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Tannoury M, Garnier D, Susin SA, Bauvois B. Current Status of Novel Agents for the Treatment of B Cell Malignancies: What's Coming Next? Cancers (Basel) 2022; 14:6026. [PMID: 36551511 PMCID: PMC9775488 DOI: 10.3390/cancers14246026] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Revised: 11/29/2022] [Accepted: 12/03/2022] [Indexed: 12/13/2022] Open
Abstract
Resistance to death is one of the hallmarks of human B cell malignancies and often contributes to the lack of a lasting response to today's commonly used treatments. Drug discovery approaches designed to activate the death machinery have generated a large number of inhibitors of anti-apoptotic proteins from the B-cell lymphoma/leukemia 2 family and the B-cell receptor (BCR) signaling pathway. Orally administered small-molecule inhibitors of Bcl-2 protein and BCR partners (e.g., Bruton's tyrosine kinase and phosphatidylinositol-3 kinase) have already been included (as monotherapies or combination therapies) in the standard of care for selected B cell malignancies. Agonistic monoclonal antibodies and their derivatives (antibody-drug conjugates, antibody-radioisotope conjugates, bispecific T cell engagers, and chimeric antigen receptor-modified T cells) targeting tumor-associated antigens (TAAs, such as CD19, CD20, CD22, and CD38) are indicated for treatment (as monotherapies or combination therapies) of patients with B cell tumors. However, given that some patients are either refractory to current therapies or relapse after treatment, novel therapeutic strategies are needed. Here, we review current strategies for managing B cell malignancies, with a focus on the ongoing clinical development of more effective, selective drugs targeting these molecules, as well as other TAAs and signaling proteins. The observed impact of metabolic reprogramming on B cell pathophysiology highlights the promise of targeting metabolic checkpoints in the treatment of these disorders.
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Affiliation(s)
| | | | | | - Brigitte Bauvois
- Centre de Recherche des Cordeliers, Sorbonne Université, Université Paris Cité, Inserm, Cell Death and Drug Resistance in Lymphoproliferative Disorders Team, F-75006 Paris, France
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Kreuzberger N, Damen JA, Trivella M, Estcourt LJ, Aldin A, Umlauff L, Vazquez-Montes MD, Wolff R, Moons KG, Monsef I, Foroutan F, Kreuzer KA, Skoetz N. Prognostic models for newly-diagnosed chronic lymphocytic leukaemia in adults: a systematic review and meta-analysis. Cochrane Database Syst Rev 2020; 7:CD012022. [PMID: 32735048 PMCID: PMC8078230 DOI: 10.1002/14651858.cd012022.pub2] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
BACKGROUND Chronic lymphocytic leukaemia (CLL) is the most common cancer of the lymphatic system in Western countries. Several clinical and biological factors for CLL have been identified. However, it remains unclear which of the available prognostic models combining those factors can be used in clinical practice to predict long-term outcome in people newly-diagnosed with CLL. OBJECTIVES To identify, describe and appraise all prognostic models developed to predict overall survival (OS), progression-free survival (PFS) or treatment-free survival (TFS) in newly-diagnosed (previously untreated) adults with CLL, and meta-analyse their predictive performances. SEARCH METHODS We searched MEDLINE (from January 1950 to June 2019 via Ovid), Embase (from 1974 to June 2019) and registries of ongoing trials (to 5 March 2020) for development and validation studies of prognostic models for untreated adults with CLL. In addition, we screened the reference lists and citation indices of included studies. SELECTION CRITERIA We included all prognostic models developed for CLL which predict OS, PFS, or TFS, provided they combined prognostic factors known before treatment initiation, and any studies that tested the performance of these models in individuals other than the ones included in model development (i.e. 'external model validation studies'). We included studies of adults with confirmed B-cell CLL who had not received treatment prior to the start of the study. We did not restrict the search based on study design. DATA COLLECTION AND ANALYSIS We developed a data extraction form to collect information based on the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS). Independent pairs of review authors screened references, extracted data and assessed risk of bias according to the Prediction model Risk Of Bias ASsessment Tool (PROBAST). For models that were externally validated at least three times, we aimed to perform a quantitative meta-analysis of their predictive performance, notably their calibration (proportion of people predicted to experience the outcome who do so) and discrimination (ability to differentiate between people with and without the event) using a random-effects model. When a model categorised individuals into risk categories, we pooled outcome frequencies per risk group (low, intermediate, high and very high). We did not apply GRADE as guidance is not yet available for reviews of prognostic models. MAIN RESULTS From 52 eligible studies, we identified 12 externally validated models: six were developed for OS, one for PFS and five for TFS. In general, reporting of the studies was poor, especially predictive performance measures for calibration and discrimination; but also basic information, such as eligibility criteria and the recruitment period of participants was often missing. We rated almost all studies at high or unclear risk of bias according to PROBAST. Overall, the applicability of the models and their validation studies was low or unclear; the most common reasons were inappropriate handling of missing data and serious reporting deficiencies concerning eligibility criteria, recruitment period, observation time and prediction performance measures. We report the results for three models predicting OS, which had available data from more than three external validation studies: CLL International Prognostic Index (CLL-IPI) This score includes five prognostic factors: age, clinical stage, IgHV mutational status, B2-microglobulin and TP53 status. Calibration: for the low-, intermediate- and high-risk groups, the pooled five-year survival per risk group from validation studies corresponded to the frequencies observed in the model development study. In the very high-risk group, predicted survival from CLL-IPI was lower than observed from external validation studies. Discrimination: the pooled c-statistic of seven external validation studies (3307 participants, 917 events) was 0.72 (95% confidence interval (CI) 0.67 to 0.77). The 95% prediction interval (PI) of this model for the c-statistic, which describes the expected interval for the model's discriminative ability in a new external validation study, ranged from 0.59 to 0.83. Barcelona-Brno score Aimed at simplifying the CLL-IPI, this score includes three prognostic factors: IgHV mutational status, del(17p) and del(11q). Calibration: for the low- and intermediate-risk group, the pooled survival per risk group corresponded to the frequencies observed in the model development study, although the score seems to overestimate survival for the high-risk group. Discrimination: the pooled c-statistic of four external validation studies (1755 participants, 416 events) was 0.64 (95% CI 0.60 to 0.67); 95% PI 0.59 to 0.68. MDACC 2007 index score The authors presented two versions of this model including six prognostic factors to predict OS: age, B2-microglobulin, absolute lymphocyte count, gender, clinical stage and number of nodal groups. Only one validation study was available for the more comprehensive version of the model, a formula with a nomogram, while seven studies (5127 participants, 994 events) validated the simplified version of the model, the index score. Calibration: for the low- and intermediate-risk groups, the pooled survival per risk group corresponded to the frequencies observed in the model development study, although the score seems to overestimate survival for the high-risk group. Discrimination: the pooled c-statistic of the seven external validation studies for the index score was 0.65 (95% CI 0.60 to 0.70); 95% PI 0.51 to 0.77. AUTHORS' CONCLUSIONS Despite the large number of published studies of prognostic models for OS, PFS or TFS for newly-diagnosed, untreated adults with CLL, only a minority of these (N = 12) have been externally validated for their respective primary outcome. Three models have undergone sufficient external validation to enable meta-analysis of the model's ability to predict survival outcomes. Lack of reporting prevented us from summarising calibration as recommended. Of the three models, the CLL-IPI shows the best discrimination, despite overestimation. However, performance of the models may change for individuals with CLL who receive improved treatment options, as the models included in this review were tested mostly on retrospective cohorts receiving a traditional treatment regimen. In conclusion, this review shows a clear need to improve the conducting and reporting of both prognostic model development and external validation studies. For prognostic models to be used as tools in clinical practice, the development of the models (and their subsequent validation studies) should adapt to include the latest therapy options to accurately predict performance. Adaptations should be timely.
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Key Words
- adult
- female
- humans
- male
- age factors
- bias
- biomarkers, tumor
- calibration
- confidence intervals
- discriminant analysis
- disease-free survival
- genes, p53
- genes, p53/genetics
- immunoglobulin heavy chains
- immunoglobulin heavy chains/genetics
- immunoglobulin variable region
- immunoglobulin variable region/genetics
- leukemia, lymphocytic, chronic, b-cell
- leukemia, lymphocytic, chronic, b-cell/mortality
- leukemia, lymphocytic, chronic, b-cell/pathology
- models, theoretical
- neoplasm staging
- prognosis
- progression-free survival
- receptors, antigen, b-cell
- receptors, antigen, b-cell/genetics
- reproducibility of results
- tumor suppressor protein p53
- tumor suppressor protein p53/genetics
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MESH Headings
- Adult
- Age Factors
- Bias
- Biomarkers, Tumor
- Calibration
- Confidence Intervals
- Discriminant Analysis
- Disease-Free Survival
- Female
- Genes, p53/genetics
- Humans
- Immunoglobulin Heavy Chains/genetics
- Immunoglobulin Variable Region/genetics
- Leukemia, Lymphocytic, Chronic, B-Cell/mortality
- Leukemia, Lymphocytic, Chronic, B-Cell/pathology
- Male
- Models, Theoretical
- Neoplasm Staging
- Prognosis
- Progression-Free Survival
- Receptors, Antigen, B-Cell/genetics
- Reproducibility of Results
- Tumor Suppressor Protein p53/genetics
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Affiliation(s)
- Nina Kreuzberger
- Cochrane Haematology, Department I of Internal Medicine, Center for Integrated Oncology Aachen Bonn Cologne Duesseldorf, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Johanna Aag Damen
- Cochrane Netherlands, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
| | | | - Lise J Estcourt
- Haematology/Transfusion Medicine, NHS Blood and Transplant, Oxford, UK
| | - Angela Aldin
- Cochrane Haematology, Department I of Internal Medicine, Center for Integrated Oncology Aachen Bonn Cologne Duesseldorf, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Lisa Umlauff
- Cochrane Haematology, Department I of Internal Medicine, Center for Integrated Oncology Aachen Bonn Cologne Duesseldorf, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | | | | | - Karel Gm Moons
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
| | - Ina Monsef
- Cochrane Haematology, Department I of Internal Medicine, Center for Integrated Oncology Aachen Bonn Cologne Duesseldorf, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Farid Foroutan
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Canada
| | - Karl-Anton Kreuzer
- Center of Integrated Oncology Cologne-Bonn, Department I of Internal Medicine, University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne, Germany
| | - Nicole Skoetz
- Cochrane Cancer, Department I of Internal Medicine, Center for Integrated Oncology Aachen Bonn Cologne Duesseldorf, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
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Roy Chowdhury S, Bouchard EDJ, Saleh R, Nugent Z, Peltier C, Mejia E, Hou S, McFall C, Squires M, Hewitt D, Davidson L, Shen GX, Johnston JB, Doucette C, Hatch GM, Fernyhough P, Marshall A, Gibson SB, Dawe DE, Banerji V. Mitochondrial Respiration Correlates with Prognostic Markers in Chronic Lymphocytic Leukemia and Is Normalized by Ibrutinib Treatment. Cancers (Basel) 2020; 12:cancers12030650. [PMID: 32168755 PMCID: PMC7139649 DOI: 10.3390/cancers12030650] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2020] [Revised: 02/28/2020] [Accepted: 03/06/2020] [Indexed: 12/20/2022] Open
Abstract
Mitochondrial bioenergetics profiling, a measure of oxygen consumption rates, correlates with prognostic markers and can be used to assess response to therapy in chronic lymphocytic leukemia (CLL) cells. In this study, we measured mitochondrial respiration rates in primary CLL cells using respirometry to evaluate mitochondrial function. We found significant increases in mitochondrial respiration rates in CLL versus control B lymphocytes. We also observed amongst CLL patients that advanced age, female sex, zeta-chain-associated protein of 70 kD (ZAP-70+), cluster of differentiation 38 (CD38+), and elevated β2-microglobulin (β2-M) predicted increased maximal respiration rates. ZAP-70+ CLL cells exhibited significantly higher bioenergetics than B lymphocytes or ZAP-70− CLL cells and were more sensitive to the uncoupler, carbonyl cyanide-p-trifluoro-methoxyphenylhydrazone (FCCP). Univariable and multivariable linear regression analysis demonstrated that ZAP-70+ predicted increased maximal respiration. ZAP-70+ is a surrogate for B cell receptor (BCR) activation and can be targeted by ibrutinib, which is a clinically approved Bruton’s tyrosine kinase (BTK) inhibitor. Therefore, we evaluated the oxygen consumption rates (OCR) of CLL cells and plasma chemokine (C-C motif) ligands 3 and 4 (CCL3/CCL4) levels from ibrutinib-treated patients and demonstrated decreased OCR similar to control B lymphocytes, suggesting that ibrutinib treatment resets the mitochondrial bioenergetics, while diminished CCL3/CCL4 levels indicate the down regulation of the BCR signaling pathway in CLL. Our data support evaluation of mitochondrial respiration as a preclinical tool for the response assessment of CLL cells.
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Affiliation(s)
- Subir Roy Chowdhury
- Research Institute in Oncology and Hematology, CancerCare Manitoba, Winnipeg, MB R3V 0V9, Canada; (S.R.C.); (E.D.J.B.); (R.S.); (Z.N.); (C.P.); (C.M.); (M.S.); (D.H.); (L.D.); (J.B.J.); (S.B.G.); (D.E.D.)
| | - Eric D. J. Bouchard
- Research Institute in Oncology and Hematology, CancerCare Manitoba, Winnipeg, MB R3V 0V9, Canada; (S.R.C.); (E.D.J.B.); (R.S.); (Z.N.); (C.P.); (C.M.); (M.S.); (D.H.); (L.D.); (J.B.J.); (S.B.G.); (D.E.D.)
| | - Ryan Saleh
- Research Institute in Oncology and Hematology, CancerCare Manitoba, Winnipeg, MB R3V 0V9, Canada; (S.R.C.); (E.D.J.B.); (R.S.); (Z.N.); (C.P.); (C.M.); (M.S.); (D.H.); (L.D.); (J.B.J.); (S.B.G.); (D.E.D.)
| | - Zoann Nugent
- Research Institute in Oncology and Hematology, CancerCare Manitoba, Winnipeg, MB R3V 0V9, Canada; (S.R.C.); (E.D.J.B.); (R.S.); (Z.N.); (C.P.); (C.M.); (M.S.); (D.H.); (L.D.); (J.B.J.); (S.B.G.); (D.E.D.)
| | - Cheryl Peltier
- Research Institute in Oncology and Hematology, CancerCare Manitoba, Winnipeg, MB R3V 0V9, Canada; (S.R.C.); (E.D.J.B.); (R.S.); (Z.N.); (C.P.); (C.M.); (M.S.); (D.H.); (L.D.); (J.B.J.); (S.B.G.); (D.E.D.)
| | - Edgard Mejia
- Departments of Immunology, Max Rady College of Medicine, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB R3E 0T5, Canada; (E.M.); (S.H.); (A.M.)
| | - Sen Hou
- Departments of Immunology, Max Rady College of Medicine, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB R3E 0T5, Canada; (E.M.); (S.H.); (A.M.)
| | - Carly McFall
- Research Institute in Oncology and Hematology, CancerCare Manitoba, Winnipeg, MB R3V 0V9, Canada; (S.R.C.); (E.D.J.B.); (R.S.); (Z.N.); (C.P.); (C.M.); (M.S.); (D.H.); (L.D.); (J.B.J.); (S.B.G.); (D.E.D.)
| | - Mandy Squires
- Research Institute in Oncology and Hematology, CancerCare Manitoba, Winnipeg, MB R3V 0V9, Canada; (S.R.C.); (E.D.J.B.); (R.S.); (Z.N.); (C.P.); (C.M.); (M.S.); (D.H.); (L.D.); (J.B.J.); (S.B.G.); (D.E.D.)
| | - Donna Hewitt
- Research Institute in Oncology and Hematology, CancerCare Manitoba, Winnipeg, MB R3V 0V9, Canada; (S.R.C.); (E.D.J.B.); (R.S.); (Z.N.); (C.P.); (C.M.); (M.S.); (D.H.); (L.D.); (J.B.J.); (S.B.G.); (D.E.D.)
| | - Linda Davidson
- Research Institute in Oncology and Hematology, CancerCare Manitoba, Winnipeg, MB R3V 0V9, Canada; (S.R.C.); (E.D.J.B.); (R.S.); (Z.N.); (C.P.); (C.M.); (M.S.); (D.H.); (L.D.); (J.B.J.); (S.B.G.); (D.E.D.)
| | - Garry X. Shen
- Departments of Internal Medicine, Max Rady College of Medicine, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB R3E 3P4, Canada;
- Children’s Hospital Research Institute of Manitoba, Winnipeg, MB R3E 3P4, Canada; (C.D.); (G.M.H.)
| | - James B. Johnston
- Research Institute in Oncology and Hematology, CancerCare Manitoba, Winnipeg, MB R3V 0V9, Canada; (S.R.C.); (E.D.J.B.); (R.S.); (Z.N.); (C.P.); (C.M.); (M.S.); (D.H.); (L.D.); (J.B.J.); (S.B.G.); (D.E.D.)
- Departments of Internal Medicine, Max Rady College of Medicine, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB R3E 3P4, Canada;
- Department of Medical Oncology and Hematology, CancerCare Manitoba, Winnipeg, MB R3E 0V9, Canada
| | - Christine Doucette
- Children’s Hospital Research Institute of Manitoba, Winnipeg, MB R3E 3P4, Canada; (C.D.); (G.M.H.)
- Departments of Physiology & Pathophysiology, Max Rady College of Medicine, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB R3E 3P4, Canada
| | - Grant M. Hatch
- Children’s Hospital Research Institute of Manitoba, Winnipeg, MB R3E 3P4, Canada; (C.D.); (G.M.H.)
- St. Boniface Hospital Albrechtsen Research Centre, Winnipeg, MB R2H 2A6, Canada
| | - Paul Fernyhough
- Departments of Pharmacology and Therapeutics, Max Rady College of Medicine, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB R3E 3P4, Canada;
- Departments of Biochemistry and Medical Genetics, Max Rady College of Medicine, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB R3E 3N4, Canada
| | - Aaron Marshall
- Departments of Immunology, Max Rady College of Medicine, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB R3E 0T5, Canada; (E.M.); (S.H.); (A.M.)
- Departments of Pharmacology and Therapeutics, Max Rady College of Medicine, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB R3E 3P4, Canada;
| | - Spencer B. Gibson
- Research Institute in Oncology and Hematology, CancerCare Manitoba, Winnipeg, MB R3V 0V9, Canada; (S.R.C.); (E.D.J.B.); (R.S.); (Z.N.); (C.P.); (C.M.); (M.S.); (D.H.); (L.D.); (J.B.J.); (S.B.G.); (D.E.D.)
- Departments of Immunology, Max Rady College of Medicine, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB R3E 0T5, Canada; (E.M.); (S.H.); (A.M.)
- Departments of Pharmacology and Therapeutics, Max Rady College of Medicine, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB R3E 3P4, Canada;
| | - David E. Dawe
- Research Institute in Oncology and Hematology, CancerCare Manitoba, Winnipeg, MB R3V 0V9, Canada; (S.R.C.); (E.D.J.B.); (R.S.); (Z.N.); (C.P.); (C.M.); (M.S.); (D.H.); (L.D.); (J.B.J.); (S.B.G.); (D.E.D.)
- Departments of Internal Medicine, Max Rady College of Medicine, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB R3E 3P4, Canada;
- Department of Medical Oncology and Hematology, CancerCare Manitoba, Winnipeg, MB R3E 0V9, Canada
| | - Versha Banerji
- Research Institute in Oncology and Hematology, CancerCare Manitoba, Winnipeg, MB R3V 0V9, Canada; (S.R.C.); (E.D.J.B.); (R.S.); (Z.N.); (C.P.); (C.M.); (M.S.); (D.H.); (L.D.); (J.B.J.); (S.B.G.); (D.E.D.)
- Departments of Internal Medicine, Max Rady College of Medicine, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB R3E 3P4, Canada;
- Department of Medical Oncology and Hematology, CancerCare Manitoba, Winnipeg, MB R3E 0V9, Canada
- Departments of Pharmacology and Therapeutics, Max Rady College of Medicine, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB R3E 3P4, Canada;
- Correspondence: ; Tel.: +1-204-7871-884; Fax: +1-204-7870-196
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