Author
Mihaela Angelova
Other affiliations: French Institute of Health and Medical Research, Pierre-and-Marie-Curie University, University of Paris ...read more
Bio: Mihaela Angelova is an academic researcher from Francis Crick Institute. The author has contributed to research in topics: Medicine & Cancer. The author has an hindex of 14, co-authored 28 publications receiving 3902 citations. Previous affiliations of Mihaela Angelova include French Institute of Health and Medical Research & Pierre-and-Marie-Curie University.
Topics: Medicine, Cancer, Biology, Lung cancer, Immunotherapy
Papers
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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
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French Institute of Health and Medical Research1, Paris Descartes University2, Pierre-and-Marie-Curie University3, Mayo Clinic4, Providence Portland Medical Center5, University of Bern6, University Hospital of Bern7, Radboud University Nijmegen8, University of Erlangen-Nuremberg9, Université catholique de Louvain10, University Health Network11, University of Toronto12, Memorial Sloan Kettering Cancer Center13, Karolinska Institutet14, First Faculty of Medicine, Charles University in Prague15, Humanitas University16, Keio University17, Yamaguchi University18, Kindai University19, Sapporo Medical University20, Kurume University21, Xi'an Jiaotong University22, Qatar Airways23, Oregon Health & Science University24
TL;DR: The immunoscore provides a reliable estimate of the risk of recurrence in patients with colon cancer and supports the implementation of the consensus Immunoscore as a new component of a TNM-Immune classification of cancer.
1,326 citations
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TL;DR: Assessment of the immune status via Immunoscore provides a potent indicator of tumor recurrence beyond microsatellite-instability staging that could be an important guide for immunotherapy strategies.
780 citations
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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
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TL;DR: The immunophenotypes of the tumors and the cancer antigenome remain widely unexplored, and the findings represent a step toward the development of personalized cancer immunotherapies.
Abstract: Background: While large-scale cancer genomic projects are comprehensively characterizing the mutational spectrum of various cancers, so far little attention has been devoted to either define the antigenicity of these mutations or to characterize the immune responses they elicit. Here we present a strategy to characterize the immunophenotypes and the antigen-ome of human colorectal cancer. Results: We apply our strategy to a large colorectal cancer cohort (n = 598) and show that subpopulations of tumor-infiltrating lymphocytes are associated with distinct molecular phenotypes. The characterization of the antigenome shows that a large number of cancer-germline antigens are expressed in all patients. In contrast, neo-antigens are rarely shared between patients, indicating that cancer vaccination requires individualized strategy. Analysis of the genetic basis of the tumors reveals distinct tumor escape mechanisms for the patient subgroups. Hypermutated tumors are depleted of immunosuppressive cells and show upregulation of immunoinhibitory molecules. Non-hypermutated tumors are enriched with immunosuppressive cells, and the expression of immunoinhibitors and MHC molecules is downregulated. Reconstruction of the interaction network of tumor-infiltrating lymphocytes and immunomodulatory molecules followed by a validation with 11 independent cohorts (n = 1,945) identifies BCMA as a novel druggable target. Finally, linear regression modeling identifies major determinants of tumor immunogenicity, which include well-characterized modulators as well as a novel candidate, CCR8, which is then tested in an orthologous immunodeficient mouse model. Conclusions: The immunophenotypes of the tumors and the cancer antigenome remain widely unexplored, and our findings represent a step toward the development of personalized cancer immunotherapies.
407 citations
Cited by
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Institute for Systems Biology1, BC Cancer Agency2, University of California, San Francisco3, University of North Carolina at Chapel Hill4, Columbia University5, Discovery Institute6, Massachusetts Institute of Technology7, Arizona State University8, Sage Bionetworks9, Harvard University10, Johns Hopkins University11, Stanford University12, University of Calgary13, Université libre de Bruxelles14, University of Texas MD Anderson Cancer Center15, Medical College of Wisconsin16, Qatar Airways17, Cold Spring Harbor Laboratory18, University of São Paulo19, Henry Ford Hospital20, University of Alabama at Birmingham21, Van Andel Institute22, Stony Brook University23
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
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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
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University of California, San Francisco1, Cold Spring Harbor Laboratory2, Icahn School of Medicine at Mount Sinai3, Oregon Health & Science University4, Wistar Institute5, University of Maryland, Baltimore County6, Huntsman Cancer Institute7, La Jolla Institute for Allergy and Immunology8, University of Pennsylvania9, Harvard University10, University of Michigan11, Massachusetts Institute of Technology12
TL;DR: By parsing the unique classes and subclasses of tumor immune microenvironment (TIME) that exist within a patient’s tumor, the ability to predict and guide immunotherapeutic responsiveness will improve, and new therapeutic targets will be revealed.
Abstract: The clinical successes in immunotherapy have been both astounding and at the same time unsatisfactory. Countless patients with varied tumor types have seen pronounced clinical response with immunotherapeutic intervention; however, many more patients have experienced minimal or no clinical benefit when provided the same treatment. As technology has advanced, so has the understanding of the complexity and diversity of the immune context of the tumor microenvironment and its influence on response to therapy. It has been possible to identify different subclasses of immune environment that have an influence on tumor initiation and response and therapy; by parsing the unique classes and subclasses of tumor immune microenvironment (TIME) that exist within a patient's tumor, the ability to predict and guide immunotherapeutic responsiveness will improve, and new therapeutic targets will be revealed.
2,920 citations
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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
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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