Prognostic value of high-sensitivity measurable residual disease assessment after front-line chemoimmunotherapy in chronic lymphocytic leukemia

Letestu, Rémi; Dahmani, Abdelmalek; Boubaya, Marouane; Baseggio, Lucile; Campos, Lydia; Châtelain, Bernard; Debliquis, Agathe; Jacob, Marie‐Christine; Legac, Éric; Garff‐Tavernier, Magali Le; Lhoumeau, Anne-Catherine; Quiney, Claire; Robillard, Nelly; Ticchioni, Michel; Aanei, Carmen Mariana; Katsahian, Sandrine; Delépine, Roselyne; Vaudaux, Sandrine; Rouille, Valérie; Béné, Marie‐Christine; Neste, Éric Van Den; Caillères, Sylvie; Damaj, Gandhi; Royer, Bruno; Gardembas, Martine; Dib, Mamoun; Truchan-Graczyk, Matgorzata; Hunault, Mathilde; Foussard, Charles; Corront, Bernadette; Parry, Anne; Orsini‐Piocelle, Frédérique; Trouillier, S.; Slama, Bohiane; Lepeu, G.; Zerazhi, Hacène; Boulat, Olivier; Azzedine, A.; Araujo, Carla; Banos, Anne; Bauduer, Frédéric; Dutel, Jean-Luc; Ghomari, Kamel; Deconinck, Éric; Brion, Annie; Vuillier, Jacqueline; Saad, Alain; Yamani, Abderrazak El; Rodon, Philippe; Soubeyran, Pierre; Étienne, Gabriel; Dilhuydy, Marie-Sarah; Bouabdallah, Krimo; Leguay, Thibaut; Chouffi, Bachra; Pollet, Bertrand; Maakaroun, Abdallah; Guillerm, Gaëlle; Berthou, Christian; Cheron, Nathalie; André, Marc; Vilque, Jean Pierre; Fruchart, Christophe; Voillat, Laurent; Pica, Gian Matteo; Corm, Sélim; Micléa, Jean-Michel; Souleau, B.; Moluçon‐Chabrot, Cécile; Renzis, Benoît de; Tournilhac, Olivier; Bay, Jacques‐Olivier; Chaleteix, Carine; Guièze, Romain; Fleury, J; Precupanu, Cristina; Bouledroua, Selwa; Haïat, Stéphanie; Petitdidier, Charlotte; Dupuis, Jehan; Belhadj, Karim; Casasnovas, Olivier; Bastie, Jean-Noel; Ferrant, Emmanuelle; Gholam, D.; Molina, Lysiane; Garban, Frédéric; Tiab, Mourad; Maisonneuve, Hervé; Villemagne, Bruno; Jacomy, Dominique; Besson, Caroline; Tertian, G; Laribi, Kamel; Morel, Pierre; Cazin, Bruno; Moreau, Stéphane; Reminieras, Liliane; Rapp, Marie‐José; Moreau, Philippe; Sebban, Catherine; Michallet, Anne‐Sophie; Salles, Gilles; Broussais, Florence; Aurran-Schleinitz, Thérèse; Coso, Diane; Abarah, Wajed; Kulekci, C.; Dorvaux, Véronique; Carassou, P.; Guibaud, Isabelle; Graux, Carlos; Rossi, Jean‐François; Quittet, Philippe; Cartron, Guillaume; Dubois, A; Eisenmann, Jean‐Claude; Drénou, Bernard; Morineau, Nadine; Mahé, Béatrice; Karsenti, Jean-Michel; Jourdan, Éric; Legouffe, Eric; Alexis-Vigier, Magda; Boulet, Jean-Michel; Aoudjhane, Malek; Thiéblemont, Catherine; Andreoli, A.; Cymbalista, Florence; Lévy, Vincent; Dreyfus, F.; Leblond, Véronique; Choquet, Sylvain; Maloum, Karim; Merle‐Béral, Hélène; Vekhoff, Anne; Decaudin, Didier; Brault, Philippe; Delarue, Richard; Janvier, Maud; Soussain, Carole; Vallantin, X; Sanhès, Laurence; Dreyfus, Brigitte; Tomowiak, Cécile; Benramdane, Riad; Gonzalez, Hugo; Blaise-Brenna, Anne; Kolb, Brigitte; Delmer, Alain; Dauriac, Charles; Houot, Roch; Escoffre‐Barbe, Martine; Lamy, Thierry; Guibert, Sophie de; Bernard, Marc; Grosbois, B.; Brehar, Oana; Leprêtre, Stéphane; Morice, Patrick; Guyotat, D.; Jaubert, J.; Portois, Christelle; Fornecker, Luc‐Matthieu; Herbrecht, Raoul; Bilger, Karin; Amé, Shanti; Ysebaert, Loïc; Dartigeas, Caroline; Feugier, Pierre; Godmer, Pascal; Jardel, Henry

doi:10.1038/s41375-020-01009-z

Cited by 21 publications

(14 citation statements)

References 40 publications

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“…Herein, we assessed the application of CD45-ROR1 panel in the MRD detection of CLL using BM, PB and LN samples. In agreement with previous studies (22,33), we found that the positive rate of MRD detected in BM samples was higher than that in PB samples. This result further veri ed that BM aspiration is not necessary if MRD is positive in PB samples for the qualitative assessment.…”

Section: Discussionsupporting

confidence: 93%

Performance of a novel 8-color flow cytometry panel in the detection of minimal residual disease assessment of chronic lymphocytic leukemia

Chen

Zhao

et al. 2022

Preprint

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Background The status of minimal residual disease (MRD) has been established as an important prognostic indicator in chronic lymphocytic leukemia (CLL). Methods Owing to the requirements of high accuracy, reproducibility and comparability of MRD, this study investigated the performance of a flow cytometric approach (CD45-ROR1 panel) in the MRD detection of CLL patients, with European Research Initiative on CLL (ERIC) 8-color panel as the “gold standard”. Results The sensitivity, specificity and concordance rate of CD45-ROR1 panel in the MRD assessment of CLL were 100% (87/87), 88.5% (23/26) and 97.3% (110/113), respectively. Two of the 3 non-consistent samples were further verified by the next-generation sequencing. In addition, the MRD results obtained from the CD45-ROR1 panel were positively associated with the ERIC 8-color results for MRD assessment (R = 0.98, p < 0.0001). MRD detection at low levels (≤ 1.0%) demonstrated a smaller difference between the two methods (bias, -0.11; 95% CI, -0.90-0.68) as compared with that at high levels (>0.1%). For the reproducibility assessment, the bias was smaller at three datapoints in the CD45-ROR1 panel as compared with that of the ERIC 8-color panel. Moreover, MRD level detected using the CD45-ROR1 panel for the same samples between different laboratories showed a strong statistical correlation (R = 0.99, p < 0.0001) with a trivial inter-laboratory variation (bias, 0.135; 95% CI, -0.439-0.709). Interesingly, the MRD level detected in the lymph nodes samples were significantly higher than that of the peripheral blood and bone marrow samples (p = 0.029). Conclusions Collectively, this study demonstrates that the CD45-ROR1 panel is a reliable method for the MRD assessment of CLL, with higher sensitivity, reproducibility, and reliability.

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Section: Discussionsupporting

confidence: 93%

Performance of a novel 8-color flow cytometry panel in the detection of minimal residual disease assessment of chronic lymphocytic leukemia

Chen

Zhao

et al. 2022

Preprint

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“…Risk stratification largely focuses on pretreatment variables. However, post-treatment biomarkers such as minimal residual disease (MRD) assessment are gaining interest [36][37][38]. Achieving undetectable MRD after receiving therapy affects PFS and OS [38].…”

Section: Dynamic Models and The Role Of Post-treatment Biomarkersmentioning

confidence: 99%

Chronic lymphocytic leukemia prognostic models in real life: still a long way off

Molica

2021

Expert Review of Hematology

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“…In CLL, these risk factors include clinical and cytogenetic indices, peripheral blood MRD status, and therapy delivered (i.e., FCR). The predictive value of MRD status on survival outcomes has been repeatedly demonstrated in the context of CIT and cellular therapies, 62,63 while recent data show that Bcl‐2 inhibitor‐based therapy has a similar correlation 64 . Although not yet part of routine clinical practice, MRD determination is increasingly used in CLL trials as a co‐primary or secondary endpoint due to its efficacy in evaluating the depth of response.…”

Section: Dynamic Models and The Role Of Post‐treatment Biomarkersmentioning

confidence: 99%

A perspective on prognostic models in chronic lymphocytic leukemia in the era of targeted agents

Molica

Seymour

Polliack

2021

Hematological Oncology

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Despite the increase in the number of prognostic models currently available for evaluating patients with chronic lymphocytic leukemia (CLL), their current application and utilization in clinical practice in the era of targeted agents is unclear. A critical reappraisal of recently developed prognostic models is presented in this review. The underlying CLL's genetic instability and changes in the host's health and comorbidities can all contribute to the acquisition of additional risk factors for adverse outcomes during the course of the disease. Therefore, available risk models solely based on pretreatment variables only partially predict patients' clinical outcome. A dynamic prognostic model that takes into account changes in the risk profile over time could indeed be useful in routine clinical practice. The next generation of risk assessment models should incorporate post-treatment and response biomarkers such as minimal residual disease. Finally, recent advances in the field of machine learning present novel opportunities to generate models capable of providing an individualized estimation of clinical outcomes in CLL. However, in the era of improved prognostic models, it is important to remember that these indices should supplement but not replace clinical expertise and medical decision-making.

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Prognostic value of high-sensitivity measurable residual disease assessment after front-line chemoimmunotherapy in chronic lymphocytic leukemia

Cited by 21 publications

References 40 publications

Performance of a novel 8-color flow cytometry panel in the detection of minimal residual disease assessment of chronic lymphocytic leukemia

Performance of a novel 8-color flow cytometry panel in the detection of minimal residual disease assessment of chronic lymphocytic leukemia

Chronic lymphocytic leukemia prognostic models in real life: still a long way off

A perspective on prognostic models in chronic lymphocytic leukemia in the era of targeted agents

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