This technique allows the identification and characterization of miRs and RBPs for any RNA sequence of interest. © 2020 Elsevier Inc. All rights reserved.We have used RNA interference (RNAi) screening technology to reveal unknown components of biological signaling pathways including survival mechanisms of estrogen-independent breast cancer cell growth and cancer cell resistance to immune attack. In this chapter, a detailed protocol describing the use of RNAi screening to identify factors important for the proliferation of estrogen-independent MCF7 breast cancer cells will be described. Resistance to therapies that target the estrogen pathway remains a challenge in the treatment of estrogen receptor-positive breast cancer. To address this challenge, small interfering-RNA (siRNA)-based libraries targeting an estrogen receptor (ER)- and aromatase-centered network, including 631 genes relevant to estrogen signaling, was designed and constructed for RNAi screening. This protocol will include the following parts (1) selection of RNAi transfection reagent for specific cells; (2) optimization of RNAi screening conditions using Z’-factor; (3) procedure of ER-network gene siRNA library screening using automated machines under optimized experimental conditions; and (4) method of analysis for RNAi screening data to identify specific determinants important for cell proliferation. 46 genes were found to be selectively required for the survival of estrogen-independent MCF7-derived cells. © 2020 Elsevier Inc. All rights reserved.Existing methodology for analysis of genetic heterogeneity generally involves digestion of the tumor tissue, followed by bulk DNA extraction or single cell preparation. Such methods destroy the tissue morphology, and therefore opportunities to analyze tumor heterogeneity and clonal architecture within the native spatial context are lost. Thus, there is a clear need for the development of generally applicable methods of in situ mutation detection (ISMD), in which tumor cells can be genetically analyzed in the context of their cellular microenvironment, including immune infiltrate. Furthermore, protocols in which ISMD can be combined with immunohistochemical analysis are highly sought after, as the combination of these two techniques enables insight not only into genetic heterogeneity, but is also permissive of genotype-phenotype analysis, whilst preserving tissue morphology and spatial context. Here we describe a novel method for in situ point mutation detection using commercially available BaseScope reagents, followed by immunohistochemical detection of immune infiltrate on the same tissue section. © 2020 Elsevier Inc. All rights reserved.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. © 2020 Elsevier Inc. All rights reserved.The remarkable success of cancer immunotherapies, especially the checkpoint blocking antibodies, in a subset of patients has reinvigorated the study of tumor-immune crosstalk and its role in heterogeneity of response. High-throughput sequencing and imaging technologies can help recapitulate various aspects of the tumor ecosystem. Computational approaches provide an arsenal of tools to efficiently analyze, quantify and integrate multiple parameters of tumor immunity mined from these diverse but complementary high-throughput datasets. This chapter describes numerous such computational approaches in tumor immunology that leverage high-throughput data from diverse sources (genomic, transcriptomics, epigenomics and digitized histopathology images) to systematically interrogate tumor immunity in context of its microenvironment, and to identify mechanisms that confer resistance or sensitivity to cancer therapies, in particular immunotherapy. © 2020 Elsevier Inc. All rights reserved.Anticancer vaccines have recently received renewed attention for immunotherapy of at least a subset of cancer-types. Such vaccines mostly involve either killed cancer or tumor cells alone, or combinations thereof with specific (co-incubated) innate immune cells. In recent years, the immunogenic characteristics of the dead or dying cancer cells have emerged as decisive factors behind the success of anticancer vaccines. This has amplified the importance of accounting for immunology of cell death while preparing anticancer vaccines. This, in turn, has increased the emphasis on the immune reactions at the site-of-vaccination since the therapeutic efficacy of the killed cancer/tumor cell vaccines is contingent upon the nature and characteristics of these reactions at the site-of-injection. In this article, we present a systematic methodology that exploits the murine ear pinna model to study differential immune cell recruitment by dead/dying cancer cells injected in vivo, thereby modeling the site-of-injection relevant for anticancer vaccines. © 2020 Elsevier Inc. All rights reserved.Exosomes are small extracellular vesicles released by prokaryotic and eukaryotic cells with a crucial role in cell-to-cell communication in both physiological and pathological conditions. Exosomes contain and transfer active biomolecules, including nucleic acids, proteins and lipids to target recipient cells. In the last decade, many methodologies have been developed for isolating specific exosomal components. In this chapter, we will detail methods to isolate exosomal DNA, considering the crucial role of exosomal DNA in regulating the behavior of recipient cells in multiple settings, including the response of malignant cells to chemo-, radio- and immunotherapy. © 2020 Elsevier Inc. All rights reserved.Transforming growth factor beta (TGF-β) is a potent pleiotropic polypeptide cytokine, with a complex and context dependent control of its activation, signaling and effector functions. This cytokine is pivotal in the regulation of immunological responses, tumor initiation and development, stromal homeostasis and all their intricate related interactions. Last decade advances in cancer immunotherapy have reactivated the clinical interest on potential drug with TGF-β inhibition effect, combined with immunomodulating enhancer drugs. The correct quantification of the in vitro and in vivo biological activity of this cytokine is essential to understand the intrinsic underlying biological mechanisms and TGF-β role in the immune system, tumor and stromal codevelopment, modulation and interactions. There is a wide variety of available procedures to quantify TGF-β activity, which includes different methodological approximations like ELISA, Bioassays including reporter gene assays, Flow cytometry (FC), Western blotting (WB), immunochemical/fluorescence microscopy, among others. Here, we detail available methods for TGF-β biological activity analysis, together with their applicability and suitability for each experimental setting, in order to get a complete analytical perspective and more comprehensive information along the development and design of combined antitumor immunotherapies, which include the inhibition of TGF-β biological activity. © 2020 Elsevier Inc. All rights reserved.The past two decades witnessed the appreciation of the importance of specific tumor-infiltrating immune cells in influencing tumor evolution. The discovery that a favorable immune contexture is linked to a prolonged patients’ survival, and more specifically that intratumoral cytotoxic T lymphocytes hold powerful prognostic value, provided the foundations for the development of the Immunoscore. Immunoscore is a digital pathology, IHC-based immune assay measuring the densities of CD3+ and CD8+ T cells at different tumor locations, linking them with patients’ clinical outcome. The present chapter provides a detailed overview of the assay development and procedure, from the bench to the data analysis, and of the internationally approved and validated guidelines to perform a consensus Immunoscore for colon cancer patients. Assay strengths and limitations are also discussed, as well as the clinical implications of its introduction in routine practice. © 2020 Elsevier Inc. All rights reserved.With the advent of whole-transcriptome studies and the growing need for public repositories, it has become essential to combine multiple heterogeneous datasets for immune cells. In this chapter, we describe the implementation of a compendium of 10,833 genes for 975 samples, corresponding to 52 resting immune cell types. We begin by describing the datasets, and their selection, in particular. We then explain the methodology implemented to create a qualified compendium the processing of each array (quality control, normalization and bias correction), integration (merging rules, global normalization and batch removal) and validation. Finally some examples of use will be detailed. The utility and limitations of the compendium are also discussed, as an introduction to the next version. © 2020 Elsevier Inc. All rights reserved.Parents’ involvement in their children’s education and parental warmth have been linked to many positive child outcomes. In addition to these positive associations, contemporary developmental theory stresses the interaction between different parenting variables and the interaction between parenting and broad contextual factors such as family socioeconomic status (SES). Thus, the purpose of this study was to examine main and interaction effects of parent home-based involvement and parental warmth on achievement outcomes. Additionally, we evaluated whether these variables also interacted with SES to predict students’ achievement growth. Using the Early Childhood Longitudinal Study – Kindergarten Cohort of 2010-11 (N = 2352), growth of academic outcomes was modeled from kindergarten to the fourth grade. We then used latent variable interaction (Maslowsky, Jager, & Hemken, 2015) procedures to examine interaction effects of our primary study variables. Few significant effects were noted for children’s reading and mathematics scores, but more substantial main (home-based involvement) and interaction (parental warmth and SES) effects emerged for science achievement. At high SES levels, warmth negatively predicted growth in science, whereas at lower SES levels, warmth positively predicted growth. Findings are discussed in relation to importance of parent involvement, differential effects across SES contexts, and curricular emphasis in contemporary schools.