Citations

Abstract Introduction: There have been substantial advances in treatment options for multiple myeloma (MM) over the past 15 years, yet little real-world evidence is available to describe the recent trends in MM treatment costs and outcomes. This study aimed to examine the trends in novel therapy use, total healthcare costs, and survival outcomes among newly diagnosed MM patients in the US since 2000. Methods: Patients aged ≥18 years who had ≥ 2 medical claims with MM (ICD-9 diagnosis code 203.0x) at least 30 days apart between January 2000 and September 2015 were identified in the Truven Health MarketScan Research Databases. Date of the first medical claim for MM was designated as index date. Patients were required to have 12-month pre-index and 3-month post-index continuous enrollment, and those with claims for MM treatment (including autologous stem-cell transplantation) in the pre-index period were excluded. Controls were selected from a pool of patients without MM and matched 1:1 with MM patients on index year, age, gender and geographic region. Patients whose death event could be obtained from the Social Security Administrations Master Death File were included in the survival outcomes analysis. Total healthcare costs were defined as sum of health-plan and patient paid costs for prescriptions and medical services, including inpatient admissions, emergency room, and physician office visits, and other outpatient services. MM treatment-related drug costs included outpatient pharmacy prescription costs and costs from outpatient services for infused products. Survival time was measured as time from index date to the date of death. Results: 19,417 newly diagnosed MM patients were included (mean± SD age at diagnosis 65.9±12.3 years, 44.9% female), of which 18,260 patients were matched to controls. The percentage of MM patients using novel therapies (pomalidomide, carfilzomib, bortezomib, lenalidomide, or thalidomide) continuously increased from 8.7% in 2000 to 61.3% in 2014. Overall, 28% of MM patients did not receive any treatment within 1-year after diagnosis during the study period. Total per-patient-per-month (PPPM) all-cause healthcare costs increased from $3,263 in 2000 to $14,656 in 2014 among newly diagnosed MM patients, which were primarily driven by costs of outpatient services (Figure 1). Hospitalization costs accounted for 21.5% of total costs in 2000, which increased to 32.7% in 2014. MM treatment-related drug costs accounted for 10.6% of total costs in 2000, 23.6% in 2009, and 28.5% in 2014. Compared with MM patients diagnosed earlier, those diagnosed after 2010 had higher rates of novel therapy use and significantly better survival outcomes (Figure 2); patients diagnosed in 2012 were 1.25 times more likely to survive two years than patients diagnosed in 2006 while two-year survival among controls was fairly stable over the study period (94-97%). MM patients also showed improved survival outcomes over the study period, with the two-year survival gap between MM patients and their matched controls decreasing at a rate of 3% per year. Conclusions: Findings from this study corroborate clinical data suggesting a paradigm shift in MM treatment over the past 15 years that is associated with substantial survival gains. Total healthcare costs among newly diagnosed MM patients have increased steadily since 2000 due to increases in all healthcare cost categories. However, the relative contribution of drug costs has remained fairly stable since 2009 despite new novel therapies coming to market in this time period. Future studies should focus on the impact of specific novel agents on patient survival, the value of clinical benefit engendered, and overall healthcare costs to better inform decision making in this setting. Disclosures Fonseca: Celgene, BMS, Bayer, Novartis, Sanofi, Janssen, Millennium a Takeda Company and AMGEN: Consultancy; Applied Bioscience: Consultancy; FISH: Patents & Royalties: Dr Fonseca has received a patent for the prognostication of MM based on genetic categorization by FISH of the disease. Abouzaid:Celgene Corporation: Employment, Equity Ownership, Research Funding. Bonafede:Truven Health: Other: I am an employee of Truven Health, which received a research contract to conduct this study with Seattle Genetics. . Cai:Truven Health Analytics: Other: I am an employee of Truven Health, which received a research contract to conduct this study with Seattle Genetics. , Research Funding. Parikh:Celgene Corporation: Employment, Equity Ownership, Research Funding. Cosler:Celgene: Consultancy. Richardson:Gentium S.p.A., Jazz Pharmaceuticals: Research Funding; Celgene, Novartis, Millennium Takeda, Gentium S.p.A., Jazz Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees.

CD19-targeted chimeric antigen receptor (CAR)-T cell therapy is a novel treatment with promising results for patients with relapsed/refractory lymphoid malignancies. CAR-T cell therapy has known early toxicities of cytokine release syndrome (CRS) and neurotoxicity, but little is known about long-term neuropsychiatric adverse effects. We have utilized patient-reported outcomes (PROs), including PROMIS®measures, to assess outcomes of patients with relapse/refractory chronic lymphocytic leukemia (CLL), non-Hodgkin lymphoma (NHL), and acute lymphoblastic leukemia (ALL) who were treated with CD19-targeted CAR-T cells on a clinical trial in our institution (NCT01865617) and survived at least one year after treatment. Between October 2018 to February 2019, 52 patients (at their 1-5 year anniversary after CAR-T cell therapy) were sent a questionnaire. The questionnaire included the PROMIS Scale v1.2-Global Health and the PROMIS-29 Profile v2.1, as well as 30 additional questions, including questions pertaining to cognitive function. As of February 28, 2019, 40 questionnaires were returned (76.9% response rate) and were included in the analysis. Patients' characteristics are summarized in Table 1. Cognitive function was assessed by asking if patients had experienced difficulties with concentration, finding words, memory, or solving problems since their CAR-T cell therapy; answer "yes" to each of the questions received "1" point to determine the total cognitive difficulty score (0-4). PROMIS measures are standardized to a T-score metric, with a score of 50 representing the general US population mean. Clinically meaningful differences were defined as a 5-point difference in scores (1/2 standard deviation). The cohort's self-reported cognitive difficulties and PROMIS mean T scores are shown in Table 2. Mean T scores of PROMIS domains of Global Mental health, Global Physical Health, Social Function, anxiety, depression, fatigue, pain and sleep disturbance were not clinically meaningfully different from the mean in the general US population. However, 19 participants (47.5%) reported at least one cognitive difficulty and/or clinically meaningful depression and/or anxiety (Figure 1), and 7 participants (17.5%) scored ≤ 40 in Global Mental Health, indicating at least one standard deviation worse than the general population mean. On risk factor analysis, younger age was found to be associated with worse Global Mental Health (p=0.02), anxiety (p=0.01) and depression (p=0.01). Anxiety prior to CAR-T cell therapy was associated with increased likelihood of anxiety after CAR-T cell therapy (p=0.001). Multivariate analysis confirmed association between age and PROMIS Global Mental Health score (p=0.03). 15 participants (37.5%) reported cognitive difficulties post CAR-T cell therapy. On multivariate analysis, depression prior to CAR-T cell therapy was statistically significantly associated with higher likelihood of self-reported cognitive difficulties after CAR-T therapy (p=0.02) and there was a trend for association between acute neurotoxicity after CAR-T cell infusion and self-reported long-term cognitive difficulties (p=0.08). Having more cognitive difficulties was associated with worse Global Mental Health (p=0.0001) and worse Global Physical Health (p= 0.01). Similarly, worse scores for pain interference, sleep disturbance, fatigue, depression, anxiety, physical function, and social function were associated with more long-term self-reported cognitive difficulties (p=0.007,p=0.0003, p=0.00006, p=0.01, p=0.0007, p=0.003, p=0.0004 respectively). Our study demonstrates overall good neuropsychiatric outcomes in 40 long-term survivors after CAR-T cell therapy. However, despite good overall mean scores, nearly 50% of patients in the cohort reported at least one negative neuropsychiatric outcome (anxiety, depression or cognitive difficulty), and almost 20% scored at least one standard deviation lower than the general US population mean in Global Mental Health, indicating that there is a significant number of patients who would likely benefit from mental health services following CAR-T cell therapy. Younger age, anxiety and depression pre-CAR-T cell therapy, and acute neurotoxicity after CAR-T cell infusion may be risk factors for long-term neuropsychiatric problems in this patient population. Larger studies are needed to confirm these findings. Disclosures Shaw: Therakos: Other: Speaker Engagement. Lee:AstraZeneca: Research Funding; Incyte: Research Funding; Syndax: Research Funding; Amgen: Research Funding; Novartis: Research Funding; Takeda: Research Funding; Kadmon: Consultancy, Research Funding; Pfizer: Consultancy, Research Funding. Turtle:T-CURX: Membership on an entity's Board of Directors or advisory committees; Kite/Gilead: Other: Ad hoc advisory board member; Caribou Biosciences: Equity Ownership, Membership on an entity's Board of Directors or advisory committees; Allogene: Other: Ad hoc advisory board member; Humanigen: Other: Ad hoc advisory board member; Juno Therapeutics: Patents & Royalties: Co-inventor with staff from Juno Therapeutics; pending, Research Funding; Nektar Therapeutics: Other: Ad hoc advisory board member, Research Funding; Precision Biosciences: Equity Ownership, Membership on an entity's Board of Directors or advisory committees; Eureka Therapeutics: Equity Ownership, Membership on an entity's Board of Directors or advisory committees; Novartis: Other: Ad hoc advisory board member. Maloney:Juno Therapeutics: Honoraria, Patents & Royalties: patients pending , Research Funding; A2 Biotherapeutics: Honoraria, Other: Stock options ; Celgene,Kite Pharma: Honoraria, Research Funding; BioLine RX, Gilead,Genentech,Novartis: Honoraria.

Introduction: Emerging evidence supports that cytogenetic abnormalities (CAs) drive myelomagenesis and heterogeneity (e.g., clinical presentation, response to therapy, and prognosis) of multiple myeloma (MM). Diversity of CAs, including primary (IgH translocations and trisomies) and secondary CAs (copy number abnormalities) argues that MM is not a "single disease". Thus, identification of CAs at diagnosis is essential for risk stratification, guiding treatment, and prognostic estimation in daily practice. However, although frequency, configuration, and significance of CAs have been well documented in the Western countries, this information is lacking in the Asian population. To this end, a multi-center retrospective analysis was carried out to examine epidemiology and prognostic significance of CAs alone or in combination in a cohort of Chinese patients with newly-diagnosed MM (NDMM). Materials and Methods: A total of 1015 NDMM patients who had the baseline information of CAs detected by FISH at four institutes nationwide were included. According to the IMWG consensus updated in 2016, 1q gain, del(17p), t[4;14], and t[14;16] were defined as high-risk CAs (HRCAs), while another HRCA t[14;20] was not tested routinely in a majority of these patients. In addition, del(1p) and del(13q14) were considered as an adverse CA. According to the mSMART3.0 proposed by Mayo Clinic in 2018, double-hit (DHMM) and triple-hit MM (THMM) were defined as co-occurrence of 2 or >= 3 HRCAs, respectively. The Kaplan-Meier approach was used to estimate progression-free survival (PFS) and overall survival (OS). Results: In this cohort, the median age of 1015 patients was 61 years; 63.5% were male. The type of IgG, light chain, IgA, IgD, non/oligosecretory, IgM, or IgE accounted for 42.9%, 26.2%, 24.5%, 3.4%, 2.5%, 0.4%, and 0.1%, respectively. Del(13q) (46.4%) and 1q gain (46.1%; 3 copies = 73.8%, >= 4 copies = 26.2%) represented the most common CAs, followed by t(4;14) (14.0%), t(11;14) (11.8%), del(1p) (11.5%), del(17p) (9.9%; 20-50% cells = 35.6%, > 50% cells = 64.4%), and t(14;16) (5.1%). While none of these CAs was detected in 23.8% of cases, the frequency of patients who carried 1 - 5 CAs was 31.9%, 28.0%, 13.4%, 2.0%, and 0.9%, respectively. In the 1q+ cases, 36.4% patients carried second CA(s), including del(13q) (61.1%), t(4;14) (20.3%), del(1p) (14.8%), del(17p) (10.7%), t(11;14) (10.4%), and t(14;16) (8.1%). In the del(17p) cases, 57.5% patients had additional CA(s), including del(13q) (75.2%), 1q gain (49.5%), del(1p) (21.6%), t(4;14) (19.6%), t(14;16) (8.7%), and t(11;14) (2.2%). In the cases bearing IgH translocations, 26.0% patients also carried other CA(s), including del(13q) and 1q gain (61.9% for each), and del(17p) (9.3%). In the cases harboring HRCAs, the percentage of patients who carried 1 - 4 HRCAs was 70.2%, 24.8%, 3.7%, and 1.1%, respectively. Overall, 14.3% and 2.9% patients had DHMM or THMM, of whom 65.0%, 18.0%, 12.0%, and 5.0% had 2 - 5 HRCAs, respectively. While there was no significant difference in PFS between the cases carrying 1 and 2 CAs (P = 0.209), the patients who had 3 or more CAs displayed a sharp reduction in median PFS (P = 0.022 and P = 0.003 for 3 vs 1 or 2 CAs). Although multiple CAs was associated with shorter median OS, no statistical significance was observed for each comparison (P > 0.05). However, patients who carried >= 2 HRCAs had significantly shorter median PFS (12.1 months; P = 0.0004) and OS (29.3 months; P = 0.027) than those who had one single HRCA (32.2 and 65.6 months for median PFS and OS). Conclusion: In comparison with the Western countries, the incidence of secondary HRCAs (e.g., 1q gain and del(17p)) is relatively higher in Chinese patients at diagnosis, while the standard-risk CA such as t(11;14) is clearly less frequent. The proportion of Chinese patients who carry multiple CAs (up to 5) or HRCAs appear to be greater as well. In this context, patients carrying two or more HRCAs, so called DHMM or THMM, exhibit significantly worse outcome than those carrying only one HRCA. Together, this study builds up an up-to-date profile of CAs for Chinese patients, which might lay a foundation for revising the criteria for risk stratification and the guideline for treatment that is more feasible and practicable in China. It also provides the information about frequency and configuration of MM-driven CAs, which might be more relevant to the Asian population. Disclosures Kumar: Takeda: Research Funding; Celgene: Consultancy, Research Funding; Janssen: Consultancy, Research Funding.

Introduction: Most patients with newly diagnosed FL treated with rituximab (R) alone or R + chemotherapy will experience prolonged progression-free survival and overall survival (OS), but it remains unclear what factors have the greatest influence on FL-associated and other causes of death in this patient population. We utilized individual patient data from 13 first-line randomized clinical trials from the FLASH database to perform a comprehensive multistate survival analysis to examine and quantify the relationships between clinical characteristics, treatment response, and early, intermediate, and late FL outcomes. Methods: The multistate survival analysis model defined states for "Alive after beginning Induction Treatment (TX)", "Alive after beginning Maintenance TX", "Death due to FL", and "Death from Other Causes" (Figure 1). We used the Aalen-Johansen estimator, a generalization of the Kaplan-Meier estimator, to calculate the likelihood of being in each model state and estimate the course of FL over time. Making no assumptions on the probability distributions and capable of coping with censored observations, the Aalen-Johansen estimator is a convenient and reliable nonparametric estimator for clinical data. Results: Among 7,465 FL patients with median age 56 (range 18-90) years, 49.2% were female; 28.7% Stage I-III, 71.3% Stage IV, and FLIPI was 0-1 (20.0%), 2 (36.8%), ≥ 3 (43.2%). Following initiation of induction treatment, 2-, 5- and 10-year death rates were 1.7%, 3.8%, and 5.8% due to FL, and 0.7%, 2.1%, and 4.8% from other causes (Figure 2). Death rates at 2, 5, and 10 years due to FL and other causes for subgroups based on clinical characteristics and treatment response are shown in Table 1. Notably, patients > 70 years and patients with FLIPI ≥ 3 had worse outcomes and patients achieving CR at 18, 24, and 30 months experienced improved outcomes. Conclusion: This is the largest study using data from randomized trials to quantify the impact of clinical factors on early, intermediate and late mortality by cause of death. We demonstrated that age > 70 years and FLIPI ≥ 3 were linked to increased FL-associated death and response to TX distinguished patients with favorable and poor outcomes. Future analyses should quantify the impact of predictors on the rate/time of FL outcomes in multivariable models. Disclosures Salles: Roche, Janssen, Gilead, Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Educational events; Amgen: Honoraria, Other: Educational events; BMS: Honoraria; Merck: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Novartis, Servier, AbbVie, Karyopharm, Kite, MorphoSys: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Educational events; Epizyme: Consultancy, Honoraria; Autolus: Consultancy, Membership on an entity's Board of Directors or advisory committees; Takeda: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Educational events. Hoster:Janssen: Research Funding; Roche Pharma AG: Other: Travel Support. Hiddemann:Bayer: Research Funding; Gilead: Consultancy, Honoraria; Vector Therapeutics: Consultancy, Honoraria; Janssen: Consultancy, Honoraria, Research Funding; Roche: Consultancy, Honoraria, Research Funding; Celgene: Consultancy, Honoraria. Herold:Roche: Honoraria, Research Funding; Janssen: Consultancy, Honoraria; Gilead: Honoraria; Celgene: Honoraria. Morschhauser:Servier: Consultancy; Gilead: Consultancy; Janssen: Honoraria; Roche/Genentech: Consultancy; BMS: Honoraria; Celgene: Honoraria. Rummel:Roche Pharma AG: Honoraria, Research Funding; Celgene: Honoraria; Janssen: Honoraria; Sandoz: Honoraria. Kimby:AbbVie,: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees; Roche: Membership on an entity's Board of Directors or advisory committees; Jansen: Membership on an entity's Board of Directors or advisory committees; Gilead: Other: educational lectures. Vitolo:Abbvie: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Kite: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; 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; Gilead: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Juno Therapeutics: Membership on an entity's Board of Directors or advisory committees; F. Hoffmann-La Roche: Speakers Bureau; Novartis: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Gyan:Pfizer: Honoraria. Ladetto:Celgene: Honoraria; Roche: Honoraria; Janssen: Honoraria; Abbvie: Honoraria; Acerta: Honoraria; Sandoz: Honoraria. Nielsen:F. Hoffmann-La Roche Ltd: Employment, Equity Ownership. Flowers:AstraZeneca: Consultancy; BeiGene: Consultancy, Research Funding; Eastern Cooperative Oncology Group: Research Funding; Burroughs Wellcome Fund: Research Funding; AbbVie: Consultancy, Research Funding; Optimum Rx: Consultancy; V Foundation: Research Funding; Pharmacyclics/Janssen: Consultancy, Research Funding; Bayer: Consultancy; Gilead: Consultancy, Research Funding; TG Therapeutics: Research Funding; Denovo Biopharma: Consultancy; Acerta: Research Funding; National Cancer Institute: Research Funding; Millenium/Takeda: Research Funding; Genentech, Inc./F. Hoffmann-La Roche Ltd: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; Spectrum: Consultancy;

Testing asymptomatic individuals for unsuspected conditions is not new to the medical and public health communities and protocols to develop screening tests are well-established. However, the application of screening principles to inherited diseases presents unique challenges. Unlike most screening tests, the natural history and disease prevalence of most rare inherited diseases in an unselected population are unknown. It is difficult or impossible to obtain a “truth set” cohort for clinical validation studies. As a result, it is not possible to accurately calculate clinical positive and negative predictive values for “likely pathogenic” genetic variants, which are commonly returned in genetic screening assays. In addition, many of the genetic conditions included in screening panels do not have clinical confirmatory tests. All of these elements are typically required to justify the development of a screening test, according to the World Health Organization screening principles. Nevertheless, as the cost of DNA sequencing continues to fall, more individuals are opting to undergo genomic testing in the absence of a clinical indication. Despite the challenges, reasonable estimates can be deduced and used to inform test design strategies. Here, we review test design principles and apply them to genetic screening.

Testing asymptomatic individuals for unsuspected conditions is not new to the medical and public health communities and protocols to develop screening tests are well-established. However, the application of screening principles to inherited diseases presents unique challenges. Unlike most screening tests, the natural history and disease prevalence of most rare inherited diseases in an unselected population are unknown. It is difficult or impossible to obtain a “truth set” cohort for clinical validation studies. As a result, it is not possible to accurately calculate clinical positive and negative predictive values for “likely pathogenic” genetic variants, which are commonly returned in genetic screening assays. In addition, many of the genetic conditions included in screening panels do not have clinical confirmatory tests. All of these elements are typically required to justify the development of a screening test, according to the World Health Organization screening principles. Nevertheless, as the cost of DNA sequencing continues to fall, more individuals are opting to undergo genomic testing in the absence of a clinical indication. Despite the challenges, reasonable estimates can be deduced and used to inform test design strategies. Here, we review test design principles and apply them to genetic screening.

Testing asymptomatic individuals for unsuspected conditions is not new to the medical and public health communities and protocols to develop screening tests are well-established. However, the application of screening principles to inherited diseases presents unique challenges. Unlike most screening tests, the natural history and disease prevalence of most rare inherited diseases in an unselected population are unknown. It is difficult or impossible to obtain a “truth set” cohort for clinical validation studies. As a result, it is not possible to accurately calculate clinical positive and negative predictive values for “likely pathogenic” genetic variants, which are commonly returned in genetic screening assays. In addition, many of the genetic conditions included in screening panels do not have clinical confirmatory tests. All of these elements are typically required to justify the development of a screening test, according to the World Health Organization screening principles. Nevertheless, as the cost of DNA sequencing continues to fall, more individuals are opting to undergo genomic testing in the absence of a clinical indication. Despite the challenges, reasonable estimates can be deduced and used to inform test design strategies. Here, we review test design principles and apply them to genetic screening.

Abstract Background Myelofibrosis (MF) is a myeloproliferative neoplasm (MPN) characterised by the frequent presence of driver mutations in genes causing activation of JAK2 signalling pathways (JAK2, CALR and MPL). Additional mutations affecting epigenetic regulators and splicing machinery are common. Anaemia with RBC-transfusion-dependence is common in patients with advanced myelofibrosis and represents a major unmet need. The RESUME study assessed the rates of RBC-transfusion independence (TI) after therapy with Pomalidomide (POM) vs placebo in persons with MPN-associated myelofibrosis and RBC-transfusion dependence. 16% of patients in both the POM and placebo arms became TI. Aims The genetic landscape of strictly confirmed transfusion dependent MF is not fully characterised. Our aim was to analyse the genetics of transfusion-dependent myelofibrosis patients in the RESUME trial and correlate with clinical characteristics, outcome and therapy response. Methods DNA samples were available from 207 of 252 patients and analysed by targeted re-sequencing using a Fluidigm Access Array gene panel followed by next generation sequencing. The panel included JAK2, CALR, MPL, TET2, ASXL1, EZH2, DNMT3A, IDH1/2, CBL, IKZF1, U2AF1, CHEK2, TP53, SF3B1, SRSF2, SH2B3, BARD1, DAP3, HRAS, IRF4, KRAS, KIT, Mir662, NFE2, POLG, SCRIB, SETBP1, TCF12 and VPS45. Results 97% (95-99%) of subjects had a mutation in ≥1 targeted gene. 2 mutations were detected in 41% (34-48%) and ≥3 in 27% (21-33%). 7 had no detectable mutation. Mutations in JAK2V617F, CALR and MPL were identified in 66% (59-72%), 14% (8-19%) and 7% (4-11%) of subjects (Figure 1), with no driver mutation in 27 patients (13%; 9-18%) (triple-negative). 68% (61-74%) had additional non-driver mutations. 42% (35-48%) (N=86) had spliceosome mutations (U2AF1 [21%]; SF3B1 [11%]; SRSF2 [8%]; ZRSR2; [6%]). More spliceosome mutations were detected in men than women (47% [39-55%] vs 27% [15-40%]; p=0.009). Spliceosome mutations were mutually exclusive in 83 subjects and were less common in subjects with prior polycythaemia vera (17% [5-37%]) compared with prior essential thrombocythaemia (39% [22-58%]) and primary MF (46% [38-54%]; p=0.024). Mutations in epigenetic regulators (ASXL1, 28%; TET2, 8%; DNMT3A 5%; EZH2 4%) were detected at similar rates to those previously reported. High molecular risk (HMR) mutations (ASXL1, EZH2, IDH1/2, SRSF2) were detected in 36% [29-43%] of subjects. Only 10 of 105 subjects with an epigenetic regulator gene mutation had ≥1 related mutation. Subjects with JAK2V617F were significantly more likely than subjects with a CALR mutation to have: (1) ≥1 additional mutation (72% [64-79%] vs. 35% [18-54%], p=0.0001); (2) a spliceosome mutation (44% [36-53%] vs. 17% [6-36%], p=0.07), in particular a U2AF1 mutation (24% [17-32%] vs. 0%; p=0.004) and (3) a HMR mutation (38% [30-47%] vs. 21% [8-40%]; p=0.07). Survival at 1.5 years was 62% (55-67%) and was not significantly associated with the presence or number of mutations in this uniformly high-risk cohort. Survival in subjects without an SF3B1 mutation was better than those SF3B1-mutated (80% [56-91%]) vs. 59% [52-65%]; p=0.07). Driver mutation status did not influence the probability of achieving red blood cell (RBC) TI, regardless of therapy. Additional non-driver mutations were more often detected in those failing to achieve RBC-TI than those achieving RBC-TI (70% [63-77%] vs 56% [40-71%], p=0.07). Furthermore, those with additional non-driver mutations were less likely to achieve ≥50% reduction in RBC transfusions (24% [17-32%] vs. 39% [27-51%]; p=0.03). A significant correlation persisted in subjects receiving POM but not in those receiving placebo. There was also a significant correlation between U2AF1 mutations and RBC-TI in POM treated subjects compared with controls; U2AF1-mutated subjects were less likely to achieve RBC-TI (3% [1, 17%]) than U2AF1-unmutated subjects (25% [17, 34%], p=0.008). No other mutations were significantly correlated with response. Conclusion We found a high incidence of spliceosome mutations in persons with MPN-associated MF and RBC-transfusion-dependence. Mutation of U2AF1 correlated with response in subjects receiving POM but not in those receiving placebo. Incorporation of mutation profiling into clinical trial design may help to inform subgroups of patients that may benefit from the intervention. Disclosures Devos: Novartis: Consultancy; Celgene: Consultancy; Takeda: Consultancy. Gisslinger:Shire: Consultancy, Honoraria; Novartis: Honoraria, Research Funding; AOP Orphan Pharmaceuticals AG: Consultancy, Honoraria, Research Funding; Janssen Cilag: Consultancy, Honoraria. Kiladjian:Celgene: Membership on an entity's Board of Directors or advisory committees; AOP Orphan: Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Membership on an entity's Board of Directors or advisory committees, Research Funding. Mesa:Celgene: Research Funding; UT Health San Antonio - Mays Cancer Center: Employment; Pfizer: Research Funding; Gilead: Research Funding; NS Pharma: Research Funding; CTI Biopharma: Research Funding; Incyte Corporation: Research Funding; Novartis: Consultancy; Promedior: Research Funding; Genentech: Research Funding. Ribrag:argenX: Research Funding; BMS: Consultancy, Honoraria, Other: travel; NanoString Technologies: Consultancy, Honoraria; pharmamar: Other: travel; Infinity: Consultancy, Honoraria; MSD: Honoraria; Amgen: Research Funding; Gilead: Consultancy, Honoraria; Servier: Consultancy, Honoraria; Roche: Honoraria, Other: travel; epizyme: Consultancy, Honoraria; Incyte Corporation: Consultancy. Schiller:Celator/Jazz Pharmaceuticals: Research Funding; Pharmacyclics: Research Funding. Vannucchi:Celgene: Membership on an entity's Board of Directors or advisory committees; ITALFARMACO: Membership on an entity's Board of Directors or advisory committees; Novartis: Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau. Reiser:Celgene: Employment. Zhong:Celgene: Employment. Mead:Novartis: Consultancy, Honoraria, Research Funding, Speakers Bureau; ARIAD: Consultancy; Bristol-Myers Squibb: Consultancy; Cell Therapeutics: Consultancy; Celgene: Research Funding; Elstar: Research Funding; Evotek: Research Funding.

Abstract Background: Patients (pts) with DLBCL who receive first-line anthracycline based immunochemotherapy and remain in remission at 2 years have excellent outcomes with overall survival (OS) in one study that was comparable to the age and sex matched general population in cohorts from the US and France (Maurer et al, JCO 2014). To evaluate whether these findings are robust and generalizable, we assessed OS stratified by progression-free survival at 24 months (PFS24) using individual patient data from pts with DLBCL enrolled in 14 multi-center, international randomized clinical trials as part of the SEAL consortium. Methods: Central data alignment was performed by the SEAL statistics and data center. All enrolled patients treated with rituximab-containing anthracycline-based immunochemotherapy as part of initial induction therapy were included in this analysis using an intent-to-treat approach. PFS was defined as time from initiation of first-line therapy to progression or death. PFS24 was defined as being alive and progression-free 24 months (731 days) after initiation of therapy. Overall survival from PFS24 was defined as time from achieving PFS24 until death due to any cause. OS was compared to each patient's age-, sex-, and country- matched general population using expected survival and standardized mortality ratios (SMR). Country-level population rate tables were obtained from www.mortality.org where available. A per-study average rate table was used when country information was unavailable. OS beyond 9 years from treatment initiation was only available the time of analysis from two trials (13% of cohort) and thus follow-up from PFS24 was restricted to the first 7 years after PFS24 for this analysis. Results: Fourteen studies with 7975 patients were included in the SEAL database. 5853 (73%) received rituximab as part of induction therapy and used in this analysis. Median age was 62 years (range 18-92) with 56% >60 years, 54% were male, 57% had elevated LDH, 63% had stage III/IV disease, 13% had ECOG PS 2-4, and IPI was 0-1 in 36%, 2 in 25%, 3 in 23%, and 4-5 in 16%. At a median follow-up of 4.4 years, 1337 (23%) had disease progression, 1489 patients (25%) had died, and 5101 patients were evaluable for PFS24. 1423 (28%) of evaluable patients failed to achieve PFS24. These patients had poor outcomes after progression with median OS of 7.2 months (95% CI 6.8-8.1), 5-year OS of 19%, and SMR of 32.1 (95% CI: 30.0-34.4). Outcome was excellent (median unreached) in the 3678 patients who were progression-free at 24 months, with subsequent OS after achieving PFS24 approaching the age and sex matched general population (Figure). The observed vs. expected OS at 3, 5, and 7 years after achieving PFS24 was 93.1% vs. 94.4%, 87.6% vs. 89.5%, and 80.0% vs. 83.7%, respectively, the SMR was 1.22 (95% CI: 1.09-1.37). In subset analyses examining pts achieving PFS24, adverse prognostic characteristics at baseline (elevated LDH, advanced stage, ECOG PS 2-4, high IPI) were associated with greater decrement in OS from the general population based expected survival (Table). Conclusions: PFS24 from the start of initial therapy stratifies DLBCL pts into populations with distinct outcomes. DLBCL pts treated with rituximab containing anthracycline-based immunotherapy on large randomized clinical trials who are alive without progression at 24 months from start of initial therapy have excellent outcomes with survival that is marginally lower but clinically indistinguishable from the age-, sex-, and country-matched background population for at least 7 years after achieving PFS24. Further follow-up time will be needed to assess any long-term impact of late relapse and survivorship issues in this population. Table Table. Figure Figure. Disclosures Shi: Mayo Clinic: Employment. Cunningham:Amgen: Research Funding; Astra-Zeneca: Research Funding; Bayer: Research Funding; Celgene: Research Funding; Merrimack: Research Funding; Medimmune: Research Funding. Seymour:AbbVie: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel support, Research Funding, Speakers Bureau; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel support, Speakers Bureau; Genentech: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Gilead: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Janssen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Roche: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Takeda: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees. Jaeger:Roche: Honoraria, Research Funding; Celgene: Honoraria, Research Funding. Haioun:Sandoz: Honoraria, Membership on an entity's Board of Directors or advisory committees; Gilead: 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; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees; Roche: Honoraria, Membership on an entity's Board of Directors or advisory committees. Ghesquieres:Mundipharma: Consultancy; Roche France: Research Funding; Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees. Merli:Roche: 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; Teva Pharmaceuticals Industries: Membership on an entity's Board of Directors or advisory committees. Herbrecht:Servier: Membership on an entity's Board of Directors or advisory committees; Gilead: Membership on an entity's Board of Directors or advisory committees; Cell Therapeutics, Inc: Membership on an entity's Board of Directors or advisory committees. Flament:Celgene: Employment, Equity Ownership. Fu:Celgene: Employment, Equity Ownership. Flowers:Gilead: Consultancy, Research Funding; Roche: Consultancy, Research Funding; Acerta: Research Funding; ECOG: Research Funding; Mayo Clinic: Research Funding; Pharmacyclics, LLC, an AbbVie Company: Research Funding; Millenium/Takeda: Research Funding; Infinity: Research Funding; TG Therapeutics: Research Funding; Genentech: Consultancy, Research Funding; NIH: Research Funding; AbbVie: Research Funding. Ou:Mayo Clinic: Employment. Sargent:Celgene: Research Funding. Coiffier:MorphoSys: Consultancy; Novartis: Consultancy, Membership on an entity's Board of Directors or advisory committees; Gilead: Consultancy, Membership on an entity's Board of Directors or advisory committees; Roche: Consultancy; Celltrion: 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, Research Funding; Pfizer: Consultancy, Membership on an entity's Board of Directors or advisory committees.

Abstract Background OSis the gold standard primary efficacy endpoint for evaluating treatment strategies for DLBCL. Because current standard of care first-line regimens with rituximab plus chemotherapy produce median OS > 8 years, an endpoint assessed earlier would accelerate the evaluation of new therapies and potentially allow patients (pts) to more rapidly benefit from study results. The SEAL group formally evaluated PFS as a surrogate endpoint for OS in first-line therapy for DLBCL. Methods Through a systematic literature search and input from lymphoma experts on the SEAL committee, we identified multicenter, randomized controlled clinical trials that evaluated active treatment and enrolled at least 100 pts with previously untreated DLBCL or aggressive non Hodgkin lymphoma by WHO/REAL classification, published after January 1, 1995. Studies which enrolled only early stage (I or II) pts and studies that could not provide individual pt data (IPD) were excluded from surrogacy analysis. Potential surrogate candidates for OS included: 1) PFS, defined as the time from initiation of induction treatment to progressive disease, relapse or death due to any cause, and 2) PFS rate at 24 months after initiation of induction treatment (PFS24). These endpoints were derived consistently for all studies based on IPD. Surrogacy was evaluated by the correlation between surrogate candidates and OS. Pt-level correlation was measured by the rank correlation coefficient (ρ) and global odds ratio for PFS and PFS24, respectively, estimated by the Copula model. Trial-level correlation was evaluated by the correlation between trial-specific treatment effects on surrogate candidates and OS with two commonly used trial-level R2s: R2WLS (Sargent et al JCO 2005) and R2Copula (Burzykowski et al J R Stat Soc C 2001 and Burzykowski et al J R Stat Soc A 2004). For the R2 measures, a value closer to 1 indicates a stronger correlation. We pre-specified that if either trial-level R2 was >=0.80 and its 95% confidence interval (CI) excluded 0.60, the candidate would meet criteria for surrogacy. Results Thirteen of 14 studies collected to date in the SEAL database met all inclusion criteria. IPD for 7507 pts (median age 63 years, 54% male, 46% ECOG performance status 0, 8% IPI 0, 50% IPI 1-2, 64% stage III/IV, median follow-up time of 52 months) were analyzed. Among 13 studies, one tested maintenance treatment only and two tested both induction and maintenance regimens. One study included four treatment arms. Thus at the trial level, 17 two-arm comparisons were formed: 14 comparisons comparing induction regimens and 3 comparing maintenance; 15 comparisons involved rituximab. Longer PFS was highly correlated with longer OS at the pt level (ρ= 0.85, 95% CI 0.84-0.86), as was PFS24 (Global odds ratio = 61.1, 95% CI 52.6-69.6). The Table shows trial-level correlations, and the Figure shows trial-level association between estimated treatment effects (log(HR) [hazard ratio]) on PFS and OS. The trial-level correlation between PFS and OS was strong and met the pre-specified surrogacy criteria, with R2WLS of 0.83 (95% CI 0.58-0.94) and R2Copula of 0.85 (95% CI 0.72-0.99). Sensitivity analyses including only comparisons including rituximab or only induction regimens showed consistent results. The overall trial-level correlation between PFS24 and OS was reduced slightly, with R2WLS of 0.77 (95% CI 0.49-0.92) and R2Copula of 0.78 (95% CI 0.59-0.96). Performance of PFS24 was improved when restricting analysis to only including induction studies (see table). Conclusion This large IPD pooled-analysis demonstrates that PFS duration met the pre-specified criteria as a robust surrogate endpoint for OS, and can be considered as a surrogate endpoint for OS for frontline DLBCL trials evaluating immunochemotherapy. The earlier endpoint of PFS24 showed promise as a potential surrogate and further validation is warranted. Figure Figure. Disclosures Shi: Mayo Clinic: Employment. Flowers:Millenium/Takeda: Research Funding; TG Therapeutics: Research Funding; Pharmacyclics, LLC, an AbbVie Company: Research Funding; Gilead: Consultancy, Research Funding; NIH: Research Funding; Mayo Clinic: Research Funding; ECOG: Research Funding; Acerta: Research Funding; Infinity: Research Funding; AbbVie: Research Funding; Roche: Consultancy, Research Funding; Genentech: Consultancy, Research Funding. Ou:Mayo Clinic: Employment. Cunningham:Merrimack: Research Funding; Celgene: Research Funding; Bayer: Research Funding; Astra-Zeneca: Research Funding; Amgen: Research Funding; Medimmune: Research Funding. Pfreundschuh:Amgen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees; Roche: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Spectrum: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding. Seymour:AbbVie Inc.: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Genentech: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Roche: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Gilead: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Janssen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees. Jaeger:Roche: Honoraria, Research Funding; Celgene: Honoraria, Research Funding. Haioun:Celgene: 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; Gilead: Honoraria, Membership on an entity's Board of Directors or advisory committees; Sandoz: Honoraria, Membership on an entity's Board of Directors or advisory committees; Roche: Honoraria, Membership on an entity's Board of Directors or advisory committees. Ghesquieres:Mundipharma: Consultancy; Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees; Roche France: Research Funding. Merli:Teva Pharmaceuticals Industries: Membership on an entity's Board of Directors or advisory committees; Roche: 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. Herbrecht:Cell Therapeutics, Inc: Membership on an entity's Board of Directors or advisory committees; Gilead: Membership on an entity's Board of Directors or advisory committees; Servier: Membership on an entity's Board of Directors or advisory committees. Flament:Celgene: Employment, Equity Ownership. Fu:Celgene: Employment, Equity Ownership. Coiffier:MorphoSys: Consultancy, Membership on an entity's Board of Directors or advisory committees; Gilead: Consultancy, Membership on an entity's Board of Directors or advisory committees; Pfizer: Consultancy, Membership on an entity's Board of Directors or advisory committees; Celltrion: 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, Research Funding; Novartis: Consultancy, Membership on an entity's Board of Directors or advisory committees. Sargent:Celgene: Research Funding.