Multi-Omics Profiling of the Tumor Microenvironment: Paving the Way to Precision Immuno-Oncology

Emerging measurements for tumor-infiltrating lymphocytes in breast cancer

Tumor-infiltrating lymphocytes are a general term for lymphocytes or immune cells infiltrating the tumor microenvironment. Numerous studies have demonstrated tumor-infiltrating lymphocytes to be robust prognostic and predictive biomarkers in breast cancer. Recently, immune checkpoint inhibitors, which directly target tumor-infiltrating lymphocytes, have become part of standard of care treatment for triple-negative breast cancer. Surprisingly, tumor-infiltrating lymphocytes quantified by conventional methods do not predict response to immune checkpoint inhibitors, which highlights the heterogeneity of tumor-infiltrating lymphocytes and the complexity of the immune network in the tumor microenvironment. Tumor-infiltrating lymphocytes are composed of diverse immune cell populations, including cytotoxic CD8-positive T lymphocytes, B cells and myeloid cells. Traditionally, tumor-infiltrating lymphocytes in tumor stroma have been evaluated by histology. However, the standardization of this approach is limited, necessitating the use of various novel technologies to elucidate the heterogeneity in the tumor microenvironment. This review outlines the evaluation methods for tumor-infiltrating lymphocytes from conventional pathological approaches that evaluate intratumoral and stromal tumor-infiltrating lymphocytes such as immunohistochemistry, to the more recent advancements in computer tissue imaging using artificial intelligence, flow cytometry sorting and multi-omics analyses using high-throughput assays to estimate tumor-infiltrating lymphocytes from bulk tumor using immune signatures or deconvolution tools. We also discuss higher resolution technologies that enable the analysis of tumor-infiltrating lymphocytes heterogeneity such as single-cell analysis and spatial transcriptomics. As we approach the era of personalized medicine, it is important for clinicians to understand these technologies.

Multidimensional progressive single-cell sequencing reveals cell microenvironment composition and cancer heterogeneity in lung cancer

Despite substantial advances in cancer biology and treatment, the clinical outcomes of patients with lung cancer remain unsatisfactory. The tumor microenvironment (TME) is a potential target. Using single-cell RNA sequencing, we could distinguish eight distinct cell types in the lung cancer microenvironment, demonstrating substantial intratumoral heterogeneity in 19 different lung cancer tumor samples. Through the re-dimensional grouping of cancer-associated fibroblasts (CAFs), myeloid cells, epithelial cells, natural killer (NK) cells, and T cells, the difference in the TME of lung cancer was revealed. We discovered SFTPB, SFN, and KRT8 as possible predictive biomarkers for lung cancer by assessing the gene expression patterns in epithelial cells. Examining cell-to-cell communications showed a robust association between the quantity of matrix CAFs, epithelial cells, and macrophages in the thrombospondin signaling pathway. Additionally, we found that the amyloid precursor protein signaling pathway primarily originated from the matrix, and inflammatory cancer-associated endothelial and fibroblast cells showed a co-expression relationship with myeloid cells and B cells. Through cell-to-cell correlation analysis, we found positive regulation between NK cells, regulatory T cells, GZMB-CD8 T cells, and GZMK-CD8 T cells, which could play a role in developing immune TMEs. These findings support studies on cancer heterogeneity and add to our understanding of lung cancer's cellular microenvironment.

Isolation of Live Immune Cells from the Tumor Microenvironment by FACS

Isolation of live cells from the tumor microenvironment (TME) has represented a challenge, particularly from metastatic nodules that need to be identified within the entire metastatic tissue. Cherry-niche, an in vivo labelling technique, allows the isolation of all the different cell populations in the TME without needing to visually locate the metastatic cancer cell colonies. Therefore, neighboring TME cells can be isolated even from the early stages of cancer cell seeding and colonization in the metastatic tissue. Here, we show how to use Cherry-niche to identify and isolate neutrophils from the lung metastatic niche. We also provide examples of downstream analyses to characterize freshly isolated neutrophils ex vivo, such as Giemsa staining, reactive oxygen species (ROS) detection, and phagocytosis assays. Similar strategies can be used to isolate other immune and non-immune cells from the metastatic TME.

The Tumor Immune Microenvironment Architecture Correlates with Risk of Recurrence in Head and Neck Squamous Cell Carcinoma

Emerging evidence suggests that not only the frequency and composition of tumor-infiltrating leukocytes but also their spatial organization might be a major determinant of tumor progression and response to therapy. Therefore, mapping and analyzing the fine tumor immune architecture could potentially provide insights for predicting cancer prognosis. Here, we performed an explorative, prospective clinical study to assess whether structures within the tumor microenvironment can predict recurrence after salvage surgery in head and neck squamous cell carcinoma (HNSCC). The major immune subsets were measured using flow cytometry and co-detection by indexing (CODEX) multiparametric imaging. Flow cytometry underestimated the number of PMN-MDSCs and neutrophils in the tumor and overestimated the tumor-infiltrating lymphocyte frequency. An ad hoc computational framework was used to identify and analyze discrete cellular neighborhoods. A high frequency of tertiary lymphoid structures composed of CD31highCD38high plasma cells was associated with reduced recurrence after surgery in HNSCC. These data support the notion that the structural architecture of the tumor immune microenvironment plays an essential role in tumor progression and indicates that type 1 tertiary lymphoid structures and long-lived CD31highCD38high plasma cells are associated with good prognosis in HNSCC.

SIGNIFICANCE

Imaging the spatial tumor immune microenvironment and evaluating the presence of type 1 tertiary lymphoid structures enables prediction of recurrence after surgery in patients with head and neck squamous cell carcinoma.

Multi-Omics Integration for the Design of Novel Therapies and the Identification of Novel Biomarkers

Multi-omics is a cutting-edge approach that combines data from different biomolecular levels, such as DNA, RNA, proteins, metabolites, and epigenetic marks, to obtain a holistic view of how living systems work and interact. Multi-omics has been used for various purposes in biomedical research, such as identifying new diseases, discovering new drugs, personalizing treatments, and optimizing therapies. This review summarizes the latest progress and challenges of multi-omics for designing new treatments for human diseases, focusing on how to integrate and analyze multiple proteome data and examples of how to use multi-proteomics data to identify new drug targets. We also discussed the future directions and opportunities of multi-omics for developing innovative and effective therapies by deciphering proteome complexity.

Assay-agnostic spatial profiling detects tumor microenvironment signatures: new diagnostic insights for triple-negative breast cancer

The role of the tumor microenvironment (TME) in immuno-oncology has driven demand for technologies that deliver in situ, or spatial, molecular information. Compartmentalized heterogeneity that traditional methods miss is becoming key to predicting both acquired drug resistance to targeted therapies and patient response to immunotherapy. Here, we describe a novel method for assay-agnostic spatial profiling and demonstrate its ability to detect immune microenvironment signatures in breast cancer patients that are unresolved by the immunohistochemical (IHC) assessment of programmed cell death ligand-1 (PD-L1) on immune cells, which represents the only FDA microenvironment-based companion diagnostic test that has been approved for triple-negative breast cancer (TNBC). Two distinct physiological states were found that are uncorrelated to tumor mutational burden (TMB), microsatellite instability (MSI), PD-L1 expression, and intrinsic cancer subtypes.

Medical gas plasma technology: Roadmap on cancer treatment and immunotherapy

Despite continuous therapeutic progress, cancer remains an often fatal disease. In the early 2010s, first evidence in rodent models suggested promising antitumor action of gas plasma technology. Medical gas plasma is a partially ionized gas depositing multiple physico-chemical effectors onto tissues, especially reactive oxygen and nitrogen species (ROS/RNS). Today, an evergrowing body of experimental evidence suggests multifaceted roles of medical gas plasma-derived therapeutic ROS/RNS in targeting cancer alone or in combination with oncological treatment schemes such as ionizing radiation, chemotherapy, and immunotherapy. Intriguingly, gas plasma technology was recently unraveled to have an immunological dimension by inducing immunogenic cell death, which could ultimately promote existing cancer immunotherapies via in situ or autologous tumor vaccine schemes. Together with first clinical evidence reporting beneficial effects in cancer patients following gas plasma therapy, it is time to summarize the main concepts along with the chances and limitations of medical gas plasma onco-therapy from a biological, immunological, clinical, and technological point of view.

Single-Cell RNA Sequencing: Technological Progress and Biomedical Application in Cancer Research

Single-cell RNA-seq (scRNA-seq) is a revolutionary technology that allows for the genomic investigation of individual cells in a population, allowing for the discovery of unusual cells associated with cancer and metastasis. ScRNA-seq has been used to discover different types of cancers with poor prognosis and medication resistance such as lung cancer, breast cancer, ovarian cancer, and gastric cancer. Besides, scRNA-seq is a promising method that helps us comprehend the biological features and dynamics of cell development, as well as other disorders. This review gives a concise summary of current scRNA-seq technology. We also explain the main technological steps involved in implementing the technology. We highlight the present applications of scRNA-seq in cancer research, including tumor heterogeneity analysis in lung cancer, breast cancer, and ovarian cancer. In addition, this review elucidates potential applications of scRNA-seq in lineage tracing, personalized medicine, illness prediction, and disease diagnosis, which reveals that scRNA-seq facilitates these events by producing genetic variations on the single-cell level.

Comprehensive analysis of single-cell RNA-seq and bulk RNA-seq revealed the heterogeneity and convergence of the immune microenvironment in renal cell carcinoma

Substantial progress has been made in cancer biology and treatment in recent years, but the clinical outcome of patients with renal cell carcinoma (RCC) remains unsatisfactory. The tumor microenvironment (TME) is a potential target. By analyzing single-cell RNA sequencing (sc-RNAseq) data from six RCC tumor samples, this study identified 11 different cell types in the RCC cellular microenvironment, indicating a high degree of intratumoral heterogeneity. Through re-dimensionality reduction clustering of epithelial cells, neutrophils, macrophages, and T cells, we deeply reveal differences in the RCC tumor microenvironment. By analyzing differentially expressed genes in normal epithelial cells and malignant epithelial cells, we identify RNASET2 and GATM as potential prognostic biomarkers in RCC. In addition, by transcriptional factor analysis, we found significant differences in the expression of GZMK-CD8 T cell and B cell transcription factors between cancer tissues and normal tissues. By cell correlation analysis, we found significant correlations between neutrophils and macrophages and between IL7R-CD4 T cells and T regulatory (Treg) cells in RCC, which may be involved in the formation of immune TMEs. By cell developmental trajectory analysis, we showed that macrophages may be derived from neutrophils, whereas Treg cells may be derived from IL7R-CD4 T cells. By cell communication analysis, we found a clear interaction between macrophages and endothelial cells, neutrophils, and GZMK-CD8 T cells. In addition, we found that ADGRE5 signaling was mainly derived from mast cells and GZMK-CD8 T cells, and had a significant communication effect with neutrophils. The COLLAGEN signaling pathway is mainly derived from fibroblasts and has a significant communication effect with mast cells. Finally, we verified that RNASET2, which is highly expressed in epithelial cells, promotes proliferation and migration of RCC in vitro. RNASET2 is likely to be a potential target for renal cell carcinoma therapy. The results based on sc-RNAseq data analysis help to further elucidate the cellular microenvironment of RCC and provide help for cancer heterogeneity studies. This will help to provide more accurate personalized treatment for patients in clinical diagnosis.

Single-Cell Analysis in Immuno-Oncology

The complexity of the cellular and non-cellular milieu surrounding human tumors plays a decisive role in the course and outcome of disease. The high variability in the distribution of the immune and non-immune compartments within the tumor microenvironments (TME) among different patients governs the mode of their response or resistance to current immunotherapeutic approaches. Through deciphering this diversity, one can tailor patients' management to meet an individual's needs. Single-cell (sc) omics technologies have given a great boost towards this direction. This review gathers recent data about how multi-omics profiling, including the utilization of single-cell RNA sequencing (scRNA-seq), assay for transposase-accessible chromatin with sequencing (scATAC-seq), T-cell receptor sequencing (scTCR-seq), mass, tissue-based, or microfluidics cytometry, and related bioinformatics tools, contributes to the high-throughput assessment of a large number of analytes at single-cell resolution. Unravelling the exact TCR clonotype of the infiltrating T cells or pinpointing the classical or novel immune checkpoints across various cell subsets of the TME provide a boost to our comprehension of adaptive immune responses, their antigen specificity and dynamics, and grant suggestions for possible therapeutic targets. Future steps are expected to merge high-dimensional data with tissue localization data, which can serve the investigation of novel multi-modal biomarkers for the selection and/or monitoring of the optimal treatment from the current anti-cancer immunotherapeutic armamentarium.

Artificial intelligence-based multi-omics analysis fuels cancer precision medicine

With biotechnological advancements, innovative omics technologies are constantly emerging that have enabled researchers to access multi-layer information from the genome, epigenome, transcriptome, proteome, metabolome, and more. A wealth of omics technologies, including bulk and single-cell omics approaches, have empowered to characterize different molecular layers at unprecedented scale and resolution, providing a holistic view of tumor behavior. Multi-omics analysis allows systematic interrogation of various molecular information at each biological layer while posing tricky challenges regarding how to extract valuable insights from the exponentially increasing amount of multi-omics data. Therefore, efficient algorithms are needed to reduce the dimensionality of the data while simultaneously dissecting the mysteries behind the complex biological processes of cancer. Artificial intelligence has demonstrated the ability to analyze complementary multi-modal data streams within the oncology realm. The coincident development of multi-omics technologies and artificial intelligence algorithms has fuelled the development of cancer precision medicine. Here, we present state-of-the-art omics technologies and outline a roadmap of multi-omics integration analysis using an artificial intelligence strategy. The advances made using artificial intelligence-based multi-omics approaches are described, especially concerning early cancer screening, diagnosis, response assessment, and prognosis prediction. Finally, we discuss the challenges faced in multi-omics analysis, along with tentative future trends in this field. With the increasing application of artificial intelligence in multi-omics analysis, we anticipate a shifting paradigm in precision medicine becoming driven by artificial intelligence-based multi-omics technologies.

Immunology of Oral Squamous Cell Carcinoma-A Comprehensive Insight with Recent Concepts

This review aims to understand the concept of oral cancer immunology through the notion of immune profiling, immunoediting and immunotherapy, and to gain knowledge regarding its application for the management of oral cancer patients. Oral cancer is an immunogenic tumor where the cells of the tumor microenvironment play an important role in tumorigenesis. Understanding the mechanism of these modulations can help design immunotherapeutic strategies in oral cancer patients. This article gives an overview of immunomodulation in the oral cancer tumor microenvironment, with concepts of immune profiling, immunoediting and immunotherapy. English literature searches via Google Scholar, Web of Science, EBSCO, Scopus, and PubMed database were performed with the key words immunology, tumor microenvironment, cells, cross talk, immune profiling, biomarkers, inflammation, gene expression, techniques, immunoediting, immunosurveillance, tumor escape, immunotherapy, immune checkpoint inhibitors, vaccines in cancer, oral cancer, and head and neck cancer. Original research articles, reviews, and case reports published from 2016-2021 (n = 81) were included to appraise different topics, and were discussed under the following subsections. Literature published on oral cancer immunology reveals that oral cancer immune profiling with appropriate markers and techniques and knowledge on immunoediting concepts can help design and play an effective role in immunotherapeutic management of oral cancer patients. An evaluation of oral cancer immunology helps to determine its role in tumorigenesis, and immunotherapy could be the emerging drift in the effective management of oral cancer.

Progress in the studies on the molecular mechanisms associated with multidrug resistance in cancers

Chemotherapy is one of the important methods to treat cancer, and the emergence of multidrug resistance (MDR) is one major cause for the failure of cancer chemotherapy. Almost all anti-tumor drugs develop drug resistance over a period of time of application in cancer patients, reducing their effects on killing cancer cells. Chemoresistance can lead to a rapid recurrence of cancers and ultimately patient death. MDR may be induced by multiple mechanisms, which are associated with a complex process of multiple genes, factors, pathways, and multiple steps, and today the MDR-associated mechanisms are largely unknown. In this paper, from the aspects of protein-protein interactions, alternative splicing (AS) in pre-mRNA, non-coding RNA (ncRNA) mediation, genome mutations, variance in cell functions, and influence from the tumor microenvironment, we summarize the molecular mechanisms associated with MDR in cancers. In the end, prospects for the exploration of antitumor drugs that can reverse MDR are briefly discussed from the angle of drug systems with improved targeting properties, biocompatibility, availability, and other advantages.

Breaking the Immune Complexity of the Tumor Microenvironment Using Single-Cell Technologies

Tumors are not a simple aggregate of transformed cells but rather a complicated ecosystem containing various components, including infiltrating immune cells, tumor-related stromal cells, endothelial cells, soluble factors, and extracellular matrix proteins. Profiling the immune contexture of this intricate framework is now mandatory to develop more effective cancer therapies and precise immunotherapeutic approaches by identifying exact targets or predictive biomarkers, respectively. Conventional technologies are limited in reaching this goal because they lack high resolution. Recent developments in single-cell technologies, such as single-cell RNA transcriptomics, mass cytometry, and multiparameter immunofluorescence, have revolutionized the cancer immunology field, capturing the heterogeneity of tumor-infiltrating immune cells and the dynamic complexity of tenets that regulate cell networks in the tumor microenvironment. In this review, we describe some of the current single-cell technologies and computational techniques applied for immune-profiling the cancer landscape and discuss future directions of how integrating multi-omics data can guide a new "precision oncology" advancement.

High-Plex Spatial RNA Profiling Reveals Cell Type‒Specific Biomarker Expression during Melanoma Development

Early diagnosis of melanoma is critical for improved survival. However, the biomarkers of early melanoma evolution and their origin within the tumor and its microenvironment, including the keratinocytes, are poorly defined. To address this, we used spatial transcript profiling that maintains the morphological tumor context to measure the expression of >1,000 RNAs in situ in patient-derived formalin-fixed, paraffin-embedded tissue sections in primary melanoma and melanocytic nevi. We profiled 134 regions of interest (each 200 μm in diameter) enriched in melanocytes, neighboring keratinocytes, or immune cells. This approach captured distinct expression patterns across cell types and tumor types during melanoma development. Unexpectedly, we discovered that S100A8 is expressed by keratinocytes within the tumor microenvironment during melanoma growth. Immunohistochemistry of 252 tumors showed prominent keratinocyte-derived S100A8 expression in melanoma but not in benign tumors and confirmed the same pattern for S100A8's binding partner S100A9, suggesting that injury to the epidermis may be an early and readily detectable indicator of melanoma development. Together, our results establish a framework for high-plex, spatial, and cell type‒specific resolution of gene expression in archival tissue applicable to the development of biomarkers and characterization of tumor microenvironment interactions in tumor evolution.

A Dynamic Transcriptome Map of Different Tissue Microenvironment Cells Identified During Gastric Cancer Development Using Single-Cell RNA Sequencing

Gastric cancer (GC) development trends have identified multiple processes ranging from inflammation to carcinogenesis, however, key pathogenic mechanisms remain unclear. Tissue microenvironment (TME) cells are critical for the progression of malignant tumors. Here, we generated a dynamic transcriptome map of various TME cells during multi-disease stages using single-cell sequencing analysis. We observed a set of key transition markers related to TME cell carcinogenic evolution, and delineated landmark dynamic carcinogenic trajectories of these cells. Of these, macrophages, fibroblasts, and endothelial cells exerted considerable effects toward epithelial cells, suggesting these cells may be key TME factors promoting GC occurrence and development. Our results suggest a phenotypic convergence of different TME cell types toward tumor formation processes in GC. We believe our data would pave the way for early GC detection, diagnosis, and treatment therapies.

Global mapping of cancers: The Cancer Genome Atlas and beyond

Cancer genomes have been explored from the early 2000s through massive exome sequencing efforts, leading to the publication of The Cancer Genome Atlas in 2013. Sequencing techniques have been developed alongside this project and have allowed scientists to bypass the limitation of costs for whole-genome sequencing (WGS) of single specimens by developing more accurate and extensive cancer sequencing projects, such as deep sequencing of whole genomes and transcriptomic analysis. The Pan-Cancer Analysis of Whole Genomes recently published WGS data from more than 2600 human cancers together with almost 1200 related transcriptomes. The application of WGS on a large database allowed, for the first time in history, a global analysis of features such as molecular signatures, large structural variations and noncoding regions of the genome, as well as the evaluation of RNA alterations in the absence of underlying DNA mutations. The vast amount of data generated still needs to be thoroughly deciphered, and the advent of machine-learning approaches will be the next step towards the generation of personalized approaches for cancer medicine. The present manuscript wants to give a broad perspective on some of the biological evidence derived from the largest sequencing attempts on human cancers so far, discussing advantages and limitations of this approach and its power in the era of machine learning.

Exploring CT Texture Parameters as Predictive and Response Imaging Biomarkers of Survival in Patients With Metastatic Melanoma Treated With PD-1 Inhibitor Nivolumab: A Pilot Study Using a Delta-Radiomics Approach

In the era of artificial intelligence and precision medicine, the use of quantitative imaging methodological approaches could improve the cancer patient’s therapeutic approaches. Specifically, our pilot study aims to explore whether CT texture features on both baseline and first post-treatment contrast-enhanced CT may act as a predictor of overall survival (OS) and progression-free survival (PFS) in metastatic melanoma (MM) patients treated with the PD-1 inhibitor Nivolumab. Ninety-four lesions from 32 patients treated with Nivolumab were analyzed. Manual segmentation was performed using a free-hand polygon approach by drawing a region of interest (ROI) around each target lesion (up to five lesions were selected per patient according to RECIST 1.1). Filtration-histogram-based texture analysis was employed using a commercially available research software called TexRAD (Feedback Medical Ltd, London, UK; https://fbkmed.com/texrad-landing-2/) Percentage changes in texture features were calculated to perform delta-radiomics analysis. Texture feature kurtosis at fine and medium filter scale predicted OS and PFS. A higher kurtosis is correlated with good prognosis; kurtosis values greater than 1.11 for SSF = 2 and 1.20 for SSF = 3 were indicators of higher OS (fine texture: 192 HR = 0.56, 95% CI = 0.32–0.96, p = 0.03; medium texture: HR = 0.54, 95% CI = 0.29–0.99, p = 0.04) and PFS (fine texture: HR = 0.53, 95% CI = 0.29–0.95, p = 0.03; medium texture: HR = 0.49, 209 95% CI = 0.25–0.96, p = 0.03). In delta-radiomics analysis, the entropy percentage variation correlated with OS and PFS. Increasing entropy indicates a worse outcome. An entropy variation greater than 5% was an indicator of bad prognosis. CT delta-texture analysis quantified as entropy predicted OS and PFS. Baseline CT texture quantified as kurtosis also predicted survival baseline. Further studies with larger cohorts are mandatory to confirm these promising exploratory results.

Segmentation-based analysis of single-cell immunoblots

From genomics to transcriptomics to proteomics, microfluidic tools underpin recent advances in single-cell biology. Detection of specific proteoforms-with single-cell resolution-presents challenges in detection specificity and sensitivity. Miniaturization of protein immunoblots to single-cell resolution mitigates these challenges. For example, in microfluidic western blotting, protein targets are separated by electrophoresis and subsequently detected using fluorescently labeled antibody probes. To quantify the expression level of each protein target, the fluorescent protein bands are fit to Gaussians; yet, this method is difficult to use with noisy, low-abundance, or low-SNR protein bands, and with significant band skew or dispersion. In this study, we investigate segmentation-based approaches to robustly quantify protein bands from single-cell protein immunoblots. As compared to a Gaussian fitting pipeline, the segmentation pipeline detects >1.5× more protein bands for downstream quantification as well as more of the low-abundance protein bands (i.e., with SNR ∼3). Utilizing deep learning-based segmentation approaches increases the recovery of low-SNR protein bands by an additional 50%. However, we find that segmentation-based approaches are less robust at quantifying poorly resolved protein bands (separation resolution, Rs < 0.6). With burgeoning needs for more single-cell protein analysis tools, we see microfluidic separations as benefitting substantially from segmentation-based analysis approaches.

Single-cell profiling of infiltrating B cells and tertiary lymphoid structures in the TME of gastric adenocarcinomas

The occurrence and development of gastric adenocarcinoma (gADC) is closely related to the interaction between tumor cells and immune cells in the tumor microenvironment (TME). Our objective was to characterize the repertoire of immune cells in the TME of gADC. To analyze the transcriptomic, immune, and spatial information of TME in gADC, we constructed single-cell RNA sequencing, 10 × Genomics V(D)J analysis, multiple immunofluorescence techniques, and OSCmap analysis of 49,765 single cells in seven samples from four gADC patients. Our integrative analysis of B cells demonstrated that a large number of mucosal associated lymphoid tissue (MALT)-B cells were detected in the gADC tissues, which have mature tertiary lymphatic structures (mTLSs), and almost no MALT-B cells in peripheral blood sample. Moreover, MALT-B cells are a class of IgA⁺ plasma cells, which are characterized with high expression of complement pathway activation-related genes. Next, natural killer T (NKT) cells mainly exist in gADC tissues accompanied by mTLSs. This study also classified monocytes/macrophages and epithelial cells into benign and malignant types. Interestingly, CSOmap (q < .05) and multiple immunofluorescence (p < .05) results indicated more types of immune cells can be enriched in tissues with mTLSs than normal TLSs, and the density of mTLSs were higher than normal TLSs. Our findings provide novel insights for the signature of immune cells and tumor cells in the TME of gADC with TLSs and highlight the potential importance of IgA-mediated humoral immunity in gADC patients with TLSs.

Interpretable systems biomarkers predict response to immune-checkpoint inhibitors

Cancer cells can leverage several cell-intrinsic and -extrinsic mechanisms to escape immune system recognition. The inherent complexity of the tumor microenvironment, with its multicellular and dynamic nature, poses great challenges for the extraction of biomarkers of immune response and immunotherapy efficacy. Here, we use RNA-sequencing (RNA-seq) data combined with different sources of prior knowledge to derive system-based signatures of the tumor microenvironment, quantifying immune-cell composition and intra- and intercellular communications. We applied multi-task learning to these signatures to predict different hallmarks of immune responses and derive cancer-type-specific models based on interpretable systems biomarkers. By applying our models to independent RNA-seq data from cancer patients treated with PD-1/PD-L1 inhibitors, we demonstrated that our method to Estimate Systems Immune Response (EaSIeR) accurately predicts therapeutic outcome. We anticipate that EaSIeR will be a valuable tool to provide a holistic description of immune responses in complex and dynamic systems such as tumors using available RNA-seq data.

Applying high-dimensional single-cell technologies to the analysis of cancer immunotherapy

Advances in molecular biology, microfluidics and bioinformatics have empowered the study of thousands or even millions of individual cells from malignant tumours at the single-cell level of resolution. This high-dimensional, multi-faceted characterization of the genomic, transcriptomic, epigenomic and proteomic features of the tumour and/or the associated immune and stromal cells enables the dissection of tumour heterogeneity, the complex interactions between tumour cells and their microenvironment, and the details of the evolutionary trajectory of each tumour. Single-cell transcriptomics, the ability to track individual T cell clones through paired sequencing of the T cell receptor genes and high-dimensional single-cell spatial analysis are all areas of particular relevance to immuno-oncology. Multidimensional biomarker signatures will increasingly be crucial to guiding clinical decision-making in each patient with cancer. High-dimensional single-cell technologies are likely to provide the resolution and richness of data required to generate such clinically relevant signatures in immuno-oncology. In this Perspective, we describe advances made using transformative single-cell analysis technologies, especially in relation to clinical response and resistance to immunotherapy, and discuss the growing utility of single-cell approaches for answering important research questions.

Phenotypic heterogeneity, stability and plasticity in tumor-promoting carcinoma-associated fibroblasts

Reciprocal interactions between cancer cells and stromal cells in the tumor microenvironment (TME) are essential for full-blown tumor development. Carcinoma-associated fibroblasts (CAFs) are a key component of the TME together with a wide variety of stromal cell types including vascular, inflammatory, and immune cells in the extracellular matrix. CAFs not only promote tumor growth, invasion, and metastasis, but also dampen the efficacy of various therapies including immune checkpoint inhibitors. CAFs are composed of distinct fibroblast populations presumably with diverse activated fibroblastic states and tumor-promoting phenotypes in a tumor, indicating intratumor heterogeneity in these fibroblasts. Given that CAFs have been implicated in both disease progression and therapeutic responses, elucidating the functional roles of each fibroblast population in CAFs and the molecular mechanisms mediating their phenotypic stability and plasticity in the TME would be crucial for understanding tumor biology. We herein discuss how distinct fibroblast populations comprising CAFs establish their cell identities, in terms of cells-of-origin, stimuli from the TME, and the phenotypes characteristic of activated states.

Differences in Tumor Immune Microenvironment in Metastatic Sites of Breast Cancer

Introduction: Tumor immune microenvironment (TIME) promotes immune escape, allowing for tumor progression and metastasis. In spite of the current evidence of the complicated role of immune cells in promoting or suppressing cancer progression, the heterogeneity of TIME according to the tumor site has been scarcely investigated. Here, we analyzed transcriptomic profiles of metastatic breast cancer to understand how TIME varies according to tumor sites.

Methods: Two gene expression datasets from metastatic breast cancer of various sites and a single-cell RNA sequencing dataset of primary breast cancer and metastatic lymph nodes were analyzed. The immune cell-type enrichment of each tumor was estimated. Immune cell types were identified by clustering analysis, and the proportions of cell types in TIME were assessed according to the tumor site.

Results: Metastatic bone lesions showed more neutrophils than breast lesions. Tumors clustered according to immune cell type were significantly associated with tumor site. In single-cell analyses, the TIMEs of metastatic lymph nodes showed fewer macrophages than those of primary tumors. Differentially expressed gene signatures in the primary tumor and metastatic lymph nodes were associated with macrophage activation.

Conclusion: We conclude that metastatic sites show variable enrichment patterns of immune cells, and that the TIME of metastatic lesions should be considered in precise immuno-oncology treatments.

Reconnaissance of tumor immune microenvironment spatial heterogeneity in metastatic renal cell carcinoma and correlation with immunotherapy response

A clearer understanding of the tumor immune microenvironment (TIME) in metastatic clear cell renal cell carcinoma (ccRCC) may help to inform precision treatment strategies. We sought to identify clinically meaningful TIME signatures in ccRCC. We studied tumors from 39 patients with metastatic ccRCC using quantitative multiplexed immunofluorescence and relevant immune marker panels. Cell densities were analyzed in three regions of interest (ROIs): tumor core, tumor-stroma interface and stroma. Patients were stratified into low- and high-marker density groups using median values as thresholds. Log-rank and Cox regression analyses while controlling for clinical variables were used to compare survival outcomes to patterns of immune cell distributions. There were significant associations with increased macrophage (CD68⁺ CD163⁺ CD206⁺ ) density and poor outcomes across multiple ROIs in primary and metastatic tumors. In primary tumors, T-bet⁺ T helper type 1 (Th1) cell density was highest at the tumor-stromal interface (P = 0·0021), and increased co-expression of CD3 and T-bet was associated with improved overall survival (P = 0·015) and survival after immunotherapy (P = 0·014). In metastatic tumor samples, decreased forkhead box protein 3 (FoxP3)⁺ T regulatory cell density correlated with improved survival after immunotherapy (P = 0·016). Increased macrophage markers and decreased Th1 T cell markers within the TIME correlated with poor overall survival and treatment outcomes. Immune markers such as FoxP3 showed consistent levels across the TIME, whereas others, such as T-bet, demonstrated significant variance across the distinct ROIs. These findings suggest that TIME profiling outside the tumor core may identify clinically relevant associations for patients with metastatic ccRCC.

Dopamine improves chemotherapeutic efficacy for pancreatic cancer by regulating macrophage-derived inflammations

Pancreatic cancer is an inflammatory malignancy, and tumor-associated macrophages (TAMs) are the predominant inflammatory cells in tumor tissue. TAMs have complicated interactions with pancreatic cancer cells, however, the details and mechanisms remain largely unknown. In this study, transcriptomics and proteomics analyses were performed to explore the interactions between murine pancreatic cancer cells and TAMs. Dopamine (DA) has been reported to suppress inflammations. However, its roles in TAMs of pancreatic cancer have not been reported. Herein, the roles and mechanisms of DA to affect the chemotherapeutic efficacy for pancreatic cancer were studied. Multi-omics results revealed that there was a tumor-promoting vicious cycle involving murine pancreatic cancer cells and TAMs. DA substantially improved the chemotherapeutic efficacy both in vitro study and in immunocompetent murine pancreatic cancer models by suppression of the M2 characters of TAMs. Further studies found that activation of DRD4 by DA led to the decrease of cAMP, and then inhibited the activation of PKA/p38 signal pathway, which suppressed the tumor-promoting inflammation of TAMs. This study uncovers the reciprocal interactions between TAMs and pancreatic cancer cells using multi-omics techniques and presents that DA has synergistic roles with chemotherapy for pancreatic cancer by suppressing of TAM-derived inflammations.

Co-culture hydrogel micro-chamber array-based plate for anti-tumor drug development at single-element resolution

In response to the need for reliable cellular models that reflect complex tumor microenvironmental properties, and enable more precise testing of anti-cancer therapeutics effects on humans, a co-culture platform for in-vitro model that enhances the physiology of breast cancer (BC) microenvironment is presented. A six well imaging plate wherein each macro-well contains several separate compartments was designed. Three-dimensional (3D) cancer spheroids are generated and cultured in the inner compartment which is embossed with an array of nano-liter micro-chambers made of hydrogel. Stromal cells are cultured in the outer chambers. The two cell types are cultured side-by-side, sharing a common space, thus enabling extra-cellular communication via secreted molecules. As proof of concept, a model of BC tumor microenvironment was recapitulated by co-cultivating 3D MCF7 spheroids in the presence of tumor-associated macrophages (TAMs). The presence of TAMs induced an aggressive phenotype by promoting spheroid growth, enhancing survivin expression levels and enabling invasive behavior. Moreover, TAMs influenced the response of BC spheroids to cytotoxic treatment as well as hormonal drug therapy, and enhanced the effects of nitric oxide donor. The platform enables time-lapse imaging and treatment without losing spatial location of the measured spheroids, thereby allowing measurements and analysis at individual-object resolution in an easy and efficient manner.

Artificial Intelligence (AI)-Based Systems Biology Approaches in Multi-Omics Data Analysis of Cancer

Cancer is the manifestation of abnormalities of different physiological processes involving genes, DNAs, RNAs, proteins, and other biomolecules whose profiles are reflected in different omics data types. As these bio-entities are very much correlated, integrative analysis of different types of omics data, multi-omics data, is required to understanding the disease from the tumorigenesis to the disease progression. Artificial intelligence (AI), specifically machine learning algorithms, has the ability to make decisive interpretation of "big"-sized complex data and, hence, appears as the most effective tool for the analysis and understanding of multi-omics data for patient-specific observations. In this review, we have discussed about the recent outcomes of employing AI in multi-omics data analysis of different types of cancer. Based on the research trends and significance in patient treatment, we have primarily focused on the AI-based analysis for determining cancer subtypes, disease prognosis, and therapeutic targets. We have also discussed about AI analysis of some non-canonical types of omics data as they have the capability of playing the determiner role in cancer patient care. Additionally, we have briefly discussed about the data repositories because of their pivotal role in multi-omics data storing, processing, and analysis.

The use of multi-omics data and approaches in breast cancer immunotherapy: a review

Breast cancer is projected to be the most common cancer in women in 2020 in the USA. Despite high remission rates treatment side effects remain an issue, hence the interest in novel approaches such as immunotherapies which aim to utilize patients' immune systems to target cancer cells. This review summarizes the basics of breast cancer including staging and treatment options, followed by a discussion on immunotherapy, including immune checkpoint blockade. After this, examples of the role of omics-type data and computational biology/bioinformatics in breast cancer are explored. Ultimately, there are several promising areas to investigate such as the prediction of neoantigens and the use of multi-omics data to direct research, with noted appropriate in clinical trial design in terms of end points.

Toward Systems Biomarkers of Response to Immune Checkpoint Blockers

Immunotherapy with checkpoint blockers (ICBs), aimed at unleashing the immune response toward tumor cells, has shown a great improvement in overall patient survival compared to standard therapy, but only in a subset of patients. While a number of recent studies have significantly improved our understanding of mechanisms playing an important role in the tumor microenvironment (TME), we still have an incomplete view of how the TME works as a whole. This hampers our ability to effectively predict the large heterogeneity of patients' response to ICBs. Systems approaches could overcome this limitation by adopting a holistic perspective to analyze the complexity of tumors. In this Mini Review, we focus on how an integrative view of the increasingly available multi-omics experimental data and computational approaches enables the definition of new systems-based predictive biomarkers. In particular, we will focus on three facets of the TME toward the definition of new systems biomarkers. First, we will review how different types of immune cells influence the efficacy of ICBs, not only in terms of their quantification, but also considering their localization and functional state. Second, we will focus on how different cells in the TME interact, analyzing how inter- and intra-cellular networks play an important role in shaping the immune response and are responsible for resistance to immunotherapy. Finally, we will describe the potential of looking at these networks as dynamic systems and how mathematical models can be used to study the rewiring of the complex interactions taking place in the TME.

Breakthrough concepts in immune-oncology: Cancer vaccines at the bedside

Clinical approval of the immune checkpoint blockade (ICB) agents for multiple cancer types has reinvigorated the long-standing work on cancer vaccines. In the pre-ICB era, clinical efforts focused on the Ag, the adjuvants, the formulation, and the mode of delivery. These translational efforts on therapeutic vaccines range from cell-based (e.g., dendritic cells vaccine Sipuleucel-T) to DNA/RNA-based platforms with various formulations (liposome), vectors (Listeria monocytogenes), or modes of delivery (intratumoral, gene gun, etc.). Despite promising preclinical results, cancer vaccine trials without ICB have historically shown little clinical activity. With the anticipation and expansion of combinatorial immunotherapeutic trials with ICB, the cancer vaccine field has entered the personalized medicine arena with recent advances in immunogenic neoantigen-based vaccines. In this article, we review the literature to organize the different cancer vaccines in the clinical space, and we will discuss their advantages, limits, and recent progress to overcome their challenges. Furthermore, we will also discuss recent preclinical advances and clinical strategies to combine vaccines with checkpoint blockade to improve therapeutic outcome and present a translational perspective on future directions.

Identification of non-cancer cells from cancer transcriptomic data

Interactions between cancer cells and non-cancer cells composing the tumour microenvironment play a primary role in determining cancer progression and shaping the response to therapy. The qualitative and quantitative characterisation of the different cell populations in the tumour microenvironment is therefore crucial to understand its role in cancer. In recent years, many experimental and computational approaches have been developed to identify the cell populations composing heterogeneous tissue samples, such as cancer. In this review, we describe the state-of-the-art approaches for the quantification of non-cancer cells from bulk and single-cell cancer transcriptomic data, with a focus on immune cells. We illustrate the main features of these approaches and highlight their applications for the analysis of the tumour microenvironment in solid cancers. We also discuss techniques that are complementary and alternative to RNA sequencing, particularly focusing on approaches that can provide spatial information on the distribution of the cells within the tumour in addition to their qualitative and quantitative measurements. This article is part of a Special Issue entitled: Transcriptional Profiles and Regulatory Gene Networks edited by Dr. Federico Manuel Giorgi and Dr. Shaun Mahony.

Determination of a Tumor-Promoting Microenvironment in Recurrent Medulloblastoma: A Multi-Omics Study of Cerebrospinal Fluid

Molecular classification of medulloblastoma (MB) is well-established and reflects the cell origin and biological properties of tumor cells. However, limited data is available regarding the MB tumor microenvironment. Here, we present a mass spectrometry-based multi-omics pilot study of cerebrospinal fluid (CSF) from recurrent MB patients. A group of age-matched patients without a neoplastic disease was used as control cohort. Proteome profiling identified characteristic tumor markers, including FSTL5, ART3, and FMOD, and revealed a strong prevalence of anti-inflammatory and tumor-promoting proteins characteristic for alternatively polarized myeloid cells in MB samples. The up-regulation of ADAMTS1, GAP43 and GPR37 indicated hypoxic conditions in the CSF of MB patients. This notion was independently supported by metabolomics, demonstrating the up-regulation of tryptophan, methionine, serine and lysine, which have all been described to be induced upon hypoxia in CSF. While cyclooxygenase products were hardly detectable, the epoxygenase product and beta-oxidation promoting lipid hormone 12,13-DiHOME was found to be strongly up-regulated. Taken together, the data suggest a vicious cycle driven by autophagy, the formation of 12,13-DiHOME and increased beta-oxidation, thus promoting a metabolic shift supporting the formation of drug resistance and stem cell properties of MB cells. In conclusion, the different omics-techniques clearly synergized and mutually supported a novel model for a specific pathomechanism.

Computational Oncology in the Multi-Omics Era: State of the Art

Cancer is the quintessential complex disease. As technologies evolve faster each day, we are able to quantify the different layers of biological elements that contribute to the emergence and development of malignancies. In this multi-omics context, the use of integrative approaches is mandatory in order to gain further insights on oncological phenomena, and to move forward toward the precision medicine paradigm. In this review, we will focus on computational oncology as an integrative discipline that incorporates knowledge from the mathematical, physical, and computational fields to further the biomedical understanding of cancer. We will discuss the current roles of computation in oncology in the context of multi-omic technologies, which include: data acquisition and processing; data management in the clinical and research settings; classification, diagnosis, and prognosis; and the development of models in the research setting, including their use for therapeutic target identification. We will discuss the machine learning and network approaches as two of the most promising emerging paradigms, in computational oncology. These approaches provide a foundation on how to integrate different layers of biological description into coherent frameworks that allow advances both in the basic and clinical settings.

Could 3D models of cancer enhance drug screening?

Cancer is a multifaceted pathology, where cellular and acellular players interact to drive cancer progression and, in the worst-case, metastasis. The current methods to investigate the heterogeneous nature of cancer are inadequate, since they rely on 2D cell cultures and animal models. The cell line-based drug efficacy and toxicity assays are not able to predict the tumor response to anti-cancer agents and it is already widely discussed how molecular pathway are not recapitulated in vitro so called flat biology. On the other side, animal models often fail to detect the side-effects of drugs, mimic the metastatic progression or the interaction between cancer and immune system, due to biologic difference in human and animals. Moreover, ethical and regulatory issues limit animal experimentation. Every year pharma/biotech companies lose resources in drug discovery and testing processes that are successful only in 5% of the cases. There is an urgent need to validate accurate and predictive platforms in order to enhance drug-testing process taking into account the physiopathology of the tumor microenvironment. Three dimensional in vitro tumor models could enhance drug manufactures in developing effective drugs for cancer diseases. The 3D in vitro cancer models can improve the predictability of toxicity and drug sensitivity in cancer. Despite the demonstrated advantages of 3D in vitro disease systems when compared to 2D culture and animal models, they still do not reach the standardization required for preclinical trials. This review highlights in vitro models that may be used as preclinical models, accelerating the drug development process towards more precise and personalized standard of care for cancer patients. We describe the state-of-the art of 3D in vitro culture systems, with a focus on how these different approaches could be coupled in order to achieve a compromise between standardization and reliability in recapitulating tumor microenvironment and drug response.

Single-cell transcriptomics of human T cells reveals tissue and activation signatures in health and disease

Human T cells coordinate adaptive immunity in diverse anatomic compartments through production of cytokines and effector molecules, but it is unclear how tissue site influences T cell persistence and function. Here, we use single cell RNA-sequencing (scRNA-seq) to define the heterogeneity of human T cells isolated from lungs, lymph nodes, bone marrow and blood, and their functional responses following stimulation. Through analysis of >50,000 resting and activated T cells, we reveal tissue T cell signatures in mucosal and lymphoid sites, and lineage-specific activation states across all sites including distinct effector states for CD8+ T cells and an interferon-response state for CD4+ T cells. Comparing scRNA-seq profiles of tumor-associated T cells to our dataset reveals predominant activated CD8+ compared to CD4+ T cell states within multiple tumor types. Our results therefore establish a high dimensional reference map of human T cell activation in health for analyzing T cells in disease.

Next-generation computational tools for interrogating cancer immunity

The remarkable success of cancer therapies with immune checkpoint blockers is revolutionizing oncology and has sparked intensive basic and translational research into the mechanisms of cancer-immune cell interactions. In parallel, numerous novel cutting-edge technologies for comprehensive molecular and cellular characterization of cancer immunity have been developed, including single-cell sequencing, mass cytometry and multiplexed spatial cellular phenotyping. In order to process, analyse and visualize multidimensional data sets generated by these technologies, computational methods and software tools are required. Here, we review computational tools for interrogating cancer immunity, discuss advantages and limitations of the various methods and provide guidelines to assist in method selection.

Analysis of Treg/Th17 cells in patients with tongue squamous cell carcinoma

The aim of the present study was to analyze the percentage of regulatory T cells (Treg) and T helper cell 17 (Th17) cells in the peripheral blood of patients with tongue squamous cell carcinoma (TSCC) to provide novel insight into the development of immune-targeting therapies for TSCC. Peripheral blood samples were collected from 40 patients with TSCC then the peripheral blood mononuclear cells (PBMCs) and plasma were isolated for flow cytometry, cytometric bead array and reverse transcription-quantitative PCR. Results demonstrated that the percentage of cluster of differentiation (CD)4+ T cells in the peripheral blood of patients with TSCC decreased significantly compared with the control. However, the percentage of Treg and Th17 cells increased significantly compared with the control. The levels of interleukin (IL)-10 and IL-17a increased significantly in patients with TSCC. Expression of IL-10 and IL-17 in the advanced stages of cancer (stage III or IV) were significantly higher compared with the early stages (I and II). The mRNA expression levels of the transcription factors forkhead box protein 3 and RAR-related orphan receptor-γ increased significantly with stage of cancer. The percentage of Treg cells and Th17 cells increased significantly in patients with TSCC suggesting that there was an imbalance between Treg and Th17 cells. In conclusion, altered Treg/Th17 balance in TSCC may promote the disease progression and these results provide a theoretical basis for the development of immunomodulators targeting Treg/Th17.

Deconvoluting tumor-infiltrating immune cells from RNA-seq data using quanTIseq

Tumor-infiltrating immune cells comprise various cells of the innate and the adaptive immune system, which influence tumor growth and response to immunotherapy by exerting anti- and protumorigenic functions. Therefore, the quantification of tumor immune infiltrates is of paramount importance for cancer immunology and immunotherapy. We recently developed quanTIseq, a computational pipeline for the quantification of immune-cell fractions from bulk RNA sequencing (RNA-seq) data from blood or tumor samples. In this chapter, we show the capabilities of quanTIseq by analyzing two publicly available data sets. In the first example, we demonstrate how quanTIseq can be used to quantify circulating immune cells from preprocessed RNA-seq data and how to validate the results using matched flow cytometry data. In the second example, we analyze raw RNA-seq data from bulk tumor samples of melanoma patients collected before and on-treatment with kinase inhibitors to show how quanTIseq can be used to reveal the immunological effects of targeted and conventional drugs.

Ion Channel Targeting with Antibodies and Antibody Fragments for Cancer Diagnosis

The antibody era has greatly impacted cancer management in recent decades. Indeed, antibodies are currently applied for both cancer diagnosis and therapy. For example, monoclonal antibodies are the main constituents of several in vitro diagnostics, which are applied at many levels of cancer diagnosis. Moreover, the great improvement provided by in vivo imaging, especially for early-stage cancer diagnosis, has traced the path for the development of a complete new class of antibodies, i.e., engineered antibody fragments. The latter embody the optimal characteristics (e.g., low renal retention, rapid clearance, and small size) which make them ideal for in vivo applications. Furthermore, the present review focuses on reviewing the main applications of antibodies and antibody fragments for solid cancer diagnosis, both in vitro and in vivo. Furthermore, we review the scientific evidence showing that ion channels represent an almost unexplored class of ideal targets for both in vitro and in vivo diagnostic purposes. In particular, we review the applications, in solid cancers, of monoclonal antibodies and engineered antibody fragments targeting the voltage-dependent ion channel Kv 11.1, also known as hERG1.

Molecular and pharmacological modulators of the tumor immune contexture revealed by deconvolution of RNA-seq data

We introduce quanTIseq, a method to quantify the fractions of ten immune cell types from bulk RNA-sequencing data. quanTIseq was extensively validated in blood and tumor samples using simulated, flow cytometry, and immunohistochemistry data.quanTIseq analysis of 8000 tumor samples revealed that cytotoxic T cell infiltration is more strongly associated with the activation of the CXCR3/CXCL9 axis than with mutational load and that deconvolution-based cell scores have prognostic value in several solid cancers. Finally, we used quanTIseq to show how kinase inhibitors modulate the immune contexture and to reveal immune-cell types that underlie differential patients' responses to checkpoint blockers.Availability: quanTIseq is available at http://icbi.at/quantiseq .

The Translational Potential of Neuroimaging Genomic Analyses To Diagnosis And Treatment In The Mental Disorders

Imaging genomics focuses on characterizing genomic influence on the variation of neurobiological traits, holding promise for illuminating the pathogenesis, reforming the diagnostic system, and precision medicine of mental disorders. This paper aims to provide an overall picture of the current status of neuroimaging-genomic analyses in mental disorders, and how we can increase their translational potential into clinical practice. The review is organized around three perspectives. (a) Towards reliability, generalizability and interpretability, where we summarize the multivariate models and discuss the considerations and trade-offs of using these methods and how reliable findings may be reached, to serve as ground for further delineation. (b) Towards improved diagnosis, where we outline the advantages and challenges of constructing a dimensional transdiagnostic model and how imaging genomic analyses map into this framework to aid in deconstructing heterogeneity and achieving an optimal stratification of patients that better inform treatment planning. (c) Towards improved treatment. Here we highlight recent efforts and progress in elucidating the functional annotations that bridge between genomic risk and neurobiological abnormalities, in detecting genomic predisposition and prodromal neurodevelopmental changes, as well as in identifying imaging genomic biomarkers for predicting treatment response. Providing an overview of the challenges and promises, this review hopefully motivates imaging genomic studies with multivariate, dimensional and transdiagnostic designs for generalizable and interpretable findings that facilitate development of personalized treatment.

Quantitative Mechanistic Modeling in Support of Pharmacological Therapeutics Development in Immuno-Oncology

Following the approval, in recent years, of the first immune checkpoint inhibitor, there has been an explosion in the development of immuno-modulating pharmacological modalities for the treatment of various cancers. From the discovery phase to late-stage clinical testing and regulatory approval, challenges in the development of immuno-oncology (IO) drugs are multi-fold and complex. In the preclinical setting, the multiplicity of potential drug targets around immune checkpoints, the growing list of immuno-modulatory molecular and cellular forces in the tumor microenvironment-with additional opportunities for IO drug targets, the emergence of exploratory biomarkers, and the unleashed potential of modality combinations all have necessitated the development of quantitative, mechanistically-oriented systems models which incorporate key biology and patho-physiology aspects of immuno-oncology and the pharmacokinetics of IO-modulating agents. In the clinical setting, the qualification of surrogate biomarkers predictive of IO treatment efficacy or outcome, and the corresponding optimization of IO trial design have become major challenges. This mini-review focuses on the evolution and state-of-the-art of quantitative systems models describing the tumor vs. immune system interplay, and their merging with quantitative pharmacology models of IO-modulating agents, as companion tools to support the addressing of these challenges.

Integration of microbiology, molecular pathology, and epidemiology: a new paradigm to explore the pathogenesis of microbiome-driven neoplasms

Molecular pathological epidemiology (MPE) is an integrative transdisciplinary field that addresses heterogeneous effects of exogenous and endogenous factors (collectively termed 'exposures'), including microorganisms, on disease occurrence and consequences, utilising molecular pathological signatures of the disease. In parallel with the paradigm of precision medicine, findings from MPE research can provide aetiological insights into tailored strategies of disease prevention and treatment. Due to the availability of molecular pathological tests on tumours, the MPE approach has been utilised predominantly in research on cancers including breast, lung, prostate, and colorectal carcinomas. Mounting evidence indicates that the microbiome (inclusive of viruses, bacteria, fungi, and parasites) plays an important role in a variety of human diseases including neoplasms. An alteration of the microbiome may be not only a cause of neoplasia but also an informative biomarker that indicates or mediates the association of an epidemiological exposure with health conditions and outcomes. To adequately educate and train investigators in this emerging area, we herein propose the integration of microbiology into the MPE model (termed 'microbiology-MPE'), which could improve our understanding of the complex interactions of environment, tumour cells, the immune system, and microbes in the tumour microenvironment during the carcinogenic process. Using this approach, we can examine how lifestyle factors, dietary patterns, medications, environmental exposures, and germline genetics influence cancer development and progression through impacting the microbial communities in the human body. Further integration of other disciplines (e.g. pharmacology, immunology, nutrition) into microbiology-MPE would expand this developing research frontier. With the advent of high-throughput next-generation sequencing technologies, researchers now have increasing access to large-scale metagenomics as well as other omics data (e.g. genomics, epigenomics, proteomics, and metabolomics) in population-based research. The integrative field of microbiology-MPE will open new opportunities for personalised medicine and public health. Copyright © 2019 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

β-Adrenoceptors as drug targets in melanoma: novel preclinical evidence for a role of β3 -adrenoceptors

Stress plays a role in tumourigenesis through catecholamines acting at β-adrenoceptors including β₁ -, β₂ - and β₃ -adrenoceptors, and the use of β-adrenoceptor antagonists seems to counteract tumour growth and progression. Preclinical evidence and meta-analysis data demonstrate that melanoma shows a positive response to β-adrenoceptor blockers and in particular to propranolol acting mainly at β₁ - and β₂ -adrenoceptors. Although evidence suggesting that β₃ -adrenoceptors may play a role as a therapeutic target in infantile haemangiomas has been recently reviewed, a comprehensive analysis of the data available from preclinical studies supporting a possible role of β₃ -adrenoceptors in melanoma was not available. Here, we review data from the literature demonstrating that propranolol may be effective at counteracting melanoma growth, and we provide preclinical evidence that β₃ -adrenoceptors may also play a role in the pathophysiology of melanoma, thus opening the door for further clinical assays trying to explore β₃ -adrenoceptor blockers as novel alternatives for its treatment. LINKED ARTICLES: This article is part of a themed section on Adrenoceptors-New Roles for Old Players. To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v176.14/issuetoc.