Citations

The NCCN Guidelines for Management of Immunotherapy-Related Toxicities provide interdisciplinary guidance on the management of immune-related adverse events (irAEs) resulting from cancer immunotherapy. These NCCN Guidelines Insights describe symptoms that may be caused by an irAE and should trigger further investigation, and summarize the NCCN Management of Immunotherapy-Related Toxicities Panel discussions for the 2020 update to the guidelines regarding immune checkpoint inhibitor–related diarrhea/colitis and cardiovascular irAEs.

Abstract Combination of carfilzomib with dexamethasone (Kd) is approved for use in relapsed and/or refractory multiple myeloma (RRMM), with carfilzomib administered twice weekly at 56 mg/m 2 (Kd56 BIW) or once weekly at 70 mg/m 2 (Kd70 QW). Post hoc cross‐trial comparisons were performed to compare efficacy and safety profiles of Kd70 QW vs Kd56 BIW dosing schedules using data from three trials of patients with RRMM: A.R.R.O.W., CHAMPION‐1, and ENDEAVOR. To select for comparable patient populations, side‐by‐side efficacy and safety comparisons were performed in subgroups of patients with 2‐3 prior lines of therapy who were not refractory to bortezomib. The overall response rate (ORR) was 69.9% (95% confidence interval [CI], 61.7‐77.2) for Kd70 QW and 72.4% (95% CI, 65.9‐78.2) for Kd56 BIW. Median progression‐free survival (PFS) was 12.1 months (95% CI, 8.4‐14.3) for Kd70 QW and 14.5 months (95% CI, 10.2—not evaluable) for Kd56 BIW. Frequency of grade ≥ 3 adverse events (AEs) was 67.6% for Kd70 QW and 85.3% for Kd56 BIW. Regression analyses (adjusting for prognostic factors) of all patients in the trials who received Kd70 QW vs Kd56 BIW estimated a PFS hazard ratio of 0.91 (95% CI, 0.69‐1.19; P = .47) and an ORR odds ratio of 1.12 (95% CI, 0.74‐1.69; P = .61). These results suggest that Kd70 QW has a comparable efficacy profile compared with Kd56 BIW and represents a convenient and well‐tolerated treatment for patients with RRMM.

Abstract Background The deep inferior epigastric perforator (DIEP) flap is the most common perforator flap for microsurgical breast reconstruction. Contrary to the conventional open approach, robotic-assisted DIEP flap harvest intends to preserve ARS integrity, thereby reducing the morbidity. We assessed the feasibility and compared performance outcomes of a robotic, cadaveric training model for DIEP flap harvest using two approaches: transabdominal preperitoneal (TAPP) and totally extraperitoneal (TEP). Methods A robotics system (da Vinci Xi) was applied in conjunction with a cadaveric training model. Ports were placed in the abdominal wall to triangulate each DIEP flap. Surgical time and technical characteristics were recorded. Values were analyzed and compared. Results Eight female cadavers (16 hemi-DIEP flaps) were dissected: 50% TAPP and 50% TEP approaches. Mean harvest time was 56 minutes (range: 48–74 minutes) and 65 minutes (range: 60–83 minutes) for TAPP versus TEP groups, respectively (p < 0.05). Mean pedicle dissection time was 36 minutes (range: 25–40 minutes) and 39 minutes (range: 30–42 minutes) for TAPP versus TEP groups, respectively (p > 0.05). Intra-abdominal contents were manipulated twice on average in the TAPP group versus 0 times in the TEP group (p < 0.05). One TAPP case had an injury to the bowel, and one TEP case was converted to conventional open due to pneumoperitoneum. Conclusion Robotic-assisted DIEP flap harvest represents a technological enhancement for advanced regenerative plastic surgery. Our model demonstrated both TAPP and TEP are feasible, with TEP less invasive, preserving the posterior rectus sheath, and decreasing complication risks. However, there is a steeper and longer learning curve for TEP.

PURPOSE Atypical teratoid/rhabdoid tumor (AT/RT) is an aggressive, early-childhood brain tumor without standard effective treatment. To our knowledge, we conducted the first AT/RT-specific cooperative group trial, ACNS0333, to examine the efficacy and safety of intensive postoperative chemotherapy and focal radiation to treat AT/RT. PATIENTS AND METHODS Patients from birth to 22 years of age with AT/RT were eligible. After surgery, they received 2 courses of multiagent chemotherapy, followed by 3 courses of high-dose chemotherapy with peripheral blood stem cell rescue and involved-field radiation therapy. Timing of radiation was based on patient age and disease location and extent. Central testing of tumor and blood for SMARCB1 status was mandated. Tumor molecular subclassification was performed retrospectively. The primary analysis was event-free survival (EFS) for patients < 36 months of age compared with a cooperative groups’ historical cohort. Although accrual was based on the therapeutic question, potential prognostic factors, including age, tumor location, M stage, surgical resection, order of therapy, germline status, and molecular subtype, were explored. RESULTS Of 65 evaluable patients, 54 were < 36 months of age. ACNS0333 therapy significantly reduced the risk of EFS events in patients < 36 months of age compared with the historical cohort ( P < .0005; hazard rate, 0.43; 95% CI, 0.28 to 0.66). Four-year EFS and overall survival for the entire cohort were 37% (95% CI, 25% to 49%) and 43% (95% CI, 31% to 55%), respectively. Timing of radiation did not affect survival, and 91% of relapses occurred by 2 years from enrollment. Treatment-related deaths occurred in 4 patients. CONCLUSION The ACNS0333 regimen dramatically improved survival compared with historical therapies for patients with AT/RT. Clinical characteristics and molecular subgrouping suggest prognostic differences. ACNS0333 results lay a foundation on which to build future studies and incorporate testing of new therapeutic agents.

Background: Novel agent induction and AHCT remains the preferred initial therapeutic strategy for transplant-eligible MM patients. Current prognostic tools in MM focus solely on disease-specific factors at diagnosis to determine patient prognosis-International Staging System (ISS) and revised-ISS (R-ISS). A major limitation to both, the ISS and R-ISS, is that they are not specific for HCT-eligible patients and do not take into account other patient factors that may enter into a decision to pursue AHCT. The data used to generate these staging systems were from broad populations with varying upfront treatment strategies and included patients who were ineligible for intensive therapy. Additionally, there is considerable interest in identifying the population that relapses early despite modern induction/AHCT approaches who are candidates for novel approaches for maintenance/consolidation. To address these problems, we used data from the Center for Blood and Marrow Transplant Research (CIBMTR) registry to identify disease-, patient-, and transplantation-specific variables that are associated with progression-free survival (PFS) in patients undergoing upfront AHCT (within 12 months of diagnosis). Methods: We used the outcomes of 2528 MM patients undergoing upfront AHCT from 2008-2017 reported to the CIBMTR. Patients were divided into training and validation sets with a 50% random split. High risk cytogenetics was defined as the presence of one or more of the following: t(4;14), t (14;16), t (14;20), del 13q, del 17p, 1q gain, or 1p deletion. We used a Cox multivariable model to identify factors prognostic of progression free survival (PFS) in a training subset. The regression coefficients of the final model was transformed into a risk score with an appropriate transformation. A weighted score using these factors was assigned to the training cohort (n = 917) and validation cohort (n=897) using subset that had all values that entered the final model. Kaplan-Meier estimates of the individual scores were used to classify patients into risk groups for both cohorts. Results: Baseline characteristics of these patients are shown in Table 1. No cytogenetic abnormality, VRD induction, pre-AHCT bone marrow plasma cells (BMPCs) &lt;10% and 1 line of induction chemotherapy were assigned 0 points. Pre-AHCT BMPCs ≥10% (hazard ratio HR, 1.47; 95% CI, 1.19-1.83), use of ≥2 lines of induction chemotherapy prior to AHCT (HR 1.32; 95% CI 1.06-1.64), standard cytogenetic risk vs. no abnormality (HR 1.41; 95% CI 1.13-1.77) and induction regimens (non-VRD regimens vs. VRD) (HR 1.4, 95% CI 1.17-1.74) were associated with increased hazard of progression and assigned 1 point in the scoring system. Presence of high-risk cytogenetics vs. no abnormality (HR 1.87; 95% CI 1.45-2.42) was assigned 2 points, and the use of thalidomide and dexamethasone (TD) as an induction regimen (HR 2.19; 95% CI 1.48-3.2) was assigned 3 points. A two-category system was created based on the scoring: low risk (0-3) and high risk (4-6). The scoring system was prognostic for PFS when applied to both cohorts. High-risk group was found to have significantly higher risk of progression and/or death compared to low risk in training (HR 2.2; 95% CI 1.74-2.86; p&lt;0.0001) and validation cohort (HR 1.7, 95% CI 1.30-2.22; p=0.0001) respectively (Table 2). The 3-year PFS in the training cohort was 60% (95% CI 56%-64%) in low risk and 27% (95% CI 17%- 36%) in high risk while in the validation cohort was 51% (95% CI 47%-55%) in low risk and 28% (95% CI 16%- 39%) in high risk (Figure 1A and 1B). Conclusions: We describe a prognostic model specifically for patients undergoing upfront AHCT in MM which can identify patients at very high risk for early relapse/progression. These patients should be ideal candidates for studies of immunotherapy or other interventions after AHCT aimed at reducing relapse. Disclosures Dhakal: Sanofi: Membership on an entity's Board of Directors or advisory committees; Janssen: Membership on an entity's Board of Directors or advisory committees; Amgen: Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Honoraria; Takeda: Membership on an entity's Board of Directors or advisory committees. Kumar:Celgene: Consultancy, Research Funding; Janssen: Consultancy, Research Funding; Takeda: Research Funding. Shah:Genentech, Seattle Genetics, Oncopeptides, Karoypharm, Surface Oncology, Precision biosciences GSK, Nektar, Amgen, Indapta Therapeutics, Sanofi: Membership on an entity's Board of Directors or advisory committees; Bristol-Myers Squibb: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Amgen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Nkarta: Consultancy, Membership on an entity's Board of Directors or advisory committees; Kite: Consultancy, Membership on an entity's Board of Directors or advisory committees; Teneobio: Consultancy, Membership on an entity's Board of Directors or advisory committees; University of California, San Francisco: Employment; Poseida: Research Funding; Indapta Therapeutics: Equity Ownership; Celgene, Janssen, Bluebird Bio, Sutro Biopharma: Research Funding. Qazilbash:Amgen: Consultancy, Other: Advisory Board; Autolus: Consultancy; Bioclinical: Consultancy; Genzyme: Other: Speaker. D'Souza:EDO-Mundapharma, Merck, Prothena, Sanofi, TeneoBio: Research Funding; Prothena: Consultancy; Pfizer, Imbrium, Akcea: Membership on an entity's Board of Directors or advisory committees. Hari:AbbVie: Consultancy, Honoraria; Cell Vault: Equity Ownership; Sanofi: Honoraria, Research Funding; Spectrum: Consultancy, Research Funding; Amgen: Research Funding; Kite: Consultancy, Honoraria; Janssen: Consultancy, Honoraria; BMS: Consultancy, Research Funding; Takeda: Consultancy, Honoraria, Research Funding; Celgene: Consultancy, Honoraria, Research Funding.

Background: Autologous hematopoietic cell transplantation (AHCT) is an effective treatment to achieve deep and durable remission in multiple myeloma (MM). Typically, AHCT is offered to patients &lt;70 years of age, although the median age of diagnosis of MM is 70 years. We compared the outcomes of upfront AHCT across age groups for newly diagnosed MM in the era of novel therapies using the Center for International Blood and Marrow Transplant Research (CIBMTR) database. Methods: We analyzed the outcomes of 15,999 MM patients aged 20 years and older from the USA who received a single AHCT with melphalan conditioning within 12 months of diagnosis between 2013 and 2017 and reported to the CIBMTR. Multivariate analysis was performed for non-relapse mortality (NRM), relapse/progression (REL), progression-free survival (PFS) and overall survival (OS) using Cox proportional hazards model with age at transplant in decades as the main effect. Hazard ratio (HR) with 95% confidence intervals (CI) are reported. Because of the large sample size, a p-value of &lt;0.01 was considered significant a priori. Results: Table 1 shows patient, disease and treatment characteristics by age group at diagnosis. All age groups had similar distribution of gender, race, ethnicity, Karnofsky performance score (KPS), comorbidity index (HCT-CI), stage III by Durie-Salmon/International Staging System. There was a higher proportion of high-risk cytogenetics in patients ≥70 years (30%), compared to age group 40-49 years (24%) and 20-39 years (20%) in this population. Older patients were more likely to be White compared to younger patients: 85% ≥70 compared to 64% 20-39 years. While 82% of the overall population received melphalan 200 mg/m2, 58% of the ≥70% received Mel 140 mg/m2. There were more ASCT performed in the ≥70-year age group in 2017 (28%) compared to 2013 (15%). Univariate outcomes by age groups shown in Table 2 revealed that 100-day NRM was higher in ≥70 years at 1% compared to younger patients (p &lt;0.01) and 2-year OS was lower in ≥70 years at 86 (85-88)% compared to 60-69 years, 89 (88-89)% (p&lt;0.01). When adjusted for other variables (Table 3), compared to reference age group of 60-69 years, patients ≥70 had similar NRM (HR 1.3, 95% CI 1, 1.7, p 0.06), REL (HR 1.03, 95% CI 0.9, 1.1, p 0.6), PFS (HR 1.06, 95% CI 1, 1.2, p 0.2), and OS (HR 1.2, 95% CI 1, 1.4, p 0.02). The leading cause of death across all age groups was primary disease. Among the ≥70 years cohort, melphalan dose was a surrogate for worse outcomes including NRM at 100 days (Mel 140, 1 (1-2)% vs Mel 200 0 (0-1)%, p 0.003, PFS at 2 years Mel 140 64 (60-67)% vs Mel 200 69 (66-73)%, p 0.003, and OS at 2 years (Mel 140 85 (82-87)% vs Mel 200 89 (86-91)%, p 0.01) (Figure). Conclusions: This is the largest study of AHCT in older adults with MM. More MM patients ≥70 years are being transplanted in the US over time. While our data may highlight referral and access biases regarding which older patients may be referred for ASCT, our results confirm that patients ≥70 years can undergo transplant safely and achieve similar benefits as 60-69 years' old patients. Our results also suggest that melphalan 200 mg/m2 may be given safely in the ≥70 years population. While melphalan conditioning dose 140 mg/m2 in the ≥70 group is associated with worse outcomes, this is likely a surrogate for higher frailty and comorbidities in this cohort of patients. Our analysis confirms that AHCT has similar benefits in terms of disease control (REL and PFS) in both young and older MM patients. This benefit is seen even in a contemporaneous era where proteasome inhibitors and/or immunomodulator drugs are used in upfront treatment. Thus, AHCT remains a safe consolidation therapy across all age groups of MM patients. Disclosures Hari: Celgene: Consultancy, Honoraria, Research Funding; Takeda: Consultancy, Honoraria, Research Funding; BMS: Consultancy, Research Funding; Janssen: Consultancy, Honoraria; Kite: Consultancy, Honoraria; Amgen: Research Funding; Spectrum: Consultancy, Research Funding; Sanofi: Honoraria, Research Funding; Cell Vault: Equity Ownership; AbbVie: Consultancy, Honoraria. Kumar:Janssen: Consultancy, Research Funding; Takeda: Research Funding; Celgene: Consultancy, Research Funding. Shah:Poseida: Research Funding; Bristol-Myers Squibb: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Nkarta: Consultancy, Membership on an entity's Board of Directors or advisory committees; Kite: Consultancy, Membership on an entity's Board of Directors or advisory committees; Celgene, Janssen, Bluebird Bio, Sutro Biopharma: Research Funding; University of California, San Francisco: Employment; Genentech, Seattle Genetics, Oncopeptides, Karoypharm, Surface Oncology, Precision biosciences GSK, Nektar, Amgen, Indapta Therapeutics, Sanofi: Membership on an entity's Board of Directors or advisory committees; Indapta Therapeutics: Equity Ownership; Amgen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Teneobio: Consultancy, Membership on an entity's Board of Directors or advisory committees. Qazilbash:Genzyme: Other: Speaker; Bioclinical: Consultancy; Amgen: Consultancy, Other: Advisory Board; Autolus: Consultancy. D'Souza:EDO-Mundapharma, Merck, Prothena, Sanofi, TeneoBio: Research Funding; Prothena: Consultancy; Pfizer, Imbrium, Akcea: Membership on an entity's Board of Directors or advisory committees.

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: 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 Primary plasma cell leukemia (pPCL) is a rare plasma cell neoplasm with a high mortality rate. There have been improvements in multiple myeloma (MM) outcomes with novel induction agents and use of hematopoietic cell transplantation (HCT) with maintenance, but similar progress has not been reported for pPCL. We examined the outcomes of pPCL patients receiving novel agents with autologous (autoHCT) or allogeneic (alloHCT) approaches as reported to the Center for International Blood and Marrow Transplant Research (CIBMTR) in the modern era. Methods From 2008 to 2015, 348 pPCL pts underwent HCT (N = 277 - autoHCT and 71 - alloHCT) with 45% and 48% having research level data available, respectively. Cumulative incidences of non-relapse mortality (NRM) and relapse/progression (REL), and probability of progression-free survival (PFS) and overall survival (OS) were calculated. Cox multivariate regression was used to model survival after autoHCT only. Median follow-up in autoHCT and alloHCT was 48 and 60 months, respectively. Results AutoHCT Cohort Median age was 60 years and 93% received HCT within 12 months of diagnosis with 76% after a single line of induction (Table 1). 35% had high risk cytogenetics. 23% received bortezomib, doxorubicin, cisplatin, cyclophosphamide, and etoposide (VDPACE). Moreover, 40% received bortezomib (BTZ) and immunomodulatory drug (IMIID)-based triplets. Disease status at HCT was VGPR or better in 47%. 27% received maintenance therapy. At 4 years post-HCT, NRM was 7% (4-11%), REL 76% (69-82%), PFS 17% (13-23%), and OS 28% (22-35%) (Figures 1A, 2A, 2B). Disease status ≥VGPR at HCT and Karnofsky Performance Score &gt;90 significantly predicted superior OS in multivariate analysis. AlloHCT Cohort Median age was 53 years and 89% received HCT within 12 months of diagnosis (Table 1). 61% received a single alloHCT, while 39% used auto-alloHCT tandem approach. 42% had high-risk cytogenetics. 61% received total body irradiation with 44% receiving myeloablative conditioning. Use of VDPACE was higher at 41% in this cohort. VGPR status at HCT was similar (48%), while maintenance was used less often (12%). Grade II-IV acute GVHD occurred in 30% and chronic GVHD in 45%. At four years post-HCT, NRM was 12% (5-21%), REL 69% (56-81%), PFS 19% (10-31%), and OS 31% (19-44%) (Figures 1A, 1B, 2A, 2B). There were no differences in outcomes based on type of HCT. A comparison of post-HCT outcomes of CIBMTR pPCL patients from 1995 to 2006 showed that PFS and OS outcomes are inferior despite lower NRM in this modern cohort (Mahindra et al. Leukemia. 2012). In addition, analysis of SEER (1995-2009) and CIBMTR databases showed that use of HCT increased from 12% (7-21%) in 1995 to 46% (34-64%) in 2009. Conclusion More newly diagnosed pPCL patients are receiving modern induction regimens translating into a higher proportion receiving HCT, but without significant further benefit post-HCT. Post-HCT relapse remains the biggest challenge and further survival in pPCL will likely need a combination of targeted and cell therapy approaches. This study provides a benchmark for future HCT studies for pPCL. Disclosures Girnius: Takeda: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Genentech: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Dhakal:Takeda: Membership on an entity's Board of Directors or advisory committees; Janssen: Membership on an entity's Board of Directors or advisory committees; Sanofi: Membership on an entity's Board of Directors or advisory committees; Amgen: Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Honoraria. Shah:University of California, San Francisco: Employment; Indapta Therapeutics: Equity Ownership; Genentech, Seattle Genetics, Oncopeptides, Karoypharm, Surface Oncology, Precision biosciences GSK, Nektar, Amgen, Indapta Therapeutics, Sanofi: Membership on an entity's Board of Directors or advisory committees; Celgene, Janssen, Bluebird Bio, Sutro Biopharma: Research Funding; Poseida: Research Funding; Bristol-Myers Squibb: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Amgen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Nkarta: Consultancy, Membership on an entity's Board of Directors or advisory committees; Kite: Consultancy, Membership on an entity's Board of Directors or advisory committees; Teneobio: Consultancy, Membership on an entity's Board of Directors or advisory committees. Qazilbash:Amgen: Consultancy, Other: Advisory Board; Bioclinical: Consultancy; Autolus: Consultancy; Genzyme: Other: Speaker. Kumar:Celgene: Consultancy, Research Funding; Takeda: Research Funding; Janssen: Consultancy, Research Funding. D'Souza:EDO-Mundapharma, Merck, Prothena, Sanofi, TeneoBio: Research Funding; Prothena: Consultancy; Pfizer, Imbrium, Akcea: Membership on an entity's Board of Directors or advisory committees. Hari:BMS: Consultancy, Research Funding; Takeda: Consultancy, Honoraria, Research Funding; Celgene: Consultancy, Honoraria, Research Funding; Janssen: Consultancy, Honoraria; Kite: Consultancy, Honoraria; Amgen: Research Funding; Spectrum: Consultancy, Research Funding; Sanofi: Honoraria, Research Funding; Cell Vault: Equity Ownership; AbbVie: Consultancy, Honoraria.