Citations

520 Background: There is considerable interest in developing HER2 testing criteria for adjuvant H. We used the 21-gene assay to examine the relationship of HER2 mRNA to benefit from H. Methods: N9831 compared adjuvant chemotherapy AC-T to concurrent chemotherapy-trastuzumab AC-TH in stage I-III HER2+ breast cancer. Recurrence Score (RS) and HER2 mRNA expression were determined by Oncotype DX (neg<10.7, equiv 10.7 to <11.5, and pos ≥11.5 log2 expression units). Cox regression was used to assess the association of HER2 expression with H benefit for distant recurrence. Results: Median follow-up: 7.4 yrs. Of 1,940 total pts, 901 had consent and sufficient tissue. HER2 by RT-PCR was neg in 130 (14%), equiv in 85 (9%), and pos in 686 (76%) pts. Concordance between HER2 assessments was 95% for RT-PCR vs central IHC (>10% + cells = +), 91% for RT-PCR vs central FISH (≥2.0 = pos) and 94% for central IHC vs central FISH. In the primary analysis, the association of HER2 expression with H benefit was marginally non-significant (P=0.057). In hormone receptor pos pts (local IHC) the association was significant (P=0.002). The association was nonlinear with the greatest estimated benefit at lower and higher HER2 mRNA expression levels. The observed treatment benefit in low HER2 pts was not due to imbalance between arms in RS and individual gene expression values. Conclusions: Concordance among HER2 assessments by central IHC, FISH, and RT-PCR was high. Association of HER2 mRNA expression with H benefit was marginally non-significant. A consistent benefit of trastuzumab irrespective of mHER2 levels was observed in the pts with either IHC+ or FISH+ tumors. Benefit was observed in pts with high HER2 by RT-PCR but also observed for the small groups of pts with negative results by quantitative RT-PCR or FISH (Table). Plausible mechanisms for this observation will be discussed. [Table: see text]

Abstract The most common genetic abnormality in multiple myeloma is the deletion of chromosome 13, seen in almost half of newly diagnosed patients. Unlike chronic lymphocytic leukemia, where a recurrent minimally deleted region including MIR15A/MIR16-1 has been mapped, the deletions in multiple myeloma predominantly involve the entire chromosome and no specific driver gene has been identified. Additional candidate loci include RB1 and DIS3, but while biallelic deletion of RB1 is associated with disease progression, DIS3 is a common essential gene and complete inactivation is not observed. The Vk*MYC transgenic mouse model of multiple myeloma spontaneously acquires del(14), syntenic to human chromosome 13, and Rb1 complete inactivation, but not Dis3 mutations. Taking advantage of this model, we explored the role in multiple myeloma initiation and progression of two candidate loci on chromosome 13: RB1 and MIR15A/MIR16-1. Monoallelic deletion of Mir15a/Mir16-1, but not Rb1, was sufficient to accelerate the development of monoclonal gammopathy in wild-type mice and the progression of multiple myeloma in Vk*MYC mice, resulting in increased expression of Mir15a/Mir16-1 target genes and plasma cell proliferation, which was similarly observed in patients with multiple myeloma. Significance: In the absence of a defined, minimally deleted region the significance of del(13) in multiple myeloma has remained controversial. Here we show that haploinsufficiency of Mir15a/Mir16-1, but not Rb1, upregulates the cell cycle–regulatory network, inducing monoclonal gammopathy in mice and promoting multiple myeloma progression in both mice and men. See related commentary by Walker, p. 16. This article is highlighted in the In This Issue feature, p. 5

Abstract Background Immunosuppression in glioblastoma (GBM) is an obstacle to effective immunotherapy. GBM-derived immunosuppressive monocytes are central to this. Programmed cell death ligand 1 (PD-L1) is an immune checkpoint molecule, expressed by GBM cells and GBM extracellular vesicles (EVs). We sought to determine the role of EV-associated PD-L1 in the formation of immunosuppressive monocytes. Methods Monocytes collected from healthy donors were conditioned with GBM-derived EVs to induce the formation of immunosuppressive monocytes, which were quantified via flow cytometry. Donor-matched T cells were subsequently co-cultured with EV-conditioned monocytes in order to assess effects on T-cell proliferation. PD-L1 constitutive overexpression or short hairpin RNA–mediated knockdown was used to determined the role of altered PD-L1 expression. Results GBM EVs interact with both T cells and monocytes but do not directly inhibit T-cell activation. However, GBM EVs induce immunosuppressive monocytes, including myeloid-derived suppressor cells (MDSCs) and nonclassical monocytes (NCMs). MDSCs and NCMs inhibit T-cell proliferation in vitro and are found within GBM in situ. EV PD-L1 expression induces NCMs but not MDSCs, and does not affect EV-conditioned monocytes T-cell inhibition. Conclusion These findings indicate that GBM EV-mediated immunosuppression occurs through induction of immunosuppressive monocytes rather than direct T-cell inhibition and that, while PD-L1 expression is important for the induction of specific immunosuppressive monocyte populations, immunosuppressive signaling mechanisms through EVs are complex and not limited to PD-L1.

Background: DLBCL has traditionally been classified by cell of origin (COO) subcategories based on tumor gene expression profiles which include Activated B-Cell (ABC) and Germinal Center B-Cell (GCB). Recently, using tumor samples from patients treated with RCHOP, new classification models have focused on DNA alterations. However, a comprehensive integrative approach using a large transcriptomic data set across both newly diagnosed (nd) and relapsed/refractory (r/r) DLBCL is yet to be accomplished. A robust clustering of this type will allow for identification of biologically driven DLBCL patient subgroups and may predict patient outcome and inform treatment approaches. Methods: RNAseq was performed on a total of 882 DLBCL tumor FFPE biopsies from 2 ndDLBCL cohorts (cohort 1 and 2) and 2 r/r DLBCL cohorts (cohorts 3 and 4). Cohort 1 (N=267) was commercially sourced and served as the discovery cohort. Cohort 2 (N=340) was from the Mayo/Iowa Lymphoma SPORE Molecular Epidemiology Resource (MER) and served as the replication cohort. Cohort 3 (N=189) was from the CC-122-ST-001 and CC-122-DLBCL-001 clinical trials (NCT01421524 and NCT02031419), and cohort 4 (N=86) was from r/r patients from the MER. Clustering input consisted of gene expression data, gene set variation analysis (GSVA) scores computed from the hallmark gene sets of MSigDB gene sets, as well as immune cell abundance estimates from a DLBCL-specific deconvolution method. An integrative clustering method iClusterPlus was applied to the input data to identify patient subgroups. A multinomial generalized linear model classifier was trained on the discovery dataset and applied to cohorts 2, 3, and 4 to assess patterns of gene expression and clinical features among the subgroups. Results: Integrative clustering identified 8 subgroups of ndDLBCL patients (DLBCL1-8; D1-D8) in cohort 1. Classifiers trained on cohort 1 were applied to cohort 2 and the same 8 clusters were identified. Among RCHOP treated patients in cohort 2, subgroups D4 (p&lt;0.01) and D8 (p&lt;0.0001) had significantly worse survival outcomes than the rest of the population. D4 comprised 21% of the MER ndDLBCL replication cohort (cohort 2) with a median event-free survival (mEFS) of 38.2 months and a median overall survival (mOS) of 80.3 months. D8 comprised 5% of the cohort with a mEFS of 7.5 months and a mOS of 12.1 months. The remaining 6 subgroups were standard risk, with mEFS ranging from 82.1 months to not reached, and none reaching mOS. The subgroups were not uniquely defined by previously known molecular classification methods such as COO or double hit signature (DHITsig), nor by clinical risk factors such as age or international prognostic index (IPI). Within D4, 92% of patients were ABC, representing a high risk subset of ABC patients. The mEFS in D4 ABCs was 38.2 months, while mEFS of non-D4 ABCs was not reached (p&lt;0.005). Transcriptomic analysis revealed a lower abundance of immune infiltration. D4 was associated with high IPI, with 49% of D4 having IPI&gt;2, compared to 33% of non-D4 with IPI&gt;2 (p&lt;0.05). D8 represented a high-risk subset which was 73% GCB. The mEFS of D8 GCBs was 5.4 months, while mEFS of non-D8 GCBs was not reached (p&lt;0.0001). Transcriptomic analysis revealed low expression of immune response and cytokine signaling pathways, consistent with the low abundance of immune cells in D8. This subgroup consisted of 63% DHITsig positive patients. Although only 20% of all DHITsig positive patients were in D8, these D8 DHITsig patients showed significantly worse survival than non-D8 DHITsig patients (mEFS 11.3 months vs. not reached, p&lt;0.0001). In the r/r DLBCL setting, D1-D8 were all present, with an increased prevalence of D4 and D8 in Cohort 3 (30% and 17%, respectively) and Cohort 4 (30% and 14%) compared to the newly diagnosed setting. Mutational data for these cohorts has been collected and is being interpreted in the context of the discovered subgroups. Conclusion: A novel integrative clustering of transformed gene expression data revealed 8 biologically homogeneous groups, two of which had inferior outcomes when treated with RCHOP therapy. Furthermore, these two subgroups were more prevalent in r/r DLBCL. This classification allows for the transcriptomic identification of high-risk patients underserved by RCHOP therapy. *Ortiz, Wenzl and Stokes contributed equally **Gandhi and Novak contributed equally Disclosures Ortiz: Celgene Corporation: Employment, Equity Ownership. Stokes:Celgene Corporation: Employment, Equity Ownership. Huang:Celgene Corporation: Employment, Equity Ownership. Maurer:Celgene: Research Funding; Morphosys: Membership on an entity's Board of Directors or advisory committees; Nanostring: Research Funding. Towfic:Celgene Corporation: Employment, Equity Ownership. Hagner:Celgene Corporation: Employment, Equity Ownership, Patents & Royalties. Ratushny:Celgene Corporation: Employment, Equity Ownership. McConnell:Celgene Corporation: Employment, Equity Ownership. Danziger:Celgene Corporation: Employment, Equity Ownership. Stong:Celgene Corporation: Employment, Equity Ownership. Lata:Celgene Corporation: Employment, Equity Ownership. Kamalakaran:Celgene Corporation: Employment, Equity Ownership. Mavrommatis:Celgene Corporation: Employment, Equity Ownership. Trotter:Celgene Corporation: Employment, Equity Ownership. Czuczman:Celgene Corporation: Employment, Equity Ownership. Ansell:Seattle Genetics: Research Funding; Affimed: Research Funding; Regeneron: Research Funding; Trillium: Research Funding; Mayo Clinic Rochester: Employment; Seattle Genetics: Research Funding; Mayo Clinic Rochester: Employment; Regeneron: Research Funding; Trillium: Research Funding; Bristol-Myers Squibb: Research Funding; Bristol-Myers Squibb: Research Funding; LAM Therapeutics: Research Funding; LAM Therapeutics: Research Funding; Affimed: Research Funding. Cerhan:NanoString: Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees; Celgene: Research Funding. Nowakowski:Genentech, Inc.: Research Funding; F. Hoffmann-La Roche Ltd: Research Funding; Curis: Research Funding; Bayer: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; Selvita: Membership on an entity's Board of Directors or advisory committees; NanoString: Research Funding; MorphoSys: Consultancy, Research Funding. Gandhi:Celgene Corporation: Employment, Equity Ownership, Patents & Royalties. Novak:Celgene Coorperation: Research Funding.

Background The CLL international prognostic index (CLL-IPI) is a validated staging system for newly diagnosed CLL patients that combines age, clinical stage, serum Beta-2 microglobulin, TP53, and IGHV mutation status into a single score and stratifies CLL patients into four prognostic risk groups with a c-statistic=0.72. Next-generation sequencing identified ~60 putative driver genes recurrently mutated in CLL, and genome-wide association studies (GWAS) identified 41 single nucleotide polymorphisms (SNPs) associated with CLL risk. We previously showed that a polygenic risk score (PRS) of the weighted average of the number of risk alleles among these 41 SNPs is associated with risk of both CLL and MBL. We and others have also shown that the tumor mutational load (TML), i.e., the total number of somatically mutated driver genes, is associated with time to first treatment (TTT). Herein, we examine whether TML and PRS add prognostic value beyond CLL-IPI for disease progression in a cohort of CLL and high-count MBLs. Methods We used the Mayo Clinic CLL resource to identify newly diagnosed (&lt;2 years) previously untreated CLL and high-count MBL patients based on the 2008 International Workshop CLL (IWCLL) criteria . We extracted DNA from CD5+/CD19+ clonal B-cells and sequenced the coding regions of 59 recurrently mutated CLL driver genes using a custom SureSelect panel and Illumina HiSeq 4000. The median coverage depth per sample was 2737X. Somatic mutations were called using MuTect2 in tumor-only mode. Germline variants were eliminated based on minor allele frequencies &gt;0.01%, and by those identified in 1000 Genomes Project, ExAC and/or ESP6500 databases, unless present in known mutation hotspots or COSMIC. After filtering high/moderate impact mutations, we calculated the TML. Peripheral blood DNA was genotyped on Illumina Omni Express array; after extensive quality control metrics, we calculated the germline PRS as the weighted average of the number of risk alleles among the CLL susceptibility SNPs. Cox regression was used to estimate hazard ratios (HR) and 95% confidence intervals (CI) to test for associations with TTT. We analyzed the low/intermediate risk groups and the high/very high risk groups in stratified analyses and tested for interactions between CLL-IPI and TML or PRS. Survival curves are displayed using the Kaplan-Meier method using P-values from the log-rank test; the PRS is displayed by tertile categories. Results Of 166 patients (120 CLL, 46 MBL), 67% were male and the median age at diagnosis was 62 years (range: 28-85). The median follow-up was 5.3 years, and 73 were treated for progressive disease as defined by the 2018 IWCLL criteria. Low risk CLL-IPI comprised 51%, intermediate risk 27%, high risk 18%, and very-high risk 4%. The most commonly somatically mutated genes were TP53 (8%), CHD2 (8%), BIRC3 (8%), SF3B1 (7%), and NOTCH1 (7%). We found that 42%, 32%, and 26% of patients had 0, 1, or 2+ genes with somatic mutations, respectively. The median PRS was 8.3 (range: 6.1-11.3) which is consistent with our prior PRS study on CLL/MBL and higher than 7.53 found in 7983 controls from our prior work. When we modeled TML, PRS, sex, CLL/MBL status and CLL-IPI together with the joint effects of CLL-IPI with TML and PRS, both interactions were significant in the model (P=0.008 for TML*CLL-IPI and P=0.014 for PRS*CLL-IPI), with c-statistic=0.81 (CI:0.77-0.85). When we stratified by CLL-IPI, TML was associated with shorter TTT in the low/intermediate risk (N=129, continuous HR=2.36 per TML category, CI: 1.61-3.47, P=1.3x10-5, Figure 1.A), but there was no evidence of an association in the high/very-high risk (N=37, continuous HR=1.12 per TML category, CI: 0.70-1. 78, P=0.64, Figure 1.B), after adjusting for PRS, sex, and CLL/MBL status. However, the PRS was associated with a shorter TTT in the high/very-high risk (continuous HR=1.77 per SD, CI: 1.00-3.12, P=0.048, Figure 1.D), with no evidence of an association in the low/intermediate risk (continuous HR=0.95 per SD, CI: 0.95-1.29, P=0.74, Figure 1.C), after adjusting for TML, sex, and CLL/MBL status. Conclusions In our study, TML was prognostic among the low/intermediate CLL-IPI risk groups while the PRS was prognostic among the high/very-high CLL-IPI risk groups. Sequencing CLL driver genes and genotyping relevant germline SNPs at time of diagnosis may provide incremental value over the CLL-IPI in predicting TTT among patients with CLL/MBL. Disclosures Parikh: MorphoSys: Research Funding; Genentech: Honoraria; AbbVie: Honoraria, Research Funding; AstraZeneca: Honoraria, Research Funding; Pharmacyclics: Honoraria, Research Funding; Janssen: Research Funding; Ascentage Pharma: Research Funding; Acerta Pharma: Research Funding. Ding:Merck: Research Funding; DTRM Biopharma: Research Funding. Cerhan:Celgene: Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees; NanoString: Research Funding. Kay:MorphoSys: Other: Data Safety Monitoring Board; Infinity Pharmaceuticals: Other: DSMB; Celgene: Other: Data Safety Monitoring Board; Agios: Other: DSMB.

Background: Diffuse large B cell lymphoma (DLBCL) is the most common aggressive lymphoid malignancy in adults. Though standard immunochemotherapy regimens can result in clinical remission and cure in a majority of patients, approximately 30% of patients are primary refractory or eventually relapse, and their prognosis is very poor. Recent progress by large scale genomic and transcriptomic profiling of DLBCL patients has resulted in a deep understanding of disease drivers in newly diagnosed DLBCL (ndDLBCL). However, previous genomic studies of relapsed and/or refractory DLBCL (rrDLBCL) are limited by small sample sizes and much less is known about the genomic landscape or the changes in clonal populations that occur within a patient undergoing R-CHOP therapy. Analysis of a large cohort of rrDLBCL is needed to uncover the importance of known drivers, define clonal shifts that occur in relapsing patients, and discover novel mutations that impact resistance to therapy. Methods: DNA sequencing (whole exome or genome sequencing) was performed on tumor and matching germline samples from two rrDLBCL patient cohorts (N=143). The first rrDLBCL cohort consisted of 68 formalin fixed paraffin embedded (FFPE) tumor samples with matched germline obtained from the Mayo/Iowa Lymphoma SPORE Molecular Epidemiology Resource (MER); including 18 diagnostic/relapse paired samples. The second cohort consisted of 75 FFPE samples from 2 rrDLBCL clinical trials CC-122-ST-001 and CC-122-DLBCL-001 (NCT01421524 and NCT02031419). In addition, a ndDLBCL cohort (n=364) consisting of MER front line samples were also analyzed. CNV and SV calling was performed with Sclust and Manta, respectively. Mutational drivers were identified with cDriver. Cosmic mutational signatures were calculated for all samples. Results: For comparison of overall mutation rates between the ndDLBCL and rrDLBCL we analyzed the frequency of mutations in known driver genes [n=211, Chapuy et al, 2018; Schmitz et al, 2018; Reddy et al 2017]. Of these, the top 5 most mutated genes found in the relapse cohort were KMT2D (34.3%), IGLL5 (25.9%), CREBBP (22.4%), TP53 (21.0%), and HIST1H1E (19.6%) all of which were previously reported as mutated genes in rrDLBCL [Morin et al. 2016; Park et al. 2016;Greenawalt et al. 2017]. None of these frequencies changed significantly from the MER ndDLBCL cohort. However, frequencies of 2 genes NCOR1 and ACTB were increased significantly from 3.3% to 13.3% (p= 5.85 x 10-05) and 0.5% to 6.3% (p=2.56 x 10-04), respectively, in the rrDLBCL cohorts. Analysis of the previously reported driver mutations in 18 paired samples revealed a significant increase in the mean variant allele frequency (VAF) from diagnosis to relapse of mutations in BCL2 and BIRC6 from 18.5% to 28.8%(p=0.016) and 4.3% to 13.7%(p=0.011), respectively. In our analysis of novel drivers in rrDLBCL cohorts, 59 significant driver mutations (FDR 0.1) were identified, 23 were novel driver mutations while the remaining 36 have been previously reported as drivers of newly diagnosed DLBCL. These include novel mutations in genes such as IL4R (8.8%), CDK5R1 (6.7%), LAPTM5 (3.7%) and others whose function in lymphoma are unknown. In a secondary analysis, we performed mutational signature analysis for rrDLBCL and identified groups of patients with COSMIC mutational signatures that are associated with aging, defects in homologous end joining resulting in large insertions/deletions, and with defective mismatch repair often found in microsatellite unstable tumors. Together with the high prevalence of TP53 mutations, these results indicate genetic instability as a hallmark of relapse/refractory DLBCL. Analysis of CNV and SV will also be reported. Conclusion: Here we report to our knowledge the largest genomic dataset in relapsed and /or refractory DLBCL using tumor biopsies. Previously identified mutated genes in rrDLBCL were confirmed and novel drivers identified. Paired samples analysis reveals clonal shifts which occur during treatment and at time of relapse. Whether the novel driver mutations appeared as a function of selection of a rare clone over the course of therapy or a therapy induced mutation will be the focus of ongoing work. Novel variants including SNV, CNV and SV can potentially lead to new insights and deepen our understanding of the mechanisms of resistance in DLBCL. * Wenzl, Ortiz and Stong contributed equally **Novak and Gandhi contributed equally Disclosures Ortiz: Celgene Corporation: Employment, Equity Ownership. Stong:Celgene Corporation: Employment, Equity Ownership. Huang:Celgene Corporation: Employment, Equity Ownership. Maurer:Morphosys: Membership on an entity's Board of Directors or advisory committees; Celgene: Research Funding; Nanostring: Research Funding. Towfic:Celgene Corporation: Employment, Equity Ownership. Hagner:Celgene Corporation: Employment, Equity Ownership, Patents & Royalties. Nikbakht:Celgene Corporation: Employment, Equity Ownership. Stokes:Celgene Corporation: Employment, Equity Ownership. Lata:Celgene Corporation: Employment, Equity Ownership. Kamalakaran:Celgene Corporation: Employment, Equity Ownership. Mavrommatis:Celgene Corporation: Employment, Equity Ownership. Trotter:Celgene Corporation: Employment, Equity Ownership. Czuczman:Celgene Corporation: Employment, Equity Ownership. Ansell:Regeneron: Research Funding; LAM Therapeutics: Research Funding; Trillium: Research Funding; Affimed: Research Funding; Bristol-Myers Squibb: Research Funding; Mayo Clinic Rochester: Employment; Affimed: Research Funding; Seattle Genetics: Research Funding; LAM Therapeutics: Research Funding; Mayo Clinic Rochester: Employment; Bristol-Myers Squibb: Research Funding; Trillium: Research Funding; Regeneron: Research Funding; Seattle Genetics: Research Funding. Cerhan:Celgene: Research Funding; NanoString: Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees. Nowakowski:Bayer: Consultancy, Research Funding; Curis: Research Funding; Celgene: Consultancy, Research Funding; MorphoSys: Consultancy, Research Funding; Genentech, Inc.: Research Funding; F. Hoffmann-La Roche Ltd: Research Funding; Selvita: Membership on an entity's Board of Directors or advisory committees; NanoString: Research Funding. Novak:Celgene Coorperation: Research Funding. Gandhi:Celgene Corporation: Employment, Equity Ownership, Patents & Royalties.

Background: Overexpression of the anti-apoptotic BCL-2 protein promotes multiple myeloma (MM) cell survival. Venetoclax (Ven) is a highly selective, potent, oral BCL-2 inhibitor that induces apoptosis and has shown synergistic activity with bortezomib (B) and dexamethasone (d). Phase 1 studies in relapsed/refractory (RR) MM demonstrated encouraging clinical efficacy of Ven + d in t(11;14) MM and in a broader patient (pt) population in combination with Bd. Recent results from the Phase 3 BELLINI study of Ven vs placebo (Pbo) + Bd in pts with RRMM demonstrated that pts treated with Ven + Bd had improved clinical response rates and progression-free survival (PFS) vs Pbo, although the overall survival (OS) result was in favor of Pbo. Subgroup analyses showed different efficacy and survival outcomes based on tumor cytogenetics and BCL-2 expression. Results of pre-specified subgroup analyses and additional retrospective correlative biomarker analyses in the Phase 3 BELLINI study are described herein. Methods: BELLINI (NCT02755597) was a randomized, double-blind, multicenter Phase 3 study of Ven or Pbo + Bd in pts with RRMM who received 1-3 prior therapies and were either sensitive or naïve to PIs. Pts were randomized 2:1 to receive Ven 800 mg/day or Pbo + Bd. The following biomarker analyses were performed by central laboratory assessments of pre-treatment tumor samples: BCL-2 protein expression by immunohistochemistry (IHC) analysis of bone marrow (BM) core biopsies; BCL2 gene expression by quantitative PCR (qPCR) and cytogenetic abnormalities by interphase fluorescence in situ hybridization (FISH) analysis of CD138-enriched BM mononuclear cells. Correlation between BCL-2 (protein and gene) expression, cytogenetics, and outcomes were examined by Kruskal-Wallis tests and by hazard ratio (HR) using the Cox proportional hazard model. Results: As of the data cut-off of 18 Mar 2019, 291 pts were randomized, 194 to the Ven arm and 97 to the Pbo arm. Out of the 291 pts randomized, 177 pts (61%) were evaluable by IHC, 257 pts (88%) by qPCR, and 262 pts (90%) by FISH. A broad range of BCL2 gene expression was observed (median 2-DCt: 0.212 [range: 0-5.21]), which strongly correlated with protein expression (median 2-DCt 0.115 in BCL-2 IHC Low vs 0.277 in BCL-2 IHC High, p=0.0021). t(11;14) MM had the highest levels of BCL-2 expression (23/23 BCL-2 High by IHC; median 2-DCt 0.406 vs 0.212 in t(11;14) negative, p=0.0132), however high BCL-2 expression was not limited to the t(11;14) subgroup. Univariate analyses showed higher BCL2 expression in pt tumor samples with del(13q) (median 2-DCt 0.333 vs 0.159 in pts without del(13q), p=0.0008) and gain(1q) (median 2-DCt 0. 295 vs 0.180 in pts without gain(1q), p=0.0059). Bootstrapping and aggregating thresholds from trees (BATTing) was used retrospectively to identify an estimated threshold value for BCL2 expression (2-DCt ≥0.323) that could provide optimum selection of pts with maximum improvement in PFS when treated with Ven+Bd. Biomarker subgroups with the greatest PFS improvement were t(11;14) (HR=0.10; 95% CI: 0.02-0.46, p=0.003) and High BCL2 by qPCR (HR=0.26; 95% CI: 0.13-0.51, p&lt;0.001; Table 1). Since the t(11;14) and High BCL2 patient populations do not completely overlap (20% of High BCL2 pts were t(11;14) and 54% of t(11;14) were High BCL2), a combined subgroup analysis was performed. For pts with t(11;14) or High BCL2, the median PFS was not reached in the Ven arm vs 9.9 mo in Pbo (HR=0.26, 95% CI: 0.14-0.48, p&lt;0.001; Table 1). Higher overall response (ORR, 88% vs 70%), very good partial response or better (≥VGPR, 73% vs 33%), and complete response or better (≥CR, 42% vs 3%) rates were observed in the Ven vs Pbo arm (Table 2). Minimal residual disease negativity (MRD-, 10-5) rate was also higher for t(11;14) or High BCL2 pts in the Ven vs Pbo arm (19% vs 0%). Median overall survival (OS) was not reached in either arm but was similar between treatment arms for the combined group with t(11;14) or High BCL2 pts (HR=0.92, 95% CI=0.39-2.16, p=0.85). In contrast, in the t(11;14) negative and Low BCL2 pts, OS favored Pbo (HR=3.13, 95% CI=1.2-8.13, p=0.019; Table 1). Conclusions: Adding Ven to Bd demonstrates significant efficacy in pts with RRMM harboring either t(11;14) or tumor cells expressing high levels of BCL2. The benefit-risk profile appears to be favorable in these subsets of pts and additional studies to gain further safety and efficacy information are warranted. Disclosures Harrison: GSK: Consultancy, Research Funding; Novartis: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; AbbVie: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: investigator on studies, Research Funding; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Amgen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen Cilag: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Takeda: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding. Cavo:celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: travel accommodations, Speakers Bureau; sanofi: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; novartis: Honoraria; takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; amgen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; AbbVie: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; janssen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: travel accommodations, Speakers Bureau; bms: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. De La Rubia:AMGEN: Consultancy; Takeda: Consultancy; AbbVie: Consultancy; Janssen: Consultancy; Celgene Corporation: Consultancy. Popat:Celgene Corporation: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: travel, accommodations, expenses; Janssen: Honoraria, Other: travel support to meetings; GSK: Consultancy, Honoraria; AbbVie: Consultancy, Membership on an entity's Board of Directors or advisory committees; Takeda: Honoraria, Other: travel, accommodations, expenses. Gasparetto:Janssen: Consultancy, Honoraria, Other: Travel, accommodations, or other expenses paid or reimbursed ; BMS: Consultancy, Honoraria, Other: Travel, accommodations, or other expenses paid or reimbursed ; Celgene: Consultancy, Honoraria, Other: Travel, accommodations, or other expenses paid or reimbursed . Hungria:Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Abbvie: Consultancy, Membership on an entity's Board of Directors or advisory committees; Janssen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Takeda: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; BMS: Consultancy, Honoraria, Speakers Bureau; Amgen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Abbvie: Consultancy, Membership on an entity's Board of Directors or advisory committees; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Janssen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; BMS: Consultancy, Honoraria, Speakers Bureau; Takeda: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Amgen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Salwender:Amgen: Honoraria, Other: Travel or accommodations; Bristol-Myers Squibb: Honoraria, Other: Travel or accommodations; Janssen Cilag: Honoraria, Other: Travel or accommodations; Sanofi: Honoraria, Other: Travel or accommodations; Celgene: Honoraria, Other: Travel or accommodations; AbbVie: Honoraria; Takeda: Honoraria, Other: Travel or accommodations. Suzuki:Ono: Research Funding; BMS: Honoraria, Research Funding; Takeda: Honoraria; Janssen: Honoraria; Celgene: Honoraria. Moreau:Celgene: Consultancy, Honoraria; Janssen: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; Takeda: Consultancy, Honoraria; AbbVie: Consultancy, Honoraria. Spencer:AbbVie: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Secura Bio: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Specialised Therapeutics Australia: Consultancy, Honoraria; Takeda: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Servier: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Sanofi: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Haemalogix: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Janssen Oncology: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Amgen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding. O'Dwyer:Janssen: Membership on an entity's Board of Directors or advisory committees, Research Funding; AbbVie: Consultancy; GlycoMimetics Inc: Research Funding; BMS: Research Funding; Onkimmune: Equity Ownership, Membership on an entity's Board of Directors or advisory committees, Research Funding. Garg:Janssen: Honoraria; Novartis, Janssen: Research Funding; Janssen, Takeda, Novartis: Other: Travel expenses. Punnoose:Roche: Other: Stock/stock options; Genentech, Inc.: Employment. Jalaluddin:AbbVie: Employment, Other: Stock/stock options. Jia:AbbVie: Employment, Other: Stock/stock options. Yang:AbbVie: Employment, Other: Stock/stock options. Sun:AbbVie: Employment, Other: Stock/stock options. Ward:AbbVie: Employment, Other: Stock/stock options. Maciag:AbbVie: Employment, Other: Stock/stock options. Ross:AbbVie: Employment, Other: Stock/stock options. Kumar:Takeda: Research Funding; Celgene: Consultancy, Research Funding; Janssen: Consultancy, Research Funding. OffLabel Disclosure: Venetoclax is a BCL-2 inhibitor that is FDA-approved in some indications. This presentation will focus on venetoclax for treatment of multiple myeloma, which is not an approved indication.

Objective To report efficacy and safety of galcanezumab in adults with chronic cluster headache. Background Galcanezumab is a humanized monoclonal antibody that binds to calcitonin gene-related peptide and inhibits its biological activity. Methods This study comprised a prospective baseline period, a 12-week double-blind, placebo-controlled treatment period, and a 52-week open-label period. Up to six protocol-specified concomitant preventive medications were allowed if patients were on a stable dose for 2 months prior to the prospective baseline period. Patients were randomized 1:1 to monthly subcutaneous galcanezumab (300 mg) or placebo. The primary endpoint was overall mean change from baseline in weekly attack frequency with galcanezumab compared to placebo. Key secondary endpoints were ≥50% response rate and percentage of patients meeting sustained response. Results from the double-blind treatment period are reported. Results A total of 237 patients were randomized and treated (120 placebo; 117 galcanezumab). At baseline, the mean age was 45 years and 63% were using ≥1 preventive drug. The primary endpoint was not met; mean change in weekly attack frequency was −4.6 placebo versus −5.4 galcanezumab ( p = 0.334). Key secondary endpoints also were not met. Injection site-related treatment-emergent adverse events were more common in the galcanezumab than the placebo group, with significantly more injection site erythema. Conclusion Treatment with galcanezumab 300 mg did not achieve its primary and key secondary endpoints. This study underscores the potential distinct biology of cCH as well as the significant unmet need for safe, effective, and well-tolerated preventive treatment. The safety profile of galcanezumab in cCH is consistent with that observed in trials of episodic CH and migraine. Trial registration NCT02438826; https://www.clinicaltrials.gov/ct2/show/NCT02438826 .

Patients diagnosed with stage III melanoma who have undergone curative-intent surgery still remain at relatively high risk of disease recurrence. Recently approved adjuvant therapies with immune checkpoint inhibitors (ICIs) have brought increased relapse-free and overall survival rates. However, they have introduced a new range of side effects that can be difficult to diagnose, are challenging to treat, and may have lifelong consequences for patients. Oncologists and other members of the oncology care team should be aware of these side effects, including atypical presentations, and be prepared to intervene to prevent increased morbidity and mortality. Oncologists also need to have a low threshold for referral to other subspecialists, as many of these immune-related adverse events (irAEs) need to be comanaged using a multidisciplinary approach. Herein, we present a case that illustrates challenging presentations of endocrinopathy and hepatic irAEs in a patient with stage III melanoma receiving ICI therapy in the adjuvant setting.