Prespecified Candidate Biomarkers Identify Follicular Lymphoma Patients Who Achieved Longer Progression-Free Survival with Bortezomib−Rituximab Versus Rituximab

Published OnlineFirst April 2, 2013; DOI: 10.1158/1078-0432.CCR-12-3069

Clinical Cancer Predictive Biomarkers and Personalized Medicine Research

Prespeciﬁed Candidate Biomarkers Identify Follicular Lymphoma Patients Who Achieved Longer Progression-Free Survival with Bortezomib–Rituximab Versus Rituximab

Bertrand Coifﬁer1, Weimin Li2, Erin D. Henitz3, Jayaprakash D. Karkera3, Reyna Favis3, Dana Gaffney3, Alice Shapiro3, Panteli Theocharous6, Yusri A. Elsayed3, Helgi van de Velde9, Michael E. Schaffer2, Evgenii A. Osmanov10, Xiaonan Hong11, Adriana Scheliga12, Jiri Mayer13, Fritz Offner8, Simon Rule7, Adriana Teixeira14, Joanna Romejko-Jarosinska15, Sven de Vos4, Michael Crump16, Ofer Shpilberg17, Pier Luigi Zinzani18, Andrew Cakana6, Dixie-Lee Esseltine5, George Mulligan5, and Deborah Ricci3

Abstract Purpose: Identify subgroups of patients with relapsed/refractory follicular lymphoma deriving substantial progression-free survival (PFS) benefit with bortezomib–rituximab versus rituximab in the phase III LYM-3001 study. Experimental Design: A total of 676 patients were randomized to five 5-week cycles of bortezomib– rituximab or rituximab. The primary end point was PFS; this prespecified analysis of candidate protein biomarkers and genes was an exploratory objective. Archived tumor tissue and whole blood samples were collected at baseline. Immunohistochemistry and genetic analyses were completed for 4 proteins and 8 genes. Results: In initial pairwise analyses, using individual single-nucleotide polymorphism genotypes, one biomarker pair (PSMB1 P11A C/G heterozygote, low CD68 expression) was associated with a significant PFS benefit with bortezomib–rituximab versus rituximab, controlling for multiple comparison corrections. The pair was analyzed under dominant, recessive, and additive genetic models, with significant association with PFS seen under the dominant model (G/GþC/G). In patients carrying this biomarker pair [PSMB1 P11A G allele, low CD68 expression (50 CD68-positive cells), population frequency: 43.6%], median PFS was 14.2 months with bortezomib–rituximab versus 9.1 months with rituximab (HR 0.47, P < 0.0001), and there was a significant overall survival benefit (HR 0.49, P ¼ 0.0461). Response rates were higher and time to next antilymphoma therapy was longer in the bortezomib–rituximab group. In biomarker-negative patients, no significant efficacy differences were seen between treatment groups. Similar proportions of patients had high-risk features in the biomarker-positive and biomarker-negative subsets. Conclusions: Patients with PSMB1 P11A (G allele) and low CD68 expression seemed to have significantly longer PFS and greater clinical benefit with bortezomib–rituximab versus rituximab. Clin Cancer Res; 19(9); 2551–61. 2013 AACR.

Introduction non-Hodgkin lymphoma (NHL; refs. 1, 2), is to prolong The goal of treatment for patients with follicular lym- progression-free survival (PFS) and improve overall survival phoma, a generally incurable, common indolent subtype of (OS). Follicular lymphoma is a highly heterogeneous

Authors' Affiliations: 1Hematologie, Hospices Civils de Lyon and Uni- Cancer Centre, Warszawa, Poland; 16Princess Margaret Hospital, versity Lyon 1, Lyon, France; 2Janssen Research and Development, University of Toronto, Toronto, Ontario, Canada; 17Institute of Hema- Spring House, Pennsylvania; 3Janssen Research and Development, tology, Rabin Medical Center, Petah Tikva, Israel; and 18Policlinico S. Raritan, New Jersey; 4David Geffen School of Medicine at the University Orsola,MalpigliIstitutodiEmatologia e Oncologia Medica, Bologna, of California and Translational Oncology Research International, Los Italy Angeles, California; 5Millennium Pharmaceuticals, Inc., Cambridge, Massachusetts; 6Janssen Research and Development, High Wycombe; Note: Supplementary data for this article are available at Clinical Cancer 7Department of Haematology, Derriford Hospital, Plymouth, United Research Online (http://clincancerres.aacrjournals.org/). Kingdom; 8Dienst Hematologie, UZ Gent, Gent, Belgium; 9Janssen fi Research and Development, Beerse, Belgium; 10Cancer Research Cen- Corresponding Author: Bertrand Coif er, Hematologie, Hospices Civils ter, Moscow, Russia; 11Cancer Hospital, FuDan University, Shanghai, de Lyon and University Lyon 1, 69310 Pierre-Benite, Lyon, France. Phone: China; 12Instituto Nacional de Cancer,^ Rio de Janeiro, Brazil; 13CEITEC 33-478-86-4300; Fax: 33-478-86-4355; E-mail: fi Brno, and Department of Internal Medicine, Hematology and Oncology, bertrand.coif [email protected] University Hospital Brno and School of Medicine, Masaryk University, doi: 10.1158/1078-0432.CCR-12-3069 Brno, Czech Republic; 14HospitaisdaUniversidadedeCoimbra,Coim- bra, Portugal; 15The Maria Skłodowska-Curie Memorial Institute and 2013 American Association for Cancer Research.

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morphisms were specified on the basis of prior studies of Translational Relevance prognostic molecular markers for lymphoma and of target Given the heterogeneity of follicular lymphoma and or response markers for bortezomib or rituximab. Here, we range of possible therapeutic options for patients with present the findings of this exploratory biomarker analysis. relapsed/refractory disease, the use of selected biomarkers based on mechanistic rationales to identify patient Materials and Methods subgroups most likely to benefit from specific therapies Patients and clinical study design is important. Candidate proteins and target gene poly- The LYM-3001 (ClinicalTrials.gov trial registration ID: morphisms were prespecified as potential prognostic NCT00312845) study design has been reported previously markers for exploratory biomarker analyses in the phase (13). Eligible patients were aged 18 years or more with III LYM-3001 study of bortezomib–rituximab versus relapsed/refractory, rituximab-na€ve, or rituximab-sensitive rituximab in patients with relapsed/refractory follicular (13) grade 1/2 follicular lymphoma. Patients with grade 2 lymphoma. Comprehensive pairwise analyses, with peripheral neuropathy or clinical evidence of transforma- genetic model testing, identified a biomarker pair, tion to aggressive lymphoma were excluded. Patients were PSMB1 P11A (C/GþG/G) plus low CD68 expression, randomized (1:1) to receive five 5-week cycles comprising associated with a significant progression-free survival bortezomib (1.6 mg/m2, days 1, 8, 15, and 22; all cycles) benefit, improved response rates, and longer overall plus rituximab (375 mg/m2, days 1, 8, 15, and 22, cycle 1, survival with bortezomib–rituximab versus rituximab. and day 1, cycles 2–5), or rituximab alone. Randomization The benefit with bortezomib–rituximab was substantial- was stratified according to Follicular Lymphoma Interna- ly greater than seen in the overall, unselected study tional Prognostic Index (FLIPI; ref. 14) score, previous population. The two biomarkers identified, for which rituximab treatment, time since last dose of antilymphoma a mechanistic hypothesis is provided for the reported treatment, and region. efficacy benefit, would be feasible and practical to screen The primary end point was PFS. Secondary efficacy end for in the clinical setting. points included ORR, CR rate, time to progression, and 1 year OS. Response was assessed using modified Interna- tional Working Group response criteria (15). Time to next antilymphoma treatment (TTNT; time from randomization disease, as evidenced by variability in disease course, to first dose of next treatment) was an additional predefined responsiveness to treatment, and outcomes (3–5). There- efficacy end point. fore, to optimize treatment for individual patients, identi- All patients provided written informed consent. Review fication of subgroups that are most likely to benefit from a boards at all participating Institutions approved the study, specific therapy is important. which was conducted according to the provisions of the The anti-CD20 monoclonal antibody rituximab is the Declaration of Helsinki, the International Conference on mainstay of treatment for follicular lymphoma (1). Addi- Harmonization, and the Guidelines for Good Clinical tional treatment options may enhance the activity of ritux- Practice. imab-based therapy in the relapsed setting. The proteasome inhibitor bortezomib has shown single-agent activity in Biomarker analysis study design follicular lymphoma and other indolent NHL subtypes This prespecified focused analysis of potential biomar- (6–8), as well as promising activity in combination with kers of sensitivity to bortezomib–rituximab or rituximab rituximab, with or without other agents, in patients with was an exploratory objective. All patients were required to relapsed/refractory follicular lymphoma (9–12). provide consent for biomarker testing, and were included in Results from the international, multicenter, randomized, the biomarker study if they had evaluable biomarker data phase III LYM-3001 study (13) showed improved PFS with and data for at least one clinical end point. Archived tumor bortezomib–rituximab versus rituximab alone in patients tissue (samples requested from diagnosis) was collected at with relapsed/refractory rituximab-na€ve or rituximab-sen- baseline, and samples were forwarded to a central labora- sitive follicular lymphoma (median 12.8 vs. 11.0 months, tory as paraffin-embedded, formalin-fixed blocks or 6- HR 0.822, P ¼ 0.039). The combination resulted in a micron slides. Whole blood samples for DNA analysis were significantly greater overall response rate (ORR; 63% vs. collected at baseline. Serum samples were collected at 49%, P ¼ 0.0004), complete response (CR) rate (25% multiple time points and stored for optional exploratory vs.18%, P ¼ 0.035), and durable (6 months) response protein analysis. rate (50% vs. 38%, P ¼ 0.002; ref. 13). After a median follow-up of 33.9 months, there was no difference in OS Prespecified candidate biomarkers between arms (13). Protein candidates were NF-kB p65, proteasome subunit The LYM-3001 protocol included as an exploratory end a-5 (PSMA5), p27, and CD68. These were chosen based on point a biomarker analysis aimed at identifying patient their attenuation by bortezomib (NF-kB, PSMA5, p27; subgroups that derived a longer PFS benefit with, and were refs. 16–20), and associations with poor prognosis in lym- more likely to respond to, bortezomib–rituximab or ritux- phoma (CD68; refs. 21, 22) and rituximab activity (CD68; imab alone. Candidate proteins and target gene poly- ref. 22).

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Drug target candidate genes were included for both aim of identifying an association with improved outcome bortezomib and rituximab. The proteasome subunit b with combination treatment. Analyses were conducted in (PSMB) genes 1, 2, and 5 interact with bortezomib and all evaluable patients, stratified by expression level of the variation in these and in PSMB 6, 7, and 8 may be important non-SNP member of the biomarker pair. Effects of geno- for interindividual variation in responses (23–25). Varia- type, treatment, and interaction of treatment and genotype tion in FCGR2A (26, 27) and FCGR3A influences affinity of were evaluated using a Cox regression model, including immunoglobulin G antibodies for the Fcg immune cell calculation of HRs and 95% CIs. The log-rank test was used surface receptor (28, 29); and an association has already for comparisons of outcomes with bortezomib–rituximab been shown between FCGR3A genotype and response to and rituximab subgroups according to genotype. rituximab-based therapy (30, 31). Kaplan–Meier methodology was used to estimate PFS, TTNT, and OS distributions. Response rates were compared Assay methods between groups using the Fisher exact test. Clinical covari- Validated immunohistochemistry tests were used for ates were FLIPI score (low, intermediate, high); prior ritux- protein analysis at a central laboratory; a single pathologist imab therapy (yes, no); time since last dose of antilym- evaluated all samples, averaging counts over 3 high-pow- phoma therapy (1 year, >1 year); region (United States/ ered fields for each marker. Assays used included: CD68, Canada, European Union, Rest of World), age (65, >65 DakoCytomation (#M0184; average macrophage counts years), sex, race, Ann Arbor stage (I, II, III, IV), number of overall and for the follicular and perifollicular spaces were prior lines of therapy (1, 2), and high tumor burden (yes, calculated; CD68 correlated directly with the number of no). Treatment exposure and safety were summarized using infiltrating macrophages); NF-kB, Cell Signaling (#C22B4); descriptive statistics in bortezomib–rituximab and rituxi- PSMA5, Biomol International (#PW8125); and p27, Trans- mab subgroups for biomarker pairs associated with a sig- duction Laboratories (BD Biosciences, #K25020). Cut nificant PFS benefit, with no statistical comparison between points for protein markers used in the analyses are sum- subgroups or between biomarker-positive and biomarker- marized in Supplementary Table S1. When insufficient negative patients. sample was available for testing all biomarkers, immuno- In the absence of independent datasets to validate find- histochemistry analyses were prioritized; CD68 had the ings, all LYM-3001 patients with no missing biomarker lowest priority. values were assigned (7:3 ratio) to discovery and confirma- TaqMan single-nucleotide polymorphism (SNP) assays tion test sets using simple randomization. The discovery set (Applied Biosystems) and custom PCR/ligase detection was used for identification of biomarkers significantly asso- reaction were used for genotyping (see Supplementary ciated with a PFS benefit. The confirmation set was used for Materials and Methods). Alleles from PSMB subunit and independent validation; patients with missing data were FCGR2A/3A genes with sufficient variation (>10%) includ- included in the confirmation set provided data were avail- ed PSMB1 P11A (rs12717), PSMB5 R24C (rs11543947), able for significant biomarkers identified in the discovery PSMB8 G8R (rs114772012), PSMB9 R60H (rs17587) and phase. All markers and covariates were treated as categorical V32I (rs241419), FCGR2A H131R (rs1801274), Q62R variables; protein biomarkers were dichotomized by stain- (rs9427398), and Q62X (rs9427397) and FCGR3A ing pattern and frequencies of patients within score groups V212F (rs396991). (Supplementary Table S1). PFS after treatment with bortezomib–rituximab and rituximab in biomarker subpopula- Statistical analysis tions was compared using the log-rank test, with 5-fold The primary single-marker association analysis was cross-validation for the biomarker pair, PSMB1 P11A (dom- aimed at identifying differentially expressed proteins or inant C/G þ G/G) and CD68 follicular expression 50, in genotypes associated with clinical study end points (PFS, the discovery set, and subsequently tested in the confirma- ORR, CR, TTNT, OS). For single-marker association anal- tion set. yses and pairwise comparisons, the log-rank test and Cox proportional hazard model were used for assessments of Results PFS, TTNT, and OS with bortezomib–rituximab and ritux- Patient characteristics imab groups, including calculation of HRs and 95% con- In total, 336 and 340 patients were randomized to fidence intervals (CI). treatment with bortezomib–rituximab and rituximab, For pairwise comparison analyses, biomarker pairs were respectively, and 334 and 339 received treatment (13). formed by exhaustive combinations of 2 markers. Geno- Patients providing evaluable samples and included in the types of SNPs were used individually to combine with biomarker analyses are summarized in Supplementary other biomarkers. Multiple testing corrections were con- Fig. S1. ducted using the false discovery rate (FDR) method for pairwise comparisons (32). Biomarker pairs containing a Single-marker associations and pairwise comparisons SNP marker that were associated with a significant PFS Initial analyses focused on single-marker associations. benefit with bortezomib–rituximab versus rituximab were Significant (P < 0.05) differences in PFS in patients treated evaluated under dominant, recessive, and additive genetic with bortezomib–rituximab versus rituximab were seen models to determine association with PFS and OS, with the in patient subgroups defined by biomarkers including

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CD68 positivity, NF-kB p65 cytoplasmic signal, and patients with low CD68 expression. PSMB1 P11A C/G þ G/G PSMB1 P11A, FCGR2A/H166R, and FCGR3A/V212F gen- was significantly associated with PFS in the rituximab arm otypes; however, the PFS benefit in these patient sub- (P ¼ 0.0238), and there was a trend of significant interaction groups (the difference in median PFS in patients treated between PSMB1 P11A genotype and treatment (P ¼ 0.0647; with bortezomib–rituximab versus rituximab) was gen- Supplementary Table S2 and Supplementary Fig. S3), indi- erally less than 5 months or population frequencies were cating that the association of the marker pair with PFS may low (Supplementary Fig. S2). be different between treatment arms. No significant asso- In pairwise analyses (1,140 comparisons), in which gen- ciations with OS were seen, although there was a trend for otypes of SNP markers were used individually in combina- association with OS in the bortezomib–rituximab arm with tion with other biomarkers, 102 biomarker pairs were low CD68 expression and PSMB1 P11A under both the identified as defining a patient subgroup in which there dominant (P ¼ 0.0801; Supplementary Fig. S3) and addi- was a significant (P < 0.05) difference in PFS in patients tive (P ¼ 0.0888) models. In subsequent analyses, the treated with bortezomib–rituximab versus rituximab. Of dominant model (G allele: C/G þ G/G) was adopted for these, the difference in median PFS in patients treated with PSMB1 P11A in the significant biomarker pair. bortezomib–rituximab versus rituximab was 6 months or more for 14 biomarker pairs (Table 1). Following FDR Clinical outcomes in patients with/without significant correction to control for multiple comparisons (32), this biomarker pair PFS difference remained significant for one biomarker pair A total of 376 patients (186 bortezomib–rituximab trea- (PSMB1 P11A C/G heterozygote, 50 CD68-positive cells). ted, 190 rituximab treated) were evaluable for both PSMB1 The population frequency of this biomarker pair was 33%. P11A and CD68. There were 164 (43.6%) patients positive This biomarker pair was subsequently tested under different for the biomarker pair (PSMB1 P11A G allele, low CD68 genetic models. expression, hereafter referred to as "biomarker-positive" The biomarker pair of PSMB1 P11A and CD68 follicular patients; 78 bortezomib–rituximab treated, 86 rituximab expression was evaluated for association with PFS and OS treated). The PSMB1/CD68-evaluable population (total, under dominant (PSMB1 P11A C/C vs. C/G þ G/G), reces- and by treatment arm) was representative of the overall sive (C/C þ C/G vs. G/G), and additive (CC vs. C/G vs. G/G) population in terms of demographics and baseline char- genetic models, stratified by CD68 follicular expression (0– acteristics (Table 2), with no significant differences (except 50 vs. >50 CD68-positive cells). By Cox regression analysis, region and race, likely due to lower collection rate of tumor among all evaluable patients, significant association with blocks/slides in China compared with the rest of the world). PFS was only seen under the dominant genetic model in In addition, there was a similar distribution in baseline

Table 1. Signiﬁcant biomarker pairs identiﬁed by pairwise analysisa

Bortezomib– rituximab Rituximab Log-rank Marker A Marker B N PFS, mo N PFS, mo D PFS, mo P FDR PSMB5 R24C C/T NF-kB p65 cytoplasmic 5 27.0 7 10.4 16.6 0.0439 0.489 signal intensity 1þ PSMB1 P11A C/G PSMA5-positive cytoplasmic 50 18.9 50 9.5 9.4 0.0145 0.447 signal >90% PSMB1 P11A C/G CD68 positive (follicular) 50 57 16.6 61 9.1 7.5 0.0001 0.051 PSMB1 P11A C/G Time since last treatment >1 year 72 18.2 74 10.7 7.5 0.0198 0.447 PSMB1 P11A C/G CD68 positive (perifollicular) >50 24 16.6 28 9.2 7.4 0.0365 0.471 PSMB9 R60H G/G NF-kB p65 nuclear positive >0% 35 16.2 28 9.5 6.7 0.0303 0.455 PSMB5 R24C C/T CD68 positive (follicular) 50 18 13.7 21 7.2 6.5 0.0220 0.447 PSMB1 P11A C/G Age 65 years 86 15.3 96 9.2 6.1 0.0071 0.437 1 prior regimen CD68 positive (follicular) 50 63 18.2 69 9.3 8.9 0.0129 0.447 Race group 'other' PSMA5 nuclear staining >20% 11 11.4 7 3.8 7.6 0.0320 0.455 High tumor burden: No CD68 positive (overall) 50 64 22.8 68 16.0 6.8 0.0177 0.447 High tumor burden: No CD68 positive (follicular) 50 64 20.5 66 13.8 6.7 0.0310 0.455 No prior rituximab CD68 positive (follicular) 50 73 15.9 86 9.2 6.7 0.0066 0.437 Male sex PSMA5 nuclear staining >20% 63 13.7 48 7.7 6.0 0.0050 0.437

NOTE: The shaded biomarker pair was determined to remain signiﬁcant following FDR correction for multiple testing (32). aSamples from China were not included in these initial pairwise biomarker assessments but were included in the subsequent genetic model analyses.

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Table 2. Comparison of baseline demographics and disease characteristics between the overall study population and the PSMB1 P11A G allele/CD68 biomarker-evaluable population

Pa, overall vs. Overall population Biomarker-evaluable population biomarker population

Btz-R R Total Btz-R R Total (n ¼ 336) (n ¼ 340) (N ¼ 676) (n ¼ 186) (n ¼ 190) (N ¼ 376) Btz-R R Total Median age, y (range) 57 (24–83) 57 (21–84) 57 (21–84) 57 (24–79) 57 (22–82) 57 (22–82) 0.9666 0.8824 0.8895 Age >65 years, n (%) 81 (24) 87 (26) 168 (25) 45 (24) 49 (26) 94 (25) 0.9971 0.9747 0.9788 Male, n (%) 172 (51) 137 (40) 309 (46) 96 (52) 71 (37) 167 (44) 0.9530 0.4916 0.6552 White, n (%)b 249 (74) 257 (76) 506 (75) 158 (85) 158 (83) 316 (84) 0.0050 0.0487 0.0007 Region, RoW, n (%)b 183 (55) 182 (54) 365 (54) 88 (47) 90 (47) 178 (47) 0.1093 0.1630 0.0341 High FLIPI (3), n (%) 138 (41) 140 (41) 278 (41) 74 (40) 83 (44) 157 (42) 0.8974 0.8642 0.9715 High tumor burden, n (%) 185 (55) 179 (53) 364 (54) 102 (55) 101 (53) 203 (54) 0.9325 0.9374 0.9959 Prior rituximab, n (%) 147 (44) 147 (43) 294 (44) 83 (45) 85 (45) 168 (45) 0.8700 0.7600 0.7399 >1 prior therapy, n (%) 192 (57) 202 (60) 394 (59) 104 (56) 109 (57) 213 (57) 0.9233 0.8776 0.8063

Abbreviations: Btz, bortezomib; R, rituximab; RoW, rest of the World, excluding USA, Canada, and Europe. at test/Mann–Whitney test. bSignificant differences likely due to lower collection rate of tumor blocks/slides in China (30%) compared with the rest of the world (80%). characteristics between the bortezomib–rituximab and Validation of these findings with the significant biomark- rituximab arms in the biomarker-evaluable and overall er pair, using LYM-3001 patient discovery and confirmation populations, including the asymmetry in male gender test sets, is summarized in Supplementary Tables S3 and S4. between the 2 treatment groups (13). Notably, similar Treatment exposure and safety profiles of bortezomib– proportions of patients with high-risk features were seen rituximab and rituximab in the overall safety population in biomarker-positive and biomarker-negative subsets; and in biomarker-positive and biomarker-negative patients FLIPI score was high in 68 (41%) and 89 (42%) patients, are summarized in Table 4. Among patients treated with respectively, intermediate in 54 (33%) and 76 (36%) bortezomib–rituximab, the proportion receiving all 5 cycles patients, and low in 42 (26%) and 47 (22%) patients, of bortezomib appeared higher, and rates of grade 3 respectively (P ¼ 0.703). A total of 93 (57%) biomarker- adverse events and serious adverse events appeared numer- positive and 110 (52%) biomarker-negative patients had ically lower, in biomarker-positive patients versus biomark- high tumor burden (P ¼ 0.404). Both high FLIPI score and er-negative patients and the overall safety population. Con- high tumor burden were seen in 52 (32%) biomarker- versely, among rituximab-treated patients, rates of grade 3 positive and 59 (28%) biomarker-negative patients, respec- adverse events and serious adverse events appeared numer- tively (P ¼ 0.427). ically higher in biomarker-positive patients. In biomarker-positive patients, PFS was significantly longer in patients treated with bortezomib–rituximab versus Discussion rituximab (median 14.2 vs. 9.1 months, HR 0.47, P < The findings of our analyses of protocol-specified bio- 0.0001; FDR P ¼ 0.047; Table 3 and Fig. 1A). There was markers in the LYM-3001 study suggest that subgroups of also a significantly higher ORR, a numerically higher CR patients with relapsed/refractory follicular lymphoma can rate, and a significantly longer TTNT. These patients also be identified that experience significantly longer PFS benefit seemed to have longer OS (HR 0.49, uncorrected P ¼ together with improved OS with addition of bortezomib to 0.0461; Fig. 1B), although this was not significant after rituximab. Our results can be considered robust, being correction for multiple testing. In biomarker-negative derived from one of the largest prospective randomized patients, no significant efficacy differences between treat- studies conducted in this setting. Per protocol, there was ment with bortezomib–rituximab and rituximab were seen. mandatory collection of archival tumor samples and whole The PFS findings were reflected when stratified by clinical blood samples, enabling substantial sample collections for covariates (Fig. 2); the PFS benefit with bortezomib–ritux- these exploratory analyses of prespecified candidate bio- imab versus rituximab was consistent and generally statis- markers. The biomarker subgroup that we identified had a tically significant in biomarker-positive patients, but no high population frequency (43.6%). There was also a sub- significant differences were seen in biomarker-negative stantially greater efficacy benefit with bortezomib–rituxi- patients. PFS findings were also similar in high-risk patients mab versus rituximab in biomarker-positive patients than with both high FLIPI score and high tumor burden (data not that seen in the overall, unselected study population (13). shown). These findings indicate the usefulness of such biomarker

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Table 3. Outcomes and response rates with bortezomib–rituximab versus rituximab in biomarker-positive [PSMB1 P11A G allele, and low CD68 expression (50 CD68-positive cells)] and biomarker-negative patients, in all biomarker evaluable patients (N ¼ 376), and in the overall study population (N ¼ 676; ref. 13)

All biomarker Biomarker positive Biomarker negative evaluable Total study population

Btz-R R Btz-R R Btz-R R Btz-R R (n ¼ 78) (n ¼ 86) (n ¼ 108) (n ¼ 104) (n ¼ 186) (n ¼ 190) (n ¼ 336) (n ¼ 340) Median PFS, months 14.2 9.1 11.6 13.7 13.6 11.0 12.8 11.0 HR (95% CI) 0.47 (0.32–0.68) 1.12 (0.80–1.58) 0.79 (0.62–1.01) 0.82 (0.68–0.99) P <0.0001 0.5017 0.0602 0.039 Median OS, months NE NE NE NE NE NE NE NE HR (95% CI) 0.49 (0.24–1.00) 1.04 (0.62–1.74) 0.80 (0.53–1.20) 0.97 (0.71–1.33) P 0.0461 0.8732 0.2805 0.854 ORRa,% 73.1 47.6 62.0 56.1 66.9 52.2 63.2 49.4 P 0.0013 0.4701 0.0053 0.0004 CR ratea,% 34.6 22.6 38.0 36.7 36.5 30.2 25.1 18.2 P 0.1166 0.8840 0.2199 0.035 Median TTNT, months 32.6 14.1 22.1 24.2 24.9 17.5 23.0 17.7 HR (95% CI) 0.44 (0.29–0.66) 1.10 (0.77–1.57) 0.75 (0.57–0.97) 0.80 (0.66–0.97) P <0.0001 0.6062 0.0293 0.024

Abbreviations: Btz, bortezomib; NE, not estimable; R, rituximab. an ¼ 57 for bortezomib–rituximab and n ¼ 60 for rituximab biomarker-positive group; n ¼ 111 and n ¼ 113, respectively, for biomarker- negative group; n ¼ 168 and n ¼ 173 for all biomarker evaluable patients; n ¼ 315 and n ¼ 324 for the total study population.

analyses for identifying specific patient subgroups that CD68 has established prognostic value in lymphoma and benefit from bortezomib–rituximab (33) and the potential with rituximab-based treatment (21, 22, 34), and readily of such analyses for optimizing treatment for individual available assays enable measurement using methodology patients with follicular lymphoma. that is likely reproducible in laboratories with experience in Using pairwise analyses, we identified one biomarker pair lymphoma diagnosis. In addition, because we used a rel- that showed a significant PFS benefit with bortezomib– atively straightforward genotyping assay for PSMB1 P11A, a rituximab versus rituximab in biomarker-positive patients validated assay could potentially be developed for clinical after multiple comparison correction. This pair was tested use. under different genetic models, and the presence of There are currently no published data on the functional PSMB1 P11A (G allele) and low CD68 expression (50 consequence of the PSMB1 P11A variant. The prognostic CD68-positive cells) was associated with a median PFS significance of variants in PSMB genes and in some protea- benefit of 5 months, improved response rates and TTNT, some a-subunits (PSMA) may be hypothesized to be asso- and an OS benefit in patients treated with bortezomib– ciated with reduced cellular levels of functional protea- rituximab versus rituximab. Notably, patients positive for somes (35). Sequence changes such as a G allele in the this biomarker pair were representative of the overall PSMB1 P11A leader sequence may interfere with the assem- LYM-3001 study population (13), with approximately bly or function of proteasomes (36) and could translate into half having high-risk disease and/or poor prognostic greater bortezomib activity. Such greater activity could be features. Furthermore, the safety profile of bortezomib– related to a reduction of active proteasome sites and a rituximab in biomarker-positive patients appeared com- consequent requirement for fewer bortezomib molecules parable with that in the overall population. It should be to sufficiently inhibit proteasome function, leading to cell noted that this was an exploratory analysis and, in the death (37). This hypothesis is supported by previous find- absence of an independent dataset with which to validate ings suggesting that low PSMA5 levels are associated with our results with the significant biomarker pair, we split longer PFS with bortezomib in mantle cell lymphoma (18). our data from LYM-3001 patients into discovery and In addition, recent RNAi screens showed that silencing of confirmation test sets. The data from these analyses sup- individual proteasome genes including PSMA5 and ported our findings. Nevertheless, independent valida- PSMB2/3/7 sensitized multiple myeloma cells to bortezo- tion studies are required for confirmation. mib (37, 38). Importantly, the two biomarkers in this pair would be The proteasome also regulates CD20, the target of ritux- feasible and practical to screen for in the clinical setting, imab; therefore, functional mutations in proteasome sub- if these findings are confirmed in independent studies. units such as PSMB1 P11A may influence the activity of

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Biomarkers for PFS Beneﬁt with Bortezomib–Rituximab in FL

Figure 1. Kaplan–Meier distributions of (A) PFS, (B) OS, and (C) TTNT with bortezomib–rituximab and rituximab in biomarker-evaluable patients (N ¼ 376) who were positive or negative for the biomarker pair PSMB1 P11A (G allele) and low CD68 expression (50 CD68-positive cells). rituximab in patients. In the presence of rituximab, there is (22, 42). Our findings (Supplementary Fig. S2A) confirm upregulation of both the ubiquitin and proteasome systems the relatively poorer outcome of patients with low CD68 (39). In patients with lower levels of functional protea- expression treated with rituximab alone (22). The anti- somes at baseline, alternative proteolytic systems (e.g., tumoral activity of rituximab is dependent on Fc-recep- autophagy) may be used for degradation of ubiquitinated tor–mediated interactions with effector cells including CD20 (40). Autophagy is less efficient than degradation neutrophils, natural killer cells, and macrophages. Macro- through the proteasome (41), and this may translate into phages can eliminate B-lymphocytes by direct Fc-recep- less efficacy for patients treated with rituximab when these tor–mediated phagocytosis (43), or they may secrete proteasome subunit anomalies are present. For such cytolytic factors or release cytokines, thereby recruiting patients, as shown in this report, treatment with bortezomib other effector cells to amplify the inflammatory response is more effective than in patients without these anomalies; (44, 45), inferring a direct relationship between CD68 this may be due to direct inhibition of the proteasome, TAM content and efficacy of rituximab. Patients with which leads to control of other survival signaling pathways PSMB1 P11A (G allele), which may indicate lower pro- such as NF-kB. teasome levels, and low CD68 TAM did much worse on It has been reported previously that patients with high rituximab alone compared with bortezomib–rituximab, levels of tumor-associated macrophages (TAM) have presumably due to less elimination of B-lymphocytes by favorable outcomes when treated with rituximab but direct Fc-receptor–mediated phagocytosis and slower poorer outcomes when treated with chemotherapy alone clearance of ubiquitinated CD20 protein through

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Coifﬁer et al.

Figure 2. PFS stratiﬁed by clinical covariates for biomarker-evaluable patients (N ¼ 376) who were positive or negative for the biomarker pair PSMB1 P11A (G allele) and low CD68 expression (50 CD68-positive cells).

autophagy. For these patients, addition of bortezomib bortezomib–rituximab versus rituximab, without a notable overcame this inefficiency, presumably by controlling impact on safety. Further confirmation in independent other survival signaling pathways such as NF-kB. cohorts of similar patients, and in patients treated with In conclusion, these findings suggest that prespecified other bortezomib–rituximab–based combination regimens biomarker combinations can identify follicular lymphoma showing activity in relapsed/refractory follicular lymphoma patient subgroups deriving substantial clinical benefit from (9, 11, 12), would be warranted.

2558 Clin Cancer Res; 19(9) May 1, 2013 Clinical Cancer Research

Biomarkers for PFS Beneﬁt with Bortezomib–Rituximab in FL

Table 4. Treatment exposure and safety with bortezomib–rituximab and rituximab in biomarker-positive and biomarker-negative patients in the biomarker-evaluable population (N ¼ 376) and in the overall safety population (N ¼ 673)

Overall safety population Biomarker positive Biomarker negative

Btz-R R Btz-R R Btz-R R (n ¼ 334) (n ¼ 339) (n ¼ 78) (n ¼ 86) (n ¼ 108) (n ¼ 104) Treatment exposure Cycles received, median (range) 5 (1–5) 5 (1–5) 5 (1–5) 5 (1–5) 5 (1–5) 5 (1–5) Patients receiving all 5 cycles, n (%) 232 (69)a 252 (74) 63 (81)a 62 (72) 74 (68.5)a 80 (77) Rituximab relative dosing intensity, mean % 96 97 97 96 90 96 Bortezomib relative dosing intensity, mean % 88 – 92 – 90 – Safety proﬁle Any AE, n (%) 316 (95) 265 (78) 74 (95) 71 (83) 105 (97) 85 (82) Any treatment-related AE, n (%) 290 (87) 156 (46) 69 (88.5) 48 (56) 93 (86) 39 (37.5) Related to rituximab 206 (62) 156 (46) 45 (58) 48 (56) 69 (64) 39 (37.5) Related to bortezomib 276 (83) – 67 (86) – 88 (81) – Any grade 3 AE, n (%) 152 (46) 70 (21) 26 (33) 20 (23) 47 (43.5) 20 (19) Any serious AE, n (%) 59 (18) 37 (11) 9 (11.5) 13 (15) 21 (19) 12 (11.5) AE leading to treatment discontinuation, n (%) 19 (6) 5 (2) 3 (4) 2 (2) 6 (6) 2 (2) Deaths due to AEs, n (%) 6 (2) 2 (<1) 0 1 (1) 2 (2) 1 (1)

Abbreviation: AE, adverse event; Btz-R, bortezomib–rituximab; R, rituximab. aData shown for cycles of bortezomib therapy; an additional 12 patients in the overall safety population, and an additional 2 and 4 patients in the biomarker-positive and biomarker-negative populations, respectively, received all 5 cycles of R.

Disclosure of Potential Conflicts of Interest Analysis and interpretation of data (e.g., statistical analysis, biosta- W. Li is employed as a senior scientist in Janssen Research & Development. tistics, computational analysis): B. Coiffier, W. Li, E.D. Henitz, A. Shapiro, J. Karkera is employed as a prinicpal reasearch scientist in Janssen Research & P. Theocharous, Y. Elsayed, H. van de Velde, M. Schaffer, A. Scheliga, P.L. Development. R. Favis is an employee of Johnson & Johnson. Y. Elsayed is Zinzani, A. Cakana, D.-L. Esseltine, G. Mulligan, D. Ricci employed as a vice president, hemeatological malignancy, and has owner- Writing, review, and/or revision of the manuscript: B. Coiffier, W. Li, E. ship interest (including patents) in Johnson & Johnson. H. van de Velde is D. Henitz, J. Karkera, R. Favis, P. Theocharous, Y. Elsayed, H. van de Velde, M. employed as a senior director oncology, Research & Development, in Janssen Schaffer, E.A. Osmanov, A. Scheliga, J. Mayer, F. Offner, S.A.J. Rule, J. and has ownership interest (including patents) in Johnson & Johnson. M. Romejko-Jarosinska, S. de Vos, M. Crump, O. Shpilberg, P.L. Zinzani, A. Schaffer has ownership interest (including patents) in Johnson & Johnson. J. Cakana, D.-L. Esseltine, G. Mulligan, D. Ricci Mayer has commercial research grant from Janssen. S.A.J. Rule has commer- Administrative, technical, or material support (i.e., reporting or orga- cial research support for a Cancer Research UK randomized trial and is nizing data, constructing databases): W. Li, E.D. Henitz, A. Shapiro, D. Ricci a consultant/advisory board member of Johnson & Johnson. S. de Vos is a Study supervision: B. Coiffier, W. Li, P. Theocharous, Y. Elsayed, H. van de consultant/advisory board member of Millennium. O. Shpilberg has a Velde, F. Offner, D. Ricci commercial research grant from Janssen. A. Cakana is an employee of Janssen Pharmaceuticals. D.-L. Esseltine is employed as a vice president in Millen- Acknowledgments nium Pharmaceuticals Inc. G. Mulligan is employed as a director in Millen- The authors thank the patients for their participation in this study, Dr nium Pharmaceuticals. D. Ricci is employed as a director, Biomarker, in Eugene Zhu at Janssen R&D for provision of adverse event and drug exposure Janssen Pharmaceuticals. No potential conflicts of interest were disclosed by data, and Steve Hill of FireKite for writing assistance in the development of the other authors. this manuscript, which was funded by Millennium Pharmaceuticals, Inc., and Janssen Global Services. Authors' Contributions Conception and design: B. Coiffier, W. Li, E.D. Henitz, P. Theocharous, Y. Grant Support Elsayed, G. Mulligan, D. Ricci This work was funded by Janssen Research and Development, L.L.C., and Development of methodology: W. Li, J. Karkera, R. Favis, D. Gaffney, A. Millennium Pharmaceuticals, Inc. The costs of publication of this article were defrayed in part by the payment Shapiro, Y. Elsayed, A. Cakana, G. Mulligan, D. Ricci advertisement Acquisition of data (provided animals, acquired and managed patients, of page charges. This article must therefore be hereby marked provided facilities, etc.): E.D. Henitz, J. Karkera, R. Favis, D. Gaffney, A. in accordance with 18 U.S.C. Section 1734 solely to indicate this fact. Shapiro, P. Theocharous, Y. Elsayed, X.-N. Hong, A. Scheliga, J. Mayer, F. Offner, S.A.J. Rule, A. Teixeira, J. Romejko-Jarosinska, S. de Vos, M. Crump, Received September 26, 2012; revised February 18, 2013; accepted March O. Shpilberg, P.L. Zinzani, A. Cakana, D. Ricci 10, 2013; published OnlineFirst April 2, 2013.

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Prespecified Candidate Biomarkers Identify Follicular Lymphoma Patients Who Achieved Longer Progression-Free Survival with Bortezomib−Rituximab Versus Rituximab

Bertrand Coiffier, Weimin Li, Erin D. Henitz, et al.

Clin Cancer Res 2013;19:2551-2561. Published OnlineFirst April 2, 2013.

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