| Literature DB >> 35577501 |
Maik Luu1, Delphine Ammar2, Paula Barbao3, Chiara Bonini4, Philippe Bousso5, Christian J Buchholz6, Monica Casucci7, Biagio De Angelis8, Emmanuel Donnadieu9, David Espie9,10, Beatrice Greco7, Richard Groen11, Johannes B Huppa12, Chahrazade Kantari-Mimoun13, Bruno Laugel13, Mary Mantock2, Janet L Markman14, Emma Morris15, Concetta Quintarelli8, Michael Rade16, Kristin Reiche16, Alba Rodriguez-Garcia3, Juan Roberto Rodriguez-Madoz17, Eliana Ruggiero4, Maria Themeli11, Sonia Guedan18, Michael Hudecek1, Ibtissam Marchiq19.
Abstract
Immunotherapy with gene engineered CAR and TCR transgenic T-cells is a transformative treatment in cancer medicine. There is a rich pipeline with target antigens and sophisticated technologies that will enable establishing this novel treatment not only in rare hematological malignancies, but also in common solid tumors. The T2EVOLVE consortium is a public private partnership directed at accelerating the preclinical development of and increasing access to engineered T-cell immunotherapies for cancer patients. A key ambition in T2EVOLVE is to assess the currently available preclinical models for evaluating safety and efficacy of engineered T cell therapy and developing new models and test parameters with higher predictive value for clinical safety and efficacy in order to improve and accelerate the selection of lead T-cell products for clinical translation. Here, we review existing and emerging preclinical models that permit assessing CAR and TCR signaling and antigen binding, the access and function of engineered T-cells to primary and metastatic tumor ligands, as well as the impact of endogenous factors such as the host immune system and microbiome. Collectively, this review article presents a perspective on an accelerated translational development path that is based on innovative standardized preclinical test systems for CAR and TCR transgenic T-cell products. © Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.Entities:
Keywords: Cell Engineering; Drug Evaluation, Preclinical; Immunotherapy, Adoptive; Receptors, Chimeric Antigen; T-Lymphocytes
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Year: 2022 PMID: 35577501 PMCID: PMC9115015 DOI: 10.1136/jitc-2021-003487
Source DB: PubMed Journal: J Immunother Cancer ISSN: 2051-1426 Impact factor: 12.469
Figure 1Factors affecting efficacy of engineered T cells. CAR-T or TCR-T efficacy can be influenced by several factors, that can improve (in green) or dampen (in red) clinical outcomes. The immunosuppressive tumor microenvironment and the heterogeneity and loss of antigen expression are an important causes of treatment failure. Specific baseline qualities of the infusion product, including optimal differentiation potential, metabolic profile and low expression of inhibitory molecules are key to mediate tumor control. Effective treatment with engineered T cells, especially in the context of solid tumors may require the activation of an endogenous T cell response, in a process known as epitope spreading. Finally, viral integration and clonal imbalance may have a favorable or deleterious impact on T cell efficacy. MHC, major histocompatibility complex. CAF, cancer-associated fibroblasts. APC, antigen-presenting cells. MDSC, Myeloid-derived suppressor cells. TAM, tumor-associated macrophages.
Figure 2Models and tools to assess efficacy of engineered T cells. Currently used preclinical models, including 2D cell culturing techniques and xenograft models in NSG mice, have been unable to predict the lack of responses or relapses observed in some clinical trials. Organotypic 3D models and humanized and syngeneic mouse models that better recapitulate the intra-tumor heterogeneity and the immunosuppressive TME are being developed and used in combination with novel analytical tools (at single cell level) and imaging techniques to better predict the clinical efficacy of next-generation engineered T-cell therapies. 2D, two-dimensional; MHC, major histocompatibility complex; TME, tumor microenvironment.
Methods and tools to assess efficacy of engineered T cells
| Assessment | Methods and tools | Advantages | Limitations | Future directions |
| CAR-T or TCR-T cell functionality | Killing, |
Optimized, easy-to-use methods to address functionality Suitable for TCR and/or CAR candidate selection and validation of T-cell engineering approaches |
Difficult to correlate with clinical outcome Very limited relevance to TME |
Perform these assays in normoxia versus hypoxia or in low glucose or low pH conditions Optimization of these techniques to be used in 3D co-culture assays |
| Planar glass-supported lipid bilayers, molecular imaging |
Highly quantitative methodology to assess CAR-T sensitivity towards antigen and CAR-proximal downstream signaling |
Requires special expertize and equipment for implementation Reductionist approach |
Broaden access to research and development (R&D) Tight collaboration between experts in biophysics and synthetic biology | |
| Human organoids |
Recapitulates many aspects of the TME, including intratumor heterogeneity |
Lack of immune cells Tumor/stroma structure not always preserved Readouts at single time points |
Addition of immunosuppressive or stimulating cells Optimization of readouts to analyze engineered T-cell functions in real time | |
| Tumor slices |
Test T-cell efficacy in a preserved human TME Identify obstacles to early T-cell responses (min to hours) |
Limited availability and viability of fresh tissue slices Readouts at single time points Allogeneic responses if using non-autologous T-cells |
Incorporation of microfluidic to improve ex vivo culture & model in vivo T infiltration into solid tumors. Optimization of culture conditions and readouts to analyze engineered T-cell functions in real-time | |
| Microphysiological 3D tumors |
Simulates invasive growth of tumor cells Engineered -T cells enter arterial medium flow actively Assessment of T cell adherence and infiltration |
Requires special expertize and equipment for implementation Costly Dependence on growth behavior of primary material or cell lines |
Setup with patient-derived primary tumor cells or antigen-density- modified cell lines to reflect tumor heterogeneity Implementation of TME components such as immunosuppressive cells | |
| RNA sequencing |
Deep characterization of T-cells (can be done at single cell level) |
Costly Complex sample preparation workflows Requires bioinformatics expertize for data analysis |
Broaden application to post-infusion patient samples Reduction of costs Simplification of data analysis | |
| Nanostring |
Easy to use, sensitive method to analyze up to 800 RNA targets without cDNA conversion or library prep |
Costly Requires access to specific equipment |
Reduction of costs Development of specific panels to analyze dysfunctional vs effective post-infusion T-cell patient samples | |
| Polyfunctionality |
Polyfunctional assessment of engineered T cells Correlates with patient outcome in CD19-specific CAR-T cell trials. |
Costly Limited access to the required equipment |
Reduction of costs Validation of their predictive efficacy value in more clinical trials | |
| Spatial RNA |
RNA expression according to T-cell spatial position within the tumor tissue |
Costly Requires bioinformatics expertize for data analysis |
Reduction of costs Simplification of data analysis | |
| Efficacy and in vivo persistence of human CAR-T and TCR-T | NSG animals |
Easy engraftment of tumors and T-cells of human origin Study of human engineered T cells, prepared as those used in clinical trials, before regulatory approval |
Difficult to test combination therapies since NSG are sensitive to irradiation and chemotherapy No human TME, could skew tumor characteristics The lack of human cytokines released by innate cells limits T-cell persistence |
Use of tumor cells expressing different levels of antigen expression to mimic tumor heterogeneity Inclusion of immunosuppressive immune cells into tumors Stable expression of genes encoding for human cytokines |
| Humanized SGM3 mice |
Reconstitution of human TME Xeno-tolerant T cells, with less risk of GVHD |
Lack of stromal component Residual lymph nodes |
Transplantation with artificial lymph nodes Further humanization Colonization with human microbiota | |
| HLA-A2 transgenic mice |
Murine tumor cells with human HLA-A2 can present peptides to human or murine T cells expressing human transgenic TCRs |
Differences in antigen processing and presentation mechanisms between mice and humans |
Development of multiple HLA-type transgenic mice to broaden applicability for testing of TCR-T cells | |
| Intravital imaging |
Real-time assessment of T cell trafficking and killing dynamics in vivo Characterization of functional heterogeneity within engineered-T cells in vivo Help to guide rationale choice of the composition of the infusion product |
Restricted to a few hours of continuous observation. |
Improvement in technology to accurately visualize deeper organs/tissues Use of imaging chambers | |
| Lymphodepletion regimens | Syngeneic models |
Intact host immune system, recapitulation of the TME Prediction of on-target off-tumor toxicities Effects of lymphodepletion in epitope spreading |
Differences between mouse and human engineered T cells Engineering of murine T cells can be challenging Limited translation to clinical setting Novel combinations may not be able to be tested in mice due to lack of target-expression or toxicity |
Optimize methods to generate mouse engineered T-cells Use of transgenic mice expressing human target antigens |
| Role of the tumor microenvironment | Syngeneic models |
Responsive host immunity, including TME and reactivity of endogenous, tumor-specific cells Synergy with other immunotherapies can be assessed |
Use of mouse CAR constructs. Differences between mouse and human T cells Engineering of murine T cells can be challenging |
Optimization of protocols to engineer and freeze murine T-cell Assessing the impact of human microbiota on TME and engineered T-cell efficacy |
| Allogeneic host vs graft rejection | Allogeneic mixed lymphocyte reactions |
Allow testing immunosuppressive drugs Efficacy of gene editing to resist lymphodepleting drugs or suppress MHC expression |
Lack of systematic evaluation of NK cell impact |
Addition of allogeneic NK cells to the assays |
3D, three-dimensional; GvHD, graft versus host disease; HLA, human leukocyte antigen; MHC, major histocompatibility complex; NSG, NOD/SCID/Il2rγc-/-; TME, tumor microenvironment.
Methods and tools to assess the expression of CARs and TCRs on engineered T cells
| Level | Method | Measurement | Observations |
| Genomic | qPCR | TCR/CAR vector copy number | Multiplexing; high throughput; cannot discriminate subtle VCN differences |
| ddPCR | Multiplexing; high throughput; costly; more precise and sensitive than qPCR | ||
| IS | Sites of vector integration | Sensitivity in assaying rarer clones; abundance of each transduced T cells can be bioinformatically inferred from IS data | |
| TCR-seq | Presence of the transgenic TCR | Defines the T-cell clonal composition of the infused product; protocol optimization needed to detect codon optimized TCR sequences | |
| Transcriptomic | RNA-seq | CAR/TCR mRNA abundance | CAR/TCR mRNA quantity depends on chromatin architecture, viral promoter, regulatory elements |
| Single-cell RNA-seq | CAR/TCR mRNA quantity depends on chromatin architecture, viral promoter, regulatory elements; coupled to multimers for proteomic and transcriptomic evaluation; multiplexing; costly | ||
| TCR-seq | TCR mRNA abundance | Defines the T cell clonal composition of the infused product; protocol optimization needed to detect codon optimized TCR sequences | |
| RNAscope ISH | CAR/TCR RNA expression in tissues | Used in FFPE and frozen tissues; co-localization with multiple RNA transcripts and/or protein markers; spatial variation of the expression patterns in tissues. No information on whether CAR/TCR are present at the T-cell membrane. | |
| Proteomic/flow cytometry | Anti-IgG Ab Protein L | CAR expression on T-cell surface | Optimized, affordable reagents; multi-step staining is required to avoid cross-reaction with IgG-like proteins; cannot independently stain different CARs on a dual-CAR expressing cell. |
| Recombinant antigen-Fc proteins; anti-idiotype Ab | High specificity for CAR: can independently stain different CARs on dual-CAR expressing cells; for recombinant proteins, interference between antigen and detection method is a potential drawback after Ag recognition | ||
| Ab against linkers or tags | Can be used in combination with other cell surface Ab in a one-step staining; Ab against linkers are typically not accessible outside industry; tags may influence CAR functionality due to structural changes | ||
| Expression of gene reporters (EGFR, CD20) | Detection of genetically modified T cells | Allows tracking of engineered T cells in patients and mice; no information on CAR/TCR expression on the T-cell membrane | |
| pHLA multimers | TCR expression on T-cell surface | Possible underestimation of the Td T cell population due to a more reliable binding of multimers to the CD8 transduced T cells; coupled to scRNAseq for proteomic and transcriptomic evaluation | |
| TCR V antibodies | Overestimation of Td T cells due to the contaminant derived by the endogenous repertoire in ex-vivo samples; in the pre-infusion product quantify TCR expression in TCR gene edited cells | ||
| Murine constant Ab | Useful when murinized TCR constant regions are used; possibility to measure with imaging if Ab is coupled to specific reporter molecules | ||
| In vivo imaging | Nuclear medicine imaging | Capture biodistribution and expansion of engineered T-cells | PET, SPECT. Radio-labeled probes; multiplexing; negative effects of radiotracers on cell function |
| MRI | Non-invasive, semi-quantitative, high-resolution whole-body tracing of the T-cell product | ||
| Optical imaging | Use of luciferase/substrate pairs. Widely available. Not used in the clinic. | ||
| Two photon microscopy | Can track engineered cells at the single cell level; increased spatial resolution; reduced photobleaching. |
Ab, antibody; ddPCR, digital droplet PCR; FFPE, formalin-fixed paraffin-embedded; IS, integration site; ISH, in situ hybridization; PET, positron emission tomography; qPCR, quantitative PCR; sc, single cell; SPECT, single photon emission CT; Td, transduced; V, variable region of the TCR; VCN, vector copy number.