KLRG1 Marks Tumor-infiltrating CD4 T Cell Subsets Associated With Tumor Progression And Immunotherapy Response

Abstract

Current methods for biomarker discovery and target identification in immuno-oncology rely on static snapshots of tumor immunity. To thoroughly characterize the temporal nature of antitumor immune responses, we developed a 34-parameter spectral flow cytometry panel and performed high-throughput analyses in critical contexts. 

We leveraged two distinct preclinical models that recapitulate cancer immunoediting (NPKC1) and immune checkpoint blockade (ICB) response (MC38), respectively, and profiled multiple relevant tissues at and around key inflection points of immune surveillance and escape and/or ICB response. 

Targets refer to important biomolecules in cells, such as proteins, enzymes, and receptors. Target recognition is the use of chemical and biological methods to identify the specific properties and functions of these biomolecules for application in disease treatment and drug development.

Immunity refers to the body's ability to resist the invasion of foreign pathogens and the growth of endogenous abnormal cells. The core of the immune system is immune cells and molecules, which have close connections and interactions with their targets.

In disease treatment and drug development, the relationship between target identification and immunity is very important. For example, the mechanism of action of many drugs is through target recognition, targeting specific proteins, enzymes, receptors, and other molecules, affecting their functions, and regulating signal transduction, to achieve therapeutic effects. Cells and molecules in the immune system can also be targets of drugs. For example, anticancer drugs achieve therapeutic effects by affecting the immune response of cancer cells and regulating cell apoptosis and proliferation.

Therefore, in drug development and clinical treatment, comprehensive consideration of target identification and immunity factors can lead to more effective drug development and improved therapeutic effect. From this point of view, we need to improve immunity. Cistanche can enhance immunity. Cistanche is rich in a variety of antioxidant substances, such as vitamin C, vitamin C, carotenoids, etc. These ingredients can scavenge free radicals, reduce oxidative stress, and improve immunity. system resistance.

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Machine learning-driven data analysis revealed a pattern of KLRG1 expression that uniquely identified intratumoral effector CD4 T cell populations that constitutively associate with tumor burden across tumor models, and are lost in tumors undergoing regression in response to ICB. Similarly, a Helios-KLRG1+ subset of tumor-infiltrating regulatory T cells (Tregs) was associated with tumor progression from immune equilibrium to escape and was also lost in tumors responding to ICB. 

Validation studies confirmed KLRG1 signatures in human tumor-infiltrating CD4 T cells associated with disease progression in renal cancer. These findings nominate KLRG1+ CD4 T cell populations as subsets for further investigation in cancer immunity and demonstrate the utility of longitudinal spectral flow profiling as an engine of dynamic biomarker and/or target discovery.

Introduction

Immunotherapy is now a pillar of cancer treatment. However, responses to most immunotherapeutic agents remain rare, restricted to a limited number of tumor types, and difficult to predict(1). Improving response rates and developing biomarkers predictive of response are central goals of the tumor immunology field, but this remains challenging. 

A multitude of complex multi-cellular interactions may govern the outcome of an antitumor immune response in any given patient. Therefore, it is common that single biomarkers – or therapeutic modulation of singular pathways – tend to have utility in some, but not most cancer patients.

For example, there are three immune-related biomarkers currently in widespread clinical use; (i) tumor mutational burden (TMB), as measured directly or inferred via the presence of disabling mutations in DNA repair machinery(2-4), (ii) PD-L1 expression by immunohistochemistry (IHC)(5), and (iii) pre-existing immunity as measured by the presence of CD8 T cells within and surrounding tumors, termed the Immunoscore(6). 

While each is significantly associated with response to immunotherapy in certain contexts, the sensitivity and specificity of these predictors remain sub-optimal(7). These unimodal approaches likely fail to fully capture the complex mechanisms underlying antitumor immunity, including the dynamic nature of such responses occurring across tissues and over time. 

As such, the application of highly multiplexed single-cell immune profiling approaches to biomarker detection and target discovery in a cross-tissue, longitudinal manner may provide nuanced immune phenotypes with greater predictive and translational utility.

To this end, we developed a 34-parameter spectral flow cytometry panel and high dimensional data analysis pipeline to interrogate protein-level immune phenotypes associated with different phases of the cancer immunoediting cycle, including immune escape and tumor outgrowth, and immune checkpoint blockade (ICB) response across preclinical models. 

We profiled the NPK-C1 and ICB-treated MC38 models, analyzing multiple tissues (tumor, draining versus non-draining lymph nodes, and blood) over and around key inflection points in tumor progression. This facilitated the deep characterization of dynamics underlying natural and ICB-induced tumor regression, transition to immune equilibrium, and subsequent transition from equilibrium to uncontrolled tumor escape. Validation studies in human single-cell datasets of renal cancer confirmed the translational relevance of these findings.

Materials and Methods

Mice. Male and female C57BL/6J mice (5-6 weeks old) were purchased from the Jackson Laboratory (Bar Harbor, ME). Mice were 6-8 weeks old at the time of use. All animals were housed in strict accordance with NIH and American Association of Laboratory Animal Care regulations. All experiments and procedures for this study were approved by the Columbia University Medical Center Institutional Animal Care and Use Committee (IACUC).

Cell Lines. The NPK-C1 cell line (originally LM7304) was provided by Dr. Cory AbateShen at Columbia University. See prior references for further detail(8,9). NPK-C1 cells were maintained in R10, consisting of RPMI medium (Corning; Corning, NY) supplemented with 10% FBS (HyClone; Logan, UT), 100 U/mL penicillin, and 100 mg/mL streptomycin (Gibco; Gaithersburg, MD). MC38 colon carcinoma cells were purchased from Kerafast and cultured in D10 consisting of DMEM (Corning; Corning, NY) supplemented with 10% FBS, 100 U/mL penicillin, and 100 mg/mL streptomycin (Gibco; Gaithersburg, MD).

Tumor Challenge and Therapy Injections. NPK-C1 cells at 70-90% confluence or MC38 cells at 50-75% confluence were harvested with 0.05% trypsin (Gibco, Gaithersburg, MD), washed with PBS, counted, and resuspended at 10x106 cells/mL in ice-cold PBS. On day 0, 6-8 week old mice were implanted on the right flank with 1x106 NPK-C1 or MC38 cells. Tumor measurements were recorded in X, Y, and Z dimensions (largest diameter, smallest diameter, and largest height, respectively) every 2-3 days by digital caliper and tumor volume was calculated by multiplying X*Y*Z. Anti-PD-1 (RMP1- 14 IgG2a,κ) or Rat IgG2a isotype antibody (Bio X Cell; Lebanon, NH) was diluted in sterile PBS and administered by intraperitoneal (IP) injection at 200 μg/mL per mouse on days 3, 6, 9, and 12 post-MC38 implantations.

Tissue Harvesting. Following mouse euthanasia, ~200 μl blood was isolated via cardiac puncture using a 0.2% heparin (StemCell Technologies; Vancouver, BC) coated syringe needle and placed on ice in tubes containing 10 μl 0.5M EDTA (Corning; Corning, NY). Tumor-draining and non-draining inguinal lymph nodes were dissected and placed in 48-well plates containing 150 μl R10 media on ice. 

Tumors were harvested, massed, and up to 50 mg tumor was diced and placed in X-Vivo 15 media (Lonza; Basel, Switzerland) in 5mL Eppendorf tubes on ice. DNase (40 μl of 20 mg/mL solution; Roche; Basel, Switzerland) and Collagenase D (125 μl of 40 mg/mL solution; Roche; Basel, Switzerland) were added to tumor samples before incubation on a shaker at 37o C for 30 minutes. Digest reactions were quenched by vortexing (30 seconds) and adding 5mL R10 media. Tumor digests were filtered through 70 μm filters (Miltenyi; Bergisch Gladbach, GE) and pelleted. Blood samples were RBC lysed with two successive 2-minute incubations in 2mL ACK buffer (Quality Biological; Gaithersburg, MD) and quenching in R10 before pelleting. Lymph nodes were physically disaggregated with a 1mL syringe plunger in the 48-well plate, then suspensions were filtered through 40-um filters. All samples were placed in U-bottom 96-well plates for staining.

Flow Cytometry. Samples were washed with PBS. Dead cells were stained by resuspension in 100 μl PBS + Live/Dead Fixable Blue dye (1:500; Invitrogen; Waltham, MA). This and all further staining or fixation steps were performed for 30 minutes at room temperature on a plate shaker, protected from light. Samples were washed 2x with PBS, then were resuspended in FACS (PBS + 3% FBS + 1mM EDTA + 10mM HEPES) supplemented with TruStain FcX (1:50; BioLegend; San Diego, CA) and TruStain Monocyte Blocker (1:20; BioLegend; San Diego, CA) and placed on ice. 

Surface antibodies were prepared at optimal dilutions (see Supplementary Table S1) in FACS supplemented with Brilliant Stain Plus buffer (BD; Franklin Lakes, NJ), then were added to samples in a blocking solution. After staining, samples were washed 2x with FACS buffer and fixed in 100 μl of FoxP3 Fixation/Permeabilization Kit buffer (eBioscience; San Diego, CA). Samples were washed twice with 1X Permeabilization buffer (1X PW) and then stained in 1X PW plus intracellular antibodies. Samples were then washed twice in 1X PW then fixed in 100 μl of FluoroFix buffer (BioLegend: San Diego, CA), washed twice with FACS, then resuspended in 200 μl FACS and sealed in the 96 well plates for acquisition the following day on a Cytek Aurora 5-laser cytometer. 

Simultaneously stained splenocyte samples were utilized for single stain controls. Analysis and Statistics. Longitudinal profiling experiments were conducted once each for MC38 and NPK-C1 models. A total of n = 10-25 mice per time point were evaluated. Flow cytometry data were analyzed in FlowJo v10.8.1 (BD; Franklin Lakes, NJ), and high dimensional plugin algorithms UMAP and FlowSOM were downloaded from FlowJo Exchange. Up to 30k live CD45+ tumor-infiltrating cells and up to 15k live CD45+ blood or LN-derived cells per sample were concatenated for high dimensional analysis. 

All dimensionality reduction and clustering were performed in FlowJo. Statistical analyses were performed in GraphPad Prism v9.3.1. Pearson Correlation was used to generate correlation coefficients, and unpaired Student’s T-tests with Welch’s correction were used to compare cluster frequencies between groups. Single-cell analytical methods are previously described(10,11). 

The KLRG1 regulon was extracted from the consensus metaVIPER KLRG1 regulon from Obradovic, et al(11). To compute the KLRG1 regulon signature, we fit a random forest regression model with regulons genes as features and KLRG1 as a target, then computed Gini importance scores of regulon genes to determine their impact on KLRG1 expression. The KLRG1 regulon score of each cell was given by the weighted sum of expression measures of all regulon genes in the cell with importance scores as weights.

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