Citations

Abstract Introduction Lenalidomide-based therapies are a standard of care for patients with newly diagnosed, transplant-ineligible MM. Daratumumab (DARA) is a human, CD38-targeted, IgG1κ monoclonal antibody which has single-agent activity in heavily pretreated MM patients. As previously reported in 3 phase 3 studies, the addition of DARA to standards of care in both relapsed refractory (D-Rd, DARA plus bortezomib and dexamethasone [D-Vd]) or transplant-ineligible NDMM (DARA plus bortezomib, melphalan, and prednisone [D-VMP]) resulted in a ≥50% reduction in the risk of disease progression or death (Palumbo A, et al. N Engl J Med 2016;375:754-766; Dimopoulos MA, et al. N Engl J Med 2016;375:1319-1331; Mateos MV, et al. N Engl J Med 2018;378:518-528). Of these, the POLLUX study with D-Rd showed the greatest benefit with a 63% reduction in risk of disease progression or death in patients with MM who had at least one prior line of therapy. Based on the efficacy and tolerable safety profile of D-Rd, we conducted a phase 3 study (MAIA) to evaluate D-Rd vs Rd in transplant-ineligible NDMM. Here we report the prespecified interim analysis of the MAIA study. Methods Patients ineligible for high-dose chemotherapy with autologous stem cell transplantation due to age ≥65 years or comorbidities were randomized 1:1 to Rd ± DARA. Stratification was based on International Staging System stage (ISS [I, II, III]), region (North America vs other), and age (<75 vs ≥75 years). All patients received 28-day cycles of treatment with Rd ± DARA. R: 25 mg (oral) QD on Days 1-21; d: 40 mg (oral) on Days 1, 8, 15 and 22. In the D-Rd arm, DARA was given at 16 mg/kg (intravenously) QW for Cycles 1-2, Q2W for Cycles 3-6, and Q4W thereafter. In both arms, patients were treated until disease progression or unacceptable toxicity. The primary endpoint was progression-free survival (PFS). Key secondary endpoints included overall response rate (ORR), minimal residual disease (MRD)-negativity rate (10-5 sensitivity, clonoSEQ® version 2.0), and safety. Results This international study randomized 737 patients (368 to D-Rd; 369 to Rd) from 14 countries. Key baseline characteristics were well balanced between the two arms. The median (range) age was 73 (45-90) years with 44% of patients ≥75 years, and 52% were male. Two thirds of patients had ECOG scores ≥1: ECOG score 0, 34%; 1, 50%; ≥2, 17%. Overall, 27%, 43%, and 29% were ISS stage I, II, and III, respectively. Of 642 patients evaluable for FISH/karyotyping analysis, 86% and 14% were standard and high cytogenetic risk, respectively. The prespecified interim analysis occurred after 239 PFS events on 24 September 2018 with a median follow up of 28 months. For the primary endpoint, the hazard ratio was 0.55 (95% CI, 0.43 to 0.72; P <0.0001), representing a 45% reduction in the risk of progression or death in patients treated with D-Rd (Figure). The median PFS for the Rd arm was 31.9 months and not reached for the D-Rd arm. Adding DARA to Rd resulted in deeper responses with a complete response (CR) or better rate of 47.6% in the D-Rd arm compared with 24.7% in the Rd arm (odds ratio [OR] 2.75, 95% CI, 2.01 to 3.76; P <0.0001). The very good partial response (VGPR) or better rate was 79.3% in the D-Rd arm compared with 53.1% in the Rd arm (OR 3.4, 95% CI, 2.45 to 4.72; P <0.0001). A total of 19% of patients have died and the HR for OS was 0.78 (95% CI, 0.56 to 1.1); follow-up is ongoing. Higher rates (5% or more difference) of grade 3/4 pneumonia, neutropenia, and leukopenia were observed in the D-Rd arm. The safety profile is consistent with previously reported DARA studies. The complete topline data set will be presented with additional efficacy endpoints, including MRD negativity rate. Conclusion The addition of DARA to Rd in patients with transplant-ineligible NDMM significantly reduced the risk of progression or death by 45%. There are no new safety signals using DARA plus Rd in NDMM. These data together with the phase 3 ALCYONE study (D-VMP vs VMP) support the addition of DARA to standard of care combinations in patients with NDMM ineligible for transplant. Disclosures Facon: Sanofi: 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; Janssen: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Takeda: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Amgen: Membership on an entity's Board of Directors or advisory committees; Karyopharm: Membership on an entity's Board of Directors or advisory committees; Oncopeptides: Membership on an entity's Board of Directors or advisory committees. Kumar:KITE: Membership on an entity's Board of Directors or advisory committees, Research Funding; AbbVie: Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees, Research Funding. Plesner:Celgene: Other: Independent Response Assessment Comittee; Janssen: Consultancy. Orlowski:Bristol-Myers Squibb: Consultancy, Membership on an entity's Board of Directors or advisory committees; Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees; Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees; Kite Pharma: Consultancy, Membership on an entity's Board of Directors or advisory committees; Sanofi-Aventis: Consultancy, Membership on an entity's Board of Directors or advisory committees; Takeda: Consultancy; Amgen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; BioTheryX: Research Funding; Spectrum Pharma: Research Funding. Moreau:Abbvie: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Amgen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Bahlis:Amgen: Consultancy, Honoraria, Research Funding; Janssen: Consultancy, Honoraria, Research Funding; Celgene: Consultancy, Honoraria, Research Funding. Hulin:Celgene: Honoraria, Research Funding; Amgen: Honoraria; Takeda: Honoraria; Janssen: Honoraria, Research Funding. Goldschmidt:Adaptive Biotechnology: Consultancy, Membership on an entity's Board of Directors or advisory committees; Amgen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Bristol Myers Squibb: 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; Janssen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Sanofi: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Chugai: Honoraria, Research Funding; Mundipharma: Research Funding; Novartis: Honoraria, Research Funding; ArtTempi: Honoraria. O'Dwyer:Janssen: Consultancy. Perrot:Janssen: Consultancy, Equity Ownership, Honoraria. Venner:Janssen: Honoraria, Research Funding; Celgene: Honoraria, Research Funding; Amgen: Honoraria; Takeda: Honoraria. Weisel:Celgene: Consultancy; Angeb: Consultancy, Honoraria, Research Funding; Juno: Consultancy; Takeda: Consultancy, Honoraria; Sanofi: Consultancy, Research Funding; Bristol Myers SquibbMS: Consultancy, Honoraria; Janssen: Consultancy, Honoraria, Research Funding. Ahmadi:Genmab: Employment, Equity Ownership. Chiu:Janssen Research & Development, LLC: Employment, Equity Ownership. Wang:Janssen Research & Development, LLC: Employment. Van Rampelbergh:Janssen: Employment. Uhlar:Janssen: Employment. Kobos:Janssen Research & Development: Employment. Qi:Janssen Research & Development, LLC: Employment. Usmani:Abbvie, Amgen, Celgene, Genmab, Merck, MundiPharma, Janssen, Seattle Genetics: Consultancy; Amgen, BMS, Celgene, Janssen, Merck, Pharmacyclics,Sanofi, Seattle Genetics, Takeda: Research Funding.

536 Background: DNA methylation is a heritable covalent modification that is developmentally regulated and is critical in tissue-type definition. Although genotype-phenotype correlations have been described for different subtypes of renal cell carcinoma (RCC), it is unknown if DNA methylation profiles correlate with morphological or ontology based phenotypes. Here we test the hypothesis that DNA methylation signatures can discriminate between putative precursor cells in the nephron. Methods: We performed deep profiling of DNA methylation in diverse histopathological RCC subtypes and validated DNA methylation and transcriptome signatures in The Cancer Genome Atlas Clear Cell and Chromophobe Renal Cell Carcinoma Datasets. Results: Our data provide the first mapping of methylome epi-signature and indicates that RCC subtypes can be grouped into two major epi-clusters: C1 which encompasses clear cell RCC, papillary RCC, mucinous and spindle cell carcinomas and translocation RCC; C2 which comprises oncocytoma and chromophobe RCC. Interestingly, C1 epi-cluster displayed three fold more hypermethylation as compared to C2 epi-cluster. Of note, differentially methylated regions between C1 and C2 epi-clusters occur in gene bodies and intergenic regions, instead of gene promoters. Transcriptome analysis of C1 epi-cluster suggests a functional convergence on Polycomb targets, whereas C2 epi-cluster displays DNA methylation defects. Furthermore, we find that our epigenetic ontogeny signature is associated with worse outcomes of patients with clear-cell RCC. Conclusions: Taken together, our data defines the epi-clusters that can discriminate between distinct RCC subtypes and for the first time, to our knowledge, define the epigenetic basis for proximal versus distal tubule derived kidney tumors.

Abstract Purpose: DNA methylation is a heritable covalent modification that is developmentally regulated and is critical in tissue-type definition. Although genotype–phenotype correlations have been described for different subtypes of renal cell carcinoma (RCC), it is unknown if DNA methylation profiles correlate with morphological or ontology based phenotypes. Here, we test the hypothesis that DNA methylation signatures can discriminate between putative precursor cells in the nephron. Experimental Designs: We performed deep profiling of DNA methylation and transcriptome in diverse histopathological RCC subtypes and validated DNA methylation in an independent dataset as well as in The Cancer Genome Atlas Clear Cell and Chromophobe Renal Cell Carcinoma Datasets. Results: Our data provide the first mapping of methylome epi-signature and indicate that RCC subtypes can be grouped into two major epi-clusters: C1, which encompasses clear-cell RCC, papillary RCC, mucinous and spindle cell carcinomas and translocation RCC; C2, which comprises oncocytoma and chromophobe RCC. Interestingly, C1 epi-cluster displayed 3-fold more hypermethylation as compared with C2 epi-cluster. Of note, differentially methylated regions between C1 and C2 epi-clusters occur in gene bodies and intergenic regions, instead of gene promoters. Transcriptome analysis of C1 epi-cluster suggests a functional convergence on Polycomb targets, whereas C2 epi-cluster displays DNA methylation defects. Furthermore, we find that our epigenetic ontogeny signature is associated with worse outcomes of patients with clear-cell RCC. Conclusions: Our data define the epi-clusters that can discriminate between distinct RCC subtypes and for the first time define the epigenetic basis for proximal versus distal tubule derived kidney tumors. Clin Cancer Res; 22(24); 6236–46. ©2016 AACR.

With the increased availability of computational resources, the past decade has seen a rise in the use of computational fluid dynamics (CFD) for medical applications. There has been an increase in the application of CFD to attempt to predict the rupture of intracranial aneurysms, however, while many hemodynamic parameters can be obtained from these computations, to date, no consistent methodology for the prediction of the rupture has been identified. One particular challenge to CFD is that many factors contribute to its accuracy; the mesh resolution and spatial/temporal discretization can alone contribute to a variation in accuracy. This failure to identify the importance of these factors and identify a methodology for the prediction of ruptures has limited the acceptance of CFD among physicians for rupture prediction. The International CFD Rupture Challenge 2013 seeks to comment on the sensitivity of these various CFD assumptions to predict the rupture by undertaking a comparison of the rupture and blood-flow predictions from a wide range of independent participants utilizing a range of CFD approaches. Twenty-six groups from 15 countries took part in the challenge. Participants were provided with surface models of two intracranial aneurysms and asked to carry out the corresponding hemodynamics simulations, free to choose their own mesh, solver, and temporal discretization. They were requested to submit velocity and pressure predictions along the centerline and on specified planes. The first phase of the challenge, described in a separate paper, was aimed at predicting which of the two aneurysms had previously ruptured and where the rupture site was located. The second phase, described in this paper, aims to assess the variability of the solutions and the sensitivity to the modeling assumptions. Participants were free to choose boundary conditions in the first phase, whereas they were prescribed in the second phase but all other CFD modeling parameters were not prescribed. In order to compare the computational results of one representative group with experimental results, steady-flow measurements using particle image velocimetry (PIV) were carried out in a silicone model of one of the provided aneurysms. Approximately 80% of the participating groups generated similar results. Both velocity and pressure computations were in good agreement with each other for cycle-averaged and peak-systolic predictions. Most apparent “outliers” (results that stand out of the collective) were observed to have underestimated velocity levels compared to the majority of solutions, but nevertheless identified comparable flow structures. In only two cases, the results deviate by over 35% from the mean solution of all the participants. Results of steady CFD simulations of the representative group and PIV experiments were in good agreement. The study demonstrated that while a range of numerical schemes, mesh resolution, and solvers was used, similar flow predictions were observed in the majority of cases. To further validate the computational results, it is suggested that time-dependent measurements should be conducted in the future. However, it is recognized that this study does not include the biological aspects of the aneurysm, which needs to be considered to be able to more precisely identify the specific rupture risk of an intracranial aneurysm.

Abstract Background: Based upon the association between pathologic response and disease free survival, the neoadjuvant setting is increasingly being used for drug development. NGS has identified unique and recurrent genetic alterations in breast cancer (BC) that are potentially targetable; however, the clinical implications are mostly unknown. We developed a prospective neoadjuvant study (BEAUTY) in high risk BC patients (pts) using weekly T followed by anthracycline-based chemo wherein percutaneous tumor biopsies (PTB) are obtained before/during/after chemo for NGS and PDX. Our goal is to identify novel biomarkers/pathways and develop PDX to test new therapeutic approaches. Methods: Pts underwent PTB at baseline and after 12 wks of T. Response to T was defined based upon 12 week Ki-67: responder (&lt;15%) vs non-responder (≥15%). Pts with histologic response and absence of invasive BC at 12 wks were classified as responders. NGS was performed using PTB/blood DNA (exome) and PTB (RNA seq). MRI response was classified using RECIST criteria. NGS data were used to identify somatic copy number variants (cnvs) and expressed single nucleotide variants (eSNVs). Non-SCID mice (estrogen supplemented) were implanted ≤ 30 minutes with PTB samples. Results: Of the first 78 pts, 44 have completed T. Here we focus on 18 pts with either triple negative or luminal B BC. Clinical characteristics according to Ki-67 response are shown in Table 1. Comparison of genomic alterations in BEAUTY pts with TCGA identified a greater overlap with copy number gains (73%) compared to deletions (40%), along with similar observations of mutations in TP53, PTEN, RYR2, and AKT1 genes. Association analysis of CNVs and eSNVs between responders/non-responders identified 33 genes (predominantly located in chromosomes 1, 8, 13) and 580 eSNVs (corresponding to 497 genes) with a p &lt; 0.05. Differential gene expression (DGE) analysis of responders/non-responders identified 198 genes with a p-value &lt; 0.05. Integrated analysis of 539 genes (CNVs, eSNVs and DGE) identified pathways such as TGF-beta, Jak-Stat, WNT and NOTCH signalling. PDX take rate was 44% [triple negative (6/10); Luminal B (2/8)]. PDX growth rate was significantly associated with clinical baseline Ki-67 (p = 0.00014). Conclusion: This is the first prospective study to demonstrate the feasibility of using PTB to obtain both NGS data and PDX in the neoadjuvant setting. PDX take rate is associated with BC subtype and baseline Ki-67. Studies are ongoing to 1) validate genes/pathways associated with treatment response in subsequent BEAUTY pts; 2) genomically characterize and assess PDX in vivo response to T and 3) Use NGS data to prioritize new drugs/drug combinations in PDX. Funded by Mayo Clinic Center for Individualized Medicine and MC Cancer Center. Clinical CharacteristicsOverallResponders: 12 week Ki-67 &lt; 15% (n = 9)Non-Responders: 12 week Ki-67 ≥ 15% (n = 9)Median Age495345T stage T2/T314 (78%)7 (78%)7 (78%)Node Positive8 (44%)4 (44%)4 (44%)Triple negative10 (56%)6 (67%)4 (44%)Luminal B8 (44%)3 (33%)5 (56%)Ki-67 after 12 Weeks of T Median 5% (0-11%)Median 35% (17-60%)Complete/Partial MRI Response after T 6 (67%)2 (22%) Citation Information: Cancer Res 2013;73(24 Suppl): Abstract nr P1-08-10.

Stimulated by a recent controversy regarding pressure drops predicted in a giant aneurysm with a proximal stenosis, the present study sought to assess variability in the prediction of pressures and flow by a wide variety of research groups. In phase I, lumen geometry, flow rates, and fluid properties were specified, leaving each research group to choose their solver, discretization, and solution strategies. Variability was assessed by having each group interpolate their results onto a standardized mesh and centerline. For phase II, a physical model of the geometry was constructed, from which pressure and flow rates were measured. Groups repeated their simulations using a geometry reconstructed from a micro-computed tomography (CT) scan of the physical model with the measured flow rates and fluid properties. Phase I results from 25 groups demonstrated remarkable consistency in the pressure patterns, with the majority predicting peak systolic pressure drops within 8% of each other. Aneurysm sac flow patterns were more variable with only a few groups reporting peak systolic flow instabilities owing to their use of high temporal resolutions. Variability for phase II was comparable, and the median predicted pressure drops were within a few millimeters of mercury of the measured values but only after accounting for submillimeter errors in the reconstruction of the life-sized flow model from micro-CT. In summary, pressure can be predicted with consistency by CFD across a wide range of solvers and solution strategies, but this may not hold true for specific flow patterns or derived quantities. Future challenges are needed and should focus on hemodynamic quantities thought to be of clinical interest.

Abstract Catechol O-methyltransferase (COMT)-catalyzed methylation of catecholestrogens has been proposed to play a protective role in estrogen-induced genotoxic carcinogenesis. We have taken a comprehensive approach to test the hypothesis that genetic variation in COMT might influence breast cancer risk. Fifteen COMT single nucleotide polymorphisms (SNPs) selected on the basis of in-depth resequencing of the COMT gene were genotyped in 1,482 DNA samples from a Mayo Clinic breast cancer case control study. Two common SNPs in the distal promoter for membrane-bound (MB) COMT, rs2020917 and rs737865, were associated with breast cancer risk reduction in premenopausal women in the Mayo Clinic study, with allele-specific odds ratios (OR) of 0.70 [95% confidence interval (CI), 0.52–0.95] and 0.68 (95% CI, 0.51–0.92), respectively. These two SNPs were then subjected to functional genomic analysis and were genotyped in an additional 3,683 DNA samples from two independent case control studies (GENICA and GESBC). Functional genomic experiments showed that these SNPs could up-regulate transcription and that they altered DNA-protein binding patterns. Furthermore, substrate kinetic and exon array analyses suggested a role for MB-COMT in catecholestrogen inactivation. The GENICA results were similar to the Mayo case control observations, with ORs of 0.85 (95% CI, 0.72–1.00) and 0.85 (95% CI, 0.72–1.01) for the two SNPs. No significant effect was observed in the GESBC study. These studies showed that two SNPs in the COMT distal promoter were associated with breast cancer risk reduction in two of three case control studies, compatible with the results of functional genomic experiments, suggesting a role for MB-COMT in breast cancer risk. [Cancer Res 2008;68(14):5997–6005]