Methods of identifying and treating patient populations amenable to cancer immunotherapy

Abstract

Methods for identifying cancer patients amenable to anti-cancer immunotherapy are provided along with methods of monitoring cancer therapy. Also provided are methods of treating cancer patients amenable to anti-cancer immunotherapy. The methods involve determining the level of CD127 <low> PD-1 <low> T cells. The patients are treated with an immune checkpoint inhibitor, such as an anti-CTLA-4 antibody, e.g. ipilimumab.

Claims

1. A method of treating cancer in a human subject having cancer comprising: (a) generating a first scaffold map from a first population of blood leukocytes isolated from the human subject having cancer, wherein the cancer is melanoma, and a second scaffold map from a second population of blood leukocytes isolated from a control, wherein the control is the human subject having cancer at an earlier time, a distinct human subject having cancer that is unresponsive to anti-cancer immunotherapy, or a healthy human subject, wherein the cancer is melanoma; (b) generating a first cell population expression profile from the first scaffold map, and a second cell population expression profile from the second scaffold map; (c) comparing the first cell population expression profile and the second cell population expression profile to determine expression level of CD127.sup.low PD-1.sup.low T cells in the human subject having cancer and the control; (d) administering a therapeutically effective amount of a composition comprising an immune checkpoint inhibitor, wherein the immune checkpoint inhibitor is ipilumimab, to the human subject having cancer, if the expression level of CD127.sup.low PD-1.sup.low T cells in the human subject having cancer is greater than the expression level of CD127.sup.low PD-1.sup.low T cells in the control, thereby treating the cancer in the human subject having cancer.

2. The method of claim 1, further comprising administering a soluble growth factor to the human subject having cancer.

3. The method of claim 2, wherein the soluble growth factor is granulocyte-macrophage colony stimulating factor (GM-CSF).

4. The method of claim 1, wherein the T cells are CD4 positive cells.

5. The method of claim 4, wherein the CD4 positive cells are regulatory T cells.

6. The method of claim 1, wherein the control is the human subject having cancer at an earlier time.

7. The method of claim 6, wherein the earlier time is prior to a cancer treatment.

8. The method of claim 1, wherein the human subject having cancer has a tumor and the first population of blood leukocytes is obtained intratumorally.

9. The method of claim 1, wherein the first population of blood leukocytes is obtained from a secondary lymphoid organ or from peripheral blood.

10. The method of claim 1, wherein the immune checkpoint inhibitor inhibits the growth of cancer cells.

11. The method of claim 1, wherein the immune checkpoint inhibitor inhibits the migration of cancer cells.

12. The method of claim 1, wherein the generating a first and second scaffold map comprises obtaining, from a third party, a dataset comprising data representing the level(s) of CD127.sup.low PD-1.sup.low cells.

13. The method of claim 1, wherein the generating a first and second scaffold map comprises processing, using flow cytometry, a sample from the human subject having cancer and/or control to experimentally determine a dataset comprising data representing the level(s) of CD127.sup.low PD-1.sup.low cells.

14. The method of claim 5, wherein the CD4 positive cells are CD4.sup.+CD25.sup.+ regulatory T cells.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

(1) FIG. 1. The Statistical Scaffold algorithm. FIG. 1 (A) Scaffold maps as originally designed (Spitzer et al., 2015). (i) A reference sample (or group thereof) is chosen for the analysis. (ii) Canonical cell populations are identified manually and all cells are also clustered in an unsupervised manner. (iii) Canonical populations are represented as landmark nodes, while clusters are represented as unsupervised nodes. (iv) These nodes are spatialized into a force-directed graph. (v) Landmark nodes are fixed in place to provide common reference points. (vi) Cells from other samples are clustered independently and (viii) graphs are generated for each sample. FIG. 1 (B) Statistical Scaffold. (i-ii) A set of reference samples is chosen and canonical cell populations as performed previously. However, all samples are clustered together. (iii-iv) Same as above. (v) Features (i.e., population frequencies, expression levels in each cluster, and the like) are extracted from the clusters for each sample. Each sample is also annotated according to the therapy to which it belongs. (vi) Significance Analysis of Microarrays is performed to identify features that are statistically significant between treatment groups. (vii) Features displaying statistically significant differences between groups are colored according to the direction of the change (increase or decrease) in the Scaffold maps to visualize which parts of the immune system are impacted by therapy.

(2) FIG. 2. Architecture of the Scaffold map. An empty Scaffold map displaying landmarks alone is presented for orientation.

(3) FIG. 3. A CD4 T cell subset from the periphery is sufficient to mediate anti-tumor immunity. FIG. 3 (A) Scaffold map of flow cytometry data from blood of melanoma patients treated with anti-CTLA-4 antibodies and GM-CSF, 3 weeks after therapy began. Red nodes are cell subsets significantly expanded in responding patients compared to non-responders. FIG. 3 (B) Frequency of CD4.sup.+ PD-1.sup.low CD127.sup.low T cells (identified manually) of total leukocytes, analyzed by two-tailed Wilcoxon rank-sum test.

(4) FIG. 4. A CD4 T cell subset from the periphery is sufficient to mediate anti-tumor immunity. FIG. 4 (A) Statistical Scaffold map highlighting differences in peripheral blood immune cell frequencies between responding and non-responding melanoma patients 6 weeks after anti-CTLA-4 and GM-CSF immunotherapy. FIG. 4 (B) Histograms comparing the protein expression profiles of those CD4 T cell clusters that were significantly different in frequency between responders and non-responders at week 3 and the remainder of the CD4 T cell subsets that were not significantly different in frequency between these patient populations.

DETAILED DESCRIPTION

(5) Immune responses involve coordination across cell types and tissues. While cancer immunotherapies can be effective, their mechanisms of action have never been characterized system-wide. For instance, cytotoxic T-lymphocyte-associated antigen 4 (CTLA-4) blockade can induce tumor regression and improve survival in cancer patients. This treatment can enhance adaptive immune responses without an exogenous vaccine, but the immunologic parameters associated with improved clinical outcome are not established. Ipilimumab is a fully humanized monoclonal antibody targeting CTLA-4 that is FDA-approved for the treatment of melanoma. In two phase III studies in advanced melanoma, ipilimumab was shown to significantly prolong overall survival (OS) (Hodi et al., 2010 and Robert et al. 2011). In the pivotal trial, melanoma patients were treated with ipilimumab plus gp100 (a melanoma peptide vaccine), ipilimumab alone, or gp100 alone (Hodi et al., 2010). The median overall survivals were 10.0, 10.1, and 6.4 months respectively. Although improvement in median overall survival was modest, a subset of patients was observed in these and other melanoma clinical trials to have durable long-term survival benefit (Prieto et al. 2012 and McDermott et al. 2014).

(6) Disclosed herein is an emergent population of peripheral CD4 T cells that conferred protection against tumors and was significantly expanded in patients who responded to immunotherapy. Those cells are CD127.sup.low PD-1.sup.low CD4 T cells. CD127.sup.low PD-1.sup.low CD4 T cells are CD4 T cells expressing both CD127 and PD-1 at levels below the median expression levels of the respective markers (i.e., CD127 and PD-1) for all CD4 T cells. Measurement of the expression level of each of these markers, i.e., CD127 and PD-1, can be accomplished using any method known in the art. For example, the expression level of CD127 and/or PD-1 can be determined using flow cytometry or any of several assays, including real-time quantitative polymerase chain reaction (RT-qPCR), and immunoassays including enzyme-linked immunosorbent assay (ELISA), BIAcore assays (plasmon resonance-based assays), radioimmunoassays, and non-immunological binding assays, as would be known in the art. Many of these assays are readily adapted to assays on solid surfaces, including chip-based or micro-array assay formats. Samples to be subjected to such assays can be blood samples obtained from peripheral blood or the serum obtained from such blood, secondary lymphoid organ samples (e.g., spleen, lymph node), or tumor samples. Markers for identifying CD4 T cells are known in the art, and include, e.g., CD4 and CD3 as well as additional markers (e.g., one or more of CD25, CD45RA, CD45RO, CCR7, and/or C62BL) that may be used to differentiate CD4 T cell sub-types. A higher level of the CD127.sup.low PD-1.sup.low T cells means that there is a greater level of such cells in the sample being measured, and in the patient from whom the sample was obtained, than found in a reference or control. The control is a sample from the cancer patient at a different point in time, such as an earlier point in time, e.g., prior to any cancer treatment. The control may also be a sample obtained from a healthy individual. The greater level is a detectably increased level of CD127.sup.low PD-1.sup.low T cells relative to the reference or control. The approach disclosed herein identifies mechanisms of adaptive resistance systemically and in distal tumors that could be targeted to rationally augment immune responses, highlighting the utility of systems-level investigation.

(7) New methods of assessing the immune state under any given condition allow for the systematic characterization of diverse cell subsets and their activation states simultaneously. Mass cytometry builds upon the success of flow cytometry and enables over 40 simultaneous parameters to be quantified by replacing fluorophores with mass tags (Bandura et al., 2009; Bendall et al., 2011). It is thus possible to discern the identity and behavior of numerous cell types from a single experiment (Bendall et al., 2011; Spitzer et al., 2015).

(8) The variance in clinical responses to immunotherapy suggests that productive immune responses against cancer are necessarily complex. There is an urgent need for methods to understand the nature of anti-tumor immunity to more reproducibly harness the immune system against cancer.

(9) Disclosed herein is a system-wide, organism-wide assessment of effective anti-tumor immune responses. Even for a therapy delivered intratumorally, a systemic immune response can be desirable for tumor rejection. With the increased use of immunotherapies, systemic responses can typically be taken into account when determining radiation or surgical regimens including lymphadenectomy.

(10) Many studies have focused on CD4 T cells (Chen and Mellman, 2013; Im et al., 2016), with less emphasis on harnessing CD4 T cells (Tran et al., 2014; Xie et al., 2010). In other contexts, CD4 T cells orchestrate functional immune responses by coordinating immune activity (Swain et al., 2012). The results disclosed herein extend this notion to anti-tumor immunity, providing a rationale for leveraging CD4 T cell responses in cancer.

(11) These results highlight the benefit of system-wide assessments. Simple prognostic metrics have been proposed for monitoring anti-tumor immunity, including Treg frequency in tumors (Bates et al., 2006; Curiel et al., 2004). Productive immunity in this setting is accompanied by an increase in Treg frequency and proliferation in the context of a powerful T cell response. With high throughput and high dimensional single-cell technologies such as mass cytometry, assessing all immune cells simultaneously is now achievable, enabling individual metrics to be contextualized into the broader immune state. For instance, the systemic proliferative response identified may provide a means for noninvasive monitoring during immunotherapy. The graphical user interface that accompanies Scaffold maps (GitHub) facilitates the application of the methods disclosed herein to the treatment and monitoring of a wide variety of cancers.

EXAMPLES

(12) The following examples illustrate embodiments of the disclosure. Below are examples of specific embodiments for carrying out the present invention. The examples are offered for illustrative purposes only, and are not intended to limit the scope of the present invention in any way. Efforts have been made to ensure accuracy with respect to numbers used (e.g., amounts, temperatures, etc.), but some experimental error and deviation should, of course, be allowed for.

Example 1

Cell Cluster Mapping

(13) Scaffold Map Generation

(14) Total live leukocytes (excluding erythrocytes) are suitable for use in the analyses, schematically illustrated in FIG. 1. Cells from each tissue were clustered together (rather than performing CLARA clustering on each file individually as originally implemented in Spitzer et al., Science, 2015.) Cells were then deconvolved into their respective samples. Cluster frequencies or the Boolean expression of Ki67 or PD-L1 for each cluster were passed into the Significance Across Microanays algorithm (Bair and Tibshirani, 2004; Bruggner et al., 2014), and results were tabulated into the Scaffold map files for visualization through the graphical user interface. Cluster frequencies were calculated as a percent of total live nucleated cells (excluding erythrocytes). For each cluster in each tissue, the most similar cluster in every other tissue was included for comparison.

(15) Scaffold maps were then generated as previously reported (Spitzer et al., 2015). See FIG. 2 for an example of the architecture of a Scaffold map. A graph was constructed by first connecting together the nodes representing the manually gated landmark populations and then connecting to them the nodes representing the cell clusters as well as connecting the clusters to one another. Each node was associated with a vector containing the median marker values of the cells in the cluster (unsupervised nodes) or gated populations (landmark nodes). Edge weights were defined as the cosine similarity between these vectors after comparing the results from the implementation of several distance metrics. Edges of low weight were filtered out. The graph was then laid out using an in-house R implementation of the ForceAtlas2 algorithm from the graph visualization software Gephi. To overlay the additional samples on the map, the position and identity of the landmark nodes were fixed and the clusters of each sample were connected to the landmark nodes as described above. Once again the graphs were laid out using ForceAtlas2 but this time only the unsupervised nodes were allowed to move. All analyses were performed using the open source Scaffold maps R package available at GitHub.

(16) Statistical Scaffold Maps

(17) To identify changes in the tumor microenvironment during immune-mediated rejection, an effort was made to systematically define changes in immune cell organization and behavior between effective and ineffective treatments. The effort was aided by a computational method called Scaffold maps for creating a reference map from high-dimensional single-cell data, facilitating comparisons across samples (Spitzer et al., 2015). These maps provided a data-driven representation of the cells present in a sample while also denoting the location of landmark immune cell populations, defined using prior knowledge of the immune system. These Landmarks (which can be visualized as black nodes in a graphic representation) function as flags to orient the investigator. In these graphs, the similarity of two groups of cells was visualized by the length of the edge connecting them. In other words, two groups of cells connected by a short line are similar to one another with respect to the proteins they express.

(18) This method was developed in an extensible manner for future datasets to be incorporated, but it did not enable precise statistical comparisons across groups of samples. Another algorithm for mass cytometry analysis, i.e., Citrus (Bruggner et al., 2014), provided statistical comparisons between groups. The results from Citrus, however, were cumbersome to interpret. Therefore, it was determined whether the statistical inference integrated into Citrus could instead be applied to Scaffold maps. This hybrid method was termed a “Statistical Scaffold”. The first step of Scaffold maps was altered, clustering data from all tumor specimens together to define cell groups in an unbiased manner. This enabled direct comparisons across samples. The Significance Analysis of Microarrays framework was then used to identify statistically significant features between the sample types (effective versus ineffective treatment groups) as in Citrus (Bair and Tibshirani, 2004; Bruggner et al., 2014). The resulting Scaffold maps were then colored by statistical significance, where features with q-values less than 0.05 (adjusted for multiple testing) were colored in either red or blue depending on the directionality of the change (for example, up or down in the group that received effective therapy). These features can either be changes in the frequency or molecular expression of a particular cell subset.

(19) Cell Population Expression Profiles

(20) Cell clusters of interest were further investigated by visualizing the distribution of protein expression within the cells comprising each cluster as a histogram. This was performed using the density visualization feature of the Scaffold maps R package. Histograms were created by exporting clusters as .FCS files using the Scaffold maps R package and using the flowCore and ggplot2 packages in R to write vector histogram plots. Scripts are available at github, using mhspitzer as an identifier.

(21) Unsupervised Force-Directed Graph Generation

(22) Cells were manually gated as Live CD45+ lineage- (Teri 19, Ly6G, Siglec-F, CD19, B220, F4/80, CD11c, PDCA-1, FcεR1α) and then CD3+ to identify T cells. The gated cell populations for each tissue/timepoint/treatment group were clustered independently in 50 clusters using clara in R. The clusters for all the tissues were combined in a single graph with edge weights defined as the cosine similarity between the vectors of median marker values of each cluster. All the pairwise distances were calculated and for each node only the 10 edges of highest weight were retained. The graph was then laid out using the ForceAtlas2 algorithm in Gephi (https://gephi.org).

(23) Correlation Network Analysis and Connectivity Analysis

(24) Immune cell subsets were gated from mass cytometry data, and the frequency of each subset in the peripheral blood samples was calculated. Pairwise Spearman correlations were calculated for each immune cell subset, and hierarchical clustering was performed to organize the correlation matrix. The hierarchical clustering result was additionally imposed on the correlation matrix as a means of comparing the networks.

(25) For the connectivity analysis, an adjacency matrix was created from the correlation matrix of animals receiving effective therapy, using a Spearman correlation coefficient of 0.5 as the threshold. The number of remaining correlations was tabulated for each immune cell population from each tissue, and these were rank ordered. The graph of the adjacency matrix visualizes all positive and negative correlations present in the adjacency matrix for each subset.

Example 2

A CD4 T Cell Subset is Associated with a Favorable Response to Immunotherapy in Melanoma Patients

(26) Eligible patients were adults with histologically confirmed unresectable metastatic melanoma as previously reported (Kwek et al., 2015). The protocol was approved by the Institutional Review Board of each participating institution and was conducted in accordance with the ethical principles of the Declaration of Helsinki and within the Good Clinical Practice guidelines as defined by the International Conference on Harmonization. All patients gave written informed consent for participation in the study. The trial was registered at the ClinicalTrials website with Identifier NCTO1363206.

(27) At the initiation of treatment (months 1-3), patients were treated with four cycles of GM-CSF and ipilimumab administered every 3 weeks. Ipilimumab was administered intravenously at a dose of 10 mg/kg on day 1 of each 21-day cycle. GM-CSF was administered subcutaneously daily for 14 days at a dose of 125 mg/m.sup.2 beginning on day 1 of each cycle. After the first four cycles of treatment, GM-CSF administration without ipilimumab continued for four more cycles on the same schedule and dose for the first 14 days of every 21-day cycle until month 6. Maintenance therapy began at month 6 and consisted of ipilimumab in the same dose (10 mg/kg) combined with 14 days of GM-CSF. This combination was administered every 3 months thereafter for up to 2 years or until disease progression or unacceptable toxicity.

(28) Blood samples were obtained at week 3 (end of cycle 1) and at week 6 (end of cycle 2) and were cryopreserved for subsequent analysis by flow cytometry. Cell-surface staining was performed in fluorescence-activated cell sorting (FACS) buffer for 30 minutes at 4° C. Intracellular forkhead box P3 (FoxP3) was performed using the FoxP3 fix/perm buffer set (BioLegend, Inc.) according to the manufacturer's protocol. The following anti-human antibodies were used: (Alexa Fluor 700)-CD3 (clone HIT3a), (Brilliant violet 570)-CD4 (clone RPA-T4), (Brilliant violet 650)-CD25 (clone BC96), (Alexa Fluor 647)-CD127 (clone A019D5), (Alexa Fluor 488)-FoxP3 (clone 206D), and (Brilliant violet 421)-PD-1 (clone EH12.2H7). All antibodies were purchased from BioLegend, Inc. Stained cells were fixed with Fluorofix buffer (BioLegend, Inc.) according to manufacturer's instructions and analyzed with an LSR II flow cytometer (BD Biosciences).

(29) The experiment was conducted to determine whether an effector memory Th1 subset of CD4 T cells that were CD44+CD69+CD62L-CD27.sup.low T-bet+, such as CD4 T cells expressing a high level of CD90, could be found in the blood of cancer patients who responded to immunotherapy. A clinical study of melanoma patients who received anti-CTLA-4 antibodies (Ipilimumab) in combination with GM-CSF was recently described (Kwek et al., 2015). Blood from these patients was analyzed both three- and six-weeks post-therapy using Statistical Scaffold. Consistent with pre-clinical results, specific clusters of CD4 T cells were significantly elevated in responders compared to non-responders at both time points (FIG. 3A). A subset of Tregs was also elevated in responders six weeks after therapy (FIG. 4A). The expanded clusters expressed lower levels of CD127 compared to the remaining CD4 T cells, indicative of activation, and lower levels of PD-1, suggesting that they were not exhausted (FIG. 4B). The results were confirmed by manually gating PD-1.sup.low CD127.sup.low CD4 T cells (FIG. 3B). A Statistical Scaffold map of blood cells obtained from the melanoma patients is provided in FIG. 3A. The frequency of PD-1.sup.low CD127.sup.low CD4 T cells relative to total leukocytes is shown in FIG. 3B. These results provide experimental evidence of a critical role for CD4 T cells in coordinating effective anti-tumor immunity in humans, and more particularly, provide experimental evidence for using the PD-1.sup.low CD127.sup.low CD4 T cell sub-population as a marker for amenability to anti-cancer immunotherapy, providing the convenience of an assay that can be performed on a peripheral blood sample or samples obtained from secondary lymphoid organs (e.g., lymph nodes, spleen) as well as samples obtained from tumors.

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(31) Each of the references cited herein is incorporated by reference herein in its entirety, or in relevant passage, as would be apparent from the context of its citation.

(32) From the disclosure it will be appreciated that, although specific embodiments of the invention have been described herein for purposes of illustration, various modifications may be made without deviating from the spirit and scope of the invention. All references, issued patents and patent applications cited within the body of the instant specification are hereby incorporated by reference in their entirety, for all purposes. It must be noted that, as used in the specification and the appended claims, the singular forms “a,” “an” and “the” include plural referents unless the context clearly dictates otherwise.