Application of Single-Cell Multi-Omics in Dissecting Cancer Cell Plasticity and Tumor Heterogeneity

Tumor heterogeneity, a hallmark of cancer, impairs the efficacy of cancer therapy and drives tumor progression. Exploring inter- and intra-tumoral heterogeneity not only provides insights into tumor development and progression, but also guides the design of personalized therapies. Previously, high-throughput sequencing techniques have been used to investigate the heterogeneity of tumor ecosystems. However, they could not provide a high-resolution landscape of cellular components in tumor ecosystem. Recently, advance in single-cell technologies has provided an unprecedented resolution to uncover the intra-tumoral heterogeneity by profiling the transcriptomes, genomes, proteomes and epigenomes of the cellular components and also their spatial distribution, which greatly accelerated the process of basic and translational cancer research. Importantly, it has been demonstrated that some cancer cells are able to transit between different states in order to adapt to the changing tumor microenvironment, which led to increased cellular plasticity and tumor heterogeneity. Understanding the molecular mechanisms driving cancer cell plasticity is critical for developing precision therapies. In this review, we summarize the recent progress in dissecting the cancer cell plasticity and tumor heterogeneity by use of single-cell multi-omics techniques.

A Novel Mice Model for Studying the Efficacy and IRAEs of Anti-CTLA4 Targeted Immunotherapy

BackgroundPatient-derived orthotopic xenograft (PDOX) is a popular animal model for translational cancer research. Immunotherapy is a promising therapy against glioblastoma (GBM). However, the PDOX model is limited to evaluating immune-related events. Our study aims to establish GBM humanized PDOX (HPDOX) mice models to study the mechanism of anti-CTLA4 immunotherapy and immune-related adverse events (IRAEs).MethodsHPDOX models were established by culturing GBM tissues and intracranially implanting them in NSG mice. Meanwhile, peripheral blood mononuclear cells (PBMCs) were separated from peripheral blood and of GBM patients and administrated in corresponding mice. The population of CD45+, CD3+, CD4+, CD8+, and regulatory T (Treg) cells was estimated in the peripheral blood or tumor.ResultsT cells derived from GBM patients were detected in HPDOX mice models. The application of anti-CTLA4 antibodies (ipilimumab and tremelimumab) significantly inhibited the growth of GBM xenografts in mice. Moreover, residual patient T cells were detected in the tumor microenvironment and peripheral blood of HPDOX mice and were significantly elevated by ipilimumab and tremelimumab. Additionally, Treg cells were decreased in mice with IRAEs. Lastly, the proportion of CD4+/CD8+ T cells dramatically increased after the administration of ipilimumab. And the degree of IRAEs may be related to CD56+ expression in HPDOX.ConclusionsOur study established HPDOX mice models for investigating the mechanism and IRAEs of immunotherapies in GBM, which would offer a promising platform for evaluating the efficacy and IRAEs of novel therapies and exploring personalized therapeutic strategies.

Oncology on FHIR: A Data Model for Distributed Cancer Research

In the field of oncology, a close integration of cancer research and patient care is indispensable. Although an exchange of data between health care providers and other institutions such as cancer registries has already been established in Germany, it does not take advantage of internationally coordinated health data standards. Translational cancer research would also benefit from such standards in the context of secondary data use. This paper employs use cases from the German Cancer Consortium (DKTK) to show how this gap can be closed using a harmonised FHIR-based data model, and how to apply it to an existing federated data platform.

A Detailed Catalogue of Multi-Omics Methodologies for Identification of Putative Biomarkers and Causal Molecular Networks in Translational Cancer Research

Recent advances in sequencing and biotechnological methodologies have led to the generation of large volumes of molecular data of different omics layers, such as genomics, transcriptomics, proteomics and metabolomics. Integration of these data with clinical information provides new opportunities to discover how perturbations in biological processes lead to disease. Using data-driven approaches for the integration and interpretation of multi-omics data could stably identify links between structural and functional information and propose causal molecular networks with potential impact on cancer pathophysiology. This knowledge can then be used to improve disease diagnosis, prognosis, prevention, and therapy. This review will summarize and categorize the most current computational methodologies and tools for integration of distinct molecular layers in the context of translational cancer research and personalized therapy. Additionally, the bioinformatics tools Multi-Omics Factor Analysis (MOFA) and netDX will be tested using omics data from public cancer resources, to assess their overall robustness, provide reproducible workflows for gaining biological knowledge from multi-omics data, and to comprehensively understand the significantly perturbed biological entities in distinct cancer types. We show that the performed supervised and unsupervised analyses result in meaningful and novel findings.

MiRNAs in Canine and Human Osteosarcoma: A Highlight Review on Comparative Biomolecular Aspects

Osteosarcoma (OS) is the most frequent primary malignant tumor of bone in humans and animals. Comparative oncology is a field of study that examines the cancer risk and tumor progression across the species. The canine model is ideally suited for translational cancer research. The biological and clinical characteristics of human and canine OS are common to hypothesize as that several living and environmental common conditions shared between the two species can influence some etiopathogenetic mechanisms, for which the canine species represents an important model of comparison with the human species. In the canine and human species, osteosarcoma is the tumor of bone with the highest frequency, with a value of about 80–85% (in respect to all other bone tumors), a high degree of invasiveness, and a high rate of metastasis and malignancy. Humans and dogs have many genetic and biomolecular similarities such as alterations in the expression of p53 and in some types of microRNAs that our working group has already described previously in several separate works. In this paper, we report and collect new comparative biomolecular features of osteosarcoma in dogs and humans, which may represent an innovative update on the biomolecular profile of this tumor.

Patient-Derived Xenograft Models in Cancer Research

This series of 12 articles, consisting of 9 original articles and 3 reviews, is presented by international leaders in translational cancer research [...]