Kipps, Thomas J.

Abstract Abstract 2441 Background: CLL patients with mutated IGHVs (M-CLL) have clinical outcomes better than patients with unmutated BCRs (U-CLL). It is believed that this difference stems from the fact that somatic mutations have altered the structure of the BCR such that it no longer binds stimulatory (auto)antigens and therefore cannot deliver trophic signals to the leukemic cells. We sought to determine if this belief could be corroborated by analyzing the types (silent vs. conservative vs. non-conservative) and locations (FRs v CDRs) of mutations that occurred in IGHVs of M-CLL clones and then comparing the time to first therapy (TTFT) in patients with different IGHV features. Methods: Using IGMT software, we analyzed the IGHV sequences of 1560 cases and characterized their mutations in several respects: first, if IGHV mutations altered amino acid structure (silent vs. replacement); second, if mutations occurred in CDRs (antigen binding domains) or FRs (scaffolds of the BCR); third, if mutations were conservative or non-conservative (as determined by charge, hydropathy, size). TTFT for patients was correlated with various combinations of the above parameters. Differences in TTFT were estimated by the method of Kaplan and Meier; differences were assessed using the log rank test. Results: First we compared TTFT in patients who had only silent mutations (n=32) to those with no mutations (n=563) and found a trend toward longer TTFT in patients with silent mutations (3.0 vs. 3.6 yrs, p=0.09). Because the statistical significance of this result was likely influenced by the small numbers of clones with only silent mutations, we expanded the group to include IGHVs with replacement mutations of only the conservative type (n=83) since these would be unlikely to havemajor impact on BCR structure. Comparison of this group with those with no mutations for differences in TTFT was significant (3.0 vs. 3.9 yrs, p=0.0007). Furthermore, comparisons of patients with silent plus conservative mutations in FRs only (n=123) versus patients with no mutations showed a clear difference in TTFT (3.0 vs. 4.1, respectively; (p<0.003). In both of the above instances, non-conservative replacement mutations do not have a significantly greater impact on TTFT than the silent plus conservative combination. Conclusions: It is assumed that M-CLL patients have a clinical course superior to U-CLL patients because IGHV somatic mutations change the structure of the M-CLL BCR such that it no longer binds stimulatory antigens. For this theory to be correct, only replacement mutations and in particular non-conservative replacement mutations that would alter amino structure of the IGHV/D/J rearrangements would have relevance. Silent mutations, which by definition do not change amino acid structure, should be irrelevant in this regard, and conservative mutations, which substitute amino acids with similar chemical properties to the original amino acid, would be unlikely to result in major changes in the BCR. Therefore, the fact that the combination of only silent plus conservative mutations, which probably would not result in changes in BCR structure and therefore antigen binding, is associated with a lengthened TTFT militates against a loss of antigen binding as a reason for improved clinical course. Therefore, we suggest that another explanation for the enhanced survival of M-CLL patients should be sought. One possibility is that the cells that have the capacity to mutate their IGHVs are different in origin and/or biologic properties from those that cannot, and these account for the differences in survival between M- and U-CLL patients. (This work was supported NIH grant PO1-CA81534 of the CLL Research Consortium.) Disclosures: Byrd: Genzyme Corporation: Research Funding. Kipps: GlaxoSmithKline: Research Funding.

Abstract Abstract 4591 Introduction: Clinicians and investigators appreciate the value of measuring HRQOL for monitoring CLL and the impact of treatments, and commonly use ECOG performance status (PS) and clinician-reported patient fatigue as surrogates for HRQOL in clinical practice. However, limited data exist on the relationships between PS, fatigue, and HRQOL in CLL patients (pts) undergoing treatment in clinical practices. We examined the associations between these measures and 3 psychometrically validated, patient-reported, HRQOL instruments: the Brief Fatigue Inventory (BFI), EQ-5D, and Functional Assessment of Cancer Therapy-Leukemia (FACT-Leu). Methods: Data were collected as part of Connect CLL®, a prospective observational registry initiated in March 2010 involving US practices. Data on pt demographics and clinical characteristics were provided by clinicians. HRQOL was self-reported by pts at enrollment using the BFI, EQ-5D, and FACT-Leu. Mean BFI, EQ-5D and FACT-Leu scores were analyzed by ECOG PS and clinician-reported fatigue. Differences in HRQOL scores between sub-cohorts were assessed by ANOVA. Results: HRQOL data were reported by 604 pts enrolled from 10 academic, 148 community, and 3 government centers. Pts were predominantly male (62%) and white (90%); mean age was 70 (standard deviation 11) years. BFI data (scale: 0 [no fatigue] - 10 [worst fatigue]) indicated that on average pts report that global fatigue, fatigue severity and fatigue-related interference worsen by ECOG severity (Table 1) and are statistically associated with clinician-reported fatigue (Table 2). Mean EQ-5D overall HRQOL as measured by a Visual Analogue Scale (VAS) from 0 (worst) to 100 (best) worsens by ECOG severity and is significantly worse in pts with fatigue. Mean EQ-5D domain scores (scale: 1 [no problem], 2 [some problems], 3 [incapacity]) indicated that pain/discomfort, mobility and usual activities increase in severity as ECOG worsens and in pts with fatigue. FACT-Leu domains except social/family were statistically worse with worse ECOG PS and in pts with fatigue. Conclusions: Initial results from Connect CLL® indicate that HRQOL worsens with worsening ECOG PS, especially in physical / functioning domains, pain/discomfort, and mobility, and worsens across multiple domains among pts whose physicians reported fatigue. Future analyses should be conducted on how HRQOL, PS and fatigue may change over time with changes in CLL, and how they are influenced by therapies. These results may serve as baseline reference. Disclosures: Pashos: Celgene: Membership on an entity's Board of Directors or advisory committees. Flowers:Genentech/Roche (unpaid): Consultancy; Celgene: Consultancy; Millennium/Takeda: Research Funding; Wyeth: Research Funding; Novartis: Research Funding. Weiss:Celgene: Membership on an entity's Board of Directors or advisory committees. Lamanna:Celgene: Membership on an entity's Board of Directors or advisory committees. Farber:Celgene: Membership on an entity's Board of Directors or advisory committees. Kipps:Igenica: Equity Ownership, Membership on an entity's Board of Directors or advisory committees; Celgene: Consultancy, Research Funding; Abbot Industries: Research Funding; Pharmacyclics: Membership on an entity's Board of Directors or advisory committees; Genentech: Research Funding; GSK: Research Funding; Gilead Sciences: Consultancy, Research Funding; Amgen: Research Funding. Lerner:Celgene: Membership on an entity's Board of Directors or advisory committees. Kay:Celgene: Membership on an entity's Board of Directors or advisory committees. Sharman:Celgene: Membership on an entity's Board of Directors or advisory committees. Grinblatt:Celgene: Membership on an entity's Board of Directors or advisory committees. Flinn:Celgene: Membership on an entity's Board of Directors or advisory committees. Kozloff:Celgene: Membership on an entity's Board of Directors or advisory committees. Swern:Celgene Corporation: Employment, Equity Ownership. Kahn:Celgene Corporation: Employment, Equity Ownership. Street:Celgene: Employment, Equity Ownership. Sullivan:Celgene: Employment, Equity Ownership. Keating:Celgene: Membership on an entity's Board of Directors or advisory committees.

Abstract Background: Chronic lymphocytic leukemia (CLL) patients exhibit variable times from diagnosis to the need to initiate therapy. Previous studies have demonstrated that the rates of CLL cell proliferation (birth) and elimination (death plus recirculation to solid tissues) could be estimated using the incorporation of deuterium (2H), taken in the form of heavy water (2H2O), into the DNA of dividing cells. In a cross-sectional study of patients with early and late stage CLL, birth rates above 0.35% per day were associated with more aggressive disease. The NIH-sponsored CLL Research Consortium (CRC) conducted a prospective study on patients with early stage disease to assess the relationship between CLL-cell birth rate and the length of time to initiation of initial therapy. Methods: Eligible patients had untreated early stage (Rai 0, 1 or 2) disease, and were no more than 3 years from diagnosis. Additional clinical parameters included age at diagnosis, gender, mutational status of the immunoglobulin heavy chain variable region (IGHV), 70-kD zeta-associated protein (ZAP-70) (binary split at 20%) and CD38 (binary split at 30%) expression, and FISH cytogenetics by the Dohner hierarchy. Heavy water was administered daily for 6 weeks, and peripheral blood was obtained for kinetic measurements at the end of weeks 3, 6, 9, 12, and 16. CLL birth rates for blood samples were measured at the same facility (KineMed, Inc, Emeryville, CA) by quantifying deuterium incorporation into the deoxyribose moiety of DNA of tumor cells, using isotope ratio mass spectrometry (IRMS). Subjects were followed for time to administration of therapy; subjects still without treatment at time of analysis were censored, as were subjects who exited the study to receive treatment outside of the established criteria. To assess the impact of birth rate and other factors on time to first therapy, Kaplan-Meier curves were compared using the logrank test for univariable analyses; stepwise Cox proportional hazards regression models were used to build multivariable models. Results: Between July 2005 and February 2009, 119 subjects were enrolled through the CRC. 107 subjects were eligible. For 10 eligible patients, exit from the study prior to completion of protocol-mandated heavy water consumption (n=5) or technical issues (n=5) made it impossible to obtain estimates of birth rate. The 97 eligible subjects for whom birth rates were available form the basis of this report. 60% of subjects were male; median age 57 years (range 40-85). Median birth rate was 0.32%/day (range 0.07-1.31%/day). 43 subjects (44%) had birth rates higher than the previously reported threshold of 0.35%/day. With a median follow up of 4 years, 33 eligible subjects (34%) required treatment. IGHV status (31 subjects unmutated), elevated ZAP-70 (31 subjects), elevated CD38 (21 subjects), and hierarchical categorization of FISH anomalies (4 del(17p), 5 del(11q), 8 tri(12), 50 del(13q)) were examined for association with differential time to first therapy in univariable analyses. All were associated with earlier need for therapy (p <0.001) except for genomic aberrations defined by FISH (p = 0.96). Birth rate > 0.35%/day was also significantly associated with shorter time to first therapy (p < 0.001). In multivariable modeling, only birth rate and IGHV status met the criteria for inclusion (Wald p-value < 0.05). Hazard ratios were 3.51 (p = 0.002) for birth rate above 0.35%/day and 5.0 for unmutated IGHV status (p < 0.001). The association between elevated birth rate and shorter time to first treatment was observed within subjects with both unmutated and mutated IGHV (see Figure 1). Conclusion: An increased birth rate of CLL cells early in the disease course is a strong predictor of an earlier need for treatment in univariable analysis, as are unmutated IGHV, elevated ZAP-70 expression and elevated CD38 expression. However, only unmutated IGHV and an increased birth rate contribute significantly to the multivariable model. This adds a direct measure of B-cell kinetics to the array of biomarkers available to subjects with CLL. Figure 1 Time to treatment by birth rate and IGHV status Figure 1. Time to treatment by birth rate and IGHV status Disclosures Murphy: KineMed, Inc: Equity Ownership. Emson:KineMed, Inc.: Employment, Equity Ownership. Li:KineMed, Inc.: Employment, Equity Ownership. McConnel:KineMed, Inc.: Equity Ownership. Turner:KineMed, Inc.: Employment, Equity Ownership. Busch:KineMed, Inc.: Equity Ownership. Hellerstein:KineMed Inc: Consultancy, Employment, Equity Ownership, Membership on an entity's Board of Directors or advisory committees. Chiorazzi:KineMed, Inc.: Stock options Other.

Abstract Background: Patients (pts) with CLL/SLL that have del11q tend to have relatively short remission durations and shorter overall survival (OS) with standard chemotherapy regimens. Ibrutinib (ibr), a first-in-class, oral, once-daily inhibitor of Bruton's tyrosine kinase (BTK) is indicated by the US FDA for the treatment of pts with CLL/SLL and allows for treatment without chemotherapy. In phase 3 studies, treatment with single-agent ibr was superior to treatment with ofatumumab (ofa) in relapsed/refractory (R/R) CLL/SLL (RESONATE; Byrd, N Engl J Med 2013) or chlorambucil (clb) in treatment-naïve (TN) CLL/SLL (RESONATE-2; Burger, N Engl J Med 2015); treatment with ibr + bendamustine/rituximab (BR) was also superior to treatment with BR in R/R CLL/SLL (HELIOS; Chanan-Khan, Lancet Oncol 2016). We examined the outcome of pts in these 3 studies who did or did not have del11q to determine the impact of del11q on clinical outcomes. Methods: In RESONATE, pts with R/R CLL/SLL received ibr 420 mg/day until progressive disease (PD) or unacceptable toxicity (tox) vs ofa for up to 24 weeks (300 mg week 1, 2000 mg weekly for 7 weeks, 2000 mg every 4 weeks for 16 weeks). In RESONATE-2, pts ≥65 years of age with TN CLL/SLL (excluding del17p) received ibr 420 mg/day until PD or tox vs clb 0.5 mg/kg (up to max of 0.8 mg/kg) on days 1 and 15 of each 28-day cycle (≤12 cycles). In HELIOS, pts with R/R CLL/SLL (excluding del17p) received BR (≤6 cycles) with or without ibr 420 mg/day (starting on day 2 of cycle 1) followed by single-agent ibr or placebo continued until PD or tox. Data from pts in the 3 studies were pooled (ibr pool, comparator pool) and analyzed based on whether or not their CLL/SLL had del11q. We performed a multivariate analysis to examine for risk factors prognostic for survival (del11q del17p, age, sex, race, current Rai stage, baseline ECOG PS, number of prior therapies, cytopenias; no multiplicity adjustment). Results: A total of 1210 pts with del11q data were included in the analysis: 609 in the ibr pool (179 with del11q, 430 without del11q) vs 601 in the comparator pool (149 with del11q, 452 without del11q). Demographics and baseline characteristics were generally similar in pts regardless of del11q status; differences of more than 10% included proportions with bulky disease ≥5 cm (63% vs 49%) and unmutated IGHV (88% vs 72%) in pts with or without del11q, respectively. Median lymphadenopathy and absolute lymphocyte counts were also higher in pts with del11q. At the time of the primary analysis, median treatment durations in the ibr pool were 20.0 mo with del11q and 18.7 mo without del11q; in the comparator pool 5.3 mo with del11q and 8.5 mo without del11q. Overall, ibr-treated pts had higher overall response rate (ORR) and complete response (CR) rates and longer progression-free survival (PFS, Figure) and OS than comparator-treated pts regardless of del11q status. In the ibr pool, the presence of del11q was associated with a trend of longer PFS and OS while in the comparator pool, pts with del11q had shorter PFS (Figure, Table), as compared to pts without del11q. Results were similar when excluding pts with del17p (10% of ibr pool [6%/11% with/without del11q] and 9% of comparator pool [8%/10% with/without del11q]; Table). By multivariate analysis, in the comparator pool, del11q, male sex, >1 prior therapy, presence of cytopenias, and unmutated IGHV (in R/R only) were associated with shorter PFS. However, these differences were not found in the ibr pool. The presence of del11q did not unfavorably impact the tempo of reductions in lymphadenopathy for ibr-treated pts, but did for comparator-treated pts. Adverse events leading to discontinuation were similar in ibr- and comparator-treated pts (12% [9% vs 13% with/without del11q] vs 13% [12% vs 13% with/without del11q]). Deaths within 30 days of last dose occurred in 31 pts (5% [3% vs 6% with/without del11q]) in the ibr pool and 28 pts (5% [6% vs 4% with/without del11q]) in the comparator pool. Conclusions: Treatment with ibr was superior to comparator regardless of del11q status; the benefit of ibr was most marked for pts with CLL/SLL with del11q. Presence of del11q was associated with a trend of longer PFS and OS in ibr-treated pts and shorter PFS and lower ORR in comparator-treated pts. By multivariate analysis, presence of del11q was a prognostic factor for shorter PFS for comparator-treated pts, but not for ibr-treated pts. Disclosures Kipps: Celgene: Consultancy, Honoraria, Research Funding; Roche: Consultancy, Honoraria; AbbVie: Consultancy, Honoraria, Research Funding; Pharmacyclics, LLC, an AbbVie Company: Consultancy, Honoraria; Gilead: Consultancy, Honoraria, Speakers Bureau. Hillmen:Pharmacyclics: Research Funding; Janssen: Honoraria, Research Funding; Roche: Honoraria, Research Funding; Gilead: Honoraria, Research Funding; Abbvie: Research Funding. Demirkan:Amgen: Consultancy. Coutre:Pharmacyclics, LLC, an AbbVie Company: Consultancy, Research Funding; AbbVie: Research Funding; Janssen: Consultancy, Research Funding. Barrientos:Gilead: Consultancy, Research Funding; Janssen: Consultancy; AbbVie: Consultancy, Research Funding. Barr:Pharmacyclics LLC, an AbbVie Company: Consultancy, Research Funding; AbbVie: Consultancy. O'Brien:Pharmacyclics, LLC, an AbbVie Company: Consultancy, Honoraria, Research Funding; Janssen: Consultancy, Honoraria. Fraser:Celgene: Research Funding; Janssen: Honoraria, Research Funding, Speakers Bureau. Jaeger:Janssen: Consultancy, Honoraria, Other: Travel, Accommodations, Expenses. Cramer:Janssen-Cilag: Consultancy, Honoraria, Other: Travel, Accommodations, Expenses, Research Funding; Novartis: Consultancy, Research Funding; GlaxoSmithKline: Research Funding; Mundipharma: Other: Travel, Accommodations, Expenses; Astellas: Other: Travel, Accommodations, Expenses; Roche: Honoraria, Other: Travel, Accommodations, Expenses, Research Funding; Gilead: Other: Travel, Accommodations, Expenses, Research Funding. Stilgenbauer:Novartis: Consultancy, Honoraria, Research Funding; Boehringer-Ingelheim: Consultancy, Honoraria, Research Funding; Genzyme: Consultancy, Honoraria, Research Funding; Janssen: Consultancy, Honoraria, Other: Travel, Accommodations, Expenses, Research Funding, Speakers Bureau; Mundipharma: Consultancy, Honoraria, Research Funding; AbbVie: Consultancy, Honoraria, Other: Travel, Accommodations, Expenses, Research Funding, Speakers Bureau; Sanofi: Consultancy, Honoraria, Research Funding; Gilead: Consultancy, Honoraria, Research Funding; Amgen: Consultancy, Honoraria, Research Funding; Celgene: Consultancy, Honoraria, Research Funding; Hoffman La-Roche: Consultancy, Honoraria, Research Funding; GlaxoSmithKline: Consultancy, Honoraria, Research Funding; Pharmacyclics LLC, an AbbVie Company: Consultancy, Honoraria, Other: Travel, Accommodations, Expenses, Research Funding, Speakers Bureau; Genentech: Consultancy, Honoraria, Research Funding. Salman:Janssen Research & Development: Employment, Equity Ownership, Other: Travel, Accommodations, Expenses. Solman:Pharmacyclics LLC, an AbbVie Company: Employment, Other: Travel, Accommodations, Expenses; AbbVie: Equity Ownership. Cheng:Johnson & Johnson: Equity Ownership; AbbVie: Equity Ownership; Pharmacyclics LLC, an AbbVie Company: Employment. Phelps:Johnson & Johnson: Employment, Equity Ownership. Ninomoto:Pharmacyclics LLC, an AbbVie Company: Employment; Amgen, Inc.: Equity Ownership; AbbVie: Equity Ownership. Howes:Janssen: Employment, Other: Leadership; Johnson & Johnson: Equity Ownership. James:Pharmacyclics LLC, an AbbVie Company: Employment; AbbVie: Equity Ownership. Hallek:Gilead: Consultancy, Honoraria, Other: travel support, Research Funding, Speakers Bureau; Amgen: Consultancy, Honoraria, Other: travel support, Research Funding, Speakers Bureau; Janssen-Cilag: Consultancy, Honoraria, Other: travel support, Research Funding, Speakers Bureau; Mundipharma: Consultancy, Honoraria, Other: travel support, Research Funding, Speakers Bureau; Celgene: Consultancy, Honoraria, Other: travel support, Research Funding, Speakers Bureau; AbbVie: Consultancy, Honoraria, Other: travel support, Research Funding, Speakers Bureau; F. Hoffmann-LaRoche: Consultancy, Honoraria, Other: travel support, Research Funding, Speakers Bureau.

Abstract Background: Smudge cells are ruptured CLL cells seen on blood smears of CLL patients. For over a century, smudge cells were thought to represent an artifact of slide preparation. We recently showed that smudge cell formation was inversely proportional to leukemic B cell vimentin content. Vimentin is a cytoskeletal protein critical for lymphocyte rigidity and migration; high vimentin content is related to poor prognosis in CLL. Concordantly, in an initial small cohort of patients from a single institution, we found that patients with a low (<30%) percentage of smudge cells on a blood smear have a shorter time to treatment (Mayo Clin Proc.2007;82:449–53). In the current study, we evaluated the impact of smudge cell percentage on prognosis of patients with CLL seen at member institutions of the CLL Research Consortium (CRC). Methods: Archived blood smears from untreated patients with CLL were evaluated for smudge levels. All blood smears were prepared manually in a standard fashion from blood obtained by CRC Tissue Core and stained with WrightGiemsa stain. Smudge cells were defined as broken cells with no intact cytoplasm and a disrupted nuclear membrane. A total of 200 lymphocytes and smudge cells were counted on each slide and the results were expressed as the percent smudge cells. The association between the percentage of smudge cells, prognostic factors (IgVH status, CD38, ZAP-70 and FISH) and time to initial therapy (TTT) was examined. Results: We calculated smudge cell percentage on blood smears obtained prior to treatment for 337 CLL patients. The median smudge cell percentage was 32 (range 2–95%). The percentage of smudge cells was lower in CD38 positive patients (mean 33% vs. 38% in CD38 negative patients, p=0.04). No difference in smudge cell percentage was observed based on IgVH gene mutation status or Zap70 expression. Smudge cell percentage as a continuous variable was associated with prolonged TTT, exponential coefficient 0.98, p=0.04. Using our previously published cutoff of 30% to stratify patients in low and high risk categories, the estimated median time to first therapy of patients with smudge cell percentage ≤30% (n=178) was 7.8 years versus not reached in patients in patients with > 30% (n=159) of smudge cells, p=0.0036, (Figure 1). Ten years from diagnosis, 62% of patients with ≤30% of smudge cells versus 39% of patients with >30% of smudge cells required therapy. In multivariate analysis, the low percentage of smudge cells (≤30%) was an independent predictor of the shortened time to treatment (HR 1.86, 95%CI 1.09–3.16, p=0.02). Conclusion: This multicenter study confirms our initial finding that the percentage of smudge cells on a blood smear is an independent predictor of clinical outcome in patients with CLL. The estimation of smudge cell percentage on routine blood smear provides a simple and inexpensive prognostic test available to nearly all patients with a diagnosis of CLL worldwide. It also allows reanalysis and risk stratification of previously completed trials provided that blood smears have been archived. Further studies of the role of the cytoskeleton in CLL biology are warranted and ongoing in our laboratory. Figure Figure

Abstract Introduction The natural history of patients (pts) with CLL varies considerably. Some pts may have asymptomatic, indolent disease, while others may require therapy soon after diagnosis. Clinical staging systems cannot identify early-stage pts at risk for inferior survival. Genetic and molecular markers can provide additional information regarding prognosis and response prediction, however their utilization is limited in routine practice (Mato, ASCO 2015) and results can be conflicting. There is a need to identify characteristics of early-stage CLL pts at highest risk of early progression and/or death. Such characteristics, if readily available and reproducible, may identify such pts as candidates for early-intervention studies; particularly in the era of kinase inhibitor therapy. The Connect CLL registry is the largest prospective study describing real-world management of a diverse cohort of 1494 CLL pts. In this analysis, we evaluate key factors that are associated with early CLL-associated mortality in clinical practice. Methods Connect CLL is a multicenter, observational cohort study aimed at understanding patterns of CLL management without a study-specific intervention. Eligible pts were adults with a clinical diagnosis of CLL, enrolled between 2010-2014 at 179 community (n=1311), 17 academic (n=155), and 3 government (n=28) sites within ≤ 2 months of initiating either a first line of therapy (LOT1), or a second-line or subsequent LOT (LOT≥2). The goal of this analysis was to assess potential predictors of death occurring within 18 months following initiation of frontline therapy (LOT1). Because CLL usually presents as an indolent disease associated with a prolonged clinical course, we chose to define early mortality as any death occurring ≤ 18 months following enrollment. Univariate logistic regression was performed against potential predictor variables including several clinical characteristics and comorbid conditions, race, treatment (Tx) site, insurance type, geographic region, presence of prior malignancies, genetic prognostic factors (FISH and cytogenetics) at enrollment, cell surface markers (Zap70, CD38), reasons for Tx initiation and Tx choice for LOT1 (BR or FCR). IgVH mutation status and genetic mutations (P53, Notch1, etc.) were not included as these tests were not readily performed in community practice. Predictors that were found to be significant at the 0.15 significance level were included in the multivariable logistic regression using a stepwise variable selection process, to identify factors associated with early mortality. Results 1344 pts completed 18 months of follow-up (n=1111) or died (n=233) within the first 18 months of study enrollment. Of these, 801 pts were enrolled in LOT1 (median time from diagnosis to LOT1 was 1.3 yrs; IQR, 0.1-3.7 yrs). 82 of 801 pts (10.2%) died within 18 months of LOT1 initiation; 41% of pts with available clinical information who died, were defined as early-stage (Rai stage 0-1). Of 26 potential predictors evaluated, 12 were associated with early mortality among LOT1 pts at the 0.15 significance level in univariate analyses including site, race, age>75, CD-38, del(17p), ECOG PS, CCI group, RAI stage, creatinine clearance, insurance status, reasons for Tx initiation, anemia and lymphocytosis. In multivariable logistic regression analyses we identified 4 independent predictors of mortality within 18 months of LOT1 initiation (Table): pt age, CD38 expression, baseline decreased hemoglobin (as a reason for Tx initiation), and race. Conclusions In CLL pts with Rai stage 0-1, we have identified 4 factors which are associated with increased risk of death within 18 months of initiation of frontline therapy. Interestingly, neither choice of FCR vs BR chemoimmunotherapy nor the presence of del(17p)/del(11q) were predictors of early mortality. Using these results, we are currently designing a model to predict early mortality in early-stage pts. Once validated, these results may provide a framework for trials targeting the "early stage, symptomatic" CLL pts for novel interventions. Table. Multivariable Analysis: Predictors of Early Mortality Odds ratio estimate 95% CI P -value LOT1, n=781 Age ≥75 years, yes vs no 2.2 1.4-3.5 0.0012 CD38 expression, positive vs negative/not specified 1.8 1.1-2.9 0.0182 Anemia as reason for treatment, yes vs no 2.1 1.3-3.4 0.0040 Race, other vs Caucasian 1.9 1.0-3.8 0.0501 Disclosures Farber: Gilead: Speakers Bureau; Genentech: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Celgene Corporation: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Janssen: Honoraria, Speakers Bureau; Pharmacyclics: Honoraria, Speakers Bureau. Mato:TG Therapeutics: Research Funding; Genentech: Consultancy; AbbVie: Consultancy, Research Funding; Pronai Pharmaceuticals: Research Funding; Gilead: Consultancy, Research Funding; Pharmacyclics: Consultancy, Research Funding; Celgene Corporation: Consultancy, Research Funding; Janssen: Consultancy. Nabhan:Celgene Corporation: Honoraria, Research Funding. Kipps:Pharmacyclics Abbvie Celgene Genentech Astra Zeneca Gilead Sciences: Other: Advisor. Flowers:Spectrum: Research Funding; Pharmacyclics: Research Funding; Infinity Pharmaceuticals: Research Funding; OptumRx: Consultancy; Gilead Sciences: Research Funding; Spectrum: Research Funding; Genentech: Research Funding; Onyx Pharmaceuticals: Research Funding; Acerta: Research Funding; OptumRx: Consultancy; Gilead Sciences: Research Funding; AbbVie: Research Funding; Pharmacyclics: Research Funding; Seattle Genetics: Consultancy; Onyx Pharmaceuticals: Research Funding; Genentech: Research Funding; Millennium/Takeda: Research Funding; Celegene: Other: Unpaid consultant, Research Funding; Janssen: Research Funding; Infinity Pharmaceuticals: Research Funding; Acerta: Research Funding; AbbVie: Research Funding; Seattle Genetics: Consultancy; Celegene: Other: Unpaid consultant, Research Funding; Janssen: Research Funding; Millennium/Takeda: Research Funding. Kay:Pharmacyclics: Research Funding; Hospira: Research Funding; Genentech: Research Funding; Gilead: Membership on an entity's Board of Directors or advisory committees, Research Funding; Tolero Pharma: Research Funding; Celgene Corporation: Membership on an entity's Board of Directors or advisory committees, Research Funding. Lamanna:Genentech-Abbvie: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Gilead: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene Corporation: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Infinity: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding. Grinblatt:Celgene Corporation: Consultancy, Honoraria, Speakers Bureau. Kozloff:Celgene: Consultancy, Speakers Bureau; Genentech: Consultancy, Speakers Bureau; Roche: Consultancy, Speakers Bureau; AbbVie: Consultancy. Sullivan:Celgene Corporation: Employment, Equity Ownership. Flick:Celgene Corporation: Employment, Equity Ownership. Kiselev:Celgene Corporation: Consultancy. Bhushan:Celgene Corporation: Employment, Equity Ownership. Swern:Celgene Corporation: Employment, Equity Ownership. Sharman:TG Therapeutics, Inc.: Research Funding; Celgene Corporation: Consultancy, Research Funding; Gilead: Consultancy, Honoraria, Research Funding, Speakers Bureau; Janssen: Research Funding; Calistoga: Honoraria; Roche: Research Funding; Pharmacyclics: Consultancy, Honoraria, Research Funding.

Abstract In chronic lymphocytic leukemia (CLL), it is recognized that germinal center experience by the cell-of-origin has an influence on the malignant phenotype and impacts clinical behavior. Measurement of the mutational status of the IGHV locus is a commonly used approach that subgroups patients according to this natural history. Other biomarkers correlating with these subgroups, such as ZAP70 expression and ZAP70 DNA methylation, are also used. Recently, we and others have found vast differences in global DNA methylation profiles among CLL cases that classifies distinct subgroups related to their natural history. Here we systematically evaluate various markers of natural history of the cell-of-origin for their ability to discriminate clinical outcomes. IGHV mutational status and ZAP70 expression are currently the most widely used prognostic markers of germinal center experience. These markers are generally concordant, with IGHV-unmutated associating with ZAP70 positivity. To compare their relative prognostic impact, we selected CLL cases from a large sample cohort collected as a prospective study maintained by the CLL Research Consortium that were discordant (IGHV-mutated and ZAP70(+) or IGHV-unmutated and ZAP70(-); n=192). We found a significant difference in time-to-first-treatment (TTFT; P=0.002) and overall survival (OS; P=0.032) between discordant groups, with curves separating according to IGHV as opposed to ZAP70 (Fig.1A). DNA methylation-based classification of CLL according to their natural history has not been compared to IGHV status. This classification strategy subgroups the majority of cases into low-programmed CLL (LP-CLL) or high-programmed (HP-CLL) subgroups that generally correlate with IGHV-unmutated/ZAP70(+) and IGHV-mutated/ZAP70(-), respectively, along with a minority of cases in an intermediate (IP-CLL) subgroup. We used our previously-described DNA methylation assay, based on MassARRAY interrogating 7 genomic loci including ZAP70, to group the 192 samples with discordant IGHV/ZAP70 results with 327 samples analyzed previously from the same cohort. DNA methylation (LP-CLL vs. HP-CLL) outperformed IGHV classification (unmutated vs. mutated) in terms of separating TTFT curves (n=432; LP-CLL vs. HP-CLL median difference=4.8 years, unmutated vs. mutated IGHV median difference=2.9 years) and OS curves (n=491; LP-CLL vs. HP-CLL median difference=12.9 years, unmutated vs. mutated IGHV median difference=6.8 years). To evaluate whether DNA methylation subgroups provide additional prognostic information to IGHV, we compared TTFT and OS within IGHV subtypes. We found that the ability of DNA methylation subgrouping to separate OS curves was maintained in the IGHV-unmutated subtype (P<0.001; Fig.1B) and TTFT in both IGHV-unmutated and mutated subtypes (P<0.001 and P=0.016, respectively). Conversely, IGHV subtypes did not significantly separate outcomes within DNA methylation subgroups (P>0.5 for all). Further investigation of the features of DNA methylation subgroups as defined revealed a strong bias in the frequency of several recurrent adverse prognostic somatic mutations. In particular 17p deletions and EGR2, SF3B1 and XPO1 mutations occurred frequently within the LP-CLL subgroup. We next explored whether the presence of each mutation correlated with clinical outcome within its corresponding DNA methylation subgroup, and found heterogenous results. EGR2 mutations separated TTFT in the LP-CLL subgroup (P=0.0009), but 17p, XPO1 and SF3B1 did not (P>0.3). This suggests that the cell-of-origin should be taken into account to accurately assess the clinical impact of somatic mutations. In summary, we demonstrate that among discordant cases for IGHV/ZAP70, IGHV appears to be a stronger prognostic factor for both TTFT and OS. When then evaluating the relative importance of DNA methylation subgrouping and IGHV status on TTFT and OS, we find that DNA methylation subgrouping is the more powerful prognostic factor. Among the markers of the cell-of-origin, DNA methylation is a strong tool for risk-stratifying patients and should be considered in development of risk prediction models. Figure 1. A) Kaplan-Meier analysis of TTFT of IGHV-mutated and ZAP70(+) versus IGHV-unmutated and ZAP70(-) (P=0.002, median difference=4.4 years). B) Kaplan-Meier analysis of OS in patients with unmutated IGHV separated by DNA methylation subgroup (P<0.001). Figure 1. A) Kaplan-Meier analysis of TTFT of IGHV-mutated and ZAP70(+) versus IGHV-unmutated and ZAP70(-) (P=0.002, median difference=4.4 years). B) Kaplan-Meier analysis of OS in patients with unmutated IGHV separated by DNA methylation subgroup (P<0.001). Disclosures Wierda: Acerta: Research Funding; Novartis: Research Funding; Gilead: Research Funding; Genentech: Research Funding; Abbvie: Research Funding. Brown:Infinity: Consultancy; Janssen: Consultancy; Gilead Sciences: Consultancy; Roche/Genentech: Consultancy; Celgene: Consultancy; Sun BioPharma: Consultancy; Pfizer: Consultancy; Abbvie: Consultancy. Kipps:Roche: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Research Funding; Gilead: Consultancy, Honoraria, Speakers Bureau; Pharmacyclics, LLC, an AbbVie Company: Consultancy, Honoraria; AbbVie: Consultancy, Honoraria, Research Funding.

Abstract Introduction: Results of several large clinical trials have demonstrated inferior outcomes when CLL patients (pts) with del(17p), or to a lesser extent del(11q), are treated with chemoimmunotherapy. Despite recent advances in targeted therapies, management of unfavorable-risk CLL pts remains challenging. The Connect CLL registry is the largest prospective study describing real-world management of a diverse cohort of CLL pts. Here we describe the management of genetically unfavorable-risk CLL pts enrolled in the Connect CLL Registry, and test associations between del(17p)/del(11q) mutations and treatment choice, timing of therapy, and outcomes. Methods: Connect CLL is a longitudinal, multicenter, observational cohort study of 1,494 CLL pts enrolled between 2010-2014 at 179 community (n=1,311), 17 academic (n=155), and 3 government (n=28) sites within ≤ 2 months of initiating any line of therapy (LOT). For this study, pts were categorized into 3 risk-groups based on FISH or cytogenetic results at enrollment: unfavorable risk [either del(17p)/del(11q) positive]; favorable risk [absence of del(17p)/del(11q)]; and unknown risk (testing not performed). Analyses were stratified by LOT at enrollment (LOT1 vs LOT≥2). The risk of an EFS defining event was compared across risk-groups stratified by LOT, including only pts who had been followed for ≥ 2 years (yr) or those who had an EFS defining event (death, progression/relapse, transformation) within 2 yr, using multivariable (MV) logistic regression. Logistic regression analyses of 18 potential predictors were performed, and significant predictors (0.10 significance level) were then included in a MV logistic regression model to identify factors associated with inferior 2 yr EFS. Results: Of 1,494 pts enrolled, 225 had unfavorable risk [del(17p), n = 94; del(11q), n = 131], 621 favorable risk, and 648 unknown risk. For pts enrolled at LOT1 (n = 889), 141 had unfavorable risk, 425 favorable risk, and 323 unknown risk. Baseline characteristics for unfavorable-risk pts in LOT1 included: median age 69 yr (62% age ≥ 65 yr); 91% White; 53% Rai stage ≥ 2; 86% community sites; 0.9 yr median time from dx to LOT1; 8% family history CLL. With the exception of age, unfavorable-risk pts treated at LOT1 with FCR (median age 63) and BR (median age 74) had similar demographic characteristics. Median time from dx to LOT1 was 1.0, 1.3, and 2.0 yr for unfavorable risk, favorable, and unknown-risk pts, respectively (P < 0.001 for comparison of the 3 risk-subgroups). Reasons for initiating treatment in unfavorable-risk pts were lymphadenopathy (42%), bone marrow failure (36%), and lymphocytosis (33%) (similar across the 3 risk-subgroups). The three most common therapies for unfavorable-risk pts in LOT1 were FCR (36%), BR (21%), and FR (6%). Favorable-risk (FCR 26%, BR 24%, and R 10%) and unknown-risk pts (FCR 22%, BR 18%, and R 17%) were treated similarly in LOT1. Median time from dx to LOT≥2 was 3.5 yr in unfavorable-risk pts (vs 4.1 yr in favorable and 4.6 yr in unknown risk pts; P = 0.04 for comparison of the 3 risk-groups). For unfavorable-risk pts in LOT≥2, BR (26%), R (11%), and FCR (10%) were most commonly prescribed. At any LOT, only 10% of unfavorable-risk pts participated in a clinical trial and 4% underwent allogeneic SCT. Overall, 33% (LOT1) and 68% (LOT≥2) of pts experienced an EFS defining event within 2 yrs. Pts with unfavorable risk (vs favorable risk) had significantly worse outcomes, with a higher-risk of death/progression/relapse/transformation within 2 yr (LOT1: 42% vs 29%, respectively, P = 0.008; and LOT≥2: 80% vs 61%, P = 0.004). Unfavorable-risk pts treated with either FCR or BR at LOT1 had similar risk of an EFS defining event (odds ratio [OR] 0.9, CI 0.6-1.5). Results from MV analyses of LOT1 pts identified unfavorable risk (OR 1.9; 95% CI 1.1-3.1; P = 0.0168) and age ≥ 75 years (OR 2.6; 95% CI 1.5-4.3; P = 0.007) as independent predictors of inferior EFS at 2 yr. Conclusions: Although similar treatment approaches were used among the 3 risk-subgroups, pts with unfavorable-risk CLL had inferior outcomes at LOT1 and LOT≥2. These results, from a real-world setting, validate the findings for unfavorable risk pts from large clinical trials. These data underscore the importance of performing genetic risk stratification on all CLL pts, and support consideration of clinical trials and/or B cell receptor targeted agents for unfavorable risk CLL pts when treatment is considered. Disclosures Mato: Janssen: Consultancy; AbbVie: Consultancy, Research Funding; Genentech: Consultancy; Pronai Pharmaceuticals: Research Funding; Celgene Corporation: Consultancy, Research Funding; Pharmacyclics: Consultancy, Research Funding; Gilead: Consultancy, Research Funding; TG Therapeutics: Research Funding. Nabhan:Celgene Corporation: Honoraria, Research Funding. Flowers:Celegene: Other: Unpaid consultant, Research Funding; Celegene: Other: Unpaid consultant, Research Funding; Spectrum: Research Funding; Infinity Pharmaceuticals: Research Funding; OptumRx: Consultancy; Onyx Pharmaceuticals: Research Funding; Janssen: Research Funding; AbbVie: Research Funding; Seattle Genetics: Consultancy; Onyx Pharmaceuticals: Research Funding; Pharmacyclics: Research Funding; Gilead Sciences: Research Funding; Genentech: Research Funding; Acerta: Research Funding; Genentech: Research Funding; Janssen: Research Funding; Millennium/Takeda: Research Funding; Infinity Pharmaceuticals: Research Funding; Spectrum: Research Funding; Millennium/Takeda: Research Funding; Pharmacyclics: Research Funding; OptumRx: Consultancy; Gilead Sciences: Research Funding; AbbVie: Research Funding; Seattle Genetics: Consultancy; Acerta: Research Funding. Kay:Pharmacyclics: Research Funding; Genentech: Research Funding; Tolero Pharma: Research Funding; Celgene Corporation: Membership on an entity's Board of Directors or advisory committees, Research Funding; Gilead: Membership on an entity's Board of Directors or advisory committees, Research Funding; Hospira: Research Funding. Kipps:Pharmacyclics Abbvie Celgene Genentech Astra Zeneca Gilead Sciences: Other: Advisor; Pharmacyclics Abbvie Celgene Genentech Astra Zeneca Gilead Sciences: Other: Advisor. Grinblatt:Celgene Corporation: Consultancy, Honoraria, Speakers Bureau. Lamanna:Genentech-Abbvie: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Gilead: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Infinity: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene Corporation: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding. Kozloff:Genentech: Consultancy, Speakers Bureau; Celgene: Consultancy, Speakers Bureau; Roche: Consultancy, Speakers Bureau; AbbVie: Consultancy. Sullivan:Celgene Corporation: Employment, Equity Ownership. Flick:Celgene Corporation: Employment, Equity Ownership. Kiselev:Celgene Corporation: Consultancy. Bhushan:Celgene Corporation: Employment, Equity Ownership. Swern:Celgene Corporation: Employment, Equity Ownership. Sharman:Gilead: Consultancy, Honoraria, Research Funding, Speakers Bureau; Calistoga: Honoraria; Roche: Research Funding; TG Therapeutics, Inc.: Research Funding; Celgene Corporation: Consultancy, Research Funding; Pharmacyclics: Consultancy, Honoraria, Research Funding; Janssen: Research Funding.

Abstract Introduction: The median age at diagnosis for CLL pts in the US is 72 years. As clinical trials in CLL have largely enrolled younger pt populations, data on disease and pts' characteristics, patterns of care, prognosis, and molecular features in aged CLL pts are limited. With this in mind, we conducted a prospective study to update critical demographic data and patterns of care for aged CLL pt. Patients and Methods: Connect®-CLL is a US-based prospective, longitudinal, multi-center, observational registry that is aimed at understanding patterns of CLL management without a study-specific intervention. We enrolled pts treated at an academic (n=155) or community (n=1340) setting between 2010 and 2014. Eligible pts were adults with a clinical diagnosis of CLL who required therapy 2 or less months after enrollment. Data on demographics, baseline characteristics, and treatment selection are presented here using descriptive statistics. Continuous variables are reported using appropriate measures of dispersion and central tendency (means, medians, ranges and standard deviations) while categorical variables are summarized as number and percentage of the analysis population. Results: Of 1495 enrolled pts, 457 (30.5%) were ≥75 years (57.3% males, 94.2% white). Rai stage III/IV was noted in 33% and B symptoms (predominantly fatigue (58.4%)) were observed in 68.1%. These and other clinical baseline characteristics appeared similar to pts younger than 75 years except that older pts had more prior malignancies (33.5% vs. 19.8%) and co-morbidities (69.3% vs. 53.6%). Cardiac, neurologic, and renal disorders were the most common morbidities in pts ≥75 years (11.5%, 8.8%, and 4.6% respectively). Imaging studies were performed in 157 (60.2%) older pts and in 416 (65.7%) pts less than 75 years prior to initial therapy. Percentage of pts with bulky nodes (> 5 cm) by imaging was similar in the two groups, 19.3% overall. Prognostic biomarker data were available on 247 pts (178 (72%) <75 years; 69 (28%) ≥75 years). While a higher proportion of older pts had CD38+ CLL (55.1% vs. 40.5%, P=0.038), the proportions of patients with ZAP-70+ CLL were similar between the two groups. In total, 137 (9%) older and 378 (25%) younger pts had 17p and 11q analysis by FISH at enrollment prior to first-line therapy. Of these, 27.0% of pts ≥75 years and 20.6% of pts <75 years had a deletion of either 17p or 11q (P=0.125). Out of all pts enrolled in the registry, 894 (60%) received first-line treatment (261 (29%) pts ≥75 years and 633 (71%) <75 years) as their indication for study entry. Amongst these treated pts, interim analysis shows (data cutoff date: 25 June 2014) progressive marrow failure was more commonly used as the indication for therapy in older pts compared to younger pts (52.1% vs. 38.5%; P<0.001), while splenomegaly was a more common cause for therapy in younger pts (16% vs. 9%; P<0.01). Rai stage III/IV at time of first therapy was 46% and 49% for younger and older pts, respectively. Progressive lymphocytosis was used as the indication for therapy in one third of pts regardless of age. Seventy-four percent of older CLL pts received first-line therapies containing rituximab (R) vs. 85% in pts <75 years (P<0.0001). R-bendamustine was the most common first-line regimen for CLL pts ≥75 (23.4%) while FCR was more commonly given to pts <75 years (32.5%). R-monotherapy was used in 18.8% of older pts versus 9.5% in pts <75 years (P<0.0001). Of note, approximately 25% of CLL pts ≥75 years did not receive R-based regimens for initial therapy. Conclusions: Connect®-CLL is the largest prospective, multicenter CLL registry in the United States. CLL Pts ≥75 years more frequently overexpress CD38 and may more commonly demonstrate high risk cytogenetics by FISH, although the difference did not reach statistical significance. Pts ≥75 years also more commonly had co-morbid diseases, and surprisingly 25% did not receive first-line R-based therapy. CLL pts are rarely included in front-line clinical trials (<3%). Given that novel therapies are increasingly available for CLL patients a continued analysis is warranted to determine their use in elderly vs younger patients as well. A longer follow up is needed to evaluate the impact of these findings on outcomes. Disclosures Nabhan: Celgene: Honoraria, Research Funding. Off Label Use: Lenalidomide in CLL. Kay:Celgene: Research Funding. Mato:Genentech, Celgene, Millenium: Speakers Bureau. Grinblatt:Celgene: Honoraria, Speakers Bureau. Kipps:Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding. Lamanna:Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding. Weiss:Celgene: Consultancy. Flinn:Celgene: Research Funding. Swern:Celgene: Employment, Equity Ownership. Street:Celgene: Employment, Equity Ownership. Kristen:Celgene: Employment, Equity Ownership. Flowers:Celgene, Prescription Solutions, Seattle Genetics, Millennium (unpaid), Genentech (unpaid) : Consultancy; Gilead, Spectrum, Millennium, Janssen: Research Funding.

Abstract BACKGROUND. The detection of minimal residual disease (MRD) at the level of 0.01%/10-4 or above is a strong independent predictor of reduced progression-free (PFS) and overall survival (OS) in patients with CLL treated with chemoimmunotherapy. Although newer agents such as B-cell receptor pathway inhibitors can result in prolonged survival without achieving complete response, there remains a important role for MRD analysis in assessing therapeutic strategies aimed at disease eradication and cure. This is particularly important in front-line trials for fit patients which now require at least five years of follow-up if PFS is used as an endpoint. The feasibility of using MRD as a surrogate or intermediate endpoint for accelerated approval of new treatments is under review by regulatory agencies but further prospective validation is required. At the same time technology is rapidly evolving and high-throughput sequencing (HTS) technologies now detect MRD at the 0.0001%/10-6 level. It is therefore important to determine the most effective approaches for quantifying MRD that are compatible with previous studies but sufficiently sensitive for current treatments. AIMS. This collaborative project had two objectives. First, to identify the simplest and most flexible flow cytometry panel capable of detecting MRD at the 0.01%/10-4 or lower, that is compatible with published data and independent of instrument/reagent manufacturer. Second, to compare the flow cytometry approach with HTS analysis using the ClonoSEQ assay (Adaptive Biotechnologies, Seattle, WA). METHODS AND RESULTS. A core panel of antibodies for MRD detection was identified by testing an 8-marker combination in 52 samples (27 post-treatment and 25 dilution study) and re-analysing data with serial exclusion of single markers to determine redundancy. A 1-tube core panel of CD19, CD20, CD5, CD43, CD79b, and CD81 was identified and validated against the previously published 2-tube 6-marker and 4-tube 4-marker ERIC panels in 76 samples (19 post-treatment and 57 dilution study). The results showed good concordance (for log-transformed data above the LoQ, linearity=0.977, Pearson correlation co-efficient=0.983, average difference=0.026 log, 95% limit of agreement 0.20log) and a limit of detection of 0.001%/10-5 for the 1-tube core panel. Inter-operator variation was similar to CML MRD monitoring with both experienced operators, or inexperienced cytometrists after ~1 hour of specific education, achieving a 95% limit of agreement less than 0.3log in samples with MRD levels ranging from 0.0001 – 100%. The flow cytometry approach was compared with the ClonoSEQ HTS assay in 109 samples (21 dilution study and 88 post-treatment samples, complete data currently available on 13/88). The assay was applicable to the vast majority CLL patients, often with two clonal markers. There was 94% concordance at the 0.01% (10-4) threshold (15 samples with ≥0.01% CLL by both methods, 14 samples with <0.01% by both methods, 1 sample with 0.03% CLL by HTS and <0.003% CLL by flow cytometry, and 1 sample with 0.005% CLL by HTS and 0.012% by flow cytometry. HTS detected CLL IGH sequences in 22% (7/31) samples with no detectable CLL cells by flow cytometry (i.e. CLL level 0.0001-0.001%, 3/13 patient samples and 4/18 dilution samples). HTS demonstrated a relatively high variability in quantification, as seen in previous studies, but with a clear superiority in the limit of detection and good linearity (linearity=0.905, Pearson correlation co-efficient=0.870, average difference=0.078 log, 95% limit of agreement 1.5 log). CONCLUSIONS. The 1-tube 6-marker flow cytometry core panel is compatible with published studies, manufacturer-independent and flexible, providing directly quantitative results to 0.001%/10-5 without requiring a pre-treatment sample. HTS requires further work to standardise the quantitative analysis and prospective validation but the ClonoSEQ assay is applicable to >95% of CLL patients, does not require viable cells and is extremely sensitive, detecting residual disease in a significant proportion of cases with <0.01% CLL. The results indicate that flow cytometry and HTS are complementary technologies with a combined approach offering the most reliable way of quantifying CLL at the 0.01%/10-4 threshold while allowing higher sensitivity in clinical trials aimed at disease eradication. Disclosures Rawstron: Roche: Honoraria; Biogen Idec: Consultancy; Gilead: Consultancy, Honoraria; Abbvie: Honoraria; BD Biosciences: Intrasure reagent Patents & Royalties; Celgene: Honoraria; GSK: Honoraria. Williamson:Adaptive Biotechnologies: Employment, Equity Ownership. Sanders:Adaptive Biotechnologies: Employment, Equity Ownership. Robins:Adaptive Biotechnologies: Consultancy, Equity Ownership, Patents & Royalties. Hallek:Celgene: Honoraria, Research Funding; Mundipharma: Honoraria, Research Funding; Roche: Honoraria, Research Funding; Janssen: Honoraria, Research Funding; GSK: Honoraria; Gilead: Honoraria. Hillmen:Roche: Honoraria, Research Funding; GSK: Honoraria, Research Funding; Janssen: Honoraria, Research Funding; Pharmacyclics: Honoraria, Research Funding; Gilead: Honoraria, Research Funding.