Recent publications

Pamela Acha, Laura Palomo, Francisco Fuster-Tormo, Bianca Xicoy, Mar Mallo, Ana Manzanares, Javier Grau, Silvia Marcé, Isabel Granada, Marta Rodríguez-Luaces, María Díez-Campelo, Lurdes Zamora, Francesc Solé

Analysis of Intratumoral Heterogeneity in Myelodysplastic Syndromes with Isolated del(5q) Using a Single Cell Approach

Cancers 2021, 13(4), 841 17 Feb 2021, .
Myelodysplastic syndromes (MDS) are a heterogeneous group of hematological diseases. Among them, the most well characterized subtype is MDS with isolated chromosome 5q deletion (MDS del(5q)), which is the only one defined by a cytogenetic abnormality that makes these patients candidates to be treated with lenalidomide. During the last decade, single cell (SC) analysis has emerged as a powerful tool to decipher clonal architecture and to further understand cancer and other diseases at higher resolution level compared to bulk sequencing techniques. In this study, a SC approach was used to analyze intratumoral heterogeneity in four patients with MDS del(5q). Single CD34+CD117+CD45+CD19- bone marrow hematopoietic stem progenitor cells were isolated using the C1 system (Fluidigm) from diagnosis or before receiving any treatment and from available follow-up samples. Selected somatic alterations were further analyzed in SC by high-throughput qPCR (Biomark HD, Fluidigm) using specific TaqMan assays. A median of 175 cells per sample were analyzed. Inferred clonal architectures were relatively simple and either linear or branching. Similar to previous studies based on bulk sequencing to infer clonal architecture, we were able to observe that an ancestral event in one patient can appear as a secondary hit in another one, thus reflecting the high intratumoral heterogeneity in MDS del(5q) and the importance of patient-specific molecular characterization.
Rosalyn W. Sayaman, Mohamad Saad, Vésteinn Thorsson, Donglei Hu, Wouter Hendrickx, Jessica Roelands, Eduard Porta-Pardo, Younes Mokrab, Farshad Farshidfar, Tomas Kirchhoff, Randy F. Sweis, Oliver F. Bathe, Carolina Heimann, Michael J. Campbell, Cynthia Stretch, Scott Huntsman, Rebecca E. Graff, Najeeb Syed, Laszlo Radvanyi, Simon Shelley, Denise Wolf, Francesco M. Marincola, Michele Ceccarelli, Jérôme Galon, Elad Ziv, Davide Bedognetti

Germline genetic contribution to the immune landscape of cancer

Immunity, VOLUME 54, ISSUE 2, P367-386.E8 9 Feb 2021, .
Understanding the contribution of the host’s genetic background to cancer immunity may lead to improved stratification for immunotherapy and to the identification of novel therapeutic targets. We investigated the effect of common and rare germline variants on 139 well-defined immune traits in ∼9000 cancer patients enrolled in TCGA. High heritability was observed for estimates of NK cell and T cell subset infiltration and for interferon signaling. Common variants of IFIH1, TMEM173 (STING1), and TMEM108 were associated with differential interferon signaling and variants mapping to RBL1 correlated with T cell subset abundance. Pathogenic or likely pathogenic variants in BRCA1 and in genes involved in telomere stabilization and Wnt-β-catenin also acted as immune modulators. Our findings provide evidence for the impact of germline genetics on the composition and functional orientation of the tumor immune microenvironment. The curated datasets, variants, and genes identified provide a resource toward further understanding of tumor-immune interactions.
Matteo Bersanelli, Erica Travaglino, Manja Meggendorfer, Tommaso Matteuzzi, Claudia Sala, Ettore Mosca, Chiara Chiereghin, Noemi Di Nanni, Matteo Gnocchi, Matteo Zampini, Marianna Rossi, Giulia Maggioni, Alberto Termanini, Emanuele Angelucci, Massimo Bernardi, Lorenza Borin, Benedetto Bruno, Francesca Bonifazi, Valeria Santini, Andrea Bacigalupo, Maria Teresa Voso, Esther Oliva, Marta Riva, Marta Ubezio, Lucio Morabito, Alessia Campagna, Claudia Saitta, Victor Savevski, Enrico Giampieri, Daniel Remondini, Francesco Passamonti, Fabio Cicer, Niccolò Bolli, Alessandro Rambaldi, Wolfgang Kern, Shahram Kordasti, Francesc Sole, Laura Palomo, Guillermo Sanz, Armando Santoro, Uwe Platzbecker, Pierre Fenaux, Luciano Milanesi, Torsten Haferlach, Gastone Castellani, Matteo G Della Porta

Classification and Personalized Prognostic Assessment on the Basis of Clinical and Genomic Features in Myelodysplastic Syndromes

J Clin Oncol . 2021 Feb 4;JCO2001659. 4 Feb 2021, .
Purpose: Recurrently mutated genes and chromosomal abnormalities have been identified in myelodysplastic syndromes (MDS). We aim to integrate these genomic features into disease classification and prognostication. Methods: We retrospectively enrolled 2,043 patients. Using Bayesian networks and Dirichlet processes, we combined mutations in 47 genes with cytogenetic abnormalities to identify genetic associations and subgroups. Random-effects Cox proportional hazards multistate modeling was used for developing prognostic models. An independent validation on 318 cases was performed. Results: We identify eight MDS groups (clusters) according to specific genomic features. In five groups, dominant genomic features include splicing gene mutations (SF3B1, SRSF2, and U2AF1) that occur early in disease history, determine specific phenotypes, and drive disease evolution. These groups display different prognosis (groups with SF3B1 mutations being associated with better survival). Specific co-mutation patterns account for clinical heterogeneity within SF3B1- and SRSF2-related MDS. MDS with complex karyotype and/or TP53 gene abnormalities and MDS with acute leukemia-like mutations show poorest prognosis. MDS with 5q deletion are clustered into two distinct groups according to the number of mutated genes and/or presence of TP53 mutations. By integrating 63 clinical and genomic variables, we define a novel prognostic model that generates personally tailored predictions of survival. The predicted and observed outcomes correlate well in internal cross-validation and in an independent external cohort. This model substantially improves predictive accuracy of currently available prognostic tools. We have created a Web portal that allows outcome predictions to be generated for user-defined constellations of genomic and clinical features. Conclusion: Genomic landscape in MDS reveals distinct subgroups associated with specific clinical features and discrete patterns of evolution, providing a proof of concept for next-generation disease classification and prognosis.
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Christian Koelsche, Daniel Schrimpf, Damian Stichel, Martin Sill, Felix Sahm, David E. Reuss, Mirjam Blattner, Barbara Worst, Christoph E. Heilig, Katja Beck, Peter Horak, Simon Kreutzfeldt, Elke Paff, Sebastian Stark, Pascal Johann, Florian Selt, Jonas Ecker, Dominik Sturm, Kristian W. Pajtler, Annekathrin Reinhardt, Annika K. Wefers, Philipp Sievers, Azadeh Ebrahimi, Abigail Suwala, Francisco Fernández-Klett, Belén Casalini, Andrey Korshunov, Volker Hovestadt, Felix K. F. Kommoss, Mark Kriegsmann, Matthias Schick, Melanie Bewerunge-Hudler, Till Milde, Olaf Witt, Andreas E. Kulozik, Marcel Kool, Laura Romero-Pérez, Thomas G. P. Grünewald, Thomas Kirchner, Wolfgang Wick, Michael Platten, Andreas Unterberg, Matthias Uhl, Amir Abdollahi, Jürgen Debus, Burkhard Lehner, Christian Thomas, Martin Hasselblatt, Werner Paulus, Christian Hartmann, Ori Staszewski, Marco Prinz, Jürgen Hench, Stephan Frank, Yvonne M. H. Versleijen-Jonkers, Marije E. Weidema, Thomas Mentzel, Klaus Griewank, Enrique de Álava, Juan Díaz Martín, Miguel A. Idoate Gastearena, Kenneth Tou-En Chang, Sharon Yin Yee Low, Adrian Cuevas-Bourdier, Michel Mittelbronn, Martin Mynarek, Stefan Rutkowski, Ulrich Schüller, Viktor F. Mautner, Jens Schittenhelm, Jonathan Serrano, Matija Snuderl, Reinhard Büttner, Thomas Klingebiel, Rolf Buslei, Manfred Gessler, Pieter Wesseling, Winand N. M. Dinjens, Sebastian Brandner, Zane Jaunmuktane, Iben Lyskjær, Peter Schirmacher, Albrecht Stenzinger, Benedikt Brors, Hanno Glimm, Christoph Heining, Oscar M. Tirado, Miguel Sáinz-Jaspeado, Jaume Mora, Javier Alonso, Xavier Garcia del Muro, Sebastian Moran, Esteller M, Jamal K. Benhamida, Marc Ladanyi, Eva Wardelmann, Cristina Antonescu, Adrienne Flanagan, Uta Dirksen, Peter Hohenberger, Daniel Baumhoer, Wolfgang Hartmann, Christian Vokuhl, Uta Flucke, Iver Petersen, Gunhild Mechtersheimer, David Capper, David T. W. Jones, Stefan Fröhling, Stefan M. Pfister & Andreas

Sarcoma classification by DNA methylation profiling

Nature Communications volume 12, Article number: 498 (2021) 21 Jan 2021, .
Sarcomas are malignant soft tissue and bone tumours affecting adults, adolescents and children. They represent a morphologically heterogeneous class of tumours and some entities lack defining histopathological features. Therefore, the diagnosis of sarcomas is burdened with a high inter-observer variability and misclassification rate. Here, we demonstrate classification of soft tissue and bone tumours using a machine learning classifier algorithm based on array-generated DNA methylation data. This sarcoma classifier is trained using a dataset of 1077 methylation profiles from comprehensively pre-characterized cases comprising 62 tumour methylation classes constituting a broad range of soft tissue and bone sarcoma subtypes across the entire age spectrum. The performance is validated in a cohort of 428 sarcomatous tumours, of which 322 cases were classified by the sarcoma classifier. Our results demonstrate the potential of the DNA methylation-based sarcoma classification for research and future diagnostic applications.
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Antonio Garcia-Gomez, Tianlu Li, Carlos de la Calle-Fabregat, Javier Rodríguez-Ubreva, Laura Ciudad, Francesc Català-Moll, Gerard Godoy-Tena, Montserrat Martín-Sánchez, Laura San-Segundo, Sandra Muntión, Xabier Morales, Carlos Ortiz-de-Solórzano, Julen Oyarzabal, Edurne San José-Enériz, Manel Esteller, Xabier Agirre, Felipe Prosper, Mercedes Garayoa, Esteban Ballestar

Targeting aberrant DNA methylation in mesenchymal stromal cells as a treatment for myeloma bone disease

Nat Commun 12, 421 (2021) 18 Jan 2021, .
Multiple myeloma (MM) progression and myeloma-associated bone disease (MBD) are highly dependent on bone marrow mesenchymal stromal cells (MSCs). MM-MSCs exhibit abnormal transcriptomes, suggesting the involvement of epigenetic mechanisms governing their tumor-promoting functions and prolonged osteoblast suppression. Here, we identify widespread DNA methylation alterations of bone marrow-isolated MSCs from distinct MM stages, particularly in Homeobox genes involved in osteogenic differentiation that associate with their aberrant expression. Moreover, these DNA methylation changes are recapitulated in vitro by exposing MSCs from healthy individuals to MM cells. Pharmacological targeting of DNMTs and G9a with dual inhibitor CM-272 reverts the expression of hypermethylated osteogenic regulators and promotes osteoblast differentiation of myeloma MSCs. Most importantly, CM-272 treatment prevents tumor-associated bone loss and reduces tumor burden in a murine myeloma model. Our results demonstrate that epigenetic aberrancies mediate the impairment of bone formation in MM, and its targeting by CM-272 is able to reverse MBD.