Ann Hematol (2015) 94:249–256 DOI 10.1007/s00277-014-2187-9
ORIGINAL ARTICLE
Modeling absolute lymphocyte counts after treatment of chronic lymphocytic leukemia with ibrutinib David D. Smith & Leanne Goldstein & Mei Cheng & Danelle F. James & Lori A. Kunkel & Maria Fardis & Ahmed Hamdy & Raquel Izumi & Joseph J. Buggy & Fong Clow
Received: 15 November 2013 / Accepted: 4 August 2014 / Published online: 3 September 2014 # Springer-Verlag Berlin Heidelberg 2014
Abstract The objective in this study was to characterize the pattern of the treatment-related lymphocytosis curve in chronic lymphocytic leukemia (CLL) patients treated with ibrutinib, and assess the relationship between the baseline factors and absolute lymphocyte counts (ALC). The PCYC-1102-CA study was a five-arm phase Ib/II open-label, nonrandomized, multicenter study in CLL/SLL. The arms and accruals were 420 and 840 mg/day treatment-naive elderly CLL/SLL (N=27 and N=4, respectively), 420 and 840 mg/day relapsed/refractory CLL/SLL (N=27 and N=34, respectively), and 420 mg/ day high-risk CLL/SLL (N=24). The results were generated through statistical modeling using data from a clinical trial (PCYC-1102) in five cohorts of treatment-naïve or relapsed/ refractory CLL patients treated at 420 and 840 mg daily of ibrutinib. In cases in which the initial increase in ALC doubles by day 28, it takes patients longer to reach their maximum ALC when compared with those with a lower rate of increase. Our models show that all of the cohorts exhibited the same pattern of treatment-related lymphocytosis from ibrutinib, and there are no significant differences between cohorts, including no detectable dose effect. The ALC of the majority of patients return to baseline ALC values by the end of cycle 5.
Electronic supplementary material The online version of this article (doi:10.1007/s00277-014-2187-9) contains supplementary material, which is available to authorized users. D. D. Smith (*) : L. Goldstein Division of Biostatistics, 1500 E Duarte Rd, City of Hope, Duarte, CA 91010-3000, USA e-mail: [email protected] M. Cheng : D. F. James : L. A. Kunkel : M. Fardis : J. J. Buggy : F. Clow Pharmacyclics Inc., Sunnyvale, CA, USA A. Hamdy : R. Izumi Acerta Pharma, San Carlos, CA, USA
Keywords Bruton’s tyrosine kinase . Lymphocytosis . B cell chronic lymphocytic leukemia . Leukemias and lymphomas . Protein tyrosine kinases . Kinase and phosphatase inhibitors . CLL . Chronic lymphocytic leukemia . Ibrutinib . Imbruvica
Introduction The novel Bruton’s tyrosine kinase inhibitor, ibrutinib (formerly known as PCI-32765), has shown promising response rates in chronic lymphocytic leukemia (CLL). Previous research has showed that this class of inhibitors may have a direct effect on B cell adhesion and migration [1–3]. de Rooij et al. [4] hypothesized that one of the effects from ibrutinib is attenuated microenvironment retention and homing of the CLL cells. One of the findings associated with the administration of ibrutinib is that CLL patients experience an increase in their absolute lymphocyte counts (ALC) [5, 6]. Characteristically, this is an early redistribution of tissue-resident CLL cells into the blood, with rapid resolution of enlarged lymph nodes. This increase generally occurs within the first few weeks of therapy, peaks within the first few months, and resolves slowly. While on continuous ibrutinib therapy, this increase in ALC may persist for months. Historically, an increase in ALC greater than 50 % from baseline was considered a sign of progression in the IWCLL 2008 Guidelines [7, 8]. However, a modification to the criteria, as recommended by the IWCLL 2008 guideline clarification [9] and the National Comprehensive Cancer Network (NCCN) 2012 guidelines [10], indicated that isolated treatment-related lymphocytosis will not be considered as evidence of disease progression with novel B cell receptor signaling pathway inhibitors. This modification also requires that there is evidence of improvement in other disease-related parameters, such as decrease in lymph
250
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node size, and improvement in hematologic parameters like neutropenia, platelet count, and hemoglobin in support of a response to treatment. The slow resolution of lymphocytosis translates into a time-dependent improvement in response as current response criteria requires a decrease in >50 % from baseline of blood lymphocytes to achieve a partial response. Given the known mechanism of action of BCR-inhibiting agents including ibrutinib, treatment-related lymphocytosis is an expected and frequent pharmacodynamic phenomenon observed with initiation (or reinitiation) of ibrutinib [11, 12]. The inhibition of BTK-mediated cellular homing and adhesion by ibrutinib results in a mobilization of CLL cells from the lymph node to the peripheral blood compartment [13]. This is supported by the observation of rapid and substantial decreases in lymph node size coinciding with the lymphocytosis in both animals and humans [4, 13]. Although it was not one of the objectives in our modeling, the correlation between ALC and decreasing lymph node sizes has been observed in the PCYC-1102-CA CLL population. This strengthens the likelihood that this phenomenon is a pharmacodynamic effect of ibrutinib. The objective in this study was to (1) characterize the pattern of the ALC curve in CLL patients treated with ibrutinib, and (2) assess the relationship between the baseline prognostic factors and ALC.
840 mg/day cohort, we combined arms 2 and 5 for our analyses. We present the results in an intent-to-treat analysis. Subjects had weekly visits for the first cycle and then once per cycle on day 1 thereafter; however, clinical sites had the option to perform hematologic lab collections more frequently for patient management. Hematologic studies included complete blood count (CBC) with differential and platelet counts. Once subjects progressed or started the use of alternative antineoplastic therapy, provided they had not withdrawn consent, they were contacted approximately every 3 months by clinic visit or telephone, to assess survival and the use of alternative antineoplastic therapy until death or lost to follow up. This study was conducted in the US in accordance with Good Clinical Practice guidelines, as provided by the International Conference on Harmonization and principles of the Declaration of Helsinki. The institutional review board at each participating site approved the study, and all patients provided written informed consent before enrollment. This trial was registered at www.clinicaltrials.gov as NCT01105247. The inclusion/exclusion criteria and demographics for PCYC-1102 have been reported recently [11, 14].
Statistical methods Subjects and methods The PCYC-1102-CA study was a five-arm phase Ib/II openlabel, nonrandomized, multicenter study in CLL/SLL. The arms and accruals were 420 and 840 mg/day treatment-naive elderly CLL/SLL (N=27 and N=4, respectively), 420 and 840 mg/day relapsed/refractory CLL/SLL (N=27 and N=34, respectively), and 420 mg/day high-risk CLL/SLL (N=24). Table 1 shows the treatment arms and sample sizes for the treated patient populations at the time of the analysis (N=116). The majority of patients were CLL; 3 of the 116 patients were SLL. Study subjects received ibrutinib capsules at the dosage given in Table 1 once daily for each 28-day cycle. Due to the small sample size in the treatment-naive elderly CLL/SLL
Table 1 Patients who received at least one treatment of ibrutinib in the PCYC-1102-CA CLL study at the time of ALC analysis
The length and peaks of the ALC in patients treated with ibrutinib were modeled using a longitudinal generalized linear mixed models with ALC and log(ALC) as the primary outcome and fit with unstructured covariance matrices. We selected the unstructured covariance matrix since it seemed to be the most flexible covariance structure that fitted the longitudinal data without oversimplifying. The time course data were estimated with least-squares means and displayed graphically. Fixed effects terms in the linear model were cubic polynomials of time since baseline (days), study arm, and days/study arm interactions. Our random effects term was cubic polynomial of time (days) or log(day+1). We performed model selection by choosing the model with the lowest Akaike information criterion (AIC) and the AIC corrected for small
Arm
Population
Dose
Group Size
Baseline ALC (×109/mL) median (range)
1 2 3 4 5
Relapsed/refractory Treatment-naïve aged ≥65 years Relapsed/refractory High risk Treatment-naïve aged ≥65 years
420 420 840 420 840
27 27 34 24 4
15 (0.3 to 138) 41 (0.3 to 233) 8 (0.9 to 234) 7 (0.5 to 200) 35 (27 to 62)
mg/day mg/day mg/day mg/day mg/day
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sample sizes (AICc) [15–17]. We assessed model fit by calculating pseudo-R2, which was defined as [Var(intercept only, random intercept and slope) − Var(full model)]/Var(intercept only, random intercept and slope). In addition, we considered regression tree models to explore their utility in predicting the maximum ALC value, time to maximum ALC, time to half of max ALC, and time to normal ALC recovery. We defined time to normal ALC recovery as the time from day 0 until the patient’s ALC went below 4.0×109 after their peak ALC. The prognostic factors in our regression trees were sex, dose, Cmax, AUC, study arm, CLL risk, IgVH gene mutation status, baseline ALC, was baseline ALC>15, 20, 25, 30×109/mL (Yes/No), ALC at 28 days, ALC ever >100×109/mL post-baseline (Yes/No), percent increase over baseline ALC at 28 days, slope of ALC at 28 days, and maximum ALC (for the time to maximum ALC endpoint only). Our criteria for determining the number of splits was to choose the parsimonious model in which the next model’s number of nodes failed to increase the fivefold cross-validation R2. All modeling was done in SAS 9.3 and JMP 11.0; graphs were constructed in SAS 9.3 and R version 3.0.1.
Results Baseline ALC blood results were taken at day 0. The last ALC measurement was taken at a median (range) of 251 days (15– 371 days). The number of ALC measurements post-baseline were median (range) of 13 visits (2–20 visits). The median and range of the baseline ALC values per group appear in Table 1. All of the SLL patients that had normal ALC values were normal (max SLL ALC baseline value was 3.3×109/mL). Figure 1 shows the values of ALC counts and log(ALC) over time among the four ibrutinib treatment groups. From the figure, treatment-related lymphocytosis developed at similar frequencies in all arms and decreased over time. It is unclear from the graph alone whether there is variation for differences in resolution of lymphocytosis between arms. Our two versions of the AIC resulted in the same conclusions for our model fitting. After fitting the model combinations described in the “Subjects and methods” section, we found that the best model had an AIC of 2,984.1 (AICc=2,984.8) with a pseudo-R2 of 0.496. This model appears in Supplemental Table 1 with type III F-test p values. The model’s terms included log(ALC) as the dependent variable, arm (fixed effect), and a cubic log(day+1) polynomial (fixed and random effects), and log(day+1)*arm interactions (fixed effect). In our model, arm was a significant predictor for the ALC values over time (Supplemental Table 1). After inspection of the arm contrast p values after Bonferroni post hoc correction, the significance of the arm term is likely due to arms 2 and 5 (treatment-naïve aged ≥65) having a larger initial ALC peak
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than arms 1, 3, and 4 (relapsed/refractory or high-risk disease). None of the interaction effects were significant, and so we may not conclude that any arm has a distinctly separate effect over time. This is illustrated in Fig. 2a, b. Eighty-three subjects had IgVH gene mutation status. When we added IgVH gene mutation status (mutated or unmutated) to a repeated measures MANOVA log(ALC) model in a complete case analysis, we found a statistically significant association (p0.9). The other arms for which we could test for a dose effect on ALC were arms 2 and 5. However, at the time of the analysis, only four subjects had been treated on arm 5. In a supportive analysis, we tested for a correlation between max ALC versus Cmax and AUC. Both correlations with ALC were less than 0.1 and not statistically significant. When we removed the nonsignificant interactions, the model’s other terms became nonsignificant, including study arm and the cubic fixed effect. The AIC increased to 2,990.3 (AICc=2,990.7) with a pseudo-R2 of 0.484. When we removed the nonsignificant study arm term from the reduced model, the AIC became 2,991.7 and AICc=2,992.0, which were both higher than the “best” model fitted in Supplemental Table 1. The pseudo-R2 of this model was lower too (pseudoR2 =0.438). We also considered a model that removed the cubic random effects terms. This model had a higher pseudo-R2 of 0.531 and significant fixed effects, including arm and high degree interactions between time and arm. However, it also had a much higher AIC (AIC=3,348.4, AICc=3,348.9). Given the dramatic increase in AIC and AICc for only a marginal increase in pseudo-R2, we considered our model in Supplemental Table 1 and Fig. 2b as the best fit. There was no clear linear correlation between baseline ALC values and either the patient’s maximum ALC or the time each patient reached their maximum ALC. We considered that the regression tree classes of models may be able to predict the important endpoints. We constructed partition tree models with the prognostic factors in the “Subjects and methods” section and with the endpoints of maximum ALC value, time to maximum ALC, time to half of maximum ALC, and time to normal ALC recovery. We defined time to normal ALC recovery as the time from day 0 until the patient’s ALC went below 4.0×109 after their peak ALC. Figure 3 shows the actual versus predicted graphs and the number of splits by R2 values for the overall model fit (red)
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1
Fig. 1 ALC values (left) and log(ALC) (right) over time among ibrutinib treatment arms
Arm 1: (Relapsed/Refractory, 420 mg)
Arm 1: (Relapsed/Refractory, 420 mg)
600 500 400 300 200 100 0
6 4 2 0 −2 Arm 2/5: (Naïve, age >= 65) 600 500 400 300 200 100 0 Arm 3: (Relapsed/Refractory, 840 mg)
600 500 400 300 200 100 0
Log Lymphocyte Count 10^9/L
Lymphocyte Count 10^9/L
Arm 2/5: (Naïve, age >= 65)
6 4 2 0 −2 Arm 3: (Relapsed/Refractory, 840 mg) 6 4 2 0 −2
Arm 4: (High risk, 420 mg)
Arm 4: (High risk, 420 mg) 600 500 400 300 200 100 0
0
100
200
300
400
500
600
Days After Baseline
and a fivefold cross-validation (blue). For the max ALC endpoint, we selected two splits (three nodes) as the best fit. There was no clear benefit in prediction utility by splitting on additional nodes. The only predictor used for this regression tree was the patient’s ALC at day 28. The overall R2 for two splits was approximately 76 %. For the time to max ALC, we selected four splits (five nodes). The overall R2 for time to max ALC was 56 %. The prognostic factors in the time to max ALC were ALC at baseline, the percent increase over baseline at 28 days, and dose (420 vs. 840 mg). The results of the partition trees appear in Table 2. The classification rules correspond to the observed versus expected plots in Fig. 3. We fit regression tree models for time to half of max ALC. The factors that were chosen were time to max ALC, sum of the products of the greatest diameter (SPD) at baseline, and percent increase over baseline with four nodes (three splits, data not shown). However, the R2 for this number of splits was 40 %, with variability among the endpoint that made prediction difficult. We concluded that regression tree models were not useful to predict time to half of max ALC. Figure 4 shows the relative cumulative frequency when patients’ ALC values decreased to their baseline ALC. The ALC of 6 % of patients decreased prior to completing cycle 1, and so their baseline ALC was their maximum throughout treatment. Twenty-five percent of patients’ ALC values did not return to their baseline levels at the last follow-up at the time of analysis (>10 cycles). Over half of patients’ ALC values returned to baseline levels by cycle 5 and 75 % of patients returned to baseline levels by cycle 10.
6 4 2 0 −2 0
100
200
300
400
500
600
Days After Baseline
An additional analysis that we considered was the time until ALC counts return to normal. We defined normal ALC counts as ALC less than 4.0×109. Across the five arms, 14 % of the patients never had ALC values exceed the upper bound for normal ALC. Of patients that had counts greater than 4.0× 109, 31 % returned to normal, and 55 % did not return to normal levels at last contact with a median follow-up among these patients of 224 days (range 15 days to 1 year) posttreatment. Baseline ALC greater or less than 20.0×109 had the most predictive utility with respect to whether ALC returned to normal within the follow up period (p= 65) Arm 3: (Relapsed/Refractory, 840 mg) Arm 4: (High risk, 420 mg)
0 0
2
4
6
Log (Days+1)
b Arm 1: (Relapsed/Refractory, 420 mg) Arm 2/5: (Naïve, age >= 65) Arm 3: (Relapsed/Refractory, 840 mg) Arm 4: (High risk, 420 mg) 90
60
30
0 0 28 56 84 11 2 14 0 16 8 19 6 22 4 25 2 28 0 30 8 33 6 36 4 39 2 42 0 44 8 47 6 50 4 53 2 56 0 58 8 61 6
Lymphocytes 10^9/L
Fig. 2 a Predicted log(ALC) over time in lowest AIC model; b back-transformed predicted log(ALC) over time of lowest AIC model
253
Days
254
0.8 0.4
Model Fit K−fold cross−validation
0.0
150
R−squared
250
350
450
550
Arm 1 (Relapsed/Refractory, 420 mg) Arm 2 (Naive, age ≥ 65, 420 mg) Arm 3 (Relapsed/Refractory, 840 mg) Arm 4 (High risk, 420 mg) Arm 5 (Naive, age ≥ 65, 840 mg)
50
Observed Max ALC Post−baseline
a
1
0
0
Fig. 3 Actual versus predicted a max ALC and b time to max ALC. Inset: cross-validation results for a max ALC and b time to max ALC. Overall model R2 is in red and fivefold crossvalidation is in blue
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0
100
200
300
400
500
2
3
4
5
6
7
Number of splits
8
9
600
Predicted Max ALC
R−squared
100
150
200
Arm 1 (Relapsed/Refractory, 420 mg) Arm 2/5 (Naive, age ≥ 65) Arm 3 (Relapsed/Refractory, 840 mg) Arm 4 (High risk, 420 mg)
0.0
0.4
50
0.8
Model Fit K−fold cross−validation
0
0
Observed Time to Max ALC Post−baseline
b
0
50
100
150
1
2
3
4
5
6
7
Number of splits
8
9
200
Predicted Time to Max ALC
mechanism of action: inhibition of BTK signaling pathways involved in B cell migration and adhesion. This effect of treatment-related lymphocytosis in CLL/SLL patients is
generally clinically asymptomatic and should not be considered a progressive disease in the absence of other clinical findings. In this modeling study, we wished to characterize
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Table 2 Nodes of the partition trees for max ALC and time to max ALC 9
9
Max ALC (×10 ) rule:
Max ALC×10 mean (Std Dev)
ALC at 28 days 170 (N=15)
32 (35) 116 (39) 345 (134)
Time to max ALC rule: Baseline ALC>0.9 (N=104) % increase over BL at day 28
ORIGINAL ARTICLE
Modeling absolute lymphocyte counts after treatment of chronic lymphocytic leukemia with ibrutinib David D. Smith & Leanne Goldstein & Mei Cheng & Danelle F. James & Lori A. Kunkel & Maria Fardis & Ahmed Hamdy & Raquel Izumi & Joseph J. Buggy & Fong Clow
Received: 15 November 2013 / Accepted: 4 August 2014 / Published online: 3 September 2014 # Springer-Verlag Berlin Heidelberg 2014
Abstract The objective in this study was to characterize the pattern of the treatment-related lymphocytosis curve in chronic lymphocytic leukemia (CLL) patients treated with ibrutinib, and assess the relationship between the baseline factors and absolute lymphocyte counts (ALC). The PCYC-1102-CA study was a five-arm phase Ib/II open-label, nonrandomized, multicenter study in CLL/SLL. The arms and accruals were 420 and 840 mg/day treatment-naive elderly CLL/SLL (N=27 and N=4, respectively), 420 and 840 mg/day relapsed/refractory CLL/SLL (N=27 and N=34, respectively), and 420 mg/ day high-risk CLL/SLL (N=24). The results were generated through statistical modeling using data from a clinical trial (PCYC-1102) in five cohorts of treatment-naïve or relapsed/ refractory CLL patients treated at 420 and 840 mg daily of ibrutinib. In cases in which the initial increase in ALC doubles by day 28, it takes patients longer to reach their maximum ALC when compared with those with a lower rate of increase. Our models show that all of the cohorts exhibited the same pattern of treatment-related lymphocytosis from ibrutinib, and there are no significant differences between cohorts, including no detectable dose effect. The ALC of the majority of patients return to baseline ALC values by the end of cycle 5.
Electronic supplementary material The online version of this article (doi:10.1007/s00277-014-2187-9) contains supplementary material, which is available to authorized users. D. D. Smith (*) : L. Goldstein Division of Biostatistics, 1500 E Duarte Rd, City of Hope, Duarte, CA 91010-3000, USA e-mail: [email protected] M. Cheng : D. F. James : L. A. Kunkel : M. Fardis : J. J. Buggy : F. Clow Pharmacyclics Inc., Sunnyvale, CA, USA A. Hamdy : R. Izumi Acerta Pharma, San Carlos, CA, USA
Keywords Bruton’s tyrosine kinase . Lymphocytosis . B cell chronic lymphocytic leukemia . Leukemias and lymphomas . Protein tyrosine kinases . Kinase and phosphatase inhibitors . CLL . Chronic lymphocytic leukemia . Ibrutinib . Imbruvica
Introduction The novel Bruton’s tyrosine kinase inhibitor, ibrutinib (formerly known as PCI-32765), has shown promising response rates in chronic lymphocytic leukemia (CLL). Previous research has showed that this class of inhibitors may have a direct effect on B cell adhesion and migration [1–3]. de Rooij et al. [4] hypothesized that one of the effects from ibrutinib is attenuated microenvironment retention and homing of the CLL cells. One of the findings associated with the administration of ibrutinib is that CLL patients experience an increase in their absolute lymphocyte counts (ALC) [5, 6]. Characteristically, this is an early redistribution of tissue-resident CLL cells into the blood, with rapid resolution of enlarged lymph nodes. This increase generally occurs within the first few weeks of therapy, peaks within the first few months, and resolves slowly. While on continuous ibrutinib therapy, this increase in ALC may persist for months. Historically, an increase in ALC greater than 50 % from baseline was considered a sign of progression in the IWCLL 2008 Guidelines [7, 8]. However, a modification to the criteria, as recommended by the IWCLL 2008 guideline clarification [9] and the National Comprehensive Cancer Network (NCCN) 2012 guidelines [10], indicated that isolated treatment-related lymphocytosis will not be considered as evidence of disease progression with novel B cell receptor signaling pathway inhibitors. This modification also requires that there is evidence of improvement in other disease-related parameters, such as decrease in lymph
250
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node size, and improvement in hematologic parameters like neutropenia, platelet count, and hemoglobin in support of a response to treatment. The slow resolution of lymphocytosis translates into a time-dependent improvement in response as current response criteria requires a decrease in >50 % from baseline of blood lymphocytes to achieve a partial response. Given the known mechanism of action of BCR-inhibiting agents including ibrutinib, treatment-related lymphocytosis is an expected and frequent pharmacodynamic phenomenon observed with initiation (or reinitiation) of ibrutinib [11, 12]. The inhibition of BTK-mediated cellular homing and adhesion by ibrutinib results in a mobilization of CLL cells from the lymph node to the peripheral blood compartment [13]. This is supported by the observation of rapid and substantial decreases in lymph node size coinciding with the lymphocytosis in both animals and humans [4, 13]. Although it was not one of the objectives in our modeling, the correlation between ALC and decreasing lymph node sizes has been observed in the PCYC-1102-CA CLL population. This strengthens the likelihood that this phenomenon is a pharmacodynamic effect of ibrutinib. The objective in this study was to (1) characterize the pattern of the ALC curve in CLL patients treated with ibrutinib, and (2) assess the relationship between the baseline prognostic factors and ALC.
840 mg/day cohort, we combined arms 2 and 5 for our analyses. We present the results in an intent-to-treat analysis. Subjects had weekly visits for the first cycle and then once per cycle on day 1 thereafter; however, clinical sites had the option to perform hematologic lab collections more frequently for patient management. Hematologic studies included complete blood count (CBC) with differential and platelet counts. Once subjects progressed or started the use of alternative antineoplastic therapy, provided they had not withdrawn consent, they were contacted approximately every 3 months by clinic visit or telephone, to assess survival and the use of alternative antineoplastic therapy until death or lost to follow up. This study was conducted in the US in accordance with Good Clinical Practice guidelines, as provided by the International Conference on Harmonization and principles of the Declaration of Helsinki. The institutional review board at each participating site approved the study, and all patients provided written informed consent before enrollment. This trial was registered at www.clinicaltrials.gov as NCT01105247. The inclusion/exclusion criteria and demographics for PCYC-1102 have been reported recently [11, 14].
Statistical methods Subjects and methods The PCYC-1102-CA study was a five-arm phase Ib/II openlabel, nonrandomized, multicenter study in CLL/SLL. The arms and accruals were 420 and 840 mg/day treatment-naive elderly CLL/SLL (N=27 and N=4, respectively), 420 and 840 mg/day relapsed/refractory CLL/SLL (N=27 and N=34, respectively), and 420 mg/day high-risk CLL/SLL (N=24). Table 1 shows the treatment arms and sample sizes for the treated patient populations at the time of the analysis (N=116). The majority of patients were CLL; 3 of the 116 patients were SLL. Study subjects received ibrutinib capsules at the dosage given in Table 1 once daily for each 28-day cycle. Due to the small sample size in the treatment-naive elderly CLL/SLL
Table 1 Patients who received at least one treatment of ibrutinib in the PCYC-1102-CA CLL study at the time of ALC analysis
The length and peaks of the ALC in patients treated with ibrutinib were modeled using a longitudinal generalized linear mixed models with ALC and log(ALC) as the primary outcome and fit with unstructured covariance matrices. We selected the unstructured covariance matrix since it seemed to be the most flexible covariance structure that fitted the longitudinal data without oversimplifying. The time course data were estimated with least-squares means and displayed graphically. Fixed effects terms in the linear model were cubic polynomials of time since baseline (days), study arm, and days/study arm interactions. Our random effects term was cubic polynomial of time (days) or log(day+1). We performed model selection by choosing the model with the lowest Akaike information criterion (AIC) and the AIC corrected for small
Arm
Population
Dose
Group Size
Baseline ALC (×109/mL) median (range)
1 2 3 4 5
Relapsed/refractory Treatment-naïve aged ≥65 years Relapsed/refractory High risk Treatment-naïve aged ≥65 years
420 420 840 420 840
27 27 34 24 4
15 (0.3 to 138) 41 (0.3 to 233) 8 (0.9 to 234) 7 (0.5 to 200) 35 (27 to 62)
mg/day mg/day mg/day mg/day mg/day
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sample sizes (AICc) [15–17]. We assessed model fit by calculating pseudo-R2, which was defined as [Var(intercept only, random intercept and slope) − Var(full model)]/Var(intercept only, random intercept and slope). In addition, we considered regression tree models to explore their utility in predicting the maximum ALC value, time to maximum ALC, time to half of max ALC, and time to normal ALC recovery. We defined time to normal ALC recovery as the time from day 0 until the patient’s ALC went below 4.0×109 after their peak ALC. The prognostic factors in our regression trees were sex, dose, Cmax, AUC, study arm, CLL risk, IgVH gene mutation status, baseline ALC, was baseline ALC>15, 20, 25, 30×109/mL (Yes/No), ALC at 28 days, ALC ever >100×109/mL post-baseline (Yes/No), percent increase over baseline ALC at 28 days, slope of ALC at 28 days, and maximum ALC (for the time to maximum ALC endpoint only). Our criteria for determining the number of splits was to choose the parsimonious model in which the next model’s number of nodes failed to increase the fivefold cross-validation R2. All modeling was done in SAS 9.3 and JMP 11.0; graphs were constructed in SAS 9.3 and R version 3.0.1.
Results Baseline ALC blood results were taken at day 0. The last ALC measurement was taken at a median (range) of 251 days (15– 371 days). The number of ALC measurements post-baseline were median (range) of 13 visits (2–20 visits). The median and range of the baseline ALC values per group appear in Table 1. All of the SLL patients that had normal ALC values were normal (max SLL ALC baseline value was 3.3×109/mL). Figure 1 shows the values of ALC counts and log(ALC) over time among the four ibrutinib treatment groups. From the figure, treatment-related lymphocytosis developed at similar frequencies in all arms and decreased over time. It is unclear from the graph alone whether there is variation for differences in resolution of lymphocytosis between arms. Our two versions of the AIC resulted in the same conclusions for our model fitting. After fitting the model combinations described in the “Subjects and methods” section, we found that the best model had an AIC of 2,984.1 (AICc=2,984.8) with a pseudo-R2 of 0.496. This model appears in Supplemental Table 1 with type III F-test p values. The model’s terms included log(ALC) as the dependent variable, arm (fixed effect), and a cubic log(day+1) polynomial (fixed and random effects), and log(day+1)*arm interactions (fixed effect). In our model, arm was a significant predictor for the ALC values over time (Supplemental Table 1). After inspection of the arm contrast p values after Bonferroni post hoc correction, the significance of the arm term is likely due to arms 2 and 5 (treatment-naïve aged ≥65) having a larger initial ALC peak
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than arms 1, 3, and 4 (relapsed/refractory or high-risk disease). None of the interaction effects were significant, and so we may not conclude that any arm has a distinctly separate effect over time. This is illustrated in Fig. 2a, b. Eighty-three subjects had IgVH gene mutation status. When we added IgVH gene mutation status (mutated or unmutated) to a repeated measures MANOVA log(ALC) model in a complete case analysis, we found a statistically significant association (p0.9). The other arms for which we could test for a dose effect on ALC were arms 2 and 5. However, at the time of the analysis, only four subjects had been treated on arm 5. In a supportive analysis, we tested for a correlation between max ALC versus Cmax and AUC. Both correlations with ALC were less than 0.1 and not statistically significant. When we removed the nonsignificant interactions, the model’s other terms became nonsignificant, including study arm and the cubic fixed effect. The AIC increased to 2,990.3 (AICc=2,990.7) with a pseudo-R2 of 0.484. When we removed the nonsignificant study arm term from the reduced model, the AIC became 2,991.7 and AICc=2,992.0, which were both higher than the “best” model fitted in Supplemental Table 1. The pseudo-R2 of this model was lower too (pseudoR2 =0.438). We also considered a model that removed the cubic random effects terms. This model had a higher pseudo-R2 of 0.531 and significant fixed effects, including arm and high degree interactions between time and arm. However, it also had a much higher AIC (AIC=3,348.4, AICc=3,348.9). Given the dramatic increase in AIC and AICc for only a marginal increase in pseudo-R2, we considered our model in Supplemental Table 1 and Fig. 2b as the best fit. There was no clear linear correlation between baseline ALC values and either the patient’s maximum ALC or the time each patient reached their maximum ALC. We considered that the regression tree classes of models may be able to predict the important endpoints. We constructed partition tree models with the prognostic factors in the “Subjects and methods” section and with the endpoints of maximum ALC value, time to maximum ALC, time to half of maximum ALC, and time to normal ALC recovery. We defined time to normal ALC recovery as the time from day 0 until the patient’s ALC went below 4.0×109 after their peak ALC. Figure 3 shows the actual versus predicted graphs and the number of splits by R2 values for the overall model fit (red)
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Fig. 1 ALC values (left) and log(ALC) (right) over time among ibrutinib treatment arms
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and a fivefold cross-validation (blue). For the max ALC endpoint, we selected two splits (three nodes) as the best fit. There was no clear benefit in prediction utility by splitting on additional nodes. The only predictor used for this regression tree was the patient’s ALC at day 28. The overall R2 for two splits was approximately 76 %. For the time to max ALC, we selected four splits (five nodes). The overall R2 for time to max ALC was 56 %. The prognostic factors in the time to max ALC were ALC at baseline, the percent increase over baseline at 28 days, and dose (420 vs. 840 mg). The results of the partition trees appear in Table 2. The classification rules correspond to the observed versus expected plots in Fig. 3. We fit regression tree models for time to half of max ALC. The factors that were chosen were time to max ALC, sum of the products of the greatest diameter (SPD) at baseline, and percent increase over baseline with four nodes (three splits, data not shown). However, the R2 for this number of splits was 40 %, with variability among the endpoint that made prediction difficult. We concluded that regression tree models were not useful to predict time to half of max ALC. Figure 4 shows the relative cumulative frequency when patients’ ALC values decreased to their baseline ALC. The ALC of 6 % of patients decreased prior to completing cycle 1, and so their baseline ALC was their maximum throughout treatment. Twenty-five percent of patients’ ALC values did not return to their baseline levels at the last follow-up at the time of analysis (>10 cycles). Over half of patients’ ALC values returned to baseline levels by cycle 5 and 75 % of patients returned to baseline levels by cycle 10.
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An additional analysis that we considered was the time until ALC counts return to normal. We defined normal ALC counts as ALC less than 4.0×109. Across the five arms, 14 % of the patients never had ALC values exceed the upper bound for normal ALC. Of patients that had counts greater than 4.0× 109, 31 % returned to normal, and 55 % did not return to normal levels at last contact with a median follow-up among these patients of 224 days (range 15 days to 1 year) posttreatment. Baseline ALC greater or less than 20.0×109 had the most predictive utility with respect to whether ALC returned to normal within the follow up period (p= 65) Arm 3: (Relapsed/Refractory, 840 mg) Arm 4: (High risk, 420 mg)
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Fig. 2 a Predicted log(ALC) over time in lowest AIC model; b back-transformed predicted log(ALC) over time of lowest AIC model
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mechanism of action: inhibition of BTK signaling pathways involved in B cell migration and adhesion. This effect of treatment-related lymphocytosis in CLL/SLL patients is
generally clinically asymptomatic and should not be considered a progressive disease in the absence of other clinical findings. In this modeling study, we wished to characterize
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Table 2 Nodes of the partition trees for max ALC and time to max ALC 9
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ALC at 28 days 170 (N=15)
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Time to max ALC rule: Baseline ALC>0.9 (N=104) % increase over BL at day 28
