Go, Ronald S.

Abstract Background: Large deletions involving the beta globin complex are relatively rare. They can be categorized generally into five groups by deletion size and/or location: 1) beta zero thalassemia (BZT); 2) delta beta thalassemia (DBT); 3) hereditary persistence of fetal hemoglobin (HPFH); 4) gamma delta beta thalassemia (GDBT); and 5) epsilon gamma delta beta thalassemia (EGDBT). These deletions are not well understood but often have significant clinical impact, either when present alone or in combination with other hemoglobin mutations. In this study, we analyze phenotypic and molecular data on a large number of cases with deletions in the beta globin gene complex to better classify these five groups of deletions as they occur in isolation. Methods: A query of the routine clinical testing patient files from the Mayo Clinic Metabolic Hematology and Molecular Genetics Laboratories from 2010 to 2015 identified 179 patients with a deletion confirmed by a Multiplex Ligation-dependent Probe Amplification (MLPA) assay. Twenty-four probes sets were placed from the 5' locus control region (LCR) to the 3' hypersensitivity region, spanning the beta globin gene complex. Using a Luminex LX200 flow cytometer, a gene dosage ratio was calculated for each probe set using the median fluorescent intensity value collected. The size and location of the deletion and patient phenotype were compared. Results: Of the 179 total cases, the following large deletions were identified: beta gene (HBB) (n = 47), delta (HBD) through HBB (n = 105), A-gamma (HBG2) through HBB (n = 20), and locus control region (LCR) through HBB (n = 7). One case had a deletion involving the LCR epsilon with the rest of the complex left intact. A subset (n = 60) of cases had compound hemoglobin mutations that altered the phenotype. The BZT cases had relatively high Hb A2 levels and variable Hb F levels consistent with promotor region loss. The main differences between DBT and HPFH included Hb F and Hb A2 levels. GDBT cases presented with median Hb F levels higher than that observed in DBTs, normal Hb A2, and microcytic anemia. EGDBT cases had variable features according to age of the patient and Hb F level; severe microcytic anemia was observed in neonates, milder microcytic anemia in young children, and microcytosis without anemia in an adult case. The phenotypic features of 119 patients with isolated large deletions are compiled in table 1. Conclusion: In general, all five categories of large deletions in an isolated heterozygous state can present with microcytic anemia and are typically benign with the exception of transient severe microcytic anemia in neonatal EGDBT cases. Although phenotypes associated with large deletions involving the beta globin gene complex are frequently distinctive, significant phenotypic overlap can be seen in a subset of cases. These cases require molecular analysis due to their clinical importance when in combination with another beta globin gene complex mutation for an adequate diagnosis and treatment approach. Table 1. Deletion type Age n HbF (%) HbA2 (%) Hb (g/dL) MCV (fL) RBC (10^12/L) RDW (%) MCH (pg/cell) BZT 20 6.3 (0.6-94.4) 6.8 (3.4-11.6) 11.1 (8.3-14.5) 65.4 (60.8-77.2) 5.4 (4.2-6.2) 19.2 (16.6-21.2) 20.9 (18.3-25.7) DBT 56 10.6 (2.7-22.4) 2.7 (2.5-3.1) 11.7 (8.6-14.4) 68.9 (61.3-83.5) 5.3 (4.1-7.3) 21.4 (18.2-26.8) 21.6 (19.9-39.2) HPFH 23 25.9 (17.6-39.7) 2.0 (1.5-2.4) 11.6 (8.1-16.7) 78.4 (60.2-101.9) 4.4 (3.0-6.3) 17.5 (14.1-22.3) 25.4 (17.6-29.7) GDBT 14 13.3 (8.2-19.0) 2.6 (1.8-2.7) 11.0 (8.6-14.1) 72.5 (57.9-82.1) 5.1 (3.5-6.2) 20.6 (17.4-23.5) 22 (17.9-25.1) EGDBT* 28 Y 1 0.3 3 13.3 59.4 6.9 15.4 19.2 1-4 Y 3 0.9 (0-1.6) 3.2 (2.9-3.5) 9.5 (8.8-13.3) 57.8 (57.6-59.4) 5.2 (4.9-6.9) 16.6 (15.4-17.4) 18.5 (18.1-19.2) <6 month 2 21.4 (14.8-27.9) 2.6 (2.2-2.9) 6.3 (6.0-6.6) 61.3 (59.9-62.6) 3.4 (3.3-3.3) 21.5 (21.2-21.7) 18.4 (18.1-18.7) medians, (min, max); *stratified by age Disclosures No relevant conflicts of interest to declare.

Abstract Background: Previous studies have demonstrated inferior overall survival (OS) among patients with AL amyloidosis who have bone marrow plasmacytosis (BMPC) >10%. It is well recognized that AL patients who achieve the deepest hematologic responses have superior organ response and OS rate. Methods: We designed a study to determine if BMPC% affects CR rate post ASCT and whether pre-transplant induction therapy improves response rate and OS among patients with a higher baseline BMPC burden. Results: Among 415 AL patients who underwent ASCT at Mayo Clinic, Rochester within 12 months of diagnosis, 116 (28%) had BMPC>10% at diagnosis. The median age was 57.2 years. The median follow up for surviving patients was 67.3 months. There was no statistically significant difference in age, gender, time from diagnosis to transplant, or use of attenuated conditioning regimen between groups with high (>10%) and low (≤ 10%) bone marrow involvement. The high BMPC group tended to receive more induction chemotherapy before ASCT, and presented with greater value of albumin, beta-2 microglobulin and difference in serum free light chain (dFLC). A higher CR rate was observed in low BMPC group (44.6% versus 26.7%, p=0.0013). In patients with high BMPC, a higher CR rate (33.9% versus 17.7%, p<0.05) was seen in those who received pre-ASCT induction therapy compared with no induction therapy. Among 3 groups of patients who had: no pre-ASCT therapy; steroid only as induction; and induction chemotherapy, the 5-year OS rates were 50.5%, 40% & 67.5%, respectively, p<0.03. Conclusions: Lower bone marrow burden at diagnosis yielded a deeper response following ASCT. Compared to no therapy, patients who were treated with steroid only pre-ASCT had an inferior OS, but other induction regimens appeared to improve survival. These data would support the concept that using induction therapy prior to ASCT conditioning among patients with higher tumor burden may be of value. Figure 1. In BMPC > 10% at diagnosis, OS comparison between induction therapies pre-ASCT Figure 1. In BMPC > 10% at diagnosis, OS comparison between induction therapies pre-ASCT Figure 2. Figure 2. Figure 3. Figure 3. Disclosures Kumar: Celgene: Research Funding; Celgene, Millenium, Sanofi, Skyline, BMS, Onyx, Noxxon,: Other: Consultant, no compensation,; Sanofi: Research Funding; AbbVie: Research Funding; Millenium/Takeda: Research Funding; Onyx: Research Funding; Skyline, Noxxon: Honoraria; Janssen: Research Funding. Gertz:Celgene: Honoraria; millenium: Consultancy, Honoraria; Onyx: Honoraria; Novartis: Honoraria; Smith Kline: Honoraria.

Abstract Background: Patients with light chain amyloidosis (AL) often have delayed diagnosis and present with significant symptomatology; this may result in decreased quality of life (QOL). With improving treatment options providing longer survival, it is increasingly important to assess QOL. However there is paucity of data in the literature addressing QOL in AL patients. We prospectively employ a "Hematology Patient Reported Symptom Screen" (HPRSS) which consists of three questions about fatigue, pain and overall QOL. The aim of this study is to understand if HPRSS parameters predict various clinical outcomes. Methods: Eligibility for this retrospective study was as follows: 1) New diagnosis of AL between 2009-2014; 2) baseline HPRSS documented in the medical record; and 3) at least a year of follow-up, which included either death within or follow-up through 12 months after diagnosis. The HPRSS questions were rated on a 1-10 scale, with 10 being the worst for fatigue and pain, and 10 being the best for overall QOL. Scores were abstracted from visits at time of diagnosis, and at 12 months +/- 1 month post-diagnosis. We considered a 2-point difference in serial scores a "change" over time. Results: For the 302 patients in this study, the baseline median scores [interquartile range] for fatigue, pain, and QOL were 6 [3,7], 2 [0,5] and 5 [3,8], respectively. Median overall survival (OS) was 39.1 months, with 102 deaths in the first year. There were significant differences in baseline HPRSS between those who lived longer than one year and the early death patients in the domains of fatigue (5 [IQR 3, 7] vs. 7 [IQR 5, 8], p<0.0001) and QOL (6 [IQR 4, 8] vs. 5 [IQR 3, 7], p=0.006), but not in pain (2 [IQR 0, 5] vs. 2 [IQR 0, 5]). There were significant baseline differences in the early death group for alkaline phosphatase, bilirubin, creatinine, and Mayo stage. Patients who received ASCT had the best baseline fatigue 4 [2.5,6] and QOL 7 [5,8] scores and were significantly different from those who did not receive ASCT [fatigue p<0.0001) and QOL (p<0.0001)] On univariate analysis fatigue and QOL were prognostic for OS. On multivariate analysis Mayo 2012 staging, autologous stem cell transplant and baseline fatigue remained independently prognostic. When analyses were restricted to the 125 patients with HPRSS measurements at 12 months, we found that over time QOL scores improved significantly 6 [IQR 3.5, 8] to 7 [IQR 5, 8] (two sided Wilcoxon signed rank p=0.01), but fatigue (5 [IQR 2, 5] to 4.5 [IQR 3, 6]) and pain scores (2 [IQR 0 ,4] to 1.5 [IQR 0, 4]) did not. Patients with worse baseline parameters tended to have improvement in QOL by 12 months while those with the best baseline parameters tended to decline in QOL although not statistically significant. When we included the 102 patients who died within 12 months to the comparison, the early death patients had the worst baseline parameters and there were significant differences across all 4 groups for most characteristics (Table 1). There was no association between achieving hematologic or organ response with change in QOL. Conclusion: Asking patients with AL to rate their fatigue and QOL using a 10-point scale has predictive value. Patient reported fatigue at diagnosis is an independent prognostic factor for survival. Survival at one year was associated with significant improvement in QOL. Table 1. Baseline parameters between patients with early death and/or survive 12 months. Grouped by Patient Reported QOL at 12 months Relative to Baseline Dead by 12 months, n=102 Improved, n=44 Stable, n=55 Worsened, n=26 pa HPRSS scores [IQR] Baseline QOL Baseline fatigue Baseline pain 12 month QOL 12 month fatigue 12 month pain 5 [3,7] 7 [5,8] 2 [0,5] NA NA NA 4 [3-5] b,c,d6 [4-8] b,c2 [0-4] 7 [6-9] b,d4 [1.25-6] 1 [0-3] 7 [4-8] 4 [2-7] 2 [1-5] 7 [4-8] 5 [3-6] 2 [0-4] 8 [7-10] 2.5 [0-5.25] 2 [0-6.25] 5 [3-7] 5 [3-6] 2.5 [1-4] <0.0001 <0.0001 NS NS 0.001 NS dFLC, mg/dL 48 (2.89-726) 30 (1.1-494) 19 (36-455) 22 (0.2-2097) 0.008 Troponin, ng/mL 0.065 (<0.01-0.84) 0.02 (<0.01-1.6) 0.01 (<0.01-0.19) 0.01 (<0.01-0.14) <0.0001 NT-proBNP pg/mL 5222 (159-70,000) 1766 (26-16868) 1381 (24-19180) 496 (56-2973) <0.0001 Received Transplant (%) 4 (4) 13 (33) 36 (50) 13 (50) 0.0002 a Significant by Wilcoxon across all 4 categories; b Significant difference between Improved and Worsened; c Significant difference between Improved and Stable; d Significant difference between Worsened and Stable Disclosures Kumar: Millenium: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; Abbvie: Research Funding; Janssen: Consultancy, Research Funding; BMS: Consultancy, Research Funding; Novartis: Research Funding; Onyx: Consultancy, Research Funding. Thompson:Kite Pharma: Research Funding.

Abstract BACKGROUND: While hematologic cancers comprise only 10% of all malignancies, they are divided into >100 distinct World Health Organization subtypes. It is known that higher volume of care is generally associated with better clinical outcomes. However, such a volume-outcome relationship in the medical management of hematologic cancers has not been rigorously explored. The American Society of Clinical Oncology (ASCO) National Census of Oncology Practices shows that the majority of hematologist-oncologists in the United States (US) have a combined hematology-oncology practice (J Oncol Pract 2013). In this study, we estimated the annual number of new and established patients with major hematologic cancers seen on average by a hematologist-oncologist in the US. METHODS: We estimated the number of hematologist-oncologists working in the US using the ASCO workforce information system data from 2011. We utilized statistics from the Surveillance Epidemiology and End Results (SEER) Program to determine the incidence and 37-year limited prevalence of hematologic cancers in 2011. We used 'first malignant tumor per site' statistics as the tumor inclusion method. For potentially curable hematologic cancers (acute lymphocytic leukemia, acute myeloid leukemia, Burkitt lymphoma, diffuse large b-cell lymphoma, Hodgkin lymphoma, and marginal zone lymphoma), we used the estimated 1-5 year survival rates from SEER and excluded patients who survived >5 years, since relapses are rare afterwards. Because prevalence estimates of chronic myelomonocytic leukemia, myelodysplastic syndromes, and certain subtypes of non-Hodgkin lymphoma are unavailable, we were unable to calculate the number of annual established cases. For myeloproliferative neoplasms, we obtained the prevalence estimate from Mehta J, et al (Leuk Lymphoma 2014). We derived the distribution of major non-Hodgkin lymphoma subtypes from the National Cancer Data Base (NCDB) Participant User File. RESULTS: The ASCO workforce information reported a total of 13,084 hematologist-oncologists working in the US in 2011. The Table summarizes the average number of specific hematologic cancer cases seen per hematologist-oncologist in 2011. CONCLUSION: Hematologic cancers are relatively rare but complex. In the US, a hematologist-oncologist on average cares for only 1-2 new patients of any subtype of hematologic cancers annually. The number of established patients is correspondingly low. These numbers are expected to vary by practice setting and disease specialization. As the diagnosis and management of hematologic cancers becomes more sophisticated, future research should explore the potential of a volume to clinical outcome relationship for these providers. Table. Hematologic Cancer Average Annual Number of Cases per Hematologist-Oncologist in the US New Cases Established Cases All Cases Acute lymphocytic leukemia 0.4 1.4 1.8 Acute myeloid leukemia 1 1.5 2.5 Chronic lymphocytic leukemia 1.1 10.7 11.8 Chronic myeloid leukemia 0.4 2.7 3.1 Chronic myelomonocytic leukemia 0.1 - - Hodgkin lymphoma 0.7 2.7 3.4 Multiple myeloma 1.6 6.3 7.9 Myelodysplastic syndromes 1.2 - - Myeloproliferative neoplasms 0.6 22.2 22.8 Non-Hodgkin lymphoma 5.1 - - Anaplastic large cell 0.1 - - Burkitt 0.1 0.3 0.4 Diffuse large B-cell 2 6 8 Follicular 1.1 - - Hairy cell leukemia 0.1 - - Lymphoplasmacytic 0.1 - - Mantle-cell 0.3 - - Marginal zone 0.5 2.1 2.6 Peripheral T-cell, not otherwise specified 0.1 - - Disclosures No relevant conflicts of interest to declare.

Background: Large granular lymphocyte (LGL) leukemia is a lymphoproliferative disorder of mature cytotoxic T- or Natural Killer (NK)-cells. T-cell LGL (T-LGL) leukemia is characterized by accumulation of cytotoxic T-cells in blood and infiltration of the bone marrow, liver, or spleen. T-LGL leukemia may manifest as asymptomatic persistent lymphocytosis or present with a myriad of autoimmune diseases including autoimmune cytopenias. Population-based studies have not been reported in LGL leukemia. Here, we present clinical characteristics, natural history, and risk factors for poor survival in patients with T-LGL leukemia using the Surveillance, Epidemiology, and End Results Program (SEER) and the National Cancer Data Base (NCDB), the two largest national cancer registries in the US. Methods: We queried the SEER and NCDB databases using ICD-O histology code 9831 corresponding to LGL leukemia. The query was further refined to include patients with T-LGL leukemia only. Incidence rates (case/1,000,000) were calculated using 2000-2011 data from SEER and age-adjusted to the US 2000 standard population. Patient level data were obtained from the NCDB and used to describe patient characteristics such as demographics, year of diagnosis, treatment, and survival. Results: The incidence of LGL leukemia was 0.2 cases/1,000,000 individuals. The incidence did not change significantly over the period of time studied. The incidences according to racial groups were (mean, 95%CI): White 0.2 (0.18-0.24), Black 0.14 (0.07-0.24), American Indian/Alaska native 0.24 (0.03-0.8), and Asian/Pacific Islander 0.15 (0.07-0.25). The incidences among Blacks, American Indians/Alaska Natives, or Asian/Pacific Islanders were not significantly different compared to Whites. Patient-level data was available for 978 patients with T-LGL leukemia. The median age at diagnosis was 66.5 years (range, 19-90) with a male to female ratio of 1.05. Females were diagnosed at a younger age compared to males (65 vs. 68 years, p<0.05). Forty-five percent of the patients with T-LGL leukemia required some form of systemic treatment at the time of diagnosis. The vital status of 760 (77.7%) patients was known at the time of last follow up with a median follow up of 32 months (range, 0-152). At the time of last follow up, 75.5% were alive with the median overall survival (OS) of 108.4 months (Figure 1). Multivariate analysis that included age at diagnosis, sex, race, year of diagnosis, and Charlson-Deyo co-morbidity score, showed that older age (>60 years) and higher co-morbidity score (≥2) were independent predictors of poorer survival (Table 1). Conclusions: We present the first population-based study of patients with LGL leukemia, an extremely rare disease. The incidence has remained stable over the last decade and does not vary significantly among races. Most patients with T-LGL leukemia do not require treatment and the overall prognosis is relatively good. Table 1. Multivariable analysis showing predictors of mortality in patients with T-LGL leukemia Parameter Hazard Ratio Lower CI Upper CI P -Value Age <60 vs ≥ 60 years 2.73 1.90 4.04 <.0001 Male vs Female 0.82 0.61 1.09 0.19 White vs Other 0.99 0.97 1.00 0.23 Charlson-Deyo score <2 vs ≥2 2.03 1.29 3.06 0.003 Year of Diagnosis (2001-2006 vs. 2007-2012) 1.11 0.76 1.69 0.58 Figure 1. Overall survival in patients with T-LGL leukemia Figure 1. Overall survival in patients with T-LGL leukemia Disclosures No relevant conflicts of interest to declare.

Abstract Background: Both MM specific and patient specific parameters place patients at risk for death. Patient frailty has been delineated primarily by age and ECOG performance score (PS) and secondarily with creatinine, eGFR, and the International Staging System (ISS), which reflect both renal function and disease burden. In an attempt to improve upon these commonly used measures, a frailty score for elderly patients that combines functional status [Activity of Daily Living (ADL) and Instrumental Activity of Daily living scores], comorbidities [Charlson comorbidity index (CCI)] and age has been developed by the IMWG. Incorporation of these tools into a busy clinical practice is challenging due to time constraints. Therefore, the identification of frail patients with an easily applicable, rapid and objective tool remains a challenge and an unmet need. A reliable, non-invasively assessable, biochemical marker of frailty would be an ideal tool to identify patients at higher risk of death. It was our goal to determine the role of NT-proBNP, a well-established cardiovascular risk biomarker, as a marker of frailty in patients with MM and in predicting overall survival (OS). Methods: Patients were elegible for this retrospective study if they were seen at the Mayo Clinic, Rochester, MN within 30 days of their myeloma diagnosis during the interval between 1/1/2007 and 12/31/2011. As part of the Mayo Clinic registration, all patients complete forms containing their past medical history, symptoms, and ADLs. Data from that first visit was abstracted and used to calculate a CCI. We excluded all patients with a concomitant diagnosis of light-chain AL amyloidosis. NT-proBNP concentration was measured in frozen sera collected within 30 days of diagnosis. NT-proBNP assay was run on the E170 Modular analyzer (Roche Diagnostics, Penzberg, Germany). We evaluated the prognostic role of NT-proBNP and these indices on OS using the Kaplan-Meier method. Results: Among the 351 consecutive patients satisfying entry criteria, median age was 65 years (range 22-95), 33% were ≥70 years, and 56% were male. Twenty-eight percent were ISS stage III, 13% had a creatinine ≥2 mg/dL, 19% had PS ≥2, and 11% had ADL score ≥2, and 30% CCI ≥2. The median value of NT-proBNP was 109 ng/L (interquartile range: 30-375 ng/L). NT-proBNP concentrations differed in the three ISS stages (median: 122 ng/L stage I, 190 ng/L stage II and 1822 ng/L stage III, P<0.0001). Median OS was 5.6 years. The best cutoff of NT-proBNP predicting OS at one year was 301 ng/L (sensitivity: 57.88%, specificity: 74.29%; AUC=0.668) and distinguished two groups with different OS (median not reached vs. 35 months, P<0.0001). Variables predictive for OS are shown in the Table and included PS, age, CCI, ISS, NT-proBNP. On multivariate analysis, ISS and NT-proBNP were independent of each other as long as age, PS and/or CCI were excluded from the model. NT-proBNP withstood these clinical variables better than did ISS (data not shown). We built two different models (table). In model 1, NT-proBNP >301 ng/L was an independent predictor of survival while age ≥70 and PS ≥2 and CCI were not significant. However, if we considered ISS stage instead of NT-proBNP in the same model, ISS stage was not significant. Conclusions: In this unselected cohort of patients with newly diagnosed multiple myeloma, NT-proBNP was a useful predictor of survival independent of age and PS. The threshold of 301 ng/L is remarkably similar to the well-established cutoff for heart failure of 300 ng/L. NT-proBNP outperformed the CCI and the ISS as risk factors. NT-proBNP is a widely available biomarker that could be added to the panel of laboratory tests of newly diagnosed MM patients and serve as a simple means of determining patient frailty in a busy clinical practice. Table. Results of univariate and multivariate analysis of various prognostic factors (NS, not significant; NI, not included). Univariate Multivariate Multivariate RR (95% CI) P Model 1 P Model 2 P PS ≥2 3.4 (2.4-4.8) <0.0001 2.5 (1.7-3.6) <0.0001 2.8 (1.9-4.0) <0.0001 Age ≥70 2.7 (1.9-3.6) <0.0001 2.1 (1.5-2.9) <0.0001 1.9 (1.3-2.7) 0.0003 CCI >=2 1.9 (1.4-2.6) <0.0001 NS NS 1.5 (1.0-2.0) 0.03 ISS stage 1.1 (1.1-1.8) 0.0004 NI NI NS NS NT-proBNP >301 ng/L 2.3 (1.7-3.2) <0.0001 1.6 (1.1-2.2) 0.007 NI NI Disclosures Merlini: Janssen-Cilag: Honoraria; Prothena: Honoraria; Millennium-Takeda: Honoraria; Pfizer: Honoraria. Kumar:Janssen: Research Funding; Celgene: Research Funding; Sanofi: Research Funding; Millenium/Takeda: Research Funding; AbbVie: Research Funding; Onyx: Research Funding; Celgene, Millenium, Sanofi, Skyline, BMS, Onyx, Noxxon,: Other: Consultant, no compensation,; Skyline, Noxxon: Honoraria.

Abstract Background: Enteropathy-associated T-cell lymphoma (EATL) is a very rare primary intestinal non-Hodgkin lymphoma (NHL) strongly associated with celiac disease. Previous studies suggest that EATL may be more common in Europe and among white population, in which celiac disease is a prevalent condition. However, comparative racial epidemiologic study of EATL in a large population is lacking and clinical studies remain limited to small series. Using two U.S. population databases, we determined the comparative incidences according to race and described the clinical features and outcome of the largest series of EATL reported to date. Methods: Incidence rates (case/1,000,000) were calculated using 2000-2010 data from the Surveillance, Epidemiology, and End Results (SEER) Program and age-adjusted to the U.S. 2000 standard population. Patient level data were obtained from the National Cancer Data Base (NCDB) Participant User File and analyzed for demographic patterns and stage distribution (1998-2011) as well as overall survival (1998-2006). SEER, a program of the U.S. National Cancer Institute, collects cancer incidence and survival data from population-based cancer registries covering approximately 28 percent of the U.S. population. NCDB, a joint program of the American College of Surgeons and the American Cancer Society, is a nationwide oncology outcomes database for more than 1,500 cancer programs in the U.S. and Puerto Rico capturing about 70% of all newly diagnosed cases of cancer in the U.S. Results: In the years 2000-2010, the overall incidence rate of EATL in the U.S. population was 0.111. Asians/Pacific Islanders had a higher incidence rate (0.236) compared to other races (whites [0.101], blacks [0.107], American Indians/Alaska natives [0.128]). The risk ratio of Asians compared to non-Asians was 2.32 (95% CI: 1.39-3.69; P =0.002). From 1998-2011, 515,026 patients were diagnosed with NHL and 337 (0.07%) had EATL. The median age was 64 years (range, 20 -90+ years) and 60% of patients were males. The stage distribution was as follows: I (26.7%), II (16.3%), III (3.3%), IV (29.1%), and unknown (24.6%). The median overall survival (OS) for the entire cohort was 5.9 months. Patients diagnosed in the more recent era (2003-2006) had a better outcome (median OS, 6.6 versus 2.6 months; Figure 1; P = 0.034). Asians/Pacific Islanders had a worse outcome compared to other races (Figure 2; P = 0.031). Multivariable analysis showed the following to be independent factors associated with inferior OS: age >60 years (HR: 1.49; 95% CI: 1.05-2.10), Asians/Pacific Islanders (HR: 2.27; 95% CI: 1.08-4.76), stage III/IV (HR: 3.03; 95%CI: 2.00-4.59), and diagnosis before 2003 (HR: 1.59; 95%CI: 1.10-2.28). Conclusions: Contrary to previous studies, we find that the highest incidence and lowest survival rates of EATL occur among Asians/Pacific Islanders, populations in which celiac disease is not considered to be prevalent. The overall survival of EATL patients has improved over time but remains poor. Figure 1 Figure 1. Figure 2 Figure 2. Disclosures No relevant conflicts of interest to declare.

Abstract INTRODUCTION: Erdheim-Chester Disease (ECD) is a rare hematologic disease manifesting as granulomatous or fibrotic infiltration of long bones and non-skeletal tissues by non-Langerhans foamy histiocytic (CD68+, CD1a-, S100-) cells. The clinical presentation can be diverse and any organ can be affected. It is estimated that less than 500 cases have been reported in the literature. METHODS: We retrospectively reviewed the medical records of patients evaluated at Mayo Clinic from January 2001 to May 2014. A tissue biopsy review by pathologists at Mayo Clinic was necessary for inclusion in the study. In all cases, the diagnosis of ECD was confirmed using clinical criteria in conjunction with histopathologic findings. Clinical data such as patient characteristics, disease presentation, management strategies, treatment responses, and survival time were collected. RESULTS: Forty-five patients with confirmed ECD were included in our study. The median age at diagnosis was 53 years (range, 32-78), while the median age of onset of symptoms prior to diagnosis was 24 months (range, 1-180). There was a male predilection with a male to female ratio of 2.4. The duration of follow-up ranged from 1-168 months (median, 20). At the time of last follow-up, 38 patients were alive and 5 were dead, while the vital statuses of 2 patients were unknown. The median and mean survival times from the time of diagnosis were 150 and 130 months respectively. The median survival time from the time of symptom onset was not reached and mean time was 154 months (Figures 1A and 1B). Bone pain was the most common presenting symptom (27%). Other common presentations included diabetes insipidus (24%), abdominal pain (22%), B symptoms (13%), visual changes (13%), renal failure/obstruction (11%), pleural effusion (9%), other neurologic symptoms (9%), ataxia (7%), pericardial effusion (7%), edema (7%), dyspnea (7%), and hypertension (7%). The organs/sites involved were bone (80%), central nervous system (59%), kidneys (56%), lungs (38%), retroperitoneum (33%), heart (22%), sinuses (22%), bone marrow (14%), adrenal glands (13%), liver (11%), spleen (11%), and skin (16%). Only 18 (40%) patients had the diagnosis established by the first biopsy. The rest required multiple biopsies before the diagnosis was confirmed (median, 2; range, 1-5). Testing for BRAF V600Ewas performed in 12 patients and 8 (67%) tested positive for the mutation. Eight patients have not required ECD-specific treatment so far. Of the 37 patients who required treatment, the median number of treatments received was 2 (range, 1-7). The systemic treatments received and their response rates are summarized in Table 1. Four patients received radiotherapy but none responded. At the time of the last follow up, 5 patients with BRAFV600E were receiving treatment with vemurafenib or dabrafenib. However, follow-up is insufficient for proper assessment of response. CONCLUSIONS: ECD is an extremely rare diagnosis as exemplified in our retrospective analysis. The diagnosis is often challenging due to protean and diverse clinical presentation as well as frequent inconclusive initial biopsies. There is usually a long-latency period of smoldering symptoms leading to definitive diagnosis. A high level of suspicion is essential in order to make the diagnosis and BRAFmutation analysis should be considered an integral part of work up even if biopsy is non-diagnostic. Traditional oncologic systemic therapies and even novel rheumatologic anti-inflammatory agents generally have limited objective activity. BRAF inhibitors offer a novel treatment option and results of prospective studies are eagerly awaited. Abstract 1405. Table 1: Summary of Response to Various Treatment Modalities in Patients with ECD Therapeutic Agent Number of Patients Treated Response (%) Complete Response Partial Response Stable Disease No response Corticosteroids 11 0 0 9 91 Methotrexate 9 0 0 11 89 Cyclophosphamide 4 0 0 25 75 2-CDA 12 8 17 25 50 Interferon-α 8 0 29 14 57 Vinorelbine 4 0 50 0 50 Tumor necrosis-α factor Inhibitors 7 0 0 14 86 Interleukin-1 Receptor Antagonists 6 17 17 0 67 Tamoxifen 2 0 50 0 50 Radiotherapy 4 0 0 0 100 Figure 1 Figure 1. Disclosures Off Label Use: We report among all treatments utilized at our institution for ECD including the use of steroids, immunosupressants, TNF blockers, interleukin-1 receptor antagonists, and BRAF inhibitors in patients with Erdheim Chester Disease. These treatments, though supported by other reports in the literature, have not been explicitly approved by the FDA for ECD..