Maia, C. (Catarina)

Search Results

Now showing 1 - 3 of 3
  • Thumbnail Image
    FlowCT for the analysis of large immunophenotypic data sets and biomarker discovery in cancer immunology
    (The American Society of Hematology, 2022) Jelinek, T. (T.); Cedena, M.T. (María Teresa); Zabaleta, A. (Aintzane); Rossi, M. (Marco); Perez, C. (Cristina); Maia, C. (Catarina); Paiva, A. (Artur); Martínez-López, J. (Joaquín); Correale, P. (Pierpaolo); Bladé, J. (Joan); Sarvide, S. (Sarai); Mateos, M.V. (María Victoria); Rosiñol, L. (Laura); Puig, N. (Noemí); Botta, C. (Cirino); Tassone, P. (Pierfrancesco); Termini, R. (Rosalinda); Garcés-Latre, J.J. (Juan José); Manrique, I. (Irene); Burgos, L. (Leire); Alignani, D. (Diego); Lahuerta, J.J. (Juan José); Goldschmidt, H. (Hartmut); Paiva, B. (Bruno); Avet-Loiseau, H. (Herve); Terpos, E. (Evangelos); Merino, J. (Juana); Roccaro, A.M. (Aldo M.); San-Miguel, J.F. (Jesús F.); Gentile, M. (Massimo); Borrello, I. (Iván)
    Large-scale immune monitoring is becoming routinely used in clinical trials to identify determinants of treatment responsiveness, particularly to immunotherapies. Flow cytometry remains one of the most versatile and high throughput approaches for single-cell analysis; however, manual interpretation of multidimensional data poses a challenge when attempting to capture full cellular diversity and provide reproducible results. We present FlowCT, a semi-automated workspace empowered to analyze large data sets. It includes pre-processing, normalization, multiple dimensionality reduction techniques, automated clustering, and predictive modeling tools. As a proof of concept, we used FlowCT to compare the T-cell compartment in bone marrow (BM) with peripheral blood (PB) from patients with smoldering multiple myeloma (SMM), identify minimally invasive immune biomarkers of progression from smoldering to active MM, define prognostic T-cell subsets in the BM of patients with active MM after treatment intensification, and assess the longitudinal effect of maintenance therapy in BM T cells. A total of 354 samples were analyzed and immune signatures predictive of malignant transformation were identified in 150 patients with SMM (hazard ratio [HR], 1.7; P < .001). We also determined progression-free survival (HR, 4.09; P < .0001) and overall survival (HR, 3.12; P = .047) in 100 patients with active MM. New data also emerged about stem cell memory T cells, the concordance between immune profiles in BM and PB, and the immunomodulatory effect of maintenance therapy. FlowCT is a new open-source computational approach that can be readily implemented by research laboratories to perform quality control, analyze high-dimensional data, unveil cellular diversity, and objectively identify biomarkers in large immune monitoring studies. These trials were registered at www.clinicaltrials.gov as #NCT01916252 and #NCT02406144.
  • Thumbnail Image
    Preclinical models for prediction of immunotherapy outcomes and immune evasion mechanisms in genetically heterogeneous multiple myeloma
    (2023) Perez, C. (Cristina); Fresquet, V. (Vicente); Maia, C. (Catarina); Gomez-Cabrero, D. (David); Lasaga, M. (Miren); Celay, J. (Jon); Lozano-Moreda, T. (Teresa); Vicent, S. (Silvestre); Roncador, G. (Giovanna); Goicoechea, I. (Ibai); García-Barchino, M.J. (María José); Martinez-Climent, J.A. (José Ángel); Walensky, L.D. (Loren D.); Panizo, C. (Carlos); Lasater, E.A. (Elisabeth A.); Katz, S.G. (Samuel G.); Larrayoz, M. (Marta); Roa, S. (Sergio); Bergsagel, P.L. (P. Leif); Gonzalez, P. (Patricia); Botta, C. (Cirino); Ordóñez-Ciriza, R. (Raquel); Takahashi, S. (Satoru); Aguirre-Ena, X. (Xabier); Kurilovich, A. (Anna); Amann, M. (Maria); Rodriguez-Otero, P. (Paula); Llopiz, D. (Diana); Paiva, B. (Bruno); Sarobe, P. (Pablo); Campos-Sanchez, E. (Elena); Ruppert, S.M. (Shannon M.); Martínez-Cano, J. (Jorge); Larrayoz, M.J. (María J.); Revuelta, M.V. (Maria V.); Cobaleda, C. (César); Prosper-Cardoso, F. (Felipe); Etxebeste-Mitxeltorena, A. (Amaia); Calasanz-Abinzano, M.J. (Maria Jose); San-Miguel, J.F. (Jesús F.); Cerchietti, L. (Leandro); Planell, N. (Núria); Jiménez-Andrés, M. (Maddalen); Kudryashova, O. (Olga); Chesi, M. (Marta); Lasarte, J.J. (Juan José)
    The historical lack of preclinical models reflecting the genetic heterogeneity of multiple myeloma (MM) hampers the advance of therapeutic discoveries. To circumvent this limitation, we screened mice engineered to carry eight MM lesions (NF-kappaB, KRAS, MYC, TP53, BCL2, cyclin D1, MMSET/NSD2 and c-MAF) combinatorially activated in B lymphocytes following T cell-driven immunization. Fifteen genetically diverse models developed bone marrow (BM) tumors fulfilling MM pathogenesis. Integrative analyses of 500 mice and 1,000 patients revealed a common MAPK-MYC genetic pathway that accelerated time to progression from precursor states across genetically heterogeneous MM. MYC-dependent time to progression conditioned immune evasion mechanisms that remodeled the BM microenvironment differently. Rapid MYC-driven progressors exhibited a high number of activated/exhausted CD8+ T cells with reduced immunosuppressive regulatory T (Treg) cells, while late MYC acquisition in slow progressors was associated with lower CD8+ T cell infiltration and more abundant Treg cells. Single-cell transcriptomics and functional assays defined a high ratio of CD8+ T cells versus Treg cells as a predictor of response to immune checkpoint blockade (ICB). In clinical series, high CD8+ T/Treg cell ratios underlie early progression in untreated smoldering MM, and correlated with early relapse in newly diagnosed patients with MM under Len/Dex therapy. In ICB-refractory MM models, increasing CD8+ T cell cytotoxicity or depleting Treg cells reversed immunotherapy resistance and yielded prolonged MM control. Our experimental models enable the correlation of MM genetic and immunological traits with preclinical therapy responses, which may inform the next-generation immunotherapy trials.
  • Thumbnail Image
    Immunological Biomarkers of Fatal COVID-19: A Study of 868 Patients
    (Frontiers Research Foundation, 2021) Landecho, M.F. (Manuel F.); Zabaleta, A. (Aintzane); Vilas, A. (Amaia); Perez, C. (Cristina); Maia, C. (Catarina); Alegre, F. (Félix); Rua, M. (Marta); Sarvide, S. (Sarai); Lopez-Diaz-de-Cerio, A. (Ascensión); Marín-Oto, M. (Marta); Pineda, Í. (Íñigo); Molano, E. (Elvira); Fernández-Alonso, M. (Miriam); Carmona-Torre, F. (Francisco de A.); Botta, C. (Cirino); Inoges, S. (Susana); Garcés-Latre, J.J. (Juan José); Alcaide, A.B. (Ana Belén); Alignani, D. (Diego); Paiva, B. (Bruno); Yuste, J.R. (José Ramón); Martín-Sánchez, E. (Esperanza); Perez-Warnisher, M.T. (María Teresa); Argemí, J. (Josepmaria); Sogbe, M. (Miguel); Blanco-Fernández, L. (Laura); Moreno, C. (Cristina)
    Information on the immunopathobiology of coronavirus disease 2019 (COVID-19) is rapidly increasing; however, there remains a need to identify immune features predictive of fatal outcome. This large-scale study characterized immune responses to severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection using multidimensional flow cytometry, with the aim of identifying high-risk immune biomarkers. Holistic and unbiased analyses of 17 immune cell-types were conducted on 1,075 peripheral blood samples obtained from 868 COVID-19 patients and on samples from 24 patients presenting with non-SARS-CoV-2 infections and 36 healthy donors. Immune profiles of COVID-19 patients were significantly different from those of age-matched healthy donors but generally similar to those of patients with non-SARS-CoV-2 infections. Unsupervised clustering analysis revealed three immunotypes during SARS-CoV-2 infection; immunotype 1 (14% of patients) was characterized by significantly lower percentages of all immune cell-types except neutrophils and circulating plasma cells, and was significantly associated with severe disease. Reduced B-cell percentage was most strongly associated with risk of death. On multivariate analysis incorporating age and comorbidities, B-cell and non-classical monocyte percentages were independent prognostic factors for survival in training (n=513) and validation (n=355) cohorts. Therefore, reduced percentages of B-cells and non-classical monocytes are high-risk immune biomarkers for risk-stratification of COVID-19 patients.