Lise Mangiante

All Publications

• The G1-S transition is promoted by Rb degradation via the E3 ligase UBR5. Science advances Zhang, S., Valenzuela, L. F., Zatulovskiy, E., Mangiante, L., Curtis, C., Skotheim, J. M. 2024; 10 (43): eadq6858

  Abstract

  Mammalian cells make the decision to divide at the G1-S transition in response to diverse signals impinging on the retinoblastoma protein Rb, a cell cycle inhibitor and tumor suppressor. Passage through the G1-S transition is initially driven by Rb inactivation via phosphorylation and by Rb's decreasing concentration in G1. While many studies have identified the mechanisms of Rb phosphorylation, the mechanism underlying Rb's decreasing concentration in G1 was unknown. Here, we found that Rb's concentration decrease in G1 requires the E3 ubiquitin ligase UBR5. UBR5 knockout cells have increased Rb concentration in early G1, exhibited a lower G1-S transition rate, and are more sensitive to inhibition of cyclin-dependent kinase 4/6 (Cdk4/6). This last observation suggests that UBR5 inhibition can strengthen the efficacy of Cdk4/6 inhibitor-based cancer therapies.

  View details for DOI 10.1126/sciadv.adq6858

  View details for PubMedID 39441926

  View details for PubMedCentralID PMC11498223

• Multi-omic dataset of patient-derived tumor organoids of neuroendocrine neoplasms. GigaScience Alcala, N., Voegele, C., Mangiante, L., Sexton-Oates, A., Clevers, H., Fernandez-Cuesta, L., Dayton, T. L., Foll, M. 2024; 13

  Abstract

  BACKGROUND: Organoids are 3-dimensional experimental models that summarize the anatomical and functional structure of an organ. Although a promising experimental model for precision medicine, patient-derived tumor organoids (PDTOs) have currently been developed only for a fraction of tumor types.RESULTS: We have generated the first multi-omic dataset (whole-genome sequencing [WGS] and RNA-sequencing [RNA-seq]) of PDTOs from the rare and understudied pulmonary neuroendocrine tumors (n = 12; 6 grade 1, 6 grade 2) and provide data from other rare neuroendocrine neoplasms: small intestine (ileal) neuroendocrine tumors (n = 6; 2 grade 1 and 4 grade 2) and large-cell neuroendocrine carcinoma (n = 5; 1 pancreatic and 4 pulmonary). This dataset includes a matched sample from the parental sample (primary tumor or metastasis) for a majority of samples (21/23) and longitudinal sampling of the PDTOs (1 to 2 time points), for a total of n = 47 RNA-seq and n = 33 WGS. We here provide quality control for each technique and the raw and processed data as well as all scripts for genomic analyses to ensure an optimal reuse of the data. In addition, we report gene expression data and somatic small variant calls and describe how they were generated, in particular how we used WGS somatic calls to train a random forest classifier to detect variants in tumor-only RNA-seq. We also report all histopathological images used for medical diagnosis: hematoxylin and eosin-stained slides, brightfield images, and immunohistochemistry images of protein markers of clinical relevance.CONCLUSIONS: This dataset will be critical to future studies relying on this PDTO biobank, such as drug screens for novel therapies and experiments investigating the mechanisms of carcinogenesis in these understudied diseases.

  View details for DOI 10.1093/gigascience/giae008

  View details for PubMedID 38451475

• Multiomic analysis of malignant pleural mesothelioma identifies molecular axes and specialized tumor profiles driving intertumor heterogeneity. Nature genetics Mangiante, L., Alcala, N., Sexton-Oates, A., Di Genova, A., Gonzalez-Perez, A., Khandekar, A., Bergstrom, E. N., Kim, J., Liu, X., Blazquez-Encinas, R., Giacobi, C., Le Stang, N., Boyault, S., Cuenin, C., Tabone-Eglinger, S., Damiola, F., Voegele, C., Ardin, M., Michallet, M. C., Soudade, L., Delhomme, T. M., Poret, A., Brevet, M., Copin, M. C., Giusiano-Courcambeck, S., Damotte, D., Girard, C., Hofman, V., Hofman, P., Mouroux, J., Cohen, C., Lacomme, S., Mazieres, J., de Montpreville, V. T., Perrin, C., Planchard, G., Rousseau, N., Rouquette, I., Sagan, C., Scherpereel, A., Thivolet, F., Vignaud, J. M., Jean, D., Ilg, A. G., Olaso, R., Meyer, V., Boland-Auge, A., Deleuze, J. F., Altmuller, J., Nuernberg, P., Ibáñez-Costa, A., Castaño, J. P., Lantuejoul, S., Ghantous, A., Maussion, C., Courtiol, P., Hernandez-Vargas, H., Caux, C., Girard, N., Lopez-Bigas, N., Alexandrov, L. B., Galateau-Salle, F., Foll, M., Fernandez-Cuesta, L. 2023

  Abstract

  Malignant pleural mesothelioma (MPM) is an aggressive cancer with rising incidence and challenging clinical management. Through a large series of whole-genome sequencing data, integrated with transcriptomic and epigenomic data using multiomics factor analysis, we demonstrate that the current World Health Organization classification only accounts for up to 10% of interpatient molecular differences. Instead, the MESOMICS project paves the way for a morphomolecular classification of MPM based on four dimensions: ploidy, tumor cell morphology, adaptive immune response and CpG island methylator profile. We show that these four dimensions are complementary, capture major interpatient molecular differences and are delimited by extreme phenotypes that-in the case of the interdependent tumor cell morphology and adapted immune response-reflect tumor specialization. These findings unearth the interplay between MPM functional biology and its genomic history, and provide insights into the variations observed in the clinical behavior of patients with MPM.

  View details for DOI 10.1038/s41588-023-01321-1

  View details for PubMedID 36928603

  View details for PubMedCentralID 8192079

• A molecular phenotypic map of malignant pleural mesothelioma. GigaScience Di Genova, A., Mangiante, L., Sexton-Oates, A., Voegele, C., Fernandez-Cuesta, L., Alcala, N., Foll, M. 2022; 12

  Abstract

  BACKGROUND: Malignant pleural mesothelioma (MPM) is a rare understudied cancer associated with exposure to asbestos. So far, MPM patients have benefited marginally from the genomics medicine revolution due to the limited size or breadth of existing molecular studies. In the context of the MESOMICS project, we have performed the most comprehensive molecular characterization of MPM to date, with the underlying dataset made of the largest whole-genome sequencing series yet reported, together with transcriptome sequencing and methylation arrays for 120 MPM patients.RESULTS: We first provide comprehensive quality controls for all samples, of both raw and processed data. Due to the difficulty in collecting specimens from such rare tumors, a part of the cohort does not include matched normal material. We provide a detailed analysis of data processing of these tumor-only samples, showing that all somatic alteration calls match very stringent criteria of precision and recall. Finally, integrating our data with previously published multiomic MPM datasets (n = 374 in total), we provide an extensive molecular phenotype map of MPM based on the multitask theory. The generated map can be interactively explored and interrogated on the UCSC TumorMap portal (https://tumormap.ucsc.edu/?p=RCG_MESOMICS/MPM_Archetypes ).CONCLUSIONS: This new high-quality MPM multiomics dataset, together with the state-of-art bioinformatics and interactive visualization tools we provide, will support the development of precision medicine in MPM that is particularly challenging to implement in rare cancers due to limited molecular studies.

  View details for DOI 10.1093/gigascience/giac128

  View details for PubMedID 36705549

• Differential Orthopedia Homeobox expression in pulmonary carcinoids is associated with changes in DNA methylation. International journal of cancer Moonen, L., Mangiante, L., Leunissen, D. J., Lap, L. M., Gabriel, A., Hillen, L. M., Roemen, G. M., Koch, A., van Engeland, M., Dingemans, A. C., Foll, M., Alcala, N., Fernandez-Cuesta, L., Derks, J. L., Speel, E. M. 2022; 150 (12): 1987-1997

  Abstract

  Limited number of tumor types have been examined for Orthopedia Homeobox (OTP) expression. In pulmonary carcinoids, loss of expression is a strong indicator of poor prognosis. Here, we investigated OTP expression in 37 different tumor types, and the association between OTP expression and DNA methylation levels in lung neuroendocrine neoplasms. We analyzed publicly available multi-omics data (whole-exome-, whole-genome-, RNA sequencing and Epic 850K-methylation array) of 58 typical carcinoids, 27 atypical carcinoids, 69 large cell neuroendocrine carcinoma and 51 small cell lung cancer patients and TCGA (The Cancer Genome Atlas) data of 33 tumor types. 850K-methylation analysis was cross-validated using targeted pyrosequencing on 35 carcinoids. We report bimodality of OTP expression in carcinoids (OTPhigh vs OTPlow group, likelihood-ratio test P = 1.5 × 10-2 ), with the OTPhigh group specific to pulmonary carcinoids while absent from all other cohorts analyzed. Significantly different DNA methylation levels were observed between OTPhigh and OTPlow carcinoids in 12/34 OTP infinium probes (FDR < 0.05 and β-value effect size > .2). OTPlow carcinoids harbor high DNA methylation levels as compared to OTPhigh carcinoids. OTPlow carcinoids showed a significantly worse overall survival (log-rank test P = .0052). Gene set enrichment analysis for somatically mutated genes associated with hallmarks of cancer showed robust enrichment of three hallmarks in the OTPlow group, that is, sustaining proliferative signaling, evading growth suppressor and genome instability and mutation. Together our data suggest that high OTP expression is a unique feature of pulmonary carcinoids with a favorable prognosis and that in poor prognostic patients, OTP expression is lost, most likely due to changes in DNA methylation levels.

  View details for DOI 10.1002/ijc.33939

  View details for PubMedID 35076935

  View details for PubMedCentralID PMC9303689

• Challenges in lung and thoracic pathology: molecular advances in the classification of pleural mesotheliomas. Virchows Archiv : an international journal of pathology Fernandez-Cuesta, L., Mangiante, L., Alcala, N., Foll, M. 2021; 478 (1): 73-80

  Abstract

  The diagnosis and classification of malignant pleural mesothelioma (MPM) is extremely challenging; obtaining an accurate histopathological diagnosis of the different types and subtypes requires expert assessment and suitable biopsies that are not always available, which can leave doctors uncertain about the patient's diagnosis, sometimes resulting in a delay in the start of treatment. In this review, we discuss recent major advances in the molecular characterisation of MPM and their implications for histological classification. We detail what is known of the molecular landscape of MPM at the genomic, transcriptomic, and epigenomic levels, describe the similarities and dissimilarities of the multiple molecular classifications that have been proposed, and provide an overview of the current state of knowledge regarding inter- and intra-tumour heterogeneity. We also highlight the current gaps in knowledge and how addressing them would benefit classification, as well as the patients in general.

  View details for DOI 10.1007/s00428-020-02980-9

  View details for PubMedID 33411030

  View details for PubMedCentralID 8192079

• A molecular map of lung neuroendocrine neoplasms. GigaScience Gabriel, A. A., Mathian, E., Mangiante, L., Voegele, C., Cahais, V., Ghantous, A., McKay, J. D., Alcala, N., Fernandez-Cuesta, L., Foll, M. 2020; 9 (11)

  Abstract

  Lung neuroendocrine neoplasms (LNENs) are rare solid cancers, with most genomic studies including a limited number of samples. Recently, generating the first multi-omic dataset for atypical pulmonary carcinoids and the first methylation dataset for large-cell neuroendocrine carcinomas led us to the discovery of clinically relevant molecular groups, as well as a new entity of pulmonary carcinoids (supra-carcinoids).To promote the integration of LNENs molecular data, we provide here detailed information on data generation and quality control for whole-genome/exome sequencing, RNA sequencing, and EPIC 850K methylation arrays for a total of 84 patients with LNENs. We integrate the transcriptomic data with other previously published data and generate the first comprehensive molecular map of LNENs using the Uniform Manifold Approximation and Projection (UMAP) dimension reduction technique. We show that this map captures the main biological findings of previous studies and can be used as reference to integrate datasets for which RNA sequencing is available. The generated map can be interactively explored and interrogated on the UCSC TumorMap portal (https://tumormap.ucsc.edu/?p=RCG_lungNENomics/LNEN). The data, source code, and compute environments used to generate and evaluate the map as well as the raw data are available, respectively, in a Nextjournal interactive notebook (https://nextjournal.com/rarecancersgenomics/a-molecular-map-of-lung-neuroendocrine-neoplasms/) and at the EMBL-EBI European Genome-phenome Archive and Gene Expression Omnibus data repositories.We provide data and all resources needed to integrate them with future LNENs transcriptomic studies, allowing meaningful conclusions to be drawn that will eventually lead to a better understanding of this rare understudied disease.

  View details for DOI 10.1093/gigascience/giaa112

  View details for PubMedID 33124659

  View details for PubMedCentralID PMC7596803

• Redefining malignant pleural mesothelioma types as a continuum uncovers immune-vascular interactions. EBioMedicine Alcala, N., Mangiante, L., Le-Stang, N., Gustafson, C. E., Boyault, S., Damiola, F., Alcala, K., Brevet, M., Thivolet-Bejui, F., Blanc-Fournier, C., Le Rochais, J. P., Planchard, G., Rousseau, N., Damotte, D., Pairon, J. C., Copin, M. C., Scherpereel, A., Wasielewski, E., Wicquart, L., Lacomme, S., Vignaud, J. M., Ancelin, G., Girard, C., Sagan, C., Bonnetaud, C., Hofman, V., Hofman, P., Mouroux, J., Thomas de Montpreville, V., Clermont-Taranchon, E., Mazieres, J., Rouquette, I., Begueret, H., Blay, J. Y., Lantuejoul, S., Bueno, R., Caux, C., Girard, N., McKay, J. D., Foll, M., Galateau-Salle, F., Fernandez-Cuesta, L. 2019; 48: 191-202

  Abstract

  Malignant Pleural Mesothelioma (MPM) is an aggressive disease related to asbestos exposure, with no effective therapeutic options.We undertook unsupervised analyses of RNA-sequencing data of 284 MPMs, with no assumption of discreteness. Using immunohistochemistry, we performed an orthogonal validation on a subset of 103 samples and a biological replication in an independent series of 77 samples.A continuum of molecular profiles explained the prognosis of the disease better than any discrete model. The immune and vascular pathways were the major sources of molecular variation, with strong differences in the expression of immune checkpoints and pro-angiogenic genes; the extrema of this continuum had specific molecular profiles: a "hot" bad-prognosis profile, with high lymphocyte infiltration and high expression of immune checkpoints and pro-angiogenic genes; a "cold" bad-prognosis profile, with low lymphocyte infiltration and high expression of pro-angiogenic genes; and a "VEGFR2+/VISTA+" better-prognosis profile, with high expression of immune checkpoint VISTA and pro-angiogenic gene VEGFR2. We validated the gene expression levels at the protein level for a subset of five selected genes belonging to the immune and vascular pathways (CD8A, PDL1, VEGFR3, VEGFR2, and VISTA), in the validation series, and replicated the molecular profiles as well as their prognostic value in the replication series.The prognosis of MPM is best explained by a continuous model, which extremes show specific expression patterns of genes involved in angiogenesis and immune response.

  View details for DOI 10.1016/j.ebiom.2019.09.003

  View details for PubMedID 31648983

  View details for PubMedCentralID PMC6838392

• Integrative and comparative genomic analyses identify clinically relevant pulmonary carcinoid groups and unveil the supra-carcinoids. Nature communications Alcala, N., Leblay, N., Gabriel, A. A., Mangiante, L., Hervas, D., Giffon, T., Sertier, A. S., Ferrari, A., Derks, J., Ghantous, A., Delhomme, T. M., Chabrier, A., Cuenin, C., Abedi-Ardekani, B., Boland, A., Olaso, R., Meyer, V., Altmuller, J., Le Calvez-Kelm, F., Durand, G., Voegele, C., Boyault, S., Moonen, L., Lemaitre, N., Lorimier, P., Toffart, A. C., Soltermann, A., Clement, J. H., Saenger, J., Field, J. K., Brevet, M., Blanc-Fournier, C., Galateau-Salle, F., Le Stang, N., Russell, P. A., Wright, G., Sozzi, G., Pastorino, U., Lacomme, S., Vignaud, J. M., Hofman, V., Hofman, P., Brustugun, O. T., Lund-Iversen, M., Thomas de Montpreville, V., Muscarella, L. A., Graziano, P., Popper, H., Stojsic, J., Deleuze, J. F., Herceg, Z., Viari, A., Nuernberg, P., Pelosi, G., Dingemans, A. M., Milione, M., Roz, L., Brcic, L., Volante, M., Papotti, M. G., Caux, C., Sandoval, J., Hernandez-Vargas, H., Brambilla, E., Speel, E. J., Girard, N., Lantuejoul, S., McKay, J. D., Foll, M., Fernandez-Cuesta, L. 2019; 10 (1): 3407

  Abstract

  The worldwide incidence of pulmonary carcinoids is increasing, but little is known about their molecular characteristics. Through machine learning and multi-omics factor analysis, we compare and contrast the genomic profiles of 116 pulmonary carcinoids (including 35 atypical), 75 large-cell neuroendocrine carcinomas (LCNEC), and 66 small-cell lung cancers. Here we report that the integrative analyses on 257 lung neuroendocrine neoplasms stratify atypical carcinoids into two prognostic groups with a 10-year overall survival of 88% and 27%, respectively. We identify therapeutically relevant molecular groups of pulmonary carcinoids, suggesting DLL3 and the immune system as candidate therapeutic targets; we confirm the value of OTP expression levels for the prognosis and diagnosis of these diseases, and we unveil the group of supra-carcinoids. This group comprises samples with carcinoid-like morphology yet the molecular and clinical features of the deadly LCNEC, further supporting the previously proposed molecular link between the low- and high-grade lung neuroendocrine neoplasms.

  View details for DOI 10.1038/s41467-019-11276-9

  View details for PubMedID 31431620

  View details for PubMedCentralID PMC6702229