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Laguillaumie MO, Titah S, Guillemette A, Neve B, Leprêtre F, Ségard P, Shaik FA, Collard D, Gerbedoen JC, Fléchon L, Hasan Bou Issa L, Vincent A, Figeac M, Sebda S, Villenet C, Kluza J, Laine W, Fournier I, Gimeno JP, Wisztorski M, Manier S, Tarhan MC, Quesnel B, Idziorek T, Touil Y. Deciphering genetic and nongenetic factors underlying tumour dormancy: insights from multiomics analysis of two syngeneic MRD models of melanoma and leukemia. Biol Res 2024; 57:59. [PMID: 39223638 PMCID: PMC11370043 DOI: 10.1186/s40659-024-00540-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2024] [Accepted: 08/23/2024] [Indexed: 09/04/2024] Open
Abstract
BACKGROUND Tumour dormancy, a resistance mechanism employed by cancer cells, is a significant challenge in cancer treatment, contributing to minimal residual disease (MRD) and potential relapse. Despite its clinical importance, the mechanisms underlying tumour dormancy and MRD remain unclear. In this study, we employed two syngeneic murine models of myeloid leukemia and melanoma to investigate the genetic, epigenetic, transcriptomic and protein signatures associated with tumour dormancy. We used a multiomics approach to elucidate the molecular mechanisms driving MRD and identify potential therapeutic targets. RESULTS We conducted an in-depth omics analysis encompassing whole-exome sequencing (WES), copy number variation (CNV) analysis, chromatin immunoprecipitation followed by sequencing (ChIP-seq), transcriptome and proteome investigations. WES analysis revealed a modest overlap of gene mutations between melanoma and leukemia dormancy models, with a significant number of mutated genes found exclusively in dormant cells. These exclusive genetic signatures suggest selective pressure during MRD, potentially conferring resistance to the microenvironment or therapies. CNV, histone marks and transcriptomic gene expression signatures combined with Gene Ontology (GO) enrichment analysis highlighted the potential functional roles of the mutated genes, providing insights into the pathways associated with MRD. In addition, we compared "murine MRD genes" profiles to the corresponding human disease through public datasets and highlighted common features according to disease progression. Proteomic analysis combined with multi-omics genetic investigations, revealed a dysregulated proteins signature in dormant cells with minimal genetic mechanism involvement. Pathway enrichment analysis revealed the metabolic, differentiation and cytoskeletal remodeling processes involved in MRD. Finally, we identified 11 common proteins differentially expressed in dormant cells from both pathologies. CONCLUSIONS Our study underscores the complexity of tumour dormancy, implicating both genetic and nongenetic factors. By comparing genomic, transcriptomic, proteomic, and epigenomic datasets, our study provides a comprehensive understanding of the molecular landscape of minimal residual disease. These results provide a robust foundation for forthcoming investigations and offer potential avenues for the advancement of targeted MRD therapies in leukemia and melanoma patients, emphasizing the importance of considering both genetic and nongenetic factors in treatment strategies.
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Affiliation(s)
- Marie-Océane Laguillaumie
- CNRS, Inserm, CHU Lille, Institut Pasteur de Lille, UMR9020-U1277-CANTHER-Cancer Heterogeneity Plasticity and Resistance to Therapies, Univ. Lille, 59000, Lille, France
- Inserm, U1003-PHYCEL-Physiologie Cellulaire, Univ. Lille, 59000, Lille, France
| | - Sofia Titah
- CNRS, Inserm, CHU Lille, Institut Pasteur de Lille, UMR9020-U1277-CANTHER-Cancer Heterogeneity Plasticity and Resistance to Therapies, Univ. Lille, 59000, Lille, France
- Inserm, U1003-PHYCEL-Physiologie Cellulaire, Univ. Lille, 59000, Lille, France
| | - Aurélie Guillemette
- CNRS, Inserm, CHU Lille, Institut Pasteur de Lille, UMR9020-U1277-CANTHER-Cancer Heterogeneity Plasticity and Resistance to Therapies, Univ. Lille, 59000, Lille, France
| | - Bernadette Neve
- CNRS, Inserm, CHU Lille, Institut Pasteur de Lille, UMR9020-U1277-CANTHER-Cancer Heterogeneity Plasticity and Resistance to Therapies, Univ. Lille, 59000, Lille, France
| | - Frederic Leprêtre
- CNRS, Inserm, CHU Lille, Institut Pasteur de Lille, US 41-UAR 2014-PLBS, Univ. Lille, 59000, Lille, France
| | - Pascaline Ségard
- CNRS, Inserm, CHU Lille, Institut Pasteur de Lille, UMR9020-U1277-CANTHER-Cancer Heterogeneity Plasticity and Resistance to Therapies, Univ. Lille, 59000, Lille, France
| | - Faruk Azam Shaik
- LIMMS/CNRS-IIS IRL2820, The University of Tokyo, Tokyo, Japan
- CNRS, IIS, COL, Univ. Lille SMMiL-E Project, Lille, France
| | - Dominique Collard
- LIMMS/CNRS-IIS IRL2820, The University of Tokyo, Tokyo, Japan
- CNRS, IIS, COL, Univ. Lille SMMiL-E Project, Lille, France
| | - Jean-Claude Gerbedoen
- LIMMS/CNRS-IIS IRL2820, The University of Tokyo, Tokyo, Japan
- CNRS, IIS, COL, Univ. Lille SMMiL-E Project, Lille, France
- Department of Health and Environment, Junia HEI-ISEN-ISA, Lille, France
| | - Léa Fléchon
- CNRS, Inserm, CHU Lille, Institut Pasteur de Lille, UMR9020-U1277-CANTHER-Cancer Heterogeneity Plasticity and Resistance to Therapies, Univ. Lille, 59000, Lille, France
| | - Lama Hasan Bou Issa
- CNRS, Inserm, CHU Lille, Institut Pasteur de Lille, UMR9020-U1277-CANTHER-Cancer Heterogeneity Plasticity and Resistance to Therapies, Univ. Lille, 59000, Lille, France
| | - Audrey Vincent
- CNRS, Inserm, CHU Lille, Institut Pasteur de Lille, UMR9020-U1277-CANTHER-Cancer Heterogeneity Plasticity and Resistance to Therapies, Univ. Lille, 59000, Lille, France
| | - Martin Figeac
- CNRS, Inserm, CHU Lille, Institut Pasteur de Lille, US 41-UAR 2014-PLBS, Univ. Lille, 59000, Lille, France
| | - Shéhérazade Sebda
- CNRS, Inserm, CHU Lille, Institut Pasteur de Lille, US 41-UAR 2014-PLBS, Univ. Lille, 59000, Lille, France
| | - Céline Villenet
- CNRS, Inserm, CHU Lille, Institut Pasteur de Lille, US 41-UAR 2014-PLBS, Univ. Lille, 59000, Lille, France
| | - Jérôme Kluza
- CNRS, Inserm, CHU Lille, Institut Pasteur de Lille, UMR9020-U1277-CANTHER-Cancer Heterogeneity Plasticity and Resistance to Therapies, Univ. Lille, 59000, Lille, France
| | - William Laine
- CNRS, Inserm, CHU Lille, Institut Pasteur de Lille, UMR9020-U1277-CANTHER-Cancer Heterogeneity Plasticity and Resistance to Therapies, Univ. Lille, 59000, Lille, France
| | - Isabelle Fournier
- Inserm, CHU Lille, U1192, Laboratoire Protéomique, Réponse Inflammatoire Et Spectrométrie de Masse (PRISM), Univ. Lille, 59000, Lille, France
| | - Jean-Pascal Gimeno
- Inserm, CHU Lille, U1192, Laboratoire Protéomique, Réponse Inflammatoire Et Spectrométrie de Masse (PRISM), Univ. Lille, 59000, Lille, France
| | - Maxence Wisztorski
- Inserm, CHU Lille, U1192, Laboratoire Protéomique, Réponse Inflammatoire Et Spectrométrie de Masse (PRISM), Univ. Lille, 59000, Lille, France
| | - Salomon Manier
- CNRS, Inserm, CHU Lille, Institut Pasteur de Lille, UMR9020-U1277-CANTHER-Cancer Heterogeneity Plasticity and Resistance to Therapies, Univ. Lille, 59000, Lille, France
| | - Mehmet Cagatay Tarhan
- CNRS, IIS, COL, Univ. Lille SMMiL-E Project, Lille, France
- Department of Health and Environment, Junia HEI-ISEN-ISA, Lille, France
- CNRS, Centrale Lille, Polytechnique Hauts-de-France, Junia, UMR 8520-IEMN, Univ. Lille, Villeneuve d'Ascq, France
| | - Bruno Quesnel
- CNRS, Inserm, CHU Lille, Institut Pasteur de Lille, UMR9020-U1277-CANTHER-Cancer Heterogeneity Plasticity and Resistance to Therapies, Univ. Lille, 59000, Lille, France
| | - Thierry Idziorek
- CNRS, Inserm, CHU Lille, Institut Pasteur de Lille, UMR9020-U1277-CANTHER-Cancer Heterogeneity Plasticity and Resistance to Therapies, Univ. Lille, 59000, Lille, France
| | - Yasmine Touil
- CNRS, Inserm, CHU Lille, Institut Pasteur de Lille, UMR9020-U1277-CANTHER-Cancer Heterogeneity Plasticity and Resistance to Therapies, Univ. Lille, 59000, Lille, France.
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A Necroptosis-Related Gene Signature to Predict the Prognosis of Skin Cutaneous Melanoma. DISEASE MARKERS 2022; 2022:8232024. [DOI: 10.1155/2022/8232024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Revised: 09/26/2022] [Accepted: 11/01/2022] [Indexed: 11/18/2022]
Abstract
The prognosis of skin cutaneous melanoma (SKCM) remains poor, and patients with SKCM show a poor response to immunotherapy. Thus, we aimed to identify necroptosis-related biomarkers, which can help predict the prognosis of SKCM and improve the effectiveness of precision medicine. Data of SKCM were obtained from The Cancer Genome Atlas (TCGA) and GEO databases. TCGA samples were classified into two clusters by consensus clustering of necroptosis-related genes. Univariate Cox and least absolute shrinkage and selection operator regression analyses led to the identification of 11 genes, which were used to construct a prognostic model. GSE65904 was used as the test set. Principal component, t-distributed stochastic neighbor embedding, and Kaplan–Meier survival analyses indicated that samples in the train and test sets could be divided into two groups, with the high-risk group showing a worse prognosis. Univariate and multivariate Cox regression analyses were performed, and a nomogram, calibration curve, and time-dependent receiver operating characteristic curve were constructed to verify the efficacy of our model. The 1-, 3-, and 5-year areas under the receiver operating characteristic curves for the train set were 0.702, 0.663, and 0.701 and for the test set were 0.613, 0.627, and 0.637, respectively. Moreover, we performed Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses between the high- and low-risk groups. Single sample gene set enrichment analysis, immune cell infiltration analysis, tumor microenvironment scores, immune checkpoint analysis, and half-maximal inhibitory concentration prediction indicated that the high-risk group showed weaker antitumor immunity; further, the response to immune checkpoint inhibitors was worse, and the high-risk group was sensitive to fewer antitumor drugs. Tumor mutational burden analysis, Kaplan–Meier survival analysis, and correlation analysis between risk score and RNA stemness score revealed that the high-risk group with low tumor mutational burden and high RNA stemness score was potentially associated with poor prognosis. To conclude, our model, which was based on 11 necroptosis-related genes, could predict the prognosis of SKCM; in addition, it has guiding significance for the selection of clinical treatment and provides new research directions to enhance necroptosis against SKCM.
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