Gene network-based and ensemble modeling-based selection of tumor-associated antigens with a predicted low risk of tissue damage for targeted immunotherapy.

BACKGROUND: Tumor-associated antigens and their derived peptides constitute an opportunity to design off-the-shelf mainline or adjuvant anti-cancer immunotherapies for a broad array of patients. A performant and rational antigen selection pipeline would lay the foundation for immunotherapy trials with the potential to enhance treatment, tremendously benefiting patients suffering from rare, understudied cancers. METHODS: We present an experimentally validated, data-driven computational pipeline that selects and ranks antigens in a multipronged approach. In addition to minimizing the risk of immune-related adverse events by selecting antigens based on their expression profile in tumor biopsies and healthy tissues, we incorporated a network analysis-derived antigen indispensability index based on computational modeling results, and candidate immunogenicity predictions from a machine learning ensemble model relying on peptide physicochemical characteristics. RESULTS: In a model study of uveal melanoma, Human Leukocyte Antigen (HLA) docking simulations and experimental quantification of the peptide-major histocompatibility complex binding affinities confirmed that our approach discriminates between high-binding and low-binding affinity peptides with a performance similar to that of established methodologies. Blinded validation experiments with autologous T-cells yielded peptide stimulation-induced interferon-γ secretion and cytotoxic activity despite high interdonor variability. Dissecting the score contribution of the tested antigens revealed that peptides with the potential to induce cytotoxicity but unsuitable due to potential tissue damage or instability of expression were properly discarded by the computational pipeline. CONCLUSIONS: In this study, we demonstrate the feasibility of the de novo computational selection of antigens with the capacity to induce an anti-tumor immune response and a predicted low risk of tissue damage. On translation to the clinic, our pipeline supports fast turn-around validation, for example, for adoptive T-cell transfer preparations, in both generalized and personalized antigen-directed immunotherapy settings.

Breaking barriers: NEK2 inhibition shines in multiple myeloma treatment.

In their article, Cheng et al.1 reveal that NEK2 loss reshapes the tumor microenvironment, reducing tumor-associated macrophages and decreasing T cell exhaustion. They show that this ultimately favors the immune system's anti-cancer response in multiple myeloma.

Melanoma 2.0. Skin cancer as a paradigm for emerging diagnostic technologies, computational modelling and artificial intelligence.

We live in an unprecedented time in oncology. We have accumulated samples and cases in cohorts larger and more complex than ever before. New technologies are available for quantifying solid or liquid samples at the molecular level. At the same time, we are now equipped with the computational power necessary to handle this enormous amount of quantitative data. Computational models are widely used helping us to substantiate and interpret data. Under the label of systems and precision medicine, we are putting all these developments together to improve and personalize the therapy of cancer. In this review, we use melanoma as a paradigm to present the successful application of these technologies but also to discuss possible future developments in patient care linked to them. Melanoma is a paradigmatic case for disruptive improvements in therapies, with a considerable number of metastatic melanoma patients benefiting from novel therapies. Nevertheless, a large proportion of patients does not respond to therapy or suffers from adverse events. Melanoma is an ideal case study to deploy advanced technologies not only due to the medical need but also to some intrinsic features of melanoma as a disease and the skin as an organ. From the perspective of data acquisition, the skin is the ideal organ due to its accessibility and suitability for many kinds of advanced imaging techniques. We put special emphasis on the necessity of computational strategies to integrate multiple sources of quantitative data describing the tumour at different scales and levels.

The IKZF1-IRF4/IRF5 Axis Controls Polarization of Myeloma-Associated Macrophages.

The bone marrow niche has a pivotal role in progression, survival, and drug resistance of multiple myeloma cells. Therefore, it is important to develop means for targeting the multiple myeloma bone marrow microenvironment. Myeloma-associated macrophages (MAM) in the bone marrow niche are M2 like. They provide nurturing signals to multiple myeloma cells and promote immune escape. Reprogramming M2-like macrophages toward a tumoricidal M1 phenotype represents an intriguing therapeutic strategy. This is especially interesting in view of the successful use of mAbs against multiple myeloma cells, as these therapies hold the potential to trigger macrophage-mediated phagocytosis and cytotoxicity. In this study, we observed that MAMs derived from patients treated with the immunomodulatory drug (IMiD) lenalidomide skewed phenotypically and functionally toward an M1 phenotype. Lenalidomide is known to exert its beneficial effects by modulating the CRBN-CRL4 E3 ligase to ubiquitinate and degrade the transcription factor IKAROS family zinc finger 1 (IKZF1). In M2-like MAMs, we observed enhanced IKZF1 levels that vanished through treatment with lenalidomide, yielding MAMs with a bioenergetic profile, T-cell stimulatory properties, and loss of tumor-promoting capabilities that resemble M1 cells. We also provide evidence that IMiDs interfere epigenetically, via degradation of IKZF1, with IFN regulatory factors 4 and 5, which in turn alters the balance of M1/M2 polarization. We validated our observations in vivo using the CrbnI391V mouse model that recapitulates the IMiD-triggered IKZF1 degradation. These data show a role for IKZF1 in macrophage polarization and can provide explanations for the clinical benefits observed when combining IMiDs with therapeutic antibodies.See related Spotlight on p. 254.

Mathematical Modelling in Biomedicine: A Primer for the Curious and the Skeptic.

In most disciplines of natural sciences and engineering, mathematical and computational modelling are mainstay methods which are usefulness beyond doubt. These disciplines would not have reached today's level of sophistication without an intensive use of mathematical and computational models together with quantitative data. This approach has not been followed in much of molecular biology and biomedicine, however, where qualitative descriptions are accepted as a satisfactory replacement for mathematical rigor and the use of computational models is seen by many as a fringe practice rather than as a powerful scientific method. This position disregards mathematical thinking as having contributed key discoveries in biology for more than a century, e.g., in the connection between genes, inheritance, and evolution or in the mechanisms of enzymatic catalysis. Here, we discuss the role of computational modelling in the arsenal of modern scientific methods in biomedicine. We list frequent misconceptions about mathematical modelling found among biomedical experimentalists and suggest some good practices that can help bridge the cognitive gap between modelers and experimental researchers in biomedicine. This manuscript was written with two readers in mind. Firstly, it is intended for mathematical modelers with a background in physics, mathematics, or engineering who want to jump into biomedicine. We provide them with ideas to motivate the use of mathematical modelling when discussing with experimental partners. Secondly, this is a text for biomedical researchers intrigued with utilizing mathematical modelling to investigate the pathophysiology of human diseases to improve their diagnostics and treatment.

Network- and systems-based re-engineering of dendritic cells with non-coding RNAs for cancer immunotherapy.

Dendritic cells (DCs) are professional antigen-presenting cells that induce and regulate adaptive immunity by presenting antigens to T cells. Due to their coordinative role in adaptive immune responses, DCs have been used as cell-based therapeutic vaccination against cancer. The capacity of DCs to induce a therapeutic immune response can be enhanced by re-wiring of cellular signalling pathways with microRNAs (miRNAs). Methods: Since the activation and maturation of DCs is controlled by an interconnected signalling network, we deploy an approach that combines RNA sequencing data and systems biology methods to delineate miRNA-based strategies that enhance DC-elicited immune responses. Results: Through RNA sequencing of IKKß-matured DCs that are currently being tested in a clinical trial on therapeutic anti-cancer vaccination, we identified 44 differentially expressed miRNAs. According to a network analysis, most of these miRNAs regulate targets that are linked to immune pathways, such as cytokine and interleukin signalling. We employed a network topology-oriented scoring model to rank the miRNAs, analysed their impact on immunogenic potency of DCs, and identified dozens of promising miRNA candidates, with miR-15a and miR-16 as the top ones. The results of our analysis are presented in a database that constitutes a tool to identify DC-relevant miRNA-gene interactions with therapeutic potential (https://www.synmirapy.net/dc-optimization). Conclusions: Our approach enables the systematic analysis and identification of functional miRNA-gene interactions that can be experimentally tested for improving DC immunogenic potency.

Single-Molecule RNA Sequencing Reveals IFNγ-Induced Differential Expression of Immune Escape Genes in Merkel Cell Polyomavirus-Positive MCC Cell Lines.

Merkel cell carcinoma (MCC) is a rare and highly aggressive cancer, which is mainly caused by genomic integration of the Merkel cell polyomavirus and subsequent expression of a truncated form of its large T antigen. The resulting primary tumor is known to be immunogenic and under constant pressure to escape immune surveillance. Because interferon gamma (IFNγ), a key player of immune response, is secreted by many immune effector cells and has been shown to exert both anti-tumoral and pro-tumoral effects, we studied the transcriptomic response of MCC cells to IFNγ. In particular, immune modulatory effects that may help the tumor evade immune surveillance were of high interest to our investigation. The effect of IFNγ treatment on the transcriptomic program of three MCC cell lines (WaGa, MKL-1, and MKL-2) was analyzed using single-molecule sequencing via the Oxford Nanopore platform. A significant differential expression of several genes was detected across all three cell lines. Subsequent pathway analysis and manual annotation showed a clear upregulation of genes involved in the immune escape of tumor due to IFNγ treatment. The analysis of selected genes on protein level underlined our sequencing results. These findings contribute to a better understanding of immune escape of MCC and may help in clinical treatment of MCC patients. Furthermore, we demonstrate that single-molecule sequencing can be used to assess characteristics of large eukaryotic transcriptomes and thus contribute to a broader access to sequencing data in the community due to its low cost of entry.

Curatopes Melanoma: A Database of Predicted T-cell Epitopes from Overly Expressed Proteins in Metastatic Cutaneous Melanoma.

Therapeutic anticancer vaccination has been adapted as an immunotherapy in several solid tumors. However, the selection of promising candidates from the total quantity of possible epitopes poses a challenge to clinicians and bioinformaticians alike, and very few epitopes have been tested in experimental or clinical settings to validate their efficacy. Here, we present a comprehensive database of predicted nonmutated peptide epitopes derived from genes that are overly expressed in a group of 32 melanoma biopsies compared with healthy tissues and that were filtered against expression in a curated list of survival-critical tissues. We hypothesize that these "self-tolerant" epitopes have two desirable properties: they do not depend on mutations, being immediately applicable to a large patient collective, and they potentially cause fewer autoimmune reactions. To support epitope selection, we provide an aggregated score of expected therapeutic efficiency as a shortlist mechanism. The database has applications in facilitating epitope selection and trial design and is freely accessible at https://www.curatopes.com. SIGNIFICANCE: A database is presented that predicts and scores antitumor T-cell epitopes, with a focus on tolerability and avoidance of severe autoimmunity, offering a supplementary epitope set for further investigation in immunotherapy.