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: Whole-exome sequencing of CLL patients who relapsed after FCR treatment revealed frequent mutations in RPS15. RPS15 mutations are likely to be early clonal events and confer poor prognosis.

Abstract: CLL stereotyped subset #2 (IGHV3-21/IGLV3-21) is uniformly aggressive independently of somatic hypermutation status. The prognosis for non–subset #2/IGHV3-21 CLL resembles that of the remaining CLL cases with similar somatic hypermutation status.

Abstract: Chronic lymphocytic leukemia (CLL) is a paradigmatic malignancy where the interplay of cell-extrinsic and cell-intrinsic factors has a major impact on disease evolution. Indeed, extrinsic triggering, e.g. antigenic stimulation through the B-cell receptor (BcR), together with intrinsic aberrations, e.g. accumulation of genetic defects, play a major role throughout the natural history of CLL. The importance of antigen involvement is underscored by the existence of 'stereotyped' BcR in up to 30% of CLL patients. Notably, CLL patients with stereotyped BcR can be grouped into different subsets, each with a subset-biased biological and clinical profile. For instance, while the clinically aggressive subset #2 (IGHV3-21/IGLV3-21, comprising both mutated (M-CLL) and unmutated (U-CLL) IGHV genes) displays a remarkably high frequency of SF3B1 mutations, subset #8, a subset with the highest risk of Richter transformation, shows a strong association with trisomy 12 and NOTCH1 mutations. ATM defects are implicated in the evolution of CLL and are associated with a dismal prognosis, however the extent to which they contribute to the genetic landscape in stereotyped subsets remains unexplored. To gain insight into this issue, we assessed the frequency of ATM mutations in 249 well-characterized CLL patients assigned to major subsets #1-8. The entire coding region of ATM (62 exons) was investigated with two different targeted deep-sequencing approaches, i.e. Haloplex technology (HiSeq, coverage ~1500X) or the Nextera XT kit (MiSeq, coverage ~4000X). A conservative variant allele frequency cut-off of 10% was selected, and mutations were validated by Sanger sequencing. Altogether, we identified 61 ATM mutations in 47/249 (19%) patients across all major subsets (Fig. 1). As expected, the majority of identified ATM mutations (n=43, 70%) have not yet been reported while the remaining 30% were listed in the HGMD or COSMIC mutation databases. The spectrum of ATM mutations included missense (n=31), nonsense (n=9), splicing (n=6), and frame-shift (n=14) mutations, and one in-frame deletion. Missense substitutions were distributed along the entire gene without any 'hotspot' region or preferred domain. The highest mutation frequency was detected in subset #2 (26%), with a significant enrichment in U-CLL vs. M-CLL cases, (13/33 vs. 8/48 subset #2 cases, respectively; p=0.021). Within poor-prognostic U-CLL subsets, ATM mutations were also frequent in subsets #6 (25%) and #7 (23%), while subsets #3, #5, #1, and #8 showed lower frequencies (17%, 17%, 13%, and 7%, respectively). The favorable prognostic M-CLL subset #4 exhibited a low frequency (7%) of ATM mutations. Notably, when comparing the two most populated subsets, i.e. #1 and #2, ATM mutations were overrepresented in the latter with a borderline significance value (p=0.086); when restricting the analysis to U-CLL #2 cases a significantly higher frequency was observed compared to #1 (13/33 vs 9/68; p=0.0045). Regarding the clinical impact of ATM defects in subset #2, we divided patients into subgroups with biallelic inactivation (def-ATM), sole 11q-, sole ATM -mutation, TP53 -aberrations and WT. While both groups with mono-allelic ATM disruption showed a significantly reduced overall survival compared to WT (median survival sole ATM -mutation, 71 months, sole 11q-, 40 months, vs. 123 months in the WT group; p=0.002 and 0.02, respectively), a non-significant reduction of overall survival was observed for patients with bi-allelic ATM aberrations (70 months, p=0.29) (Fig. 2). The few subset #2 patients with TP53 defects showed a similar survival as WT group, underscoring previous observations that TP53 dysfunction per se plays a minor role in this subset. In summary, we demonstrate that ATM mutations can be added to the list of genetic defects with a biased distribution in stereotyped subsets. The enrichment of ATM defects in subset #2 was associated with a negative impact on overall survival, suggesting a role for ATM inactivation in shaping the aggressive phenotype of this subset. This study further supports the recent suggestion that CLL development is driven by antigenic selection, coupled with preferential acquisition of specific genetic defects. The work was supported by the projects MSMT CR CZ.1.05/1.1.00/02.0068, IGA NT13493-4/2012 and TACR TE02000058. Figure 1. Figure 1. Figure 2. Figure 2. Disclosures Langerak: Roche: Other: Lab services in the field of MRD diagnostics provided by Dept of Immunology, Erasmus MC (Rotterdam); DAKO: Patents & Royalties: Licensing of IP and Patent on Split-Signal FISH. Royalties for Dept. of Immunology, Erasmus MC, Rotterdam, NL; InVivoScribe: Patents & Royalties: Licensing of IP and Patent on BIOMED-2-based methods for PCR-based Clonality Diagnostics. . Strefford:Roche: Research Funding. Stamatopoulos:Gilead Sciences: Research Funding; Janssen Pharmaceuticals: Research Funding.

Abstract: B cells residing the marginal zone (MZ) provide a first line of defense against blood borne pathogens, producing the greater part of circulating natural antibodies conferring protection against infection. Dysregulated homeostasis and function of MZ B cells has been implicated in a wide range of B lymphoproliferations, encompassing the distinct MZ lymphomas recognized by the WHO Classification, the related provisional entities and even chronic lymphocytic leukemia (CLL), for which a MZ derivation has been proposed. Here, taking advantage of a large multi-institutional series, we aimed at obtaining insight into the ontogenetic relationship of MZ lymphoproliferations, related entities and CLL through cross-comparison of their B cell receptor immunoglobulin (BcR IG) gene repertoires. Our sequence dataset included 3660 unique IGHV-IGHD-IGHJ gene rearrangement sequences from our collaborative centers and/or public databases derived from: (1) MZ lymphomas: splenic (SMZL), n=379; nodal (NMZL), n=37; extranodal (ENMZL), n=95; (2) provisional entities of postulated MZ origin, including splenic diffuse red pulp lymphoma (SDRL, n=16) and clonal B cell lymphocytosis of MZ origin (CBL-MZ, n=60); (3) persistent polyclonal B cell lymphocytosis (PPBL), n=286 (from 2 cases); (4) MZ cells isolated from six spleen specimens free of neoplastic cells at histological inspection (non-malignant MZ), obtained at surgery for cancer, n=489; (5) autoimmune conditions, n=1243; (6) various types of normal B cells, n=1055. The most pronounced IG gene repertoire skewing was observed in SMZL with the IGHV1-2*04 gene accounting for 26% of cases. Restrictions, though less striking, were also identified in the other MZ lymphomas as well: (i) the IGHV4-34 gene predominated in NMZL (14.3%); and, (ii) the IGHV1-69 gene predominated in ENMZL (14.6%), albeit with significantly different distribution depending on the primary site of involvement, ranging from 38% in salivary ENMZL to 11% in gastric ENMZL to 4% in ocular adnexa ENMZL (p 〈 0.01). The vast majority of MZL cases showed at least some impact of somatic hypermutation (SHM), with the proportion of cases lacking any SHM ranging from 0% in salivary ENMZ to only 13% in SMZL. Following established bioinformatics approaches, we searched for stereotyped BcR IG sequences i.e. IGHV-IGHD-IGHJ gene rearrangements with restricted antigen-binding site sequence motifs. For the purposes of this analysis, the present sequence dataset was cross-compared to a large dataset of 20451 IGHV-IGHD-IGHJ gene rearrangement sequences from CLL patients from the IMGT/CLL-DB. Overall, 6437 different clusters with stereotyped BcR IG sequences were identified in the merged dataset, including from only 2 to more than 350 sequences. Two categories of clusters with stereotyped BcR IG were identified: disease-specific (n=4813) and 'mixed' (n=1624) i.e. comprised of cases with different diagnosis. The great majority of clusters in the former category concerned exclusively CLL and corresponded to well-established CLL stereotyped subsets, while only a small minority concerned exclusively MZ lymphomas, all with a diagnosis of SMZL. Mixed clusters were relatively small in size, with only 4 populated by more than 10 cases; of these, 2 utilized the IGHV1-69 gene, while the remaining 2 utilized the IGHV3-7 and IGHV4-59 gene, respectively. They comprised rearrangements from various entities, including SMZL, ENMZL (gastric, salivary gland, ocular adnexa), CLL, hepatitis C virus-associated diffuse large B cell lymphoma (DLBCL), but also rheumatoid factors and non-malignant spleen MZ cells. Notably, shared (recurrent) amino acid changes introduced by SHM (i.e. the same amino acid replacement at the same position) were identified in each mixed cluster. In conclusion, we document different immunogenetic signatures for MZ lymphomas, with limited overlap both amongst the various distinct and provisional WHO entities but also versus CLL. These findings indicate distinct antigen exposure histories and/or different (micro)environments underlying the ontogeny of MZ lymphomas. That said, the existence of rare public stereotypes raises the intriguing possibility that common, pathogen-triggered, immune-mediated mechanisms, may result in diverse B lymphoproliferations due to targeting versatile progenitor B cells and/or operating in particular microenvironments. Disclosures Ghia: Janssen Pharmaceuticals: Research Funding.

Abstract: Preliminary observations from essentially small patient series indicate that certain recurrent gene mutations may be enriched in subsets of chronic lymphocytic leukemia (CLL) with stereotyped B-cell receptors (BcR). On these grounds, it could be argued that differential modes of immune signaling, in the context of subset-biased antigen-immunoglobulin (IG) interactions, may be associated with the acquisition and/or selection of certain genomic aberrations within various stereotyped CLL subsets. With this in mind, we here sought to explore the genetic background of 10 major stereotyped subsets which collectively account for ~11% of all CLL and represent both IGHV unmutated (U-CLL) and/or mutated (M-CLL) cases. We focused on recurrent mutations within the NOTCH1 (entire exon 34 or targeted analysis for del7544-45), TP53 (exons 4-9), SF3B1 (exons 14-16), BIRC3 (exons 6-9) and MYD88 (exon 5) genes. Overall, 647 cases were analyzed, belonging to the following major subsets: (i) U-CLL: #1 (the largest within U-CLL, clinically aggressive), n=139; #3, n=39; #5, n=22; #6, n=48; #7, n=74; #8, n=46; #59, n=19 and #99, n=18; (ii) M-CLL: #4 (the largest within M-CLL, particularly indolent), n=78; and, (iii) subset #2 (the largest overall, variable mutational status and clinically aggressive), n=164. All cases were devoid of MYD88 mutations, which was not surprising given that our cohort was predominantly composed of U-CLL. Mutations within the BIRC3 gene were either absent (#2, #4, #6 and #59) or rare (#1, #3, #5, #7, #8 and #99; frequency 1.5%-7%) with no clear bias to any subset. BIRC3-mutant cases frequently co-existed with either del(11q) or trisomy 12. NOTCH1 mutations were more frequent in subsets #1, #6, #8, #59 and #99 (frequency, 22%-32%), sharply contrasting subsets #2 or #3 (4% and 7%, respectively) (p 〈 0.0001). Of note, although NOTCH1 mutations tended to coincide with trisomy 12 in certain subsets e.g. #1 and #8, their co-occurrence differed significantly with only 33% of NOTCH1mut subset #1 cases carrying trisomy 12 compared to 75% of NOTCH1mut subset #8 cases (p=0.036). Moving to SF3B1, we noted that subsets harboring NOTCH1 mutations were either absent for or carried few SF3B1 mutations, while the inverse was also true i.e. very high frequency of SF3B1 mutations in subsets #2 and #3, 45% and 36%, respectively. Almost 80% of mutations observed in subset #2 were localized to two codons (p.K700E: n=44/76, 58%: p.G742D: n=15/76, 20%) within the HEAT domain of the SF3B1 protein; p.K700E accounted for only 29% (4/14) of all SF3B1 mutations detected in subset #3 while p.G742D was absent (p=0.043 and p=0.068 respectively). Thus, although the functional relevance of these mutations is currently unknown, their high frequency and striking bias to subset #2 bodes strongly for their critical role in the pathobiology of subset #2. Finally, TP53 mutations were: (i) enriched in subsets #3 (11%) and #7 (19%) and, in contrast, absent or rare in subsets #5 (0%) and #6 (4%), despite all utilizing the IGHV1-69 gene (p=0.02); (ii) enriched in subset #1 (15%) and subset #99 (33%), a less populated subset that is highly similar to subset #1; and, (iii) very rare in subsets #2 and #8 (2% in both), the latter known to display the highest risk for Richter's transformation among all CLL. In conclusion, we confirm and significantly extend recent observations indicating that different CLL stereotyped subsets display distinct genetic makeup. These findings imply that distinctive modes of microenvironmental interactions, mediated by certain stereotyped BcRs, may be associated with selection or occurrence of particular genetic aberrations, with the combined effect determining both clonal and clinical evolution, and ultimately disease outcome. Figure 1 Figure 1. Disclosures No relevant conflicts of interest to declare.

Abstract: The B cell receptor immunoglobulin (Ig) gene repertoires of marginal zone (MZ) lymphoproliferations were analyzed in order to obtain insight into their ontogenetic relationships. Our cohort included cases with MZ lymphomas ( n = 488), i.e. splenic (SMZL), nodal (NMZL) and extranodal (ENMZL), as well as provisional entities ( n = 76), according to the WHO classification. The most striking Ig gene repertoire skewing was observed in SMZL. However, restrictions were also identified in all other MZ lymphomas studied, particularly ENMZL, with significantly different Ig gene distributions depending on the primary site of involvement. Cross‐entity comparisons of the MZ Ig sequence dataset with a large dataset of Ig sequences (MZ‐related or not; n = 65 837) revealed four major clusters of cases sharing homologous (‘public’) heavy variable complementarity‐determining region 3. These clusters included rearrangements from SMZL, ENMZL (gastric, salivary gland, ocular adnexa), chronic lymphocytic leukemia, but also rheumatoid factors and non‐malignant splenic MZ cells. In conclusion, different MZ lymphomas display biased immunogenetic signatures indicating distinct antigen exposure histories. The existence of rare public stereotypes raises the intriguing possibility that common, pathogen‐triggered, immune‐mediated mechanisms may result in diverse B lymphoproliferations due to targeting versatile progenitor B cells and/or operating in particular microenvironments. Copyright © 2018 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Abstract: The IGHV4-34 gene is very frequent (~10%) in the B cell receptor immunoglobulin (BcR IG) gene repertoire of chronic lymphocytic leukemia (CLL). Over 30% of IGHV4-34 CLL cases can be assigned to different subsets with stereotyped BcR IG. The largest is subset #4 which represents ~1% of all CLL and ~10% of IGHV4-34 CLL and is considered a prototype for indolent disease. The BcR IG of a great majority (~85%) of IGHV4-34 CLL cases carry a significant load of somatic hypermutation (SHM), often with distinctive SHM patterns. This holds especially true for stereotyped subsets and is suggestive of particular modes of interactions with the selecting antigen(s). In detail, subsets #4 and #16, both involving IgG-switched cases (IgG-CLL), exhibit the greatest sequence similarity in SHM profiles, whereas they differ in this respect from IgM/D subsets #29 and #201. Prompted by these observations, here we explored the extent that these subset-biased SHM profiles in different IGHV4-34 stereotyped subsets were reflected in distinct demographics, clinical presentation, genomic aberrations and outcomes. Within a multi-institutional series of 20,331 CLL patients, 1790 (8.8%) expressed IGHV4-34 BcR IG. Following established bioinformatics approaches for the identification of BcR IG stereotypy, 573/1790 IGHV4-34 CLL cases (32%) were assigned to stereotyped subsets; of these, 340 cases (19% of all IGHV4-34 CLL and 60% of stereotyped IGHV4-34 cases) belonged to subsets #4, #16, #29 and #201, all concerning IGHV-mutated CLL (M-CLL). Clinicobiological information was available for 275/340 patients: #4, n=150; #16, n=44; #29, n=39; and #201, n=42. Comparisons between subsets revealed no differences in gender and age distribution. Interestingly, however, 36-43% of each subset cases were young for CLL (defined as patients aged ≤55 years), which is higher compared to general CLL cohorts, where young patients generally account for ~25% of cases. In contrast, significant differences were identified between subsets regarding: (i) disease stage at diagnosis, with 〉 90% of IgG subsets #4 and #16 diagnosed at Binet stage A versus 83% in subset #201 and 74% in subset #29 (p=0.029); (ii) CD38 expression, ranging from 1% in subset #4 to 10% in subset #201 (p=0.013); (iii) the distribution of del(13q), peaking at a remarkable 92% in subset #29 versus only 37% in subset #16 (p 〈 0.0001). Regarding other genomic aberrations, they were either absent (NOTCH1 mutations) or rare (SF3B1 mutations, trisomy 12, del(11q), TP53 aberrations due to either del(17p) and/or TP53 mutations). The sole exception concerned a high frequency (14%) of TP53 aberrations in subset #29 (p 〈 0.05 compared with the other subsets), which is notable for M-CLL cases in general. Time to first treatment (TTFT) could be analyzed in 228 cases. IgG subsets #4 and #16 had significantly (p=0.036) longer TTFT (median TTFT: not yet reached) compared to the IgM/D subsets #29 and #201 (median TTFT: 11 and 12 years, respectively). In conclusion, we have identified distinct clinicobiological profiles for different stereotyped IGHV4-34 M-CLL subsets, highlighting subsets #4 and #16 as particularly indolent, which is important for both medical and social reasons, especially considering that a significant proportion of patients in these subsets are diagnosed at younger ages. Our findings support the notion that BcR IG stereotypy refines prognostication in CLL, superseding the crude immunogenetic distinction based on SHM load only. Additionally, the observed heterogeneity suggests that not all M-CLL are equal, prompting further research into the underlying biological background with the ultimate aim of tailored patient management. Disclosures Tausch: Gilead: Other: Travel support. Shanafelt:Glaxo-Smith_Kline: Research Funding; Genentech: Research Funding; Celgene: Research Funding; Polyphenon E Int'l: Research Funding; Hospira: Research Funding; Janssen: Research Funding; Pharmactckucs: Research Funding; Cephalon: Research Funding. Niemann:Gilead: Consultancy; Janssen: Consultancy; Roche: Consultancy; Novartis: Other: Travel grant. Langerak:InVivoScribe: Patents & Royalties: Licensing of IP and Patent on BIOMED-2-based methods for PCR-based Clonality Diagnostics.; DAKO: Patents & Royalties: Licensing of IP and Patent on Split-Signal FISH. Royalties for Dept. of Immunology, Erasmus MC, Rotterdam, NL; Roche: Other: Lab services in the field of MRD diagnostics provided by Dept of Immunology, Erasmus MC (Rotterdam). Hallek:Celgene: Honoraria, Other: Speakers Bureau and/or Advisory Boards, Research Funding; AbbVie: Honoraria, Other: Speakers Bureau and/or Advisory Boards, Research Funding; Roche: Honoraria, Other: Speakers Bureau and/or Advisory Boards, Research Funding; Boehringher Ingelheim: Honoraria, Other: Speakers Bureau and/or Advisory Boards; Pharmacyclics: Honoraria, Other: Speakers Bureau and/or Advisory Boards, Research Funding; Mundipharma: Honoraria, Other: Speakers Bureau and/or Advisory Boards, Research Funding; Janssen: Honoraria, Other: Speakers Bureau and/or Advisory Boards, Research Funding; Gilead: Honoraria, Other: Speakers Bureau and/or Advisory Boards, Research Funding. Ghia:Janssen Pharmaceuticals: Research Funding.