Abstract: Abstract 1587 Monoclonal B-cell lymphocytosis (MBL) with an immunophenotype consistent with marginal zone origin (MBL-MZ), that can be either CD5− or CD5+ but atypical for CLL, and also lacking an IGH/CCND1 translocation, is an increasingly recognised entity with poorly understood biological background and clinical significance. In particular, it is not yet clarified whether it represents a precursor state to one of the distinct lymphoma entities recognized by WHO as deriving from MZ cells or whether it constitutes a novel entity, likely with similar ontogeny. To obtain insight into this issue we retrospectively evaluated a series of 71 patients (male/female: 35/36, median age: 73.3 years) with lymphocytosis (median lymphocyte count: 5.77 × 109/l) detected incidentally on a routine blood test. No case had lymphadenopathy, organomegaly or any clinical features to suggest a concurrent marginal zone lymphoma. Hemoglobin and platelet counts were normal in all cases; 15/57 (26%) cases had paraproteinemia. Peripheral blood immunophenotyping revealed the presence of a clonal B-cell population with Matutes score 〈 2 in all cases. Individual markers were expressed as follows: CD5: 15/71, CD23: 7/70, CD79β: 60/64, FMC7: 50/67, CD49d: 35/35, CD38: 6/53. In 8/45 cases assessed with CT-scan and/or ultrasound, borderline splenomegaly was observed. Histopathological examination of the bone marrow biopsy (BMB) was available in 11 cases and demonstrated mostly mixed patterns of neoplastic lymphocytic inflitration from small B cells. Karyotype data were available in 66 cases; 48/66 (72.7%) had abnormal karyotype. Main cytogenetic findings are as follows: (1) translocations: n=16 cases of which 3 carried t(2;7)(p11;q21/22); (2) isochromosome 17q: n=8; (3) trisomy 12: n=8; (4) del(7q): n=7; (5) trisomy 3: n=4. Immunogenetic analysis revealed overusage of the IGHV4–34 gene (15/63, 23.8%). Notably, the IGHV1–2 gene was utilized by a single case, thus sharply contrasting (p 〈 0.0001) splenic marginal-zone lymphoma (SMZL) with a reported frequency of IGHV1–2 in excess of 30%. Seven of 63 rearrangements (11.1%) carried IGHV genes with no somatic mutations, whereas the remainder (56/63, 88.9%) exhibited some impact of somatic hypermutation (SHM), ranging from minimal to (mostly) pronounced. Overall, cases of the present study exhibited a significantly (p 〈 0.005) higher SHM load compared to SMZL. With a median follow-up of 4.9 years (0.8–20), 54 cases (group A) remain stable with no signs of progression. The remaining 17 cases (group B) have MBL along with clinical or histopathologic evidence of lymphoma. In particular: (i) a female with MBL as an incidental finding, also carrying t(2;7), was eventually diagnosed with gastric MALT lymphoma at +11 months from presentation; immunogenetic analysis confirmed clonal identity between the MBL and the lymphoma; (ii) 2 cases developed lymphadenopathy; (iii) a single case developed diffuse large B cell lymphoma of the skin; and, (iv) 13 cases developed splenomegaly and, thus, can be considered as either SMZL or splenic lymphoma/leukemia unclassifiable (SLLU). Groups A and B did not differ in terms of demographics, diagnostic blood counts (including clonal MBL count) and SHM status; the only difference concerned cytogenetic profiles, with i(17)(q10) and del(7q) being almost exclusive to group A. In conclusion, we demonstrate that MBL-MZ can be the presenting feature of occult MZ lymphoma, most frequently SMZL/SLLU. However, in a sizeable proportion of cases, MBL-MZ remains stable over time with no evidence of organ involvement and distinct immunogenetic features from SMZL, thereby raising the possibility that it might represent a newcomer to the spectrum of B-cell lymphoproliferations of MZ origin. Disclosures: No relevant conflicts of interest to declare.

Abstract: The classification of CLL patients according to the somatic hypermutation status (SHM) of the immunoglobulin heavy variable (IGHV) genes, namely mutated (M-CLL) versus unmutated (U-CLL), reflects fundamental differences in disease biology and clinical course. Realizing this, here we followed a compartmentalized approach and addressed the issue of prognostication separately for M-CLL and U-CLL. In a multi-institutional cohort of 2366 patients [M-CLL, n=1364 (58%); U-CLL, n=1002 (42%)] consolidated within ERIC, the European Initiative in CLL, we assessed the clinical impact of 'traditional' (age and clinical stage at the time of diagnosis, gender, CD38 expression, FISH detected abnormalities included in the Döhner hierarchical model of cytogenetic aberrations), and novel prognosticators (recurrent mutations within the TP53, SF3B1, NOTCH1, MYD88, and BIRC3 genes; IGHV gene usage; membership in stereotyped subsets) within M-CLL and U-CLL. Our statistical approach was based both on Cox regression models and recursive partitioning algorithms; internal validation was performed via bootstrapping procedures. Given the retrospective nature of our study, time-to-first-treatment (TTFT) was the primary endpoint. As expected, M-CLL exhibited significantly longer TTFT compared to U-CLL [median TTFT: not yet reached (M-CLL) vs 1.9 years (95% CI: 0.01-12.3 years, U-CLL), p 〈 0.0001]. Advanced clinical stages (Binet B-C) were associated with shorter TTFT in both M-CLL and U-CLL; a significantly worse outcome was also identified for Binet C versus Binet B cases (p 〈 0.0001). Binet A patients received our special focus, representing 90% and 67% of M-CLL and U-CLL studied cases, respectively. Amongst Binet A M-CLL cases, TP53 aberrations [TP53abs, deletions of chromosome 17p, del(17p) and/or TP53 mutations], stereotyped subset #2 membership and trisomy 12 were identified as equally adverse prognostic indicators [median TTFT: 5.5 (95% CI: 0.2-12.8), 4 (95% CI: 0.6-6.8) and 7.3 (95% CI: 0.7-13.4) years, respectively; p-value: non-significant when applying the log-rank test for all paired comparisons); of note, TP53abs were mutually exclusive with the other two features. Amongst Binet A U-CLL cases, TP53abs, SF3B1 mutations and deletion of chromosome 11q [del(11q)] had an overall similar adverse impact [median TTFT for TP53abs, SF3B1 mutations and del(11q): 1.8 (95% CI: 0.01-4.4), 2 (95% CI: 0.01-7.7) and 2.1 (95% CI: 0.01-8.1) years, respectively, p-value: non-significant when applying the log-rank test for all paired comparisons]. Within the remaining Binet A U-CLL cases [i.e. those lacking TP53abs and/or SF3B1 mutations and/or del(11q)] , the only parameter associated with shorter TTFT was male gender (median TTFT: 3.5 years, 95% CI: 0.5-8.1 years). Based on these findings, we developed two prognostic indices for assessing TTFT tailored specifically to M-CLL and U-CLL, respectively. Within M-CLL (Figure 1A), 4 subgroups were identified: (i) very high risk: Binet C with identical 5- and 10-year treatment-probability (TP) of 92%; (ii) high risk: Binet B, 5y-TP and 10y-TP: 64% and 84%, respectively; (iii) intermediate risk: Binet A with one of the following: TP53abs or +12 or subset #2 membership, 5y-TP and 10y-TP: 40% and 55%, respectively; and (iv) low risk: Binet A nonTP53abs/+12/subset#2, 5y-TP and 10y-TP: 12% and 25%, respectively. Within U-CLL (Figure 1B), 5 subgroups were identified: (i) very high risk: Binet C with 5- and 10-year TP of 100%; (ii) high risk: Binet B, identical 5y-TP and 10y-TP: 90% and 100%, respectively; (iii) intermediate risk: Binet A with one of the following: TP53abs or SF3B1 mutations or del(11q), 5y-TP and 10y-TP: 78% and 98%, respectively; (v) low risk: Binet A, male nonTP53abs/SF3B1mut/del(11q), 5y-TP and 10y-TP: 65% and 85%, respectively and (iv) very low risk: Binet A, female nonTP53abs/SF3B1mut/del(11q), 5y-TP and 10y-TP: 45% and 65%, respectively. In conclusion, we identified clinicobiological parameters with distinct prognostic implications for M-CLL and U-CLL. These parameters were used in order to develop prognostic indices tailored to SHM status that were found capable of distinguishing subgroups with markedly different outcomes. We argue that such a compartmentalized approach may supersede previous attempts, thus overcoming the pronounced heterogeneity of CLL and optimizing prognostication. PB and TM contributed equally as first authors Figure 1 Figure 1. Disclosures Rosenquist: Gilead Sciences: Speakers Bureau.

Abstract: Clonal B-cell lymphocytosis of potential marginal-zone origin (CBL-MZ) rarely progresses to a well-recognized lymphoma. CBL-MZ does not require treatment in the absence of progressive disease.

Abstract: Selecting the most appropriate chronic lymphocytic leukaemia (CLL) treatment is challenging. Patient‐reported health‐related quality of life (HRQoL) is therefore a critical aspect to consider. This international study by the European Organization for Research and Treatment of Cancer (EORTC) tested the psychometric properties of a newly developed measure for CLL patients: the EORTC QLQ‐CLL17 to supplement the core questionnaire (EORTC QLQ‐C30). Patients with CLL ( n  = 341) from 12 countries completed the QLQ‐C30, QLQ‐CLL17 and a debriefing questionnaire. Sociodemographic and clinical data were recorded from medical records. A high percentage (30%–66%) reported symptoms and/or worries (e.g. aches/pains in muscles, lack of energy and worry/fears about health). Confirmatory factor analysis showed an acceptable to good fit of the 17 items on the three scales (i.e. symptom burden, physical condition/fatigue and worries/fears about health and functioning). Completion took on average 8 min. Test–retest and convergent validity was demonstrated. The QLQ‐CLL17 differentiated between patients with an Eastern Cooperative Oncology group (ECOG) performance of 0 versus 1–3 ( p 's  〈  0.01 and clinically relevant). The newly developed EORTC QLQ‐CLL17 will increase sensitivity of HRQoL assessment in patients with CLL. Implementation of this questionnaire both in clinical research and practice will help to generate unique clinically relevant data to better inform CLL treatment decision‐making.

Abstract: The remarkable clinical heterogeneity of CLL has prompted several initiatives towards the development of prognostic models aiming to stratify patients into subgroups with distinct outcome. However, despite progress, the resultant prognostic models, mostly based on Cox regression analysis, have not been adopted in everyday clinical practice, mainly due to failure to provide sufficiently accurate predictions on a per patient basis. Here, we approached the issue of prognostication amongst Binet stage A CLL cases following a novel approach, in particular using Adaboost, an ensemble learning algorithm based on decision trees. Adaboost jointly considers all available parameters providing a specific prediction for each patient, unlike Cox regression models which are based on identifying parameters with independent prognostic significance. In addition, Adaboost models are completely automated with minimal time for training and prediction generation. This is in contrast to Cox models which are manually trained and require significantly more time for prediction generation. Both Cox regression and Adaboost models were evaluated regarding their predictive accuracy i.e. the number of patients successfully assigned to their true risk group divided by the total number of patients. For the development of the prognostic models, 5-fold cross-validation was used. The patients were equally subdivided into 5 subgroups. Each time, 4 out of the 5 subgroups were used to train the Cox regression and the Adaboost models while the 5th was kept as the validation cohort, where the models were applied to. The study cohort included 789 Binet A CLL patients with available data regarding gender, age, immunogenetic profile, CD38 expression, Döhner model cytogenetic aberrations and treatment status with a median follow up of 8.5 years (range 0-40.5 years, at least 5 years for untreated cases). Patients were subdivided in 3 groups: (i) high risk (HR): time-to-first-treatment (TTFT) 〈 2 years, n=215 (27%); (ii) intermediate risk (IR): TTFT≥2 years and 〈 5 years, n=151 (20%); and, (iii) low risk (LR): no need for treatment within 5 years from diagnosis, n=422 (53%). Applying Adaboost, the HR, IR and LR groups included 326 (41.5%), 0 (0%) and 463 (58.5%) cases, respectively. On multivariate analysis, unmutated IGHV genes U-CLL, subset #2 assignement and CD38 expression emerged as independently predictive of shorter TTFT; in contrast, adverse prognosis cytogenetic aberrations i.e. del(17p) and del(11q) did not retain significance (p=0.06 and 0.052, respectively), likely due to their strong association with U-CLL. Applying Cox regression models based on the significant independent parameters, patients were classified as follows: (i) HR: unmutated IGHV genes (U-CLL) and/or assignment to stereotyped subset #2 (n=357, 45%); (ii) IR: mutated IGHV genes (M-CLL) and high CD38 expression (CD38+) (n=41, 5%); and, (iii) LR: M-CLL and low CD38 expression (CD38-) (n=397, 50%). Prediction accuracies were 58.2% and 61.1% for the Cox regression and the Adaboost model, respectively (McNemar's test: p 〈 0.0025). Both models often failed to identify patients belonging to the IR group. Further, we gave the same clinico-biological parameters used for the development of the prognostic models to 7 trained hematologists and asked them to assign each patient included in the study to one of the 3 risk groups. Among the trained hematologists, responses varied within the range of 51.2-58.4%, leading to an average prediction accuracy of 54.6%: particularly challenging was the discrimination between the HR vs the IR group. In conclusion, Adaboost outperforms to a small, yet statistically significant, degree the predictive accuracy of both Cox regression and expert judgment, suggesting its potential for clinical testing. However, the predictive accuracy rates of both the Adaboost and Cox regression approach are still unsatisfactory, highlighting that further development is required in order to provide robust personalized predictive modeling, while also suggesting that statistical significance does not automatically translate into clinical utility. This indicates the need for incorporating disease- and host-related parameters not yet evaluated for their prognostic/predictive value in CLL in order to refine risk stratification, thus meaningfully empowering physicians in clinical decision-making. Disclosures Niemann: Janssen: Consultancy; Roche: Consultancy; Gilead: Consultancy; Novartis: Other: Travel grant. Ghia:Janssen Pharmaceuticals: Research Funding.

Abstract: The existence of stereotyped B cell receptor immunoglobulins (BcR IG) in chronic lymphocytic leukemia (CLL) strongly implicated antigen selection in disease ontogeny. We have previously shown that the stereotyped fraction encompasses ~30% of all CLL and includes multiple subsets with distinct BcR IG configuration and variable size. Eventually, certain major subsets emerged as distinct clinical entities, exemplified by subset #2 (IGHV3-21/IGLV3-21, ~2.5-3% of all CLL, mixed somatic hypermutation (SHM) status) of a particularly aggressive clinical course, thus, sharply contrasting subset #4 (IGHV4-34/IGKV2-30, ~1% of all CLL, mutated IGHV genes, M-CLL), a prototype for indolent disease. Here, taking advantage of a multi-institutional cohort of 21,123 CLL IG rearrangements, almost three times the size of the largest previous study, and the availability of validated, purpose-built immunoinformatics methods, we reappraised BcR IG stereotypy especially focusing on major subsets and the degree of their sequence similarity to related minor subsets. Stereotypy discovery was performed with ARResT/Teiresias, while stereotypy assignment to existing subsets previously deemed as major was performed with ARResT/AssignSubsets (http://bat.infspire.org/arrest/). In the present study, a subset was characterized as major if representing 〉 0.2% of the cohort (i.e. at least 50 cases). Minor subsets closely related to major ones (termed satellite) were identified applying the following criteria: (i) usage of IGHV genes from the same phylogenetic clan; (ii) VH CDR3 length difference ranging from -2 to +2 compared to the respective major subset; (iii) shared VH CDR3 sequence motif; and, (iv) -2 to +2 difference in the offset of the VH CDR3 motif compared to the respective major subset. In total, 7378/21123 (34.9%) IG sequences were grouped into subsets with stereotyped VH CDR3, with the previously characterized 19 major subsets accounting collectively for 2594 sequences (12.3%) of the cohort: of these, 12 included cases with unmutated IGHV genes (U-CLL), 6 concerned M-CLL and 1 (subset #2) included cases with mixed SHM status. Four additional subsets exceeded 50 cases, and, thus, were also considered as 'major'. These results reinforce the notion that not all CLL will end up being stereotyped but rather that a plateau for stereotypy exists at ~1/3 of the cohort. Subset #2 was the largest subset (n=572, 2.7%), while subset #1 (IGHV clan I (IGHV1,5,7 subgroups)/IGKV1(D)-39) was the most frequent subset within U-CLL (n=515, 2.4%) and subset #4 the most common M-CLL subset (n=192, 0.9%), hence displaying remarkable consistency regarding their frequency in all cohorts published since the pioneering studies. Altogether, Teiresias and AssignSubsets gave concordant results for previously identified major subsets, illustrating the validity of our approach. Satellite subsets were sought for individually for each major subset. In general, few satellite subsets were identified, most of which concerned U-CLL major subsets. That notwithstanding, notable cases of satellite subsets were exemplified by major subset #1 and its satellite subset #99 from which it differed only in VH CDR3 length (13 aminoacids in subset #1 versus 14 in subset #99); interestingly, both subsets displayed equally aggressive clinical course. Another example concerned subset #8 (IGHV4-39/IGKV1(D)-39, U-CLL), an aggressive subset with very high risk for Richter's transformation, that, except for a one-aminoacid difference in VH CDR3 length, was otherwise identical to satellite subset #215, also displaying clinical aggressiveness. Overall, our results confirm that major subsets can be robustly identified and are consistent in relative size, hence representing distinct disease subgroups amenable to compartmentalized research with the potential of overcoming the pronounced heterogeneity of CLL. Most major subsets display unique sequence motifs, however satellite subsets exist, especially within U-CLL. Considering ever-increasing evidence that major stereotyped subsets may represent distinct disease subgroups, the existence of satellite subsets reveals a novel aspect of repertoire restriction and has implications for refined molecular classification of CLL. Disclosures Shanafelt: Genentech: Research Funding; GlaxoSmithkKine: Research Funding; Celgene: Research Funding; Janssen: Research Funding; Pharmacyclics: Research Funding; Cephalon: Research Funding; Hospira: Research Funding. Gaidano:Roche: Consultancy, Honoraria, Speakers Bureau; Karyopharm: Consultancy, Honoraria; Morphosys: Consultancy, Honoraria; Gilead: Consultancy, Honoraria, Speakers Bureau; Janssen: Consultancy, Honoraria, Speakers Bureau; Novartis: Consultancy, Honoraria, Speakers Bureau. Niemann:Janssen: Consultancy; Abbvie: Consultancy; Roche: Consultancy; Gilead: Consultancy. Langerak:F. Hofmann-LaRoche, Genentech: Research Funding; InVivoScribe Technologies: Patents & Royalties: Royalties are provided to European Network (EuroClonality). Jaeger:Roche: Honoraria, Research Funding; Celgene: Honoraria, Research Funding. Kater:Celgene: Research Funding; Gilead: Research Funding; Janssen: Consultancy, Research Funding; Roche: Consultancy, Research Funding; Abbvie: Consultancy, Research Funding. Stilgenbauer:Amgen: Consultancy, Honoraria, Other: Travel grants, Research Funding; Gilead: Consultancy, Honoraria, Other: Travel grants , Research Funding; Genentech: Consultancy, Honoraria, Other: Travel grants , Research Funding; Celgene: Consultancy, Honoraria, Other: Travel grants , Research Funding; Boehringer Ingelheim: Consultancy, Honoraria, Other: Travel grants , Research Funding; Genzyme: Consultancy, Honoraria, Other: Travel grants , Research Funding; AbbVie: Consultancy, Honoraria, Other: Travel grants, Research Funding; GSK: Consultancy, Honoraria, Other: Travel grants , Research Funding; Janssen: Consultancy, Honoraria, Other: Travel grants , Research Funding; Mundipharma: Consultancy, Honoraria, Other: Travel grants , Research Funding; Novartis: Consultancy, Honoraria, Other: Travel grants , Research Funding; Pharmacyclics: Consultancy, Honoraria, Other: Travel grants , Research Funding; Hoffmann-La Roche: Consultancy, Honoraria, Other: Travel grants , Research Funding; Sanofi: Consultancy, Honoraria, Other: Travel grants , Research Funding. Hallek:Gilead: Consultancy, Honoraria, Other: travel support, Research Funding, Speakers Bureau; F. Hoffmann-LaRoche: Consultancy, Honoraria, Other: travel support, Research Funding, Speakers Bureau; Mundipharma: Consultancy, Honoraria, Other: travel support, Research Funding, Speakers Bureau; Celgene: Consultancy, Honoraria, Other: travel support, Research Funding, Speakers Bureau; AbbVie: Consultancy, Honoraria, Other: travel support, Research Funding, Speakers Bureau; Janssen-Cilag: Consultancy, Honoraria, Other: travel support, Research Funding, Speakers Bureau; Amgen: Consultancy, Honoraria, Other: travel support, Research Funding, Speakers Bureau. Rosenquist:Gilead Sciences: Speakers Bureau. Ghia:Gilead: Consultancy, Honoraria, Research Funding, Speakers Bureau; Janssen: Consultancy, Honoraria, Speakers Bureau; Roche: Honoraria, Research Funding; Adaptive: Consultancy; Abbvie: Consultancy, Honoraria. Stamatopoulos:Novartis: Honoraria, Research Funding; Abbvie: Honoraria, Other: Travel expenses; Janssen: Honoraria, Other: Travel expenses, Research Funding; Gilead: Consultancy, Honoraria, Research Funding.

Abstract: Purpose: Prompted by the extensive biases in the immunoglobulin (IG) gene repertoire of splenic marginal-zone lymphoma (SMZL), supporting antigen selection in SMZL ontogeny, we sought to investigate whether antigen involvement is also relevant post-transformation. Experimental Design: We conducted a large-scale subcloning study of the IG rearrangements of 40 SMZL cases aimed at assessing intraclonal diversification (ID) due to ongoing somatic hypermutation (SHM). Results: ID was identified in 17 of 21 (81%) rearrangements using the immunoglobulin heavy variable (IGHV)1-2*04 gene versus 8 of 19 (40%) rearrangements utilizing other IGHV genes (P = 0.001). ID was also evident in most analyzed IG light chain gene rearrangements, albeit was more limited compared with IG heavy chains. Identical sequence changes were shared by subclones from different patients utilizing the IGHV1-2*04 gene, confirming restricted ongoing SHM profiles. Non-IGHV1-2*04 cases displayed both a lower number of ongoing SHMs and a lack of shared mutations (per group of cases utilizing the same IGHV gene). Conclusions: These findings support ongoing antigen involvement in a sizable portion of SMZL and further argue that IGHV1-2*04 SMZL may represent a distinct molecular subtype of the disease. Clin Cancer Res; 22(8); 2032–40. ©2015 AACR.