Machine Learning for Cancer Immunotherapies Based on Epitope Recognition by T Cell Receptors.

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.

Expitope 2.0: a tool to assess immunotherapeutic antigens for their potential cross-reactivity against naturally expressed proteins in human tissues.

BACKGROUND: 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. RESULTS: 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. CONCLUSIONS: 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.

Assessment of cancer and virus antigens for cross-reactivity in human tissues.

MOTIVATION: 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. RESULTS: 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 Kesmir 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. AVAILABILITY AND IMPLEMENTATION: 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.

Expitope: a web server for epitope expression.

MOTIVATION: Adoptive T cell therapies based on introduction of new T cell receptors (TCRs) into patient recipient T cells is a promising new treatment for various kinds of cancers. A major challenge, however, is the choice of target antigens. If an engineered TCR can cross-react with self-antigens in healthy tissue, the side-effects can be devastating. We present the first web server for assessing epitope sharing when designing new potential lead targets. We enable the users to find all known proteins containing their peptide of interest. The web server returns not only exact matches, but also approximate ones, allowing a number of mismatches of the users choice. For the identified candidate proteins the expression values in various healthy tissues, representing all vital human organs, are extracted from RNA Sequencing (RNA-Seq) data as well as from some cancer tissues as control. All results are returned to the user sorted by a score, which is calculated using well-established methods and tools for immunological predictions. It depends on the probability that the epitope is created by proteasomal cleavage and its affinities to the transporter associated with antigen processing and the major histocompatibility complex class I alleles. With this framework, we hope to provide a helpful tool to exclude potential cross-reactivity in the early stage of TCR selection for use in design of adoptive T cell immunotherapy. AVAILABILITY AND IMPLEMENTATION: The Expitope web server can be accessed via http://webclu.bio.wzw.tum.de/expitope.

Pharmacologic inhibition of MALT1 protease by phenothiazines as a therapeutic approach for the treatment of aggressive ABC-DLBCL.

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.

The CD6 scavenger receptor is differentially expressed on a CD56 natural killer cell subpopulation and contributes to natural killer-derived cytokine and chemokine secretion.

The CD6 scavenger receptor is known to be expressed on virtually all T cells and is supposed to be involved in costimulation, synapse formation, thymic selection and leukocyte migration. Here, we demonstrate that CD6 is differentially expressed by a subpopulation of peripheral CD56(dim) natural killer (NK) cells and absent on CD56(bright) NK cells. CD56(dim)CD16(+) cells represent the major NK subset in the periphery, and most cells within this group are positive for CD6. Most killer immunoglobulin-like receptor- and immunoglobulin-like transcript-positive cells also belong to the CD6(+) subpopulation, as expected from their restricted expression on CD56(dim) NK cells. In addition, CD6(+) NK cells are similar to the newly identified CD94(low)CD56(dim) NK subpopulation and most distant from the recently defined CD27(+) NK subpopulation based on the reverse correlation of expression between CD6 and CD27, a marker associated primarily with CD56(bright) NK cells. With respect to CD6 function on NK cells, direct CD6 triggering did not result in degranulation but induced secretion of cytokines (interferon-γ and tumor necrosis factor-α) and chemokines [CXCL10 (IP-10), CXCL1 (GRO-α)]. Thus, CD6 expression on peripheral NK cells marks a novel CD56(dim) subpopulation associated with distinct patterns of cytokine and chemokine secretion.

Diverse hematological malignancies including hodgkin-like lymphomas develop in chimeric MHC class II transgenic mice.

A chimeric HLA-DR4-H2-E (DR4) homozygous transgenic mouse line spontaneously develops diverse hematological malignancies with high frequency (70%). The majority of malignancies were distributed equally between T and B cell neoplasms and included lymphoblastic T cell lymphoma (LTCL), lymphoblastic B cell lymphoma (LBCL), diffuse large B cell lymphoma (DLBCL), the histiocyte/T cell rich variant of DLBCL (DLBCL-HA/T cell rich DLBCL), splenic marginal zone lymphoma (SMZL), follicular B cell lymphoma (FBL) and plasmacytoma (PCT). Most of these neoplasms were highly similar to human diseases. Also, some non-lymphoid malignancies such as acute myeloid leukemia (AML) and histiocytic sarcoma were found. Interestingly, composite lymphomas, including Hodgkin-like lymphomas, were also detected that had CD30(+) Hodgkin/Reed-Sternberg (H/RS)-like cells, representing a tumor type not previously described in mice. Analysis of microdissected H/RS-like cells revealed their origin as germinal center B cells bearing somatic hypermutations and, in some instances, crippled mutations, as described for human Hodgkin lymphoma (HL). Transgene integration in an oncogene was excluded as an exclusive driving force of tumorigenesis and age-related lymphoma development suggests a multi-step process. Thus, this DR4 line is a useful model to investigate common molecular mechanisms that may contribute to important neoplastic diseases in man.

Antigen presenting cells transfected with LMP2a RNA induce CD4+ LMP2a-specific cytotoxic T lymphocytes which kill via a Fas-independent mechanism.

Recent reports have demonstrated that EBV can be used as a target of specific CTL-based treatments in severe chronic EBV, immunoblastic B cell lymphoma and Hodgkin's disease (HD). Based upon the promising results form these in vivo studies, it has been suggested that an antigen-specific CTL-based immunotherapy may be of benefit in treating EBV-associated tumors such as HD and nasopharyngeal carcinoma (NPC) which express the potentially immunogenic antigens, LMP1 and LMP2a. Recent work form our group has demonstrated that LMP2a-specific CTLs may be generated in vitro using autologous antigen presenting cells which have been transfected with polyadenylated LMP2a RNA in the presence of a cationic lipid. In this study, we demonstrate that the presence of the lipid enhances dendritic cell (DC) transfection efficiency and appears to protect the intracellular LMP2a RNA from degradation by cellular RNAses. Significantly, these improvements resulted in the transfected DCs having a superior ability to stimulate autologous T cell proliferation. These LMP2a + DCs were used to stimulate LMP2a-specific effector cells which were predominantly a mixture of cytotoxic and helper CD4+ T cells. The molecular mechanisms whereby these CD4+ T cells lyzed their LMP2a-expressing targets was investigated and we show that, although expressing Fas ligand on their surface, LMP2a-specific CD4+ effector cells kill their targets using the Ca2+-dependent perforin/granzyme pathway which is the same mechanism used by CD8+ CTLs.