Abstract
PURPOSE
Mantle cell lymphoma (MCL) is a B-cell lymphoma characterized by cyclin D1 expression. Autologous hematopoietic cell transplantation (AHCT) consolidation after induction chemotherapy is often used for eligible patients; however, the benefit remains uncertain in the rituximab era. Herein we retrospectively assessed the impact of AHCT consolidation on survival in a large cohort of transplantation-eligible patients age 65 years or younger.
PATIENTS AND METHODS
We retrospectively studied transplantation-eligible adults age 65 years or younger with newly diagnosed MCL treated between 2000 and 2015. The primary objective was to assess for improved progression-free survival (PFS) with AHCT consolidation and secondarily to assess for improved overall survival (OS). Cox multivariable regression analysis and propensity score–weighted (PSW) analysis were performed.
RESULTS
Data were collected from 25 medical centers for 1,254 patients; 1,029 met inclusion criteria. Median follow-up for the cohort was 76 months. Median PFS and OS were 62 and 139 months, respectively. On unadjusted analysis, AHCT was associated with improved PFS (75 v 44 months with v without AHCT, respectively; P < .01) and OS (147 v 115 months with v without AHCT, respectively; P < .05). On multivariable regression analysis, AHCT was associated with improved PFS (hazard ratio [HR], 0.54; 95% CI, 0.44 to 0.66; P < .01) and a trend toward improved OS (HR, 0.77; 95% CI, 0.59 to 1.01; P = .06). After PSW analysis, AHCT remained associated with improved PFS (HR, 0.70; 95% CI, 0.59 to 0.84; P < .05) but not improved OS (HR, 0.87; 95% CI, 0.69 to 1.1; P = .2).
CONCLUSION
In this large cohort of younger, transplantation-eligible patients with MCL, AHCT consolidation after induction was associated with significantly improved PFS but not OS after PSW analysis. Within the limitations of a retrospective analysis, our findings suggest that in younger, fit patients, AHCT consolidation may improve PFS.
INTRODUCTION
Mantle cell lymphoma (MCL) is a subset of B-cell non-Hodgkin lymphoma characterized by the t(11,14) translocation that leads to overexpression of cyclin D1.1-3 Clinical outcomes of MCL are heterogeneous4-6; high-risk patients have a median survival of only 37 months and 5-year overall survival (OS) of 20%.4,7-9 Efforts to better prognosticate resulted in the MCL International Prognostic Index (MIPI)10, MIPIB, and combined MIPI with Ki-67 index.11-13
First-line treatment options are varied and depend on age, performance status (PS), and comorbidities.14 No approach has shown superiority, although inclusion of cytarabine is associated with improved outcome.15-17 The best outcomes for younger, fit patients were achieved using intensive induction chemoimmunotherapy followed by autologous hematopoietic cell transplantation (AHCT) consolidation15,17; this approach has become the current de facto standard. Examples include R-maxi-CHOP (rituximab plus high-dose cyclophosphamide, doxorubicin, vincristine, and prednisone) with high-dose cytarabine followed by AHCT15 and R-CHOP alternating with R-DHAP (rituximab plus dexamethasone, cisplatin, and cytarabine) followed by AHCT.17 The use of AHCT consolidation is supported by a randomized trial of younger patients with MCL that demonstrated improved progression-free survival (PFS) with AHCT consolidation (39 v 17 months) over maintenance with interferon alfa.18 However, the lack of rituximab during induction, lack of cytarabine, and use of interferon maintenance make this approach less applicable to today’s patients. Furthermore, intensive cytarabine-containing regimens (eg, R-hyperCVAD [rituximab plus hyperfractionated cyclophosphamide, vincristine, doxorubicin, and dexamethasone]) have shown prolonged disease-free survival without AHCT.19,20 Last, targeted agents in first- or later-line therapy (eg, bortezomib, lenalidomide, and ibrutinib) may negate the need for aggressive induction.21 Therefore, the true benefit of AHCT consolidation in younger, fit patients with MCL in the modern era is not clearly established. Herein we retrospectively assessed the impact of AHCT consolidation on survival in a large cohort of younger patients with MCL treated at multiple North American academic medical centers in the rituximab era.
PATIENTS AND METHODS
Patients
Patients were eligible if age ≤ 65 years, newly diagnosed with MCL, and deemed transplantation eligible at diagnosis by the institutional investigator by review of medical records. The diagnosis of MCL was made by a hematopathologist at each institution as per routine clinical practice. Patients must have received induction from 2000 to 2015 and achieved a partial response (PR) or complete response to induction; responses were defined by the local investigator using institutional standard imaging modalities at time of treatment (ie, computed tomography and/or positron emission tomography). Patients who received radiation therapy alone, achieved less than a PR, were deemed not transplantation eligible because of comorbidities or poor PS, or underwent consolidative allogeneic hematopoietic cell transplantation (allo-HCT) were excluded. AHCT consolidation was defined as transplantation within 6 months of induction. Centers performing transplantation in 0% or 100% of patients were excluded, as were patients with unknown histology, unknown induction regimen, or missing outcome data. The protocol was approved by the institutional review board of each participating center.
Data Collection
Data were collected for each patient on baseline characteristics and treatment, transplantation, and outcome (Appendix Table A1, online only). MIPI score was calculated for each patient with sufficient data as previously published.10
Statistical Analysis
The primary objective was to assess whether AHCT consolidation in first remission was associated with improved PFS, as calculated from day of diagnosis. The secondary objective was to assess for improved OS, also calculated from day of diagnosis. Patient, tumor, and treatment factors were compared between patients undergoing or not undergoing AHCT using χ2, Fisher’s exact, and Wilcoxon rank-sum tests, as appropriate.
Survival curves were calculated using the Kaplan-Meier method. Cox proportional hazards models were used to analyze the association of AHCT consolidation with survival after adjusting for confounders (sex, MIPI, cyclin D1 status, bone marrow or peripheral blood involvement, extranodal disease, induction regimen, blastoid or pleomorphic morphology, response to induction, and receipt of maintenance therapy). Because of the time between diagnosis and consolidation with AHCT, we addressed potential immortal-time bias using two different methods.22 For graphic presentation of survival curves, we excluded patients who died within 6 months of diagnosis in a landmark analysis. For all Cox model–based analyses, treatment was included in the model as a time-varying covariable, so time before transplantation was coded as “no AHCT,” whereas time after transplantation was coded as “AHCT.”23 Covariates with 0% to 30% missing data were imputed via chained equations.24 We used multiple imputation for variables with missing data, and standard deviations were calculated using Rubin’s equation.25 Ki-67 was excluded from the primary regression models because it was available in < 50% of patients. All regression models were stratified by treating institution, with separate baseline hazard functions fit to each stratum.
To assess the assumptions of the model, we conducted several sensitivity analyses (SAs). We fit models excluding stage, bone marrow/peripheral blood, and extranodal disease, because these covariates may be affected by institutional staging practices. We also ran models without imputing data, instead including categorical indicators for missing data.25 We determined subgroup effects using regression models with subgroup × AHCT interactions; these models were conducted only in the patients with complete data on the particular variable of interest.
Propensity Score–Weighted Analysis
A propensity score–weighted (PSW) analysis was subsequently performed. Propensity scores (probability of AHCT) were estimated via logistic regression models on the basis of the imputed data sets, including the covariates listed in the main model and an indicator variable for missing data pattern.26 We then applied variance-stabilized inverse probability of treatment weights to generate a pseudo sample in which covariates used to estimate the propensity score were balanced between treatment arms. Propensity score ranges were checked for sufficient overlap, and balance was assessed via standardized differences.27 The weighted sample was then used to create Kaplan-Meier curves and fit time-varying Cox regression models, both of which accounted for confounders included in the propensity score via the weights. This method has been shown to successfully correct bias from measured confounders, but it does not address unmeasured confounding. We therefore conducted an SA to assess the degree of unmeasured confounding that would be required to change the conclusions of the analysis. We explored the potential impact of a particular unmeasured confounder on the estimated effect of AHCT under a range of possible scenarios.28
RESULTS
Patients and Disease Characteristics
Data for a total of 1,254 patients were collected from 25 North American academic centers. To identify patients, 32% (n = 8) of centers used a lymphoma database only, 4% (n = 1) used a transplantation registry only, 12% (n = 3) used a pathology database, 24% (n = 6) used both a lymphoma database and a transplantation registry, and 28% (n = 7) used another method. Median number of patients contributed per institution was 30, with a range of two to 285 patients. A majority of patients received AHCT consolidation, with a median rate of 67% (range, 26% to 92%); three centers had AHCT rates < 50%. Of 1,254 patients, we excluded: those age > 65 years (n = 27), those who underwent allo-HCT (n = 47), those achieving less than a PR to induction (n = 74), and those missing substantial data (n = 44; Fig 1).
FIG 1.
CONSORT diagram. allo-HCT, allogeneic hematopoietic cell transplantation; PD, progressive disease; SD, stable disease.
A total of 1,029 patients were included in the final analysis. Patient and treatment characteristics are listed in Table 1. Sixty-four percent (n = 657) of patients received AHCT consolidation after induction. Of the 372 patients who did not undergo AHCT, the reason for no transplantation was physician choice in 67% (n = 249), patient preference in 18% (n = 66), other reason (eg, mobilization failure) in 3% (n = 12), and missing reason in 12% (n = 45). Median age at time of diagnosis was 57 years. The induction regimen was CHOP-like in 43% of patients (n = 443), intensive (hyperCVAD, maxi-CHOP, DHAP) in 44% (n = 454), bendamustine based in 11% (n = 119), and other (eg, clinical trial) in 1% (n = 13). Best response to induction was complete response for 76% of patients (n = 783). Overall, both groups were balanced with regard to prognostic features, tumor characteristics, and treatment modalities; small but statistically significant differences were detected in certain variables. The lymphomas of 89% (n = 915) of patients were cyclin D1 positive. Ki-67 expression in 43% and 57% of patient was < 30% and ≥ 30%, respectively (median value, 30%). Thirteen percent (n = 136) were diagnosed with blastoid or pleomorphic morphology. A majority of patients (95%; n = 973) received an anti-CD20 monoclonal antibody with induction; 30% (n = 306) received maintenance rituximab. Only 2.5% (n = 26) received a novel agent with induction.
TABLE 1.
Baseline Patient Demographic and Clinical Characteristics
Survival
After a median follow-up of 76 months (6.3 years; range, 1 to 205 months), median PFS for the entire cohort was 62 months (5.2 years; range, 1 month to 17.1 years), and median OS was 138 months (11.5 years; range, 1 month to 17.1 years; Appendix Fig A1, online only). Only three patients died < 6 months after induction. Unadjusted landmark analysis demonstrated a statistically significant improvement in PFS favoring use of consolidative AHCT after induction, with median PFS of 44 months without AHCT versus 75 months with AHCT (hazard ratio [HR], 0.64; 95% CI, 0.54 to 0.78; P < .01; Fig 2A). We also observed a significant improvement in OS with use of AHCT, from 115 to 147 months (HR, 0.79; 95% CI, 0.63 to 0.99; P < .05; Fig 2B).
FIG 2.
Kaplan-Meier curves for (A) progression-free survival (PFS) and (B) overall survival (OS) at 6 months and for (C) propensity score–weighted (PSW) PFS and (D) PSW OS at 6 months. AHCT, autologous hematopoietic cell transplantation. (*) Log-rank test.
On multivariable regression analysis (MVA) and with imputation for missing data, AHCT was associated with improved PFS (HR, 0.53; 95% CI, 0.43 to 0.66; P < .01) and a trend toward improved OS (HR, 0.77; 95% CI, 0.98 to 1.01; P = .06). Factors associated with improved PFS and OS are listed in Table 2. When analysis was restricted to the 91% of patient cases that were cyclin D1 positive, the HR for PFS and OS did not change significantly (Appendix Table A2, online only). On subgroup analyses, all subgroups demonstrated improved PFS with consolidative AHCT (Fig 3A). Improved OS was seen only in patients with high-risk MIPI scores, those who received CHOP-like induction, those with blastoid or pleomorphic morphology, and those who did not receive cytarabine with induction (Fig 3B). Kaplan-Meier curves demonstrated these findings (Appendix Fig A2, online only).
TABLE 2.
Factors Associated With Improved Survival on Multivariable Analysis (N = 1,029)
FIG 3.
Forest plots for (A) progression-free survival (PFS) and (B) overall survival (OS) with autologous hematopoietic cell transplantation (AHCT). CR, complete response; FISH, fluorescence in situ hybridization; MIPI, Mantle Cell Lymphoma International Prognostic Index; PR, partial response.
Of patients not undergoing AHCT, 224 had a progression event. Of these, 64 underwent AHCT or allo-HCT in the second-line setting. OS was significantly improved for patients receiving any type of transplant after relapse (Appendix Fig A3, online only).
Survival After PSW Analysis
After PSW analysis (n = 1,003), median PFS improved from a median of 48.5 months without AHCT to 78.0 months with AHCT (HR, 0.70; 95% CI, 0.59 to 0.84, P < .05; Fig 2C). Improvement in OS was not observed, with a median OS of 138 months without AHCT and 147 months with AHCT (HR, 0.87; 95% CI, 0.69 to 1.10; P = .24; Table 3; Fig 2D).
TABLE 3.
Survival After PSW Analysis (N = 1,029)
Sensitivity for Unmeasured Confounding
In any observational study, a range of unmeasured confounders may bias the results. We hypothesized that PS after induction therapy would be the largest such confounder and chose to use it as an illustrative example. We expected PS would be worse in patients not undergoing AHCT and that PS > 0 would be associated with an HR of 2 to 3 for OS outcomes and 1.25 to 2 for PFS outcomes.29-31 We assumed that rates of PS > 0 would be fairly low in this younger population (ie, 5% to 10% in the AHCT arm). We assessed the effect of varying HRs and differences in the rate of PS > 0 on the estimated OS and PFS effect of AHCT. Although the effect of AHCT on OS was nonsignificant after adjustment, we found that with modest imbalances in PS, the trend toward benefit with AHCT would disappear (ie, HR = 1 for AHCT on OS when HR for PS > 0 equaled 3 and the difference in rates between arms was 10%). The effect of AHCT on PFS was much more robust; to negate the improved PFS by AHCT, substantial effects of PS and imbalance between groups would be necessary (ie, when HR for PS > 0 equaled 2 and the difference in rates between arms was 25%; Appendix Table A3, online only).
Safety
In patients who underwent consolidative AHCT, 1.2% (n = 7) died within 100 days of transplantation. In the entire cohort and at a median follow-up of 76.8 months, 2% (n = 21) of patients developed secondary myelodysplastic syndrome or acute myeloid leukemia. The incidence of myelodysplastic syndrome or acute myeloid leukemia was not different in the AHCT and non-AHCT groups (2.5%; n = 16 v 1.3%; n = 5, respectively; P = .36).
DISCUSSION
In this large retrospective cohort of younger, transplantation-eligible patients with MCL who achieved a PR or better after induction chemotherapy, we demonstrated improved PFS for patients who underwent consolidative AHCT. After MVA, certain subgroups (patients with blastoid or pleomorphic morphology, those with high-risk MIPI scores, those treated with nonintensive CHOP-like induction, and those who did not receive cytarabine with induction) derived the largest improvement in OS. To reduce inherent biases of retrospective analyses, we elected to perform a PSW analysis and demonstrated persistence of the observed improvement in PFS. In contrast, although improved OS was observed on unadjusted analysis, this improvement did not persist for the entire cohort after PSW analysis, raising the possibility that any observed benefits may have resulted from confounding.
MCL remains an incurable lymphoma with no clearly defined standard-of-care first-line treatment strategy. Prospective trials using intensive induction regimens such as the Nordic regimen followed by AHCT,15 DHAP alternating with R-CHOP followed by AHCT,17 and R-hyperCVAD with methotrexate and high-dose cytarabine without AHCT19,20 have demonstrated improved survival compared with historical controls. Results from a smaller retrospective study32 and a recently reported analysis of > 10,000 patients obtained using the National Cancer Database demonstrated an association between consolidative AHCT and improved OS.33 With the caveats of retrospective analysis, our data also suggest an improvement in PFS with AHCT consolidation after induction in transplantation-eligible patients. The lack of improvement in OS after PSW analysis may be a result of effective salvage therapy (eg, novel agents and/or AHCT/allo-HCT) after relapse, which may abrogate any improvement of consolidative AHCT after induction.
In our subgroup analysis after MVA, most groups demonstrated improved PFS with AHCT, whereas improved OS with AHCT was limited to patients who received CHOP-like induction or induction without cytarabine, had blastoid or pleomorphic variant, or were MIPI-high. This suggests that patients who do not receive cytarabine with induction, receive CHOP-like induction, or have high-risk features (eg, MIPI-high or blastoid/pleomorphic MCL) may benefit most from AHCT consolidation. No differential treatment effect was observed for MIPI-low or -intermediate patients. MIPI score remained prognostic irrespective of receipt of AHCT consolidation. Furthermore, we did not observe a benefit of AHCT with respect to the Ki-67 index. These findings are consistent with the overarching prognostic impact of the combined MIPI score observed in both younger and older patients, as described by Hoster et al.13
Novel combinations using ibrutinib,34 bortezomib,35 and lenalidomide36 have demonstrated favorable outcomes. In our data set, the number of patients who received novel agents with induction was small, limiting conclusions of this approach. The use of these agents at relapse may help explain the lack of improvement in OS after PSW analysis despite improved PFS.
The strengths of our study are the large number of patients (> 1,000) included in analysis; the high rate (89%) of cyclin D1–positive tumors, which balances lack of central pathology review; high rates of receipt of anti-CD20 antibodies with induction, confirming this population was treated with modern therapy; use of PSW analysis to limit selection bias; use of nontransplantation registries to identify patients at all but one center, limiting recall bias; and use of an SA to determine the effect of PS.
There are a number of limitations to our study, mostly inherent to its retrospective nature. Selection bias may have informed the decision for AHCT. We attempted to collect the reasons patients did not undergo AHCT, but there was potential for significant heterogeneity in the true reasons. For example, physicians may have recommended against AHCT for patients who experienced significant toxicity, those with comorbid conditions that were not strict contraindications, or those with aggressive disease over concern for lack of benefit. In addition, a substantial portion (18%) of patients elected to not undergo HCT, which may have reflected underlying differences in family support, socioeconomic status, or deterioration in PS after induction. Moreover, PS is subjective and can change during induction, and emotional states and quality-of-life measures of patients were not captured. Although we could not directly account for these factors, we did conduct an SA to assess the potential impact of unmeasured confounding on treatment outcomes; however, the underlying potential bias in retrospective data remains. Other limitations include that data were collected from tertiary centers only, which may have led to bias from referral patterns. Induction regimen varied both between and within centers, leading to heterogeneity in first-line treatment. There was a lack of standardized response assessment to induction, as well as no central review of the response assessment. There was no central pathology review, raising the possibility of incorrect diagnosis for some patients (although notably, results were not significantly different when analysis was restricted to patients positive for cyclin D1). Finally, certain data fields (eg, Ki-67) had a relatively large percentage of missing data.
In summary, in this cohort of > 1,000 young, transplantation-eligible patients treated in the rituximab era, the use of consolidative AHCT after induction was associated with improved PFS even after controlling for disease severity using PSW analysis. Although no improvement in OS was observed for the entire cohort after PSW analysis, certain high-risk patients and those who did not receive intensive induction or cytarabine with induction seemed to benefit. These findings must certainly be interpreted in light of the limitations inherent to the retrospective nature of our study. AHCT is not a random event, and although we adjusted for confounding, unmeasurable differences between patients may have influenced our findings. Prospective, randomized trials are urgently needed to determine the true benefit of consolidative AHCT. It is likely that some subgroups derive minimal benefit from AHCT consolidation, such as patients with certain genetic abnormalities (eg, TP53 mutations) and those who achieve minimal residual disease negativity after induction, the latter of whom are being investigated in the ongoing EA4151 clinical trial (ClinicalTrials.gov identifier: NCT03267433). With this and other well-designed prospective trials as well as with well-validated predictive biomarkers, clinicians will be better able to provide a more refined, risk-adapted approach to first-line management of MCL.
Appendix
TABLE A1.
Data Collection
TABLE A2.
Adjusted Model for Survival Limited to Cyclin D1–Positive Patients
TABLE A3.
Sensitivity Analysis for PS
FIG A1.
Overall survival (OS) of full cohort. Median survival, 76.8 months (6.4 years).
FIG A2.
Overall survival (OS) for patients (A) undergoing or (B) not undergoing autologous hematopoietic cell transplantation (AHCT) by Mantle Cell Lymphoma International Prognostic Index (MIPI) score and for patients (C) by AHCT and receipt of cytarabine and (D) by AHCT and induction regimen. CHOP, cyclophosphamide, doxurobucin, vincristine, and prednisone; NA, not available.
FIG A3.
Overall survival (OS) after progression by second-line transplantation.
Footnotes
Presented as an oral abstract at the 59th Annual Meeting of the American Society of Hematology, Atlanta GA, December 9-12, 2017.
AUTHOR CONTRIBUTIONS
Conception and design: James N. Gerson, Stefan K. Barta
Financial support: Stefan K. Barta
Administrative support: Richard I. Fisher
Provision of study material or patients: All authors
Collection and assembly of data: All authors
Data analysis and interpretation: James N. Gerson, Stefan K. Barta, Elizabeth Handorf
Manuscript writing: All authors
Final approval of manuscript: All authors
Accountable for all aspects of the work: All authors
AUTHORS' DISCLOSURES OF POTENTIAL CONFLICTS OF INTEREST
Survival Outcomes of Younger Patients With Mantle Cell Lymphoma Treated in the Rituximab Era
The following represents disclosure information provided by authors of this manuscript. All relationships are considered compensated. Relationships are self-held unless noted. I = Immediate Family Member, Inst = My Institution. Relationships may not relate to the subject matter of this manuscript. For more information about ASCO's conflict of interest policy, please refer to www.asco.org/rwc or ascopubs.org/jco/site/ifc.
Elizabeth Handorf
Research Funding: Pfizer (Inst)
Diego Villa
Honoraria: Roche Canada, Janssen, Lundbeck Canada, Seattle Genetics, Gilead Sciences, Acerta Pharma/AstraZeneca, Celgene, Merck
Consulting or Advisory Role: Roche Canada, Janssen, Lundbeck Canada, Seattle Genetics, Gilead Sciences, Acerta Pharma/AstraZeneca, Celgene
Research Funding: Roche (Inst)
Travel, Accommodations, Expenses: Roche Canada, Janssen, Lundbeck Canada, Acerta Pharma
Alina S. Gerrie
Honoraria: Janssen
Consulting or Advisory Role: Janssen, AbbVie, Seattle Genetics
Research Funding: AbbVie (Inst), Accerta (Inst), Roche Canada (Inst), Lundbeck Canada (Inst)
Parv Chapani
Patents, Royalties, Other Intellectual Property: Intellectual property of recombinant oncoloytic viruses; the invention relates to recombinant oncolytic viruses; more specifically, the present invention relates to recombinant oncolytic viruses expressing a heterologous B cell–attractant polypeptide or a T cell–attractant polypeptide
Michael I. Wang
Honoraria: Janssen Research & Development, AstraZeneca, National Cancer Institute, Medscape, Peerview
Consulting or Advisory Role: AstraZeneca, Janssen Research & Development, Celgene, MoreHealth
Research Funding: AstraZeneca, Janssen Research & Development, Pharmacyclics, Kite Pharma, Juno Therapeutics, BeiGene, Novartis, Acerta Pharma
Travel, Accommodations, Expenses: Janssen Research & Development, AstraZeneca
Jonathon B. Cohen
Consulting or Advisory Role: Pharmacyclics, Celgene, Millennium Pharmaceuticals, Seattle Genetics, Novartis, Infinity Pharmaceuticals, AbbVie
Research Funding: Bristol-Myers Squibb, Janssen, Novartis, Takeda Pharmaceuticals
Oscar Calzada
Research Funding: Seattle Genetics
Brian T. Hill
Honoraria: Pharmacyclics, Gilead Sciences, Genentech, AbbVie, Seattle Genetics, Bayer HealthCare Pharmaceuticals
Consulting or Advisory Role: Seattle Genetics, Novartis, AbbVie/Genentech
Research Funding: AbbVie (Inst), Karyopharm Therapeutics (Inst), Celgene (Inst), Takeda Pharmaceuticals (Inst), Amgen (Inst)
Francisco J. Hernandez-Ilizaliturri
Consulting or Advisory Role: Celgene, Amgen, Seattle Genetics, Pharmacyclics, Takeda Pharmaceuticals, Novartis, GlaxoSmithKline
Shalin Kothari
Stock and Other Ownership Interests: Portola Pharmaceuticals
Julie M. Vose
Honoraria: Novartis, AbbVie, Epizyme, Roche, Legend Biotech, Karyopharm Therapeutics, Sandoz, Vaniam Group, Janssen Scientific Affairs, Kite Pharma/Gilead Sciences, Acerta Pharma/AstraZeneca, Nordic Nanovector
Consulting or Advisory Role: Bio Connections
Research Funding: Celgene (Inst), Genentech (Inst), Incyte (Inst), Acerta Pharma (Inst), Kite Pharma (Inst), Seattle Genetics (Inst), Novartis (Inst), Bristol-Myers Squibb (Inst), Merck Sharp & Dohme (Inst), AstraZeneca
Timothy S. Fenske
Stock and Other Ownership Interests: Merck
Honoraria: Sanofi, AstraZeneca, Celgene, Adaptive Biotechnologies, Janssen Oncology, Seattle Genetics, Genentech
Consulting or Advisory Role: Adaptive Biotechnologies, Janssen Oncology, Seattle Genetics, Genentech
Speakers’ Bureau: Sanofi, AstraZeneca, Seattle Genetics, Celgene
Travel, Accommodations, Expenses: Sanofi, AstraZeneca, Celgene, Adaptive Biotechnologies, Janssen Oncology, Seattle Genetics, Genentech
Kami J. Maddocks
Honoraria: Pharmacyclics, Bayer HealthCare Pharmaceuticals, Novartis, TEVA Pharmaceuticals Industries
Research Funding: Pharmacyclics, Merck, Bristol-Myers Squibb
Veronika Bachanova
Consulting or Advisory Role: Seattle Genetics, Kite Pharma
Research Funding: Gamida Cell, Unum Therapeutics (Inst), Novartis (Inst)
Travel, Accommodations, Expenses: Amgen
Bhaskar Kolla
Stock and Other Ownership Interests: Amgen
Julio Chavez
Consulting or Advisory Role: Kite Pharma/Gilead Sciences, Novartis, Genentech, Bayer HealthCare Pharmaceuticals
Speakers’ Bureau: Kite Pharma/Gilead Sciences, Novartis, Genentech, Janssen, Merck
Bijal Shah
Honoraria: Pharmacyclics/Janssen
Consulting or Advisory Role: Adaptive Biotechnologies
Research Funding: Incyte, Jazz Pharmaceuticals (Inst)
Frederick Lansigan
Consulting or Advisory Role: Spectrum Pharmaceuticals, Celgene, Seattle Genetics
Research Funding: Spectrum Pharmaceuticals (Inst)
Nina Wagner-Johnston
Consulting or Advisory Role: Juno Therapeutics, ADC Therapeutics, Janssen Oncology, Gilead Sciences
Research Funding: Merck, Novartis/Pfizer, Genentech, Astex Pharmaceuticals
Amitkumar Mehta
Stock and Other Ownership Interests: Witty Health
Consulting or Advisory Role: Spectrum Pharmaceuticals, Aileron Therapeutics, Bristol-Myers Squibb, Seattle Genetics, Kite Pharma, Carevive
Speakers’ Bureau: Spectrum Pharmaceuticals, AstraZeneca, Kite Pharma, Gilead Sciences
Research Funding: Incyte (Inst), Roche/Genentech (Inst), Merck (Inst), Bristol-Myers Squibb (Inst), Juno Therapeutics (Inst), Gilead Sciences (Inst), Forty Seven (Inst), Takeda Pharmaceuticals (Inst), Astex Pharmaceuticals (Inst), Pharmacyclics/Janssen (Inst), Epizyme (Inst), Aileron Therapeutics (Inst), Carevive (Inst)
Nishitha Reddy
Consulting or Advisory Role: Celgene, AbbVie, Bristol-Myers Squibb, Adaptive Biotechnologies
Speakers’ Bureau: Gilead Sciences
Research Funding: Bristol-Myers Squibb (Inst)
Alexandra E. Kovach
Stock and Other Ownership Interests: Lixte Biotechnology
Daniel J. Landsburg
Consulting or Advisory Role: Celgene, Curis
Research Funding: Takeda Pharmaceuticals (Inst), Triphase Accelerator (Inst), Curis, Curis (Inst)
Martha Glenn
Employment: ExactSciences (I)
Research Funding: Genentech
Reem Karmali
Consulting or Advisory Role: Kite Pharma/Gilead Sciences, Juno Therapeutics
Speakers’ Bureau: AstraZeneca, Kite Pharma/Gilead Sciences
Research Funding: Bristol-Myers Squibb (Inst), Takeda Pharmaceuticals (Inst)
Jason B. Kaplan
Consulting or Advisory Role: Seattle Genetics
Research Funding: Janssen (Inst), Seattle Genetics (Inst)
Travel, Accommodations, Expenses: Curis
Paolo F. Caimi
Consulting or Advisory Role: Genentech/Roche, Kite Pharma
Speakers’ Bureau: Spectrum Pharmaceuticals
Patents, Royalties, Other Intellectual Property: XaTEC patent holder (I)
Andrew Evens
Honoraria: Seattle Genetics, Celgene, Spectrum Pharmaceuticals, Pharmacyclics, Affimed Therapeutics, Merck, Acerta Pharma, AbbVie, Janssen Biotech, Novartis, Bayer HealthCare Pharmaceuticals, Verastem, Kite Pharma/Gilead Sciences, Research to Practice
Consulting or Advisory Role: Celgene, Spectrum Pharmaceuticals, Seattle Genetics, Affimed Therapeutics, Merck, Kite Pharma, Janssen Oncology, Bayer HealthCare Pharmaceuticals, AbbVie/Genentech
Research Funding: Tesaro, Seattle Genetics, Merck
Travel, Accommodations, Expenses: Seattle Genetics, Research to Practice, Bayer HealthCare Pharmaceuticals, Affimed Therapeutics, Pharmacyclics, Janssen Biotech, Novartis, Merck, Verastem, AbbVie/Genentech, Spectrum Pharmaceuticals, Celgene
Andreas Klein
Honoraria: Takeda Pharmaceuticals
Consulting or Advisory Role: Shire
Travel, Accommodations, Expenses: Takeda Pharmaceuticals
Jennifer E. Amengual
Honoraria: Janssen
Research Funding: Appia Pharmaceuticals
Catherine Diefenbach
Stock and Other Ownership Interests: Gilead Sciences
Consulting or Advisory Role: Seattle Genetics, Bayer HealthCare Pharmaceuticals, Bristol-Myers Squibb, Genentech/Roche, Merck
Research Funding: Seattle Genetics (Inst), Genentech (Inst), Incyte (Inst), LAM Therapeutics (Inst), Merck (Inst), Bristol-Myers Squibb (Inst), Millennium Pharmaceuticals (Inst), MEI Pharma (Inst)
Richard I. Fisher
Consulting or Advisory Role: Pharmacyclics/Janssen, Roche, Kite Pharma, Seattle Genetics, Sandoz, Celgene, Genentech, Bayer HealthCare Pharmaceuticals, AstraZeneca, Adaptive Biotechnologies, Ion Solutions
Expert Testimony: Roche
No other potential conflicts of interest were reported.
REFERENCES
- 1.Pérez-Galán P, Dreyling M, Wiestner A. Mantle cell lymphoma: Biology, pathogenesis, and the molecular basis of treatment in the genomic era. Blood. 2011;117:26–38. doi: 10.1182/blood-2010-04-189977. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Barista I, Romaguera J.E., Cabanillas F. Mantle-cell lymphoma. Lancet Oncol. 2001;2:141–148. doi: 10.1016/S1470-2045(00)00255-2. [DOI] [PubMed] [Google Scholar]
- 3.Williams ME, Bernstein SH, Jares P, et al. Recent advances in mantle cell lymphoma: Report of the 2012 Mantle Cell Lymphoma Consortium Workshop. Leuk Lymphoma. 2013;54:1882–1890. doi: 10.3109/10428194.2013.771400. [DOI] [PubMed] [Google Scholar]
- 4.Sander B, Quintanilla-Martinez L, Ott G, et al. Mantle cell lymphoma: A spectrum from indolent to aggressive disease. Virchows Arch. 2016;468:245–257. doi: 10.1007/s00428-015-1840-6. [DOI] [PubMed] [Google Scholar]
- 5.Jares P, Colomer D, Campo E. Molecular pathogenesis of mantle cell lymphoma. J Clin Invest. 2012;122:3416–3423. doi: 10.1172/JCI61272. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Navarro A, Clot G, Royo C, et al. Molecular subsets of mantle cell lymphoma defined by the IGHV mutational status and SOX11 expression have distinct biologic and clinical features. Cancer Res. 2012;72:5307–5316. doi: 10.1158/0008-5472.CAN-12-1615. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Chandran R, Gardiner SK, Simon M, et al. Survival trends in mantle cell lymphoma in the United States over 16 years 1992-2007. Leuk Lymphoma. 2012;53:1488–1493. doi: 10.3109/10428194.2012.656628. [DOI] [PubMed] [Google Scholar]
- 8.Duggan MJ, Weisenburger DD, Ye YL, et al. Mantle zone lymphoma: A clinicopathologic study of 22 cases. Cancer. 1990;66:522–529. doi: 10.1002/1097-0142(19900801)66:3<522::aid-cncr2820660320>3.0.co;2-4. [DOI] [PubMed] [Google Scholar]
- 9.Vandenberghe E, De Wolf-Peeters C, Vaughan Hudson G, et al. The clinical outcome of 65 cases of mantle cell lymphoma initially treated with non-intensive therapy by the British National Lymphoma Investigation Group. Br J Haematol. 1997;99:842–847. doi: 10.1046/j.1365-2141.1997.4693273.x. [DOI] [PubMed] [Google Scholar]
- 10.Hoster E, Dreyling M, Klapper W, et al. A new prognostic index (MIPI) for patients with advanced-stage mantle cell lymphoma. Blood. 2008;111:558–565. doi: 10.1182/blood-2007-06-095331. [DOI] [PubMed] [Google Scholar]
- 11.Hoster E, Klapper W, Hermine O, et al. Confirmation of the mantle-cell lymphoma International Prognostic Index in randomized trials of the European Mantle-Cell Lymphoma Network. J Clin Oncol. 2014;32:1338–1346. doi: 10.1200/JCO.2013.52.2466. [DOI] [PubMed] [Google Scholar]
- 12.Geisler C.H., Kolstad A, Laurell A, et al. The Mantle Cell Lymphoma International Prognostic Index (MIPI) is superior to the International Prognostic Index (IPI) in predicting survival following intensive first-line immunochemotherapy and autologous stem cell transplantation (ASCT) Blood. 2010;115:1530–1533. doi: 10.1182/blood-2009-08-236570. [DOI] [PubMed] [Google Scholar]
- 13.Hoster E, Rosenwald A, Berger F, et al. Prognostic value of Ki-67 index, cytology, and growth pattern in mantle-cell lymphoma: Results from randomized trials of the European Mantle Cell Lymphoma Network. J Clin Oncol. 2016;34:1386–1394. doi: 10.1200/JCO.2015.63.8387. [DOI] [PubMed] [Google Scholar]
- 14.Dreyling M, Campo E, Hermine O, et al: Newly diagnosed and relapsed mantle cell lymphoma: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann Oncol 28:iv62-iv71, 2017 (suppl 4): [DOI] [PubMed]
- 15.Geisler C.H., Kolstad A, Laurell A, et al. Long-term progression-free survival of mantle cell lymphoma after intensive front-line immunochemotherapy with in vivo-purged stem cell rescue: A nonrandomized phase 2 multicenter study by the Nordic Lymphoma Group. Blood. 2008;112:2687–2693. doi: 10.1182/blood-2008-03-147025. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Campo E, Rule S. Mantle cell lymphoma: Evolving management strategies. Blood. 2015;125:48–55. doi: 10.1182/blood-2014-05-521898. [DOI] [PubMed] [Google Scholar]
- 17.Hermine O, Hoster E, Walewski J, et al. Addition of high-dose cytarabine to immunochemotherapy before autologous stem-cell transplantation in patients aged 65 years or younger with mantle cell lymphoma (MCL Younger): A randomised, open-label, phase 3 trial of the European Mantle Cell Lymphoma Network. Lancet. 2016;388:565–575. doi: 10.1016/S0140-6736(16)00739-X. [DOI] [PubMed] [Google Scholar]
- 18.Dreyling M, Lenz G, Hoster E, et al. Early consolidation by myeloablative radiochemotherapy followed by autologous stem cell transplantation in first remission significantly prolongs progression-free survival in mantle-cell lymphoma: Results of a prospective randomized trial of the European MCL Network. Blood. 2005;105:2677–2684. doi: 10.1182/blood-2004-10-3883. [DOI] [PubMed] [Google Scholar]
- 19.Bernstein SH, Epner E, Unger JM, et al. A phase II multicenter trial of hyperCVAD MTX/Ara-C and rituximab in patients with previously untreated mantle cell lymphoma: SWOG 0213. Ann Oncol. 2013;24:1587–1593. doi: 10.1093/annonc/mdt070. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Romaguera JE, Fayad L, Rodriguez MA, et al. High rate of durable remissions after treatment of newly diagnosed aggressive mantle-cell lymphoma with rituximab plus hyper-CVAD alternating with rituximab plus high-dose methotrexate and cytarabine. J Clin Oncol. 2005;23:7013–7023. doi: 10.1200/JCO.2005.01.1825. [DOI] [PubMed] [Google Scholar]
- 21.Martin P, Ruan J, Leonard JP. The potential for chemotherapy-free strategies in mantle cell lymphoma. Blood. 2017;130:1881–1888. doi: 10.1182/blood-2017-05-737510. [DOI] [PubMed] [Google Scholar]
- 22.Giobbie-Hurder A, Gelber RD, Regan MM. Challenges of guarantee-time bias. J Clin Oncol. 2013;31:2963–2969. doi: 10.1200/JCO.2013.49.5283. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23. Klein J, Moeschberger ML: Survival Analysis: Techniques for Censored and Truncated Data (ed 2). New York, NY, Springer-Verlag, 2003. [Google Scholar]
- 24. van Buuren S, Groothuis-Oudshoorn K: Multivariate imputation by chained equations in R. https://www.jstatsoft.org/article/view/v045i03.
- 25. Little RJA, Rubin DB: Statistical Analysis With Missing Data (ed 2). Hoboken, NJ, John Wiley & Sons, 2014.
- 26.Qu Y, Lipkovich I. Propensity score estimation with missing values using a multiple imputation missingness pattern (MIMP) approach. Stat Med. 2009;28:1402–1414. doi: 10.1002/sim.3549. [DOI] [PubMed] [Google Scholar]
- 27.Austin PC, Stuart EA. Moving towards best practice when using inverse probability of treatment weighting (IPTW) using the propensity score to estimate causal treatment effects in observational studies. Stat Med. 2015;34:3661–3679. doi: 10.1002/sim.6607. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Lin DY, Psaty BM, Kronmal RA. Assessing the sensitivity of regression results to unmeasured confounders in observational studies. Biometrics. 1998;54:948–963. [PubMed] [Google Scholar]
- 29.Hedström G, Hagberg O, Jerkeman M, et al. The impact of age on survival of diffuse large B-cell lymphoma: A population-based study. Acta Oncol. 2015;54:916–923. doi: 10.3109/0284186X.2014.978367. [DOI] [PubMed] [Google Scholar]
- 30.Smith A, Crouch S, Howell D, et al. Impact of age and socioeconomic status on treatment and survival from aggressive lymphoma: A UK population-based study of diffuse large B-cell lymphoma. Cancer Epidemiol. 2015;39:1103–1112. doi: 10.1016/j.canep.2015.08.015. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Carey M.S., Bacon M, Tu D, et al. The prognostic effects of performance status and quality of life scores on progression-free survival and overall survival in advanced ovarian cancer. Gynecol Oncol. 2008;108:100–105. doi: 10.1016/j.ygyno.2007.08.088. [DOI] [PubMed] [Google Scholar]
- 32. doi: 10.1007/s00277-013-1860-8. Touzeau C, Leux C, Bouabdallah R, et al: Autologous stem cell transplantation in mantle cell lymphoma: a report from the SFGM-TC. Ann Hematol 93:233-242, 2014. [DOI] [PubMed] [Google Scholar]
- 33. Sawalha Y, Radivoyevitch T, Tullio K, et al: The role of upfront autologous hematopoietic cell transplantation in the treatment of mantle cell lymphoma, a population based study using the National Cancer Data Base (NCDB). Presented at the 59th Annual Meeting of the American Society of Hematology, Atlanta GA, December 9-12, 2017. [Google Scholar]
- 34.Maddocks K, Christian B, Jaglowski S, et al. A phase 1/1b study of rituximab, bendamustine, and ibrutinib in patients with untreated and relapsed/refractory non-Hodgkin lymphoma. Blood. 2015;125:242–248. doi: 10.1182/blood-2014-08-597914. [DOI] [PubMed] [Google Scholar]
- 35.Robak T, Huang H, Jin J, et al. Bortezomib-based therapy for newly diagnosed mantle-cell lymphoma. N Engl J Med. 2015;372:944–953. doi: 10.1056/NEJMoa1412096. [DOI] [PubMed] [Google Scholar]
- 36.Ruan J, Martin P, Shah B, et al. Lenalidomide plus rituximab as initial treatment for mantle-cell lymphoma. N Engl J Med. 2015;373:1835–1844. doi: 10.1056/NEJMoa1505237. [DOI] [PMC free article] [PubMed] [Google Scholar]