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Desponds E, Croci D, Wosika V, Hadadi N, Fonseca Costa SS, Ciarloni L, Ongaro M, Zdimerova H, Leblond MM, Hosseinian Ehrensberger S, Romero P, Verdeil G. Immuno-Transcriptomic Profiling of Blood and Tumor Tissue Identifies Gene Signatures Associated with Immunotherapy Response in Metastatic Bladder Cancer. Cancers (Basel) 2024; 16:433. [PMID: 38275874 PMCID: PMC10814931 DOI: 10.3390/cancers16020433] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Revised: 01/11/2024] [Accepted: 01/15/2024] [Indexed: 01/27/2024] Open
Abstract
Blood-based biomarkers represent ideal candidates for the development of non-invasive immuno-oncology-based assays. However, to date, no blood biomarker has been validated to predict clinical responses to immunotherapy. In this study, we used next-generation sequencing (RNAseq) on bulk RNA extracted from whole blood and tumor samples in a pre-clinical MIBC mouse model. We aimed to identify biomarkers associated with immunotherapy response and assess the potential application of simple non-invasive blood biomarkers as a therapeutic decision-making assay compared to tissue-based biomarkers. We established that circulating immune cells and the tumor microenvironment (TME) display highly organ-specific transcriptional responses to ICIs. Interestingly, in both, a common lymphocytic activation signature can be identified associated with the efficient response to immunotherapy, including a blood-specific CD8+ T cell activation/proliferation signature which predicts the immunotherapy response.
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Affiliation(s)
- Emma Desponds
- Department of Oncology UNIL CHUV, University of Lausanne, 1015 Lausanne, Switzerland; (E.D.); (M.O.); (H.Z.); (M.M.L.)
- Ludwig Institute for Cancer Research, University of Lausanne, 1015 Lausanne, Switzerland
| | - Davide Croci
- Novigenix SA, 1066 Epalinges, Switzerland; (D.C.); (N.H.); (S.S.F.C.); (L.C.); (S.H.E.); (P.R.)
| | - Victoria Wosika
- Novigenix SA, 1066 Epalinges, Switzerland; (D.C.); (N.H.); (S.S.F.C.); (L.C.); (S.H.E.); (P.R.)
| | - Noushin Hadadi
- Novigenix SA, 1066 Epalinges, Switzerland; (D.C.); (N.H.); (S.S.F.C.); (L.C.); (S.H.E.); (P.R.)
| | - Sara S. Fonseca Costa
- Novigenix SA, 1066 Epalinges, Switzerland; (D.C.); (N.H.); (S.S.F.C.); (L.C.); (S.H.E.); (P.R.)
| | - Laura Ciarloni
- Novigenix SA, 1066 Epalinges, Switzerland; (D.C.); (N.H.); (S.S.F.C.); (L.C.); (S.H.E.); (P.R.)
| | - Marco Ongaro
- Department of Oncology UNIL CHUV, University of Lausanne, 1015 Lausanne, Switzerland; (E.D.); (M.O.); (H.Z.); (M.M.L.)
- Ludwig Institute for Cancer Research, University of Lausanne, 1015 Lausanne, Switzerland
| | - Hana Zdimerova
- Department of Oncology UNIL CHUV, University of Lausanne, 1015 Lausanne, Switzerland; (E.D.); (M.O.); (H.Z.); (M.M.L.)
- Ludwig Institute for Cancer Research, University of Lausanne, 1015 Lausanne, Switzerland
| | - Marine M. Leblond
- Department of Oncology UNIL CHUV, University of Lausanne, 1015 Lausanne, Switzerland; (E.D.); (M.O.); (H.Z.); (M.M.L.)
- Ludwig Institute for Cancer Research, University of Lausanne, 1015 Lausanne, Switzerland
| | | | - Pedro Romero
- Novigenix SA, 1066 Epalinges, Switzerland; (D.C.); (N.H.); (S.S.F.C.); (L.C.); (S.H.E.); (P.R.)
| | - Grégory Verdeil
- Department of Oncology UNIL CHUV, University of Lausanne, 1015 Lausanne, Switzerland; (E.D.); (M.O.); (H.Z.); (M.M.L.)
- Ludwig Institute for Cancer Research, University of Lausanne, 1015 Lausanne, Switzerland
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Bastian FB, Roux J, Niknejad A, Comte A, Fonseca Costa SS, de Farias TM, Moretti S, Parmentier G, de Laval VR, Rosikiewicz M, Wollbrett J, Echchiki A, Escoriza A, Gharib WH, Gonzales-Porta M, Jarosz Y, Laurenczy B, Moret P, Person E, Roelli P, Sanjeev K, Seppey M, Robinson-Rechavi M. The Bgee suite: integrated curated expression atlas and comparative transcriptomics in animals. Nucleic Acids Res 2021; 49:D831-D847. [PMID: 33037820 PMCID: PMC7778977 DOI: 10.1093/nar/gkaa793] [Citation(s) in RCA: 76] [Impact Index Per Article: 25.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Revised: 08/24/2020] [Accepted: 09/15/2020] [Indexed: 01/24/2023] Open
Abstract
Bgee is a database to retrieve and compare gene expression patterns in multiple animal species, produced by integrating multiple data types (RNA-Seq, Affymetrix, in situ hybridization, and EST data). It is based exclusively on curated healthy wild-type expression data (e.g., no gene knock-out, no treatment, no disease), to provide a comparable reference of normal gene expression. Curation includes very large datasets such as GTEx (re-annotation of samples as ‘healthy’ or not) as well as many small ones. Data are integrated and made comparable between species thanks to consistent data annotation and processing, and to calls of presence/absence of expression, along with expression scores. As a result, Bgee is capable of detecting the conditions of expression of any single gene, accommodating any data type and species. Bgee provides several tools for analyses, allowing, e.g., automated comparisons of gene expression patterns within and between species, retrieval of the prefered conditions of expression of any gene, or enrichment analyses of conditions with expression of sets of genes. Bgee release 14.1 includes 29 animal species, and is available at https://bgee.org/ and through its Bioconductor R package BgeeDB.
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Affiliation(s)
- Frederic B Bastian
- Department of Ecology and Evolution, University of Lausanne, 1015 Lausanne, Switzerland.,SIB Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Julien Roux
- Department of Ecology and Evolution, University of Lausanne, 1015 Lausanne, Switzerland.,SIB Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Anne Niknejad
- Department of Ecology and Evolution, University of Lausanne, 1015 Lausanne, Switzerland.,SIB Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Aurélie Comte
- Department of Ecology and Evolution, University of Lausanne, 1015 Lausanne, Switzerland.,SIB Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Sara S Fonseca Costa
- Department of Ecology and Evolution, University of Lausanne, 1015 Lausanne, Switzerland.,SIB Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Tarcisio Mendes de Farias
- Department of Ecology and Evolution, University of Lausanne, 1015 Lausanne, Switzerland.,SIB Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Sébastien Moretti
- Department of Ecology and Evolution, University of Lausanne, 1015 Lausanne, Switzerland.,SIB Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Gilles Parmentier
- Department of Ecology and Evolution, University of Lausanne, 1015 Lausanne, Switzerland.,SIB Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Valentine Rech de Laval
- Department of Ecology and Evolution, University of Lausanne, 1015 Lausanne, Switzerland.,SIB Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Marta Rosikiewicz
- Department of Ecology and Evolution, University of Lausanne, 1015 Lausanne, Switzerland.,SIB Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Julien Wollbrett
- Department of Ecology and Evolution, University of Lausanne, 1015 Lausanne, Switzerland.,SIB Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Amina Echchiki
- Department of Ecology and Evolution, University of Lausanne, 1015 Lausanne, Switzerland.,SIB Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Angélique Escoriza
- Department of Ecology and Evolution, University of Lausanne, 1015 Lausanne, Switzerland.,SIB Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Walid H Gharib
- Department of Ecology and Evolution, University of Lausanne, 1015 Lausanne, Switzerland.,SIB Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Mar Gonzales-Porta
- Department of Ecology and Evolution, University of Lausanne, 1015 Lausanne, Switzerland.,SIB Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Yohan Jarosz
- Department of Ecology and Evolution, University of Lausanne, 1015 Lausanne, Switzerland.,SIB Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Balazs Laurenczy
- Department of Ecology and Evolution, University of Lausanne, 1015 Lausanne, Switzerland.,SIB Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Philippe Moret
- Department of Ecology and Evolution, University of Lausanne, 1015 Lausanne, Switzerland.,SIB Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Emilie Person
- Department of Ecology and Evolution, University of Lausanne, 1015 Lausanne, Switzerland.,SIB Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Patrick Roelli
- Department of Ecology and Evolution, University of Lausanne, 1015 Lausanne, Switzerland.,SIB Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Komal Sanjeev
- Department of Ecology and Evolution, University of Lausanne, 1015 Lausanne, Switzerland.,SIB Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Mathieu Seppey
- Department of Ecology and Evolution, University of Lausanne, 1015 Lausanne, Switzerland.,SIB Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Marc Robinson-Rechavi
- Department of Ecology and Evolution, University of Lausanne, 1015 Lausanne, Switzerland.,SIB Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
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Fonseca Costa SS, Robinson-Rechavi M, Ripperger JA. Single-cell transcriptomics allows novel insights into aging and circadian processes. Brief Funct Genomics 2020; 19:343-349. [PMID: 32633783 PMCID: PMC7716582 DOI: 10.1093/bfgp/elaa014] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2020] [Revised: 05/18/2020] [Accepted: 06/04/2020] [Indexed: 12/14/2022] Open
Abstract
Aging and circadian rhythms are two biological processes that affect an organism, although at different time scales. Nevertheless, due to the overlap of their actions, it was speculated that both interfere or interact with each other. However, to address this question, a much deeper insight into these processes is necessary, especially at the cellular level. New methods such as single-cell RNA-sequencing (scRNA-Seq) have the potential to close this gap in our knowledge. In this review, we analyze applications of scRNA-Seq from the aging and circadian rhythm fields and highlight new findings emerging from the analysis of single cells, especially in humans or rodents. Furthermore, we judge the potential of scRNA-Seq to identify common traits of both processes. Overall, this method offers several advantages over more traditional methods analyzing gene expression and will become an important tool to unravel the link between these biological processes.
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Affiliation(s)
- Sara S Fonseca Costa
- Department of Ecology and Evolution, University of Lausanne, 1015 Lausanne, Switzerland.,SIB Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Marc Robinson-Rechavi
- Department of Ecology and Evolution, University of Lausanne, 1015 Lausanne, Switzerland.,SIB Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
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Fonseca Costa SS, Wegmann D, Ripperger JA. Normalisation against Circadian and Age-Related Disturbances Enables Robust Detection of Gene Expression Changes in Liver of Aged Mice. PLoS One 2017; 12:e0169615. [PMID: 28068403 PMCID: PMC5222604 DOI: 10.1371/journal.pone.0169615] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2016] [Accepted: 12/08/2016] [Indexed: 12/29/2022] Open
Abstract
The expression of some genes is affected by age. To detect such age-related changes, their expression levels are related to constant marker genes. However, transcriptional noise increasing with advancing age renders difficult the identification of real age-related changes because it may affect the marker genes as well. Here, we report a selection procedure for genes appropriate to normalise the mouse liver transcriptome under various conditions including age. These genes were chosen from an initial set of 16 candidate genes defined based on a RNA-sequencing experiment and published literature. A subset of genes was selected based on rigorous statistical assessment of their variability using both RNA-sequencing and Nanostring hybridization experiments. The robustness of these marker genes was then verified by the analysis of 130 publicly available data sets using the mouse liver transcriptome. Altogether, a set of three genes, Atp5h, Gsk3β, and Sirt2 fulfilled our strict selection criteria in all assessments, while four more genes, Nono, Tprkb, Tspo, and Ttr passed all but one assessment and were included into the final set of marker genes to enhance robustness of normalisation against outliers. Using the geometric mean of expression of the genes to normalise Nanostring hybridization experiments we reliably identified age-related increases in the expression of Casein kinase 1δ and 1ϵ, and Sfpq, while the expression of the glucose transporter Glut2 decreased. The age-related changes were verified by real-time PCR and Western blot analysis. As conclusion, proper normalisation enhances the robustness of quantitative methods addressing age-related changes of a transcriptome.
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Affiliation(s)
| | - Daniel Wegmann
- Department of Biology, University of Fribourg, Fribourg, Switzerland
- Swiss Institute of Bioinformatics, University of Lausanne, Lausanne, Switzerland
- * E-mail: (DW); (JAR)
| | - Jürgen A. Ripperger
- Department of Biology, University of Fribourg, Fribourg, Switzerland
- * E-mail: (DW); (JAR)
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Okabe T, Chavan R, Fonseca Costa SS, Brenna A, Ripperger JA, Albrecht U. REV-ERBα influences the stability and nuclear localization of the glucocorticoid receptor. J Cell Sci 2016; 129:4143-4154. [PMID: 27686098 PMCID: PMC5117207 DOI: 10.1242/jcs.190959] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2016] [Accepted: 09/17/2016] [Indexed: 12/22/2022] Open
Abstract
REV-ERBα (encoded by Nr1d1) is a nuclear receptor that is part of the circadian clock mechanism and regulates metabolism and inflammatory processes. The glucocorticoid receptor (GR, encoded by Nr3c1) influences similar processes, but is not part of the circadian clock, although glucocorticoid signaling affects resetting of the circadian clock in peripheral tissues. Because of their similar impact on physiological processes, we studied the interplay between these two nuclear receptors. We found that REV-ERBα binds to the C-terminal portion and GR to the N-terminal portion of HSP90α and HSP90β, a chaperone responsible for the activation of proteins to ensure survival of a cell. The presence of REV-ERBα influences the stability and nuclear localization of GR by an unknown mechanism, thereby affecting expression of GR target genes, such as IκBα (Nfkbia) and alcohol dehydrogenase 1 (Adh1). Our findings highlight an important interplay between two nuclear receptors that influence the transcriptional potential of each other. This indicates that the transcriptional landscape is strongly dependent on dynamic processes at the protein level.
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Affiliation(s)
- Takashi Okabe
- Dept. of Biology, Biochemistry, University of Fribourg, Fribourg 1700, Switzerland
| | - Rohit Chavan
- Dept. of Biology, Biochemistry, University of Fribourg, Fribourg 1700, Switzerland
| | - Sara S Fonseca Costa
- Dept. of Biology, Biochemistry, University of Fribourg, Fribourg 1700, Switzerland
| | - Andrea Brenna
- Dept. of Biology, Biochemistry, University of Fribourg, Fribourg 1700, Switzerland
| | - Jürgen A Ripperger
- Dept. of Biology, Biochemistry, University of Fribourg, Fribourg 1700, Switzerland
| | - Urs Albrecht
- Dept. of Biology, Biochemistry, University of Fribourg, Fribourg 1700, Switzerland
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6
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Chavan R, Feillet C, Costa SSF, Delorme JE, Okabe T, Ripperger JA, Albrecht U. Liver-derived ketone bodies are necessary for food anticipation. Nat Commun 2016; 7:10580. [PMID: 26838474 PMCID: PMC4742855 DOI: 10.1038/ncomms10580] [Citation(s) in RCA: 84] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2015] [Accepted: 12/30/2015] [Indexed: 12/16/2022] Open
Abstract
The circadian system has endowed animals with the ability to anticipate recurring food availability at particular times of day. As daily food anticipation (FA) is independent of the suprachiasmatic nuclei, the central pacemaker of the circadian system, questions arise of where FA signals originate and what role components of the circadian clock might play. Here we show that liver-specific deletion of Per2 in mice abolishes FA, an effect that is rescued by viral overexpression of Per2 in the liver. RNA sequencing indicates that Per2 regulates β-hydroxybutyrate (βOHB) production to induce FA leading to the conclusion that liver Per2 is important for this process. Unexpectedly, we show that FA originates in the liver and not in the brain. However, manifestation of FA involves processing of the liver-derived βOHB signal in the brain, indicating that the food-entrainable oscillator is not located in a single tissue but is of systemic nature.
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Affiliation(s)
- Rohit Chavan
- Department of Biology, Unit of Biochemistry, University of Fribourg, Fribourg 1700, Switzerland
| | - Céline Feillet
- Department of Biology, Unit of Biochemistry, University of Fribourg, Fribourg 1700, Switzerland
| | - Sara S Fonseca Costa
- Department of Biology, Unit of Biochemistry, University of Fribourg, Fribourg 1700, Switzerland
| | - James E Delorme
- Department of Biology, Unit of Biochemistry, University of Fribourg, Fribourg 1700, Switzerland
| | - Takashi Okabe
- Department of Biology, Unit of Biochemistry, University of Fribourg, Fribourg 1700, Switzerland
| | - Jürgen A Ripperger
- Department of Biology, Unit of Biochemistry, University of Fribourg, Fribourg 1700, Switzerland
| | - Urs Albrecht
- Department of Biology, Unit of Biochemistry, University of Fribourg, Fribourg 1700, Switzerland
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