Braggio, Esteban

Abstract Background B-cell chronic lymphocytic leukemia (B-CLL) is a well-defined clinical entity with heterogeneous molecular and cytogenetic features. Chromosome aberrations could be associated to specific CLL clinical features and outcomes and their impact on clonal evolution should have been addressed by using temporal analysis. In order to demonstrate the role of temporal analysis in CLL we conducted a comprehensive karyotypic survey of a large number of CLL cases with already known genetic aberrations to fully describe their meaning in terms of biological evolution. Methods A total of 1749 karyotypes were retrieved from the Mitelman Database of Chromosome Aberrations in Cancer. A matrix depicting the 360-band human chromosome ideogram was created. Regions that were either lost or gained in more than 3% of the cases were retained and identified as recurrent imbalances. Early and late imbalances were defined according to the appearance on the complex karyotypes. Descriptive statistics were used to summarize the genetic abnormalities. Results The median chromosome number and the median number of abnormalities per tumor (NAPT) were 46 and 1 respectively. (Figure 1 A and 1B) The most common abnormality seen was trisomy 12 which occurred in 29% (508 cases) followed by 13q14del (10.34%), 13q13del (6.63%) and add14q32 (6.63%). The temporal analysis revealed +12, 13q-, −17, 17p-, −Y and −X to be early imbalances (TO<5), followed predominately by a late loss with TO=5–10.5 (11q-). Hierarchical clustering suggested there are different groups including: trisomy 12, 13q-, 11q-, and 6q-, which at least in this clustering analysis appeared to be mutually exclusive. In summary, we can conclude that overall CLL is a neoplasia that shows remarkable chromosome stability even when only abnormal karyotypes are evaluated. Interestingly, clustering analysis suggests there are non-overlapping, unique subsets of CLL cases where trisomy 12 is the most common and along with 13q-emerged as early events on CLL clonal evolution. Figure 1A. Chromosomes distribution in chronic Lymphocytes Leukemia karyotypes Figure 1A. Chromosomes distribution in chronic Lymphocytes Leukemia karyotypes Figure 1b. Chronic Lymphocytes Leukemia and Number of Abnormalities Per Tumor (NAPT) distribution Figure 1b. Chronic Lymphocytes Leukemia and Number of Abnormalities Per Tumor (NAPT) distribution Figure Figure

Abstract Constitutive activation of NF-κB signaling pathways has been identified in a variety of malignancies, however the molecular basis has remained largely unknown. Recently, we have shown mutations in 10 genes encoding positive and negative regulators of NF-κB signaling pathways in 20% of multiple myeloma (MM) patients, with TRAF3 being the most commonly affected gene. TRAF3 is a negative regulator of NF-κB signal and its inactivation results in the constitutive activation of the alternative or noncanonical NF-κB signaling pathway. With the exception of two recent studies in MM, there is no available data regarding the mechanisms that dysregulate NF-κB in other B-cell malignancies. The aim of this study was to analyze the TRAF3 status in a cohort of Waldenström’s macroglobulinemia (WM) patients. Forty-nine WM were analyzed by using a combination of array-based comparative genomic hybridization (aCGH) and cIgM-FISH. Genome sequencing from 28 of these patients was performed on all coding exons of TRAF3 and the adjacent intron-exon junctions. A NF-κB index, as a measure of NF-κB transcription activity, was calculated from 21 patients with gene expression data. The index is the mean expression level of four probe sets differentially expressed between human myeloma cell lines with identified mutations compared to those without identified NF-κB pathway mutations (CD74, IL2RG and 2x TNFAIP3). By performing aCGH we identified a small monoallelic deletion (∼460 Kb), including the TRAF3 locus in one of 22 patients. Genome sequencing identified a missense mutation (D483N) in the remaining allele. This substitution affects a highly conserved amino acid throughout vertebrate evolution, suggesting its functional relevance. We used cIgM-FISH to screen 27 patients with no available aCGH data and we found one biallelic TRAF3 deletion and one chromosome 14 monosomy. In the later case, no DNA was available to sequence the remaining allele. Both samples with biallelic inactivation of TRAF3 possess the two highest NFKB index values of our WM cohort, suggesting NF-κB transcription activity. Moreover, the case with biallelic deletion showed the lowest level of TRAF3 gene expression. Additionally, the entire TRAF3 coding region was sequenced in 28 of these 49 patients and one homozygous nonsense mutation (K286X) was identified. No GEP was available in this sample to calculate the NF-κB index. TRAF3 was only recently implicated in a malignant process, when inactivating mutations and biallelic deletions were reported in approximately 13% of MM patients, making it the most frequently inactivated tumor suppressor in MM. Our study has identified biallelic TRAF3 inactivation in at least 3 of 49 WM patients, suggesting that the constitutive activation of NF-κB signaling pathways may be involved in the pathogenesis of WM. Recent findings showing an association between TRAF3 loss of function and good response to bortezomib in MM patients suggest that proteasome inhibitor-based therapy may be an attractive option in the treatment of the WM patients with constitutive activation of NF-κB signaling pathways. Additional mutational screening of TRAF3 and other regulators of these pathways are ongoing to determine the extent of constitutive activation present in WM and other B-cell malignancies.

Abstract The use of high-resolution aCGH allows for whole genome screening for copy number changes even in tumors that rarely produce metaphase spreads. The aim of this study was to characterize the aCGH findings in B-CLL, lung MALT lymphoma, Waldenström’s Macroglobulinemia (WM), Multiple Myeloma (MM) and human myeloma cell lines (HMCLs). This study includes 301 B-cell malignancy samples (19 B-CLL, 19 MALT, 20 WM, 48 HMCLs and 195 MM) and an additional 76 melanoma samples. We found MM to have the highest number of abnormalities per karyotype, with 15.8±7.5 (mean ± SD) and 20.6±17.0 in hyperdiploid (H) and non-hyperdiploid MM (NH), respectively. The remaining diseases have a significantly lower number of abnormalities: 5.5±4.5 in B-CLL, 3.5±1.96 in MALT and 4±3.5 in WM. Aneuploidy and biallelic deletions were rare events in these three diseases. B-CLL has two highly recurrent abnormalities involving 11q22-q23 (29%) and 13q14.3 (33%). A minimal deleted region (MDR) of ∼108Kb was identified at 11q comprising ACAT1, NPAT and ATM. In 13q, a 280Kb MDR was recognized, including TRIM13, KCNRG, mir-15a and mir-16. The subgroup of MALT patients with t(11;18) had very few abnormalities and the majority are a consequence of unbalanced translocations (MALT, BIRC2 and BIRC3 deletions). In the remaining MALT samples, trisomy 3 and 18q gain were the most common abnormalities (31% each). The 6q16.3-q26 deletion was the most common abnormality in WM (45%). In 4 of 9 patients with 6q deletion there was an associated 6p gain. The same observation was found in 2 of 3 patients with 8p loss and concomitant 8q gain. Interestingly, 6p and 8q gain were not identified in patients without 6q and 8p deletions. Deletion of 17p was the most common recurrent abnormality observed in our panel of B-cell cancers, being identified in MM, WM and B-CLL. In B-CLL and WM, small mono and biallelic deletions of the NF-kB negative regulators, TRAF3 and NFKBIA, were identified, suggesting a potential involvement of this pathway in the pathogenesis of these diseases. Hierarchical clustering of the aCGH data in MM identified 3 distinct groups, NH-MM, H-MM and MM with deletions of chromosome 13 and 14. Interestingly, MGUS and SMM were more commonly clustered with H-MM. Several sub-clusters were observed in the H-MM group, the most prominent being made up exclusively of patients from TC class D1. This cluster was characterized by the presence of trisomy 11 and 21. The 11q13 TC group clustered and was highly associated with 11q amplifications due to unbalanced t(11;14). The 6q deletion defined a cluster with WM, MM and Melanoma patient with that abnormality. In the case of B-CLL and MALT, the only identifiable clusters were observed in cases with 13q loss and trisomy 3, respectively. These results highlight both, the common and unique aCGH findings in several B-cell cancers. By hierarchical clustering, MM was significantly divergent from the rest of the B-cell diseases. Due to the limited number of abnormalities in B-CLL/MALT/WM, the clustering of these groups was exclusively based on the common abnormalities. The use of aCGH has refined the MDRs of 11q and 13q in B-CLL. Additionally, the identification of deletions affecting the NF-kB pathway in B-CLL and WM has prompted us to hypothesize on the importance of constitutive NF-κB activation in these diseases.

Abstract Next‐generation sequencing identified about 60 genes recurrently mutated in chronic lymphocytic leukemia (CLL). We examined the additive prognostic value of the total number of recurrently mutated CLL genes (i.e., tumor mutational load [TML]) or the individually mutated genes beyond the CLL international prognostic index (CLL‐IPI) in newly diagnosed CLL and high‐count monoclonal B‐cell lymphocytosis (HC MBL). We sequenced 59 genes among 557 individuals (112 HC MBL/445 CLL) in a multi‐stage design, to estimate hazard ratios (HR) and 95% confidence intervals (CI) for time‐to‐first treatment (TTT), adjusted for CLL‐IPI and sex. TML was associated with shorter TTT in the discovery and validation cohorts, with a combined estimate of continuous HR = 1.27 (CI:1.17‐1.39, P = 2.6 × 10 −8 ; c ‐statistic = 0.76). When stratified by CLL‐IPI, the association of TML with TTT was stronger and validated within low/intermediate risk (combined HR = 1.54, CI:1.37‐1.72, P = 7.0 × 10 −14 ). Overall, 80% of low/intermediate CLL‐IPI cases with two or more mutated genes progressed to require therapy within 5 years, compared to 24% among those without mutations. TML was also associated with shorter TTT in the HC MBL cohort (HR = 1.53, CI:1.12‐2.07, P = .007; c ‐statistic = 0.71). TML is a strong prognostic factor for TTT independent of CLL‐IPI, especially among low/intermediate CLL‐IPI risk, and a better predictor than any single gene. Mutational screening at early stages may improve risk stratification and better predict TTT.

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

BACKGROUND: Chemoimmunotherapy with fludarabine, cyclophosphamide, and rituximab (FCR) has been the standard therapy for younger patients with CLL. FCR therapy is particularly effective in patients with immunoglobulin heavy chain variable region (IGHV) mutated CLL. Approximately half of IGHV mutated patients are progression free 8 years after FCR therapy. At the ASH 2018 meeting, we reported the initial results of the ECOG 1912 (E1912) trial, a phase 3 trial comparing the FCR regimen to the combination of ibrutinib and rituximab (IR) for previously untreated CLL patients age 70 or younger who required therapy. With median follow-up of approximately 34 months, the trial demonstrated both a progression-free survival (PFS) and an overall survival (OS) benefit relative to FCR. On sub-set analysis by IGHV mutation status, the difference in PFS was statistically significant for IGHV unmutated patients but, with current follow-up, not IGHV mutated patients. Here, we present updated results for PFS in the E1912 trial. METHODS: As previously reported, eligible patients were treatment-naive individuals with CLL age 70 or younger. Patients with deletion 17p- were excluded from participating in the E1912 trial given their known poor response to FCR therapy. Patients were randomly assigned in a two-to-one ratio to receive ibrutinib (420 mg/day until disease progression or unacceptable toxicity) and rituximab (50 mg/m2 on day 1 of cycle 2, 325 mg/m2 on day 2 of cycle 2, and then 500 mg/m2 on day 1 of cycles 3-7) or six courses of intravenous fludarabine (25 mg/m2 days 1-3), cyclophosphamide (250 mg/m2 days 1-3) and rituximab (50 mg/m2 on day 1 of cycle 1, 325 mg/m2 on day 2 of cycle 1, and then 500 mg/m2 on day 1 of cycles 2-6) every 28-days. Adverse events (AEs) were graded according to the NCI Common Toxicity Criteria (version 4). Dose adjustments for cytopenias were based on the IWCLL CLL Working Group grading scale. The primary endpoint of the trial was PFS with OS a secondary endpoint. Analysis was by intention to treat. RESULTS: With median follow-up of 45 months, 257 (73%) of 354 patients randomized to IR remain on ibrutinib. With extended follow-up, grade 3 and above treatment-related AEs were observed in 70% of IR and 80% of FCR treated patients (OR=0.56; 95% CI 0.34 - 0.90; p=0.013). Among IR-treated patients, the median time on treatment is currently 43 months (range=0.2-61). Among the 95 patients who have discontinued ibrutinib, the reason for discontinuation was progression or death in 23 (7% of patients who started IR; 24% of those who discontinued treatment), AE or complication in 48 (14% of patients who started IR; 51% of those who discontinued treatment), and withdrawal of consent or other reasons in 24 (7% of patients who started IR; 25% of those who discontinued treatment). On multivariable Cox regression adjusting for Timed Up and Go test score, Cumulative Illness Rating Scale (CIRS) score, age, gender, ECOG performance status, creatinine clearance, and baseline anemia/thrombocytopenia, only CIRS score (range 0 - 14) predicted discontinuation of ibrutinib for a reason other than progression or death (HR=1.13 per unit increase; 95% CI 1.03 - 1.23; p=0.009). Among the 72 patients who discontinued ibrutinib for a reason other than progression or death, the median time on ibrutinib was 15.1 months (range 0.2-58.2 months). The median time from ibrutinib discontinuation to disease progression or death was 23 months. With current follow-up, we observed 110 PFS events. The hazard ratio (HR) for PFS favored IR over FCR (HR=0.39; 95% CI 0.26-0.57; p<0.0001). Updated analysis of OS will be presented. On sub-group analysis by IGHV mutation status, IR was superior to FCR for IGHV unmutated patients (HR=0.28; 95% CI 0.17-0.48; p<0.0001). With current follow-up the difference is not significant in IGHV mutated patients (HR=0.42; 95% CI 0.16-1.16; p=0.086). Kaplan-Meier estimates for PFS are shown in Figure 1. CONCLUSIONS: Although less than 7% of ibrutinib treated patients progressed while on therapy, roughly 1 in 5 patients have discontinued ibrutinib for a reason other than progression or death. The only parameter significantly associated with discontinuation for a reason other than progression was increased baseline CIRS score. With extended follow-up, the combination of ibrutinib and rituximab continues to provide superior PFS compared to FCR for younger patients with previously untreated CLL. Disclosures Shanafelt: Pharmacyclics: Research Funding; Patent: Patents & Royalties: US14/292,075 on green tea extract epigallocatechin gallate in combination with chemotherapy for chronic lymphocytic leukemia; Polyphenon E International: Research Funding; Merck: Research Funding; Abbvie: Research Funding; Genentech: Research Funding; Celgene: Research Funding; Glaxo-SmithKline: Research Funding; Hospira: Research Funding; Cephalon: Research Funding. Kay:MorphoSys: Other: Data Safety Monitoring Board; Infinity Pharmaceuticals: Other: DSMB; Celgene: Other: Data Safety Monitoring Board; Agios: Other: DSMB. O'Brien:Janssen: Consultancy, Honoraria; Pharmacyclics LLC, an AbbVie Company: Consultancy, Research Funding; Pfizer: Consultancy, Honoraria, Research Funding; AbbVie: Consultancy, Honoraria; Astellas: Consultancy; Aptose Biosciences, Inc: Consultancy; Amgen: Consultancy; Alexion: Consultancy; Acerta: Research Funding; Eisai: Consultancy; Gilead: Consultancy, Research Funding; GlaxoSmithKline: Consultancy; Verastem: Consultancy; Celgene: Consultancy; Sunesis: Consultancy, Research Funding; TG Therapeutics: Consultancy, Research Funding; Vaniam Group LLC: Consultancy; Regeneron: Research Funding; Kite: Research Funding. Barrientos:AbbVie: Consultancy, Research Funding; AstraZeneca: Consultancy; Genentech: Consultancy; Bayer: Consultancy; Gilead: Consultancy; Janssen: Honoraria; Pharmacyclics LLC, an AbbVie Company: Consultancy, Research Funding; Sandoz: Consultancy; Oncternal Therapeutics: Research Funding. Coutre:Astellas: Consultancy, Membership on an entity's Board of Directors or advisory committees; Pharmacyclics: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel, Accommodations, Expenses; Janssen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel, Accommodations, Expenses, Research Funding; AbbVie: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other: Travel, Accommodations, Expenses, Research Funding; Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other: Travel, Accommodations, Expenses, Research Funding; Astra Zeneca: Consultancy, Membership on an entity's Board of Directors or advisory committees; Acerta: Research Funding; Gilead: Research Funding; BeiGene: Other: Travel, Accommodations, Expenses & Data Safety Monitoring Committee; Genentech: Consultancy. Barr:Pharmacyclics LLC, an AbbVie company: Consultancy, Research Funding; Gilead: Consultancy; Verastem: Consultancy; Genentech: Consultancy; Seattle Genetics: Consultancy; Merck: Consultancy; Celgene: Consultancy; Janssen: Consultancy; AbbVie: Consultancy; Astra Zeneca: Consultancy, Research Funding; TG Therapeutics: Consultancy, Research Funding. Cashen:Celgene: Other: Speaker's Bureau; Seattle Genetics: Other: Speaker's Bureau; Novartis: Other: Speaker's Bureau. Mato:AstraZeneca: Consultancy, Research Funding; AbbVie: Consultancy, Research Funding; Sunesis: Consultancy, Research Funding; Celgene: Consultancy; Johnson & Johnson: Consultancy, Research Funding; TG Therapeutics: Consultancy, Other: DSMB member , Research Funding; LOXO: Consultancy, Research Funding; DTRM Biopharma: Research Funding; Genentech: Consultancy, Research Funding; Pharmacyclics: Consultancy, Research Funding; Gilead: Research Funding; Acerta: Consultancy; Janssen: Consultancy. Erba:Amgen: Consultancy; MacroGenics: Consultancy, Other: Lecture fees, Research Funding; MacroGenics: Consultancy, Other: Lecture fees, Research Funding; Novartis: Consultancy, Research Funding, Speakers Bureau; Novartis: Consultancy, Research Funding, Speakers Bureau; Jazz Pharmaceuticals: Consultancy, Speakers Bureau; Agios: Consultancy, Speakers Bureau; Agios: Consultancy, Speakers Bureau; Incyte: Consultancy, Speakers Bureau; Jazz Pharmaceuticals: Consultancy, Speakers Bureau; Incyte: Consultancy, Speakers Bureau; ImmunoGen: Consultancy, Research Funding; Astellas Pharma: Consultancy; Astellas Pharma: Consultancy; Amgen: Consultancy; GlycoMimetics: Consultancy, Other: Chair, data and safety monitoring board, Research Funding; Daiichi Sankyo: Consultancy, Research Funding; Celgene: Consultancy, Other: chair, AML Registry Scientific Steering Committee, Speakers Bureau; ImmunoGen: Consultancy, Research Funding; AbbVie: Consultancy, Other: Chair, IRC for phase III studies, Research Funding; GlycoMimetics: Consultancy, Other: Chair, data and safety monitoring board, Research Funding; Covance: Other: Fees for serving as chair on an independent review board for AbbVie Phase III studies; Covance: Other: Fees for serving as chair on an independent review board for AbbVie Phase III studies; AbbVie: Consultancy, Other: Chair, IRC for phase III studies, Research Funding; Celgene: Consultancy, Other: chair, AML Registry Scientific Steering Committee, Speakers Bureau; Pfizer: Consultancy; Pfizer: Consultancy; Seattle Genetics: Consultancy; Seattle Genetics: Consultancy; Daiichi Sankyo: Consultancy, Research Funding. Stone:Arog Pharmaceuticals: Consultancy, Honoraria, Research Funding; Otsuka-Astex Pharmaceuticals: Consultancy; Fujifilm: Consultancy; AstraZeneca: Consultancy; Celator Pharmaceuticals: Consultancy; Abbvie: Consultancy, Research Funding; Ono Pharmaceutical-Theradex Oncology: Consultancy; Cornerstone Pharmaceuticals: Consultancy; Daiichi Sankyo: Consultancy; Takeda: Other: Fees for serving on a data and safety monitoring board ; Orsenix: Consultancy; MacroGenics: Consultancy; Jazz Pharmaceuticals: Consultancy; Agios: Consultancy, Research Funding; Stemline Therapeutics: Consultancy; Pfizer: Consultancy; Celgene: Consultancy, Other: Fees for serving on a steering committee, and fees for serving on a data and safety monitoring board; Novartis: Consultancy, Research Funding; Actinium Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees; Amgen: Membership on an entity's Board of Directors or advisory committees; Astellas Pharma: Membership on an entity's Board of Directors or advisory committees; Argenx: Other: Fees for serving on a data and safety monitoring board ; Roche: Consultancy. Tallman:Jazz Pharmaceuticals: Consultancy, Membership on an entity's Board of Directors or advisory committees; Oncolyze: Consultancy, Membership on an entity's Board of Directors or advisory committees.

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 (<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 >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.

Introduction Despite continuous improvement of clinical outcome in multiple myeloma (MM), disease relapse remains a major challenge, leading to progressively shorter remissions and fewer treatment options. Strategies attempting to counteract this challenge include recent efforts resulting in an increase in the availability of novel promising anti-MM agents and targeting specific genetic profiles of the disease. In this context, we aim to develop predictive models of sensitivity and resistance to novel compounds by connecting an ex vivo high-throughput drug screen with genetic, transcriptomics, FISH, and clinical features. Methods Twenty compounds (afatinib, afuresertib, belinostat, buparlisib, cobimetinib, CPI-0610, crenolanib, dinaciclib, dovitinib, JQ1, LGH447, osimertinib, OTX015, panobinostat, romidepsin, selinexor, sunitinib, trametinib, venetoclax, and vorinostat) were selected based on overall promising anti-MM activity from an ex vivo high throughput drug screen with a panel of 79 single agents incubated for 24 hours. The area under the curve (AUC) was used to rank order the ex vivo responses for each compound and the lowest and highest quartile samples were identified for further analysis. Clinical data and FISH data, including t(11;14), t(4;14), t(14;16), del(17p), +1q, monosomy 13, and MYC rearrangement, were collected. Targeted DNA sequencing was performed using a 2.3 Mb custom capture panel covering 139 MM-relevant genes. mRNA-sequencing was performed and differential gene expression analysis in the highest and lowest quartile identified subsets of markers positively and negatively associated with the AUC response for a given compound. An additional unbiased selection of markers using lasso techniques was performed, resulting in predictive generalized linear models (GLM) for each agent. Responses from the remaining intermediate samples were estimated with the predictive models, with overall predictive ability assessed by correlating predicted AUCs with their actual counterparts. Results Our integrative analysis was performed on 50 primary patient samples (36% untreated and 64% relapsed MM). Venetoclax, dinaciclib, romidepsin, panobinostat, osimertinib, belinostat and selinexor were the most active compounds in the cohort. Interestingly, LGH447, dovitinib, selinexor, JQ1, OTX-015, cobimetinib, and trametinib showed increased activity in relapsed MM when compared to untreated samples (Wilcoxon Test; p<0.05). We generated GLMs using an average of 92 markers (range 64-107) per compound, combining mRNA-sequencing expression with FISH and mutation data. The analysis proposed in the present study was validated through the unbiased selection of BCL2 among the subset of markers included in the GLM predicting sensitivity to venetoclax, a first-in-class orally bioavailable selective BCL2 inhibitor. Expression level of critical NF-kB and cell cycle genes, such as BIRC3, CKS1B, PAX5, NFKB2, and CCND2, were included in 60% of our predictive models. Mutations of DNA repair genes (ATM,TP53) were included in the GLMs of three epigenetic therapies, one histone deacetylase inhibitor and two BET inhibitors, associated to ex vivo resistance to the drugs. The presence of monosomy 13 was also a marker for ex vivo resistance for five epigenetic therapies, four HDAC inhibitors and one BET inhibitor. The three BET inhibitors, JQ1, CPI-0610, and OTX015, were among the compounds most accurately predicted by our integrative approach, with Spearman correlation values between 0.773-0.858. Overall, our models accurately predicted the ex vivo response for 16 (80%) of the compounds (r>0.7). Five (25%) of these compounds displayed a remarkably accurate prediction model in both training (highest and lowest quartiles) and validation (intermediate quartiles) samples (r>0.8). Conclusions The GLM data integration approach enabled the establishment of effective predictive models, identifying FISH, transcriptomics, and mutations of putative driver genes important in anti-MM agent responsiveness. In addition, the resulting dataset is promising for future research focusing on the discovery of novel mechanisms of action and establishing markers of sensitivity and resistance to novel compounds. We are currently increasing our dataset and seek to create an omnibus approach that predicts responses to multiple anti-MM agents simultaneously. Disclosures Bergsagel: Celgene: Consultancy; Ionis Pharmaceuticals: Consultancy; Janssen Pharmaceuticals: Consultancy. Stewart:Amgen: Consultancy, Research Funding; Bristol Myers-Squibb: Consultancy; Celgene: Consultancy, Research Funding; Ionis: Consultancy; Janssen: Consultancy, Research Funding; Oncopeptides: Consultancy; Ono: Consultancy; Roche: Consultancy; Seattle Genetics: Consultancy; Takeda: Consultancy.

Abstract MYC expression is frequently dysregulated in multiple myeloma (MM). In one comprehensive study, MYC structural variations (SV) were found in nearly half of MM cases (Affer et al. Leukemia 2014). The prevalence was higher in hyperdiploid (HRD) tumors (65%) compared to non-hyperdiploid (NHRD) tumors (36%). The large amount of tumor DNA required for all of the genomic studies performed may have biased the samples analyzed (e.g., to those with higher tumor burdens). To validate the findings of recurrent MYC SV in another dataset, we analyzed the CoMMpass data. We analyzed long-insert whole genome sequencing (WGS) data from diagnostic samples in 420 patients from the IA7 release (dbGAP phs000748) for SV. The results of clinical data, processed WES (SNV), RNASeq (gene expression) and WGS (copy number) data from IA8 (http://research.themmrf.org) were used to calculate survival, RAS and NFKB pathway mutation (WES), TC class (RNA), NFKB index (RNA) and hyperdiploid index (WGS). MYC SVs were identified in 38% of tumors. They were present in 53% HRD and 28% NHRD, and by TC in 55% D1, 43% D2, 36% MMSET, 26% MAF, 13% CCND. Juxtaposition of an Ig enhancer (IgH, IgK, IgL) close to MYC was the most common MYC SV, representing ~40% of the MYC SV. Other enhancers identified have mostly been reported previously, with the most frequent being NSMCE2 (12% of SV) and TXNDC5 (5% of SV). Intrachromosomal SV (deletions, inversions, duplications) not associated with any known enhancer were also frequent (18% of SV). As expected, MYC expression was higher in tumors with MYC SV compared to those without (~2.4 fold, p-value<0.0001). We used Gene Set Enrichment Analysis (GSEA) to identify activated pathways that might substitute for MYC in HRD patients without MYC SV and observed significant activation of the NFkB pathway. Interestingly, examining patients with RAS/MAPK pathway (NRAS, KRAS, BRAF, FGFR3 - abbreviated RAS) mutations (identified in 50% of all patients) the same pattern was observed: in the absence of RAS mutation, there was a significantly higher NFkB index. In general, the HRD tumors seem to have either activation of RAS and/or MYC, or activation of NFkB (Figure). There was no difference in overall survival in patients with versus those without MYC SV. We developed a clinically applicable sequencing platform to identify MYC SV, which cannot be reliably identified by FISH. We sought to validate this targeted capture approach, where in addition to the coding exons of 81 interesting genes described previously (M3P), we also pulled down the region surrounding MYC (2 Mb), IgH (0.5 Mb), IgK (50 kb) and IgL (100 kb) allowing us to additionally identify SV in MYC and Ig loci. Using this approach we identified IgH translocations in 29/30 samples with translocations previously identified by FISH (97%). Moreover, we identified MYC SV in 19/22 patients with SV previously identified by mate-pair WGS (86%). Importantly, sequencing identified the precise translocation breakpoint, and identity of the enhancer dysregulating MYC, which may be important variables. In one informative patient two different MYC SV were present at diagnosis, only one of which was still present following a partial response to four cycles of lenalidomide and dexamethasone. This suggests that the two MYC SV are in different subclones, one of which was much more sensitive to the treatment. Interestingly, the enhancer dysregulating MYC in the sensitive subclone harbors 5 strong Ikaros binding sites identified by ChIPseq, suggesting one intriguing mechanism for sensitivity to lenalidomide. To summarize, we verified in a large dataset that MYC expression is frequently dysregulated by SV in MM (38%), and the RAS/MAPK (50%) and NFKB (23%) pathways are frequently activated by mutations. Surprisingly, given their generally good prognosis, nearly half of HRD tumors seem to be MYC driven, while this is true for only a quarter of NHRD tumors. HRD tumors not driven by MYC or RAS appear to be driven by NFKB. Remarkably, the pathways most commonly activated by mutation in MM: CCND, MYC, RAS, NFKB are common to many cancers and have been studied extensively individually. To understand their clinical impact in MM we have developed a comprehensive custom capture sequencing panel that identified 97% of IgH translocations, 86% of MYC SV, and as well as SNV and CNV of 81 recurrently mutated genes. It will be important to include such a comprehensive genetic analysis to complement clinical trials in the future. Figure. Figure. Disclosures Stewart: Bristol Myers Squibb: Consultancy; Celgene: Consultancy; Takeda Oncology: Consultancy; Janssen Pharmaceuticals: Consultancy.