12th Congress of the European Hematology ... - Haematologica

12 th Congress of the European Hematology Association
men. Phase 1 (immuno-chemotherapy), consisted of 2-4 courses with
Rituximab day 1, vincristine day 2, and cyclophosphamide day 2 to 6.
Phase 2 (in vivo purging and PBSC mobilization), coupled Rituximab on
days 1 and 9 with HD-AraC (2 g/m 2 every 12 h on days 2 and 3). Phase
3 (high-dose chemotherapy with autotransplant) consisted of BEAM
followed by infusion of in vivo purged PBSC plus 2 consolidation doses
of Rituximab on days +14 and +21 posttransplant. Results. Median age
50. Histology: 29 grade I, 27 grade II, 8 grade III; 84% stage III-IV; 56%
had bone marrow involvement; 59% were PCR + for bcl-2 rearrangement
in blood and/or marrow. At enrollment, FLIPI was low in 44%,
intermediate in 34%, high in 11%, NA in 11%. Of 64 patients, after
debulking 7 reached CR, 52 PR, and 5 stable disease; of 38 PCR+
patients, 9 (24%) achieved molecular response. After immunochemotherapy,
40 patients obtained CR, 22 PR, and 2 progressed; of the
38 PCR + patients, additional 24 achieved molecular response. Overall, 33
patients (87%) were molecularly negative before mobilization. Sixty-one
patients proceeded to PBSC mobilization with in vivo purging. Toxicity
consisted of WHO grade 3-4 granulocytopenia in 72%. One patient
who developed E. Coli sepsis during cytopenia did not harvest. Leukaphereses
were started after a median of 12 days (range 5-16) after the first
dose of AraC. The median number of CD34 + cells collected was
16.6×10 6 /kg (range 3.8'80.6). All the 31 harvests tested for Bcl-2 were
PCR-negative. Autotransplant was performed in 58 patients. The median
time to neutrophils recovery over 0.5×10 9 /L was 10 days (8-14), and
to platelets over 20×10 9 /L was 10 days (6-13). One patient developed
transient cerebral vasculitis during the cytopenic phase. The two Rituximab
doses post-transplant did not affect hemopoietic recovery. Asymptomatic
WHO grade 3-4 neutropenia developed in 10 patients after a
median of 90 days after transplant. After a median F-U of 3.4 years
(range 1-8), 42 patients are in clinical remission (24 for more than 4
years). One patient became PCR + 34 months after transplant without
clinical relapse and was treated with 4 doses of Rituximab achieving
PCR – , seven had clinical and molecular recurrence after a median of 16
months after transplant (range 5-40). The 5-year event-free survival of
the whole series is 67%. Conclusions. This study demonstrates the role
of harvesting lymphoma-free PBSC for the attainment of long-lasting
clinical and molecular responses in refractory/relapsed follicular lymphoma
patients. Asymptomatic late onset neutropenia may occur after
Rituximab consolidation of autologous transplant with no clinical influence
on the outcome of the disease.
0730
HEPATITIS C VIRUS INFECTION AND LYMPHOPROLIFERATIVE DISORDERS: A BAYESIAN
APPROACH
L. Arcaini, 1 C. Pascutto, 2 R. Bruno, 3 M. Merli, 2 S. Rizzi, 2 F. Passamonti, 2
E. Rumi, 2 F. Montanari, 2 M. Lucioni, 4 M. Paulli, 4 M. Lazzarino2 1 IRCCS Policlinico S. Matteo Pavia, PAVIA; 2 Hematology, IRCCS Policlinico
S. Matteo, PAVIA; 3 Hepatology, IRCCS Policlinico S. Matteo, PAVIA; 4 Pathology,
IRCCS Policlinico S. Matteo, PAVIA, Italy
Background. A recent meta-analysis of epidemiologic studies (Dal
Maso 2006) confirmed association between hepatitis C virus (HCV)
infection and lymphoid malignancies but did not identified specific
HCV-related diseases. The Bayesian approach offers an appealing alternative
to classical statistical inference in order to detect specific HCVrelated
type of lymphoid neoplasms. Methods. We analysed 2,621
patients with lymphoproliferative disorders diagnosed since 1992 to
2006; HCV serology was available for 1,434 patients. We compared the
estimates of HCV prevalence in each type of disorder obtained with
four different statistical models. Model A: the classical maximum likelihood
(ML) estimator for the binomial distribution, with the corresponding
95% confidence intervals. Model B: a fully Bayesian fixed-effects
model based on the assumption that the number of HCV+ patients are
independent binary response variables with group-specific prevalence as
failure probabilities. On each failure probability a non informative prior
distribution was specified. Model C: a fully Bayesian hierarchical
model, based on the assumption that all the (logistic transformed) prevalence
rates are all drawn from the same normally distributed random
variable. The mean of the random variable is an overall prevalence to be
estimated (with a flat normal hyperprior distribution), and the standard
deviation is a random parameter on which a non informative (uniform)
hyperprior distribution has been specified. Model D: a fully Bayesian
hierarchical model, in which the (logistic transformed) prevalence of
each diagnosis group has been assigned to one of two normal distributions
with different mean and standard deviation parameters, but with
the same non informative hyperpriors as for model C. All Bayesian computations
were carried out using WinBUGS 1.4.1 © Imperial College
272 | haematologica/the hematology journal | 2007; 92(s1)
and MRC (UK) http://www.mrc-bsu.cam.ac.uk/bugs Results. The resulting
estimates and the 95% credible intervals obtained from Model B
were overlapping to the estimates and the 95% confidence intervals
obtained with the classic ML approach (Model A) (see Figure 1). The estimated
prevalence ranged from around 5% for Hodgkin’s lymphoma
and hairy cell leukemia to more than 35% for diffuse large B-cell lymphoma
(DLBCL) (43%), MALT lymphoma (44%), splenic MZL (34%).
The estimates of the group-specific prevalences obtained with Model C
were slightly pulled towards the overall mean value, shown at the top
of Figure 1. However, it was still possible to point out the histotypes
with high HCV seroprevalence (DLBCL 43%, MALT lymphoma 43%,
splenic MZL 33%). In those groups, the credible intervals had little overlapping
(or no overlapping at all) with the credible interval of the overall
prevalence. Model C allowed to further highlight the presence of
high and low HCV-seroprevalence neoplasms. The estimated overall
HCV seroprevalences of the two subsets of lymphoma (high vs. low)
had non-overlapping credible intervals, and the estimates in each lymphoma
subtype were pulled towards the overall prevalence of the corresponding
subset. This is particularly evident for splenic MZL. Conclusions.
Bayesian hierarchical models are a very flexible tool, allowing to
build complex models in which our prior beliefs and acquisitions are formalized
and integrated in the analysis. In our analysis, the Bayesian
approach highlighted a group of histotypes with high HCV-seroprevalence:
DLBCL, MALT lymphoma and splenic MZL.
Figure 1. HCV and lymphoid malignancies: 4 different models.