Dissecting the tumor-immune landscape in chimeric antigen ... · machine learning methods, such as...

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Dissecting the tumor-immune landscape in chimeric antigen receptor T-cell therapy: key

challenges and opportunities for a systems immunology approach

Gregory M. Chen1,4,5

, Andrew Azzam4, Yang-Yang Ding

1, David M. Barrett

1, Stephan A.

Grupp1,2,6

, Kai Tan1,3-8,*

1Division of Oncology, Center for Childhood Cancer Research, Children’s Hospital of

Philadelphia 2Division of Oncology, Cancer Immunotherapy Program, Children's Hospital of Philadelphia

3Department of Biomedical and Health Informatics, Children’s Hospital of Philadelphia

4Perelman School of Medicine, University of Pennsylvania

5Graduate Group in Genomics and Computational Biology, University of Pennsylvania

6Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania

7Department of Cell and Developmental Biology, Perelman School of Medicine, University of

Pennsylvania 8Department of Genetics, Perelman School of Medicine, University of Pennsylvania

* Corresponding author

Running title: Systems Biology of CAR T-cell Therapy

Contact information for the corresponding author:

Kai Tan

The Children’s Hospital of Philadelphia

4002 Colket Translational Research Building (CTRB)

3501 Civic Center Boulevard

Philadelphia, PA 19104

USA

Phone: 267-425-0050

Email: [email protected]

Disclosure of potential conflicts of interest

The authors declare no competing interests.

Research. on July 16, 2020. © 2020 American Association for Cancerclincancerres.aacrjournals.org Downloaded from

Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on March 3, 2020; DOI: 10.1158/1078-0432.CCR-19-3888

http://clincancerres.aacrjournals.org/

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Abstract

The adoptive transfer of genetically engineered Chimeric Antigen Receptor (CAR) T-cells has

opened a new frontier in cancer therapy. Unlike the paradigm of targeted therapies, the efficacy

of CAR T-cell therapy depends not only on the choice of target, but also on a complex interplay

of tumor, immune, and stromal cell communication. This presents both challenges and

opportunities from a discovery standpoint. Whereas cancer consortia have traditionally focused

on the genomic, transcriptomic, epigenomic, and proteomic landscape of cancer cells, there is an

increasing need to expand studies to analyze the interactions between tumor, immune, and

stromal cell populations in their relevant anatomical and functional compartments. Here, we

focus on the promising application of systems biology to address key challenges in CAR T-cell

therapy, from understanding the mechanisms of therapeutic resistance in hematologic and solid

tumors to addressing important clinical challenges in biomarker discovery and therapeutic

toxicity. We propose a systems biology view of key clinical objectives in CAR T-cell therapy,

and suggest a path forward for a biomedical discovery process that leverages modern

technological approaches in systems biology.




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Introduction

CAR T-cell therapy has seen exceptional success in several hematologic malignancies (1–5),

marked by the first round of FDA approvals and Phase II trials (4,6). Yet these studies have also

highlighted key clinical challenges including therapeutic resistance in a subset of patients,

challenges in translation to solid tumors, and therapeutic toxicity (7–9). As clinical knowledge

increases, there is a promising opportunity to apply modern molecular technologies under a

systems immunology approach to accelerate progress. Furthermore, the limited availability of

clinical samples from early clinical trials is a major motivating factor for using advanced

technologies to gain the most knowledge from the samples that exist. Broadly speaking, systems

immunology encompasses the tools of systems biology framework to the unique biological

characteristics of innate and adaptive immunity (10,11). The modern roots of systems biology

date to the turn of the 21st century, as genome sequencing technologies began to rapidly

accelerate (12,13). Under a systems biology paradigm, biological entities are modeled as a

network of interacting units, typically requiring high-throughput data generation paired with

integrative computational and statistical models. Each foundational unit may be defined as an

intracellular entity such as a gene or metabolite, or a cellular entity such as an individual cell or

cell type (10). Technological advances have made possible many approaches that were

previously untenable except under theoretical circumstances. Massively parallel molecular

assays can quantify not only the levels of various molecular analytes such as RNA, protein, and

metabolites, but also interrogate their interactions at increasingly higher throughput. These

molecular assays can also be applied to measure the cellular states and interactions after either

genetic or chemical perturbations in a high-throughput fashion (14–16). The advent of single-cell

technologies in particular has led to an influx of data, and ongoing consortia such as the Human




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Cell Atlas (17), the Human BioMolecular Atlas Program (18), and the Human Tumor Atlas

Network will provide key resources to characterize the underlying basis of normal and malignant

cellular phenotypes and tumor microenvironment. The immune system has been an important

focus in multiple consortia, leading to opportunities to integrate knowledge into the field of CAR

T-cell therapy and other immunotherapies.

In parallel to experimental technology development, a vast array of computational and statistical

approaches have been developed to advance systems biology. Mathematical models of tumor-

immune interactions have been developed using principles of optimal control theory, treating

cellular populations as interacting units within a dynamic system (19,20). Another key class of

systems biology algorithms model biological processes as molecular interaction networks, and

both computational and technological advancements have allowed for construction and analysis

of these networks at a tissue- or cell type- specific resolution (21,22). The relatively high sample

sizes produced by single-cell experiments have been increasingly capable of powering for

machine learning methods, such as probabilistic graphical models and deep learning, to interpret

the data and identify regulatory mechanisms (23–25). A major ongoing challenge in the field is

the integration of data across experimental protocols and molecular assays, and approaches such

as factor analysis and transfer learning have been developed to address this challenge (26,27).

The mechanisms of CAR T-cell treatment success and failure are likely multimodal and involve

tumor, T-cell, and microenvironmental factors (9). From a systems biology perspective, tumor-

immune interactions can be viewed as a complex adaptive system that underlies the major

clinical challenges, from treatment failures to therapeutic toxicities (Figure 1). In hematological




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malignancies such as B-cell acute lymphoblastic leukemia (B-ALL), cancer cells may be present

in large numbers in peripheral circulation as well as the bone marrow, allowing T-cells to have

relatively free access to the tumor throughout the body and in the marrow microenvironment. In

contrast, CAR T-cell therapy for solid tumors faces multiple additional barriers including T-cell

exclusion, vascular and hypoxic constraints, and immune suppression mediated by additional cell

types such as cancer associated fibroblasts, tumor-associated macrophages, and myeloid-derived

suppressor cells (28,29). Understanding these factors will be a crucial component of improving

existing CAR T-cell therapies and developing new therapies for hematological and solid tumors.

As the number of clinical and preclinical trials of CAR T-cell therapies expand, a systems

biology approach will become a powerful complement to advance biological discovery (Table

1). Paramount to a systems biology approach is an understanding of the strengths and limitations

of each technology, including sources of technical variability and the fundamental role of each

molecular analyte in a biological system.

Biomarker discovery: leveraging prior immunological knowledge

The identification of prognostic biomarkers of response to CAR T-cell therapy is of great clinical

interest due to the potential to aid in risk stratification and identification of patients most likely to

benefit from therapy. There is an urgent need to define T-cell characteristics in the CAR T-cell

product that predict key attributes such as proliferation and persistence of CAR T-cells after

infusion. A number of cellular and molecular biomarkers have been identified in immune

checkpoint blockade: circulating factors such as lactate dehydrogenase (LDH) (30,31);

immunological markers such as immune cell composition, T-cell activation pathways,

lymphocytic infiltration, and T-cell repertoire (32–35); and tumor markers such as PD-L1




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signaling (36) and mutational load (36–38). Some, but not all, of these biomarkers have the

potential to generalize to CAR T-cell therapy. Transcriptomic phenotypes of lymphocytic

activation and migration may be shared between immunotherapy modalities. On the other hand,

biomarkers that assess the diversity in antigen recognition, such as T-cell repertoire and

mutational load, may be of lesser significance in CAR T-cell therapy compared to

immunotherapies that depend primarily on cytotoxicity through endogenous T-cell receptor

signaling. The utility of these biomarkers in combined checkpoint blockade and CAR T-cell

therapy, or in future CAR T-cell therapies that may require general T-cell activation and epitope

spreading for efficacy, has yet to be assessed in clinical trials.

Circulating factors may play a general role in suppression or activation of adaptive immune

responses. Lactic acid has been shown to blunt antitumor responses by T-cell and NK-cells (39),

and serum lactate dehydrogenase (LDH) has been shown to correlate with clinical outcomes in

immune checkpoint blockade. In CAR T-cell therapy, post-infusion serum cytokine markers

have been shown to be predictive of cytokine release syndrome (CRS) (40), the most significant

characteristic adverse effect of the therapy; but biomarkers predicting or modulating efficacy in

either the product or the patient have not been clearly identified. Transcriptional pathways in T-

cell populations, such as T-cell exhaustion, have been associated with resistance to both immune

checkpoint blockade and CAR T-cell therapy (41,42). Insights related to tumor evasion of

adaptive immunity may be shared between CAR T-cell therapy and other therapeutic modalities.

Among the best-known example is expression of the programmed death-ligand 1 (PD-L1) on

tumor cells, which interacts with PD-1 on T-cells to suppress T-cell activity. In an in vivo mouse




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model of pleural mesothelioma, tumor-cell PD-L1 expression inhibited CAR T-cell effector

functions (43).

The transfer of knowledge from prior studies of immunology and cancer immunotherapy to CAR

T-cell therapy will be key to the systems biology approach. The Immunological Genome Project

(44) and more recently, the Human Cell Atlas (17), the Human Tumor Atlas Network, and the

Immuno-Oncology Translational Network will provide a wealth of reference data on the

molecular physiology of multiple immune populations in normal and malignant environments. In

clinical and pre-clinical studies of cancer immunotherapy, particularly in immune checkpoint

blockade, there has been an expansion in the use of high-throughput technologies to generate

large datasets and computational models of tumor and immune populations. For example, Jiang

et al. used publicly available bulk RNA-Seq profiles to derive a predictive signature of T-cell

exclusion and dysfunction in anti-PD1 and anti-CTLA4 therapy (45); and Krieg et al. used

single-cell mass cytometry to assess cell surface protein phenotypes of peripheral blood

mononuclear cells at multiple therapeutic time points (46).

The computational framework for biomarker discovery for CAR T-cell therapy from prior

immunological data can be formulated under the principles of transfer learning, which involves

the exchange of knowledge from a source domain and task to a target domain and task (47).

Under this framework, the source domain consists of data generated from a previous

immunological study, and target domain consists of data generated from one or more CAR T-cell

studies (Figure 2). Knowledge transfer can occur in the form of model parameters, feature

representations, or instances in an integrative model. In the case of immunological data, the most




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direct methods of knowledge transfer can involve gene signatures, pathway signatures, gene

network topology and edge weights, or model coefficients to be used as a statistical prior.

Transfer learning has been successfully used for prediction of MHC class I protein binding,

clinical and histopathological image analysis, integration of molecular data from genomic and

RNAi screens, and integration of single-cell RNA-Seq data (48–52). Immune cell populations

are among the most common cells to be studied by high-throughput technologies, and the wealth

of publicly available immunological data is an opportunity for the development and deployment

of integrative computational methods to be used in studies of CAR T-cell therapy.

Pre-treatment effects and the search for combination therapy

Patients on clinical trials of CAR T-cell therapy to date have received CAR T-cell therapy

subsequent to other treatments, often including multiple rounds of cytotoxic chemotherapy.

These prior treatment effects may have a profound impact on the immune cells, tumor cells, and

tumor microenvironment involved in CAR T-cell therapy. In studies of other immunotherapies,

chemotherapeutic agents have been shown to increase immunogenicity of tumor cells through

increased antigenicity and depletion of suppressive immune cell populations (53,54) and increase

tumor infiltration by T-cells in solid tumors (55). Combination therapy between certain

chemotherapeutic agents and immune checkpoint blockade is the standard of care in some

cancers (56). It is unclear to what extent these factors apply to CAR T-cell therapy.

Lymphodepleting chemotherapy is routinely given to patients prior to CAR T-cell infusion, and

has been associated with improved in vivo expansion and persistence (57), owing to cytokine

upregulation as well as suppression of regulatory T-cells and myeloid-derived suppressor cells

that has been observed in earlier adoptive cell transfer therapies (58). Clinical and preclinical




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studies comparing the impact of prior therapy on CAR T-cells themselves are scarce, and such

studies are limited by the heterogeneity of prior treatment regimens among patients enrolled in

clinical trials. It is possible that certain chemotherapies could have a deleterious impact on CAR

T-cell therapy due to their impact on T-cells and activation of pathways hindering expansion in

vitro or post-infusion (59).

With over a hundred approved anticancer drugs and over a thousand in development (60), the

identification of agents that sensitize cancer cells or augment immunological function in CAR T-

cell therapy is a promising objective. Some drug combinations, such as combining immune

checkpoint blockade and CAR T-cell therapy, have initial clinical evidence of benefit (61). A

highly active area of systems biology has been the development of technologies and

computational methods to identify synergistic and antagonistic drug combinations in cancer

therapy. High-throughput drug sensitivity screens on cell lines, paired with genomic and

transcriptomic data, have provided a resource for modelling individual drug perturbations in

cancer cells (62,63), and CRISPR screens of gene pairs provide a functional assay to assess for

genetic interactions among known drug targets in cancer cells (64). A number of computational

methods have been developed to model the gene regulatory networks of cancer cells, with the

objective of identifying synergistic drug combinations or pairs of potentially targetable genes

that exhibit synthetic lethality or synthetic dose lethality (65–68). These approaches, however,

are challenging to generalize to immunotherapy. Immunotherapy fundamentally differs from

conventional pharmacotherapy, in that the mechanism of tumor cell killing is dependent on

complex interactions between immune cells and tumor cells, which are not adequately modeled

in assays of cancer cell lines. One approach has been to identify differentially expressed genes




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and gene modules between immunotherapy responders and non-responders, and consider

regulators of these pathways as candidate targets. For example, Lesterhuis et al. performed

network analysis of immunotherapy responsive and resistant tumor transcriptomes to identify

candidate target genes to augment checkpoint blockade (69), and Jerby-Arnon et al. performed

single-cell RNA-Seq on 33 melanoma tumors to identify an immune evasion cellular program

that suggested CDK4/6 inhibition in combination with immune checkpoint blockade (70).

Intratumor targets, however, only represent a subset of drug targets among the potentially

targetable cellular populations. For example, stimulation of innate immunity, modification of the

tumor immunosuppressive environment, and modulation of T-cell function represent classes of

potential drug targets directed toward non-tumoral cells (71). To address these broader

opportunities of combination therapies, computational models must consider immune cell

populations and tumor-immune interactions.

Use of single-cell technology to better understand the T-cell contribution to CAR T-cell

therapy

The underlying T-cell populations used for generation of CAR T-cell products likely play a

substantial role in determining the ability of the CAR T-cells to proliferate and kill tumor cells in

vivo. Until recently, most protocols for CAR T-cell generation have not selected for T-cell

subtypes for engineering, and thus contain a heterogeneous mix of CD4+ and CD8

+ T-cells along

the full spectrum of naive to effector phenotypes. A recent exception to this is the JCAR017

approach, using defined populations of CD4 and CD8 cells, with impacts on efficacy and

persistence that are still being studied (72). A number of studies have assessed T-cell predictors

of therapeutic response in chronic lymphocytic leukemia (42), non-Hodgkin lymphoma (73), and




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multiple myeloma (74). Fraietta et al. found that sustained response to CAR T-cell therapy in

chronic lymphocytic leukemia was associated with increased prevalence of CD27+CD45RO

-

CD8+ memory-like T-cells prior to CAR T-cell generation, and the presence of CD27

+PD-1

-

CD8+ CAR T-cells expressing high levels of the IL-6 receptor-β chain was predictive of

therapeutic response (42). In a study of non-Hodgkin lymphoma, Rossi et al. applied a single-

cell secreted cytokine assay on pre-infusion CAR T-cells, and developed a predictive score based

on the proportion of cells secreting multiple cytokines and the mean fluorescence intensity of

secreted proteins per cell; they reported that their score was significantly associated with

treatment response (73). In a study of multiple myeloma, Cohen et al. found that a higher

CD4:CD8 ratio in the initial leukapheresis product was associated with greater CAR T-cell

expansion in vivo (74). An important area of study has been understanding of the role of early

memory phenotypes (75), such as naive T-cells, in establishing proliferative populations and

long-term memory. Selection or enrichment of T-cell subtypes has been proposed as a major area

of interest for improving CAR T-cell therapies (76,77), which may be aided by the recent

proliferation of single-cell transcriptomic, epigenomic, and proteomic technologies.

T-cell subset assessment has conventionally relied on the use of individual cell-surface protein

markers, but recent technologies have made it possible to interrogate large numbers of markers at

a single-cell level in combination with other molecular and imaging assays. Single-cell mass

cytometry uses flow cytometry and mass spectrometry with discrete isotopes to characterize

large numbers of cell surface protein markers simultaneously at a single-cell level (78).

Simultaneous profiling of single-cell RNA transcriptomes with proteomics has been made

possible with CITE-Seq and REAP-Seq, methods that combine single-cell RNA-Seq with




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oligonucleotide-labeled antibodies that bind to cell surface proteins (79,80). These methods may

be used to distinguish between CAR T-cells and non-CAR T-cells in single-cell transcriptomic

studies, allowing for the capacity to identify gene expression pathways specific to the

endogenous and engineered cell populations.

Single-cell RNA-Seq has been a breakthrough technology for characterizing whole-

transcriptome gene expression at a single-cell resolution. Single-cell RNA-Seq has been used to

profile infiltrating T-cells in several solid tumor types (81,82), demonstrating the ability to

investigate T-cell populations within the tumor microenvironment. Tikhonova et al. and

Baryawno et al. recently characterized the mouse bone marrow microenvironment using single-

cell RNA-Seq, providing key insights into cellular phenotypes and signaling that underlies the

bone marrow niche (83,84). Applying single-cell transcriptomics to complex populations of

peripheral and infiltrating T-cells in CAR T-cell therapy may aid in identifying subpopulations

and cellular pathways that mediate proliferation, persistence, and memory formation.

Characterization of suppressive cell types in the tumor microenvironment, such as tumor-

associated macrophages, cancer-associated fibroblasts, myeloid-derived suppressor cells, and

regulatory T-cells, may aid in understanding the contribution of these cells in the efficacy of

CAR T-cell therapy. Without doubt, current single-cell technologies still have a number of

limitations, such as coverage and throughput. There has been significant work to address these

and other technical challenges in single-cell RNA-Seq, including variability in sequencing depth

and cell size, drop out, and batch effect correction (85).




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Improving the engineering of CAR T-cells with the goal of increasing efficacy, speeding

manufacturing, and decreasing toxicity is a central challenge in the field, with the potential to

enhance existing therapies and accelerate the adoption of cellular therapeutics to other cancer

types. Beyond cancer therapy, CAR T-cells have been designed for other therapeutic purposes,

including autoimmune disease (86), prevention of allogeneic transplant rejection (87), and

against infectious disease (88,89). Modeling of intracellular and extracellular interactions may

play an important role in the design of novel engineering of CAR components. The intracellular

pathway mediating cytotoxic response in CAR T-cells is incompletely understood, and recent

studies have applied computational modeling to understand phosphorylation kinetics of certain

CAR constructs (90). A number of synthetic biology approaches have been designed to augment

and enhance CAR construction, including T-cells engineered with kill switches, growth switches,

chemokine receptors, multiple targets, and avoidance of allogeneic rejection (91,92).

Addressing challenges in solid tumors

CAR T-cell therapies have seen their greatest success in hematological B-cell malignancies, and

the design of an effective CAR T-cell therapy for solid tumors is a key objective in pediatric and

adult clinical oncology. In contrast to hematological cancers, solid tumors pose particular

challenges including the need for CAR T-cell trafficking and infiltration, overcoming a hypoxic

and metabolically constrained tumor microenvironment, and the presence of suppressive cell

populations (29). These factors have been hypothesized to be major barriers to the effective

implementation of CAR T-cell therapy in solid tumors.




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In CAR T-cell therapy, T-cells are typically extracted from and re-infused into the peripheral

circulation, making it necessary for CAR T-cells to enter the tumor microenvironment. To

address this challenge, some groups have explored intracavitary or intratumoral injection of CAR

T-cells (93,94). This strategy may be technically challenging, and even more importantly, it is

unclear to what extent regionally delivered CAR T-cells can migrate to other tumor sites in

highly disseminated disease. The level of T-cell infiltration into solid tumors has been correlated

with chemokine expression in the tumor microenvironment, and variability in chemokine

expression may explain the low level of T-cell infiltration in some tumors (95). Some groups

have focused on engineering approaches to localize CAR T-cells, such as the manufacturing of

T-cells with receptors for chemokine CCR2 to increase cellular migration (96) or injection of

amphiphile CAR T-cell ligands that traffic with endogenous albumin to lymph nodes and

enhance the interaction between CAR T-cells and antigen-presenting cells (97).

Recent technological advances in proteomics and single-cell RNA-sequencing has improved our

understanding of the composition and cellular interactions within the tumor-immune

microenvironment (98). For example, Spitzer et al. performed mass cytometry of immune cells

in multiple anatomical compartments of a mouse model of cancer immunotherapy, and

demonstrated that immune activation occurs systemically in initial therapy but only in the

peripheral compartment during tumor rejection (99). In particular, the role of regulatory T-cells,

M2 macrophages, and myeloid-derived suppressor cells in immune evasion has been

demonstrated in clinical and pre-clinical studies of other immunotherapies in solid tumors (100);

however, limited sample sizes and technical challenges in raising immunocompetent mouse

models have made it challenging to directly study these interactions of CAR T-cells with these




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cellular populations (28). Ongoing consortia, such as the Human Tumor Atlas Network and the

Adult Immunotherapy Network, have dedicated significant resources into generating publicly

accessible data that will be key to unravelling the basis of immune suppression in the tumor-

immune microenvironment. An exciting advance in the field has been the integration of cellular

imaging with single-cell molecular profiling. Technologies such as Spatial Transcriptomics and

Slide-Seq have been recently developed to integrate single-cell RNA-Seq with cellular imaging

(101,102), providing an opportunity to understand the spatial context of cellular populations in

the tumor-immune microenvironment. Highly multiplexed analysis of proteins in the context of

tissue imaging has been made possible with several imaging technologies, including Imaging

Mass Cytometry (IMC) (103), Multiplexed Ion Beam Imaging (MIBI) (104), and co-detection by

indexing (CODEX) (105). These resources will provide rich datasets for modeling the signal

transduction pathways within the tumor microenvironment in CAR T-cell therapy.

Identifying tumor-specific antigens is a challenge for the development of future CAR T-cell

therapies for non-hematopoietic malignancies. One of the reasons for the success of CAR T-cell

therapies for B-cell malignancies is that the ablation of normal B-cell populations is a

manageable toxicity via immunoglobulin replacement. Cancer-associated antigens have to be

chosen carefully to avoid off-tumor, on-target toxicity, which has been a challenge in some solid

tumor CAR T-cell trials (14). Computational and functional screens have been designed to

identify cell surface proteins that are ubiquitously expressed on tumor cells but not on normal

cells (106,107). A major challenge to this approach is the lack of a comprehensive reference

catalog of normal cell populations. Consortia such as GTEx have performed bulk RNA-Seq on

multiple normal human tissues (108), but bulk assays may miss rare but important cell




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populations such as adult stem cells and non-parenchymal cells. Single-cell data sources have the

potential to address this problem through their capacity to characterize rare and previously

unknown cell populations (109). Designing CAR T-cell therapies to target multiple antigens may

be an approach to circumvent antigen escape, as well as increase tumor specificity. Toward this

goal, an integration of single-cell atlases with data from individual studies may provide the

much-needed reference from which tumor-specific antigens and combinatorial antigen targets

may be identified.

Therapy-induced toxicity

CRS is the most common and significant toxicity of CAR T-cell therapy, reported to occur in 54-

91% of patients receiving anti-CD19 CAR T-cell therapy (110). This syndrome was not apparent

in preclinical models, and only became manifest in early clinical trials (2). Neurotoxicity,

referred to as immune effector cell-associated neurotoxicity syndrome, or ICANS (111), has

been observed as an adverse event, with the most concerning trait being cerebral edema. While

the underlying pathophysiology of these toxicities remain incompletely understood, clinical trials

have implicated macrophages, IL-1, endothelial activation, and especially IL-6 in the

pathogenesis of CRS. IL-6 blockage with tocilizumab is the only currently approved therapy for

CRS (2,112). Novel murine models have been recently developed, and have suggested a role of

myeloid lineages and endothelial activation in CRS (113). Giavirdis et al. (114) found that CAR

T-cells recruit and activate macrophages, leading the myeloid cells to produce IL-6 and iNOS

which contribute CRS. Norelli et al. (115) found that monocytes and macrophages were

primarily responsible for increases in IL-1 and IL-6 in their mouse model of CRS, with IL-1

production preceding and possibly stimulating IL-6 production. In a clinical trial of CD19 CAR




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T-cell therapy in B-ALL, CLL, and NHL, investigators reported evidence of increased

endothelial activation in patients with severe CRS, via elevated Von Willebrand Factor and

increased Ang2:Ang1 ratio (110). Moreover, IL-6 has been shown to disrupt the blood-brain

barrier in vitro, suggesting a possible mechanistic explanation for neurotoxicity that has been

reported in some patients.

Toxicities of CAR T-cell therapy such as CRS and ICANS remain as important concerns that

may or may not be class effects of the therapy, and prognostic and therapeutic strategies will

require a deeper understanding of the underlying biological basis. While initial clinical studies

have focused on tumor cells and T-cells, a systems immunology approach will involve

investigation of cell-cell interactions with various other immune cell types in the peripheral and

marrow microenvironments. Comprehensive analyses of myeloid and lymphoid compartments in

clinical trials, as well as expansion of murine models, will be important paths forward for the

study of these therapy-limiting toxicities.

Conclusions and future directions

CAR T-cell therapy has been a breakthrough in clinical oncology. Understanding the T-cell,

tumor-cell, and tumor microenvironment factors mediating therapeutic efficacy will play a

significant role in optimizing current therapies and applying CAR T-cells to other tumor types.

Modern omics and systems biology technologies provide opportunities for biomarker discovery

and therapeutic development. As clinical cohorts of patients treated with CAR T-cell therapy

continue to grow, there will be increasing availability of clinical samples from which rich multi-

omic profiling can advance the discovery process. Like many biomedical challenges in the 21st




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century, translational research will be best performed through a collaborative effort by

interdisciplinary teams of clinicians, experimental and computational biologists.

Acknowledgements

This work was supported by National Institutes of Health of United States of America grants, GM108716,

GM104369, CA226187, CA233285 (to K.T.), CA232361 (to D.B. and S.G.) and a Doris Duke Charitable

Foundation Clinical Scientist Development Award (to D.B.).

Figure Legend

Figure 1. A systems immunology view of biomedical discovery for CAR T-cell therapy.

Immunotherapy requires cellular interactions between tumor and immune populations in multiple

physiological compartments including the peripheral circulation, tumor microenvironment, and

lymphoid tissues. Addressing the key challenges in CAR T-cell therapy will require an

understanding of these interactions among cellular phenotypes and microenvironments.

Experimental and computational advances in systems immunology provide the capacity to

simultaneously profile multiple cellular populations within each anatomical component, making

it possible to investigate the tumor-immune interactions that underlie the major challenges in

CAR T-cell therapy. Recent technological advances enable investigators to query genomic,

transcriptomic, proteomic, and epigenomic features at a single-cell level. A number of methods

for joint profiling of multiple molecular features have been developed, and imaging-based

approaches have been developed to resolve spatial context of transcriptomic and proteomic data.

Figure 2. The challenge of integrating data from heterogeneous molecular assays and

experimental sources. Prior and ongoing consortia have provided a wealth of data for multiple

immune cell populations in isolation and in interaction with each other and tumor cells.

Integrative data analysis is a major challenge in the field, and leveraging publicly available

immunological data may be a valuable approach for systems immunology model development.

The use of unlabeled immune cell data may be framed under an unsupervised transfer learning

framework, in which the target task may be dimensionality reduction or clustering of immune

cell populations. The use of labeled source data - for example, transcriptomic profiles paired with

clinical outcome of another immunotherapy modality – may follow a framework of inductive

transfer learning, in which the target task is the prediction of CAR T-cell clinical outcome.




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Table 1. A framework for addressing the major clinical challenges of CAR T-cell therapy

using systems immunology approaches*

Clinical challenge Systems immunology approach

Biomarker discovery Profiling of serum, tumor, and immune factors prior to

infusion

Leveraging pre-existing immunological data from other

domains using principles of transfer learning

Pre-treatment effects

and identifying

combination therapies

Modeling the perturbation of immune cell populations in

the tumor and peripheral compartments

Development of computational methods that account for

gene-gene as well as cell-cell interactions between tumor

and immune populations

Understanding T-cell

aspects to therapy failure

Joint analysis of endogenous and CAR T-cell populations

in circulation and in the tumor microenvironment

Characterization of the interactions between T-cells and

suppressive cell populations

Expanding CAR T-cell

therapy to solid tumors

Joint molecular profiling and imaging of cells in the tumor-

immune microenvironment

Leveraging data generation by ongoing consortia to

understand activating and suppressive immune factors

Antigen escape and

novel target

identification

Understanding genetic and non-genetic causes of antigen

loss

Systematic identification of candidate targets in tumor and

normal tissues

Addressing major

toxicities

Understanding immune cell interactions, particularly

interactions between lymphoid and myeloid phenotypes

Investigating the interactions between CAR T-cells,

cytokines, and organ-specific cell types

*Completed and ongoing clinical trials have brought to light a number of major clinical

challenges for CAR T-cell therapy moving forward: Biomarker discovery, pre-treatment effects

and identifying combination therapies, understanding T-cell aspects to therapeutic failure,

expanding CAR T-cell therapy to solid tumors, antigen escape and novel target identification,

and addressing major toxicities. For each of these major clinical challenges, the tools of systems

immunology provide a strategy to better understand the complex interactions between tumor and

immune cell populations, providing a path forward for the next generation of cellular

therapeutics.




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Figure 1:

Peripheral circulation Solid tumor microenvironmentKey technologies in systems immunology

Key challenges in CAR T-cell therapy

Genomics:- Whole-genome and whole-exome sequencing- Single-cell DNA-Seq- Deep genomic sequencing- TCR/BCR sequencing

Transcriptomics and proteomics:- Single-cell RNA-Seq- CITE-Seq and REAP-Seq- Mass cytometry- Slide-Seq, spatial transcriptomics- Imaging mass cytometry (IMC), multiplexed ion beam imaging (MIBI), co-detection by indexing (CODEX)

Epigenomics:- Single-cell ATAC-Seq- Single-cell DNA methylation- Single-cell Hi-C

Prior treatment e�ects and circulating factorsIntrinsic T-cell dysfunctionInadequate T-cell infiltrationSuppressive tumor microenvironmentDevelopment of antigen-negative subclonesFailure of T cells to establish memoryCytokine release syndrome and other toxicities

1

EndogenousT cell

FibroblastAntigen (-)cancer cell

Myeloid-derivedsuppressor cell

RegulatoryT cell

Antigen-presenting

cell

Bone marrow microenvironment

Blast cell

Lymphoid tissue

5

4CAR T cell

Antigen (+)cancer cell

Endothelialcell

Macrophage

IL-6IL-1IFNγ

1

2

3

6

7

2

3

4

5

6

7




Figure 2:

Publicly available immunological data

CAR T-cell clinical and preclinical data

DNA

Tumor/immune cellsfrom human cell atlas,ImmGen, or immunecheckpoint blockade

study

High-throughput datageneration

Clinical or phenotypicannotation

Computationalmodels of cell-cell

and gene-geneinteractions

Knowledgetransfer

Computationalmodels of cell-cell

and gene-geneinteractions

Biomarker discovery for patient selection and prognosis

Mechanistic insights to guide CAR T-cell engineering in solid and liquid tumors

Synergistic and antagonistic combination therapies

Tumor/immune cellsfrom peripheral

circulation,tumor-immune

microenvironment

High-throughput datageneration Clinical annotation

RNAProtein

Imaging

DNARNA

ProteinImaging




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http://clincancerres.aacrjournals.org/content/early/2020/03/03/1078-0432.CCR-19-3888


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