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.

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.

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...

Creating designer enzymes with the ability to catalyse abiological transformations is a formidable challenge. Efforts toward this goal typically consider only canonical amino acids in the initial design process. However, incorporating unnatural amino acids that feature uniquely reactive side chains could significantly expand the catalytic repertoire of designer enzymes. To explore the potential of such artificial building blocks for enzyme design, here we selected p-aminophenylalanine as a potentially novel catalytic residue. We demonstrate that the catalytic activity of the aniline side chain for hydrazone and oxime formation reactions is increased by embedding p-aminophenylalanine into the hydrophobic pore of the multidrug transcriptional regulator from Lactococcus lactis. Both the recruitment of reactants by the promiscuous binding pocket and a judiciously placed aniline that functions as a catalytic residue contribute to the success of the identified artificial enzyme. We anticipate that our design strategy will prove rewarding to significantly expand the catalytic repertoire of designer enzymes in the future.

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RNA G-quadruplexes (rG4s) are secondary structures in mRNAs known to influence RNA post-transcriptional mechanisms thereby impacting neurodegenerative disease and cancer. A detailed knowledge of rG4–protein interactions is vital to understand rG4 function. Herein, we describe a systematic affinity proteomics approach that identified 80 high-confidence interactors that assemble on the rG4 located in the 5′-untranslated region (UTR) of the NRAS oncogene. Novel rG4 interactors included DDX3X, DDX5, DDX17, GRSF1 and NSUN5. The majority of identified proteins contained a glycine-arginine (GAR) domain and notably GAR-domain mutation in DDX3X and DDX17 abrogated rG4 binding. Identification of DDX3X targets by transcriptome-wide individual-nucleotide resolution UV-crosslinking and affinity enrichment (iCLAE) revealed a striking association with 5′-UTR rG4-containing transcripts which was reduced upon GAR-domain mutation. Our work highlights hitherto unrecognized features of rG4 structure–protein interactions that highlight new roles of rG4 structures in mRNA post-transcriptional control.

By combining targeted mutagenesis, computational refinement, and directed evolution, a modestly active, computationally designed DielsAlderase was converted into the most proficient biocatalyst for [4+2] cycloadditions known. The high stereoselectivity and minimal product inhibition of the evolved enzyme enabled preparative scale synthesis of a single product diastereomer. X-ray crystallography of the enzyme-product complex shows that the molecular changes introduced over the course of optimization, including addition of a lid structure, gradually reshaped the pocket for more effective substrate preorganization and transition state stabilization. The good overall agreement between the experimental structure and the original design model with respect to the orientations of both the bound product and the catalytic side chains contrasts with other computationally designed enzymes. Because design accuracy appears to correlate with scaffold rigidity, improved control over backbone conformation will likely be the key to future efforts to design more efficient enzymes for diverse chemical reactions.biocatalysis | computational enzyme design | Diels-Alder reaction | laboratory evolution | enzyme mechanism