Explore the vast range of titles available.

By exerting pro- and anti-tumorigenic actions, tumor-infiltrating immune cells can profoundly influence tumor progression, as well as the success of anti-cancer therapies. Therefore, the quantification of tumor-infiltrating immune cells holds the promise to unveil the multi-faceted role of the immune system in human cancers and its involvement in tumor escape mechanisms and response to therapy. Tumor-infiltrating immune cells can be quantified from RNA sequencing data of human tumors using bioinformatics approaches. In this review, we describe state-of-the-art computational methods for the quantification of immune cells from transcriptomics data and discuss the open challenges that must be addressed to accurately quantify immune infiltrates from RNA sequencing data of human bulk tumors.

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.

Recent preclinical and clinical studies have proved the long-standing hypothesis that tumors elicit adaptive immune responses and that the antigens driving effective T-cell response are neoantigens, i.e., peptides that are generated from somatically mutated genes. Hence, the characterization of neoantigens and the identification of the immunogenic ones are of utmost importance for improving cancer immunotherapy and broadening its efficacy to a larger fraction of patients. In this review, we first introduce the methods used for the quantification of neoantigens using next-generation sequencing data and then summarize results obtained using these tools to characterize the neoantigen landscape in solid cancers. We then discuss the importance of neoantigens for cancer immunotherapy using checkpoint blockers, vaccination, and adoptive T-cell transfer. Finally, we give an overview over emerging aspects in cancer immunity, including tumor heterogeneity and immunoediting, and give an outlook on future prospects.

Autophagy supports both cellular and organismal homeostasis. However, whether autophagy should be inhibited or activated for cancer therapy remains unclear. Deletion of essential autophagy genes increased the sensitivity of mouse mammary carcinoma cells to radiation therapy in vitro and in vivo (in immunocompetent syngeneic hosts). Autophagy-deficient cells secreted increased amounts of type I interferon (IFN), which could be limited by CGAS or STING knockdown, mitochondrial DNA depletion or mitochondrial outer membrane permeabilization blockage via BCL2 overexpression or BAX deletion. In vivo, irradiated autophagy-incompetent mammary tumors elicited robust immunity, leading to improved control of distant nonirradiated lesions via systemic type I IFN signaling. Finally, a genetic signature of autophagy had negative prognostic value in patients with breast cancer, inversely correlating with mitochondrial abundance, type I IFN signaling and effector immunity. As clinically useful autophagy inhibitors are elusive, our findings suggest that mitochondrial outer membrane permeabilization may represent a valid target for boosting radiation therapy immunogenicity in patients with breast cancer. Autophagy controls cellular homeostasis and influences immune responses. Galluzzi and colleagues show that tumor cell autophagy opposes inflammatory cell death following radiation therapy and can be inhibited to enhance antitumor responses.

The cancer immunoediting hypothesis postulates a dual role of the immune system: protecting the host by eliminating tumor cells, and shaping the tumor by editing its genome. Here, we elucidate the impact of evolutionary and immune-related forces on editing the tumor in a mouse model for hypermutated and microsatellite-instable colorectal cancer. Analyses of wild-type and immunodeficient RAG1 knockout mice transplanted with MC38 cells reveal that upregulation of checkpoint molecules and infiltration by Tregs are the major tumor escape mechanisms. Our results show that the effects of immunoediting are weak and that neutral accumulation of mutations dominates. Targeting the PD-1/PD-L1 pathway using immune checkpoint blocker effectively potentiates immunoediting. The immunoediting effects are less pronounced in the CT26 cell line, a non-hypermutated/microsatellite-instable model. Our study demonstrates that neutral evolution is another force that contributes to sculpting the tumor and that checkpoint blockade effectively enforces T-cell-dependent immunoselective pressure. The cancer immunoediting hypothesis assumes the immune system sculpts the cancer genome. Here the authors show, in a mouse model, that neutral evolution outweighs the effects of immunoselection and that immune checkpoint blockade potentiates the immunoediting, switching the system to non-neutral evolution.

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 .

Tumor-infiltrating immune cells are highly relevant for prognosis and identification of immunotherapy targets in hepatocellular carcinoma (HCC). The recently developed CIBERSORT method allows immune cell profiling by deconvolution of gene expression microarray data. By applying CIBERSORT, we assessed the relative proportions of immune cells in 41 healthy human livers, 305 HCC samples and 82 HCC adjacent tissues. The obtained immune cell profiles provided enumeration and activation status of 22 immune cell subtypes. Mast cells were evaluated by immunohistochemistry in ten HCC patients. Activated mast cells, monocytes and plasma cells were decreased in HCC, while resting mast cells, total and naïve B cells, CD4 + memory resting and CD8 + T cells were increased when compared to healthy livers. Previously described S1, S2 and S3 molecular HCC subclasses demonstrated increased M1-polarized macrophages in the S3 subclass with good prognosis. Strong total immune cell infiltration into HCC correlated with total B cells, memory B cells, T follicular helper cells and M1 macrophages, whereas weak infiltration was linked to resting NK cells, neutrophils and resting mast cells. Immunohistochemical analysis of patient samples confirmed the reduced frequency of mast cells in human HCC tumor tissue as compared to tumor adjacent tissue. Our data demonstrate that deconvolution of gene expression data by CIBERSORT provides valuable information about immune cell composition of HCC patients.

Significance We provide a comprehensive catalog of novel genetic variants influencing gene expression and metabolic phenotypes in human pancreatic islets. The data also show that the path from genetic variation (SNP) to gene expression is more complex than hitherto often assumed, and that we need to consider that genetic variation can also influence function of a gene by influencing exon usage or splice isoforms (sQTL), allelic imbalance, RNA editing, and expression of noncoding RNAs, which in turn can influence expression of target genes. Genetic variation can modulate gene expression, and thereby phenotypic variation and susceptibility to complex diseases such as type 2 diabetes (T2D). Here we harnessed the potential of DNA and RNA sequencing in human pancreatic islets from 89 deceased donors to identify genes of potential importance in the pathogenesis of T2D. We present a catalog of genetic variants regulating gene expression (eQTL) and exon use (sQTL), including many long noncoding RNAs, which are enriched in known T2D-associated loci. Of 35 eQTL genes, whose expression differed between normoglycemic and hyperglycemic individuals, siRNA of tetraspanin 33 (TSPAN33), 5′-nucleotidase, ecto (NT5E), transmembrane emp24 protein transport domain containing 6 (TMED6), and p21 protein activated kinase 7 (PAK7) in INS1 cells resulted in reduced glucose-stimulated insulin secretion. In addition, we provide a genome-wide catalog of allelic expression imbalance, which is also enriched in known T2D-associated loci. Notably, allelic imbalance in paternally expressed gene 3 (PEG3) was associated with its promoter methylation and T2D status. Finally, RNA editing events were less common in islets than previously suggested in other tissues. Taken together, this study provides new insights into the complexity of gene regulation in human pancreatic islets and better understanding of how genetic variation can influence glucose metabolism.

Background Targeted amplicon sequencing of the 16S ribosomal RNA gene is one of the key tools for studying microbial diversity. The accuracy of this approach strongly depends on the choice of primer pairs and, in particular, on the balance between efficiency, specificity and sensitivity in the amplification of the different bacterial 16S sequences contained in a sample. There is thus the need for computational methods to design optimal bacterial 16S primers able to take into account the knowledge provided by the new sequencing technologies. Results We propose here a computational method for optimizing the choice of primer sets, based on multi-objective optimization, which simultaneously: 1) maximizes efficiency and specificity of target amplification; 2) maximizes the number of different bacterial 16S sequences matched by at least one primer; 3) minimizes the differences in the number of primers matching each bacterial 16S sequence. Our algorithm can be applied to any desired amplicon length without affecting computational performance. The source code of the developed algorithm is released as the mopo16S software tool (Multi-Objective Primer Optimization for 16S experiments) under the GNU General Public License and is available at http://sysbiobig.dei.unipd.it/?q=Software#mopo16S . Conclusions Results show that our strategy is able to find better primer pairs than the ones available in the literature according to all three optimization criteria. We also experimentally validated three of the primer pairs identified by our method on multiple bacterial species, belonging to different genera and phyla. Results confirm the predicted efficiency and the ability to maximize the number of different bacterial 16S sequences matched by primers.

Background In silico cell-type deconvolution from bulk transcriptomics data is a powerful technique to gain insights into the cellular composition of complex tissues. While first-generation methods used precomputed expression signatures covering limited cell types and tissues, second-generation tools use single-cell RNA sequencing data to build custom signatures for deconvoluting arbitrary cell types, tissues, and organisms. This flexibility poses significant challenges in assessing their deconvolution performance. Results Here, we comprehensively benchmark second-generation tools, disentangling different sources of variation and bias using a diverse panel of real and simulated data. Our results reveal substantial differences in accuracy, scalability, and robustness across methods, depending on factors such as cell-type similarity, reference composition, and dataset origin. Conclusions Our study highlights the strengths, limitations, and complementarity of state-of-the-art tools, shedding light on how different data characteristics and confounders impact deconvolution performance. We provide the scientific community with an ecosystem of tools and resources, omnideconv , simplifying the application, benchmarking, and optimization of deconvolution methods.