The Cancer Genome Atlas revealed the genomic landscapes of human cancers. In parallel, immunotherapy is transforming the treatment of advanced cancers. Unfortunately, the majority of patients do not respond to immunotherapy, making the identification of predictive markers and the mechanisms of resistance an area of intense research. To increase our understanding of tumor-immune cell interactions, we characterized the intratumoral immune landscapes and the cancer antigenomes from 20 solid cancers and created The Cancer Immunome Atlas (https://tcia.at/). Cellular characterization of the immune infiltrates showed that tumor genotypes determine immunophenotypes and tumor escape mechanisms. Using machine learning, we identified determinants of tumor immunogenicity and developed a scoring scheme for the quantification termed immunophenoscore. The immunophenoscore was a superior predictor of response to anti-cytotoxic T lymphocyte antigen-4 (CTLA-4) and anti-programmed cell death protein 1 (anti-PD-1) antibodies in two independent validation cohorts. Our findings and this resource may help inform cancer immunotherapy and facilitate the development of precision immuno-oncology.

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 . Electronic supplementary material The online version of this article (10.1186/s13073-019-0638-6) contains supplementary material, which is available to authorized users.

BackgroundRecent work has identified and mapped a range of posttranscriptional modifications in mRNA, including methylation of the N6 and N1 positions in adenine, pseudouridylation, and methylation of carbon 5 in cytosine (m5C). However, knowledge about the prevalence and transcriptome-wide distribution of m5C is still extremely limited; thus, studies in different cell types, tissues, and organisms are needed to gain insight into possible functions of this modification and implications for other regulatory processes.ResultsWe have carried out an unbiased global analysis of m5C in total and nuclear poly(A) RNA of mouse embryonic stem cells and murine brain. We show that there are intriguing differences in these samples and cell compartments with respect to the degree of methylation, functional classification of methylated transcripts, and position bias within the transcript. Specifically, we observe a pronounced accumulation of m5C sites in the vicinity of the translational start codon, depletion in coding sequences, and mixed patterns of enrichment in the 3′ UTR. Degree and pattern of methylation distinguish transcripts modified in both embryonic stem cells and brain from those methylated in either one of the samples. We also analyze potential correlations between m5C and micro RNA target sites, binding sites of RNA binding proteins, and N6-methyladenosine.ConclusionOur study presents the first comprehensive picture of cytosine methylation in the epitranscriptome of pluripotent and differentiated stages in the mouse. These data provide an invaluable resource for future studies of function and biological significance of m5C in mRNA in mammals.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-016-1139-1) contains supplementary material, which is available to authorized users.

BackgroundAfter the 2002/2003 SARS outbreak, 30% of survivors exhibited persisting structural pulmonary abnormalities. The long-term pulmonary sequelae of coronavirus disease 2019 (COVID-19) are yet unknown, and comprehensive clinical follow-up data are lacking.MethodsIn this prospective, multicentre, observational study, we systematically evaluated the cardiopulmonary damage in subjects recovering from COVID-19 at 60 and 100 days after confirmed diagnosis. We conducted a detailed questionnaire, clinical examination, laboratory testing, lung function analysis, echocardiography, and thoracic low-dose computed tomography (CT).ResultsData from 145 COVID-19 patients were evaluated, and 41% of all subjects exhibited persistent symptoms 100 days after COVID-19 onset, with dyspnea being most frequent (36%). Accordingly, patients still displayed an impaired lung function, with a reduced diffusing capacity in 21% of the cohort being the most prominent finding. Cardiac impairment, including a reduced left ventricular function or signs of pulmonary hypertension, was only present in a minority of subjects. CT scans unveiled persisting lung pathologies in 63% of patients, mainly consisting of bilateral ground-glass opacities and/or reticulation in the lower lung lobes, without radiological signs of pulmonary fibrosis. Sequential follow-up evaluations at 60 and 100 days after COVID-19 onset demonstrated a vast improvement of both, symptoms and CT abnormalities over time.ConclusionA relevant percentage of post-COVID-19 patients presented with persisting symptoms and lung function impairment along with pulmonary abnormalities more than 100 days after the diagnosis of COVID-19. However, our results indicate a significant improvement in symptoms and cardiopulmonary status over time.

Current major challenges in cancer immunotherapy include identification of patients likely to respond to therapy and development of strategies to treat non-responders. To address these problems and facilitate understanding of the tumor-immune cell interactions we inferred the cellular composition and functional orientation of immune infiltrates, and characterized tumor antigens in 19 solid cancers from The Cancer Genome Atlas (TCGA). Decomposition of immune infiltrates revealed prognostic cellular profiles for distinct cancers, and showed that the tumor genotypes determine immunophenotypes and tumor escape mechanisms. The genotype-immunophenotype relationships were evident at the high-level view (mutational load, tumor heterogenity) and at the low-level view (mutational origin) of the genomic landscapes. Using random forest approach we identified determinants of immunogenicity and developed an immunophenoscore based on the infiltration of immune subsets and expression of immunomodulatory molecules. The immunophenoscore predicted response to immunotherapy with anti-CTLA-4 and anti-PD-1 antibodies in two validation cohorts. Our findings and the database we developed (TCIA-The Cancer Immunome Atlas, http://tcia.at) may help informing cancer immunotherapy and facilitate the development of precision immuno-oncology. ACKNOWLEDGMENTSThe results shown here are in part based upon data generated by the TCGA Research Network: http://cancergenome.nih.gov.

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.

We introduce quanTIseq, a method to quantify the tumor immune contexture, determined by the type and density of tumor-infiltrating immune cells. quanTIseq is based on a novel deconvolution algorithm for RNA sequencing data that was validated with independent data sets. Complementing the deconvolution output with image data from tissue slides enables in silico multiplexed immunodetection and provides an efficient method for the immunophenotyping of a large number of tumor samples.Cancer immunotherapy with antibodies targeting immune checkpoints has shown durable benefit or even curative potential in various cancers 1,2 . As only a fraction of patients are responsive to immune checkpoint blockers, efforts are underway to identify predictive markers as well as mechanistic rationale for combination therapies with synergistic potential. Thus, comprehensive and quantitative immunological characterization of tumors in a large number of clinical samples is of utmost importance, but it is currently hampered by the lack of simple and efficient methods. Therefore, we developed quanTIseq, a computational pipeline for the quantification of the Tumor Immune contexture using RNA-seq data and images of haematoxylin and eosin (H&E)-stained tissue slides (Fig. 1a). As part of quanTIseq, we first developed a deconvolution algorithm based on constrained least squares regression 12 . We then designed a signature matrix from a compendium of 51 RNA-seq data sets (Supplementary (Fig. 1c).To validate quanTIseq we first used both simulated data and published data. We simulated 1,700RNA-seq data sets from human breast tumors by mixing various numbers of reads from tumor and immune-cell RNA-seq data, considering different immune compositions and sequencing depths.quanTIseq obtained a high correlation between the true and the estimated fractions and accurately quantified tumor content (measured by the fraction of "other" cells) (Supplementary Figure 1). We then validated quanTIseq using experimental data from a previous study 13 , in which peripheral blood mononuclear cell (PBMC) mixtures were subjected to both, RNA-seq and flow cytometry. A high accuracy of quanTIseq estimates was also observed with this data set ( Fig. 1d and Supplementary Figure 2). Additionally, we successfully validated quanTIseq using two previous published data sets (Supplementary Figures 3 and 4).As most of the validation data sets available in the literature are based on microarray data or consider a limited number of phenotypes, we generated RNA-seq and flow cytometry data from mixtures of peripheral-blood immune cells collected from nine healthy donors. Flow cytometry was used to quantify all the immune sub-populations considered by quanTIseq signature matrix except macrophages, which are not present in blood. Comparison between quanTIseq cell estimates and flow cytometry fractions showed a high correlation at a single and multiple cell-type level ( Fig. 1e and Supplementary Figure 5).We then validated quanTIseq using two independent data sets. The first data...

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.