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Peptide-loaded MHC class I (pMHC-I) multimers have revolutionized our capabilities to monitor disease-associated T cell responses with high sensitivity and specificity. To improve the discovery of T cell receptors (TCR) targeting neoantigens of individual tumor patients with recombinant MHC molecules, we developed a peptide-loadable MHC class I platform termed MediMer. MediMers are based on soluble disulfide-stabilized β2-microglobulin/heavy chain ectodomain single-chain dimers (dsSCD) that can be easily produced in large quantities in eukaryotic cells and tailored to individual patients’ HLA allotypes with only little hands-on time. Upon transient expression in CHO-S cells together with ER-targeted BirA biotin ligase, biotinylated dsSCD are purified from the cell supernatant and are ready to use. We show that CHO-produced dsSCD are free of endogenous peptide ligands. Empty dsSCD from more than 30 different HLA-A,B,C allotypes, that were produced and validated so far, can be loaded with synthetic peptides matching the known binding criteria of the respective allotypes, and stored at low temperature without loss of binding activity. We demonstrate the usability of peptide-loaded dsSCD multimers for the detection of human antigen-specific T cells with comparable sensitivities as multimers generated with peptide-tethered β2m-HLA heavy chain single-chain trimers (SCT) and wild-type peptide-MHC-I complexes prior formed in small-scale refolding reactions. Using allotype-specific, fluorophore-labeled competitor peptides, we present a novel dsSCD-based peptide binding assay capable of interrogating large libraries of in silico predicted neoepitope peptides by flow cytometry in a high-throughput and rapid format. We discovered rare T cell populations with specificity for tumor neoepitopes and epitopes from shared tumor-associated antigens in peripheral blood of a melanoma patient including a so far unreported HLA-C*08:02-restricted NY-ESO-1-specific CD8+ T cell population. Two representative TCR of this T cell population, which could be of potential value for a broader spectrum of patients, were identified by dsSCD-guided single-cell sequencing and were validated by cognate pMHC-I multimer staining and functional responses to autologous peptide-pulsed antigen presenting cells. By deploying the technically accessible dsSCD MHC-I MediMer platform, we hope to significantly improve success rates for the discovery of personalized neoepitope-specific TCR in the future by being able to also cover rare HLA allotypes.

Background Primary and secondary resistance is a major hurdle in cancer immunotherapy. Therefore, a better understanding of the underlying mechanisms involved in immunotherapy resistance is of pivotal importance to improve therapy outcome.Method Here, two mouse models with resistance against therapeutic vaccine-induced tumor regression were studied. Exploration of the tumor microenvironment by high dimensional flow cytometry in combination with therapeutic in vivo settings allowed for the identification of immunological factors driving immunotherapy resistance.Results Comparison of the tumor immune infiltrate during early and late regression revealed a change from tumor-rejecting toward tumor-promoting macrophages. In concert, a rapid exhaustion of tumor-infiltrating T cells was observed. Perturbation studies identified a small but discernible CD163hi macrophage population, with high expression of several tumor-promoting macrophage markers and a functional anti-inflammatory transcriptome profile, but not other macrophages, to be responsible. In-depth analyses revealed that they localize at the tumor invasive margins and are more resistant to Csf1r inhibition when compared with other macrophages. In vivo studies validated the activity of heme oxygenase-1 as an underlying mechanism of immunotherapy resistance. The transcriptomic profile of CD163hi macrophages is highly similar to a human monocyte/macrophage population, indicating that they represent a target to improve immunotherapy efficacy.Conclusions In this study, a small population of CD163hi tissue-resident macrophages is identified to be responsible for primary and secondary resistance against T-cell-based immunotherapies. While these CD163hi M2 macrophages are resistant to Csf1r-targeted therapies, in-depth characterization and identification of the underlying mechanisms driving immunotherapy resistance allows the specific targeting of this subset of macrophages, thereby creating new opportunities for therapeutic intervention with the aim to overcome immunotherapy resistance.

The re-wiring of the metabolic machinery is a common feature in cancer. Here, the authors show, using paired normal and prostate cancer samples that the cancer samples exhibit a shift to succinate respiration, which is associated with elevated levels of mitochondrial DNA mutations.

Abstract 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 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.

It was highlighted that the original article [1] contained a typesetting mistake in the name of Noel Filipe da Cunha Carvalho de Miranda. This was incorrectly captured as Noel Filipe da Cunha Carvahlo de Miranda. It was also highlighted that in Fig. 3C the left panels Y-axis were cropped and in Fig. 5C, CD8 bar was cropped. This Correction article shows the correct Figs. 3 and 5. The original article has been updated.

BACKGROUND:For virtually every patient with colorectal cancer (CRC), hematoxylin-eosin (HE)-stained tissue slides are available. These images contain quantitative information, which is not routinely used to objectively extract prognostic biomarkers. In the present study, we investigated whether deep convolutional neural networks (CNNs) can extract prognosticators directly from these widely available images. METHODS AND FINDINGS:We hand-delineated single-tissue regions in 86 CRC tissue slides, yielding more than 100,000 HE image patches, and used these to train a CNN by transfer learning, reaching a nine-class accuracy of >94% in an independent data set of 7,180 images from 25 CRC patients. With this tool, we performed automated tissue decomposition of representative multitissue HE images from 862 HE slides in 500 stage I-IV CRC patients in the The Cancer Genome Atlas (TCGA) cohort, a large international multicenter collection of CRC tissue. Based on the output neuron activations in the CNN, we calculated a "deep stroma score," which was an independent prognostic factor for overall survival (OS) in a multivariable Cox proportional hazard model (hazard ratio [HR] with 95% confidence interval [CI]: 1.99 [1.27-3.12], p = 0.0028), while in the same cohort, manual quantification of stromal areas and a gene expression signature of cancer-associated fibroblasts (CAFs) were only prognostic in specific tumor stages. We validated these findings in an independent cohort of 409 stage I-IV CRC patients from the "Darmkrebs: Chancen der Verhütung durch Screening" (DACHS) study who were recruited between 2003 and 2007 in multiple institutions in Germany. Again, the score was an independent prognostic factor for OS (HR 1.63 [1.14-2.33], p = 0.008), CRC-specific OS (HR 2.29 [1.5-3.48], p = 0.0004), and relapse-free survival (RFS; HR 1.92 [1.34-2.76], p = 0.0004). A prospective validation is required before this biomarker can be implemented in clinical workflows. CONCLUSIONS:In our retrospective study, we show that a CNN can assess the human tumor microenvironment and predict prognosis directly from histopathological images.

Metastasis is the most life threatening aspect of breast cancer. It is a multi-step process involving invasion and migration of primary tumor cells with a subsequent colonization of these cells at a secondary location. The aim of the present study was to investigate the role of thioredoxin (Trx1) in the invasion and migration of breast cancer cells and to assess the strength of the association between high levels of Trx1 and thioredoxin reductase (TrxR1) expression with breast cancer patient survival. Our results indicate that the expression of both Trx1 and TrxR1 are statistically significantly increased in breast cancer patient cells compared with paired normal breast tissue from the same patient. Over-expression of Trx1 in MDA-MB-231 breast cancer cell lines enhanced cell invasion in in vitro assays while expression of a redox inactive mutant form of Trx1 (designated 1SS) or the antisense mRNA inhibited cell invasion. Addition of exogenous Trx1 also enhanced cell invasion, while addition of a specific monoclonal antibody that inhibits Trx1 redox function decreased cell invasion. Over-expression of intracellular Trx1 did not increase cell migration but expression of intracellular 1SS inhibited migration. Addition of exogenous Trx1 enhanced cell migration while 1SS had no effect. Treatment with auranofin inhibited TrxR activity, cell migration and clonogenic activity of MDA-MB-231 cells, while increasing reactive oxygen species (ROS) levels. Analysis of 25 independent cohorts with 5910 patients showed that Trx1 and TrxR1 were both associated with a poor patient prognosis in terms of overall survival, distant metastasis free survival and disease free survival. Therefore, targeting the Trx system with auranofin or other specific inhibitors may provide improved breast cancer patient outcomes through inhibition of cancer invasion and migration.

Lymphoid and myeloid cells are abundant in the tumor microenvironment, can be quantified by immunohistochemistry and shape the disease course of human solid tumors. Yet, there is no comprehensive understanding of spatial immune infiltration patterns (‘topography’) across cancer entities and across various immune cell types. In this study, we systematically measure the topography of multiple immune cell types in 965 histological tissue slides from N = 177 patients in a pan-cancer cohort. We provide a definition of inflamed (‘hot’), non-inflamed (‘cold’) and immune excluded patterns and investigate how these patterns differ between immune cell types and between cancer types. In an independent cohort of N = 287 colorectal cancer patients, we show that hot, cold and excluded topographies for effector lymphocytes (CD8) and tumor-associated macrophages (CD163) alone are not prognostic, but that a bivariate classification system can stratify patients. Our study adds evidence to consider immune topographies as biomarkers for patients with solid tumors.

BACKGROUND:Chronic inflammation is frequently observed on histological analysis of malignant and non-malignant prostate specimens. It is a suspected supporting factor for prostate diseases and their progression and a main cause of false positive PSA tests in cancer screening. We hypothesized that inflammation induces autoantibodies, which may be useful biomarkers. We aimed to identify and validate prostate inflammation associated serum autoantibodies in prostate cancer patients and evaluate the expression of corresponding autoantigens. METHODS:Radical prostatectomy specimens of prostate cancer patients (N = 70) were classified into high and low inflammation groups according to the amount of tissue infiltrating lymphocytes. The corresponding pre-surgery blood serum samples were scrutinized for autoantibodies using a low-density protein array. Selected autoantigens were identified in prostate tissue and their expression pattern analyzed by immunohistochemistry and qPCR. The identified autoantibody profile was cross-checked in an independent sample set (N = 63) using the Luminex-bead protein array technology. RESULTS:Protein array screening identified 165 autoantibodies differentially abundant in the serum of high compared to low inflammation patients. The expression pattern of three corresponding antigens were established in benign and cancer tissue by immunohistochemistry and qPCR: SPAST (Spastin), STX18 (Syntaxin 18) and SPOP (speckle-type POZ protein). Of these, SPAST was significantly increased in prostate tissue with high inflammation. All three autoantigens were differentially expressed in primary and/or castration resistant prostate tumors when analyzed in an inflammation-independent tissue microarray. Cross-validation of the inflammation autoantibody profile on an independent sample set using a Luminex-bead protein array, retrieved 51 of the significantly discriminating autoantibodies. Three autoantibodies were significantly upregulated in both screens, MUT, RAB11B and CSRP2 (p>0.05), two, SPOP and ZNF671, close to statistical significance (p = 0.051 and 0.076). CONCLUSIONS:We provide evidence of an inflammation-specific autoantibody profile and confirm the expression of corresponding autoantigens in prostate tissue. This supports evaluation of autoantibodies as non-invasive markers for prostate inflammation.

Cancer progression is a complex process involving host-tumor interactions by multiple molecular and cellular factors of the tumor microenvironment. Tumor cells that challenge immune activity may be vulnerable to immune destruction. To address this question we have directed major efforts towards data integration and developed and installed a database for cancer immunology with more than 1700 patients and associated clinical data and biomolecular data. Mining of the database revealed novel insights into the molecular mechanisms of tumor-immune cell interaction. In this paper we present the computational tools used to analyze integrated clinical and biomolecular data. Specifically, we describe a database for heterogenous data types, the interfacing bioinformatics and statistical tools including clustering methods, survival analysis, as well as visualization methods. Additionally, we discuss generic issues relevant to the integration of clinical and biomolecular data, as well as recent developments in integrative data analyses including biomolecular network reconstruction and mathematical modeling. [ABSTRACT FROM AUTHOR]