Adoptive cell therapies continually evolve through science-based innovation. Specialized innovations for TCR-T therapies are described here that are embedded in an End-to-End Platform for TCR-T ...Therapy Development which aims to provide solutions for key unmet patient needs by addressing challenges of TCR-T therapy, including selection of target antigens and suitable T cell receptors, generation of TCR-T therapies that provide long term, durable efficacy and safety and development of efficient and scalable production of patient-specific (personalized) TCR-T therapy for solid tumors. Multiple, combinable, innovative technologies are used in a systematic and sequential manner in the development of TCR-T therapies. One group of technologies encompasses
product enhancements
that enable TCR-T therapies to be safer, more specific and more effective. The second group of technologies addresses
development optimization
that supports discovery and development processes for TCR-T therapies to be performed more quickly, with higher quality and greater efficiency. Each module incorporates innovations layered onto basic technologies common to the field of immunology. An active approach of “evolution by innovation” supports the overall goal to develop best-in-class TCR-T therapies for treatment of patients with solid cancer.
Proteolytic activity of the mucosa-associated lymphoid tissue lymphoma translocation protein-1 (MALT1) paracaspase is required for survival of the activated B cell subtype of diffuse large B cell ...lymphoma (ABC-DLBCL). We have identified distinct derivatives of medicinal active phenothiazines, namely mepazine, thioridazine, and promazine, as small molecule inhibitors of the MALT1 protease. These phenothiazines selectively inhibit cleavage activity of recombinant and cellular MALT1 by a noncompetitive mechanism. Consequently, the compounds inhibit anti-apoptotic NF-κB signaling and elicit toxic effects selectively on MALT1-dependent ABC-DLBCL cells in vitro and in vivo. Our data provide a conceptual proof for a clinical application of distinct phenothiazines in the treatment of ABC-DLBCL.
► Distinct phenothiazine derivatives act as specific MALT1 inhibitors ► Mepazine and thioridazine inhibit MALT1 in activated T cells and in ABC-DLBCL ► Both compounds elicit toxic effects on MALT1-dependent ABC-DLBCL in vitro and in vivo
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
In many solid tumors, cancer stem cells (CSC) represent a population with tumor‐initiating, self‐renewal, and differentiation potential, which can be identified by surface protein markers. No ...generally applicable markers are yet known for renal cell carcinoma (RCC). Two RCC cell lines (RCC‐26, RCC‐53) were found to differ widely in their capacity to form spheres in vitro and to establish tumors in mice, potentially reflecting differences in CSC content. A subpopulation expressing the CXC chemokine receptor 4 (CXCR4) was present only in the more tumorigenic cell line RCC‐53. When grown as spheres, most of the RCC‐53 cells were CXCR4‐positive, expressed stem cell‐associated transcription factor genes at elevated levels, and were more resistant toward the tyrosine kinase inhibitors sunitinib, sorafenib, and pazopanib. Sorted CXCR4‐positive cells exhibited greater capacity for sphere formation and tumor growth‐inducing potential in vivo than CXCR4‐negative cells. Significantly, higher CXCR4 mRNA levels in primary RCC tumors from patients with localized but not disseminated disease predicted shorter survival. Downregulation of CXCR4 expression by small interfering RNA (siRNA) or pharmacological inhibition by AMD3100 compromised tumor sphere formation, viability of CXCR4‐positive cells, and increased their responsiveness toward tyrosine kinase inhibitors. In conclusion, CXCR4 identifies a subpopulation of tumor‐initiating cells in RCC cell lines and plays a role in their maintenance. The relative insensitivity of such cells to tyrosine kinase inhibitors might contribute to the development of therapy resistance in RCC patients. Future therapies therefore could combine blockade of the CXCR4 signaling pathway with standard therapies for more effective treatments of metastatic RCC. STEM Cells 2013;31:1467–1476
In the last years, immunotherapies have shown tremendous success as treatments for multiple types of cancer. However, there are still many obstacles to overcome in order to increase response rates ...and identify effective therapies for every individual patient. Since there are many possibilities to boost a patient’s immune response against a tumor and not all can be covered, this review is focused on T cell receptor-mediated therapies. CD8
+
T cells can detect and destroy malignant cells by binding to peptides presented on cell surfaces by MHC (major histocompatibility complex) class I molecules. CD4
+
T cells can also mediate powerful immune responses but their peptide recognition by MHC class II molecules is more complex, which is why the attention has been focused on CD8
+
T cells. Therapies based on the power of T cells can, on the one hand, enhance T cell recognition by introducing TCRs that preferentially direct T cells to tumor sites (so called TCR-T therapy) or through vaccination to induce T cells
in vivo
. On the other hand, T cell activity can be improved by immune checkpoint inhibition or other means that help create a microenvironment favorable for cytotoxic T cell activity. The manifold ways in which the immune system and cancer interact with each other require not only the use of large omics datasets from gene, to transcript, to protein, and to peptide but also make the application of machine learning methods inevitable. Currently, discovering and selecting suitable TCRs is a very costly and work intensive
in vitro
process. To facilitate this process and to additionally allow for highly personalized therapies that can simultaneously target multiple patient-specific antigens, especially neoepitopes, breakthrough computational methods for predicting antigen presentation and TCR binding are urgently required. Particularly, potential cross-reactivity is a major consideration since off-target toxicity can pose a major threat to patient safety. The current speed at which not only datasets grow and are made available to the public, but also at which new machine learning methods evolve, is assuring that computational approaches will be able to help to solve problems that immunotherapies are still facing.
As cancer strikes, individuals vary not only in terms of factors that contribute to its occurrence and development, but as importantly, in their capacity to respond to treatment. While exciting new ...therapeutic options that mobilize the immune system against cancer have led to breakthroughs for a variety of malignancies, success is limited to a subset of patients. Pre-existing immunological features of both the host and the tumor may contribute to how patients will eventually fare with immunotherapy. A broad understanding of baseline immunity, both in the periphery and in the tumor microenvironment, is needed in order to fully realize the potential of cancer immunotherapy. Such interrogation of the tumor, blood, and host immune parameters prior to treatment is expected to identify biomarkers predictive of clinical outcome as well as to elucidate why some patients fail to respond to immunotherapy. To approach these opportunities for progress, the Society for Immunotherapy of Cancer (SITC) reconvened the Immune Biomarkers Task Force. Comprised of an international multidisciplinary panel of experts, Working Group 4 sought to make recommendations that focus on the complexity of the tumor microenvironment, with its diversity of immune genes, proteins, cells, and pathways naturally present at baseline and in circulation, and novel tools to aid in such broad analyses.
Identifying T-cell receptors (TCRs) that bind tumor-associated antigens (TAAs) with optimal affinity is a key bottleneck in the development of adoptive T-cell therapy of cancer. TAAs are unmutated ...self proteins, and T cells bearing high-affinity TCRs specific for such antigens are commonly deleted in the thymus. To identify optimal-affinity TCRs, we generated antigen-negative humanized mice with a diverse human TCR repertoire restricted to the human leukocyte antigen (HLA) A*02:01 (ref. 3). These mice were immunized with human TAAs, for which they are not tolerant, allowing induction of CD8⁺ T cells with optimal-affinity TCRs. We isolate TCRs specific for the cancer/testis (CT) antigen MAGE-A1 (ref. 4) and show that two of them have an anti-tumor effect in vivo. By comparison, human-derived TCRs have lower affinity and do not mediate substantial therapeutic effects. We also identify optimal-affinity TCRs specific for the CT antigen NY-ESO. Our humanized mouse model provides a useful tool for the generation of optimal-affinity TCRs for T-cell therapy.
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IJS, NUK, SBMB, UL, UM, UPUK
Background & Aims Cancer therapies are being developed based on our ability to direct T cells against tumor antigens. Glypican-3 (GPC3) is expressed by 75% of all hepatocellular carcinomas (HCC), but ...not in healthy liver tissue or other organs. We aimed to generate T cells with GPC3-specific receptors that recognize HCC and used them to eliminate GPC3-expressing xenograft tumors grown from human HCC cells in mice. Methods We used mass spectrometry to obtain a comprehensive peptidome from GPC3-expressing hepatoma cells after immune-affinity purification of human leukocyte antigen (HLA)-A2 and bioinformatics to identify immunodominant peptides. To circumvent GPC3 tolerance resulting from fetal expression, dendritic cells from HLA-A2−negative donors were cotransfected with GPC3 and HLA-A2 RNA to stimulate and expand antigen-specific T cells. Results Peptide GPC3367 was identified as a predominant peptide on HLA-A2. We used A2-GPC3367 multimers to detect, select for, and clone GPC3-specific T cells. These clones bound the A2-GPC3367 multimer and secreted interferon-γ when cultured with GPC3367 , but not with control peptide-loaded cells. By genomic sequencing of these T-cell clones, we identified a gene encoding a dominant T-cell receptor. The gene was cloned and the sequence was codon optimized and expressed from a retroviral vector. Primary CD8+ T cells that expressed the transgenic T-cell receptor specifically bound GPC3367 on HLA-A2. These T cells killed GPC3-expressing hepatoma cells in culture and slowed growth of HCC xenograft tumors in mice. Conclusions We identified a GPC3367 -specific T-cell receptor. Expression of this receptor by T cells allows them to recognize and kill GPC3-positive hepatoma cells. This finding could be used to advance development of adoptive T-cell therapy for HCC.
The hostile tumor microenvironment (TME) is a major challenge for the treatment of solid tumors with T-cell receptor (TCR)-modified T-cells (TCR-Ts), as it negatively influences T-cell efficacy, ...fitness, and persistence. These negative influences are caused, among others, by the inhibitory checkpoint PD-1/PD-L1 axis. The Preferentially Expressed Antigen in Melanoma (PRAME) is a highly relevant cancer/testis antigen for TCR-T immunotherapy due to broad expression in multiple solid cancer indications. A TCR with high specificity and sensitivity for PRAME was isolated from non-tolerized T-cell repertoires and introduced into T-cells alongside a chimeric PD1-41BB receptor, consisting of the natural extracellular domain of PD-1 and the intracellular signaling domain of 4-1BB, turning an inhibitory pathway into a T-cell co-stimulatory pathway. The addition of PD1-41BB to CD8+ T-cells expressing the transgenic PRAME-TCR enhanced IFN-γ secretion, improved cytotoxic capacity, and prevented exhaustion upon repetitive re-challenge with tumor cells in vitro without altering the in vitro safety profile. Furthermore, a single dose of TCR-Ts co-expressing PD1-41BB was sufficient to clear a hard-to-treat melanoma xenograft in a mouse model, whereas TCR-Ts without PD1-41BB could not eradicate the PD-L1-positive tumors. This cutting-edge strategy supports development efforts to provide more effective TCR-T immunotherapies for the treatment of solid tumors.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Cross-reactivity (CR) or invocation of autoimmune side effects in various tissues has important safety implications in adoptive immunotherapy directed against selected antigens. The ability to ...predict CR (on-target and off-target toxicities) may help in the early selection of safer therapeutically relevant target antigens.
We developed a methodology for the calculation of quantitative CR for any defined peptide epitope. Using this approach, we performed assessment of 4 groups of 283 currently known human MHC-class-I epitopes including differentiation antigens, overexpressed proteins, cancer-testis antigens and mutations displayed by tumor cells. In addition, 89 epitopes originating from viral sources were investigated. The natural occurrence of these epitopes in human tissues was assessed based on proteomics abundance data, while the probability of their presentation by MHC-class-I molecules was modelled by the method of Keşmir et al. which combines proteasomal cleavage, TAP affinity and MHC-binding predictions. The results of these analyses for many previously defined peptides are presented as CR indices and tissue profiles. The methodology thus allows for quantitative comparisons of epitopes and is suggested to be suited for the assessment of epitopes of candidate antigens in an early stage of development of adoptive immunotherapy.
Our method is implemented as a Java program, with curated datasets stored in a MySQL database. It predicts all naturally possible self-antigens for a given sequence of a therapeutic antigen (or epitope) and after filtering for predicted immunogenicity outputs results as an index and profile of CR to the self-antigens in 22 human tissues. The program is implemented as part of the iCrossR webserver, which is publicly available at http://webclu.bio.wzw.tum.de/icrossr/ CONTACT: d.frishman@wzw.tum.deSupplementary information: Supplementary data are available at Bioinformatics online.
Adoptive immunotherapy offers great potential for treating many types of cancer but its clinical application is hampered by cross-reactive T cell responses in healthy human tissues, representing ...serious safety risks for patients. We previously developed a computational tool called Expitope for assessing cross-reactivity (CR) of antigens based on tissue-specific gene expression. However, transcript abundance only indirectly indicates protein expression. The recent availability of proteome-wide human protein abundance information now facilitates a more direct approach for CR prediction. Here we present a new version 2.0 of Expitope, which computes all naturally possible epitopes of a peptide sequence and the corresponding CR indices using both protein and transcript abundance levels weighted by a proposed hierarchy of importance of various human tissues.
We tested the tool in two case studies: The first study quantitatively assessed the potential CR of the epitopes used for cancer immunotherapy. The second study evaluated HLA-A*02:01-restricted epitopes obtained from the Immune Epitope Database for different disease groups and demonstrated for the first time that there is a high variation in the background CR depending on the disease state of the host: compared to a healthy individual the CR index is on average two-fold higher for the autoimmune state, and five-fold higher for the cancer state.
The ability to predict potential side effects in normal tissues helps in the development and selection of safer antigens, enabling more successful immunotherapy of cancer and other diseases.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
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