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Clemens Mayer

Bio: Clemens Mayer is an academic researcher from Innsbruck Medical University. The author has contributed to research in topics: Immunotherapy & Medicine. The author has an hindex of 7, co-authored 8 publications receiving 1369 citations.

Papers
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Journal ArticleDOI
TL;DR: 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 and may help inform cancer immunotherapy and facilitate the development of precision immuno-oncology.

2,292 citations

Posted ContentDOI
31 May 2016-bioRxiv
TL;DR: Cellular characterization of the immune infiltrates revealed a role of cancer-germline antigens in spontaneous immunity and showed that tumor genotypes determine immunophenotypes and tumor escape mechanisms and a scoring scheme for the quantification termed immunophenoscore was developed.
Abstract: 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://tciaat) may help informing cancer immunotherapy and facilitate the development of precision immuno-oncology

615 citations

Journal ArticleDOI
TL;DR: QuanTIseq as discussed by the authors is a method to quantify the fractions of ten immune cell types from bulk RNA-sequencing data, which is extensively validated in blood and tumor samples using simulated, flow cytometry, and immunohistochemistry data.
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.

572 citations

Posted ContentDOI
17 Aug 2018-bioRxiv
TL;DR: QuanTIseq, a deconvolution method that quantifies the densities of ten immune cell types from bulk RNA sequencing data and tissue imaging data, is developed and used to show how kinase inhibitors modulate the immune contexture and suggest that it might have predictive value for immunotherapy.
Abstract: The immune contexture has a prognostic value in several cancers and the study of its pharmacological modulation could identify drugs acting synergistically with immune checkpoint blockers. However, the quantification of the immune contexture is hampered by the lack of simple and efficient methods. We developed quanTIseq, a deconvolution method that quantifies the densities of ten immune cell types from bulk RNA sequencing data and tissue imaging data. We performed extensive validation using simulated data, flow cytometry data, and immunohistochemistry data from three cancer cohorts. Analysis of 8,000 samples showed that the activation of the CXCR3/CXCL9 axis, rather than the mutational load is associated with cytotoxic T cell infiltration. We also show the prognostic value of deconvolution-based immunoscore and T cell/B cell score in several solid cancers. Finally, we used quanTIseq to show how kinase inhibitors modulate the immune contexture, and we suggest that it might have predictive value for immunotherapy.

337 citations

Journal ArticleDOI
TL;DR: TIminer, an easy‐to‐use computational pipeline for mining tumor‐immune cell interactions from next‐generation sequencing data, enables integrative immunogenomic analyses, including: human leukocyte antigens typing, neoantigen prediction, characterization of immune infiltrates and quantification of tumor immunogenicity.
Abstract: Summary Recently, a number of powerful computational tools for dissecting tumor-immune cell interactions from next-generation sequencing data have been developed. However, the assembly of analytical pipelines and execution of multi-step workflows are laborious and involve a large number of intermediate steps with many dependencies and parameter settings. Here we present TIminer, an easy-to-use computational pipeline for mining tumor-immune cell interactions from next-generation sequencing data. TIminer enables integrative immunogenomic analyses, including: human leukocyte antigens typing, neoantigen prediction, characterization of immune infiltrates and quantification of tumor immunogenicity. Availability and implementation TIminer is freely available at http://icbi.i-med.ac.at/software/timiner/timiner.shtml. Contact zlatko.trajanoski@i-med.ac.at. Supplementary information Supplementary data are available at Bioinformatics online.

69 citations


Cited by
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Journal ArticleDOI
17 Apr 2018-Immunity
TL;DR: An extensive immunogenomic analysis of more than 10,000 tumors comprising 33 diverse cancer types by utilizing data compiled by TCGA identifies six immune subtypes that encompass multiple cancer types and are hypothesized to define immune response patterns impacting prognosis.

3,246 citations

Journal ArticleDOI
TL;DR: Tumor Immune Estimation Resource (TIMER) is presented to comprehensively investigate molecular characterization of tumor-immune interactions and provides a user-friendly web interface for dynamic analysis and visualization of these associations, which will be of broad utilities to cancer researchers.
Abstract: Recent clinical successes of cancer immunotherapy necessitate the investigation of the interaction between malignant cells and the host immune system. However, elucidation of complex tumor-immune interactions presents major computational and experimental challenges. Here, we present Tumor Immune Estimation Resource (TIMER; cistrome.shinyapps.io/timer) to comprehensively investigate molecular characterization of tumor-immune interactions. Levels of six tumor-infiltrating immune subsets are precalculated for 10,897 tumors from 32 cancer types. TIMER provides 6 major analytic modules that allow users to interactively explore the associations between immune infiltrates and a wide spectrum of factors, including gene expression, clinical outcomes, somatic mutations, and somatic copy number alterations. TIMER provides a user-friendly web interface for dynamic analysis and visualization of these associations, which will be of broad utilities to cancer researchers. Cancer Res; 77(21); e108-10. ©2017 AACR.

3,236 citations

Journal ArticleDOI
TL;DR: An algorithm-selected gene signature focused on tumor immune evasion and suppression predicts response to immune checkpoint blockade in melanoma, exceeding the accuracy of current clinical biomarkers.
Abstract: Cancer treatment by immune checkpoint blockade (ICB) can bring long-lasting clinical benefits, but only a fraction of patients respond to treatment. To predict ICB response, we developed TIDE, a computational method to model two primary mechanisms of tumor immune evasion: the induction of T cell dysfunction in tumors with high infiltration of cytotoxic T lymphocytes (CTL) and the prevention of T cell infiltration in tumors with low CTL level. We identified signatures of T cell dysfunction from large tumor cohorts by testing how the expression of each gene in tumors interacts with the CTL infiltration level to influence patient survival. We also modeled factors that exclude T cell infiltration into tumors using expression signatures from immunosuppressive cells. Using this framework and pre-treatment RNA-Seq or NanoString tumor expression profiles, TIDE predicted the outcome of melanoma patients treated with first-line anti-PD1 or anti-CTLA4 more accurately than other biomarkers such as PD-L1 level and mutation load. TIDE also revealed new candidate ICB resistance regulators, such as SERPINB9, demonstrating utility for immunotherapy research.

2,185 citations

Journal ArticleDOI
TL;DR: This work presents xCell, a novel gene signature-based method, and uses it to infer 64 immune and stromal cell types and shows that xCell outperforms other methods.
Abstract: Tissues are complex milieus consisting of numerous cell types. Several recent methods have attempted to enumerate cell subsets from transcriptomes. However, the available methods have used limited sources for training and give only a partial portrayal of the full cellular landscape. Here we present xCell, a novel gene signature-based method, and use it to infer 64 immune and stromal cell types. We harmonized 1822 pure human cell type transcriptomes from various sources and employed a curve fitting approach for linear comparison of cell types and introduced a novel spillover compensation technique for separating them. Using extensive in silico analyses and comparison to cytometry immunophenotyping, we show that xCell outperforms other methods. xCell is available at http://xCell.ucsf.edu/ .

2,040 citations

Journal ArticleDOI
TL;DR: TIMER2.0 (http://timer.cistrome.org/) provides more robust estimation of immune infiltration levels for The Cancer Genome Atlas (TCGA) or user-provided tumor profiles using six state-of-the-art algorithms.
Abstract: Tumor progression and the efficacy of immunotherapy are strongly influenced by the composition and abundance of immune cells in the tumor microenvironment. Due to the limitations of direct measurement methods, computational algorithms are often used to infer immune cell composition from bulk tumor transcriptome profiles. These estimated tumor immune infiltrate populations have been associated with genomic and transcriptomic changes in the tumors, providing insight into tumor-immune interactions. However, such investigations on large-scale public data remain challenging. To lower the barriers for the analysis of complex tumor-immune interactions, we significantly improved our previous web platform TIMER. Instead of just using one algorithm, TIMER2.0 (http://timer.cistrome.org/) provides more robust estimation of immune infiltration levels for The Cancer Genome Atlas (TCGA) or user-provided tumor profiles using six state-of-the-art algorithms. TIMER2.0 provides four modules for investigating the associations between immune infiltrates and genetic or clinical features, and four modules for exploring cancer-related associations in the TCGA cohorts. Each module can generate a functional heatmap table, enabling the user to easily identify significant associations in multiple cancer types simultaneously. Overall, the TIMER2.0 web server provides comprehensive analysis and visualization functions of tumor infiltrating immune cells.

1,992 citations