Hogan, William J.

7094 Background: Obesity is associated with comorbidities that could cause negative outcome upon delivering intensive care. In pediatric AML patients (pts), obesity was associated with more toxicity and worse prognosis. Here, we study Body-Mass Index (BMI) impact on clinical outcome of adult AML pts. Methods: A total of 180 adult pts with AML between 2003-2011 were enrolled. Retrospective data included demographics, labs, cytogenetics and outcome. LeukemiaNET Standardization (LNS), complete remission (CR), overall survival (OS) and relapse free survival (RFS) were obtained (Dohner E, Blood 2010). BMI of 25-30 was defined as overweight, while >30 as obesity. Fischer’s and Wilcoxon tests were used for comparatives between groups, cox proportional hazards and logistic regression for associations for OS/RFS and CR, Kaplan-Meier test for OS and RFS estimates via JMP software V9.0. IRB approval was obtained according to Helsinki declaration. Results: The median age was 63 years, with 115 (64%) were men. Of 159 pts, karyotype was favorable, Intermediate I, II and adverse in 21 (13%), 76 (48%), 23 (%14) and 39 (25%) pts respectively. Median BMI was 28.2 (range 16.8-47.8). 48 (26%) had normal BMI, 62 (34%) were overweight, and 70 (38%) were obese. At diagnosis, BMI classes were not associated with age, sex, glucose, white blood count (WBC), platelets, blasts, ECOG status, LNS, FLT3/NPM1 status; nor treatment toxicities, CR rates, or relapse after CR. BMI classes were associated with presence of concomitant comorbidities (p=0.047) and glucose levels (p=0.044). In univariate analysis, overweight (OR=1.8, p=0.16) and obesity (OR=1.9, p=0.13) did not affect CR rates. On adjusting for age, sex, LNS, WBC and blast count at diagnosis, only overweight pts had a significant higher CR (76% vs 63%) rates (OR=2.99, p=0.043). OS and RFS were not associated with BMI in univariate (p=0.51) and multivariate (p=0.32) models. Median OS and RFS were not different across BMI subgroups (p=0.52 and 0.59). Conclusions: BMI subgroups showed no correlation with treatment toxicity, LNS, relapse rates, OS or RFS. This should encourage giving therapy to pts regardless of their BMI status. Overweight was associated with better CR rates despite increased concomitant morbidities.

7064 Background: Obesity is a growing medical challenge that has strong association with many comorbid diseases. Body mass index (BMI) is used as one indicator for obesity. A few papers have linked hematological cancers with a worse prognosis if obesity was present. Aim: to study the influence of obesity clinical outcome in patients (pts) with CMML. Methods: A retrospective chart review of pts with CMML at Mayo Clinic Rochester between 1994–2011 was done. BMI was calculated at diagnosis or first time visit. Overweight was defined as BMI ≥25, while obesity as BMI >30. IRB approval was obtained. Comparison between medians was done using Wilcoxon test, while survival estimates were calculated using Kaplan-Meier estimates via JMP software v.9. Results: We found227 pts with a median follow-up of 311 days, 68% of which weremales. Median age was 71 years, hemoglobin 11 gm/dL, white blood cells (WBC) 12x 109/L, and platelets 90x 109/L. Obese pts had a median age of 68 (p=0.0058), hemoglobin 11 gm/dL (p=0.0018), WBC 13.5 x 109/L, platelets 115x109/L (p=0.012), PB blasts 0, bone marrow (BM) blasts 3.5% compared to non-obese pts (73, 10, 11, 78, 0, 3%, respectively). Diploid cytogenetics were similar in both groups (68% vs 64%, respectively, p=0.54). Leukemic transformation into acute myeloid leukemia (LT) was found in 13% vs 10% (p=0.83). Overall response (OR) was 24% and 23%, respectively (p=0.2), when limited to hypomethylating agents OR was 33% vs 27%, respectively (p=0.62). Median OS was found to be similar (571 day vs 569 days, respectively, p=0.56). When compared, overweight pts had similar outcome to non-overweight. Median LT was 11% vs 15% respectively, OR was 21% vs 28% respectively (p=0.5), and OS was 571 days vs 549 days respectively (p=0.21). On a multivariate analysis age, hemoglobin, platelets, BM blasts%, gender, and obesity (p=0.048) were of a significant value for OS. Conclusions: When compared, both obesity and overweight did not affect demographic characteristics, overall response, or transformation into AML. When comparing median overall survival, obesity was not a significant factor, possibly due to short follow-up. However, on multivariate analysis obesity did have a negative impact on overall survival.

7041 Background: Prognostic factors for diffuse large B-cell lymphoma (DLBCL) at day 100 post-autologous peripheral blood hematopoietic stem cell transplantation (APBHSCT) have not been evaluated. We previously reported that absolute monocyte (AMC)/lymphocyte count (ALC) prognostic score (AMLCPS) at diagnosis of DLBCL patients is a prognostic factor of survival, stratifying patients into three risk groups: low- (AMC<630/μL and ALC>1000/μL), intermediate- (AMC ≥ 630/μL or ALC ≤ 1000/μL) and high-risk (AMC ≥ 630/μL and ALC ≤ 1000/μL) (Leukemia 25: 1502-9, 2011). Therefore, we evaluated day 100 AMLCPS as a prognostic factor of survival by landmark analysis from day 100 post-APBHSCT in DLBCL patients. Methods: DLBCL patients that achieved complete response by day 100 post-APBHSCT qualified for the study. From 2000 to 2007, 134 consecutive DLBCL patients were evaluated. Overall survival (OS) and progression free survival (PFS) were calculated using Kaplan-Meier analysis. Results: The median follow up from day 100 for the cohort was 5.5 years (range: 0.17-12.17 years) and for living patients (N=93) was 6.9 years (range: 2.5-12.17 years). Day 100 AMLCPS was able to stratify patients into three risk groups low-, intermediate-, and high-risk for OS [low: median OS = not reached, 5-year survival rate = 94% (95%CI 83.3%-98.1%); intermediate: median OS = not reached, 5-year survival rate = 70% (95%CI 58.0%-79.8%); high: median OS = 2.2 years, 5-year survival rate = 13% (95%CI 3.4%-40.5%); p<0.0001] and PFS [low: median PFS = not reached, 5-year progression free rate = 87% (95%CI 74.5%-94.3%); intermediate: median PFS = 10.9 years, 5-year progression free rate = 67% (95%CI 55.4%-77.5%); high: median PFS = 1 year, 5-year progression free rate = 13% (95%CI 3.4%-40.5%); p<0.0001]. The day 100 AMLCPS was balanced in relation to the International Prognostic Index (IPI; p=0.22), infused CD34+ stem cells (p=0.92), and disease status pre-APBHSCT (complete remission versus partial response; p=0.11). Conclusions: The day 100 AMLCPS is a simple biomarker that can help identify DLBCL patients post-APBHSCT with poor clinical outcomes.

8092 Background: High dose therapy and autologous stem cell transplant (SCT) remain an integral part of the treatment of multiple myeloma (MM). Despite the introduction of several new drugs, disease relapse remains inevitable and a second SCT often allows continued disease control. Methods: We examined the outcomes in 105 patients (pts) undergoing a second SCT (SCT2) for relapsed MM, from among 1033 pts receiving initial transplant between 1994 and 2009. Patients receiving an allogeneic SCT post SCT2 were not included in the survival analysis. Results: Median (range) age at SCT2 was 60 yrs (35-74) and the time between SCTs was 46 mos (10-130). The median follow-up was 5 yrs from SCT2. The median (range) number of regimens between the two SCTs and from diagnosis was 2 (0-7) and 3 (1-11), respectively. Conditioning regimen was Mel 200 in 85 (81%) pts. Median (range) CD34 cells infused was 4.6 (2.5-25 million/kg). Hospitalization was required for 53 pts (50%), median hospital stay was 7 days.. Median time to ANC of 500 and platelets of 50,000 were 13 and 16 days, respectively. Treatment related mortality was seen in 5 (5%) pts. A partial response or better was seen in 95 (90%) pts, including a stringent CR in 10%, CR in 19%, and VGPR in 21%. Stable disease or progression was seen in 7 pts and 3 pts died prior to disease assessment. The median (95% CI) PFS and OS after SCT2 were 10.4 (8, 14) and 33 (28, 51) mos, respectively. The median OS was not reached and 33 mos respectively, for those obtained a CR and those with <CR, P=0.03. FISH pre-SCT2 was available for 73 pts; the PFS and OS were not affected by the presence of high-risk MM. In multivariate analyses, shorter duration of response to SCT1, higher plasma cell (PC) labeling index at SCT2, more regimens used prior to SCT2 all predicted shorter PFS and OS post SCT2. Lack of CR to SCT2 was also significantly associated with shorter PFS. Conclusions: Second SCT is an effective therapy for eligible pts relapsing after other treatments. It provides a meaningful duration of response and appears to be well tolerated. Pts with longer duration of response to first transplant and those achieving a CR appear to have maximum benefit.

6614 Background: CDKN2A locus on chromosome 9p21 is implicated in altered molecular pathways leading to leukemogenesis as a tumor suppressor by inhibiting the proliferative kinases CDK4/CDK6 and blocking the cell-cycle division during G1/S phase. Deletion of CDKN2A locus is frequently seen in ALL, although prognostic significance remains unclear. Methods: Retrospective chart review at Mayo Clinic between 8/2005-11/2011 was performed on adult B-ALL patients (pts) who underwent fluorescent in situ hybridization (FISH) studies at diagnosis. Survival estimate was calculated using Kaplan-Meier statistics. Event-free survival (EFS) was defined as time between diagnosis and induction failure, relapse, or death, while overall survival (OS) as time between diagnosis and death Results: Out of 27 pts who had FISH studies for CDKN2A, 15 (56%) pts had CDKN2A deletions (8 homozygous/7 hemizygous), while 12 (44%) pts had diploid cytogenetics (none had abnormalities of MLL, bcr/abl fusion or CDKN2A deletion on FISH). There was no significant difference in median age, gender, WBC, hemoglobin, platelet count, LDH or complete remission (CR) rate (p=0.924) between the two groups. CDKN2A-deleted pts had higher circulating blasts (34% vs 16%, p=0.017). Of 13 CDKN2A-deleted pts, chromosome 9p was intact in 9 (69%) pts using routine cytogenetics. In the CDKN2A-deleted group, allogeneic stem cell transplant (ASCT) was performed in 7 (47%) pts at first CR and 1 at second CR. Four of 15 (27%) CDKN2A-deleted pts also had bcr/abl fusion. Five-year EFS was poorer in CDKN2A-deleted pts compared to normal FISH group (22% vs 60%, p=0.042), while 5-year OS was 72% vs 60% (p=0.308), respectively. Excluding the 4 bcr/abl fusion pts, 5-year EFS was still statistically significant (p=0.008). In the CDKN2A deleted group, both 5-yr EFS (80% vs 0%, p=0.006) and OS (100% vs 42%, p=0.016) were significantly superior in pts who received ASCT. Conclusions: CDKN2A deletions predict earlier relapse in pts with B-ALL. Majority of 9p abnormalities were only found by FISH testing and missed by conventional cytogenetics. In CDKN2A deleted pts, ASCT gave overall survival advantage. Further investigation through larger cohorts of pts is needed to validate these findings.

Systemic glucocorticoids are the principal treatment for acute graft-versus-host disease (GVHD), which remains the major cause of non-relapse mortality (NRM) after allogeneic hematopoietic cell transplantation (HCT). However, there are no validated biomarkers that measure a patient's response to glucocorticoid therapy, and thus response is evaluated by the change in clinical symptom severity. A major weakness in the predictive power of clinical responses is that changes to all organs are weighted equally even though the major driver of NRM is irreversible damage to the crypts of the GI tract. Recent studies from the Mount Sinai Acute GVHD International Consortium (MAGIC) have validated an algorithm probability (MAP) that combines serum concentrations of two biomarkers of GVHD (REG3α and ST2) to generate an estimated probability of 6 month NRM for individual patients. The MAP has been considered a "liquid biopsy" that estimates the damage caused by GVHD to crypts throughout the lower GI tract (Hartwell et al., JCI Insight, 2017; Major-Monfried et al., Blood, 2018). We hypothesized that the change in MAP between start of treatment and 28 days later could serve as a response biomarker for GVHD and might compare favorably to the change in clinical symptoms that measures response to GVHD treatment, which is widely used as a surrogate for long term survival and is the primary endpoint in most GVHD treatment trials (Martin et al., BBMT, 2009; MacMillan et al., Blood, 2010). We prospectively collected serum samples and clinical staging from 368 sequential HCT patients who received systemic treatment for acute GVHD in one of 20 MAGIC centers between January 2016 and February 2018. We measured the serum concentrations of REG3α and ST2 before and after systemic therapy for acute GVHD and computed MAPs, the changes in MAPs, and clinical responses for each patient. MAPs of patients who experienced 6 month NRM showed significantly greater increases than MAPs of patients who survived (p=0.0004). In patients whose MAPs at the start of treatment were low (Ann Arbor 1, MAP &lt; 0.141) or intermediate (Ann Arbor 2, 0.141 ≤ MAP ≤ 0.290), 6 month NRM clustered among those who had the greatest increases in MAP after 28 days (Fig 1A,B). In patients with high MAPs at the start of treatment (Ann Arbor 3, MAP &gt; 0.290), those who survived tended to have the largest decreases in MAP (Fig 1C). These changes in MAP suggested crossing a single threshold could predict risk of mortality. We found that patients whose MAPs rose above a threshold MAP of 0.290 (5% of Ann Arbor 1, 27% of Ann Arbor 2) had significantly worse survival compared to those who remained below it, whereas the large number patients with initially high MAPs that remained above the threshold (66% of Ann Arbor 3) had a large increases in mortality (Fig 2). When measured at day 28, the MAP was significantly more accurate in predicting NRM than the gold standard of the clinical response, with areas under the receiver operating characteristic curve (AUC) of 0.86 and 0.70, respectively (p&lt;0.0001). An algorithm that combined clinical response with biomarkers generated the same AUC as the MAP alone (0.83 v 0.86, p = NS). We next tested whether the same MAP threshold of 0.290 could predict risk within clinical response subsets. A significant minority (10%) of clinical responders had high MAPs and experienced three-fold greater NRM than those with low MAPs (40% v 12%, p&lt;0.0001) whereas the majority (57%) of non-responders had low MAPs and experienced almost three-fold lower NRM than those with high MAPs (24% v 65%, p&lt;0.0001) (Fig 3). Thus the MAP provides important prognostic information over and above the change in clinical symptoms, further stratifying both responders and non-responders at four weeks of treatment. The MAP threshold classified patients both with and without significant lower GI symptoms because the MAP is a more specific measure of irreversible cryptic damage in patients with copious diarrhea and more sensitive in patients with less than 0.5 liters of daily diarrhea (Fig 4). We conclude that the MAP is, to our knowledge, the first validated laboratory test to serve as response biomarker for the treatment for acute GVHD and a more accurate predictor of survival than clinical response after four weeks of treatment. The MAP may serve as a novel endpoint and an important complement to changes in clinical symptom severity in future trials of GVHD treatment. Disclosures Srinagesh: National Institutes of Health: Research Funding. Ozbek:Viracor: Patents & Royalties: Biomarker Patent. Ayuk:Novartis: Honoraria, Other: Advisory Board, Research Funding. Aziz:Doris Duke Charitable Foundation: Research Funding. Defilipp:Incyte: Research Funding. Grupp:Novartis: Consultancy, Research Funding; Roche: Consultancy; GSK: Consultancy; CBMG: Consultancy; Novartis: Research Funding; Kite: Research Funding; Servier: Research Funding; Jazz: Other: study steering committees or scientific advisory boards; Adaptimmune: Other: study steering committees or scientific advisory boards; Cure Genetics: Consultancy; Humanigen: Consultancy. Hexner:novartis: Research Funding. Kitko:Mallinckrodt: Honoraria; Novartis: Consultancy, Honoraria. Mielke:EBMT/EHA: Other: Travel support; ISCT: Other: Travel support; Miltenyi: Consultancy, Honoraria, Other: Travel and speakers fee (via institution), Speakers Bureau; Jazz Pharma: Honoraria, Other: Travel support, Speakers Bureau; IACH: Other: Travel support; Kiadis Pharma: Consultancy, Honoraria, Other: Travel support (via institution), Speakers Bureau; DGHO: Other: Travel support; Bellicum: Consultancy, Honoraria, Other: Travel (via institution); GILEAD: Consultancy, Honoraria, Other: travel (via institution), Speakers Bureau; Celgene: Honoraria, Other: Travel support (via institution), Speakers Bureau. Merli:Sobi: Consultancy; Amgen: Honoraria; Novartis: Honoraria; Bellicum: Consultancy. Pulsipher:Amgen: Other: Lecture; Miltenyi: Research Funding; Bellicum: Consultancy; Novartis: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Jazz: Other: Education for employees; CSL Behring: Membership on an entity's Board of Directors or advisory committees; Adaptive: Membership on an entity's Board of Directors or advisory committees, Research Funding; Medac: Honoraria. Qayed:Bristol-Myers Squibb: Honoraria. Reshef:Pfizer: Consultancy; Magenta: Consultancy; Kite: Consultancy, Research Funding; Atara: Consultancy, Research Funding; BMS: Consultancy; Pharmacyclics: Consultancy, Research Funding; Incyte: Consultancy, Research Funding; Celgene: Research Funding; Shire: Research Funding. Levine:Incyte: Consultancy, Research Funding; Biogen: Other: non-financial support; Viracor: Patents & Royalties: biomarker patent; Ironwood: Honoraria; bluebird bio: Consultancy; National Cancer Institute: Research Funding; Novartis: Honoraria; Kamada: Research Funding. Ferrara:National Institutes of Health: Research Funding; ViraCor: Consultancy; Incyte: Consultancy; Kamada: Consultancy; Mallinckrodt: Consultancy; Enlivex: Consultancy; Xenikos: Consultancy; CSL Behring: Consultancy.