Patent Application
20220365091
The present invention relates in general to the field of cancer immunotherapy, and more particularly, to the use of intratumoral TFR cells as a biomarker informing the choice of immune checkpoint blockade therapy. It moreover pertains to the occurrence of immunotherapy-mediated immune related adverse events (irAEs) and minimization thereof.
Without limiting the scope of the invention, its background is described in connection with anti-PD-1 therapy.
An increased density of T regulatory cells (TREG) in tumors has been linked to poor survival outcomes1. In non-cancer settings, TREG cells have been shown to differentiate into PD-1 expressing follicular regulatory T cells (TFR) that restrain germinal center responses2. It was not previously known whether such differentiation also occurs in the tumor microenvironment, and if so, whether such tumor-infiltrating TFR cells are molecularly distinct from TREG cells or are activated by anti-PD1 therapy.
What is needed is to prevent immunosuppression caused by anti-PD-1 therapy and/or reduction of irAE frequency or severity caused by immunotherapy.
The present invention pertains to T follicular regulatory cells (TFR cells) and their functional role in cancer. To date, TFR cells have been overlooked in cancer. This is a critical oversight, as these cells account for a substantial proportion of tumor-infiltrating CD4+ T cells, and importantly, are highly responsive to immune checkpoint blockade. It was found that TFR cells play a pivotal role in anti-tumor immunity and in determining anti-PD-1 treatment efficacy. The present inventors demonstrate that among tumor-infiltrating lymphocytes, TFR cells express the highest levels of the checkpoint receptor PD-1, making them highly susceptive to anti-PD-1 therapy and in turn lead to an accelerated accumulation of highly suppressive intratumoral TFR cells. Thus, by increasing the abundance and/or activity of intratumoral TFR cells, anti-PD-1 therapy cannot only facilitate, but also dampen anti-tumor immune attack. Moreover, it is shown herein that the efficacy of anti-PD-1 therapy can be improved by depleting TFR cells prior to initiating anti-PD1 treatment (e.g., with anti-IL1R2 antibodies).
Combination therapy utilizing anti-CTLA-4 and nivolumab (anti-PD-1) induces more frequent and severe immune related adverse events (irAEs), thus limiting its use. The inventors have found that selective depletion of TFR cells with novel immunotherapy drugs (e.g., anti-IL1R2) that do not significantly affect or deplete TREG cells, result in fewer and less severe irAEs while maintaining treatment efficacy.
In one embodiment, the present invention includes a method of detecting follicular regulatory T cells (TFR) comprising: obtaining a biological sample from a subject; and detecting whether TFR cells are present or increased in the biological sample by contacting the biological sample with antibodies that detect CD3+CD4+FOXP3+BCL6+ T cells, CD3+CD4+CXCR5+GITR+ T cells, or both. In one aspect, the biological sample is contacted with antibodies that detect CD3+CD4+CXCR5+FOXP3+BCL6+ T cells, CD3+CD4+ CXCR5+FOXP3+ T cells, or CD3+CD4+CXCR5+ BCL6+ GITR+ T cells, or any combination thereof. In another aspect, the increase of TFR cells is detected in the biological sample as compared to a healthy subject. In other aspect, the increase of TFR cells is detected in the biological sample as compared to a healthy subject. In another aspect, the method further comprises detecting the presence or a high level of expression of at least one of: PD-1, CTLA-4, 4-1BB, ICOS, Tox, Ki67, or TCF1 on the TFR. In another aspect, the step of detecting is measuring mRNA, protein, or both. In another aspect, the TFR cells are CD3+CD4+FOXP3+BCL6+ T cells, CD3+CD4+CXCR5+GITR+ T cells, CD3+CD4+CXCR5+FOXP3+BCL6+ T cells, CD3+CD4+CXCR5+FOXP3+ T cells, or CD3+CD4+CXCR5+ BCL6+ GITR+ T cells or any combination thereof. In another aspect, the TFR cells are not LIN− CD45+CD3+CD4+CXCR5−FOXP3+BCL6−PD-1− cells. In another aspect, the biological sample is a tumor sample is selected from a colorectal, a melanoma, a lung, a liver, a head and neck, or a breast cancer issue. In another aspect, the tumor sample is obtained from a subject suspected of having an immune reactive adverse effect (IRAE). In another aspect, the TFR cells are PD-1high.
In another embodiment, the present invention includes a method of diagnosing and treating a cancer in a patient, the method comprising the steps of: determining whether the patient has an increase in PD-1 expressing follicular regulatory T (TFR) cells in or about the cancer by: obtaining or having obtained a biological sample from the patient; performing or having performed an assay on the biological sample to determine if the patient has an increase in PD-1 expressing TFR cells, wherein the TFR cells are CD3+CD4+FOXP3+BCL6+ T cells, CD3+CD4+CXCR5+GITR+ T cells, CD3+CD4+CXCR5+FOXP3+BCL6+ T cells, CD3+CD4+CXCR5+FOXP3+ T cells, or CD3+CD4+CXCR5+BCL6+GITR+ T cells or any combination thereof, when compared to a reference level generated for specific tumor types or a healthy patient by: identifying that the patient has an increase in TFR cells that will limit the effectiveness of anti-PD-1 cancer therapy; and if the patient has TFR cells or shows an increase in TFR cells, then internally administering a selective TFR cell depleting therapy to the patient, and if the patient does not have TFR cells, an increase in the TFR cells, or if the TFR cells have been depleted by administering a TFR cell depleting therapy to the patient, then administering anti-PD-1 therapy to the patient in an amount sufficient to treat the cancer, wherein a failure to control cancer growth, or an immune related adverse effects (irAE), is lower following the depletion of FoxP3-expressing regulatory T (TREG) cells and the TFR cells in the patient. In one aspect, the presence of TFR cells is determined in a tumor biopsy. In another aspect, the step of detecting is measuring mRNA, protein, or both. In another aspect, the selective TFR cell depleting therapy is at least one of anti-CTLA-4, anti-IL1R2, anti-4-1BB, anti-ICOS, anti-GITR, anti-OX40, or anti-IL1R2, anti-CCR8 therapy, or other targets specifically expressed or enriched on TFR cells when compared to TREG cells and other T cell populations. In another aspect, the cancer is selected from a colorectal, a melanoma, a lung, a liver, a head and neck, and a breast cancer. In another aspect, the TFR cells express one or more of the following markers: FOXP3, GITR, CTLA-4, 4-1BB, ICOS, Tox, Ki67, and TCF1. In another aspect, the presence of TFR cells is further determined by measuring the expression of one or more genes selected from Tnfrsf1b, Lag3, Tigit, Batf, Illr2, Ccr8, Pdcd1, Tox, CCR8, TNFRSF1B, DUSP14, CLP1. In another aspect, the selective TFR cell depleting therapy does not reduce or eliminate TREGS.
In another embodiment, the present invention includes a method for treating a patient suffering from a cancer susceptible to anti-PD-1 therapy, the method comprising the steps of: determining whether the patient has an increase in PD-1 expressing follicular regulatory T (TFR) cells in or about the cancer, when compared to a reference level generated for specific tumor types or a healthy patient by: obtaining or having obtained a biological sample from the patient; and performing or having performed an assay on the biological sample to determine if the patient has PD-1 expressing TFR cells; and if the patient has the PD-1 expressing TFR cells, then administering a PD-1 expressing TFR depleting therapy to the patient, and if the patient does not have the TFR cells or if the TFR cells have been depleted by administering a selective TFR cell depleting therapy to the patient, then administering anti-PD-1 therapy to the patient in an amount sufficient to treat the cancer susceptible to anti-PD-1 therapy and to reduce immune related adverse effects (irAEs), wherein a risk of failure to control cancer growth is lower following the depletion of the TFR cells In one aspect, the presence of TFR cells is determined from a cancer tissue biopsy. In another aspect, the step of detecting is measuring mRNA, protein, or both. In another aspect, the selective TFR cell depleting therapy is at least one of, but not limited to, anti-IL1R2, anti-OX40, anti-TNFR2, anti-CCR8 antibodies or other targets specifically expressed or enriched on TFR cells when compared to TREG cells and other T cell populations. In another aspect, the selective TFR cells depleting therapy is at least one of an anti-CTLA-4, anti-IL1R2, anti-4-1BB, anti-ICOS, anti-GITR, anti-OX40, or anti-IL1R2, anti-CCR8 depletion therapy. In another aspect, the TFR cells are CD3+CD4+ FOXP3+BCL6+ T cells, CD3+CD4+CXCR5+GITR+ T cells, CD3+CD4+CXCR5+FOXP3+BCL6+ T cells, CD3+CD4+ CXCR5+FOXP3+ T cells, or CD3+CD4+CXCR5+ BCL6+ GITR+ T cells or any combination thereof. In another aspect, the cancer is selected from a colorectal, a melanoma, a lung, a liver, a head and neck, and a breast cancer. In another aspect, the TFR cells express or have a high level of expression one or more of the following markers: PD-1, BCL6, FOXP3, CXCR5, GITR, CTLA-4, 4-1BB, ICOS, Tox, Ki67, and TCF1. In another aspect, the presence of TFR cells is determined by measuring the expression of two or more genes or proteins selected from Tnfrsf1b, Lag3, Tigit, Batf, Illr2, Ccr8, Pdcd1, Tox, CCR8, TNFRSF1B.
In another embodiment, the present invention includes a method of determining if a patient has follicular regulatory T (TFR) cells that will increase cancer growth, or cause an immune-related adverse effect (irAE), when treated with anti-PD-1 therapy comprising: obtaining a biological sample from a patient; and detecting the TFR cells in the biological sample by contacting the biological sample with antibodies that detect CD3+CD4+ FOXP3+BCL6+ T cells, CD3+CD4+CXCR5+GITR+ T cells, CD3+CD4+CXCR5+FOXP3+BCL6+ T cells, CD3+CD4+ CXCR5+FOXP3+ T cells, or CD3+CD4+CXCR5+ BCL6+ GITR+ T cells or any combination thereof, when compared to a reference level generated for specific tumor types or a healthy patient, and detecting the TFR cells in the biological sample, wherein if the patient has an increase in TFR cells in the biological sample anti-PD-1 therapy will increase cancer growth or cause the irAE. In one aspect, the method further comprises detecting the presence or a high level of expression of at least one of: GITR, CTLA-4, 4-1BB, ICOS, Tox, Ki67, or TCF1 on the TFR cells. In another aspect, the step of detecting is measuring mRNA, protein, or both. In another aspect, the selective TFR cell depleting therapy is at least one of, but not limited to, anti-IL1R2, anti-OX40, anti-TNFR2, anti-CCR8 antibodies or other targets specifically expressed or enriched on TFR cells when compared to TREG cells and other T cell populations. In another aspect, the TFR cell depleting therapy is at least one of anti-CTLA-4, anti-IL1R2, anti-4-1BB, anti-ICOS, anti-GITR, anti-OX40, or anti-IL1R2, anti-CCR8 depletion therapy. In another aspect, the TFR cells are LIN−CD45+CD3+CD4+BCL6+ FOXP3+PD-1+GITR+ or LIN−CD45+CD3+CD4+CXCR5+PD-1+GITR+. In another aspect, the TFR cells are not LIN−CD45+CD3+CD4+CXCR5−FOXP3+BCL6−PD-1− cells. In another aspect, the biological sample is a cancer tissue. In another aspect, the biological sample is selected from a colorectal, a melanoma, a lung, a liver, a head and neck, or a breast cancer tissue. In another aspect, the TFR cells are PD-1high.
In another embodiment, the present invention includes a method of depleting follicular regulatory T cells (TFR) cells without affecting regulatory T (TREGS) cells, comprising: treating a T cell population with a treatment that reduces or eliminates PD-1 expressing TFR cells and co-administering anti-IL1R2 antibodies to protect TREGS, in order to prevent or reduce immune related adverse events (irAEs). In one aspect, the irAE is stimulation of CD4 or CD8 T cell proliferation, TFR cells infiltrating a tumor, or that reduces or abrogates the effectiveness of an anticancer therapy. In another aspect, the anticancer therapy is anti-PD-1 therapy of a cancer selected from colorectal, melanoma, lung, liver, head and neck, or breast cancer. In another aspect, the TFR cells are PD-1high. In another aspect, the selective elimination of TFR cells is in vitro.
In another embodiment, the present invention includes a method of depleting follicular regulatory T cells (TFR) cells without affecting regulatory T (TREGS) cells, comprising: treating a patient with reagents that selectively eliminate (PD-1 expressing) TFR cells without significantly affecting or depleting TREG cells, in order to prevent or reduce the occurrence of immune related adverse events (irAEs). In another aspect, the anticancer therapy is anti-PD-1 therapy of a cancer selected from colorectal, melanoma, lung, liver, head and neck, or breast cancer. In another aspect, the TFR cells are PD-1high.
In another embodiment, the present invention includes a method of reducing immune related adverse events (irAEs) comprising: selectively depleting TFR cells, but not all FOXP3-expressing (Tregs, TFR, or both) cells, by specifically targeting TFR-specific cells with at least one of anti-CTLA-4, anti-IL1R2, anti-4-1BB, anti-ICOS, anti-GITR, anti-OX40, or anti-IL1R2, anti-CCR8 depletion. In one aspect, the irAE is stimulation of at least one of CD4 or CD8 T cell proliferation, TFR infiltrating a tumor, or that reduces or abrogates the effectiveness of an anticancer therapy. In another aspect, the anticancer therapy is anti-PD-1 therapy of cancers selected from colorectal, melanoma, lung, liver, head and neck, or breast cancer. In another aspect, the TFR cells are PD-1high.
For a more complete understanding of the features and advantages of the present invention, reference is now made to the detailed description of the invention along with the accompanying figures and in which:
While the making and using of various embodiments of the present invention are discussed in detail below, it should be appreciated that the present invention provides many applicable inventive concepts that can be embodied in a wide variety of specific contexts. The specific embodiments discussed herein are merely illustrative of specific ways to make and use the invention and do not delimit the scope of the invention.
To facilitate the understanding of this invention, a number of terms are defined below. Terms defined herein have meanings as commonly understood by a person of ordinary skill in the areas relevant to the present invention. Terms such as “a”, “an” and “the” are not intended to refer to only a singular entity but include the general class of which a specific example may be used for illustration. The terminology herein is used to describe specific embodiments of the invention, but their usage does not limit the invention, except as outlined in the claims.
Appended hereto in electronic format only, and as an additional part of the disclosure, are submitted two supplemental Tables 1 and 2 that are an integral part of the disclosure.
As used herein, the terms “treatment”, “treating”, and the like, may include amelioration or elimination of a developed disease or condition once it has been established or alleviation of the characteristic symptoms of such disease or condition, specifically, immune suppression by follicular regulatory T cells (TFR). Specifically, the treatment of a disease or condition that would benefit from preventing excessive activation or accumulation of PD-1 expressing follicular regulatory T cells (TFR), e.g., anti-PD1 treatment in cancer. The treatment would also target the indiscriminate depletion of FoxP3-expressing (TREG+TFR) cells, which results in immune related adverse events (irAEs). As used herein, these terms “treatment”, “treating”, and the like, may also encompass, depending on the condition of the subject, preventing the onset of a disease or condition or of symptoms associated with the disease or condition, including, for example, reducing the immune suppression caused by TFR cells of cytotoxic tumor infiltrating lymphocytes (TIL). Such prevention or reduction prior to affliction may refer to administration of a therapeutic compound to a subject that is not at the time of administration afflicted with the disease or condition.
As used herein, the term “preventing” refers to preventing the indiscriminate depletion of FoxP3-expressing (TREG+TFR) cells, which causes irAEs. Selectively depleting TFR cells (without or with minimal depletion of TREG cells) minimizes irAEs while maintaining treatment efficacy, especially in combination with anti-PD-1 therapy.
As used herein, these terms “subject” or “patient” can be any mammal, including a human and the treatment can be provided in vivo or cells can be treated in vitro and then returned to the subject or patient.
As used herein, the terms “standard control” “control” or “control biological sample” refers to a sample, measurement, or value that serves as a reference, usually a known reference, for comparison to a subject biological sample, test sample, measurement, or value. For example, a test biological sample can be taken from a patient suspected of having a cancer or through the generation of a reference level for specific tumor types or immune related adverse events (irAEs). A standard control can represent an average measurement or value gathered from a TFR population of similar individuals that do not have a given disease or condition (i.e., standard control population), e.g., healthy individuals with a similar medical background, same age, weight, etc. that do not have a cancer or immune related adverse events (irAEs). As shown herein, irAEs are caused by immunotherapy-mediated depletion (e.g., CTLA-4 antibodies) of suppressive FoxP3-expressing cells in multiple tissues (not only cancer tissue). Thus, specifically targeting and depleting TFR cells decreases irAEs while maintaining treatment efficacy. A standard control value can also be obtained from the same individual, e.g., from an earlier-obtained sample, prior to disease or condition (e.g., a cancer or immune related adverse events (irAEs)), or prior to treatment. One of skill in the art will understand which standard controls are valuable in a given situation and be able to analyze data based on comparisons to standard control values. Standard controls are also valuable for determining the significance of data. For example, if values for a given parameter are widely variant in standard controls, variation in test samples will not be considered as significant.
As used herein, the term “TFR cell population” refers to a cell population which has been processed so as to identify the cell population from other cell populations with which it is normally associated in its naturally occurring state using the various markers described herein, including both cell surface markers, but also the expression of genes or proteins that remain intracellularly and can be measures in vivo or ex vivo. The purified TFR cell population can, thus, represent an enriched cell population in that the relative concentration of the cell population in a sample can be increased following such processing in comparison to its natural state. Alternatively, the TFR cell population can be reduced by at least 50%, 60%, 70%, 80%, or at least 90%, or at least 95% or 100% in comparison to its natural state (i.e. pre-treatment) to prevent their immune suppressive activity. Such purified cell population may, thus, represent a cell preparation which can be further processed so as to obtain commercially viable preparations.
Agents for reducing or eliminating TFR cells may be processed so as to be part of a pharmaceutical composition, such as those taught herein. Non-limiting examples include anti-CTLA-4, anti-IL1R2, anti-4-1BB, anti-TNFR2, anti-ICOS, anti-GITR, anti-OX40, and/or anti-CCR8, that lead to TFR depletion. For example, in one embodiment, the cell preparation can be prepared for transportation or storage in a serum-based solution containing necessary additives, which can then be stored or transported in a frozen form. In doing so, the person of skill will readily understand that the cell preparation is in a composition that includes a suitable carrier, which composition is significantly different from the natural occurring separate elements.
For example, the serum-based preparation may comprise human serum or fetal bovine serum, which is a structural form that is markedly different from the form of the naturally occurring elements of the preparation. The resulting preparation includes cells that are in dormant state, for example, that may have slowed-down or stopped intracellular metabolic reactions and/or that may have structural modifications to their cellular membranes. The resulting preparation includes cells that can, thus, be packaged or shipped while minimizing cell loss which would otherwise occur with the naturally occurring cells. This property of minimizing cell loss of the resulting preparation/composition is markedly different from properties of the cells by themselves in nature. A person skilled in the art would be able to determine a suitable preparation without departing from the present disclosure.
As used herein, the term “carrier” refers to any carrier, diluent or excipient that is compatible with the herein described composition that reduces or eliminates TFR, such as, anti-CTLA-4, anti-IL1R2, anti-4-1BB, anti-ICOS, anti-GITR, anti-OX40, and/or anti-CCR8 (including antibodies) that cause TFR depletion or inactivation. Suitable acceptable carriers known in the art include, but are not limited to, water, saline, glucose, dextrose, buffered solutions, and the like. Such a carrier is advantageously non-toxic or has a limited effect on non-TFR immune cells and not harmful to the subject. It may also be biodegradable. The carrier may be a solid or liquid acceptable carrier. A suitable solid acceptable carrier is a non-toxic carrier. For instance, this solid acceptable carrier may be a common solid micronized injectable such as the component of a typical injectable composition for example, but without being limited to, kaolin, talc, calcium carbonate, chitosan, starch, lactose, and the like. A suitable liquid acceptable carrier may be, for example, water, saline, DMSO, culture medium such as DMEM, and the like. The person skilled in the art will be able to determine a suitable acceptable carrier for a specific application without departing from the present disclosure.
As used herein, the terms “determining,” “measuring,” “evaluating,” “assessing,” and “assaying,” as used herein, generally refer to any form of measurement, and include determining if TFR cells are present or not in a biological sample. In addition, these can also be used to determine the abundance of TFR cells. These terms include both quantitative and/or qualitative determinations, which both require sample processing and transformation steps of the biological sample. Assessing may be relative or absolute. The phrase “assessing the presence of” can include determining the amount of something present, as well as determining whether it is present or absent.
As used herein, the term “therapeutically effective amount” may include the amount necessary to allow the component or composition that prevent the TFR cells from performing their immunological role without causing overly negative effects in the host to which the component or composition is administered. In one example, the agents reduce or eliminate TFR cells (or their activity), but do not significantly affect or deplete TREG cells. The exact amount of the components to be used or the composition to be administered will vary according to factors such as the type of condition being treated, the type and age of the subject to be treated, the mode of administration, as well as the other ingredients in the composition.
As used herein, the term “expression” refers to a level of expression of a gene or a protein, that is transcribed from the gene. Generally, an “expression” level is determined by measuring the expression level of a gene of interest for a given cell population, determining the median expression level of that gene for the cell population, and comparing the expression level of the same gene for a particular cell to the median expression level for a different cell population. For example, if the expression level of a gene of interest for the single cell population is determined to be above the median expression level of the patient population, that cell is determined to have high expression of the gene of interest. Alternatively, if the expression level of a gene of interest for the cell population is determined to be below the median expression level of a normal cell population, that cell is determined to have low expression of the gene of interest.
As used herein when referring to a cell surface or other detectable marker, the terms “high” or “high expression” refers to a statistically significant increase in expression compared to naïve T cells. In certain embodiments, a statistically significant increase in expression refers to at least one log higher expression when compared to naïve T cells; in other cases, it can be 2 or even 3 logs higher. The expression can be measured with any number of methods, for example, fluorescence activated cell sorting, RNA-expression, luminescent or chemiluminescent platforms, Illumina®, MesoScale®, or other similar systems. As used herein, for example, when referring to the surface expression of PD-1high in the context of T cells (e.g., TFR), are T cells that have a statistically significant increased expression when compared to naïve T cells. This statistically significant increase refers to, in certain embodiments, at least one log higher surface expression.
An increased density of regulatory T cells (TREG) in tumors has been linked to poor survival outcomes1. In non-cancer settings, TREG cells have been shown to differentiate into PD-1 expressing follicular regulatory T cells (TFR) that restrain germinal center responses2. It is not known whether such differentiation also occurs in the tumor microenvironment, and if so, whether such tumor-infiltrating TFR cells are molecularly distinct from TREG cells or are activated by anti-PD1 therapy. In this example, the inventors show that TFR cells are present in high numbers in human and murine tumor tissues, share T cell receptor (TCR) clonotypes with intratumoral TREG cells and express high levels of PD-1. Single-cell TCR data, trajectory analyses and adoptive transfer studies indicate intratumoral conversion of TREG to TFR cells. When compared to TREG cells, TFR cells exhibited enhanced suppressive capacity in vitro and in vivo and expressed higher levels of molecules known to be linked to co-stimulation (4-1BB, ICOS, GITR), cell proliferation (Ki67), suppressive function (CTLA-4), and self-renewal potential (TCF1), all features suggestive of superior functional properties.
In syngeneic tumor models, anti-PD-1 treatment increased the number of tumor-infiltrating TFR cells. Conditional knockout of TFR cells or depletion of TFR cells with anti-CTLA4 antibody prior to anti-PD1 treatment, improved tumor control in mice. Notably, in a cohort of 271 melanoma patients, treatment with anti-CTLA-4 followed by anti-PD-1 at progression was associated with better long-term survival outcomes than anti-PD-1 or anti-CTLA-4 monotherapy, anti-PD-1 followed by anti CTLA-4 at progression or concomitant combination therapy. These findings illustrate that anti-PD1 therapy has the potential to regulate abundance and/or functionality of TFR cells, and can thus induce a profoundly immunosuppressive milieu impeding anti-tumor immunity. Thus, indiscriminate use of anti-PD-1 therapy can prove detrimental in some patients.
Follicular regulatory T cells (TFR) inhibit T and B cell responses to mitigate germinal center reactions in secondary lymphoid organs3, impede humoral immunity towards self-antigens2 and display heightened suppressive capacity when compared to TREG cells4,5. TFR cells are being characterized by their joint expression of the surface molecules Cxcr5 and Gitr2,6, or by their co-expression of the transcription factors FoxP3 and Bcl67. Several studies have demonstrated that, depending on disease context and organ, cells of the T follicular lineage express varying levels of CXCR5 and BCL68,9. Moreover, it has been shown that deletion of Cxcr5 expression in FoxP3-expressing cells does not abrogate the development and maintenance of Bcl6+ TFR cells7, indicating that distinct subsets of TFR cell cells exist, which not only differ in their expression of Cxcr5 and Bcl6, but also in their expression of CD2510,11.
Most tumors contain tertiary lymphoid structures and because cancerous cells frequently express self or altered-self antigens, the inventors investigated if TREG and TFR cells accumulate in parallel in the tumor microenvironment (TME) as a means of effective immune evasion.
TFR cells are present in multiple cancer types. The inventors integrated 9 published single-cell RNA-seq datasets and performed a meta-analysis of tumor-infiltrating CD4+ T cells (n=25,149) from patients with six different cancer types (Supplemental Data Table 1). As expected, FOXP3-expressing CD4+ T cells (i.e., TREG cells) clustered distinctly and represented 5-55% of all tumor-infiltrating CD4+ T cells (
TFR cells exhibit unique transcriptomic features. As few studies have thoroughly analyzed the transcriptomic features of TFR cells, the inventors first utilized well-established immunization models in mice to gain mechanistic insights into TFR cell function and to assess whether the features identified in human tumor-infiltrating TFR cells are also applicable to murine TFR cells. Immunization with ovalbumin and adjuvant (CFA or MPLA) induced robust TFR responses (
Next, the inventors performed bulk RNA-seq analyses of enriched populations of TREG (CD4+CD25+CXCR5−) and TFR cells (CD4+CXCR5+GITR+) (
Intratumoral TREG and TFR cells share clonotypes but differ in their molecular profile. Recent data demonstrate that tumor-infiltrating TREG cells potently recognize tumor (neo)antigens and, upon antigen-encounter, undergo clonal expansion24. Given that antigen-specific activation of TREG cells in the context of viral infection has been implicated in promoting their differentiation into TFR cells via Tcf1-mediated induction of Bcl625, the inventors hypothesized that tumor-associated antigen (TAA) recognition may also trigger TREG to TFR conversion within the TME. To assess this, the inventors performed combined single-cell RNA-seq and TCR-seq of sorted CD4+ (TFH, TREG and TFR) and CD8+ TILs from primary tumor tissue and tumor-infiltrated lymph nodes of two HNSCC patients (n=8,722 cells). Unsupervised clustering revealed two distinct CD4+ T cell clusters (1 and 6) that were enriched for FOXP3 expression (
These observations are consistent with a model in which the TME is initially infiltrated by a large and highly diverse pool of bystander (i.e., not TAA-specific) TREG cells, and a smaller pool of TAA-specific TREG clones, which are poised for differentiation into tissue resident TFR cells. This implies that TFR cells comprise a larger proportion of tumor-reactive clones than TREG cells, which is substantiated herein by the finding that TFR cells expressed significantly higher levels of 4-1BB than TREG cells (
TFR cells exhibit superior suppressive capacity. Next, the inventors assessed frequency, activity and functional responsiveness of TFR cells in murine tumor models. TFR cells (CD3+CD4+Bcl6+Foxp3+) were present in tumor samples from two syngeneic tumor model systems (B16F10 melanoma and MC38 colorectal tumor cell lines) (
Similar to human TFR cells, murine TFR cells exhibited increased proliferative potential, as evidenced by Ki-67 expression levels, and increased expression of Tcf1 and 4-1bb compared to TREG cells (
To experimentally validate that TFR cells are more suppressive than TREG cells, the inventors performed functional assays in vitro and in vivo. Strikingly, it was found that TFR cells suppressed CD8+ T cell proliferation significantly more efficiently than TREG cells (
TFR cells are responsive to anti-PD-1 therapy. Anti-PD-1 monotherapy resulted in a significant increase in the frequency of TFR cells in both MC38 and B16F10 tumor models (
To uncouple the effects of TREG and TFR cells on anti-tumor immunity and anti-PD-1 treatment efficacy, the inventors utilized a genetic knockdown system, in which TFR cells can be selectively depleted. Tamoxifen-induced depletion of TFR cells in female heterozygous FoxP3eGFP-cre-ERT2cre/wt×Bcl6fl/fl mice33 prior to initiation of anti-PD-1 therapy, significantly decreased tumor growth, demonstrating that TFR cells curtail anti-PD-1 treatment efficacy (
In an independent cohort of patients with melanoma (n=29), who received anti-PD1 treatment, the inventors observed poor survival outcomes in patients with a higher proportion of CD4+ T cells co-expressing FOXP3 and BCL6 (BCL6+ TFR cells) in tumor (
Sequential ICB is beneficial in melanoma patients. To overcome the suppressive milieu induced by anti-PD-1-mediated increase in TFR cells, the inventors reasoned that it may be necessary to deplete TFR cells in the tumor prior to initiating anti-PD1 therapy. Anti-CTLA-4 treatment is believed to deplete intratumoral TREG cells via antibody-dependent cellular cytotoxicity37. Given that tumor-infiltrating TFR cells expressed higher levels of CTLA-4 than TREG cells in both human and mouse (
To test the clinical significance of sequential ICB treatment, the inventors retrospectively assessed the survival outcomes of patients with inoperable melanoma (n=271), who were, based on their treatment regimens, stratified into 5 groups: 1st line anti-CTLA-4, 1st line anti-PD-1, simultaneous combination therapy, sequential therapy with anti-CTLA-4 followed by anti-PD-1 at progression and vice versa. Sequential treatment with anti-CTLA-4 followed by anti-PD-1 was associated with better long-term overall survival (OS) outcomes when compared to the 4 other groups (p<0.001) (
In summary, these results demonstrate in both human and murine tumors, the existence of suppressive and highly proliferative TFR cells that, among tumor-infiltrating lymphocytes, expressed the highest levels of CTLA-4 and PD-1. The findings in the murine tumor model demonstrate that intratumoral TFR cells are responsive to ICB and that by increasing the abundance of TFR cells, anti-PD-1 therapy can not only facilitate, but also dampen anti-tumor immune attack. The inventors provide critical insights into how anti-CTLA-4 and anti-PD-1 therapies mediate their function, and highlight the clinical benefit of sequential dosing to render tumors responsive to anti-PD1 therapy. The well-described clinical scenario in which some tumors hyper-progress following anti-PD1 therapy40,41 is finally explained due to the effects of treatment on a highly suppressive immune cell compartment (TFR cells), especially in patients with an initially high level of tumor-specific TREG (TFR precursor) cells. Conversely, exacerbated immune-related adverse events observed upon combination therapy, might be caused by anti-CTLA-4-mediated depletion of suppressive TFR cells and subsequent uninhibited anti-PD-1-mediated activation of effector CD4+ and CD8+ T cells, hypotheses which can be addressed in future studies. Finally, these results provide for the first time a unique composition of stimulatory versus suppressive T cells in the TME of each patient, as well as their differentiation status (i.e., PD-1 expression levels and frequency of TFR cells within CD4+ T cells), as important immunological determinants driving anti-PD-1 treatment efficacy.
Human tumor samples. The study was approved by the Southampton and South West Hampshire Research Ethics Board, and written informed consent was obtained from all subjects. Newly diagnosed, untreated patients with NSCLC, were prospectively recruited once referred. Freshly resected tumor tissue was obtained from lung cancer patients following surgical resection and after histological confirmation. The patient cohort for the survival analysis was collected by retrospective evaluation of a centralized prescribing system (Aria, Varian Medical Systems Inc, Crawley, UK). All patients started on immunotherapy at a single institution (Southampton University Hospitals NHS Foundation Trust) with immunotherapy for melanoma between July 2014 to October 2018 were included. Patients were divided into cohorts according to first type immunotherapy treatment approved in the United Kingdom (aPD-1, either nivolumab or pembrolizumab, N=98), aCTLA-4 antibody (ipilimumab (88) or joint administration of nivolumab plus ipilimumab on up to four occasions (N=85), followed by maintenance nivolumab where appropriate. Dosing was according to standard of care at the time (3 mg/kg ipilimumab×4, 2 mg/kg of pembrolizumab 3 weekly, later 200 mg flat dosing, 3 mg/kg nivolumab, then 480 mg flat dosing, and in combination 3 mg/kg ipilimumab+1 mg/kg nivolumab, four doses, followed by 3 mg/kg nivolumab). All patients were included who had at least one dose of immunotherapy. Clinical data were obtained from an electronic hospital record for age, gender, BRAF status, LDH, M stage, performance status. For clinical outcome overall survival was collected to death or censored at last clinical review. Data were anonymized by the treating clinician (I.K. and C.H.O.) once the data had been collated and verified. Prism 8 (Graph Pad Software, San Diego, USA) was used for ANOVA and to plot Kaplan Meier Survival Graphs and estimate treatment differences using a Log-rank (Mantel-Cox) test on survival curves. SPSS v26 (IBM Corp, New York, USA) was used to evaluate imbalances between treatment groups via Chi Square testing followed by Cox Regression analysis. Fur multiple testing a Bonferroni error correction was applied.
Mice. C57BL/6J, Bcl6fl/fl, OT-I and RAG1−/− mice were obtained from Jackson labs. FoxP3eGFP-cre-ERT2 and FoxP3YFP-cre mice were a kind gift from Klaus Ley (LJI) and FoxP3eGFP mice were a kind gift from Amnon Altman (LJI). All mice were between 6-12 weeks old at the beginning of experiments. All animal work was approved by the relevant La Jolla institute for Immunology Animal Ethics Committee.
Tumor cell lines. B16F10-OVA cells were a gift from the laboratory of Prof. Linden (LJI) and MC38-OVA cells were a gift from the lab Prof. Fuchs (UPenn) and approved for use by Prof. Smyth (Peter MacCallum cancer centre). B16F10-OVA cells form distinct melanoma tumors and are thus true melanoma cells. MC38-OVA cells were generated at the Peter MacCallum cancer centre and therefore authenticated by the developer. Cell lines tested negative for mycoplasma infection and were subsequently treated with Plasmocin to prevent contamination.
Tumor models. Tumor cell lines were tested negatively for mycoplasma infection and Plasmocin (InvivoGen) was used as a routine addition to culture media to prevent mycoplasma contamination. Mice were inoculated with 1-1.5×105 B16F10-OVA cells or 2×106 MC38-OVA cells subcutaneously into the right flank. Mice were injected intraperitoneally at indicated time points with either 200 μg anti-PD-1 (29F1.A12, InvivoPlus anti-mouse PD-1, Bioxcell), anti-CTLA-4 (9H10, InvivoPlus anti-mouse CTLA-4, Bioxcell) or respective isotype controls (anti-CTLA-4 isotype ctrl, InVivoPlus polyclonal Syrian hamster IgG, Bioxcell) (anti-PD-1 isotype control, InVivoPlus rat IgG2a isotype control, anti-trinitrophenol, Bioxcell). Tumor size was monitored every other day, and tumor harvested at indicated time points for analysis of tumor-infiltrating lymphocytes. Tumor volume was calculated as ½×D×d2, where D is the major axis and d is the minor axis, as described previously43.
Suppression and Competition Assay. Mice were immunized i.p. with Ovalbumin in alum (100 ug in 100 ul sterile PBS mixed with 100 ul 2% alum). At day 3-5 after immunization, mice were immunized i.p. with an IL-2/anti-IL-2Receptor complex (1 ug IL-2, 5 ug anti-IL-2Receptor Ab, mixed for 30 min at 37 degrees Celsius) to achieve polyclonal expansion of TREG cells in vivo, as described previously44. Lymphocytes (CD4+ and CD8+ T cells) were isolated from spleen by mechanical dispersion through a 70-μm cell strainer (Miltenyi) to generate single-cell suspensions. CD4+ and CD8+ T cells were purified (Stemcell) according to manufacturer's instructions.
In Vitro—CD8+ T cells were labelled with CellTrace Violet (CTV) (Thermofisher) and 40,000 cells were added to 96 well cell culture plated, pre-coated with anti-CD3, in 200 ul complete RPMI media. Purified CD4+ T cells were stained and different numbers of viable (Fixable Viability dye) TREG cells (CD4+CXCR5−CD25+GITR+) or TFR cells (CD4+CXCR5+CD25+GITR+) were sorted into the cell culture plate containing the CTV-labeled CD8+ T cells. CD8+ T cell proliferation (CTV dilution) was determined 3 days later.
In Vivo—OT-I CD8+ T cells were purified (Stemcell), CD4+ T cells were purified, stained and TREG and TFR cells were sorted as described above. Cells were counted and 2×105 OT-I T cells, 2×105 OT-I T cells+5×104 TREG cells (4:1 ratio) or 2×105 OT-I T cells+5×104 TFR cells (4:1 ratio) were adoptively transferred into B16F10-OVA tumor-bearing Rag1−/− recipient mice 3 days after tumor inoculation.
Competition assay—OT-I CD8+ T cells were purified (Stemcell), FoxP3+ T cells were purified from FoxP3YFP-cre×Bcl6fl/fl mice (YFP+) and FoxP3eGFP mice (GFP+) and 4×105 cells (2×105 OT-I T cells, 1×105 GFP+ TREG cells and 1×105YFP+ TREG cells) were adoptively transferred into B16F10-OVA tumor-bearing Rag1−/− recipient mice 3 days after tumor inoculation.
Flow cytometry. Human T cells were isolated from cryopreserved tumor tissue using a combination of enzymatic and mechanical dissociation, as previously described45. Cells were prepared in staining buffer (PBS with 2% FBS and 2 mM EDTA), FcR blocked (clone 2.4G2, BD Biosciences) and stained with indicated primary antibodies for 30 minutes at 4° C.; secondary stains were done for selected markers. Samples were then sorted or fixed and intracellularly stained using a FoxP3 transcription factor kit according to manufacturer's instructions (eBioscience). Cell viability was determined using fixable viability dye (ThermoFisher).
Murine samples—Lymphocytes were isolated from spleen by mechanical dispersion through a 70-μm cell strainer (Miltenyi) to generate single-cell suspensions. RBC lysis (Biolegend) was performed to remove red blood cells. Tumor samples were harvested and lymphocytes were isolated by dispersing the tumor tissue in 2 ml of PBS, followed by incubation of samples at 37° C. for 15 min with DNase I (Sigma) and Liberase DL (Roche). The suspension was then diluted with MACS buffer and passed through a 70-μm cell strainer to generate a single cell suspension. Cells were prepared in staining buffer (PBS with 2% FBS and 2 mM EDTA) and FcR blocked (clone 2.4G2, BD Biosciences) and stained with indicated primary antibodies for 30 minutes at 4° C.; secondary stains were done for selected markers. Samples were then sorted or fixed and intracellularly stained using a FoxP3 transcription factor kit according to manufacturer's instructions (eBioscience). Cell viability was determined using fixable viability dye (ThermoFisher). For bulk-RNA-seq analyses, the inventors sorted tumor-infiltrating TFR cells based on the co-expression of CXCR5 and GITR2,6 (
Histology and immunohistochemistry. Deparaffinisation, rehydration, antigen retrieval and IHC staining was carried out using a Dako PT Link Autostainer. Antigen retrieval was performed using the EnVision FLEX Target Retrieval Solution, High pH (Agilent) for all antibodies. The primary antibodies used for IHC include anti-CD8 (pre-diluted, C8/144B, Agilent Dako), anti-CD4 (1:100, 4B12, Agilent Dako), anti-FOXP3 (1:100, ab20034, Abcam), anti-CXCR5 (1:50, D6L3C, CellSignaling), anti-BCL6 (1:30, NCL-L-Bcl6-6-564, Leica), anti-CD31 (pre-diluted product diluted further 1:5, Agilent Dako) and anti-PanCK (AE1/AE3; pre-diluted; Agilent Dako). Primary antibodies were detected using EnVision FLEX HRP (Agilent Dako) and either Rabbit or Mouse Link reagents (Agilent Dako) as appropriate. Chromogenic visualization was completed with either two washes for five minutes in DAB or one wash for thirty minutes in AEC and counterstained with hematoxylin. To analyze multiple markers on single sections, multiplexed IHC staining was performed as described previously. 4 micron tissue sections were stained with anti-PanCK (for NSCLC samples) or anti-CD31 (for melanoma samples) antibodies, visualized using DAB chromogenic substrate and scanned using a ZEISS Axio Scan.Z1 with a 20× air immersion objective. Each immune marker was then visualized using AEC chromogenic substrate and scanned. Between each staining iteration, antigen retrieval was performed, preparing for the subsequent round of staining and denaturing of the preceding antibodies; along with removal of the labile AEC staining in organic solvent (50% ethanol, 2 min; 100% ethanol, 2 min; 100% xylene; 2 min, 100% ethanol, 2 min; and 50% ethanol, 2 min).
For each tissue section the PanCK or CD31 alone image was used as a reference for tiled registration of each staining iteration, using the linear stack alignment with SIFT plugin. Color deconvolution was performed using the “H AEC” vector matrix from the Fiji plugin generating three images representing blue (hematoxylin), red (AEC and DAB) and green (hematoxylin and DAB) staining intensities. These images were inverted, so that higher pixel values represented greater staining intensity. To generate an AEC specific image the “green” pixel intensities were subtracted from the “red” pixel intensities. 8 bit deconvoluted images were then visually inspected to determine a pixel intensity threshold of positive staining for each marker and this value was subtracted from each image to remove non-specific staining. Cell simulation and analysis was then performed using Tissue Studio image analysis software (Definiens). Cells were identified by nucleus detection and cytoplasmic regions were simulated up to 5 μm, per cell protein expression was measured using the mean staining intensity within simulated cell regions.
Bulk-RNA sequencing. Total RNA was purified using a miRNAeasy kit (Qiagen) from human tumor-infiltrating TREG (LIN−CD45+CD3+CD4+CXCR5−CD127−CD25+) and TFR (LIN− CD45+CD3+CD4+CXCR5+GITR+) cells and was quantified as described previously45,46. Cells from mice immunized with either Ovalbumin in Complete Freund's adjuvant (InvivoGen), Ovalbumin in Monophosphoryl Lipid A (InvivoGen) or mock PBS: TEFF (Cd19−Cd45+Cd3+Cd4+Cxcr5′Gitr−Cd25− Cd62L−Cd44+), TREG (Cd19−Cd45+Cd3+Cd4+Cxcr5−Gitr+Cd25+), TFH (Cd19−Cd45+Cd3+Cd4+Cxcr5+Gitr−) and TFR (Cd19−Cd45+Cd3+Cd4+Cxcr5+Gitr+) were sorted and RNA was purified as described above. RNA-seq libraries were prepared using Smart-seq2 protocol, as previously described47. Samples were sequenced using Illumina HiSeq2500 to obtain 50-bp single-end reads. For quality control, steps were included to determine total RNA quality and quantity, the optimal number of PCR pre-amplification cycles, and cDNA fragment size as previously described45. Samples that failed quality control, or had a low number of starting cells were eliminated from further sequencing and analysis.
Bulk RNA-seq analysis. Bulk RNA-seq data from human samples were mapped against the hg19 reference using TopHat48 (--bowtie 1 -max-multihits 1 -microexon search) with FastQC (v0.11.2), Bowtie49 (v1.1.2), Samtools (v0.1.19.0)50 and the inventors employed htseq-count -m union -s no -t exon -i gene_name (part of the HTSeq framework, version v0.7.1)51. Trimmomatic (v0.36) was used to remove adapters52. Bulk RNA-seq from mouse samples were mapped against mm10 reference using TopHat (1.4.1) with library-type fr-unstranded parameter. Values throughout are displayed as log2 TPM (transcripts per million) counts; a value of 1 was added prior to log transformation. To identify genes expressed differentially by various cell types, the inventors performed negative binomial tests for unpaired comparisons by employing the Bioconductor package DESeq253 (v1.14.1), disabling the default options for independent filtering and Cooks cutoff. The inventors considered genes to be expressed differentially by any comparison when the DESeq2 analysis resulted in a Benjamini-Hochberg-adjusted P value of ≤0.05 and a fold change of at least 2. Euler diagrams were generated using the eulerr package (v5.1.0). Correlations and heatmaps were generated as previously described 46,54,55 Visualizations were generated in ggplot2 using custom scripts. For tSNE analysis, the data frame was filtered to genes with mean≥1 TPM counts expression in at least one condition and visualizations created using the top 500 most variable genes, as calculated in DESeq253 (v1.16.1); this allowed for unbiased visualization of the Log2 (TPM counts+1) data, using package Rtsne (v0.13).
Weighted Gene Coexpression Network Analysis. WGCNA was completed in R (v.3.5.0) with the package WGCNA (v1.61) using the TPM data matrix. Well-expressed genes with TPM>=10 in at least one sample, were used in both TFR and TREG data from human. Gene modules were generated using blockwiseModules function (parameters: checkMissingData=TRUE, power=5, TOMType=“signed”, minModuleSize=30, maxBlockSize=13441, mergeCutHeight=0.80). The remaining parameters were as per default in WGCNA. The default ‘grey’ module generated by WGCNA for non-co-expressed genes, was excluded from further analysis. As each module by definition is comprised of highly correlated genes, their combined expression may be usefully summarized by module eigengene (ME) profiles, effectively the first principal component of a given module. A small number of module eigengene profiles may therefore effectively ‘summarize’ the principle patterns within the cellular transcriptome with minimal loss of information. This dimensionality-reduction approach also facilitates correlation of MEs with clinical traits as module-trait relationship matrix. Significance of correlation between this trait and MEs was assessed using linear regression with Benjamini-Hochberg adjustment to correct for multiple testing. The TOMplot was generated using the TOMplot function in WGCNA with default parameters for clustering and color scheme. To visualize co-expression networks were generated in gplots (v3.0.1) using the heatmap2 function, while weighted correlation analysis was completed using WGCNA56 (v1.61) from the Log2 (TPM counts+1) data matrix and the function TOMsimilarityfromExpr (Beta=5) and exportNetworkToCytoscape, weighted=true, threshold=0.05. Highlighted genes were ordered as per the order in the correlation plot. Networks were generated in Gephi (v0.92)57,58 using ForceAtlas2 and Noverlap functions. The size and color were scaled according to the Average Degree as calculated in Gephi, while the edge width was scaled according to the WGCNA edge weight value. All analyses were completed in R version in R (v3.5.0).
Meta-analysis of published single-cell RNA-seq studies. The inventors integrated 9 published single-cell RNA-seq datasets17,23,59-65 of tumor-infiltrating CD4-expressing T cells with UMAP. The integration was performed using the R package Seurat v3.0. For each dataset, cells that expressed less than 200 genes were considered outliers and discarded. The inventors integrated data from all cohorts using the alignment by ‘anchors’ option in Seurat 3.0. Briefly, the alignment is a computational strategy to “anchor” diverse datasets together, facilitating the integration and comparison of single cell measurements from different technologies and modalities. The “anchors” correspond to similar biological states between datasets. These pairwise correspondences between datasets allows the transformation of datasets into a shared space regardless of the existence of large technical and/or biological divergences. This improved function in Seurat 3.0 allows integration of multiple RNA-seq datasets generated by different platforms66. While single cell RNA-seq can be utilized to identify distinct states within a given cell population, it does not offer higher resolution compared to bulk RNA-seq in terms of number of transcripts recovered due to high drop-out rates with single-cell RNA-seq assays, more so with 10×-based assays. The inventors used the FindIntegrationAnchors function to find correspondences across the different study datasets with default parameters (dimensionality=1:30). Furthermore, the inventors used the IntegrateData function to generate a Seurat Object with an integrated and batch-corrected expression matrix. In total, 25,149 cells and 2,000 most variable genes were used for clustering. The inventors used the standard workflow from Seurat, scaling the integrated data, finding relevant components with PCA and visualizing the results with UMAP. The number of relevant components was determined from an elbow plot. UMAP dimensionality reduction and clustering were applied with the following parameters: 2000 genes, 15 principal components, resolution of 0.2, min·dis 0.05 and spread 2. Cells used for the integration were selected from clusters labeled in the original studies as tumor CD4 T cells and from pre-treatment samples when necessary. Cells with expression of CD8B>1 CPM (UMI data) or 10 TPM (Smart-seq2) were filtered out as indicated in Table 1. TREG cells and TFR cells were identified based on criteria defined in Table 1. Only smart-seq2 datasets were used to compare TFR cells from different cancer types.
Single-cell differential expression analysis. Differential expression was calculated with MAST67 and SCDE68 (v1.99.1) as previously described54. For each comparison, the inventors obtained the differentially expressed gene list by taking the union of the gene lists from both the methods using adjusted P<0.05 and log2 fold change >1 from each method.
Single-cell TCR and transcriptome analysis. Single-cell smart-seq2 data from17 were re-analyzed (Table 1), using custom scripts to identify αβ chains and showing only cells were both TCR chains were detected, as described previously69. Visualizations were completed in ggplot2, Prism (v8.1.1) and custom scripts in TraCer. A cell was considered expanded when both the most highly expressed α and β TCR chain sequences matched other cells with the same stringent criteria. Cells were considered not expanded when α and β TCR productive chain sequences did not match those of any other cells. A cell was considered a TREG cell when the expression of CD4 and FOXP3 were >10 TPM, and lacked expression of CXCR5 and BCL6 (TPM ≤10). A cell was characterized as a TFR cell if expression of CD4 and FOXP3 were >10 TPM and the expression of CXCR5 or BCL6 was >10 TPM. A cell was considered 4-1BB+ when the expression of 4-1BB was >10 TPM as indicated in Table 1. Cell-state hierarchy maps were generated using Monocle (v3.0)76 and default settings with expressionFamily=negbinomial.size( ), lowerDetectionLimit=0.1 after transformation of TPM counts with relative2abs function as recommended in the manual, including the top 2000 most variable genes identified in Seurat (v3.0) and taking 14 PCs based on the elbow plot. The shared signature was calculated with AddModuleScore function from Seurat after setting the object with default parameters and using the intersection of differentially expressed genes from comparing 4-1BB− TREG cells with three populations: 4-1BB+ TREG, clonally-expanded TREG cells sharing their TCRs with TFR and clonally-expanded TFR cells with Benjamini-Hochberg-adjusted P value of <0.05 and a log 2 fold change of 1.Single-cell smart-seq2 data from23 were utilized to compare the single-cell transcriptome of tumor-infiltrating TFR cells from pre- and post-anti-PD-1 treatment samples.
Hierarchical clustering. Distance between clusters was calculated by obtaining a particular cells location in PCA space (Principal component 1:5) using the function Embeddings from Seurat. The number of principal components was determined from an elbow plot. A distance matrix was calculated (dist function, core R, method=Euclidean) from the PCA matrix and the clustering was performed (hclust function, method=“average”) in R and generated from the distance matrix. Function colored bars from the WGCNA package was used to annotate different groups in the dendrogram.
Single-cell transcriptome analysis of primary tumor tissue and metastatic tumor-infiltrated lymph nodes. Human T cells from 2 HNSCC patients (primary tumor tissue and metastatic tumor-infiltrated lymph nodes) were isolated and prepared for flow-cytometric sorting from cryopreserved tumor tissue as described above. CD4+ TH cells (CXCR5+GITR− and CXCR5−CD25−), TREG cells (CD4+CXCR5− CD25+CD127lo), TFR cells (CD4+CXCR5+GITR+) and CD8+CD69+ cells were sorted and cDNA libraries were constructed using the standard 10× sequencing protocol. A total of n=9,562 (n=4,975 from metastatic tumor-infiltrated lymph node, n=4,589 from primary tumor tissue) cells were sequenced and cells with less than 200 and more than 5,000 expressed genes, less than 15,000 counts, and more than 10% of mitochondrial counts were filtered out. For clustering with Seurat (3.0) the inventors used 17 PCs from a set of highly variable genes (n=609) taking 30% of the variance after filtering out genes with mean expression less than 0.1 and removing TCR genes. TCR analysis: clonotype output (clonotypes and filtered contig annotation) from Cell Ranger for tumor and lymph node libraries were re-calculated (matching sequences were assigned the same clonotype id) and the overlap between cluster 1 and 6 was determined with these ‘aggregated’ tables. Gene Set Enrichment analysis: the Log 2 fold change was used as ranking metric and enrichment was calculated for each list. fgsea (1.13.0) in R with default parameters was used to calculate the enrichment and create GSEA plots. Monocle (2.99.1) was used to generate the trajectory plots, reduction_method=DDRTree for the dimensional reduction taking 15 principal components. Hierarchical clustering was performed as stated above with 20 PCs.
Quantification and statistical analysis. The number of subjects, samples or mice/group, replication in independent experiments, and statistical tests can be found in the figure legends. Details on quality control, sample elimination and displayed data are stated the method details and figure legends. Sample sizes were chosen based on published studies to ensure sufficient numbers of mice in each group enabling reliable statistical testing and accounting for variability. Sample sizes are indicated in Figure legends. Mice, which didn't develop any tumors by 10 after inoculation were excluded from analyses, prior to any therapeutic intervention. RNA-seq samples that didn't pass quality control weren't included in the analyses. Experiments were reliably reproduced in independent experiments at least twice. Animals of same sex and age were randomly assigned to experimental groups Statistical analyses were performed with Graph Pad Prism 8 and statistical tests used are indicated in the figure legends and experimental model and subject details.
Immune checkpoint blockade (ICB) targeting CTLA-4 or PD-1 can lead to dramatic, long-lasting responses; nonetheless, fewer than 30% of patients respond to monotherapy with either agent. While anti-CTLA-4 therapy is believed to deplete T regulatory (TREG) cells, anti-PD-1 blocking antibodies are thought to primarily activate CD8+ T cells. Combination therapy, though more effective, causes more frequent and severe immune-related adverse events (irAEs), potentially caused by undirected anti-CTLA-4-mediated TREG cell depletion and subsequent uninhibited anti-PD-1-mediated activation of effector T cells. As described hereinabove, a novel population of T cells, follicular regulatory T cells (TFR), are a district population of regulatory T cells that inhibit CD8 T cells. As shows in the example above, by increasing the abundance of TFR cells, anti-PD-1 therapy not only facilitates, but also dampens anti-tumor immunity. Conditional knockout of TFR cells or depletion of TFR cells with anti-CTLA-4 antibody prior to anti-PD-1 treatment, improved tumor control in mice. In a large melanoma cohort, sequential ICB (anti-CTLA-4 prior to anti-PD-1 therapy) was associated with better long-term survival outcomes when compared to concomitant combination therapy or monotherapy with either agent, highlighting the clinical benefit of sequential ICB to render tumors responsive to anti-PD1 therapy. Translating the benefits of ICB to a broader patient cohort while minimizing irAEs requires a deeper understanding of its cellular targets and specific mode of action. A better understanding of the tumor microenvironmental cues that trigger TREG to TFR differentiation, how TFR cells inhibit anti-tumor immunity and whether targeting them more specifically (without affecting TREG cells) to decrease irAEs while maintaining treatment efficacy, is included herein.
Adoptive transfer studies can be used in conjunction with TCR trajectory analyses to show intratumoral TREG to TFR conversion. The factors driving this differentiation step can be determined using large-scale, high-resolution smart-seq3 single-cell RNA-seq and single-cell ATAC-seq of human tumor-infiltrating TREG and TFR cells from three common cancer types (NSCLC, HNSCC and melanoma). The transcriptional profiles and enhancer landscapes (PageRank analysis) of TREG and TFR cells to identify common are compared (shared between cell types), as well as unique, cell type specific molecules and transcription factors (TFs) potentially driving differentiation. Protein expression and DNA binding studies can be used to verify the most significant TFs using micro-scaled ChIP assays, and test functional significance of the top ‘candidate’ molecule in murine tumor models. Novel immunotherapy target to selectively deplete TFR cells. Given that anti-CTLA-4 antibodies non-specifically deplete both, TREG and TFR cells, potentially causing irAEs, targeting IL1R2, a surface molecule specifically expressed on TFR cells, can be used to study the anti-tumor effects of TFR cell-deficient mice and in the model systems described in Example 1. Antibody clones can be generated and studied using the Beacon platform (Berkeley Lights), select clones with heightened ADCC activity are isolated and their functionality assessed in multiple murine tumor models.
Molecular mechanisms driving augmented anti-tumor immunity. Having established that mice that are selectively deficient in TFR cells (without affecting the TREG cell compartment) display augmented anti-tumor immunity, the molecular mechanisms driving this effect can be determined. For example, whether TFR cells, and not TREG cells, impact the priming or activity of tumor-antigen specific cytotoxic T cells and whether TFR cells induce transcriptomic changes in CTLs and other tumor-infiltrating lymphocyte compartments can be assessed as outlined in Example 1. Moreover, how TFR cells curtail anti-PD-1 treatment efficacy and mediate irAEs can be studied by specific TFR cell depletion (anti-IL1R2, genetic depletion), which minimizes irAEs when compared to undirected depletion of all FoxP3+ cells (TREG and TFR) (anti-CTLA-4, FoxP3DTR) while maintaining treatment efficacy of combination therapy, thus paving the way for novel combination therapies (anti-IL1R2+anti-PD-1).
Additional transcriptomic differences and similarities between intratumoral TREG and TFR cells to further discern the roles they play in anti-tumor immunity using cell type-specific enhancer regions to uncover binding sites for TFs that play a role in maintenance, functionality or differentiation. As cell numbers for human analyses are likely to be limited, single-cell ATAC-seq and micro-scaled ChIP-seq assays can be used. Coupling high-resolution single-cell RNA-seq and single-cell ATAC-seq with ChIP-seq has several benefits: (i) Single-cell RNA-seq to identify cell type-specific TFs. (ii) TFs drive lineage differentiation and cell-specific functions can be traced back by assessing the enhancer landscape. During cell fate specification, lineage-driving TFs leave a ‘footprint’ on the cell's enhancer landscape, often accompanied by increased chromatin accessibility (measured by ATAC-Seq). (iii) Genes being bound and thus affected by the identified TFs are defined by utilizing micro-scaled ChIP-seq.
Diverging roles for TREG and TFR cells in anti-tumor immunity. In the example above, TREG cells have been shown to differentiate into PD-1 expressing follicular regulatory T cells (TFR) that restrain germinal center responses, impede humoral immunity towards self-antigens and display heightened suppressive capacity when compared to TREG.
As shown hereinabove, TFR cells are characterized by their joint expression of, e.g., the surface molecules Cxcr5 and Gitr, or by their co-expression of the transcription factors FoxP3 and Bcl6. While it is well-established that TREG cells can inhibit anti-tumor immunity, few studies have examined the potential effects of ICB on this cell compartment. Moreover, TFR cells, their functional role in cancer, and their responsiveness to immunotherapy drugs have been completely disregarded so far.
It was now possible to investigate if TREG and TFR cells accumulate in parallel in the tumor microenvironment (TME) as a means of effective immune evasion. As shown above, tumor-infiltrating TFR cells were highly prevalent in a variety of different cancer types. When compared to TREG cells, TFR cells showed superior suppressive capacity, enhanced proliferative capacity and Bcl6-dependent in vivo persistence. Recent data demonstrate that tumor-infiltrating TREG cells potently recognize tumor (neo)antigens and, upon antigen-encounter, undergo clonal expansion. RNA-seq, TCR-seq and trajectory analyses of tumor-infiltrating TREG and TFR cells show that the tumor microenvironment (TME) is initially infiltrated by a large and highly diverse pool of bystander (i.e., not TAA-specific) TREG cells, and a smaller pool of TAA-specific TREG clones, which are poised for differentiation into tissue resident TFR cells. In a genetic murine model permitting selective TFR cell depletion, it was found that TFR cells inhibit anti-tumor immunity to a similar degree as depletion of all FoxP3-expressing cells (TREG and TFR). Thus, selective depletion of TFR cells is clinically beneficial and will cause fewer irAEs. Thus, it is important to use therapeutic strategies to specifically deplete TFR cells without affecting the bystander TREG cell compartment. Compared to TREG cells and CD8+ TILs, TFR cells expressed the highest levels of CTLA-4 and PD-1. As such, TFR cells are highly responsive to ICB targeting these molecules. Accordingly, while anti-CTLA-4 treatment preferentially depleted TFR cells in tumor tissues, the inventors observed substantial depletion of bystander TREG cells, potentially causative of irAEs found in the clinical setting. These data not only demonstrate that anti-PD-1 therapy can not only facilitate, but also dampen anti-tumor immune attack by activating highly suppressive TFR cells in tumor tissues, but also highlight the clinical benefit of sequential ICB. These findings provide critical insights into how anti-CTLA-4 and anti-PD-1 therapies mediate their function and point to a potential cause of exacerbated irAEs observed upon combination therapy. Thus, these support further study of the diverging roles of TREG and TFR cells in cancer and the molecular mechanisms driving intratumoral TREG to TFR conversion. The observed effects of specific TFR cell depletion can be recapitulated by generating antibodies against IL1R2, a novel immunotherapy target to assess how TFR cells impact the priming or activity of tumor antigen-specific CTLs by profiling TFR cell-induced changed in their transcriptomic signatures and will discern the relative contribution of TREG and TFR cells onto anti-tumor immunity and anti-PD-1 treatment efficacy and irAEs, in the same manner as described to anti-CLTA4 hereinabove.
The present invention can also be used to clarify the diverging roles that TREG and TFR cells play in anti-tumor immunity and identifying key molecular players and pathways driving the development and functionality of highly suppressive TFR cells in tumor tissues will inform the discovery of novel immunotherapy drug targets to treat cancer. The factors that trigger developmental and functional changes in tumor-infiltrating TREG and TFR cells can also be determined using this model system. Further, pre-clinical and clinical findings on TFR cells to further improve immunotherapy efficiency and to translate its benefits to a larger group of patients can also be determined using the present model.
Further, it is possible to combine genetic and drug-related strategies to unravel the diverse roles TREG and TFR cells play in anti-tumor immunity and improve current treatment regimens as well as evaluate its effects on priming and activity of tumor antigen-specific CTLs and generate and test a novel immunotherapy antibody to mirror the anti-tumor effects of specific TFR cell depletion.
Further evaluation of the highly suppressive intratumoral TFR cells disclosed herein can be used to improve anti-tumor immunity and ICB treatment efficacy. It can also be used enhance the understanding of cellular and molecular mechanisms facilitating efficacious anti-tumor reactions and may lead to more specific therapies as well as identify novel targets for immunotherapy and therapeutic interventions.
Sequential ICB treatment is associated with better survival outcomes.
To test the clinical significance of sequential ICB treatment, the inventors retrospectively assessed the survival outcomes of patients with inoperable melanoma (n=271), who were, based on their treatment regimens, stratified into 5 groups: 1st line anti-CTLA-4, 1st line anti-PD-1, simultaneous combination therapy, sequential therapy with anti-CTLA-4 followed by anti-PD-1 at progression and vice versa. Sequential treatment with anti-CTLA-4 followed by anti-PD-1 was associated with better long-term overall survival (OS) outcomes when compared to the 4 other groups (p<0.001) (
Crucially and in-line with our findings in mouse models (
Identify molecular programs driving TREG to TFR differentiation in tumor tissue.
High-resolution smart-seq3 single-cell RNA-seq has a significantly higher (2-5 fold) gene coverage than previous (smart-seq2 and 10×) methods and can thus reveal currently unknown patterns of gene expression. The high-resolution smart-seq3 single-cell RNA-seq sequencing platform can be used to fully characterize the transcriptomic signatures of tumor-infiltrating TREG (CD4+CD127loCD25+CXCR5−) and TFR (CD4+CXCR5+GITR+) cells. The results will define genes and transcription factors (TFs) that are pivotal for the heightened suppressive capacity of TFR cells (see Example 1) and for their differentiation. Single-cell RNA-seq, single-cell ATAC-seq and micro-scaled ChIP-seq assays will allow us to bypass potentially low cell numbers, identify novel genes and TFs, permit assessing the enhancer landscape of intratumoral TREG and TFR cells, and delineate potential alterations in gene expression by bound TFs.
To identify genes and TFs specific to human tumor-infiltrating TREG and TFR cells, high-resolution single-cell RNA-seq in purified (˜500 TREG or TFR) cells isolated can be performed from 3 human cancer types (NSCLC, HNSCC and melanoma; n=8 samples/tumor type. Human tumor samples will be obtained from studies at a tertiary center in the UK that is actively recruiting patients (˜20 cases/month). This will allow for the efficient characterization of the transcriptomic signatures of tumor-infiltrating TREG and TFR cells of different cancer types. Additionally, patient-matched TREG and TFR cells can be sorted for single-cell ATAC-seq and enhancer profiling. Comparing the enhancer landscapes of tumor-infiltrating TREG cells with that of TFR cells will determine which enhancers are cell type specific. Subsequently, enhancers specific to each cell type, and then employ PageRank16 analysis to predict TFs can be used to establish the enhancer landscape of TFR cells; such TFs are likely to be pivotal for TFR cell differentiation. Lastly, the TFs identified in the previous steps can be subjected to in micro-scaled ChIP assays17 to determine genes that are being bound by these TFs.
Functional studies: Demonstrating that a given TF, such as BCL6, is critical for the differentiation of TREG cells into TFR cells in tumors requires assessing the impact of manipulating the expression levels of that TF by depletion or overexpression in vivo. Such mechanistic studies are best performed in murine tumor models, where the abundance and function of TREG and TFR cells can be easily assessed. To test the functional role of the top ‘candidate’ TF or molecule emerging from our studies (conserved between human and mouse), CRISPR-Cas9-mediated depletion of the ‘candidate’ molecule in TREG cells can be used to assess the impact of their differentiation into TFR cells in murine tumor models. Knockdown efficiency can be verified at the transcript level and at the protein level, if suitable antibodies are available. Performing CRISPR-Cas9 based gene depletion in primary T cells, and show feasibility of depleting ICOS in T cells can also be used to adoptively transfer TREG cells that are sufficient or deficient in the ‘candidate’ molecule into B16F10-OVA tumor-bearing recipient (congenic C57BL/6J or Rag1−/−) mice. Knockout mice for the ‘candidate’ molecule can be used to purify and adoptively transfer TREG cells deficient with the ‘candidate’ molecule. Tumor growth over the course of the experiment and sacrifice mice at day 13-18 after adoptive transfer and assess alterations in TFR cell frequency.
Finally, an unbiased “genomics-based” approach I am taking is likely to identify several key players driving TREG to TFR differentiation and cell functionality, that will fuel additional important hypotheses and strands of research.
The data in
It is contemplated that any embodiment discussed in this specification can be implemented with respect to any method, kit, reagent, or composition of the invention, and vice versa. Furthermore, compositions of the invention can be used to achieve methods of the invention.
It will be understood that particular embodiments described herein are shown by way of illustration and not as limitations of the invention. The principal features of this invention can be employed in various embodiments without departing from the scope of the invention. Those skilled in the art will recognize, or be able to ascertain using no more than routine experimentation, numerous equivalents to the specific procedures described herein. Such equivalents are considered to be within the scope of this invention and are covered by the claims.
All publications and patent applications mentioned in the specification are indicative of the level of skill of those skilled in the art to which this invention pertains. All publications and patent applications are herein incorporated by reference to the same extent as if each individual publication or patent application was specifically and individually indicated to be incorporated by reference.
The use of the word “a” or “an” when used in conjunction with the term “comprising” in the claims and/or the specification may mean “one,” but it is also consistent with the meaning of “one or more,” “at least one,” and “one or more than one.” The use of the term “or” in the claims is used to mean “and/or” unless explicitly indicated to refer to alternatives only or the alternatives are mutually exclusive, although the disclosure supports a definition that refers to only alternatives and “and/or.” Throughout this application, the term “about” is used to indicate that a value includes the inherent variation of error for the device, the method being employed to determine the value, or the variation that exists among the study subjects.
As used in this specification and claim(s), the words “comprising” (and any form of comprising, such as “comprise” and “comprises”), “having” (and any form of having, such as “have” and “has”), “including” (and any form of including, such as “includes” and “include”) or “containing” (and any form of containing, such as “contains” and “contain”) are inclusive or open-ended and do not exclude additional, unrecited features, elements, components, groups, integers, and/or steps, but do not exclude the presence of other unstated features, elements, components, groups, integers and/or steps. In embodiments of any of the compositions and methods provided herein, “comprising” may be replaced with “consisting essentially of” or “consisting of”. As used herein, the term “consisting” is used to indicate the presence of the recited integer (e.g., a feature, an element, a characteristic, a property, a method/process step or a limitation) or group of integers (e.g., feature(s), element(s), characteristic(s), property(ies), method/process steps or limitation(s)) only. As used herein, the phrase “consisting essentially of” requires the specified features, elements, components, groups, integers, and/or steps, but do not exclude the presence of other unstated features, elements, components, groups, integers and/or steps as well as those that do not materially affect the basic and novel characteristic(s) and/or function of the claimed invention.
The term “or combinations thereof” as used herein refers to all permutations and combinations of the listed items preceding the term. For example, “A, B, C, or combinations thereof” is intended to include at least one of: A, B, C, AB, AC, BC, or ABC, and if order is important in a particular context, also BA, CA, CB, CBA, BCA, ACB, BAC, or CAB. Continuing with this example, expressly included are combinations that contain repeats of one or more item or term, such as BB, AAA, AB, BBC, AAABCCCC, CBBAAA, CABABB, and so forth. The skilled artisan will understand that typically there is no limit on the number of items or terms in any combination, unless otherwise apparent from the context.
As used herein, words of approximation such as, without limitation, “about”, “substantial” or “substantially” refers to a condition that when so modified is understood to not necessarily be absolute or perfect but would be considered close enough to those of ordinary skill in the art to warrant designating the condition as being present. The extent to which the description may vary will depend on how great a change can be instituted and still have one of ordinary skill in the art recognize the modified feature as still having the required characteristics and capabilities of the unmodified feature. In general, but subject to the preceding discussion, a numerical value herein that is modified by a word of approximation such as “about” may vary from the stated value by at least ±0.1, 0.5, 1, 2, 3, 4, 5, 6, 7, 10, 12 or 15%, or as understood to be within a normal tolerance in the art, for example, within 2 standard deviations of the mean. Unless otherwise clear from the context, all numerical values provided herein are modified by the term about.
All of the compositions and/or methods disclosed and claimed herein can be made and executed without undue experimentation in light of the present disclosure. While the compositions and methods of this invention have been described in terms of preferred embodiments, it will be apparent to those of skill in the art that variations may be applied to the compositions and/or methods and in the steps or in the sequence of steps of the method described herein without departing from the concept, spirit and scope of the invention. All such similar substitutes and modifications apparent to those skilled in the art are deemed to be within the spirit, scope and concept of the invention as defined by the appended claims.
To aid the Patent Office, and any readers of any patent issued on this application in interpreting the claims appended hereto, applicants wish to note that they do not intend any of the appended claims to invoke paragraph 6 of 35 U.S.C. § 112, U.S.C. § 112 paragraph (f), or equivalent, as it exists on the date of filing hereof unless the words “means for” or “step for” are explicitly used in the particular claim.
For each of the claims, each dependent claim can depend both from the independent claim and from each of the prior dependent claims for each and every claim so long as the prior claim provides a proper antecedent basis for a claim term or element.
This application claims priority to U.S. Provisional Patent Application Nos. 62/873,185, filed Jul. 11, 2019, and 62/971,603, filed Feb. 7, 2020, the entire contents of each of which are incorporated herein by reference.
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/US2020/041734 | 7/11/2020 | WO |
| Number | Date | Country | |
|---|---|---|---|
| 62873185 | Jul 2019 | US | |
| 62971603 | Feb 2020 | US |
