Literature DB >> 31582354

Time-to-progression after front-line fludarabine, cyclophosphamide, and rituximab chemoimmunotherapy for chronic lymphocytic leukaemia: a retrospective, multicohort study.

Carmen D Herling1, Kevin R Coombes2, Axel Benner3, Johannes Bloehdorn4, Lynn L Barron5, Zachary B Abrams2, Tadeusz Majewski6, Jolanta E Bondaruk6, Jasmin Bahlo1, Kirsten Fischer1, Michael Hallek1, Stephan Stilgenbauer4, Bogdan A Czerniak6, Christopher C Oakes7, Alessandra Ferrajoli8, Michael J Keating8, Lynne V Abruzzo9.   

Abstract

BACKGROUND: Fludarabine, cyclophosphamide, and rituximab (FCR) has become a gold-standard chemoimmunotherapy regimen for patients with chronic lymphocytic leukaemia. However, the question remains of how to treat treatment-naive patients with IGHV-unmutated chronic lymphocytic leukaemia. We therefore aimed to develop and validate a gene expression signature to identify which of these patients are likely to achieve durable remissions with FCR chemoimmunotherapy.
METHODS: We did a retrospective cohort study in two cohorts of treatment-naive patients (aged ≥18 years) with chronic lymphocytic leukaemia. The discovery and training cohort consisted of peripheral blood samples collected from patients treated at the University of Texas MD Anderson Cancer Center (Houston, TX, USA), who fulfilled the diagnostic criteria of the International Workshop on Chronic Lymphocytic Leukemia, had received at least three cycles of FCR chemoimmunotherapy, and had been treated between Oct 10, 2000, and Oct 26, 2006 (ie, the MDACC cohort). We did transcriptional profiling on samples obtained from the MDACC cohort to identify genes associated with time to progression. We did univariate Cox proportional hazards analyses and used significant genes to cluster IGHV-unmutated samples into two groups (intermediate prognosis and unfavourable prognosis). After using cross-validation to assess robustness, we applied the Lasso method to standardise the gene expression values to find a minimum gene signature. We validated this signature in an external cohort of treatment-naive patients with IGHV-unmutated chronic lymphocytic leukaemia enrolled on the CLL8 trial of the German Chronic Lymphocytic Leukaemia Study Group who were treated between July 21, 2003, and April 4, 2006 (ie, the CLL8 cohort).
FINDINGS: The MDACC cohort consisted of 101 patients and the CLL8 cohort consisted of 109 patients. Using the MDACC cohort, we identified and developed a 17-gene expression signature that distinguished IGHV-unmutated patients who were likely to achieve a long-term remission following front-line FCR chemoimmunotherapy from those who might benefit from alternative front-line regimens (hazard ratio 3·83, 95% CI 1·94-7·59; p<0·0001). We validated this gene signature in the CLL8 cohort; patients with an unfavourable prognosis versus those with an intermediate prognosis had a cause-specific hazard ratio of 1·90 (95% CI 1·18-3·06; p=0·008). Median time to progression was 39 months (IQR 22-69) for those with an unfavourable prognosis compared with 59 months (28-84) for those with an intermediate prognosis.
INTERPRETATION: We have developed a robust, reproducible 17-gene signature that identifies a subset of treatment-naive patients with IGHV-unmutated chronic lymphocytic leukaemia who might substantially benefit from treatment with FCR chemoimmunotherapy. We recommend testing the value of this gene signature in a prospective study that compares FCR treatment with newer alternative therapies as part of a randomised clinical trial. FUNDING: Chronic Lymphocytic Leukaemia Global Research Foundation and the National Institutes of Health/National Cancer Institute.
Copyright © 2019 Elsevier Ltd. All rights reserved.

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Year:  2019        PMID: 31582354      PMCID: PMC7147008          DOI: 10.1016/S1470-2045(19)30503-0

Source DB:  PubMed          Journal:  Lancet Oncol        ISSN: 1470-2045            Impact factor:   41.316


  33 in total

1.  Estimating the occurrence of false positives and false negatives in microarray studies by approximating and partitioning the empirical distribution of p-values.

Authors:  Stan Pounds; Stephan W Morris
Journal:  Bioinformatics       Date:  2003-07-01       Impact factor: 6.937

2.  Hunting for robust gene signature from cancer profiling data: sources of variability, different interpretations, and recent methodological developments.

Authors:  Jian-Zhen Xu; Chi-Wai Wong
Journal:  Cancer Lett       Date:  2010-06-25       Impact factor: 8.679

3.  Addition of rituximab to fludarabine and cyclophosphamide in patients with chronic lymphocytic leukaemia: a randomised, open-label, phase 3 trial.

Authors:  M Hallek; K Fischer; G Fingerle-Rowson; A M Fink; R Busch; J Mayer; M Hensel; G Hopfinger; G Hess; U von Grünhagen; M Bergmann; J Catalano; P L Zinzani; F Caligaris-Cappio; J F Seymour; A Berrebi; U Jäger; B Cazin; M Trneny; A Westermann; C M Wendtner; B F Eichhorst; P Staib; A Bühler; D Winkler; T Zenz; S Böttcher; M Ritgen; M Mendila; M Kneba; H Döhner; S Stilgenbauer
Journal:  Lancet       Date:  2010-10-02       Impact factor: 79.321

4.  National Cancer Institute-sponsored Working Group guidelines for chronic lymphocytic leukemia: revised guidelines for diagnosis and treatment.

Authors:  B D Cheson; J M Bennett; M Grever; N Kay; M J Keating; S O'Brien; K R Rai
Journal:  Blood       Date:  1996-06-15       Impact factor: 22.113

5.  Chemoimmunotherapy Is Not Dead Yet in Chronic Lymphocytic Leukemia.

Authors:  Jennifer R Brown; Neil E Kay
Journal:  J Clin Oncol       Date:  2017-07-25       Impact factor: 44.544

6.  Treatment-related myelodysplasia following fludarabine combination chemotherapy.

Authors:  Constantine S Tam; John F Seymour; H Miles Prince; Melita Kenealy; Max Wolf; E Henry Januszewicz; David Westerman
Journal:  Haematologica       Date:  2006-11       Impact factor: 9.941

7.  Cytogenetic complexity in chronic lymphocytic leukemia: definitions, associations, and clinical impact.

Authors:  Panagiotis Baliakas; Sabine Jeromin; Michalis Iskas; Anna Puiggros; Karla Plevova; Florence Nguyen-Khac; Zadie Davis; Gian Matteo Rigolin; Andrea Visentin; Aliki Xochelli; Julio Delgado; Fanny Baran-Marszak; Evangelia Stalika; Pau Abrisqueta; Kristina Durechova; George Papaioannou; Virginie Eclache; Maria Dimou; Theodoros Iliakis; Rosa Collado; Michael Doubek; M Jose Calasanz; Neus Ruiz-Xiville; Carolina Moreno; Marie Jarosova; Alexander C Leeksma; Panayiotis Panayiotidis; Helena Podgornik; Florence Cymbalista; Achilles Anagnostopoulos; Livio Trentin; Niki Stavroyianni; Fred Davi; Paolo Ghia; Arnon P Kater; Antonio Cuneo; Sarka Pospisilova; Blanca Espinet; Anastasia Athanasiadou; David Oscier; Claudia Haferlach; Kostas Stamatopoulos
Journal:  Blood       Date:  2019-01-02       Impact factor: 22.113

8.  Immunohistochemical detection of ZAP70 in chronic lymphocytic leukemia predicts immunoglobulin heavy chain gene mutation status and time to progression.

Authors:  Joan H Admirand; Ronald J Knoblock; Kevin R Coombes; Constantine Tam; Ellen J Schlette; William G Wierda; Alessandra Ferrajoli; Susan O'Brien; Michael J Keating; Rajyalakshmi Luthra; L Jeffrey Medeiros; Lynne V Abruzzo
Journal:  Mod Pathol       Date:  2010-07-23       Impact factor: 7.842

Review 9.  Predictive ability of DNA microarrays for cancer outcomes and correlates: an empirical assessment.

Authors:  Evangelia E Ntzani; John P A Ioannidis
Journal:  Lancet       Date:  2003-11-01       Impact factor: 79.321

Review 10.  Interpretation of microarray data in cancer.

Authors:  S Michiels; S Koscielny; C Hill
Journal:  Br J Cancer       Date:  2007-03-06       Impact factor: 7.640

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3.  Unsupervised machine learning and prognostic factors of survival in chronic lymphocytic leukemia.

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4.  Evolution of Advanced Chronic Lymphoid Leukemia Unveiled by Single-Cell Transcriptomics: A Case Report.

Authors:  Pavel Ostasov; Henry Robertson; Paolo Piazza; Avik Datta; Jane Apperley; Lucie Houdova; Daniel Lysak; Monika Holubova; Katerina Tesarova; Valentina S Caputo; Iros Barozzi
Journal:  Front Oncol       Date:  2020-10-30       Impact factor: 6.244

5.  An Original Ferroptosis-Related Gene Signature Effectively Predicts the Prognosis and Clinical Status for Colorectal Cancer Patients.

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