The Genotype Of Mlh1 Identifies A Subgroup Of

Published Ahead of Print on January 16, 2015, as doi:10.3324/haematol.2014.108183.
Copyright 2015 Ferrata Storti Foundation.
The genotype of MLH1 identifies a subgroup of follicular
lymphoma patients that do not benefit from doxorubicin:
FIL-FOLL05 study
by Davide Rossi, Alessio Bruscaggin, Piera La Cava, Sara Galimberti, Elena Ciabatti,
Stefano Luminari, Luigi Rigacci, Alessandra Tucci, Alessandro Pulsoni, Giovanni Bertoldero,
Daniele Vallisa, Chiara Rusconi, Michele Spina, Luca Arcaini, Francesco Angrilli,
Caterina Stelitano, Francesco Merli, Gianluca Gaidano, Massimo Federico,
and Giuseppe A. Palumbo
Haematologica 2015 [Epub ahead of print]
Citation: Rossi D, Bruscaggin A, La Cava P, Galimberti S, Ciabatti E, Luminari S, Rigacci L, Tucci A,
Pulsoni A, Bertoldero G, Vallisa D, Rusconi C, Spina M, Arcaini L, Angrilli F, Stelitano C, Merli F,
Gaidano G, Federico M, and Palumbo GA. The genotype of MLH1 identifies a subgroup of follicular
lymphoma patients that do not benefit from doxorubicin: FIL-FOLL05 study.
Haematologica. 2015; 100:xxx
doi:10.3324/haematol.2014.108183
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THE GENOTYPE OF MLH1 IDENTIFIES A SUBGROUP OF FOLLICULAR
LYMPHOMA PATIENTS THAT DO NOT BENEFIT FROM DOXORUBICIN:
FIL-FOLL05 STUDY
Davide Rossi,1 Alessio Bruscaggin,1 Piera La Cava,2 Sara Galimberti,3 Elena Ciabatti,3 Stefano
Luminari,4 Luigi Rigacci,5 Alessandra Tucci,6 Alessandro Pulsoni,7 Giovanni Bertoldero,8 Daniele
Vallisa,9 Chiara Rusconi,10 Michele Spina,11 Luca Arcaini,12 Francesco Angrilli,13 Caterina
Stelitano,14 Francesco Merli,15 Gianluca Gaidano,1* Massimo Federico,4* Giuseppe A. Palumbo2*
*
G.G., M.F., and G.A.P. equally contributed
1
Division of Hematology, Department of Translational Medicine, Amedeo Avogadro University of
Eastern Piedmont, Novara, Italy; 2Division of Hematology, AOU "Policlinico V. Emanuele",
Catania, Italy; 3Department of Oncology, Transplants and Advanced Technologies, Section of
Haematology, University of Pisa, Pisa, Italy; 4Department of Oncology and Hematology, University
of Modena and Reggio Emilia, Modena, Italy; 5Division of Hematology, Azienda Ospedaliero
Universitaria Careggi, Florence, Italy; 6Division of Hematology, Spedali Civili Hospital and
University, Brescia, Italy; 7Hematology, Department of Cellular Biotechnologies and Hematology,
Sapienza University, Rome, Italy; 8Department of Oncology, Civic Hospital, Noale, Venice, Italy;
9
Division of Hematology, Ospedale Civile, Piacenza, Italy; 10Division of Hematology, Department
of Hematology and Oncology, Niguarda Hospital, Milan, Italy; 11Division of Medical Oncology A,
National Cancer Institute, Aviano, Italy; 12Divisions of Hematology, Fondazione IRCCS Policlinico
San Matteo, University of Pavia, Pavia, Italy; 13Department of Hematology, Local Health Unit of
Pescara, Italy; 14Division of Hematology, Ospedale di Reggio Calabria, Reggio Calabria, Italy;
15
Hematology Unit, Arcispedale Santa Maria Nuova, Istituto di Ricovero e Cura a Carattere
Scientifico, Reggio Emilia, Italy
Running head: MHL1 polymorphims in FL
Correspondence: Giuseppe Palumbo, M.D., Division of Hematology, AOU "Policlinico V.
Emanuele", Catania, Italy; E-mail [email protected];
Abstract word count: 246; Text word count: 2327; Tables: 3; Figures: 4; Supplementary files:
1.
Trial registration: clinicaltrials.gov identifier: NCT00774826
Acknowledgments: This study was supported by Special Program Molecular Clinical Oncology 5
x 1000 No. 10007 and My First AIRC Grant No. 13470 Associazione Italiana per la Ricerca sul
Cancro Foundation Milan, Italy; Progetto Giovani Ricercatori 2010, Grant No. GR-2010-2317594,
Ministero della Salute, Rome, Italy; Compagnia di San Paolo, Grant No. PMN_call_2012_0071,
Turin, Italy; Fondazione Cariplo, Grant No. 2012-0689; Futuro in Ricerca 2012 Grant No.
RBFR12D1CB, Ministero dell'Istruzione, dell'Università e della Ricerca, Rome, Italy.
ABSTRACT
Though most follicular lymphoma biomarkers rely on tumor features, the host genetic
background may also be relevant for outcome. Here we aimed at verifying the contribution of
candidate polymorphisms of FCγ receptor, DNA repair and detoxification genes to prognostic
stratification of follicular lymphoma treated with immunochemotherapy. The study was based on
428 patients enrolled in the FOLL05 prospective trial that compared three standard-of-care
regimens (rituximab-cyclophosphamide-vincristine-prednisone vs rituximab-cyclophosphamidedoxorubicin-vincristine-prednisone vs rituximab-fludarabine-mitoxantrone) for the first line therapy
of advanced follicular lymphoma. Polymorphisms were genotyped on peripheral blood DNA
samples. Primary endpoint was time-to-treatment-failure. Polymorphisms of FCGR2A and
FCGR3A, which have been suggested to influence rituximab single agent activity, did not affect
time-to-treatment-failure in the pooled analysis of the three FOLL05 treatment arms that combined
rituximab with chemotherapy (p=.742, p=.252, respectively). These results were consistent even
when the analysis was conducted by intention to treatment arm, indicating that different
chemotherapy regimens and loads did not differentially interact with FCGR2A and FCGR3A
genotypes. The genotype of MLH1, which regulates the genotoxic effect of doxorubicin,
significantly affected time-to-treatment-failure in the rituximab-cyclophosphamide-doxorubicinvincristine-prednisone arm (p=.001; q<.1), but not in arms lacking doxorubicin (rituximabcyclophosphamide-vincristine-prednisone, rituximab-fludarabine-mitoxantrone). The impact of
MLH1 on time-to-treatment-failure was independent after adjusting for the Follicular Lymphoma
International Prognostic Index and other potential confounding variables by multivariate analysis.
These data indicate that MLH1 genotype is a predictor of rituximab-cyclophosphamidedoxorubicin-vincristine-prednisone treatment failure in advanced follicular lymphoma and confirm
that FCGR2A and FCGR3A polymorphisms have no impact when follicular lymphoma is treated
with rituximab plus chemotherapy (clinicaltrials.gov identifier: NCT00774826).
2
INTRODUCTION
The current standard of treatment for advanced follicular lymphoma (FL) is
immunochemotherapy, that combines the anti-CD20 monoclonal antibody rituximab to a variety of
multiagent
chemotherapy
regimens
incorporating
anthracyclines
(i.e.
doxorubicin),
anthracenediones (i.e. mitoxantrone), alkylating agents (i.e. cyclophosphamide), or purine
analogues (i.e. fludarabine).1,2 A number of clinical markers have been proposed as tools for
refining survival prognostication in FL, most of which rely on the features of the tumor clone.1,2 On
the contrary, a limited set of biomarkers is available to predict treatment outcome in this
lymphoma.3,4
The activity of drugs employed for the treatment of FL may be affected by the patient
genetic background. The anti-tumoral effect of monoclonal antibodies may be modulated by
polymorphisms of the FCγ receptors, that are expressed on cells responsible of the antibodydependent cell-mediated cytotoxicity (ADCC) and devoted to attract and activate the immune
response against antibody-coated tumor cells.5 While FCγ receptor polymorphisms may influence
the outcome of rituximab monotherapy in FL, their role in the context of immunochemoterapy is
questionable.6-15 The therapeutic activity of doxorubicin may be modulated by a polymorphism of
MLH1,16 a molecule that is involved in the induction of cell cycle arrest and apoptosis in response
to the DNA damage produced by doxorubicin.17,18 The outcome of doxorubicin-based
chemotherapy is also affected by functional polymorphisms of CYBA, a subunit of the NADPH
oxidase complex that produces reactive oxygen species in response to chemotherapy.19,20 The
therapeutic activity of cyclophosphamide is dependent on polymorphisms of genes deputed to its
detoxification, such as GSTA1.19
The FOLL05 study compared three standard-of-care regimens for the first line therapy of
advanced FL.21 Patients were randomized to receive rituximab plus cyclophosphamide, vincristine,
and prednisone (R-CVP), or rituximab plus cyclophosphamide, doxorubicin, vincristine, and
3
prednisone (R-CHOP), or rituximab plus fludarabine and mitoxantrone (R-FM). The FOLL05 study
showed that R-CHOP and R-FM are superior to R-CVP in terms of time to treatment failure
(TTF).21
We took advantage of the FOLL05 study to: i) clarify the role of FCγ receptor
polymorphism in advanced FL patients treated with rituximab-based immunochemotherapy; and ii)
assess whether MLH1, CYBA and GSTA1 polymorphisms selectively predict the outcome of a
specific immunochemotherapy regimen.
4
METHODS
Patients
Peripheral blood samples were prospectively obtained from 428/504 (84.9%) untreated
advanced FL patients enrolled in the multicenter randomized FOLL05 study (Table 1; Figure 1).21
The study was designed to assess differences in TTF, that was the primary end point of the FOLL05
study (See Supplementary Appendix).21,22 The REMARK and STREGA guidelines were followed
throughout this study.23,24 FOLL05 (clinicaltrials.gov identifier: NCT00774826) was conducted in
compliance with the Declaration of Helsinki, was approved by the appropriate research ethics
committee, required each patient to provide written informed consent and also included
centralization of DNA from patients samples for ancillary studies.
SNP genotyping
Genomic DNA was extracted from peripheral blood samples. Genotyping of the FCGR2A
rs1801274, FCGR3A rs396991, CYBA rs4673, and GSTA1 rs3957357 SNPs was performed on high
molecular weight genomic DNA by single nucleotide polymorphism (SNP) minisequencing (ABI
Prism SNaPshot Multiplex kit, Applied Biosystems, Foster City, CA), after validation of this
approach by DNA direct sequencing of each SNP in a pilot panel of cases (n=40). Genotyping of
the MLH1 rs1799977 SNP was performed on high molecular weight genomic DNA by Sanger
sequencing. Details are in the Supplementary Appendix. Quality control of genotyping was
performed by replicate sample analysis (100% concordance in replicates for all the candidate
SNPs). Deviation of SNP genotype distribution from Hardy-Weinberg equilibrium was tested by
chi-square test or Fisher exact test if appropriate. SNP genotyping was performed in blind with the
study endpoint.
5
Statistical analysis
TTF was the primary end point of the study, was evaluated according to the intention-totreat principle, and was defined as time from date of study entry to last follow-up or to the first of
the following events: less than partial remission (PR), shift to a different therapy for any reason
after at least cycle one, PD or relapse, or death.21 Molecular studies were blinded to the study end
points. Analysis of TTF was performed by Kaplan-Meier method using the log-rank test to assess
differences between genotype groups.25 False discovery rate (FDR) was used to control for multiple
statistical testing.26 Cox-regression analysis was used to estimate genotype-specific hazard ratios
(HR) and 95% confidence intervals (CI), adjusting for potentially confounding covariates.27 For
each SNP genotype, the HRs were generated using common allele homozygotes as the reference
group. For SNPs with 10 or fewer minor allele homozygotes, only the combination of minor allele
homozygotes with heterozygotes was analyzed. If this combined frequency was still <10, then the
SNP was removed from the analysis. Proportional hazard regression assumptions were
appropriately assessed. Bias corrected c-index, calibration slope and heuristic shrinkage estimator
of the Cox model were calculated.28 Cox model stability was internally validated using
bootstrapping procedures.29-31 These approaches provided an estimate of prediction accuracy of the
Cox model to protect against overfitting. Categorical variables were compared by chi-square test
and exact test, when appropriate. All statistical tests were two-sided. Statistical significance was
defined as p-value<0.05 and a q-value <0.1. The analysis was performed with SPSS v.21.0 and with
R statistical package 3.0.1 (http://www.r-project.org).
6
RESULTS
FCγ receptor polymorphisms have no prognostic impact when advanced FL is treated with
chemoimmunotherapy
The clinical features of the 428 advanced FL patients available for SNP genotyping (84.9%
of the whole FOLL05 study cohort; Figure 1) did not differ from those of the 76 patients not
available for genotyping (Table 1). These data indicate that the lack of biological material for
genotyping was not due to an unintended selection bias. Out of the 428 genotyped cases, the
FCGR2A and FCGR3A polymorphisms were assessable in 407 and 406 patients, respectively
(Figure 1). In the remaining cases, the quality and/or quantity of genomic DNA prevented its
amplification and sequencing. The distributions of the FCGR2A and FCGR3A polymorphisms were
in Hardy-Weinberg equilibrium, thus excluding poor genotyping or population biases (Table S1).
Patient characteristics at diagnosis as well as treatment allocation distributed without significant
differences across the three genotypes of the FCGR2A and FCGR3A polymorphisms (Table S2 and
S3).
All FL patients enrolled in the FOLL05 study, independent of the treatment arm, were
planned to receive 8 rituximab doses combined with chemotherapy.21 Therefore, the impact of
FCGR2A and FCGR3A genotypes on the primary clinical endpoint of the study (i.e. TTF) was
initially assessed in the whole study cohort. By pooled analysis of the three treatment arms, TTF
was not influenced by the FCGR2A (p=.742) and FCGR3A genotypes (p=.252) (Table 2; Figure 2).
FCGR2A and FCGR3A genotypes did not influence TTF either in clinical subgroups defined by
disease bulk or patients' gender, which might affect disease sensitivity to rituximab, or in different
FLIPI prognostic groups (Figure S1, S2 and S3). Also, the overall response rate distributed without
significant differences across the three genotypes of the FCGR2A (p=.994) and FCGR3A (p=.606)
polymorphisms. By multivariate analysis, FLIPI and treatment allocation, but not FCGR2A and
FCGR3A genotypes, were independent predictors of TTF (Table 2), thus confirming that the
7
FOLL05 study population included in this genotype-phenotype association analysis is
representative of advanced FL patients.
Patients enrolled in the FOLL05 study were randomized to receive different loads of
chemotherapy combined to rituximab, with the lowest load in the R-CVP arm.21 In order to verify
whether different chemotherapy regimens and loads might differentially interact with FCGR2A and
FCGR3A genotypes, the impact of these SNPs on TTF was also assessed by treatment arm.
However, even when the analysis was conducted by intention to treatment arm, TTF did not differ
according to FCGR2A and FCGR3A genotypes (Figure S1 and S2).
Similarly to FCγ SNPs, also polymorphisms of GSTA1 and CYBA had no role in FL outcome
prediction (Figure S4).
The genotype of MLH1 is a predictor of R-CHOP treatment failure in advanced FL
The MLH1 polymorphism was assessable in 411 FL patients (Figure 1), and its distribution
was in Hardy-Weinberg equilibrium (Table S1). Among the drugs utilized in the FOLL05 study,
MLH1 is known to regulate the genotoxic effects of doxorubicin.17,18 According to this biologic
rationale, the clinical impact of the MLH1 polymorphism was initially assessed in FL patients
randomized to the R-CHOP arm. Among FL patients allocated to R-CHOP, characteristics at
diagnosis distributed without significant differences across the three genotypes of the MLH1
polymorphism, with the sole exception of a trend towards a more frequent involvement of >1
extranodal sites in patients homozygous for the variant allele (Table S4).
Univariate analysis for TTF controlled for multiple comparisons by FDR testing identified
the MLH1 polymorphism as a predictor of R-CHOP treatment failure in advanced FL (p=.011;
q<.1) (Table 3; Figure 3A). After R-CHOP treatment, FL patients who carried the homozygous GG
variant genotype of MLH1 showed a significantly lower 3-year TTF (30.3%) compared to patients
who carried the MLH1 AG (3-year TTF: 66.2% ) or AA (3-year TTF: 68.8%) genotypes (p=.010
and p=.003 in the pairwise comparisons) (Figure 3A). Consistent with the selective involvement of
8
MLH1 in doxorubicin pharmacodynamics, the MLH1 polymorphism did not affect the outcome of
FL patients treated with regimens lacking this drug (i.e. R-CVP and R-FM) (Figure S3). By
multivariate analysis, FL patients who carried the homozygous GG variant genotype of MLH1
displayed a 2.8 fold increase in risk of failing R-CHOP (HR: 2.81; 95% CI: 1.18-6.73; p=.020),
after adjusting for clinically relevant covariates including FLIPI, number of extranodal sites, bone
marrow involvement and beta-2-microglobulin elevation (Table 3). The increased risk of failing RCHOP translated into a significantly shorter OS in FL patients harboring the homozygous GG
variant genotype of MLH1 (Figure 3B).
The genotype of MLH1 predicts reduced benefit from the addition of doxorubicin in FL
treatments
The FOLL05 randomized study demonstrates that R-CHOP significantly improves TTF
compared to R-CVP in patients with previously untreated FL, thus documenting a relevant clinical
benefit when doxorubicin is added to the R-CVP backbone in this lymphoma type.21 Consistent
with these clinical data, the addition of doxorubicin to the R-CVP backbone resulted into a
significant improvement in the 3-year TTF (19% increase; p=0.002) in FL patients harboring the
common allele of MLH1 (AA and AG genotypes) (Figure 4A). On the contrary, FL patients who
carried the homozygous GG variant genotype of MLH1 (~10% of the FOLL05 population) did not
gain benefit from doxorubicin (Figure 4B).
9
DISCUSSION
This large prospective substudy of the FOLL05 trial, shows that: i) FCγ receptor
polymorphisms
have
no
prognostic
impact
when
advanced
FL
is
treated
with
chemoimmunotherapy; and ii) the MLH1 genotype is a predictor of R-CHOP failure in FL.
Though several small studies in FL have shown that FCγ receptor polymorphisms may be
useful in predicting response to single agent rituximab, their clinical impact in the setting of
immunochemotherapy is still controversial.6-14 Our study represent the most complete prospective
examination of the effects of FCγ polymorphisms on the outcome of advanced FL patients treated
with rituximab combined with chemotherapy. Consistent with the data from the PRIMA study,15
our analysis definitively indicates that FCγ receptor polymorphisms have no prognostic impact
when advanced FL is treated with chemoimmunotherapy, independent of the tumor burden and the
type and load of drugs that are combined with rituximab. Therefore, FCγ SNPs must not be further
considered and implemented as biomarkers in the setting of advanced FL treated with
immunochemotherapy.
The MLH1 polymorphism is a predictor of R-CHOP treatment failure in advanced FL.
Consistent with the selective involvement of MLH1 in doxorubicin pharmacodynamics,16-18 the
MLH1 genotype did not affect the outcome of FL patients treated with regimens lacking this drug.
The selective association between MLH1 genotype and outcome after R-CHOP has been clinically
validated in independent retrospective series of lymphoma patients, including two retrospective
DLBCL cohorts and this prospective FL series.16 Overall, these notions point to MLH1 genotype as
a predictor of R-CHOP failure in B-cell lymphoma.
The mechanicistic explanation of the phenotype observed in FL patients who carried the
homozygous GG variant genotype of MLH1 remains to be clarified. In other disease models, the
MLH1 rs1799977 polymorphism associates with reduced MLH1 protein expression in tumor
10
cells.16,32,33 Alternatively, the MLH1 rs1799977 polymorphism might be in linkage disequilibrium
with other functionally relevant SNPs of the MLH1 gene,34 suggesting that multiple variants within
the MLH1 locus may contribute to the risk of treatment failure in FL.
The association between CYBA and GSTA1 SNPs and R-CHOP outcome was not replicated
in the current study cohort. It is likely that moderate sample size, inter-subtype and other genetic
heterogeneity, as well as small true effect sizes account for the lack of replication. Alternatively, the
lack of replication might be the consequence of the false positive report probability that is known to
affect candidate gene association studies, and indicates that, at variance with MLH1 rs179977,
neither CYBA nor GSTA1 SNPs represent prognosticators in B-cell lymphomas treated with RCHOP.
Despite the limitations imposed by the sample size, our data provide a signal of reduced
benefit from the addition of doxorubicin to the R-CVP backbone in FL patients harboring the
homozygous GG variant genotype of MLH1. Conversely, the MLH1 genotype has no clinical
relevance in FL patients treated with R-FM, which, in turns, seems equally effective as R-CHOP in
the setting of advanced FL. Therefore, R-FM might represent a suitable initial chemotherapy
approach for FL patients carrying the homozygous GG variant genotype of MLH1. Replication of
these findings in other FL patient cohorts will be necessary to assess their generalizability.
The FOLL05 study was designed before the establishment of rituximab maintenance as a
standard of care for advanced FL.2 Also, the FOLL05 study does not include bendamustine-based
regimens among treatment strategies.2 These notions prompts investigations aimed at clarifying
whether maintenance after initial R-CHOP or bendamustine-based immunochemotherapy might
abrogate the prognostic impact of the MLH1 genotype.
11
AUTHORSHIP AND DISCLOSURES
Authors contribution: D.R., G.G., M.F. and G.A.P designed the study, interpreted data and wrote
the manuscript; D.R. and G.A.P. performed statistical analysis; A.B and P.L.C. performed and
interpreted molecular studies; S.G., E.C., S.L., F.A, L.A., G.B., F.M., A.P., L.R., G.R., C.R., M.S.,
C.S., and D.V. performed the research and contributed to preparation of manuscript.
Conflict-of-interest disclosure: D.R., A.B., P.L.C., S.G., E.C., S.L., L.R., A.T., A.P., G.B., D.V.,
C.R., M.S., L.A., F.A., C.S., F.M., M.F., and G.P. declare no conflict of interest. G.G. as consulted
in Advisory Boards for Celgene, GSK, Novartis, Roche, Amgen.
12
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34. Takahashi M, Shimodaira H, Andreutti-Zaugg C, Iggo R, Kolodner RD, Ishioka C. Functional
analysis of human MLH1 variants using yeast and in vitro mismatch repair assays. Cancer Res.
2007;67(10):4595-4604.
16
Table 1. Clinical features by availability of biological samples for genotyping a
Available for genotyping
(n=428)
n
%
FLIPI
0-1
2
3-5
Age >60 years
Male
ECOG PS >1
Ann Arbor stage III-IV
Nodal areas >4
Extranodal sites >1
Bone marrow involvement
Largest involved node >6 cm
Hb <12 g/dl
LDH >ULN
Beta-2-microglobulin >ULN
Grading
1
2
3
Unclassified
Treatment (ITT)
R-CVP
R-CHOP
R-FM
CR
3-years TTF
3-years PFS
3-years OS
Not available for genotyping
(n=76)
n
%
76
242
110
139
224
12
391
275
159
232
116
64
76
189
17.8
56.5
25.7
32.5
52.3
2.8
91.4
64.3
37.1
54.2
27.1
15.0
17.8
44.2
11
40
25
28
40
2
70
44
37
42
18
16
25
35
14.5
52.6
32.9
36.8
52.6
2.7
92.1
57.9
48.7
55.3
23.7
21.1
32.9
46.1
147
192
56
33
34.3
44.9
13.1
7.7
20
34
12
10
26.3
44.7
15.8
13.2
p
.401
.456
.962
.933
.829
.289
.057
.865
.543
.180
.002
.759
.278
.124
135
143
150
303
31.6
33.4
35.0
70.8
57.3
62.9
95.8
33
22
21
52
43.5
28.9
27.6
68.4
47.3
51.4
90.2
.641
.222
.128
.209
a
FLIPI, follicular lymphoma international prognostic index; ULN, upper limit of normal; ITT, intention to treatment; Hb, hemoglobin; ECOG PS, Eastern Cooperative Oncology Group Performance Status, LDH, lactate
dehydrogenase; R-CVP, rituximab, cyclophosphamide, vincristine, prednisone; RCHOP, rituximab, cyclophosphamide, doxorubicin, prednisone; RFM, rituximab, fludarabine, mitoxantrone
17
Table 2. Univariate and multivariate analysis for TTF in the whole study cohort a
HR
FLIPI
0-1
2
3-5
Age >60 years
Male
ECOG PS >1
Ann Arbor stage III-IV
Nodal areas >4
Extranodal sites >1
Bone marrow involvement
Largest involved node >6 cm
Hb <12 g/dl
LDH >ULN
Beta-2-microglobulin >ULN
Treatment (ITT)
R-CVP
R-CHOP
R-FM
FCGR2A rs18011274
AA
AG
GG
FCGR3A rs396991
TT
GT
GG
Univariate analysis
LCI
UCI
p
HR
Multivariate analysis
LCI
UCI
p
2.78
3.92
1.29
1.00
2.68
1.78
1.51
1.56
1.87
1.39
1.51
1.51
1.61
1.67
2.30
0.97
0.87
1.46
0.99
1.12
1.19
1.41
1.03
1.07
1.10
1.23
4.62
6.68
1.73
1.14
4.92
3.18
2.03
2.04
2.49
1.86
2.14
2.07
2.11
<.001
<.001
.075
.963
.001
.052
.006
.001
<.001
.027
.018
.010
.001
2.12
2.82
1.09
1.14
4.11
6.94
.025
.024
2.11
1.53
1.05
0.71
4.22
3.29
.034
.275
1.35
0.96
1.12
0.95
0.61
0.76
1.93
1.50
1.66
.094
.872
0.553
1.20
0.79
1.83
.372
0.59
0.64
0.43
0.46
0.83
0.88
.002
.007
0.52
0.54
0.35
0.37
0.76
0.78
.001
.001
1.12
1.00
0.80
0.63
1.58
1.58
.486
.988
1.03
1.06
0.72
0.66
1.47
1.68
.844
.795
0.82
1.51
0.53
0.81
1.26
1.63
.371
.429
0.91
1.32
0.58
0.2
1.42
1.89
.685
.126
a
TTF, time to treatment failure; HR, hazard ratio; LCI, 95% lower confidence interval; UCI, 95% upper confidence interval; FLIPI, follicular lymphoma international prognostic index; ULN, upper limit of normal; ITT,
intention to treatment; Hb, hemoglobin; ECOG PS, Eastern Cooperative Oncology Group Performance Status, LDH, lactate dehydrogenase; R-CVP, rituximab, cyclophosphamide, vincristine, prednisone; RCHOP,
rituximab, cyclophosphamide, doxorubicin, prednisone; RFM, rituximab, fludarabine, mitoxantrone
b
p-trend
Total number of patients included in the multivariate analysis: 406; Events: 165; FCGR2A and FCGR3A genotypes were not assessable in 22 cases
18
Table 3. Univariate and multivariate analysis for TTF in patients treated with R-CHOP a
HR
FLIPI
0-1
2
3-5
Age >60 years
Male
ECOG PS >1
Ann Arbor stage III-IV
Nodal areas >4
Extranodal sites >1
Bone marrow involvement
Largest involved node >6 cm
Hb <12 g/dl
LDH >ULN
Beta-2-microglobulin >ULN
MLH1 rs1799977
AA
AG
GG
Univariate analysis
LCI
UCI
p
HR
Multivariate analysis
LCI
UCI
p
1.75
2.50
1.02
0.94
1.12
1.34
1.51
1.95
1.74
1.05
1.39
1.42
1.56
0.80
1.09
.059
0.73
.027
0.54
0.87
1.17
1.02
0.57
0.72
0.79
0.94
3.79
5.74
1.76
1.21
4.61
3.37
2.60
3.24
2.95
1.92
2.68
2.55
2.60
.155
.030
.938
.649
.873
.522
.138
.010
.039
.863
.319
.241
.083
1.27
1.35
0.43
0.35
3.71
5.25
.654
.660
2.54
0.87
1.24
0.38
5.19
1.98
.010
.745
1.17
0.56
2.44
.662
0.82
2.89
0.44
1.24
1.53
6.72
.544
.014
0.90
2.81
0.48
1.18
1.70
6.73
.757
.020
a
TTF, time to treatment failure; HR, hazard ratio; LCI, 95% lower confidence interval; UCI, 95% upper confidence interval; FLIPI, follicular lymphoma international prognostic index; ULN, upper limit of normal; ITT,
intention to treatment; Hb, hemoglobin; ECOG PS, Eastern Cooperative Oncology Group Performance Status, LDH, lactate dehydrogenase; R-CVP, rituximab, cyclophosphamide, vincristine, prednisone; RCHOP,
rituximab, cyclophosphamide, doxorubicin, prednisone; RFM, rituximab, fludarabine, mitoxantrone
Total number of patients included in the multivariate analysis: 138; Events: 48; MLH1 genotypes was not assessable in 22 cases
19
FIGURE LEGENDS
Figure 1. CONSORT diagram representing the number of patients included in the analysis
Figure 2. Kaplan-Meier estimates of time to treatment failure in the polled treatment arms
according to FCGR2A and FCGR3A genotypes. (A) Comparison of time to treatment failure
(TTF) between patients homozygous for the common FCGR2A rs1801274 allele (blue line),
patients heterozygous for the FCGR2A rs1801274 genotype (yellow line), and patients homozygous
for the variant FCGR2A rs1801274 allele (red line). (B) Comparison of TTF between patients
homozygous for the common FCGR3A rs396991 allele (blue line), patients heterozygous for the
FCGR3A rs396991 genotype (yellow line), and patients homozygous for the variant FCGR3A
rs396991 allele (red line). p, p values by log-rank test.
Figure 3. Kaplan-Meier estimates of time to treatment failure and overall survival in patients
randomized to the R-CHOP arm according to the MLH1 rs1799977 genotype. (A) Comparison
of time to treatment failure (TTF) between patients homozygous for the common MLH1 rs1799977
allele (blue line), patients heterozygous for the MLH1 rs1799977 genotype (yellow line), and
patients homozygous for the variant MLH1 rs1799977 allele (red line). (B) Comparison of overall
survival (OS) between patients homozygous for the common MLH1 rs1799977 allele (blue line),
patients heterozygous for the MLH1 rs1799977 genotype (yellow line), and patients homozygous
for the variant MLH1 rs1799977 allele (red line). p, p values by log-rank test; q, q values by false
discovery rate.
Figure 4. Kaplan-Meier estimates of time to treatment failure stratified according to the
MLH1 rs1799977 genotype and treatment randomization. (A) Comparison of time to treatment
failure (TTF) between R-CHOP (blue line), R-CVP (red line) and R-FM (yellow line) among
20
patients harboring the MLH1 rs1799977 AA/AG genotype. (B) Comparison of time to treatment
failure (TTF) between R-CHOP (blue line), R-CVP (red line) and R-FM (yellow line) among
patients harboring the MLH1 rs1799977 GG genotype. p, p values by log-rank test.
21
SUPPLEMENTARY APPENDIX CONTENTS
THE GENOTYPE OF MLH1 IDENTIFIES A SUBGROUP OF FOLLICULAR LYMPHOMA PATIENTS
THAT DO NOT BENEFIT FROM DOXORUBICIN: FIL-FOLL05 STUDY
Davide Rossi,1 Alessio Bruscaggin,1 Piera La Cava,2 Sara Galimberti,3 Elena Ciabatti,3 Stefano Luminari,4 Luigi
Rigacci,5 Alessandra Tucci,6 Alessandro Pulsoni,7 Giovanni Bertoldero,8 Daniele Vallisa,9 Chiara Rusconi,10 Michele
Spina,11 Luca Arcaini,12 Francesco Angrilli,13 Caterina Stelitano,14 Francesco Merli,15 Gianluca Gaidano,1* Massimo
Federico,4* Giuseppe A. Palumbo2*
*
1
G.G., M.F., and G.A.P. equally contributed
Division of Hematology, Department of Translational Medicine, Amedeo Avogadro University of Eastern Piedmont,
Novara, Italy; 2Division of Hematology, AOU "Policlinico V. Emanuele", Catania, Italy; 3Department of Oncology,
Transplants and Advanced Technologies, Section of Haematology, University of Pisa, Pisa, Italy; 4Department of
Oncology and Hematology, University of Modena and Reggio Emilia, Modena, Italy; 5Division of Hematology,
Azienda Ospedaliero Universitaria Careggi, Florence, Italy; 6Division of Hematology, Spedali Civili Hospital and
University, Brescia, Italy; 7Hematology, Department of Cellular Biotechnologies and Hematology, Sapienza University,
Rome, Italy; 8Department of Oncology, Civic Hospital, Noale, Venice, Italy; 9Division of Hematology, Ospedale Civile,
Piacenza, Italy; 10Division of Hematology, Department of Hematology and Oncology, Niguarda Hospital, Milan, Italy;
11
Division of Medical Oncology A, National Cancer Institute, Aviano, Italy; 12Divisions of Hematology, Fondazione
IRCCS Policlinico San Matteo, University of Pavia, Pavia, Italy; 13Department of Hematology, Local Health Unit of
Pescara, Italy; 14Division of Hematology, Ospedale di Reggio Calabria, Reggio Calabria, Italy; 15Hematology Unit,
Arcispedale Santa Maria Nuova, Istituto di Ricovero e Cura a Carattere Scientifico, Reggio Emilia, Italy
Supplementary Methods
Figure S1. Kaplan-Meier estimates of time to treatment failure stratified according to the FCGR2A rs1801274 genotype,
clinical features at presentation and treatment randomization.
Figure S2. Kaplan-Meier estimates of time to treatment failure stratified according to the FCGR3A rs396991 genotype,
clinical features at presentation and treatment randomization.
Figure S3. Kaplan-Meier estimates of time to treatment failure stratified according to the genotypes of FCGR3A
rs396991 and FCGR3A rs396991 and patients' gender.
Figure S4. Kaplan-Meier estimates of time to treatment failure stratified according to the genotypes of MLH1
rs1799977, GSTA1 rs3957357, and CYBA rs4673, and treatment randomization.
Table S1. Distribution of the alleles and Hardy-Weinberg equilibrium
Table S2. Clinical features of the whole study cohort by FCGR2A rs1801274 genotype
Table S3. Clinical features of the whole study cohort by FCGR3A rs396991 genotype
Table S4. Clinical features of R-CHOP arm cohort by MLH1 rs1799977 genotype
2
Supplementary Methods
Patients
Patients were enrolled in the FOLL05 study between March 2006 and September 2010. The clinical database was locked on May
2012. The FLIPI score was a pre-specified criterion of patient stratification and was assessed at the time of patient enrollment and
before treatment. The three study arms included eight doses of rituximab combined with eight courses of CVP, or six cycles of
CHOP or six cycles of FM, every 3 weeks. Doses and administration schedules for each regimen were as follows: CVP
(cyclophosphamide 750 mg/m2 on day 1, vincristine 1.4 mg/m2 [capped at 2 mg] on day 1, prednisone 40 mg/m2 on days 1-5),
CHOP (cyclophosphamide 750 mg/m2 on day 1, vincristine 1.4 mg/m2 [capped at 2 mg] on day 1, doxorubicin 50 mg/m2 on day
1, prednisone 100 mg on days 1-5,), and FM (fludarabine 25 mg/m2 on days 1-3, mitoxantrone 10 mg/m2 on day 1). Rituximab
(375 mg/m2 at each infusion) was administered on day 1 of each chemotherapy course. Growth factors were administered at
physicians' discretion. Dose reduction/treatment delay rules were pre-specified in the protocol. Median overall delivered doseintensities were 0.956, 0.964, and 0.918 for R-CVP, R-CHOP, and R-FM, respectively. Final response was assessed within one
month after last rituximab infusion. Response assessment was performed by physical examination, laboratory tests, and total-body
CT scan. Bone marrow biopsy was required only for assessment of final response if positive at baseline. Patients showing
progressive (PD) or stable disease (SD) were coded as experiencing treatment failure and shifted to salvage treatment. According
to study protocol, maintenance therapy was not allowed. During follow-up, disease status was to be assessed at months +3, +6,
+12, +18, +24, and +36 with CT scan and with bone marrow biopsy if positive at baseline.
Study design
Sample size calculation of this biologically ancillary study was performed according to the assumption of a proportion of patients
with the genotype at risk >10%. By pooled analysis of the three treatment arms, we estimated that 428 patients would allow
detecting at least a 20% difference in 3-year TTF between patients harboring the genotype at risk (3-year-TTF=35%) and patients
harboring the common genotype (3-year-TTF=56%) (power=82%; alpha=0.05). In the R-CVP arm, we estimated that 135 patients
would allow detecting at least a 30% difference in 3-year TTF between patients harboring the genotype at risk (3-year-TTF=15%)
and patients harboring the common genotype (3-year-TTF=45%) (power=80%; alpha=0.05). In the R-CHOP arm, we estimated
that 143 patients would allow detecting at least a 30% difference in 3-year TTF between patients harboring the genotype at risk
(3-year-TTF=28%) and patients harboring the common genotype (3-year-TTF=63%) (power=80%; alpha=0.05). In the R-FM
arm, we estimated that 143 patients would allow detecting at least a 30% difference in 3-year TTF between patients harboring the
genotype at risk (3-year-TTF=28%) and patients harboring the common genotype (3-year-TTF=58%) (power=84%; alpha=0.05).
SNP genotyping
Genotyping of FCGR2A rs1801274, FCGR3A rs396991, CYBA (rs4673) and GSTA1 (rs3957357) was analyzed by single base
sequencing on a ABI Prism 3100 Genetic Analyzer (Applied Biosystem), using the ABI Prism SNaPshot Multiplex Kit according
to manufacturer instructions and the following sequencing primers: GATGGAGAAGGTGGGATCCAAA,
TTTTTTTTTCCTACTTCTGCAGGGGGCTT,
AAAAAAAACCTCCCCCAGGGGACAGAAG
and
AAAAAAAAAAAAAAAAAAAAAAAATCTCTCCCACTGAAAGAAG. In brief, 100 ng of genomic DNA were amplyfied in
50 µl, using the same primer couples as above at a final concentration of 0.2 microM, on a GeneAmp 9700 thermal cycler
(Applied Biosystem) for 50 cycles (1’ at 94°C, 1’ at 60°C, 1’ at 72°C), using 1.25 units of AmpliTaq Gold (Applied Biosystem),
its 1X buffer with MgCl2 and dNTPs (Applied Biosystem) at a final concentration of 100 µM each. Then, to remove
unincorporated dNTPs, 5 µl of the PCR product were treated with 2 µl of ExoSAP-IT (USB) at 37°C for 15’ and inactivated at
80°C for 15’; 3 µl of the purified template were added to 5 µl of SNaPshot Multiplex Ready Reaction Mix, and 0.2 µM of the
sequencing primer, and placed in the thermal cycler for 25 cycles (96°C for 10”, 50°C for 5”. 60°C for 30”). Post-extension
treatment, to remove unincorporated ddNTPs, consisted in adding 1 U of Shrimp Alkaline Phospatase (USB), followed by
incubation at 37°C for 1 hour and inactivation at 75°C for 15’. Finally, samples underwent electrophoresis on the Genetic
Analyzer at 60°C with POP-4 polymer using the E5 Run Module (injection time: 5”; electrophoresis and EP voltages: 15 kV;
collection time 24’; syringe pump time: 150”; preinjection EP: 120”). Collected data were analyzed using the GeneScan software
ver. 3.1.2. Primers used for amplification and direct Sanger sequencing of the MLH1 rs1799977 polymorphism were. 5’GCCTCAACCGTGGACAATA and 3’- TCACGCCACAGAATCTAGGA.
3
Table S1. Distribution of the alleles and Hardy-Weinberg equilibrium
SNP
FCGR2A rs1801274
FCGR3A rs396991
MLH1 rs1799977
NCF4 rs1883112
CYBA rs4673
GSTA1 rs3957357
Assessable
407
406
411
417
417
416
Homozygote for the
common allele
139 (AA)
127 (TT)
182 (AA)
164 (GG)
144 (CC)
126 (CC)
Heterozygote
193 (AG)
183 (GT)
189 (AG)
202 (AG)
198 (CT)
217 (CT)
Homozygote for the
minor allele
75 (GG)
96 (GG)
40 (GG)
51 (AA)
75 (TT)
73 (TT)
HWE p
.578
.060
.368
.351
.629
.271
4
Table S2. Clinical features of the whole study cohort by FCGR2A rs1801274 genotype
FLIPI
0-1
2
3-5
Age >60 years
Male
ECOG PS >1
Ann Arbor stage III-IV
Nodal areas >4
Extranodal sites >1
Bone marrow involvement
Largest involved node >6 cm
Hb <12 g/dl
LDH >ULN
Beta-2-microglobulin >ULN
Grading
1
2
3
Unclassified
Treatment (ITT)
R-CVP
R-CHOP
R-FM
CR
3-years TTF
3-years PFS
3-years OS
Homozygote for the common allele
(AA; n=139)
n
%
Heterozygote
(AG; n=193)
n
%
Homozygote for the minor allele
(GG; n=75)
n
%
27
83
29
46
79
3
126
88
46
73
29
16
26
62
19.4
59.7
20.9
33.1
56.8
2.2
90.6
63.3
33.1
52.5
20.9
11.5
18.7
44.6
34
102
57
67
93
8
176
121
77
103
56
34
29
86
17.6
52.8
29.5
34.7
48.2
4.1
91.2
62.7
39.9
53.4
29.0
17.6
15.0
44.6
13
43
19
19
41
1
69
51
28
41
24
11
18
35
17.3
57.3
25.3
25.3
54.7
1.3
92.0
68.0
37.3
54.7
32.0
14.7
24.0
46.7
40
72
12
15
28.8
51.8
8.6
10.8
71
82
30
10
36.8
42.5
15.5
5.2
26
32
11
6
34.7
42.7
14.7
8.0
44
37
58
103
31.7
26.6
41.7
76.3
59.1
65.0
95.8
60
72
61
135
31.1
37.3
31.6
71.1
57.9
61.8
96.2
23
28
24
52
30.7
37.3
32.0
69.3
62.1
69.6
97.2
p
.517
.331
.269
.305
.946
.709
.448
.956
.134
.305
.377
.947
.133
.219
.460
.742
.452
.077
5
Table S3. Clinical features of the whole study cohort by FCGR3A rs396991 genotype
FLIPI
0-1
2
3-5
Age >60 years
Male
ECOG PS >1
Ann Arbor stage III-IV
Nodal areas >4
Extranodal sites >1
Bone marrow involvement
Largest involved node >6 cm
Hb <12 g/dl
LDH >ULN
Beta-2-microglobulin >ULN
Grading
1
2
3
Unclassified
Treatment (ITT)
R-CVP
R-CHOP
R-FM
CR
3-years TTF
3-years PFS
3-years OS
Homozygote for the common allele
(TT; n=127)
n
%
Heterozygote
(GT; n=183)
n
%
Homozygote for the minor allele
(GG; n=96)
n
%
18
74
35
43
66
7
119
80
51
73
38
19
23
66
14.2
58.3
27.6
33.9
52.0
5.5
93.7
63.0
40.2
57.5
29.9
15.0
18.1
52.0
43
94
46
54
97
4
166
118
65
89
50
26
30
75
23.5
51.4
25.1
29.5
53.0
2.2
90.7
64.5
35.5
48.6
27.3
14.2
16.4
41.0
13
60
23
34
50
1
85
61
35
54
21
15
20
41
13.5
62.5
24.0
35.4
52.1
1.0
88.5
63.5
36.5
56.3
21.9
15.6
20.8
42.7
48
51
16
12
37.8
40.2
12.6
9.4
65
81
21
16
35.5
44.3
11.5
8.7
24
53
16
3
25.0
55.1
16.7
3.1
43
40
44
89
33.9
31.5
34.6
71.8
61.7
64.7
97.1
56
67
60
130
30.6
36.6
32.8
71.8
55.0
59.9
95.4
28
29
39
70
29.2
30.2
40.6
74.5
61.5
69.1
96.8
p
.139
.543
.980
.106
.392
.963
.698
.243
.398
.949
.656
.143
.104
.637
.880
.252
.213
.904
6
Table S4. Clinical features of R-CHOP arm cohort by MLH1 rs1799977 genotype
Homozygote for the common allele
(AA; n=65)
n
%
FLIPI
0-1
2
3-5
Age >60 years
Male
ECOG PS >1
Ann Arbor stage III-IV
Nodal areas >4
Extranodal sites >1
Bone marrow involvement
Largest involved node >6 cm
Hb <12 g/dl
LDH >ULN
Beta-2-microglobulin >ULN
Grading
1
2
3
Unclassified
CR
3-years TTF
3-years PFS
3-years OS
Heterozygote
(AG; n=62)
n
%
Homozygote for the minor allele
(GG; n=11)
n
%
14
37
14
22
35
2
60
40
25
32
12
10
11
28
21.5
56.9
21.5
33.8
53.8
3.1
92.3
61.5
38.5
49.2
18.5
15.4
16.9
43.1
10
35
17
19
31
1
54
36
19
34
18
11
13
29
16.1
56.5
27.4
30.6
50.0
1.6
87.1
58.1
30.6
54.8
29.0
17.7
21.0
46.8
1
5
5
4
4
1
10
8
8
9
3
2
2
8
9.1
45.5
45.5
36.4
36.4
9.1
90.9
72.7
72.7
81.8
27.3
18.2
18.2
72.7
27
27
8
3
17
41.5
41.5
12.3
4.6
26.2
66.2
68.3
97.5
26
18
10
8
18
41.9
29.0
16.1
12.9
29.0
68.8
75.6
100
3
4
1
3
2
27.3
36.4
9.1
27.3
25.0
30.3
48.0
90.0
p
.543
.855
.556
.371
.634
.697
.035
.129
.328
.942
.892
.198
.227
.950
.011
.088
.002
7
8
9
10
11
12