ALBERT

Description: 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

Description: Chronic lymphocytic leukemia (CLL) is characterized by progressive hypogammaglobulinemia that can affect one or more immunoglobulin subclasses. Although many underlying mechanisms have been suggested, the pathogenesis of this phenomenon remains to be elucidated. In the present study, we revisit hypogammaglobulinemia in CLL through a combined clinicobiological approach aiming at identifying associations with particular disease profiles that would offer pathogenetic insight and guidance for further research. The study group included 412 CLL patients with available information about serum immunoglobulins either at diagnosis (n=380) or before treatment initiation (n=32). Patient characteristics were as follows: median age: 65 years; males/females: 266/146; Binet stage A: 272/335, unmutated IGHV genes (U-CLL): 140/412 cases (34%); CD38 expression: 59/330 cases (18%); clonotypic IG of the MD or G isotype: 250 and 43 cases, respectively; isolated del(13q): 64/136 (47%); trisomy 12: 18/183 (10%); del(11q): 18/186 (10%); del(17p): 11/189 (6%); NOTCH1 del7544-45/p.P2514Rfs*4: 8/219 (4%). With a median follow up of 5 years, 152/329 cases (46%) received treatment. Decreased immunoglobulin serum levels in at least one subclass were identified in 220/412 patients (53%), as follows: (i) decreased IgM, 172/412 cases (41%); (ii) decreased IgG, 78/412 cases (19%); (iii) decreased IgA, 100/412 cases (24%). In 36/412 cases (9%), a decrease in all serum immunoglobulin subclasses was noted. No statistically significant differences were identified between patients with normal serum immunoglobulin levels versus those with hypogammaglobulinemia regarding age, gender, disease burden at diagnosis, IGHV gene mutational status, CD38 expression, cytogenetic aberrations, NOTCH1 mutations and the incidence of a second malignancy. However patients with hypogammaglobulinemia exhibited increased need for treatment compared to patients with normal serum immunoglobulins (91/175 vs 61/154 respectively, p=0.025). Among cases with hypogammaglobulinemia, 90 (41%) and 26 (12%) exhibited isolated IgM and IgA subclass deficiency, respectively; isolated IgG decrease, was relatively rare (10/220 cases, 4%). Interestingly, when comparing isolated IgA versus other subclass deficiencies, statistically significant associations were identified with (i) advanced clinical stage (Binet B/C, Rai III/IV) (p=0.002); (ii) female gender (p=0.041); and, (iii) NOTCH1 mutations (p=0.004). A propos of the latter, it is noteworthy that in 5/8 (63%) mutant NOTCH1 cases with hypogammaglobulinemia, the affected subclass was IgA. Within our cohort, we identified cases belonging to one of three different, well characterized subsets with stereotyped B-cell receptor immunoglobulin (BcR IG), namely: (1) subset #1 (clan I IGHV genes/IGKV1(D)-39): U-CLL, clinically aggressive, n=12; (2) subset #2 (IGHV3-21/IGLV3-21), mixed IGHV mutational status, noted clinical aggressiveness, n=5; and, (3) subset #4, mutated IGHV4-34/IGKV2-30 BcR IG, clinically indolent, n=12. Notably, all subset #2 cases showed low levels of at least one serum subclass, while in 4/5 and 3/5 cases, two or all three immunoglobulin subclasses were affected. Although numbers are small, the incidence of hypogammaglobulinemia in subset #2 was significantly (p

Description: A remarkable feature of chronic lymphocytic leukemia (CLL) is the existence of quasi-identical, stereotyped B-cell receptor immunoglobulins (BcR IGs), strongly supporting an antigen-driven pathway to CLL development. Subsets of cases with distinct stereotyped BcRs collectively account for almost one-third of all CLL. Furthermore, just a handful of major stereotyped subsets represent a substantial fraction of the entire cohort and, perhaps more importantly, an even larger fraction of clinically aggressive CLL. In several major subsets, stereotypy extends from shared primary IG sequences to shared clinical and biological features, including immune signaling, mRNA and miRNA expression, DNA methylation, and genomic aberrations. Regarding the latter, recent evidence indicates that different subsets display distinct profiles of recurrent gene mutations, even when limiting the analysis to subsets with similar IGHV gene mutational status. However, it should be emphasized that even the largest subsets account for only ∼3% of the cases with available IGHV-D-J sequence information, indicating that for meaningful conclusions to be reached, large patient cohorts are essential. Here, taking advantage of a series of 2482 CLL cases consolidated in the context of a multicenter collaboration coordinated by ERIC, the European Research Initiative on CLL, we systematically explored the genetic background of stereotyped subsets. Our main focus was on recurrent mutations in 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, 1313 cases (52.9%) carried mutated IGHV genes (M-CLL), whereas the remaining 1169 cases (47.1%) carried unmutated IGHV genes (U-CLL). Cases were sub-classified into the following major subsets: (i) U-CLL: #1, n=72; #3, n=25; #5, n=11; #6, n=22; #7, n=37; #8, n=20; (ii) M-CLL: #4, n=32; #77, n=12; #148, n=20; and, (iii) subset #2 (IGHV3-21, variable mutational status), n=57. Mutations in the MYD88 and BIRC3 genes were relatively rare, with no clear bias to any subset. With regards to the other three genes, only a single mutation in the TP53 gene was identified in a total of 80 M-CLL subset cases. Among U-CLL subsets and clinically aggressive subset #2, we noted asymmetric mutation frequencies, summarized as follows. (1) TP53 mutations were (a) enriched in subsets #3 and #7 (frequency 〉10%) and, in contrast, absent in subsets #5 and #6, though all these subsets utilize the IGHV1-69 gene; (b) enriched in subset #1 (9%) and, interestingly, subset #99, a less populated subset that is highly similar to subset #1 (2/4 cases positive for TP53 mutations); (c) absent in subset #2; and, (d) relatively infrequent in subset #8 (5%), the latter known to display the highest risk for Richter’s transformation among all CLL. Differences between these subsets showed a trend for statistical significance (p=0.09). (2) NOTCH1 mutations exhibited (a) increased frequency in subsets #1 (28%) and #8 (25%); (b) among IGHV1-69 expressing subsets, lower frequencies in subsets #3 (8%), #5 (10%) and #7 (3%) compared to subset #6 (25%); and (c) intermediate frequency (9%) in subset #2 (p=0.0078 for comparison between subsets). (3) SF3B1 mutations were (a) significantly (p

Description: 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

Description: In chronic lymphocytic leukemia (CLL), genomic aberrations identify subgroups of patients with distinct treatment outcomes. In particular, patients with deletion of chromosome 17p and/or TP53 gene mutations have inferior prognosis with standard therapy. Hence, screening for these abnormalities is mandatory prior to enrolling patients in clinical trials (CT) and can be also useful in general practice (GP). Recent studies suggest that aberrant p53 function (p53ab), due to TP53 gene mutations and/or del(17p), is only one of several mechanisms underlying clinical aggressiveness. Indeed, mutations in the NOTCH1, SF3B1 and BIRC3 genes have been reported as recurrent in CLL and correlated to poor outcome which is in contrast to mutations in MYD88. An important caveat stemming from published studies is the relative small and hetereogeneous sample size thus making it difficult to draw conclusions regarding the actual incidence and prognostic relevance of these mutations in CLL. The European Research Initiative on CLL (ERIC) is pursuing several studies to shed light on these issues in large and well-annotated CLL series, comprising both GP CLL and CT CLL cases, and here we report the initial results of this project. To date, a total of 3185 patients have been included in the analysis; 596 (18.7%) are CT cases and 2589 (81.3%) GP cases. Mutational screening provided data on 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). For each gene, almost two-thirds of the cases were analyzed within a year of diagnosis. Overall, BIRC3 and MYD88 mutations were relatively rare [25/923 (2.7%) and 24/1085 (2.2%) respectively], however: (i) BIRC3 mutations were significantly (p

Description: In chronic lymphocytic leukemia (CLL), the molecular features of the clonotypic B-cell receptor immunoglobulins (BcR IG) are set from the birth of the clone and in contrast to genetic aberrations, remain stable overtime, rendering the BcR IG a reference biomarker that is usually not significantly affected by clonal evolution. Approximately 30% of CLL cases carry quasi-identical BcR IGs and can be assigned to distinct stereotyped subsets. While preliminary evidence alludes to BcR IG stereotypy being relevant from a clinical viewpoint, this aspect has never been explored systematically or in a cohort of adequate size to enable meaningful conclusions. In order to assess the clinical implications of BcR IG stereotypy, we evaluated clinicobiological data from 8593 CLL patients, particularly focusing on 14 major stereotyped subsets of cases with unmutated (U-CLL) or mutated IGHV genes (M-CLL). The largest subset was #2 (n=254, 3%), within which 156 and 98 cases were M-CLL and U-CLL, respectively, followed by U-CLL subsets #1 (n=173, 2%) and #7 (n=123, 1.4%). Amongst M-CLL, the largest subsets were #4 (n=94, 1.1%) and #148 (n=92, 1%). Stereotyped subsets, even of the same mutational category i.e. U-CLL or M-CLL, exhibited significant clinicobiological differences regarding: age at diagnosis (median age ranging from 53-68 years, p

Description: Key Points 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.

Description: 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.

Description: Introduction: Transformation of Marginal Zone Lymphoma (MZL) into an aggressive histology is uncommon phenomenon that can occur at any time after diagnosis and is expected to have a detrimental impact on prognosis. Biological and clinical knowledge on transformed Marginal Zone Lymphoma (tMZL) is poor and no standard treatment is established in the Rituximab era for these patients (pts). We retrospectively analyzed all consecutive biopsy proven tMZL in two Italian Hematological Divisions from 2002 to 2014 and we focused on post-transformation treatment and outcome. Methods: The dataset included 378 MZL pts diagnosed between 2002 and 2014 at Division of Hematology in Pavia and in Milan (Niguarda Hospital): 204 pts (53.9%) by extranodal MALT lymphomas, 113 pts (29.8%) by splenic MZL, and 61 pts (16.3%) by nodal MZL. Histological transformation (HT) was defined as transformation into an aggressive lymphoma at any time from previous MZL diagnosis; only cases with biopsy confirmed HT were comprised in the present analysis, while cases with only clinical suspicion of transformation were not counted as tMZL. Results: HT was documented in 18 of the 378 pts (4.8%), 6.5% in nodal MZL, 7.0% in splenic MZL and 2.9% in MALT. Histology at transformation was Diffuse Large B-Cell lymphoma in all but one case; the remaining pt was diagnosed as high-grade B-cell lymphoma, unclassifiable. CD20 was negative only in one Rituximab-naïve pt. Median time from first diagnosis to HT was 31 months (range: 10-124) and median number of previous therapies was 1 (range 0-1); pts received first line therapy listed in table 1. Median age at transformation was 68 years (range: 46-85), M/F ratio was 0.8. In the tMZL population, first diagnosis was nodal MZL in 4 pts (22%), splenic MZL in 8 pts (45%) and MALT in 6 pts (33%). At first diagnosis of MZL, 72% of t-MZL pts had stage IV disease, 17% had B symptoms, 11% had elevated LDH and ECOG performance status was lower than 2 in all the cases. HCV serology was positive in 5/17 cases; HCV status was not available for one pt. At HT disease stage was III or IV in 14 pts (78%), B symptoms were present in 7 pts (39%), LDH and beta2microglobulin were both elevated in 7 pts (39%) and ECOG performance status was lower than 2 in all the cases. Pts received post-HT treatment listed in table 2. At time of analysis 6 pts died (33%), and the main cause of death was progressive disease. With a median post-transformation follow-up of 16.6 months (range: 2-98), the 2-years Progression-Free Survival (PFS) was 45,4 % and the 2-years Overall Survival (OS) was 56.75%. No correlation was found between the following characteristics and survival: MZL type at first diagnosis, stage, symptoms, LDH and ECOG at HT, number and types of pre-HT therapies. Conclusions: This large cohort confirms that HT is a relatively rare and early event in MZL. At present time, we did not identify any feature predictive of outcome for transformed MZL. Chemotherapy in combination with Rituximab showed to be an effective treatment for tMZL. Table 1 First line treatment N. of patients % Therapy strategy CVP 7 39 Anthracycline-containing regimen 2 11 Chlorambucil monotherapy 5 28 Splenectomy 1 5.5 H.pylori eradication 1 5.5 Watch and wait strategy 2 11 Rituximab incorporation Included in first line therapy 6 33 Maintenance 0 0 Response ORR (%) 67 CRR (%) 33 Table 2. Post-HT treatment N. of patients % Therapy strategy CHOP regimen 16 89 Platinum-containing regimen 1 5.5 Missing 1 5.5 Rituximab incorporation Included in first line therapy 17 94 Maintenance 0 0 ASCT consolidation 3 17 Response ORR (%) 61 CRR (%) 59 Disclosures Rusconi: Roche: Honoraria.

Description: BACKGROUND In the setting of cHL, ABVD (adriamycin, bleomycin, vinblastine, dacarbazine) is the most widely used first line chemotherapeutic treatment and it is well known that this regimen is associated with a high emetic risk (HEC). Palonosetron (PALO) currently represents one of the most effective and implemented drug for CINV prevention, but after many ABVD cycles patients (pts) frequently need to add other antiemetic drugs to obtain a good control of their symptoms. Netupitant (NETU) is the NK1-RA (neurokinin receptor antagonist) component of NEPA, the first antiemetic drug available as oral fixed combination: NETU (300mg) + PALO (0.5mg). Both ABVD drugs and NETU are metabolized by cytochrome P-450 isoform 3A4 (CYP3A4), but respect to other NK1-receptor antagonist available, NETU has demonstrated to have no clinical relevant interaction with chemotherapy drugs like etoposide, cyclophosphamide and docetaxel. However, no data are currently available about the safety profile of NETU in the setting of ABVD treatment; for that reason we started the use of this drug as salvage therapy after PALO failure. METHODS We retrospectively analyzed the cHL pts treated with ABVD at our Center from September 2016 to January 2018. We used PALO + dexamethasone as first-line anti-CINV prophylaxis, while NEPA was introduced as salvage drug for those pts with inadequately controlled CINV. We collected data regarding demographics; diagnosis; planned chemotherapeutic treatment; performed chemotherapeutic treatment; acute, delayed and anticipatory CINV (before and after NEPA); laboratory findings including transaminases, creatinine and electrolytes (before and after NEPA); adverse reactions (before and after NEPA). The primary endpoint of the study was safety of NEPA in ABVD treated pts, while CINV control (no nausea or vomiting) was the secondary endpoint. NEPA-related safety data have been compared to the same data collected at the moment of the last previous PALO-containing regimen. RESULTS Among the 32 pts treated with ABVD during the study period, 13 (41%) received NEPA. Three pts were males and 10 females, and median age was 33 years (range 18-61). According to disease characteristics at diagnosis the planned ABVD administrations were 12 (6 cycles) in 9 pts, 8 (4 cycles) in 3 pts and 4 (2 cycles) in 1 pt. Nine pts completed the planned chemotherapy, 1 pt skipped the last cycle for personal decision, 3 pts are ongoing treatment at the time of analysis. Reasons for shift to NEPA are as follows: acute (grade 2) CINV alone in 3/13 pts; late (grade 2) CINV alone in 3/13 pts; combined acute (4 grade 2, 1 grade 1) and late (4 grade 2, 1 grade 1) CINV in 5/13 pts; combined anticipatory (grade 1), acute (grade 1) and late (grade 2) CINV in 2/13 pts. NEPA was started after a median of 4 ABVD administrations (range 1-10). Globally 53 NEPA administrations were delivered during subsequent cycles (median number of 3 NEPA administrations for each pt, range 1-11). With regard to the primary endpoint, the observed adverse events are listed in Table 1. With regard to the secondary endpoint, anticipatory, acute and delayed CINV were detected in 15%, 77%, 77%, of PALO pts and 15%, 46% and 15% of NEPA pts, respectively (see Table 2) CONCLUSION In our experience NETU did not show drug-drug interaction with ABVD chemotherapy agents, and NEPA administration was globally well tolerated with mild and transient adverse events. Furthermore, in cHL ABVD treated pts who experienced nausea and/or vomiting after failure of PALO + dexamethasone antiemetic prophylaxis, NEPA has demonstrated to be effective in CINV control. In 4 out of 13 cases, after an initial improvement in CINV control, pts subsequently required to shift to anti-CINV third line treatments. On the other hand, the 9 pts who continued on NEPA administration could experience an optimal CINV control immediately after the first administration. At our knowledge no data have been published regarding NEPA toxicities in the ABVD setting. Our safety and efficacy data come from a real life experience of consecutive pts treated homogeneously at a single center and would suggest the use of NEPA as primary anti-CINV prophylaxis in previously untreated cHL pts. Disclosures Rusconi: Celgene: Research Funding.