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Mihaela Angelova

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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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

Journal ArticleDOI
Franck Pagès, Bernhard Mlecnik, Florence Marliot, Gabriela Bindea1, Gabriela Bindea2, Gabriela Bindea3, Fang Shu Ou4, Carlo Bifulco5, Alessandro Lugli6, Inti Zlobec6, Tilman T. Rau6, Martin D. Berger7, Iris D. Nagtegaal8, Elisa Vink-Börger8, Arndt Hartmann9, Carol Geppert9, Julie Kolwelter9, Susanne Merkel, Robert Grützmann, Marc Van den Eynde10, Anne Jouret-Mourin10, Alex Kartheuser10, Daniel Léonard10, Christophe Remue10, Julia Y. Wang11, Julia Y. Wang12, Prashant Bavi12, Michael H.A. Roehrl12, Michael H.A. Roehrl13, Michael H.A. Roehrl11, Pamela S. Ohashi11, Linh T. Nguyen11, Seong Jun Han11, Heather L. MacGregor11, Sara Hafezi-Bakhtiari11, Bradly G. Wouters11, Giuseppe Masucci14, Emilia Andersson14, Eva Zavadova15, Michal Vocka15, Jan Spacek15, Lubos Petruzelka15, Bohuslav Konopasek15, Pavel Dundr15, Helena Skalova15, Kristyna Nemejcova15, Gerardo Botti, Fabiana Tatangelo, Paolo Delrio, Gennaro Ciliberto, Michele Maio, Luigi Laghi16, Fabio Grizzi16, Tessa Fredriksen1, Tessa Fredriksen2, Tessa Fredriksen3, Bénédicte Buttard1, Bénédicte Buttard2, Bénédicte Buttard3, Mihaela Angelova2, Mihaela Angelova3, Mihaela Angelova1, Angela Vasaturo3, Angela Vasaturo1, Angela Vasaturo2, Pauline Maby3, Pauline Maby2, Pauline Maby1, Sarah E. Church, Helen K. Angell, Lucie Lafontaine1, Lucie Lafontaine3, Lucie Lafontaine2, Daniela Bruni2, Daniela Bruni3, Daniela Bruni1, Carine El Sissy, Nacilla Haicheur, Amos Kirilovsky, Anne Berger, Christine Lagorce, Jeffrey P. Meyers4, Christopher Paustian5, Zipei Feng5, Carmen Ballesteros-Merino5, Jeroen R. Dijkstra8, Carlijn van de Water8, Shannon van Vliet8, Nikki Knijn8, Ana Maria Mușină, Dragos Viorel Scripcariu, Boryana Popivanova17, Mingli Xu17, Tomonobu Fujita17, Shoichi Hazama18, Nobuaki Suzuki18, Hiroaki Nagano18, Kiyotaka Okuno19, Toshihiko Torigoe20, Noriyuki Sato20, Tomohisa Furuhata20, Ichiro Takemasa20, Kyogo Itoh21, P. Patel, Hemangini H. Vora, Birva Shah, Jayendrakumar B. Patel, Kruti N. Rajvik, Shashank J. Pandya, Shilin N. Shukla, Yili Wang22, Guanjun Zhang22, Yutaka Kawakami17, Francesco M. Marincola23, Paolo A. Ascierto, Daniel J. Sargent4, Bernard A. Fox5, Bernard A. Fox24, Jérôme Galon1, Jérôme Galon2, Jérôme Galon3 
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

Journal ArticleDOI
15 Mar 2016-Immunity
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

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: 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


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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: 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

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

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