the expression of Fli1 in T cells when Tex are more common compared to Tpex in the biological sample and/or when Tex are the most common T cells in the biological sample.

The present invention also provides the use of a Transcription factor FLi1 or a nucleic acid molecule encoding a Transcription factor FLi1 or a compound enhancing the expression of Fli1 for stimulating immune response, in particular for preventing or reducing Tpex differentiation into Tex.

The present invention also provides a method for reprogramming T cells, in particular Tpex. Such a method comprises the steps of:

- Providing a biological sample comprising T cells previously obtained from a subject, in particular wherein the sample is issued from the microenvironment of a disease,

- Extracting T cells from the biological sample,

- Treating T cells with a Transcription factor FLi1 or a nucleic acid molecule encoding a Transcription factor FLi1 or a compound enhancing the expression of Fli1 in T cells.

These aspects, preferred embodiments and further advantages of the invention will be readily apparent from the following detailed description of the invention and in the claims of the present disclosure.

Short description of the drawings

These and further aspects of the invention will be explained in greater detail by way of examples and with reference to the accompanying drawings in which:

Figure 1 : Single cell TE subfamily expression distinguishes TIL subpopulations.

(A) Uniform Manifold Projections (UMAP) based on genes and TE subfamilies expression visualizing predicted cell type clusters from Carmona et al. (left) and cell clusters identified by the graph-based algorithm (resolution 0.3) (right). Each dot represents an individual cell and the colors indicate cell types or cell clusters.

(B) Barplot displaying the percentages of cells for each cluster and colored by predicted cell types. (C) Volcano plots displaying differential TE expression between terminally exhausted CD8+ T cells (Tex) and other T cells populations. Up- regulated and down-regulated genes (black) and TE subfamilies (pink) are indicated. (D) Venn diagrams representing the overlap of down-regulated (top) and up-regulated (bottom) TE subfamilies in Tex. (E) Heatmap showing the scaled and centered genes and TE subfamilies expression. Each column represents a cell, and each row represents a gene or TE subfamily found differential expressed in (C). (F) Violin plots displaying the expression of four up- regulated (top) and six down-regulated (bottom) TE subfamilies shown as differential expressed in terminally exhausted CD8+ T cells. Pairwise Wilcoxon test results comparing Tex and other T cells populations are represented above violin plots (ns: p > 0.05; *: p <= 0.05; **: p <= 0.01 ;

***: p <= 0.001 ; p <= 0.0001 ).

(

Figure 2: Recent autonomous TE are repressed in terminally exhausted CD8+ T cells.

(A) Volcano plots displaying differential genes, TE subfamilies and individual TE expression between progenitor (Slamf6+) and terminally (Tim3+) exhausted CD8+ T cells. Black dashes represent adjusted P-value <0.05 on y-axis. Up- regulated and down-regulated features (genes or TEs) relative to the population indicated first in the title are colored in green (Slamf6+ TILs) and red (Tim3+ TILs), respectively. Numbers of differentially expressed genes or TEs are indicated on each plot. (B) Pie charts displaying TE class distribution of up-regulated TEs in Slam6+ (left) or Tim3+ (right) TILs. (C) Plot showing TE enrichment analysis against genome distribution between Slamf6+ and Tim3+ TILs using individual TE signatures. On each axis is represented the ‘adjusted p-value’ as -Iog1 Devalue)) using the Benjamini-Hochberg procedure. (D) Hierarchical heatmap showing the scaled and centered TE expression of individual TEs from MMVL30- int and RLTR6-int subfamilies. Each column represents a sample and each row represents an individual TE. TE expression above and below the mean is depicted in blue and red, respectively. (E-F) Plots showing the median age (E) and the mean length (F) of individual TEs for all annotated subfamilies in the genome (black) and for all up-regulated TE subfamilies in Slamf6+ (green) or Tim3+ (red) TILs. TE subfamilies are classified per TE class. Pairwise Wilcoxon tests were applied for statistical analysis. Figure 3: VL30 TEs are overexpressed in progenitor exhausted CD8+ T cells.

(A) Plot modelling the enrichment of VL30 subfamilies in Slamf6+ TE signature (green) and Tim3+ TE signature red. Disk size is proportional to the fold enrichment and x position is determined by the adjusted p-value. (B) Plot showing the percentage of VL30 TE subfamilies per signature (Slamf6 in green and Tim3 in red). (C) Plot displaying the median expression in Slamf6 and Tim3 samples for each individual TE in MMVL30-int, RLTR6-int and RLTR6-Mm subfamilies. (D) Slamf6+ or Tim3+ populations were sorted from TILs from B16OVA tumors and RNA was extracted. Custom subfamily-specific or locus-specific Taqman probes were used to measure TE expression by quantitative real-time PCR. Each dot indicates an individual mouse. Mann-Whitney test (* - p<0.05, ** - p < 0.01 , *** - p < 0.001 ). (E) Piecharts displaying the percentage of VL30 sequences categorized as full-length, truncated copies and solo LTRs in genomic and Slamf6+ and Tim3+ TE signatures. (F) Piecharts showing the distribution of LTR or VL30 elements expressed by genomic location (left) and by distance to closest gene (right). (G) Plots summarizing the association between TEs and genes in Slamf6+ TE signature (top) and Tim3+ TE signature (bottom). TE+Gene- indicates that the TE is differentially expressed and not its closest gene. TE+Gene+ indicated that both the TE and its closest gene are differentially expressed.

Figure 4: VL30 TEs are also repressed in Tex in chronic LCMV infection.

(A) Hierarchical heatmap representing the scaled and centered expression of VL30 TEs in progenitor (Slamf6+) and terminally (Tim3+) CD8+ T cells isolated from mice with chronic LCMV infection (36). (B) Volcano plots displaying results from differential analyses of TE expression between Slam6+ and Tim3+ in LCMV samples at subfamily (left) and individual TE (right) levels. (C) Barplots showing the mean TE expression of tumor TE signatures in LCMV samples. (D-E) GSEA was performed to assess the specific enrichment of the gene and TE tumor signatures (Slamf6 in green and Tim3 in red) (D) and VL30 TE subfamilies (E) in LCMV samples. Figure 5: Flil controls VL30 expression in Tpex.

(A) Plot summarizing the motif enrichment analysis as percentage of true positive in genomic, genes and TE signatures. Highly significant motifs are highlighted in red. (B) Principal component analysis (PCA) based on 5000 most variable genes (left) and VL30 TE signature (right). Principal components PC1 and PC2 are projected and the variance of each is indicated along axes. (C) Barplots displaying the normalized expression of the tumor Slamf6+ (left), the tumor Tim3+ (middle) and the VL30 expressed (right) TE signatures in WT or FH1 KO T cells. (D) Plot displaying the mean expression in WT and Fli1 KO samples for each individual TE in MMVL30-int, RLTR6-int and RLTR6-Mm subfamilies. (E) Plot showing the GSEA curves and statistics (NES and FDR) for VL30 expressed TE signature and for VL30 TE subfamilies between WT and Fli1 KO.

Figure 6: Anti-PD-1 re-programing modulates TE expression.

(A) Principal component analysis (PCA) based on 5000 most variable TEs (left) and VL30 TE signature (right), showing the clustering of transcriptional profiles of progenitor and terminally exhausted CD8+ T cells from mice treated or not with anti-PD-1 . Principal components PC1 and PC2 are projected and the variance of each is indicated along axes. (B) Volcano plots displaying differential TE expression between treated and not treated CD8+ T cells in Slamf6+ (Tpex) and Tim3+ (Tex) samples. (C) Plots showing expression of FPKM (Fragments Per Kilobase of transcript per Million) normalized counts in progenitor and terminally exhausted CD8+ T cells from mice not treated and treated with anti-PD-1. (D) Heatmaps and unsupervised hierarchical clustering of VL30 TEs profiling in treated and untreated samples. The scaled and centered values are represented. The progenitor Slamf6+(left) and terminally Tim3+ (right) exhausted TIL samples are colored in green and red, respectively. Pink and blue colors indicate experimental treatment (control or aPD-1 ). (E) GSEA curves and statistics (NES and FDR) for VL30 TE signature (left) and VL30 TE subfamilies (right) between control and anti-PD-1 -treated samples. (F) Plots displaying the age of genomic or differentially expressed individual TEs from MMVL30-int and RLTR6-int subfamilies. Each dot represents an individual TE. Figure 7: TE expression in human TILs.

(A) Schematic representation of protocol used for isolation of Tex and Tpex from human NSCLC tumors and bulk polyA+ RNA sequencing. (B) Principal component analysis (PCA) based on expressed distal TEs showing the clustering of transcriptional profiles of progenitor (Tpex) and terminally exhausted (Tex) CD8+ T cells from human samples. (C) MA plot showing Iog2 fold-change distributions (on y-axis) between TEx and TPex human samples based on the mean expression (on x-axis). Colored dots represent more than two-fold up- regulated TEs in TEx (red) or TPex(green). The numbers are indicated on top and bottom. (D) Pie charts showing TE distribution per class at genomic level (top) and within distal TE specifically expressed in TPex (bottom left) and TEx (bottom right) samples with more than two-fold. (E) Plots showing the age of up- regulated TE subfamilies in TPex (green) or TEx+ (red) TILs. TE subfamilies are classified per TE class. Unpaired Wilcoxon test was applied for statistical analysis. (F) Heatmaps based on unsupervised hierarchical clustering of differentially expressed TEs between TEx and TPex. The scaled and centered values are represented. The progenitor Tpex (left) and terminally Tex (right) exhausted TILs samples are colored in green and red, respectively. (G) UMAP visualizing cell clusters identified in CD8+ T cells in lung cancer scRNAseq from (43). (H) Feature plots and violin plots displaying expression of TE signature score of TPex (top) and TEx signatures (bottom) using only samples 3’-oriented single cell chemistries. (I) VolcanoPlot of differentially expressed TE subfamilies between Tex (red) and Tpex (green). (J) VolcanoPlot of differentially expressed of individual copies of TEs between Tex (red) and Tpex (green). (K) Plot modelling the enrichment of some TE subfamilies in TEx population (left) and TPex population (right). Disk size is proportional to the fold enrichment and x position is determined by the adjusted p-value.

Figure 8: TE expression in human TILs.

(A-B) Boxplot of TPex (top) and TEx (bottom) gene-based (A) and TE-based (B) signatures previously identified in human samples between in pre- and posttreatment samples in both trials from (44) and (45). (C) BubbleGUM representation of GSEA analyses between pre and post treatment samples at gene and TE levels. The strength of the enrichment is quantified by the NES and the significance of the enrichment is measured by the false discovery rate (FDR) value. The bigger the bubbles, the bigger the enrichment and the darker the bubbles, the lower the FDR. The bottom panel summarizes the normalized enrichment score (NES) and false discovery rate (FDR) parameters obtained by GSEA.

Detailed description of specific embodiments of the invention

In a first aspect, the invention relates to a method for determining if a human subject having a disease, in particular a cancer or an infectious disease, is likely to benefit from a treatment with an immune checkpoint blocker or immune checkpoint activator, the method comprising:

Providing a biological sample previously obtained from the human subject wherein the sample is from the microenvironment of the disease and comprises T cells, in particular the sample is a biopsy of the microenvironment of the disease,

Identifying T cells as early progenitor T cells (Tpex) when distal TEs belonging to the MER31 -int subfamily are overexpressed as compared to the genomic MER31 -int subfamily expression,

Identifying T cells as terminally exhausted T cells (Tex) when distal TEs belonging to the L1 MDb subfamily are overexpressed as compared to the genomic L1 MDb subfamily expression,

Classifying the human subject as being likely to benefit from a treatment with the immune checkpoint blocker or immune checkpoint activator when Tex cells are not the most common type of T cells in the biological sample and/or when Tpex are more common compared to Tex in the biological sample. The terms "disease”” has its general meaning in the art and refers to any harmful deviation from the normal structural or functional state of an organism, generally associated with certain signs and symptom, diseased organism commonly exhibits signs or symptoms indicative of its abnormal state. In a particular embodiment of the invention, the disease is a cancer or an infectious disease. Infectious diseases are disorders caused by organisms, such as bacteria, viruses, fungi or parasites. In a particular embodiment of the invention, the disease is a viral infectious disease. In an embodiment of the invention, the disease is a chronic viral infection.

The terms "cancer" and “tumor” have their general meaning in the art and refer to a group of diseases involving abnormal cell growth with the potential to invade or spread to other parts of the body. The term "cancer" further encompasses both primary and metastatic cancers. A cancer is a disease involving abnormal cell growth with the potential to invade or spread to other parts of the body. According to the invention, the cancer which affects or affected a patient may be selected from the list consisting of bladder cancer, bone cancer, brain cancer, breast cancer, including Triple-Negative Breast cancer, cervical cancer, colon cancer, endometrical cancer, esophageal cancer, gastric cancer, head & neck cancers, hepatocellular carcinoma, hodgkin’s lymphoma leukemia, liver cancer, lung cancer, melanoma, mesothelioma, multiple myeloma myelodysplastic syndrome, non-hodgkin’s lymphoma, ovarian cancer, pancreatic cancer, prostate cancer, rectal cancer, renal cancer, sarcoma, skin cancer, testicular cancer, thyroid cancer or uterine cancer. In a particular embodiment, the cancer which affects a patient is a breast cancer, ovarian cancer, liver cancer, endometrial cancer, or hepatocellular carcinoma. The microenvironment of the tumor is the ecosystem that surrounds a tumor inside the body. It includes immune cells, the extracellular matrix, blood vessels and other cells, like fibroblasts. The microenvironment of the tumour comprises the space surroundings the tumor, and includes the surrounding blood vessels, immune cells, fibroblasts, signaling molecules and the extracellular matrix. The tumor and the surrounding microenvironment are closely related and interact constantly. Immune cells in the microenvironment can affect the growth and evolution of cancerous cells. The microenvironment of the disease refers to the cellular environment in which tumor cells, immune cells, stromal cells and other non-cancerous cells exist. In particular, the microenvironment of the disease corresponds to tumor cells and immune cells. The microenvironment of a tumor is composed of tumor cells (or tumor parenchyma), stromal cells (including T cells) and inflammatory mediators.

Microenvironment of a tumor can correspond to the description made by Bozyk at al. (Biology (Basel). 2022 Jun; 11 (6): 929. doi: 10.3390/biologyl 1060929).

In an embodiment, the microenvironment of the disease is the microenvironment of a tumor.

In an embodiment, the microenvironment of the disease is the tumor parenchyma.

In an embodiment, the microenvironment of the disease corresponds to tumor cells and immune cells present within the tumor parenchyma.

In an embodiment, the microenvironment of the disease corresponds to a tumor.

The cancer may be a “solid cancer” or a “liquid tumor” such as cancers affecting the blood, bone marrow and lymphoid system, also known as tumors of the hematopoietic and lymphoid tissues, which notably include leukemia and lymphoma. Liquid tumors include for example acute myelogenous leukemia (AML), chronic myelogenous leukemia (CML), acute lymphocytic leukemia (ALL), and chronic lymphocytic leukemia (CLL), (including various lymphomas such as mantle cell lymphoma, non-Hodgkins lymphoma (NHL), adenoma, squamous cell carcinoma, laryngeal carcinoma, gallbladder and bile duct cancers, cancers of the retina such as retinoblastoma).

Solid cancers notably include cancers affecting one of the organs selected from the group consisting of colon, rectum, skin, endometrium, lung (including nonsmall cell lung carcinoma), uterus, bones (such as Osteosarcoma, Chondrosarcomas, Ewing's sarcoma, Fibrosarcomas, Giant cell tumors, Adamantinomas, and Chordomas), liver, kidney, esophagus, stomach, bladder, pancreas, cervix, brain (such as Meningiomas, Glioblastomas, Lower-Grade Astrocytomas, Oligodendrocytomas, Pituitary Tumors, Schwannomas, and Metastatic brain cancers), ovary, breast, head and neck region, testis, prostate and the thyroid gland. Preferably, a cancer according to the invention is a cancer affecting the blood, bone marrow and lymphoid system as described above. In some embodiments, the cancer is, or is associated, with multiple myeloma.

Diseases according to the invention also encompass infectious diseases or conditions, such as, but not limited to, viral, retroviral, bacterial, and protozoal infections, HIV immunodeficiency, Cytomegalovirus (CMV), Epstein-Barr virus (EBV), adenovirus, BK polyomavirus.

Diseases according to the invention also encompass autoimmune or inflammatory diseases or conditions, such as arthritis, e.g., rheumatoid arthritis (RA), Type I diabetes, systemic lupus erythematosus (SLE), inflammatory bowel disease, psoriasis, scleroderma, autoimmune thyroid disease, Grave's disease, Crohn's disease multiple sclerosis, asthma, and/or diseases or conditions associated with transplant.

The human subject may be any human who had or is suspected to have or who develops a disease, in particular a cancer or an infectious disease. In particular, the subject may be any human who has cancer and has been diagnosed accordingly, particular, the subject may be any human who has an infectious disease and has been diagnosed accordingly The patient may be a child, an adolescent, an adult. The subject may or may not have been treated for symptoms associated with its disease. In an embodiment of the invention, the subject is likely to benefit from an immune treatment, for example by administration of an Immune checkpoint blocker or activator. In an embodiment of the invention, the subject is not or has not yet been treated against its disease. The invention may optionally comprise determining one or more clinical factors of said subject, such as selected from sex, age, body mass index, health history. As used herein, “treatment” or “treating” or “therapy” is an approach for obtaining beneficial or desired results including clinical results. For purposes of this invention, beneficial or desired clinical results include, but are not limited to, one or more of the following: alleviating one or more symptoms resulting from the disease, diminishing the extent of the disease, stabilizing the disease (e.g., preventing or delaying the worsening of the disease), preventing or delaying the spread of the disease, preventing or delaying the recurrence or relapse of the disease, delaying or slowing the progression of the disease, ameliorating the disease state, providing a remission (partial or total) of the disease, enabling to decrease the administered dose of one or more other medications required or used to treat the disease, increasing the quality of life, enabling progression-free survival (PFS) increasing time to disease progression and/or prolonging survival, in particular overall survival (OS), preventing or alleviating side-effects of current treatment, or treatments that will be developed. Immunotherapy refers to a treatment with a compound or a composition that acts on the immune system (like but not limited to Immune Checkpoint Blockers or Activators).

An immune checkpoint blocker or immune checkpoint activator is a compound that blocks, or actives proteins called checkpoints that are expressed by some types of immune system cells, such as T cells, and some cancer cells. These checkpoints help keep or enhance or activate immune responses. When these checkpoints are blocked or activated, T cells can be more effective to kill diseased cells, like infected cells or cancer cells. Examples of checkpoint proteins found on T cells or cancer cells include PD-1/PD-L1 and CTLA-4/B7-1/B7-2. In an embodiment of the present invention, the immune checkpoint blocker is an anti- PD-1 or PD-L1 compound. In particular, the immune checkpoint blocker is an anti-PD-1 or PD-L1 antagonist compound (i.e. a compound that blocks, inhibits or reduces the PD-/PD-LA interaction). In particular, the immune checkpoint blocker is an anti-PD-1 or PD-L1 antagonist antibody or antigen-binding fragment thereof. In a particular embodiment, the immune checkpoint blocker is an anti- PD-1 antagonist antibody.

A biological sample obtained from the patient can be any biological sample, such as tissue, blood, urine, whole cell lysate, biopsy, tumor, tumor cells. Methods of obtaining a biological sample from a patient are well known in the art and include obtaining samples from surgically excised tissue. Tissue, blood, urine, biopsy, tumor and cellular samples can also be obtained without the need for invasive surgery, for example by puncturing the subject with a fine needle and withdrawing cellular material or by biopsy. In certain embodiments, samples taken from a patient can be treated or processed to obtain processed biological samples such as supernatant, whole cell lysate, or fractions or extract from cells obtained directly from the patient. In other embodiments, biological samples issued from a patient can also be used with no further treatment or processing. In a preferred embodiment, the biological sample obtained from the subject is a tissue, in particular a tissue from a tumor or a tumor extract, preferably obtained by biopsy. A biological sample issued from a subject may, for example, be a sample removed or collected or susceptible of being removed or collected from an internal organ or tissue or tumor of said subject, in particular from tumor, or a biological fluid from said subject such as the blood, serum, plasma, tumor microenvironment or urine. A biological sample collected or removed from the subject may, for example, be a sample comprising cancer cells which have been or are susceptible of being removed or collected from a tissue, in particular a tumor, of said subject. A step for lysis of the cells, in particular lysis of the cancer cells contained in said biological sample, may be carried out in advance in order to render nucleic acids or, if appropriate, proteins and/or polypeptides and/or peptides, directly accessible to the analysis.

As used herein, the term “T cells” has its general meaning in the art and refers to T lymphocyte which is a type of lymphocyte having a T-cell receptor on the cell surface and playing a central role in cell-mediated immunity. In a particular embodiment, the T cells are human T cells. In a particular embodiment, the T cells are selected from the group consisting of terminally exhausted T cells, progenitor exhausted T cells, CD4+ T cells, CD8+ T cells, naive T cells, effector T cells, memory T cells, stem cell T cells, central memory T cells, effector memory T cells, terminally differentiated effector memory T cells, tumor-infiltrating lymphocytes, immature T cells, mature T cells, helper T cells, cytotoxic T cells, mucosa-associated invariant T cells, naturally occurring and adaptive regulatory T cells, follicular helper T cells, alpha/beta T cells, CAR-T cells, CD-19 targeting CAR T cells, and delta/gamma T cells. In a preferred embodiment, the T cells are selected from the group consisting of CD8+ T cells, and more particularly of Slam- 6+ T cells and Tim3+ T cells. In an embodiment, T cells refers to terminally exhausted T cells (also referenced Tex or Ttex) and progenitor exhausted T cells (also referenced Tpex).

Tex refers to terminally exhausted T cell or exhausted T cell. Tex are T cells that may express the markers Cxcl13, Entpdl , Layn, Havcr2 and Lag3. In an embodiment of the invention, Tex are CD103+, CD39+, PD1 ^hi, TIM3+ cells. Tpex refers to progenitor exhausted T cells. Tpex are T cells that may express the markers Tcf7, Ccr7, Gzmk and Sell. In an embodiment of the invention, Tpex comprises TP circulating cells, that are CD103-, KLRG1 +, and TPresident cells, that are CD103+, PD1 ^low/int, TIM3-, CD39-.

A “marker” (or biomarker) is defined as a biochemical, molecular, or cellular alteration that is measurable in biological tissue such as tissues, cells, or fluids, and that indicates, e.g., is functionally related to normal or abnormal process of a condition or disease. The term “biomarker” refers to molecule which can be measured accurately and reproducibly, thereby leading to the provision of a “signature” that is objectively measured and evaluated as an indicator of normal biological processes, or pathogenic processes, or pharmacologic responses. In the context of the present invention, a biomarker corresponds to biological molecule(s) expressed by and/or present within cells of a human being. Thus, in the present invention biological markers include protein biomarkers, genetic biomarkers (corresponding to the transcript products of genes) and epigenetic biomarker (corresponding to methylation of DNA for example). In the present invention, biomarkers include DNA, RNA and proteins. MER31 -int, L1 MDb, RLTR6-int, RTLR6-Mm, MMLV30-int and Fill are considered as markers in the context of the present invention.

Viral-like 30 elements (VL30s) are a family of 5-6 Kb retrovirus-like DNA sequences present in the genome of mice and human among others. VL30 LTRs contain a large repertoire of transcription factor binding sites that provide a plasticity for VL30 RNA expression.

Distal TEs correspond to the TEs localized more than 2000 base pairs from the closest gene.

MER31 -int subfamily of TEs belong to the LTR subfamily of TEs.

L1 MDb subfamily of TEs belong to the LTR subfamily of TEs.

Fli1 corresponds to a protein encoded by the Fli-1 proto-oncogene. Fli1 protein is Friend leukemia integration 1 transcription factor. Fli1 is also referenced as BDPLT21 , EWSR2, FLI-1 , and SIC-1. Fli1 may be encoded by the human gene of sequence reference Gene ID: 2313. Several isoforms of Fli1 are expressed in humans. Any Fli 1 protein referenced as an isoform of Friend leukemia integration 1 transcription factor may be Fli1 used in the present invention. The measurement or assay may be carried out in a biological sample which has been collected or removed from said subject and which has been transformed, for example by extraction and/or purification of proteins and/or polypeptides and/or peptides and/or DNA and/or RNA, or by extraction and/or purification of a protein fraction or cell fraction such as serum or plasma or cells extracted from blood.

Determining by measuring or assaying the level of expression of selected markers may be carried out in a sample which has been obtained from said subject, such as a biological sample removed from or collected from said subject, or a sample comprising nucleic acids (in particular RNAs) and/or proteins and/or polypeptides and/or peptides of said biological sample, in particular a sample comprising nucleic acids and/or proteins and/or polypeptides and/or peptides which have been or are susceptible of having been extracted and/or purified from said biological sample, or a sample comprising cDNAs which have been or are susceptible of having been obtained by reverse transcription of said RNAs.

One feature of the method according to the invention is that it includes the determination by measuring or assaying the level to which the selected biomarkers, in particular genes, are expressed in particular cells, in particular T cells, more particularly Tex and/or Tpex of said subject. The expression “level of expression of a gene” or equivalent expression as used here designates both the level to which this gene is transcribed into RNA, more particularly into mRNA, and also the level to which a protein encoded by that gene is expressed. The term “measure” or “assay” or equivalent term is to be construed as being in accordance with its general use in the field, and refers to quantification, in particular relative quantification. The level of transcription (RNA) of each of said biomarkers or the level of translation (protein) of each of said biomarkers, or indeed the level of transcription for certain of said selected biomarkers and the level of translation for the others of these selected biomarkers can be measured. In accordance with one embodiment of the invention, either the level of transcription or the level of translation of each of said selected biomarkers is measured. The fact of measuring (or assaying) the level of transcription of a biomarker includes the fact of quantifying the RNAs transcribed from that gene, more particularly of determining the concentration of RNA transcribed by that biomarker (for example the quantity of those RNAs with respect to the total quantity of RNA initially present in the sample. The fact of measuring (or assaying) the level of translation of a biomarker includes the fact of quantifying proteins encoded by that biomarker, more particularly of determining the concentration of proteins encoded by a gene corresponding to the selected marker(s), (for example the quantity of that protein per volume of biological fluid). Certain proteins encoded by a mammalian gene, in particular a human gene, may occasionally be subjected to post-translation modifications such as, for example, cleavage into polypeptides and/or peptides. If appropriate, the fact of measuring (or assaying) the level of translation of a biomarker may then comprise the fact of quantifying or determining the concentration, not of the protein or proteins themselves, but of one or more post-translational forms of this or these proteins, such as, for example, polypeptides and/or peptides which are specific fragments of this or these proteins.

In order to measure or assay the level of expression of a biomarker, it is thus possible to quantify the RNA transcripts of a gene, or proteins expressed by a gene or post-translational forms of such proteins, such as polypeptides or peptides which are specific fragments of these proteins, or particular forms of proteins expressed, for examples epigenetic modifications including degree of methylation of one or several amino acid residues on proteins.

In order to measure the level of transcription of a biological marker, in particular a gene, its level of RNA transcription may be measured. Such a measurement may, for example, comprise assaying the concentration of transcribed RNA of each of said selected biological marker, either by assaying the concentration of these RNAs or by assaying the concentration of cDNAs obtained by reverse transcription of these RNAs. The measurement of nucleic acids is well known to the skilled person. As an example, the measurement of RNA or corresponding cDNAs may be carried out by amplifying nucleic acid, in particular by PCR. As an example, in the case of measurement of the level of expression of a gene by measurement of transcribed RNAs, i.e. in the case of measurement of the level of transcription of this gene, the measurement is generally carried out by amplification of the RNAs by reverse transcription and PCR (RT-PCR) and by measuring values for Ct (cycle threshold). In order to measure the level of translation of a biological marker, in particular a gene, its level of protein translation may be measured. Such a measurement may, for example, comprise assaying the concentration of proteins translated from each of said selected genes (for example, measuring the proteins in cell extracts). Protein measurement is well known to the skilled person. As an example, the proteins (and/or polypeptides and/or peptides) may be measured by ELISA or any other immunometric method which is known to the skilled person, or by a method using mass spectrometry which is known to the skilled person. As an example, in the case of measuring the level of expression of a biological marker by measuring proteins expressed by the gene encoding the biological marker, i.e. in the case of measuring a level of translation of that gene, the measurement is generally carried out by an immunometric method using specific antibodies, and by expression of the measurements made thereby in quantities by weight or international units using a standard curve. Examples of specific antibodies are indicated in the examples of the present disclosure. A value for the measurement of the level of translation of a gene may, for example, be expressed as the quantity of this protein per volume of biological fluid, for example per volume of serum (in mg/mL or in pg/mL or in ng/mL or in pg/mL, for example)

The measurement values are preferably values corresponding to the concentration or the quantity or the proportion of the levels of expression of each of said selected biological marker and reflect as accurately as possible, at least with respect to each other, the degree to which each of biological marker is expressed (degree of transcription or degree of translation), in particular by being proportional to these respective degrees. Thus, the expression level of marker in the biological sample may correspond to the proportion or the concentration or the quantity of the biomarker. The proportion may be expressed as a percentage (%) of the biomarker with the overall amount of protein within the sample or in respect to the other biological marker which expression is measured. Alternatively, instead of determining a proportion, a concentration or a quantity of the biological marker within the sample may be measured. The determination of the over-expression of at least one marker among the list of selected biomarkers is made by comparison with a reference level. The determination of overexpression of a biomarker in said subject may be deduced or determined by comparing the determined value(s) of each measured marker obtained from said subject with the value(s) associated with the same biomarker, or the distribution of the value(s) associated with the same biomarker, in reference subject(s) (for example a healthy subject, or healthy cells of the subject from whom the biological sample is issued, in particular healthy cells issued from the organ of the subject from whom the biological sample is issued), or cohorts of subjects. Alternatively, the determination of over-expression of a biomarker in said subject may be deduced or determined by comparing the determined value(s) of each measured biomarker obtained from said subject with the value(s) associated with the same biomarker in reference cells known not to be Tpex or Tex cells as defined herein, like progenitor T cells, CD8+ T cells, a pool of different T cells, etc. The determination of the expression of the selected biomarker made on the subject and on the reference may correspond to measurements of the levels of gene expression (transcription or translation).

Overexpression of a biomarker may correspond to excessive expression (transcription or translation) of a biomarker of at least 10% as compared to the reference level, in particular at least 20% as compared to the reference level, in particular in particular at least 30% as compared to the reference level, in particular at least 40% as compared to the reference level, and more particularly at least 50% as compared to the reference level. Under expression of a biomarker may correspond to insufficient expression (transcription or translation) of a biomarker of at least 10% as compared to the reference level, in particular at least 20% as compared to the reference level, in particular in particular at least 30% as compared to the reference level, in particular at least 40% as compared to the reference level, and more particularly at least 50% as compared to the reference level.

A reference level of a particular form of protein may correspond to the quantity of the particular form of the considered protein in a reference cell, like a pool of T cells, in particular a pool of CD8+ T cells.

Over expression and under expression of a biological marker is relative to a reference level. Thus, if according to the reference level, a marker is not expressed (for example when a reference marker is not expressed), over expression of a biological marker may correspond to the expression of the biological marker; Alternatively, under expression may correspond to an absence of expression.

In an embodiment of the invention, the human subject has a cancer.

In an embodiment of the invention, the human subject has a cancer, and the biological sample is issued from the microenvironment of the subject’s tumor.

In an embodiment of the invention, the human subject has an infectious disease, in particular a viral infectious disease or a chronic viral infection.

In an embodiment of the invention, the expression of MER31 -int subfamily of TEs and L1 MDb subfamily of TEs is measured.

In an embodiment of the invention, the expression of TF Fli 1 is measured.

In an embodiment of the invention, the expression of MER31 -int subfamily of TEs and L1 MDb subfamily of TEs is measured, and the expression of TF Fli1 is measured is measured.

The invention also related to a method to prognose a disease in a human subject, in particular a cancer or a viral infectious disease or a chronic viral infection, the method comprising:

Determining the presence in the biological sample of exhausted T cells (Tex) or progenitor exhausted T cells (Tpex) by measuring in T cells the expression of at least one distal Transposable Element selected from the group consisting of MER31 -int subfamily and L1 MDb subfamily, and/or a Transcription Factor Fli1 ,

Identifying the presence of Tex in the biological sample as T cells overexpressing the distal TEs belonging to the L1 MDb subfamily and/or underexpressing the Transcription Factor Fli1 ,

Identifying the presence of Tpex in the biological sample as T cells overexpressing the TEs belonging to the MER31 -int subfamily and/or the Transcription Factor Fli1 ,

As before, the measurement of markers can include the measurement of 0, 1 , 2 or the 3 Transposable Elements, and/or the measurement of Fli1 . The invention also relates to a method to predict the response of a human subject to a treatment by immunotherapy, the method comprising:

Classifying the human subject as being likely to benefit from the immunotherapy when Tpex cells are more common compared to Tex, and/or classifying the human subject as being unlikely to benefit from the immunotherapy when Tex cells are more common compared to Tpex in the biological sample.

In an embodiment, the immunotherapy is a therapy with an Immune Checkpoint Blocker or activator, in particular an immune checkpoint blocker. In particular, the immune checkpoint blocker is an anti-PD-1 or PD-L1 compound. In particular, the immune checkpoint blocker is an anti-PD-1 or PD-L1 antagonist compound (i.e. a compound that blocks, inhibits or reduces the PD-/PD-LA interaction). In particular, the immune checkpoint blocker is an anti-PD-1 or PD-L1 antagonist antibody or antigen-binding fragment thereof. In a particular embodiment, the immune checkpoint blocker is an anti-PD-1 antagonist antibody.

As before, the measurement of markers can include the measurement of 0, 1 , 2 or the 3 Transposable Elements, and/or the measurement of Fli1 . The invention also relates to a method for treating a human subject having a disease, in particular a cancer or an infectious disease, wherein the method comprises:

Identifying the presence of Tpex in the biological sample as T cells overexpressing the TEs belonging to the MER31 -int subfamily and/or the Transcription Factor Fli1.

Administering to the human subject a checkpoint blocker or checkpoint activator, in particular an anti-PD1 or an anti-PDL1 antagonist compound, when Tex cells are not the most common type of T cells in the biological sample and/or when Tpex are more common compared to Tex in the biological sample.

The invention also relates to a method for treating a human subject having a disease, in particular a cancer or an infectious disease, wherein the method comprises:

Administering to the human subject a Transcription factor FLi1 or a nucleic acid molecule encoding a Transcription factor FLi 1 or a compound enhancing the expression of Fli1 in T cells when Tex are more common compared to Tpex in the biological sample and/or when Tex are the most common T cells in the biological sample.

In a preferred embodiment, it is administered an anti- PD1 antagonist antibody to the patient.

Identifying the presence of Tex in the biological sample as T cells overexpressing the distal TEs belonging to the L1 MDb subfamily and/or underexpressing the Transcription Factor Fli1 , Identifying the presence of Tpex in the biological sample as T cells overexpressing the TEs belonging to the MER31 -int subfamily and/or the Transcription Factor Fli1.

Administering to the human subject a checkpoint blocker or checkpoint activator, in particular an anti-PD1 or an anti-PDL1 antagonist compound, when Tex cells are not the most common type of T cells in the biological sample and/or when Tpex are more common compared to Tex in the biological sample, and

As before, the measurement of markers can include the measurement of 0, 1 , 2 or the 3 Transposable Elements, and/or the measurement of Fli1 .

The present invention also relates to a Transcription factor FLi1 or a nucleic acid molecule encoding a Transcription factor FLi1 or a compound enhancing the expression of Fli1 in T cells, or a T cell overexpressing Fli1 , for use in the treatment of a cancer or an infectious disease, in particular a viral infectious disease or a chronic viral infection.

The Transcription factor FLi1 may be a recombinant protein.

The nucleic acid molecule encoding a Transcription factor FLi1 may be a genetic expression vector that will allow expression of Fli1 in T cells after transfection of the nucleic acid molecule within T cells.

A T cell overexpressing Fli1 may be a T cell genetically engineered for overexpressing Fli1 as compared to an unmodified T cell. Overexpression of Fli1 may be the result of introduction into the T cells of nucleic acid molecules encoding Fli1 .

The present invention also relates to the Transcription factor FLi1 or a nucleic acid molecule encoding a Transcription factor FLi 1 or a compound enhancing the expression of Fli 1 in T cells, or T cells overexpressing Fli 1 , for use in the treatment of a disease, in particular a cancer or an infectious disease, by preventing Tpex differentiation into Tex. T cells overexpressing Fli1 are less likely to differentiate into Tex, thereby strengthening the immune response against the disease.

The invention also related to the use of a Transcription factor FLi1 or a nucleic acid molecule encoding a Transcription factor FLi 1 or a compound enhancing the expression of Fli 1 or T cells overexpressing Fli 1 for stimulating immune response, in particular for preventing or reducing Tpex differentiation into Tex.

The invention also relates to a method for reprogramming T cells, comprising:

Providing a biological sample comprising T cells previously obtained from a subject, in particular wherein the sample is issued from the microenvironment of a disease,

Extracting T cells from the biological sample,

Treating T cells with a Transcription factor FLi1 or a nucleic acid molecule encoding a Transcription factor FLi1 or a compound enhancing the expression of Fli1 in T cells.

This method may be performed on any kind of T cells, In a particular embodiment, this method is performed on Tex, or Tpex, or Tpex and Tex, more particularly issued from a subject having a disease. These T cells are thereafter administered to the subject (i.e. as an adoptive immunotherapy like allogenic immunotherapy) or to another patient in need thereof.

Examples illustrating the invention

Material and method

Animal handling

Mice

C57BL6/J mice were obtained from Charles River. Typically, mice were imported into the conventional animal facilities at Institut Curie between 6 and 7 weeks of age and used for experiments between 8 and 10 weeks. All animal procedures were approved by the Curie Institute ethical committee, in agreement with French and European regulations (Apafis n^Q13360-201802021535690-v1 ).

Tumor experiments Tumor cells (0.5x105 B16OVA cells) were injected into WT recipients by subcutaneous injection. Where stated, when tumors became palpable, mice were treated twice weekly with 150pg of anti-PD-1 (Clone RMP1 -14, BioXcell, BE0146). Treatment groups were formed to ensure homogeneous distribution of tumor volumes prior to treatment. Tumor growth was monitored twice weekly using an electronic caliper (Mahr) and volume estimated as 0,5*L2*l (ref). Animals were sacrificed when tumors reached 1000mm3 volume, when they reached the ethical endpoint (as specified in the ethical protocol), or latest on d21 for TIL isolation.

TIL isolation

Tumors were collected from tumor-bearing mice on d21 post-tumor injection. Tumors were placed in 6-well plates, cut using two scalpels, and digested with Liberase TL (Roche, 0.67mg/mL) and DNase I (Roche, 100pg/mL), in CO2 independent medium (Gibco), at 37^QC for 45 minutes. Digested tumors were collected, filtered through 100pm2 cell strainers, and remaining tissue pieces manually dissociated using a syrine plunger. Samples were washed, and debris were removed using the Debris Removal kit (Miltenyi Biotec) following manufacturer’s instructions. Further TIL enrichment was performed using a Percol gradient. Cells present on the ring were collected, washed, and stained with near-IR LiveDead (LifeTechnologies) in presence of FcBlock (eBioscience), for 20 minutes, in FACS buffer (PBS, 0.5%BSA, 2mM EDTA). Cells were washed and stained with anti-CD45 FITC, anti-PD-1 PECy7, anti-Tim3 BV421 , anti- Ly108 APC, anti-CD44 PECy5 and anti-CD8a PE, as described in [cite the Slamf6/Tim3 original paper], in FACS buffer for 30 minutes on ice. Cells were washed and Slamf6 and Tim3 TIL populations were sorted using a FACS Aria, using a 100pm2 nozzle (BD). Cells were sorted directly into Trizol (LifeTechnologies).

RNA extraction

Cells were detached, counted, pelleted, and lysed in 500pL-1 mL Trizol (Invitrogen/Fisher Scientific). Chloroform was added to the samples (200pL for 1 mL of Trizol), samples mixed thoroughly and spun for 20min, maximum speed on a cold tabletop centrifuge. Aqueous phase was collected, mixed in a 1 :1 ratio with 100% Ethanol, and transferred to RNAeasy micro columns (Qiagen). RNA was extracted following manufacturer’s instructions, including the on- column digestion using RNAse-free DNAse set (Qiagen). Samples were eluted twice with 13pL of RNAse-free ddH20. cDNA synthesisl 1 pL of RNA were used to perform first-strand synthesis using Superscript III, with oligodT primers, following manufacturer’s instructions. cDNA was diluted 7 times in nuclease free ddH20 for further use. qRT-PCR

TE-specific minor groove binding Taqman probes were designed using the AllelelD software (Premier Biosoft), using default parameters. Custom Taqman probes were ordered from Fisher Scientific and primers from Eurogentec. 20X concentrated Taqman assays were prepared by mixing 5pL of Taqman probe, 18pL of each primer, and 59pL of ddH20. GAPDH and HPRT Taqman assays (Fisher scientific) were used as housekeeping gene controls. qRT-PCR were performed in 384 well plates, using dTTTP MasterMix (Eurogentec), according to manufacturer’s instructions (for each well, 0.5pL Taqman assay, 5pL Mastermix and 4.5pL of cDNA). Amplification was performed as recommended by the manufacturer, on a Roche Lighcycler 480, and the absolute quantification (fit points) tool from the analysis software was applied. The detection threshold was placed manually in the first 1/3 of the linear amplification phase. Data were analyzed Microsoft Excel to calculate III Ct, and further analyzed in Graphpad Prism.

Transposable Element annotations Classification and TE metadata Transposable elements annotations are derived from TEtranscript hg38, hg19 and mm10 gtf annotations files which are based from RepeatMasker database. Features included in SQuiRE output tables and not present in these annotations were excluded (most of them were annotated as snRNA, rRNA, srpRNA, tRNA, scRNA). Age of TEs

Using Homer repeat gtf annotation files (mm10, hg 19, hg38), and matching them with our TE metadata based on TE location, we could retrieve the percentage of divergence of transposable elements. Based on the percentage of divergence, we calculated transposable element age using following formula from this article (49): Divergence I (2.2 * 10-9) for human genome and Divergence I (4.5 * 10-9) for mouse genome.

TE-gene proximity

Closest and intersect tools from bedtools suite (v2.29.2) have been used to retrieve, for each TE, the distance from their closest protein-coding genes. Their location was compared to protein-coding genes locations based on gencode hg38, hg19 and mm10 gtf annotation files.

Single-cell RNA-seq analysis mouse Downloading gene expression

Gene expression data (GSE1 16390_aggregated_filtered_matrix.tar.gz) were downloaded from GEO database (accession number: GSE116390). barcodes.tsv, genes.tsv and matrix. mtx files were provided in the expression data.

Downloading data, genome alignment and TE quantification

Single cell RNA-seq fastq reads from 7 mouse samples were downloaded from the European Nucleotide Archive database (study number: PRJNA478372). Reads were processed and mapped to mm10 genome using cellranger (v6.0.0) with default parameters and mm10 transcriptome reference (version 2020-A). After retrieving output BAM files from cellranger (possorted_genome_bam.bam), individual TE expression was quantified on each sample using scTE (version 1 .0) : 1 ) An mm10 index was created using scTE_build function (parameter -m exclusive was set to exclude reads that can map to exon of protein coding genes and IncRNAs) by providing gencode GRCm38.p6 release m22 gtf file for gene annotations, TEtranscripts mml Ogtf file (https://labshare.cshl.edu/shares/mhammelllab/www- data/TEtranscripts/TE_GTF/) for transposable elements annotations and the parameter; 2) Genes and individual TEs expression were quantified using scTE function with previously created index and BAM files; 3) For each TE subfamily, raw counts of all individual TEs were aggregated to obtain their global TE subfamily expression; 4) Individual TEs expression matrix was filtered to keep only TEs expressed in at least 5 cells.

Combining gene an TE subfamily expression

Gene expression data were loaded and processed in R (version 4.2.1 ) using Seurat (version 4.2.0) and its function Readl OX. A Seurat object was then created filtering out cells expressing less than 200 features using CreateSeu ratObject function. After calculating the fraction of mitochondrial counts per cell, cells with more than 10% of counts originated from mitochondrial genes were removed. TE subfamily aggregated expression was then loaded in R and imported in a new assay in the previously created Seurat object using CreateAssayObject function. A third assay was then created combining raw counts from both gene expression assay and TE subfamily expression assay using rbind. Downstream analysis was finally performed using this assay as the default assay.

Downstream single cell analysis

Raw counts were normalized and the 4000 most variable features were established using respectively NormalizeData and FindVariableFeatures functions from Seurat. Ribosomal genes were filtered out in the list of most variable features. After scaling their expression, PCA was performed using the most variables features (30 PCs computed and stored). To correct for batch effect, RunHarmony function from R package harmony (version 0.1.0) was performed using 30 PCs computed correcting for sample origin (7 samples). UMAP reduction and clustering were then computed using 10 harmony components using Seurat functions RunllMAP, FindNeighbors and FindClusters (resolution 0.3 was chosen). TILS state prediction

Cell states of CD8 cells were predicted using ProjecTILs R package from Carmona lab. First default assay was set to the gene expression assay. Then cells projected to the reference dataset of the package using make. projection function. Finally, cell state was predicted for each cell using cellstate. predict function. Five different cell states were predicted: CD8_NaiveLike, CD8_EarlyActiv, CD8_EffectorMemory, CD8_Tpex, CD8_Tex.

Differential expression analysis

To identify significant differentially expressed features between CD8 differentiation cell states, FindMarkers function was used with default parameters except for the threshold of Iog2 fold change which was set to 0.01 . Differential expression analysis was performed on the assay combining both the gene and TE subfamily expression.

Trajectory analysis

R package collection dynverse was used to perform trajectory analysis which consist of the following packages: dyno (version 0.1.2), dyndimred (version 1.0.4), dynmethods (version 1.0.5), dynwrap (version 1.2.2), dynutils (version 1 .0.1 1 ), dynplot (version 1 .1 .2), dynparam (version 1 .0.2), dynguidelines (version 1 .0.1 ), dynfeature (version 1 .0.0). A dynverse object was created using raw and normalized expression from the combined assay using wrap_expression function. Slingshot (ti_slingshot) was used as the method for the trajectory analysis using infer_trajectory function.

Bulk RNA-seq analysis on mouse data

Downloading data, genome alignment and feature quantification

Bulk RNA-seq fastq reads were downloaded from the European Nucleotide Archive database (study number: PRJNA507854 and PRJNA630257). SQuiRE (version 0.9.9.92) was used to mapped reads and perform gene, TE subfamily and individual TE counts quantification. Within SQuiRE, sequencing reads were mapped against mm10 reference mouse genome using STAR. For each sample, SQuiRE quantifies raw counts and calculates normalized FPKM values of 1 ) all genes annotated in Ensembl mm10 database; 2) all TE subfamilies and individual TEs annotated in RepeatMasker database.

Filtering, normalization and differential expression analysis

Genes and TEs FPKM counts matrices were imported to R (version 4.2.1 ) to plot FPKM density of the features. Based on the genes bi-modal density curve, a threshold of 0.5 FPKM was chosen as our threshold to filter out non expressed features. Genes and TEs were kept in the analysis if at least all triplicates of 1 group were expressing these features above the 0.5 FPKM threshold. Genes and TEs identified as non-expressed were filtered out in the raw count matrices. Counts were normalized and the differential analysis was performed using DESeq2 R package (version 1.36.0). Genes and TEs with Benjamin- Hochberg (BH) adjusted p-value below

0.05 were considered as significantly differentially expressed between conditions.

Visualization

IGV (version 2.13.0) was used to visualize read coverage of bulk-RNA samples.

Principal Component Analysis

PCAs were computed using dudi.pca function from the R package Ade4 (version 1 .7-19) and visualized using fviz_pca_ind function from the R package factoExtra (version 1 .0.7). Confidence ellipses were computed around the set of samples of each group with a level of

0.95 .

Heatmap

Heatmaps were computed with R packages Pheatmap (version 1.0.12) and ComplexHeatmap (version 2.12.1 ). Hierarchical clustering were performed on rows and columns with default parameters except the clustering method which was ward.D2 method. Motif analysis

AME web interface (version 5.4.1 ) from the MEME suite was used to do motif analysis selecting JASPAR CORE and UniPROBE Mouse databases with default parameters. For genes, fasta sequences were generated with the promotor sequence (2kb upstream till start) whereas for TEs, the full individual TE sequences were used.

Figures

Barplots, volcano plots, violin plots, pie charts, MA plots and enrichment plots were created using R (version 4.2.1 ) and ggplot2 (version 3.3.6). Venn diagrams were created using R package VennDiagram (version 1 .7.3).

Human data processing

Study design

The overall objective of this study was to characterize the diversity and ontogeny of CD8+ T cells in untreated lung tumors. This was accomplished by performing bulk RNAseq and flow cytometry analysis on CD8+ T cell subsets isolated from tumor tissue, normal adjacent to tumor tissue, and blood samples from patients undergoing surgical resection for early-stage lung cancer. All samples were collected from the Institute Mutualist Montsouris, under a dedicated protocol for lung cancer specimens approved by the French Ethics and Informatics Commission (EUdract 2017-A03081 -52). All patients in this study provided written informed consent for sample collection and data analysis. Cohort size was selected to assess interpatient variability in subpopulation levels.

Human samples

Tumor tissue, normal adjacent (juxta) tissue and blood samples were obtained from early stage, untreated patients with NSCLC. All patients enrolled in this study patients in this study provided written informed consent for sample collection and data analyses.

Tumor tissue samples were obtained from surgical specimens, after macroscopical examination of the tissue by a pathologist. Tissue samples were stored in C02-independent medium (Invitrogen) with 5% human serum and transferred within 1 -hour post-surgery to the research institute. For each specimen, a fragment was formalin-fixed, and paraffin embedded for histology and immunohistochemistry.

Tissue dissociation

Tumor tissue samples were gently cut in approximately 1 mm3 pieces. Tissues were digested enzymatically, by an incubation of 20-40 minutes, based on the size of the tissue, at 37oC in agitation, in C02-independent medium (Invitrogen) with Collagenase I (2mg/ml, Sigma-Aldrich), Hyaluronidase (2mg/ml, Sigma- Aldrich) and DNase (25pg/ml, Sigma-Aldrich).

The tissue pieces were gently triturated with a 20 ml syringe plunger on a 40 pm cell strainer (BD) in 1X PBS (Invitrogen) with 1 % fetal bovine serum (FBS) and 2mM EDTA (Gibco), until uniform cell suspensions were obtained. The suspended cells were subsequently centrifuged for 10 minutes at 400g.

Tumor- infiltrating lymphocytes isolation

Tumor-infiltrating lymphocytes were isolated using Ficoll-Paque PLUS solution (Sigma-Aldrich). After tissue digestion, cells are resuspended in C02- independent medium and layered onto Ficoll-Paque PLUS solution. Subsequently cells were centrifuged for 20 minutes at room temperature at 800g without breaks. After centrifugation tumor-infiltrating lymphocytes were carefully transferred to a new tube and washed with 1 X PBS with 1 % FBS and 2m M EDTA (Gibco).

CD3+ T cell isolation and purification

CD3+ T cells were isolated using the Pan T cell Isolation Kit (Miltenyi), that leads to approximately 80% purity. CD3+ T cells were resuspended 1 X PBS with 1 % FBS and 2mM EDTA (Gibco). Cell numbers and viability were measured using a Countess II Automated Cell Counter (Thermo Fisher Scientific) and hemocytometer/ trypan blue. CD8+ T cell sorting

For isolation of the distinct CD8+ T cell subsets, upon TILs isolation, cells were washed and resuspended in 10Oul staining buffer containing antibodies. After 20 minutes incubation at 4oC, cells were washed twice and resuspended in 200ul of 1 X Phosphate Buffered Saline (PBS) buffer with 1 mM EDTA and 1 % fetal bovine serum. Cell sorting was performed using MoFlo ASTRIOS cell sorter from Beckman Coulter Life Sciences. Cells were stained with a combination of surface markers, using a set of monoclonal antibodies from the following: CD3-AF532 (1 :50, clone UCHT1 , #2067979, Thermofischer Scientific), CD8-BV510 (1 :50, clone 2ST8SH7, #9259590, BD Biosciences), PD-1 -BV421 (1 :40, clone EH12.2H7, #B268454, Biolegend), KLRG1 -PE-Cy7 (1 :40, clone SA231 A2 ,#B256810, Biolegend), TIM3-BV786 (1 :40, clone 7D3, #9259393, BD Biosciences), CD39-APC (1 :50, clone eBioAl , 2#2071264, Thermofischer Scientific). Cells were gated on single cells based on the FSC-A/FSC-H and SSC- A/ SSC-H scatters, live cells (Zombie NIR negative), CD3+, CD8+ cells and afterwards on the population of interest, on the basis of the expression of the protein markers: KLRG1 , CD103, PD-1 , TIM3 and CD39.

Samples preparation for RNAseq

Upon sorting of the CD8+ subsets, cells were centrifuged at 300g for 3 minutes, resuspended in 350ul of TCL buffer (QIAGEN) with 1 % b-mercaptoethanol inside a 1 .5ml eppendorf tube and rapidly stored at -80oC.

Within a period of a maximum 7 days, cells were thawed and prepared for RNA isolation. Norgen single cell RNA purification kit was used for RNA extraction. Residual genomic DNA was removed using the RNase free DNase set (Qiagen). RNA quantity and quality was measured by Agilent RNA 6000 Pico kit (Agilent Technologies). Samples with high quality RNA (RIN>7) were further selected. On average the samples had between 500pg-1 ng of total RNA. To synthesize and amplify cDNA from those samples, the SMART-Seq v4 Ultra Low Input RNA kit (TaKaRa) was used, according to manufacturer’s instructions. cDNA quantity and quality were measured by High Sensitivity DNA kit (Agilent Technologies). 9ng of amplified cDNA was used for library synthesis, using the KAPA HyperPlus library preparation kit (Roche) according to manufacturer’s instructions. Sequencing libraries were quantified with the LabChip® GX Touch™ nucleic acid analyzer and libraries were sequenced on a HiSeq4000 sequencer (Illumina).

Human data analysis

Downloading melanoma datasets

Bulk RNA-seq fastq files from Gide et al. and Riaz et al. melanoma datasets were downloaded from the European Nucleotide Archive database (study number: PRJEB23709 and PRJNA356761 ).

Bulk RNA-seq analysis on human data

Following processing steps were done on both melanoma public datasets and homemade CD8 sorted samples. Like the mouse analysis, SQuiRE (version 0.9.9.92) was used to mapped reads and perform gene, TE subfamily and individual TE counts quantification. Within SQuiRE, sequencing reads were mapped against hg38 reference human genome using STAR. Genes and TEs FPKM counts matrices were imported to R (version 4.2.1 ). For CD8 sorted samples, genes and individual TEs were kept in the analysis if at least all samples of 1 group (Tex or Tpex) were expressing these features above the 0.5 FPKM threshold. Genes and TEs identified as non- expressed were filtered out in the raw count matrices. Counts were normalized and the differential analysis was performed using DESeq2 R package (version 1.36.0). Genes and TEs with Benjamin-Hochberg (BH) adjusted p-value below 0.05 were considered as significantly differentially expressed between conditions. Signatures scores were calculated using R package singscore (version 1.16.0)

Single cell RNA-seq analysis on human data

Single cell RNA-seq fastq reads from 1 1 human patients were processed and mapped to hg38 genome using cellranger (v6.0.0) with default parameters and hg38 transcriptome reference (version 2020-A). Similar to the mouse dataset, output BAM files from cellranger (possorted_genome_bam.bam) were used and individual TE expression was quantified on each sample using scTE (version 1 .0) : 1 ) An hg38 index was created using scTE_build function (parameter -m exclusive was set to exclude reads that can map to exon of protein coding genes and IncRNAs) by providing gencode GRCh38.p13 release 38 gtf file for gene annotations, TEtranscripts hg38 gtf file

(https://labshare.cshl.edu/shares/mhammelllab/www- data/TEtranscripts/TE GTF/) for transposable elements annotations and the parameter ; 2) Genes and individual TEs expression were quantified using scTE function with previously created index and BAM files; 3) For each TE subfamily, raw counts of all individual TEs were aggregated to obtain their global TE subfamily expression; 4) Individual TEs expression matrix was filtered to keep only TEs expressed in at least 20 cells. Previously processed Seurat object from Gueguen et al. was imported into R and a new assay with individual TEs raw count matrix was created. Individual TE expression was normalized and signature scores were computed using AddModuleScore function from Seurat.

Gene Set Enrichment Analysis

Gene Set Enrichment Analysis (GSEA) was performed with gene and TE signatures from significant differentially expressed features previously identified on DESeq2 normalized count matrices. GSEA (version 4.2.3) was running with default parameters. BubbleGUM and GSEA curve representations were done using ggplot2 and imported GSEA results (NES and adjusted p value corrected by FDR).

Statistical Analysis

A hypergeometric test was performed to test enrichment of TE classes, families and sub- families. Adjusted p-values were corrected with False Discovery Rate method. In violin plot representations, Wilcoxon tests were performed to compare either Slamf6+ and Tim3+ samples or treated and non-treated samples with R package ggpubr (version 0.3.0). The corresponding p-values to symbols are as follows: ns: p > 0.05; *: p <= 0.05; **: p <= 0.01 ; ***: p <= 0.001 ; p <= 0.0001 .

Results Single cell TE subfamily expression distinguishes TIL subpopulations

To investigate TE expression in CD8+ TIL subpopulations, we analyzed single cell transcriptomics of CD8 T lymphocytes infiltrating mouse B16 melanoma tumors (34). Following downstream quality control steps, 3540 cells were processed using a recently published pipeline, scTE (35) that integrates expression of coding genes and TE subfamilies. Five main cell clusters were thus identified (Fig. 1 A), corresponding to the main known TIL subpopulations, including exhausted progenitors (Tpex) and terminally exhausted (Tex) (Fig. 1 A and 1 B). Differential expression analysis between these two clusters revealed genes known to be differentially expressed in Tpex and Tex, including Tcf7, Ccr7, H7r, Slamf6 for Tpex and Tox, Havcr2, Cxcr6, Ccl5, Gzmb, for Tex (Fig. 1 C). In addition, several TE subfamilies were differentially expressed between Tex and Tpex, including 4 subfamilies of LTR that are differentially down- regulated in Tex (e.g. MMVL30-int, RLTR6-Mm, IAPLTR1 -Mn and RLTR6C-Mm) (Fig. 1 C, left panel). These families are also down modulated in Tex as compared to the other T cell clusters, i.e. naive-like, early activated and effector memory cells (Fig 1 c, middle and right panels). Different subfamilies of LTR, LINEs and MIR3, are differentially up- regulated in Tex (Fig. 1 D-1 E). Therefore, like genes, TE subfamilies are differentially regulated in intra-tumor T cell populations and exhaustion stages are associated to distinct TE expression profiles.

Pseudo-time trajectory reconstitution based on both differentially expressed genes and TE subfamilies organizes T cells along a linear path that starts with stem/memory-like and ends with Tex (TILs). Examples of individual TE subfamilies induced or repressed during TIL differentiation are shown in Fig. 1 F. As suggested by the differential expression analysis, expression of MMLV30-int, RLTR6-Mm, RLTR6C-Mm and IAPLTR2-Mm subfamilies is quite stable along the differentiation path from naive-like to Tpex, and then drops when cells become Tex. These results show selective control of TE expression at the subfamily level, during T cell differentiation in tumors and indicate strong repression of certain TE subfamilies when exhausted progenitors become terminally exhausted. TE repression in terminally exhausted TILs

Because of the limitations of TE mapping from single cell 3’ RNAseq data, and to investigate the expression of TEs in CD8+ TILs in greater details, we next analyzed public bulk polyA+ RNA (bRNAseq) from purified PD-1 +SLAMF6+TIM- 3- (Slamf6+ Tpex) and terminally exhausted PD- 1 +SLAMF6-TIM-3+ (Tim3+ Tex) from B16-OVA tumors (36). Alignment to the mm10 genome, as well as gene and TE quantification were performed using SQuiRE (37). Expression threshold for both genes and TEs was set to 0.5 FPKM. Collectively, we identified 10802 genes and 89239 individual TEs (belonging to the 1189 distinct subfamilies) expressed in at least one condition. When compared to their genomic frequencies, the proportion of transcribed SINEs is higher, while that of LTRs and LINEs are lower. Expressed TEs are predominantly located in introns (less than 10% are expressed from intergenic regions). To investigate how TE expression varies between TIL sub-populations Tpex and

Tex, we used principal component analysis (PCA) based on; (1 ) most variable genes, (2) TE subfamilies, and (3) individual or (4) “distal” TEs (i.e., elements located at least 2 Kb away from the nearest annotated TSS). In all cases, the first PCA dimension separates Slamf6+ and Tim3+ populations, indicating that like coding genes, TEs alone can differentiate the two TIL populations. Distal TEs (mostly intergenic) show the same effect, suggesting that differential TE expression patterns do not simply reflect gene expression.

Selective repression of recent autonomous TE subfamilies in Tex

To better understand the selectivity of TE expression in Tpex and Tex, we next performed differential expression analysis between Slamf6+ and Tim3+ populations, for genes, TE subfamilies and individually mapped TEs (Fig. 2A). Differentially expressed genes include known Tpex and Tex markers (including Slamf6, Tcf7, Ccr7 for the former, and Hacvr2, different Gzm, EntPD-1 , for the latter). Analysis based on TE subfamily expression is concordant with our scRNAseq analysis (RLTR6-Mm and MMLV30-int subfamilies are upregulated in Slamf6+ cells, MIR3 in Tim3+ cells) (Fig. 2A, middle panel for example). New differentially expressed TE subfamilies are identified here (as compared to the scRNAseq analysis), most likely due to the higher sequencing depth of the bulk RNA-seq libraries. Among differentially expressed TE subfamilies (Fig. 2A), the proportion of those belonging to the LINE and LTR classes is overall increased in Slamf6+ cells, while differentially expressed DNA transposons and SINE subfamilies are only detected in Tim3+ cells (Fig. 2B). Enrichment tests on differentially expressed individual TEs (compared to genomic abundance of the same families) confirmed that MMVL30-int elements are selectively enriched in the Slamf6+ population (Fig. 2C), while RLTR6-int are enriched in both TIL types (Fig. 2C). This analysis also shows that TE subfamily enrichment in Tpex is much stronger (10-fold differences in adjusted p-values, compare Y- and X-axes in Fig. 2C) than in Tex.

Heatmap representation of the row expression z-score for MMVL30-int and RLTR6-int individual TE copies active in Slamf6+ progenitors (Fig. 2D) shows coordinated repression of most elements in Tim3+ cells. Quantification of the expression of TE subfamilies at single cell level identified those specifically repressed in Tex (for example, MMLV30-int, RLTR6-Mm, RLTR6C-Mm and ETnERV2-int). Analysis of evolutionary ages and lengths shows that expressed MMVL30-int and RLTR6-int copies are younger and longer in Slamf6+ than in Tim3+ TILs (grouped by class in Fig. 2E and 2F). DNA and SINE subfamilies have age and length distributions comparable to that of their genomic counterparts. Similarly, LINE and LTR species differentially expressed in Tim3+ cells have age and length distribution patterns similar to the genomic copies. These results show that a group of young and relatively long LINE and LTR elements are selectively expressed in Tpex, and repressed when Tpex become Tex.

VL30 TEs are overexpressed in Tpex in tumor

Several subfamilies differentially expressed in Slamf6+ Tpex belong to the mouse-specific VL30 family. VL30s are composed of 372 curated mouse genomic DNA sequences with retroviral structure (5'LTR-gag-pol-env-3'LTR), including 86 full-length, 49 truncated copies, and 237 solo LTRs (32). MMVL30-int, RLTR6-int, RLTR6-Mm subfamilies account for over 60% of genomic VL30 elements. For the following analyses, we will call VL30 elements all TEs from MMVL30-int, RLTR6-int, RLTR6-Mm subfamilies. Enrichment tests based on differentially expressed VL30 subfamilies (as compared to their genomic abundance), confirmed that MMVL30-int, RLTR6- Mm and RLTR6-int are all enriched in Slamf6+ TE signature (Fig. 3A and B). In contrast, only RLTR6-int TEs are enriched in the Tim3+ TE signature, (Fig. 3A and B). Most individual copies from these three subfamilies are overexpressed in Slamf6+ compared to Tim3+ TILs (Fig. 3C). Quantification of expression of VL30 subfamilies at single cell level shows a similar trend. To validate this result experimentally, we sorted Slamf6+ or Tim3+ cells from B16-OVA tumors and performed qRT-PCR using custom subfamily- or TE locus- specific Taqman probes. This analysis confirms that VL30 TE subfamilies are repressed in Tex (Fig. 3D).

We next sought to further analyze the VL30 TEs expressed in Tpex and repressed in Tex. Among the 3592 TEs differentially expressed in Slamf6+ Tpex, 56 are VL30, including 33 MMLV30-int, 16 RLTR6C-int and 7 RLTR6-Mm. Eight VL30 copies differentially expressed in TIM3+ Tex belong to the RLTR6-int subfamily (out of 2474 total differentially expressed TEs, Fig. 3E). The majority of the VL30 copies differentially expressed in Tpex are full-length (36 out of 56, 64.3%), compared to only 16,5% genome-wide (Fig. 3E). The three major VL30 subfamilies are equally represented among the full length TEs (Fig. 3E). These elements are preferentially intergenic (70.7%, as compared to 15.1 % of other expressed LTRs) and distal (47.1 % are over 10kb away from the closest gene, compared to 8.2% of other expressed LTRs) (Fig. 3F). In addition, most of the Slam6+-associated VL30s (91.2%, top right panel, Fig. 3G) are expressed independently of their closest genes compared to other Slamf6+ -linked TEs (26.1 %, top left panel, Fig. 3G) or Tim3+-linked TEs (18.1 %, bottom left panel, Fig 3G). We conclude that Tpex specifically express a cohort of young, mostly full-length, intergenic VL30 elements, most probably transcribed autonomously. These elements undergo transcriptional repression in terminally exhausted

Tim3+ TILs.

VL30 TEs are also repressed in Tex in chronic LCMV infection

T cell exhaustion was initially defined in LCMV models of chronic viral infection (38). If overexpression of VL30 elements is characteristic of exhausted progenitors, compared to terminally exhausted cells, it should also occur in exhausted T cell in LCMV. We first analyzed expression of the previously defined VL30 signature in Tpex and Tex isolated from mice infected with LCMV clone 13 (36). Unsupervised clustering shows that this signature can distinguish LCMV- induced Tpex and Tex (Fig. 4A). Differential gene and TE expression analyses identify known genes associated with T cell exhaustion (Tcf7, H7r for Tpex and Havcr2, EntPD-1 , Gzm a/b, Cxcr6 for Tex). Consistent with the previous analysis in TILs, the same VL30 subfamilies and mostly the same individual TE copies are differentially expressed in the clone 13 Tpex and Tex (Fig. 4B, right panel). Likewise, both TE and genes signatures from tumor Tpex and Tex are overexpressed in LCMV Tpex and Tex, respectively (Fig. 4C). Like for tumor Tpex and Tex, the VL30 signature clearly distinguishes Tpex and Tex cells from clone 13 infected T cells (Fig. 4C, right panel). Number of differentially expressed TE subfamilies, individual TE copies from these subfamilies and VL30 TEs are shared between Tpex and Tex from tumors and LCMV clone 13 infected mice. Gene Set Enrichment Analysis (GSEA) shows that the Tpex and Tex gene-based and TE-based signatures previously identified in tumor are enriched in the corresponding clone 13 Tpex and Tex populations (Fig. 4D). LCMV clone 13 Tpex are enriched for all MMLV30-int, RLTR6-Mm and RLTR6-int TE signatures (Fig. 4D), as well as for VL30 elements signature (Fig. 4E). We conclude that the same VL30 elements are differentially expressed in Tpex vs. Tex from tumor and from viral experimental model of exhaustion, and that repression of VL30 elements is a common characteristic of terminally exhausted T cells. In that aspect, the pattern of T cell exhaustion is comparable in the tumor and viral settings.

Fli1 controls VL30 expression in Tpex To identify transcription factors potentially involved in the control of TE expression in Tpex, we analyzed transcription factor (TF)-binding motifs in the TE and VL30 TIL signatures. Motif analysis of the VL30 signature (56 elements) in Slamf6+ Tpex (Fig. 3E), revealed enrichment for several transcription-factor binding motifs (Fig. 5A). The same analysis on Slamf6+ and Tim3+ TE signatures did not show any significant motif enrichment (Fig. 5A). Some of the identified motifs correspond to transcription factors involved in Tpex/Tex differentiation, including Irf8, Id4, Irf7, Fli1 and Tcf3/4 (39).

One of these TFs, Fli 1 , restrains effector and exhausted T cell differentiation. Fli 1 inactivation in mouse T cells causes accumulation of effector/exhausted populations in both LCMV and tumor models (40). Since Fli1 -binding motifs are enriched among VL30 elements expressed in Tpex, we next analyzed the expression of VL30 TEs in Fli1 -deficient Tpex and Tex from tumors or LCMV clone 13. PCA analysis based on the most variable genes separates first (PC1 , 42.7%) LCMV from tumor populations (Fig. 5B, left panel). PC2 (14.3% of variance) separates Slamf6+ from Tim3+ cells in both tumor-bearing and LCMV- infected mice (Fig. 5B). The same PC2 separates Fli 1 -sufficient total LCMV clone 13 T cells (which cluster together with Slamf6+ populations), from Fli1 -deficient T cells (which cluster with Tex, Tim3+ cells). These results show that the main differences in gene expression patterns are between cells from tumor and LCMV models, and that Fli1 -ablation favors T cell exhaustion in both models. PCA of the VL30 signature from each dataset (tumors, LCMV and Fli1 samples), in contrast, separates Slamf6+ from Tim3+ cells along a strong PC1 dimension (38.1 % of variance) (Fig. 5B, right panel). PC2 (with lower variance of 12.3%) separates LCMV from tumor T cells Tpex and Tex. Based on the expression of VL30 elements, Fli1 -sufficient T cells cluster close to Slamf6+ populations, while Fli 1 -deficient T cells cluster with Tim3+ cells. These results indicate that the major differences in VL30 expression between Slamf6+ and Tim3+ cells also occur between Fli1 -sufficient and - deficient T cells in the clone 13 LCMV model.

The impact of the Fli1 depletion on expression of the complete TE Slamf6+ or Tim3+ signatures is minor, compared to the strong reduction in the VL30 Tpex signature (Fig. 5C). Pairwise analysis of individual TE expression from MMLV30- int, RLTR6-int and RLTR6-Mm subfamilies shows significant decrease for numerous TEs in FH1 -KO T cells (Fig. 5D). We next asked whether specific TE subfamilies are preferentially enriched in either wildtype or FH1 -KO samples compared to the genome. We extracted TEs modulated by at least twofold between wildtype and FH1 -KO T cells (VL30 elements are highlighted). We observe that the majority of VL30 element present a positive fold change. Next, we performed hypergeometric enrichment tests using TE are two-fold overexpressed TE. This analysis reveals that VL30 subfamilies, such as MMVL30-int and RLTR6-Mm, are enriched among overexpressed TEs in the wildtype, but this enrichment is not observed for overexpressed TEs in the Fli1 knock-out samples.

Finally, Gene Set Enrichment Analysis (GSEA) of the complete VL30 Slamf6+ signature, or signatures for the main VL30 subfamilies (MMLV30-int, RLTR6-Mm and RLTR6C-int), showed highly significant enrichment in the WT compared to the Fli1 KO (Fig. 5E). Together, these results show that depletion of Fli1 in bulk T cells from the clone 13-infected mice is associated with decreased in expression of VL30 elements differentially expressed in Tpex. Since Fli1 - binding motifs are selectively enriched in these VL30 elements, it is likely that Fli 1 directly regulates expression of these TEs in Slamf6+ cells.

Anti-PD-1 re-programing modulates TE expression

The results presented so far show selective regulation of TEs during exhausted T cell differentiation. We next investigated the possibility to use TE expression in T cells as a biomarker of response to immunotherapy. PD-1 blockade leads to expansion of Tpex at expense of Tex populations, causing effective rejection of established tumors in both mouse models and cancer patients (6). To evaluate the effect of PD-1 blockade on gene and TE expression, we first used PCA analysis based on either 5000 most variable genes from bulk RNAseq analysis (36) or most variable individual TEs (Fig 6A, left panel). In both cases, the first PCA dimension separates Slamf6+ and Tim3+ samples, as expected (PC1 = 17.3%). The second PCA dimension (contribution of only 7.7% to the variability) separates untreated from treated samples, suggesting that the anti-PD-1 treatment does not induce strong gene or TE expression changes in these conditions. A similar PCA analysis based on the VL30 signature defined above, separates samples along one major PC1 (representing 50.8% of the variability) (Fig 6A, right panel). Moreover, only Slamf6+ treated and non-treated samples are separated with the second PCA axe, indicating that PD-1 blockade affects VL30 TE expression strongly in Slamf6+ TIL populations. These results indicate that VL30 elements are strongly modulated in response to PD-1 blockade.

To better understand the effect of PD-1 blockade on TE expression, we differentially analyzed expression of genes or TE subfamilies (Fig 6B). 1575 and 1866 genes, and 35 and 62 TE subfamilies, are differentially expressed between untreated and anti-PD-1 -treated Slamf6+ and Tim3+ TILs, respectively (Fig. 6B). MMLV30-int, RLTR6-Mm and RLTR6-int VL30 subfamilies are among the most strongly induced in anti-PD-1 -treated Slamf6+ and Tim3+ TILs (Fig. 6B). Quantification of VL30 expression at the subfamily level confirms that MMLV30- int, RLTR6-int and RLTR6-Mm are significantly increased in anti-PD-1 -treated Slamf6+ and Tim3+ TIL populations (Fig. 6C). Heatmap of Z-scores for the previously described VL30 signature shows increased expression of most individual TEs in the VL30 signature in anti-PD-1 treated slamf6+ and Tim3+ samples (Fig. 6D). GSEA analysis shows significant enrichment for the MMLV30- int, RLTR6-Mm subfamilies, and VL30 TEs in anti-PD-1 -treated samples (Fig. 6E). The median age of VL30 elements from the signatures of Tpex or Tex, both treated and non-treated, is lower for MMVL30-int and RLTR6-int elements compared to their genomic counterparts (Fig. 6F). We conclude that PD-1 blockade affects expression of recent VL30 elements in both Slamf6+ Tpex and Tim3+ Tex populations.

TE expression in human TILs

The results presented so far show selective TE expression during differentiation from Tpex to Tex in mouse T cells, and during treatment by anti-PD-1. Multiple recent studies have shown that during chronic stimulation, in both viral infections and cancer, human T cells undergo a similar Tpex to Tex differentiation (6, 41 , 42). To investigate the control of TE expression in human exhausted TILs, we next isolated Tex and Tpex from human NSCLC tumors and sequenced bulk polyA+ RNA (Fig.7A). PCA analysis based on all distal expressed TEs distinguishes Tex from Tpex (Fig. 7B). Expression analysis of distal TEs identifies around 1000 TEs that are over expressed by at least 2-fold in Tex or Tpex (Fig. 7C). Analysis of TE classes shows over-representation of SINEs and underrepresentation of LINEs among TEs overexpressed in Tex or Tpex, as compared to genomic TE class frequencies (Fig. 7D). Like in mice (Fig. 2E) distal LTRs overexpressed in Tpex are significatively younger than the LTRs overexpressed in Tex (Fig. 7E). This is not the case for other TE classes. We conclude that TE expression is also selectively controlled during human Tpex to Tex differentiation in tumors.

Differentially expressed genes can be used as specific molecular signatures to detect changes in cell populations during therapeutic treatments in patients. To investigate if TE signatures could be used in a similar way, we first defined gene and TE signatures specific for Tpex or Tex. Differential gene expression analysis defined Tpex and Tex-specific signatures that include genes known to be overexpressed in the corresponding exhausted subpopulations (n=873 and n=1041 genes in Tpex and Tex, respectively). These genes include, for example, Tcf7, Ccr7, Gzmk and Sell for Tpex and CxcH 3, EntPD-1 , Layn, Havcr2 and Lag3 for Tex. Similar differential expression analysis for TEs defined Tpex- and Texspecific signatures that include TEs overexpressed in Tpex (n=180 TEs) or Tex (n=484 TEs) (Z-score heatmap for the two signatures are shown in Fig. 7F). Feature maps for expression of the two TE signatures in single cell RNAseq from CD8+ T cells from NSCLC patients (43) show their specificity for Tpex or Tex, respectively (Fig. 7G and H for 3’). These results suggest that, like genes, TEs expressed selectively in human T cell subpopulations could be used as biomarkers in bulk RNAseq samples from patients. On figures 7I and 7J, differentially expressed TE subfamilies between Tex (red) and Tpex (green) are illustrated. It can be seen that among the differentially expressed TEs, MER31 and L1 MDb are present in Tpex and Tex respectively. On figure 7K, it can be seen that MER31 is enriched in Tpex, and that L1 MDb is enriched in Tex. To test this possibility, we next used the gene and TE signatures to follow the representation of Tex and Tpex in published cohorts of melanoma patients before and after ICB immunotherapy (in two published data sets from (44) and (45). To evaluate the proportion of Tpex and Tex in bulk RNAseq samples from patients, we used both signature scores and GSEA. The Tpex and Tex gene signatures are relatively ineffective to distinguish pre- from post (or on-) treatment tumors (Fig. 8A and C, upper panels). Only the gene Tpex signature shows significant enrichment in the post-treatment samples, in one of the two cohorts, using both signature score and GSEA analyses. The Tpex and Tex TE signatures, in contrast, are significantly enriched in post-treatment samples in both trials, using both signature scores and GSEA analyses (Fig. 8B-C, bottom panels). The TE signatures show that following treatment with ICB, melanoma tumors are infiltrated with T cells, including Tpex and Tex, a process that is not evidenced based on the gene signatures in the same patient cohorts. These results also show that TE expression can be used to define biomarkers that, in these two cohorts, detect the effect of the treatment better than conventional gene signatures.

Discussion

Transition from progenitors to terminally exhausted T cells is associated with limited responsiveness to reprograming by immune checkpoint blockade, and resistance to treatment in cancer patients. This transition is mediated by defined transcription programs, driven by key transcription factors, that have been precisely analyzed (6, 41 , 42). Here, we unveil an unexpected aspect of these transcriptomic programs related to TEs. We show that the transcription of numerous TEs is repressed in terminally exhausted mouse T cells, in both tumors and chronic infection. Silencing of TEs affects certain TE subfamilies more drastically, including a very well described family of autonomous, intergenic and young TEs, VL30 (some of which still bear intact ORFs). We show that transcription of VL30 is active in all T cell populations in tumors and chronically infected mice but is repressed in Tex. VL30 transcription in Tpex is likely driven by Fli1 , a transcription factor that controls progression to exhaustion (40). We also show that exhausted T cell reprograming by ICB de-represses VL30 expression, indicating that TE expression is part of the re-programing induced by ICB treatments. Finally, we used coding genes or TEs differentially expressed by T pex or Tex to generate specific signatures that were used to analyze T infiltration in cancer patients. In two independent patient cohorts, TE signatures reveal Tpex and Tex infiltration in tumors of ICB-treated patients more convincingly than conventional gene signatures.

The functions of TEs in general, and VL30 in particular, are still incompletely understood. In various cell types and tissues, TEs have been associated with different functions. TEs can be constitutive of genomic regulatory gene expression elements, such as promoters or enhancers (31 ). Active enhancers in T cells are enriched in specific TE subfamilies that participate to their architecture and bear TF binding motifs (31 ). Certain TEs overlap long non-coding RNAs (IncRNAs), with specific functions, and conserved motifs. TEs can participate to epigenetic modulation, regulating deposition of histone marks, for example. Moreover, TEs can be sensed, directly as double stranded RNA or DNA (after retrotranscription) by innate sensors and trigger inflammation (46) (24, 28). Indeed, very recent evidence indicates that LINE-1 splicing isoforms can play a role in maintaining T cell quiescence (47). Finally, some TEs, especially env proteins from ERVs, are translated into proteins with immunoregulatory functions (48). We don’t know today which functions (if any) are associated with the TEs selectively expressed or repressed in Tpex and in Tex. The fact that VL30 repression is selective to Tex in mice and is not a byproduct of silencing of coding genes (VL30 elements are intergenic, autonomous, and expressed independently of their neighbor genes), suggests that their silencing is functionally relevant.

We also show a role for Fli1 in driving VL30 expression in T cells, based on both the identification of Fli1 -binding motifs and the reduction of VL30 expression in Fli1 (Fig 5). The Fli1 -binding motifs are enriched in a cohort of VL30 elements, but not in other TE species enriched in Tex, and the reduction of VL30 expression in FH1 -KO T cells is stronger than the reduction of other TE biotypes (Fig. 5). These results indicate a specific and direct role for Fli1 in the control of VL30 expression in Tpex. Very little is known about the TF that drive TE expression in general, and VL30 in particular. The finding that cells share TFs for the control of canonical genes and TEs across differentiation paths supports the possibility that TEs play some role in that process. The contribution of Fli1 to regulating their transcription, suggests that both coding genes and TEs are part of integrated transcription programs that determine cell fates during T cell differentiation. Future analyses of these integrated gene expression programs will certainly modify our view of T cell exhaustion and help generate tool to better understand their control.

Our results also highlight the potential of TEs for developing signatures for biomarker applications. In the case analyzed here, TE signatures allow better detection of Tpex and Tex infiltration in two independent patient cohorts after ICB treatment (Fig. 8). This may or may not be the case for other cell types or cell states. Nevertheless, these results show that signatures based on expression of individual TEs can be used across different technologies, including bulk and single cell transcriptomics, and in independently generated data sets and cohorts. The results also validate the use of TE-derived signatures as response biomarkers in cancer. Analysis of individual TE expression in human single cells will certainly open new avenues for developing predictive and response signatures in cancer and other human diseases.

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Claims

1. A method for determining if a human subject having a disease, in particular a cancer or an infectious disease, is benefiting from a treatment, in particular with an immune checkpoint blocker or immune checkpoint activator, the method comprising:

- Providing a biological sample previously obtained from the human subject wherein the sample is from the microenvironment of the disease and comprises T cells, in particular the sample is a biopsy of the microenvironment of the disease, more particularly is a biopsy of a tumor,

- Measuring the expression of distal Transposable Elements (TEs), in particular distal TEs belonging to the LTR retrotransposons (LTRs) and Long interspersed nuclear elements (LINEs) subclasses, and/or the expression of Transcription Factor Fli1 , in T cells,

- Classifying the human subject as benefiting from the treatment when Tpex, or Tex, or Tpex and Tex, are detected in the biological sample.

2. The method according to claim 1 , wherein the disease is a cancer or a viral infectious disease or a chronic viral infection.

3. The method according to claim 1 or 2, wherein the disease is a cancer, and the biological sample is issued from the microenvironment of the subject’s tumor.

4. The method according to any one of claims 1 to 3, wherein the treatment from which the subject is likely to benefit is an immunotherapy by administration of an anti-PD-1 or an anti-PD-L1 compound, in particular an anti-PD-1 or an anti-PD-L1 antagonist compound, more particularly an anti-PD-1 or an anti-PD-L1 antagonist antibody of antigen-binding fragment thereof.

5. A method to prognose a disease in a human subject, in particular a cancer or a viral infectious disease or a chronic viral infection, the method comprising:

L1 MDb subfamily, and/or a Transcription Factor Fli1 ,

- Identifying the presence of Tpex in the biological sample as T cells over-expressing the TEs belonging to the MER31 -int subfamily and/or the Transcription Factor Fli1 .

6. A method to predict the response of a human subject to a treatment by immunotherapy, the method comprising:

L1 MDb subfamily, and/or a Transcription Factor Fli1 ,

- Identifying the presence of Tex in the biological sample as T cells over-expressing the distal TEs belonging to the L1 MDb subfamily and/or under-expressing the Transcription Factor Fli1 , - Identifying the presence of Tpex in the biological sample as T cells over-expressing the TEs belonging to the MER31 -int subfamily and/or the Transcription Factor Fli1 ,

7. A Transcription factor FLi1 or a nucleic acid molecule encoding a Transcription factor FLi1 or a compound enhancing the expression of Fli1 in T cells, for use in the treatment of a cancer or an infectious disease.

8. The Transcription factor FLi1 or a nucleic acid molecule encoding a Transcription factor FLi1 or a compound enhancing the expression of Fli1 in T cells, for use in the treatment of a disease, in particular a cancer or an infectious disease, by preventing Tpex differentiation into Tex.

9. A composition comprising a Transcription factor FLi1 or a nucleic acid molecule encoding a Transcription factor FLi1 or a compound enhancing the expression of Fli1 in T cells and T cells, in particular Tpex.

10. A method for treating a human subject having a disease, in particular a cancer or an infectious disease, wherein the method comprises:

L1 MDb subfamily, and/or a Transcription Factor Fli1 ,

11. Use of a Transcription factor FLi1 or a nucleic acid molecule encoding a Transcription factor FLi1 or a compound enhancing the expression of Fli1 for stimulating immune response, in particular for preventing or reducing Tpex differentiation into Tex.

12. A method for reprogramming T cells, comprising:

- Extracting T cells from the biological sample,

13. The method of claim 12, wherein T cells are Tex, or T pex, or T pex and

Tex.