Chaffee, Kari G.

Abstract Introduction: Monoclonal B cell lymphocytosis (MBL) is an asymptomatic expansion of clonal CD19+/CD5+ B cells with less than 5x109/L cells in the peripheral blood and without other manifestations of chronic lymphocytic leukemia (CLL). Approximately 1% of MBL evolves to CLL requiring therapy per year; thus it is critical to develop more precise tools to identify which MBL will progress to CLL and require treatment. Patients and Methods: In this study, we performed targeted deep sequencing (TDS) on 49 high-count MBL individuals (median B-cell count 3.7x109/L; range 0.8-4.9x109/L) and explored the mutation status of 20 driver genes recurrently mutated in CLL. We analyzed the clonal evolution in 45 of these 49 MBLs by screening 2-4 sequential samples (average time between samples 56 months, range 10-119 months). At last follow-up, 19 cases (39%) had progressed to Rai>0, and 10 cases (20%) required treatment. Tumor and germ line DNAs were isolated from sorted CD5+/CD19+ and CD5-/CD19- cell populations, respectively. Overall, 154 samples from 49 MBL cases (105 tumor and 49 germ line) were screened using semiconductor sequencing technology. The latter genetic information was integrated with relevant clinical and biological parameters, and we evaluated the effect of driver mutations and clonal expansion on time to CLL progression and time to treatment (TTT). Results and Discussion: Our cohort consisted in 17 women and 32 men, with a median age of 66 years (range: 44-80). Five cases presented secondary diseases, including melanoma, lung and bladder cancer. Clinical and biological parameters were collected, including IGHV mutation status (mutated 66%, unmutated 34%), ZAP70 and CD49 expression (25% each). At presentation, 46% of cases had del(13q), 27% trisomy 12, 6% del(11q), and 4% del(17p). Overall, we found somatic non-synonymous mutations in 23 of 49 MBLs (47%) at the initial time point including 22% of cases with more than one mutated driver gene. The average depth of coverage was 730x, thus allowing the identification of small subclonal mutations. Recurrent mutations were found in most of the drivers: CHD2, DDX3X (8% of cases), FBXW7, NOTCH1, SF3B1 (6% each), ATM, BCOR, BIRC3, BRAF, KRAS, MED12, MYD88 and ZMYM3 (4% each). Furthermore, ITPKB, POT1, SAMHD1 and XPO1 were mutated in only one case, whereas no mutations were found in HIST1H1E, RIPK1 and TP53. In 4 individuals, we found two mutations in the same gene (BRAF, DDX3X, KRAS and SAMHD1). Genes that are known to be associated with disease progression in CLL were either mutated with significantly lower incidence (NOTCH1, SF3B1) or not mutated (TP53). Mutations were detected on average 45 months (range 9-73) prior to progression to CLL Rai>0 indicating the early origin of most driver gene mutations in the MBL/CLL continuum. The presence of driver mutations in MBL was associated with shorter TTT (median TTT: present: 96 months vs. not present: not reached, HR: 5.52, 95% CI: 1.2-26.2, P =0.015). Next, we looked at clonal expansion of driver mutations over time (defined as >2-fold change in the allelic frequency of driver mutations between time points). Of 20 MBLs with mutations at baseline who had sequential samples available, 10 cases showed clonal expansion. Seven out of 10 MBLs who required therapy showed clonal expansion, which was detected on average 15 months (range 6-30 month) prior to treatment. Finally, the detection of clonal expansion was significantly associated with reduced TTT (median TTT: clonal expansion: 21 months vs. no clonal expansion: 84 months, HR: 7.79, 95% CI: 1.94-31.3, P <0.001). Conclusion: We have confirmed the existence of recurrent mutations in most CLL putative driver genes at the premalignant MBL stage many years before progression to CLL. Furthermore, the early identification of driver mutations and its clonal expansion predicts a shorter TTT. Of note, clonal evolution under selective pressure has recently been linked to the onset of CLL progression after therapy. In this study, we characterized the clonal dynamics in the pre-malignant stages of the disease and underlined its impact on clinical outcome. Despite the relatively small size of the cohort, these findings suggest that the sequential monitoring of MBL individuals with a simple and reliable technique, such as TDS, will be at least of prognostic use and thus its incorporation in the disease stratification and clinical management should be further tested. Disclosures Fonseca: Novartis: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Patents & Royalties, Research Funding; Applied Biosciences: Membership on an entity's Board of Directors or advisory committees; Sanofi: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Patents & Royalties, Research Funding; Millennium: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Patents & Royalties, Research Funding; Binding Site: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Patents & Royalties, Research Funding; Onyx/Amgen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Patents & Royalties, Research Funding; Bayer: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Patents & Royalties, Research Funding; BMS: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Patents & Royalties, Research Funding; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Patents & Royalties, Research Funding. Kay:Gilead: Membership on an entity's Board of Directors or advisory committees, Research Funding; Pharmacyclics: Research Funding; Tolero Pharma: Research Funding; Genentech: Research Funding; Celgene Corporation: Membership on an entity's Board of Directors or advisory committees, Research Funding; Hospira: Research Funding.

Abstract Introduction. CLL primarily affects elderly individuals who frequently have comorbid health conditions. It is typically assumed non-CLL-related etiologies will be the ultimate cause of death for most CLL patients, particularly those with comorbid conditions at diagnosis. Methods. Between 9/2002 and 11/2014, 1174 patients with newly diagnosed CLL were enrolled in a prospective cohort study evaluating the natural history of CLL. Comorbidities were prospectively recorded at the time of diagnosis. Comorbidities arising during the course of disease were not considered for this analysis. Standardized longitudinal follow-up was performed in all patients every 6 months for the first 3 years after diagnosis and annually thereafter through 04/2015. Internal and external medical records, death certificates, and information from next of kin were centrally reviewed to determine cause of death, using a standardized protocol. Categorical and continuous variables were evaluated using the c2 or Fisher exact tests and the Mann-Whitney test, as appropriate. Results. Baseline characteristics at time of CLL diagnosis are shown in the Table. Over 80% of patients had 2 or more comorbidities at diagnosis (median 3, range 0-11). After a median follow up of 5 years, 224 (19%) patients have died. The cause of death could be accurately determined in 183 (82%) of these patients. The cause of death was due to CLL in 135 (74%), including 84 (46%) CLL progressions, 14 (8%) infections, and 37 (20%) other cancers. Death was due to non-CLL-related causes (such as congestive heart failure, stroke or chronic obstructive pulmonary disease) in the remaining 48 (26%) patients. On univariable analysis, age and number of comorbid health conditions were not related to whether or not the cause of death was related/unrelated to CLL. The only specific co-morbid condition at diagnosis that predicted for non-CLL related death was stroke (8% vs 1%, p=0.04). Unmutated IGHV was the only prognostic factor thatpredicted greater likelihood of CLL-related death (70% vs 50%, p=0.03). Conclusions. The majority of patients with CLL have multiple comorbidities at time of diagnosis. Despite this fact, CLL progression and/or CLL-related complications are the primary cause of death. The number and type of comorbidities at diagnosis have minimal relationship to whether or not the ultimate cause of death was CLL-related. In contrast, the CLL-specific characteristic IGHV status (but interestingly not FISH defects) correlates with cause of death. Collectively, these findings illustrate the need for more effective CLL therapy, particularly treatments that can be tolerated by patients with comorbid health conditions. It is hoped the signaling inhibitors may help address this unmet need. Table. Number (%), median [range] N=1174 Age (years) 63 [23-89] Males Females 791 (67) 383 (33) Creatinine-Clearance > (mL/min) 86 [10-252] B2M (mg/dL) 2.3 [1.1-13.2] Rai stage 0 I-II III-IV 604 (52) 512 (38) 54 (10) CD49d positive negative 277 (30) 638 (70) CD38 positive negative 332 (30) 780 (70) ZAP70 positive negative 380 (37) 650 (63) IGHV unmutated mutated 394 (44) 506 (56) FISH del13q negative +12 del11q del17p 395 (40) 175 (18) 276 (28) 90 (9) 50 (5) Comorbid health conditions Other cancers 237 (20) Stroke 38 (3) Cardiac disease 326 (28) Hypertension 472 (40) Respiratory 210 (18) Endocrinologic 165 (14) Diabetes 118 (10) Hyperlipidemia 485 (41) Rheumatologic 489 (42) Gastrointestinal 384 (33) Genitourinary 412 (35) Psychiatric 197 (17) DVT/PE 33 (3) Substance abuse 58 (5) Sexually transmitted disease 35 (3) Obesity 376 (32) # of Comorbidities 0 1 2 3 4 > 4 66 (6) 148 (13) 203 (17) 220 (19) 206 (17) 331 (28) Disclosures Kay: Hospira: Research Funding; Genentech: 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; Pharmacyclics: Research Funding; Tolero Pharma: Research Funding. Cerhan:Kite Pharma: Research Funding.

Monoclonal B cell lymphocytosis (MBL) has been shown to be the precursor condition that precedes overt diagnosis of chronic lymphocytic leukemia (CLL). Whereas CLL is classified with greater than or equal to 5 × 109/L B lymphocytes in the peripheral blood, MBL is a clonal expansion of B-cells below this threshold. MBL can be further divided into high- or low-count based on whether the B-cell count is above or below 0.5 × 109/L. Approximately 10% of the population over 40 is estimated to have MBL, increasing to >50% over the age of 90. While low-count MBL is unlikely to progress, 1-2% of high-count (HC) MBL individuals progress to CLL requiring therapy per year. It is debatable if all patients with detectable MBL should be classified as an entity requiring monitoring by a hematologist, especially for low-count MBL. In addition, the diagnosis of leukemia is distressing to patients; therefore, it is important to identify HC MBL patients that are more likely to progress to disease requiring treatment and thus should be monitored more closely. As the majority of MBL cases phenotypically resemble CLL, established prognostic markers including recurrent chromosomal aberrations, beta-2 microglobulin levels, and the mutational status of the Immunoglobulin heavy-chain variable region locus (IGHV) have been shown to predict time to treatment (TTT) and overall survival (OS) in a large retrospective study of MBL1. CLL patients can also be divided into three distinct epigenetic subtypes that reflect progressive DNA methylation changes that occurs during B cell development. These 'epitypes' termed low-programmed (LP), intermediate-programmed (IP), and high-programmed (HP) independently predict clinical outcomes irrespective of disease stage and treatment2. LP-CLL patients follow a generally unfavorable clinical course compared to the more indolent HP-CLL patients, while IP CLL patients display an intermediate outcome. Here we sought to determine if epitype forecasts progression to CLL and eventual clinical outcome for individuals with MBL. We analyzed 66 individuals diagnosed with HC MBL at the Mayo Clinic with a median follow-up of 6.3 years. Developmental epitype was determined using our novel Methylation-iPLEX technique that interrogates 34 CpGs and assigns epitype using a random forest model2. Seventy-seven percent of the MBL cases were assigned to one of the three epitypes: 42.4% HP, 19.7% IP, and 15.2% LP. The residual 23% remained unclassified due to ambiguous (low confidence) epigenetic patterns or insufficient purity (Figure 1A). The overall proportion of HP and IP epitypes in MBL were significantly greater than proportions observed in CLL cohorts (P<0.01). Epitypes remained stable over time as 20/21 cases for which we obtained a high-confidence epitype classification at multiple time points remained unchanged. Epitype significantly predicted MBL individuals progressing to treatment (P=0.001) with LP individuals progressing in a median of 5.6 years (P=0.049 and P=0.0002 versus IP and HP, respectively); median was not reached in HP or IP (Figure 1B). Epitype also predicted overall survival (OS) in MBL (P=0.04), with LP individuals having a significantly shorter OS (median of 11 years versus not reached in IP and HP; P=0.056 and P=0.048 versus IP and HP, respectively). In this study we evaluated a cohort of 66 HC MBL cases and determined that classification using developmental DNA methylation epitypes can be employed in HC MBL to aid in risk stratification. HC MBL patients classified as LP are more likely to progress to requiring treatment and have a significantly reduced OS. The epigenetic classification may help clinicians decide how closely and frequently a HC MBL individual needs to be monitored. Figure 1: (A) Breakdown of the epigenetic subtype assigned to 66 HC MBL samples. (B) Kaplan-Meier analysis of time to treatment and (C) overall survival of MBL patients separated by epitype. 1. Parikh, S. A. et al. Outcomes of a large cohort of individuals with clinically ascertained high-count monoclonal B-cell lymphocytosis. Haematologica103, e237-e240 (2018). 2. Giacopelli, B. et al. Developmental subtypes assessed by DNA methylation-iPLEX forecast the natural history of chronic lymphocytic leukemia. Blood blood.2019000490 (2019). doi:10.1182/blood.2019000490 Disclosures Byrd: Ohio State University: Patents & Royalties: OSU-2S; Janssen: Consultancy, Other: Travel Expenses, Research Funding, Speakers Bureau; BeiGene: Research Funding; TG Therapeutics: Other: Travel Expenses, Research Funding, Speakers Bureau; Novartis: Other: Travel Expenses, Speakers Bureau; TG Therapeutics: Other: Travel Expenses, Research Funding, Speakers Bureau; Genentech: Research Funding; Gilead: Other: Travel Expenses, Research Funding, Speakers Bureau; Novartis: Other: Travel Expenses, Speakers Bureau; Ohio State University: Patents & Royalties: OSU-2S; Gilead: Other: Travel Expenses, Research Funding, Speakers Bureau; Acerta: Research Funding; Genentech: Research Funding; Pharmacyclics LLC, an AbbVie Company: Other: Travel Expenses, Research Funding, Speakers Bureau; Pharmacyclics LLC, an AbbVie Company: Other: Travel Expenses, Research Funding, Speakers Bureau; Acerta: Research Funding. Shanafelt:Patent: Patents & Royalties: US14/292,075 on green tea extract epigallocatechin gallate in combination with chemotherapy for chronic lymphocytic leukemia; Merck: Research Funding; Polyphenon E International: Research Funding; Pharmacyclics: Research Funding; Genentech: Research Funding; Hospira: Research Funding; Glaxo-SmithKline: Research Funding; Abbvie: Research Funding; Cephalon: Research Funding; Celgene: Research Funding. Parikh:Genentech: Honoraria; Janssen: Research Funding; AstraZeneca: Honoraria, Research Funding; Pharmacyclics: Honoraria, Research Funding; MorphoSys: Research Funding; AbbVie: Honoraria, Research Funding; Acerta Pharma: Research Funding; Ascentage Pharma: Research Funding. Kay:Agios: Other: DSMB; MorphoSys: Other: Data Safety Monitoring Board; Infinity Pharmaceuticals: Other: DSMB; Celgene: Other: Data Safety Monitoring Board.

Abstract Introduction: Several prognostic models for overall survival (OS) in chronic lymphocytic leukemia (CLL) have been developed, but none were derived for patients (pts) with relapsed or refractory (R/R) CLL treated in the era of novel agents. We used a comprehensive approach to derive and validate a novel risk model for OS in 2,475 pts with R/R CLL who received therapy in randomized phase 3 trials evaluating ibrutinib (IBR), idelalisib (IDELA), and venetoclax (VEN) vs control arms, or the Mayo Clinic CLL Database (MCCD). Methods: In collaboration with Janssen, Gilead, Pharmacyclics, and Genentech/Roche, the analysis included 2,475 pts from 6 multicenter randomized phase 3 trials or the MCCD. Trials included were: IBR plus bendamustine-rituximab (BR) vs placebo plus BR (HELIOS: NCT01611090), IBR vs ofatumumab (RESONATE: NCT01578707), IDELA plus BR vs placebo plus BR (Study 115: NCT01569295); IDELA plus rituximab vs placebo plus rituximab (Study 116: NCT01539512); IDELA plus ofatumumab vs ofatumumab (Study 119: NCT01659021); VEN plus rituximab vs BR (MURANO: NCT02005471). All pts had R/R CLL and required treatment by iwCLL criteria. The model-building dataset (n=969) included pts from HELIOS and RESONATE, and was randomly assigned to the IBR/chemoimmunotherapy (CIT) training dataset (n=727) and IBR/CIT internal-validation dataset(n=242). Three independent external validation datasets included IDELA/CIT (n=897), VEN/CIT (n=389), and MCCD (n=220). We applied univariate and multivariate analyses (MVA) to 28 candidate clinical/laboratory and genetic prognostic factors to derive the risk model in the training dataset. The primary endpoint was OS. We assessed the need for separate models for targeted agents by interactions with treatment in univariate analyses. We proceeded to develop a single model for all pts with R/R CLL, as only one candidate factor (del(13q), interaction p value <0.1) demonstrated a marginal differential effect. We evaluated cutoffs for dichotomized covariates and for definition of risk groups after the final factors were selected. We fit a Cox regression on the training and internal/external validation datasets using the risk categories as the covariate to test the difference among the groups, and calculated the C-statistic as a measure of discrimination. Results: Of 11 selected factors with a significant univariate effect on OS (p<0.1), 6 were independently prognostic in the MVA of OS (p<0.05): Baseline β2-microglobubulin (B2M), lactate dehydrogenase (LDH), hemoglobin (HGB), IGHV mutational status, number of prior therapies, and time from initiation of last therapy. We excluded number of prior therapies from the model, reasoning that its optimal cutoff might change with advances in CLL therapies. IGHV did not remain significant (p=0.0592) in MVA of 5 remaining prognostic factors. Notably, del(17p) was not independently prognostic for OS. The final risk model consisted of 4 prognostic factors: B2M ≥5 mg/L, LDH elevated, HGB <110 mg/L for women or <120 mg/L for men, and time from initiation of last therapy <24 months. Because hazard ratios for each factor were similar (range, 0.55-0.68) with overlapping confidence intervals, 1 point was assigned to each factor. The resulting score separated pts into low (score 0-1), intermediate (score 2-3), and high risk (score 4) groups. Our risk model was prognostic for OS in the IBR/CIT training dataset (C-statistic 0.74 [95% CI 0.60-0.85]; log-rank p<0.0001) and was validated in the IBR/CIT internal validation (C-statistic 0.79 [95% CI 0.56-0.97]; log-rank p<0.0001), as well as in all 3 external validations: IDELA/CIT dataset (CS=0.71, 95% CI 0.59-0.81; log-rank p<0.001); VEN/CIT dataset (CS=0.76, 95% CI 0.66-0.85; log-rank p=0.01); MCCD (CS=0.61, 95% CI 0.56-0.66; log-rank p<0.001). Conclusions: We present the first validated risk model for OS in R/R CLL in the era of targeted therapies. Our model consists of 4 readily available factors (B2M ≥5 mg/L, LDH elevated, HGB <110 mg/L for women or <120 mg/L for men, and time from initiation of last therapy <24 months), identifies 3 prognostic groups with significantly different OS, and has been validated for pts treated with all FDA approved targeted therapies: IBR, IDELA, and VEN. This identifies a well-defined cohort of pts with R/R disease with an unmet clinical need who are suitable for prospective trials targeting these pts. An exploratory analysis regarding prediction of MRD is ongoing. Figure. Figure. Disclosures Darif: Jannsen: Employment. Londhe:Jannsen: Employment. Xing:Gilead Sciences, Inc.: Employment. Mun:Genentech: Employment, Equity Ownership. Kay:Cytomx Therapeutics: Membership on an entity's Board of Directors or advisory committees; Pharmacyclics: Membership on an entity's Board of Directors or advisory committees, Research Funding; Tolero Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees; Infinity Pharm: Membership on an entity's Board of Directors or advisory committees; Agios Pharm: Membership on an entity's Board of Directors or advisory committees; Morpho-sys: Membership on an entity's Board of Directors or advisory committees; Acerta: Research Funding; Gilead: Membership on an entity's Board of Directors or advisory committees; Janssen: Membership on an entity's Board of Directors or advisory committees. Shanafelt:Mayo Clinic: Patents & Royalties: Physician Well-being Index, Medical Student Well-being Index, Well-being index; GlaxoSmithKline: Research Funding; Genentech: Research Funding; Pharmacyclics: Research Funding; Janssen: Research Funding; Abbvie: Research Funding; Celgene: Research Funding. Furman:Loxo Oncology: Consultancy; Pharmacyclics LLC, an AbbVie Company: Consultancy; Sunesis: Consultancy; TG Therapeutics: Consultancy; Verastem: Consultancy; Gilead: Consultancy; Incyte: Consultancy, Other: DSMB; Acerta: Consultancy, Research Funding; Genentech: Consultancy; Janssen: Consultancy; AbbVie: Consultancy. Hillmen:Janssen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Abbvie: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Research Funding; Pharmacyclics: Research Funding; Alexion Pharmaceuticals, Inc: Consultancy, Honoraria; Novartis: Research Funding; Gilead Sciences, Inc.: Honoraria, Research Funding; F. Hoffmann-La Roche Ltd: Research Funding; Acerta: Membership on an entity's Board of Directors or advisory committees. Sharman:Acerta: Consultancy, Research Funding; Pharmacyclics, an AbbVie Company: Consultancy, Research Funding. Seymour:AbbVie: Consultancy, Honoraria, Research Funding; Celgene: Consultancy; F. Hoffmann-La Roche Ltd: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Genentech Inc: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Honoraria, Research Funding. Jones:Celgene: Employment, Equity Ownership. Ferrante:Jannsen: Employment. Dreiling:Gilead: Employment. Mobasher:F. Hoffmann-La Roche Ltd: Other: Ownership interests non-PLC; Genentech Inc: Employment. Stark:Genentech: Employment. Reddy:Actinium Pharmaceuticals: Employment, Equity Ownership; Pharmacyclics: Employment. Howes:Janssen: Employment. James:Pharmacyclics: Employment. Bhargava:Gilead: Employment. Zelenetz:Novartis/Sandoz: Consultancy; Abbvie: Research Funding; Celgene: Consultancy; Amgen: Consultancy; Gilead: Consultancy, Research Funding; AstraZeneca: Consultancy; Genentech/Roche: Consultancy, Research Funding.

Abstract Introduction: Observation is the standard of care for asymptomatic early stage chronic lymphocytic leukemia (CLL) however these cases follow a heterogenous course. Recent studies show novel biomarkers can delineate indolent from aggressive early stage disease and current clinical trials are exploring the role of early intervention in high risk cases. Although several scoring systems have been established in CLL, most are designed for overall survival, do not circumscribe early stage disease, and require cumbersome calculations relying on extensive laboratory and clinical information. Aim: We propose a novel laboratory-based prognostic calculator to risk stratify time to first treatment (TTFT) in early stage CLL and guide candidate selection for early intervention. Methods: We included 1574 cases of early stage CLL in an international cohort from Italy, the United Kingdom and the United States using a training-validation model. Patient information was obtained from participating centers in accordance with the Declaration of Helsinki. The training cohort included 478 Rai 0 cases from a multicenter Italian cohort, all referred to a single center (Clinical and Experimental Onco-Hematology Unit of the Centro Riferimento Oncologico in Aviano, IT) for immunocytogenetic lab analyses. Considering TTFT as an endpoint, we evaluated 8 variables (age>65, WBC>32K, 17p-, 11q-, +12, IGHV status, CD49d+, gender) with univariate and multivariate Cox regression internally validated using bootstrapping procedures. FISH thresholds were 5% for 11q-, and +12 and 10% for 17p-. Cases were categorized according to the hierarchical model proposed by Dohner. IGHV status was considered unmutated at ≥98%. CD49d+ was set at >30%. WBC cutoff of >32K was established by maximally selected log rank analysis. Variables were weighted based on the proportion of their normalized hazard ratios rounded to the nearest whole integer. We used recursive partitioning for risk-category determination and Kaplan-Meier analysis to generate survival curves. We compared the concordance index (C-index) of our model with the CLL international prognostic index (CLL-IPI) for 381/478 cases in the training cohort with available beta-2-microglobulin data and for all validation cohorts. We used 3 independent single-center cohorts for external validation. Results: The training cohort had 478 cases of Rai 0 CLL with a median (95% CI) TTFT of 124 months (m) (104-183m). Five prognostic variables emerged with respect to TTFT, and each assigned a point value of 1 or 2 according to their respective normalized HR values as follows: 17p-, and UM IGHV (2 pts); 11q-, +12, and WBC>32K (1 pt). We identified three risk groups, based on point cut-offs of 0, 1-2, and 3-5 established by recursive partitioning analysis with a median (95% CI) TTFT of 216m (216-216m), 104m (93-140m) and 58m (44-68m) (p<0.0001, C-index 0.75) for the low, intermediate, and high-risk groups, respectively (figure 1). A comparison with the CLL-IPI was possible in 381 cases with available beta-2-microglobulin data. In this subset, the C-index was 0.75 compared to 0.68 when patient risk groups were split according to the CLL-IPI. The scoring system was then validated in 3 independent cohorts of early stage CLL: i) Gemelli Hospital in Rome, IT provided 144 Rai 0 cases. Median (95% CI) TTFT was 86m (80-94m, 95% CI). Median (95% CI) TTFT for the low, intermediate and high-risk groups was 239m (239-239m), 98m (92-132m) and 85m (60-109m) respectively (p=0.002 between low and intermediate groups, p=0.09 between intermediate and high groups; C-index 0.64 v 0.60 for CLL-IPI). ii) Cardiff University Hospital in Wales, UK provided 395 Binet A cases. Median (95% CI) TTFT was 74 m (67-81m) overall and NR, 111m (97-146m) and 70m (29-114m) for the low, intermediate and high-risk groups respectively (p<0.001 between low and intermediate groups, p=0.009 between intermediate and high groups; C-index 0.63 v 0.63 for CLL-IPI). iii) Mayo Clinic in Rochester, MN provided 557 Rai 0 cases. Median (95% CI) TTFT was 127m (96m-NR) overall and NR, 76m (64m-NR) and 36m (31-59m) for the low, intermediate and high-risk groups respectively (p<0.0001; C-index 0.72 v 0.68 for CLL-IPI). Conclusion: We present a novel laboratory-based scoring system for Rai 0/Binet A CLL to aid case selection in risk-adapted treatment for early disease. Further comparison to existing indices is needed to verify its utility in the clinical setting. Disclosures Zaja: Novartis: Honoraria, Research Funding; Takeda: Honoraria; Abbvie: Honoraria; Celgene: Honoraria, Research Funding; Amgen: Honoraria; Janssen: Honoraria; Sandoz: Honoraria. Fegan:Roche: Honoraria; Napp: Honoraria; Janssen: Honoraria; Gilead Sciences, Inc.: Honoraria; Abbvie: Honoraria. Pepper:Cardiff University: Patents & Royalties: Telomere measurement patents. Parikh:AstraZeneca: Honoraria, Research Funding; Janssen: Research Funding; MorphoSys: Research Funding; Abbvie: Honoraria, Research Funding; Gilead: Honoraria; Pharmacyclics: Honoraria, Research Funding. Kay:Janssen: Membership on an entity's Board of Directors or advisory committees; Acerta: Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees; Pharmacyclics: Membership on an entity's Board of Directors or advisory committees, Research Funding; Infinity Pharm: Membership on an entity's Board of Directors or advisory committees; Cytomx Therapeutics: Membership on an entity's Board of Directors or advisory committees; Tolero Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees, Research Funding; Morpho-sys: Membership on an entity's Board of Directors or advisory committees; Gilead: Membership on an entity's Board of Directors or advisory committees; Agios Pharm: Membership on an entity's Board of Directors or advisory committees.

Abstract Introduction: Richter transformation (RT) refers to transformation of chronic lymphocytic leukemia (CLL) to an aggressive lymphoma, most commonly diffuse large B-cell lymphoma (DLBCL). Most studies on the management of RT were either small retrospective series or early phase non-randomized trials before the era of novel agents. The natural history, prognostication and optimal treatment of patients with RT remain undefined. Here we report the clinical characteristics and outcomes of a large series of RT from a single center. Methods: Biopsy-confirmed RT (limited to non-Hodgkin lymphoma) diagnosed from 4/1993 to 4/2018 were identified from the Mayo Clinic CLL database. Clinical characteristics, treatment information and follow-up data were abstracted by chart review. Overall survival (OS) was defined as time from RT diagnosis to death from any cause and analyzed using the Kaplan-Meier method. Statistical analysis was done in SAS 9.4. Results: A total of 204 patients with CLL who developed RT were identified. The median age at CLL diagnosis was 62 years (range 22-85), and 148 (73%) were male. The median time to transformation was 4.7 years (range 0-34.5). Prior to RT, 68 (33.3%) patients received no treatment for CLL, 109 (53.4%) received chemoimmunotherapy (CIT) only, and 27 (13.2%) received at least one novel agent (idelalisib, ibrutinib, or venetoclax) for CLL. The median lines of CLL therapy prior to RT was 2 (range 0-13). The median age at RT diagnosis was 69 years (range 30-88). Pathology of RT was DLBCL and high grade B-cell lymphoma in 193 (94.6%) and 11 (5.4%) patients, respectively. The median LDH was 306 IU/L (range 99-9000). 62/125 (49.6%) patients had bulky disease (≥ 5 cm), and the median PET SUVmax was 13.9 (range 2.9-30.0). 45/131 (34.4%) patients had del(17p) or TP53 mutation, 12 (9.2%) had del(11q), 21 (16.0%) had trisomy 12, 27 (20.6%) had del(13q), and 25 (19.1%) had normal FISH. The CLL and RT were clonally related in 12/21 (57.1%) patients. For the transformed lymphoma, cell of origin by Han's algorithm was germinal center B cell-like (GCB) and non-GCB in 31/100 (31.0%) and 69/100 (69.0%) patients, respectively. EBV was positive in 14/52 (26.9%) patients. The median Ki-67 was 80% (range 10-100). Myc and Bcl-2 were positive by IHC in 31/43 (72.1%) and 83/103 (80.6%) patients, respectively; 27/56 (48.2%) were double-expressors. MYC, BCL2, and BCL6 rearrangement was positive by FISH in 18/68 (26.5%), 10/34 (29.4%), and 4/31 (12.9%) patients, respectively; 8/66 (12.1%) were double/triple-hit. The most common first-line treatment (Table 1 notes) of RT was R-CHOP-like regimen (n=114, 65.5%). Other treatments included R-EPOCH-like (n=6, 3.4%), high-intensity chemotherapy (n=15, 8.6%), novel agents (eg, ibrutinib, venetoclax, pembrolizumab; n=19, 10.9%), other chemotherapy (n=12, 6.9%), and palliative therapy (n=8, 4.6%). Response to first-line treatment was CR in 57 (38.0%), PR in 33 (22.0%), SD in 18 (12.0%), and PD in 42 (28.0%) patients. The median OS of the entire cohort after RT diagnosis was 12.0 months. The median OS for patients who received no prior CLL treatment, CIT only or at least one novel agent for CLL were 65.5, 7.3, and 12.0 months, respectively (P<0.0001; Figure 1). Of note, in patients who received CIT only for CLL, ~10% and 60% received high-intensity and R-CHOP/R-EPOCH-like chemotherapy, respectively, as first-line RT therapy. In contrast, in patients who had prior novel agents for CLL, 56% and 26% were treated with novel agents and R-CHOP/R-EPOCH-like chemotherapy, respectively, as first-line RT therapy. Patients with or without del(17p)/TP53 mutation had a median OS of 8.3 and 12.8 months, respectively (P=0.046). Patients who were treated with high intensity chemotherapy, R-CHOP/R-EPOCH-like regimens, novel agents, and other therapies for RT had a median OS of 35.1, 14.4, 10.9 and 6.1 months, respectively (P=0.02; Figure 2). OS comparisons by CLL/RT clonal relationship, double expressor or double/triple-hit status are shown in Table 1, with no significant differences noted. Conclusions: Over two thirds of RT were the non-GCB subtype, and about half were Myc/Bcl-2 double expressors. Patients who developed RT without prior CLL therapies had a significantly better OS. In contrast, patients who had received prior CLL therapies had poor outcomes. Myc/Bcl-2 double expressor and MYC/BCL2/BCL6 double/triple hit status had no impact on OS. Disclosures Kenderian: Humanigen: Research Funding; Novartis: Patents & Royalties; Tolero Pharmaceuticals: Research Funding. Kay:Infinity Pharm: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees; Agios Pharm: Membership on an entity's Board of Directors or advisory committees; Morpho-sys: Membership on an entity's Board of Directors or advisory committees; Tolero Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees; Acerta: Research Funding; Cytomx Therapeutics: Membership on an entity's Board of Directors or advisory committees; Pharmacyclics: 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. Parikh:Janssen: Research Funding; Abbvie: Honoraria, Research Funding; AstraZeneca: Honoraria, Research Funding; MorphoSys: Research Funding; Gilead: Honoraria; Pharmacyclics: Honoraria, Research Funding. Ding:Merck: Research Funding.

Abstract The characteristic clinical heterogeneity observed in chronic lymphocytic leukemia (CLL) reflects its biological diversity and as a consequence, better understanding of the biology of this disease has led to the identification of a number of useful prognostic tools. Advances in massively parallel sequencing technologies have unearthed genetic and epigenetic heterogeneity and reinforced the concept that specific genetic lesions and the development of genomic complexity can modulate the pathology of CLL. In addition, DNA methylation profiling has shown that CLL patients can be grouped into at least three clinically relevant epigenetic subgroups with distinct outcomes. However, despite this explosion in knowledge, defining the prognosis of individual CLL patients remains a significant clinical challenge. Consequently, there is a need to identify prognostic tests that can provide reliable personalized risk assessments. We have recently shown that a subset of CLL patients manifest telomere dysfunction, which drives genomic instability and clonal evolution. We went on to show an association between short, dysfunctional telomeres and 'high-risk' genetic lesions1,2. Importantly, longitudinal analysis confirmed that tumor cell telomere length remained remarkably stable throughout the course of the disease suggesting that the telomere length is fixed at an early point in disease pathogenesis making it a reliable predictor of both clinical progression and overall survival (OS) regardless of when the measurement is made. In this study, we generated telomere length profiles in 224 newly diagnosed, early stage CLL patients seen at the Mayo clinic in Rochester using high-throughput single telomere length analysis (HT-STELA). We then tested the ability of telomere length to predict time to treatment (TTT) and OS and compared its predictive power with DNA methylation profiling and CLL-IPI scores. Patients with a mean telomere length inside the fusogenic range (IFR) had a significantly shorter time to treatment (P<0.0001; HR = 3.2) and OS (P = 0.0027; HR = 2.3) when compared to patients with a mean telomere length outside the fusogenic range (OFR) (Figure 1A and B). When considering epigenetic subgroups, we found a strong association between telomere length and 'epitype'; high programmed (HP) CLL cells had significantly longer telomeres than intermediate programmed (IP), and low programmed (LP) CLL cells (P = 0.0002 and P<0.0001 respectively). Furthermore, bifurcation of the HP and IP groups into telomere length OFR and IFR subsets identified patients with distinctly different TTT (P = 0.0008; HR = 2.74) and OS (P = 0.004; HR = 2.4). 79/81 (97.5%) of the LP group had short telomere length IFR, but when we divided the LP group above and below the mean telomere length of the fusogenic range (≤/>2.17kb), patients with the shortest telomeres had a significantly worse OS (P = 0.034; HR = 2.83). Telomere length was also strongly associated with CLL-IPI score; the CLL-IPI low-risk group (CLL-IPI 1) had the longest telomeres and the CLL-IPI high-risk group (CLL-IPI 4) had the shortest telomeres (P<0.0001). More heterogeneity in telomere length was observed in the CLL-IPI 1 and 2 groups and patients with telomere length IFR in these 'low-risk' groups showed significantly shorter TTT (P<0.0001; HR = 4.24) and overall survival (P<0.0001; HR = 10.1) (Figure 1C and 1D). Finally, multivariate modelling, using Cox proportional hazards with forward selection, revealed that telomere length retained independent prognostic significance when CLL-IPI score was entered in the model for time to treatment (HR = 1.42; P = 0.023) and OS (HR = 1.68; P = 0.008). In conclusion, telomere length analysis provides more accurate prognostic information about the progression and outcome of CLL patients and can augment the prognostic power of the CLL-IPI for early stage CLL. References Lin TT et al. Blood. 2010;116(11):1899-907. Britt-Compton et al. Leukemia. 2012; 26(4):826-30. Disclosures Norris: Cardiff University: Patents & Royalties: Telomere measurement patent. Shanafelt:GlaxoSmithKline: Research Funding; Genentech: Research Funding; Pharmacyclics: Research Funding; Jansen: Research Funding. Kay:Acerta: Research Funding; Tolero Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees, Research Funding; Cytomx Therapeutics: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees; Janssen: Membership on an entity's Board of Directors or advisory committees; Pharmacyclics: Membership on an entity's Board of Directors or advisory committees, Research Funding; Agios Pharm: Membership on an entity's Board of Directors or advisory committees; Gilead: Membership on an entity's Board of Directors or advisory committees; Infinity Pharm: Membership on an entity's Board of Directors or advisory committees; Morpho-sys: Membership on an entity's Board of Directors or advisory committees. Fegan:Napp: Honoraria; Gilead Sciences, Inc.: Honoraria; Roche: Honoraria; Abbvie: Honoraria; Janssen: Honoraria. Baird:Cardiff University: Patents & Royalties: Telomere measurement patents. Pepper:Cardiff University: Patents & Royalties: Telomere measurement patents.

Abstract Clonal hematopoiesis of indeterminate potential (CHIP) is defined as somatic mutations in clonal blood cells of individuals without any other hematological abnormalities and have been identified using whole exome sequencing (WES) from pre-specified genes. CHIP is reportedly associated with aging, reduced overall survival, cardiovascular disease, and hematologic disease, mainly myeloid cancers. Additionally, whole genome sequencing and WES studies in CLL cases have identified putative CLL driver genes with recurrent somatic variants or copy number alterations. Here, we investigate the associations of CHIP and mutations in CLL somatic driver genes with risk of CLL and MBL, a precursor to CLL. Using WES, 571 individuals from 132 CLL families were sequenced from two different sites, Mayo Clinic (92 families) and National Cancer Institute (NCI, 40 families), including 251 CLLs, 74 MBLs, and 246 unaffected relatives. DNA was extracted from buccal cells (Mayo Clinic) and peripheral blood (NCI). Allele counts from gVCF files were extracted for specific genes and analyzed. Variants were annotated using the Variant Effect Predictor in Ensembl, genome build GRCh37 (http://useast.ensembl.org/index.html). Highly-sensitive flow cytometry of the blood was used to screen for MBL. CHIP was defined as in Genovese et al., 2014 as the presence of any of the following: A) disruptive mutations in ASXL1, TET2, or PPM1D; B) missense mutation p.617F in JAK2; C) disruptive or missense mutations in DNMT3Alocalized to exons 7 to 23D) any mutations reported at least 7 times in hematological malignancies in the Catalogue of Somatic Mutations in Cancer (COSMIC); and (E) No variant allelic fraction (VAF) threshold. CLL driver genes included disruptive or missense mutations in ATM, BIRC3, MYD88, NOTCH1, SF3B1, TP53, EGR2, POT1, NFKBIE, XPO1, or FBXW7with VAF greater than 0.1. Mutations from COSMIC and CLL driver genes were excluded if the minor allele frequency in 1000 Genomes was greater than 0.5%. Data was analyzed using logistic regression adjusting for age, sex, and site. Odds ratios (OR) and 95% confidence intervals (CI) were estimated. Median age for CLL, MBL, and unaffected was 63, 61.5, and 54, respectively. The unaffected relatives were significantly younger than both the CLL and MBL groups (p<0.0001), with no difference between CLL and MBL (p=0.46). A total of 70 (12%) individuals from 54 different families had CHIP. CHIP was characterized in the majority of samples as mutations in DNMT3A, ASXL1, and TET2with 43, 9 and 8 individuals, respectively. As expected, CHIP was associated with age (p<0.001) but not sex (p=0.87). CHIP was present in 42 (17%) CLLs, 9 (12%) MBLs, and 19 (8%) unaffected relatives. There was a significant association between the presence of CHIP and CLL risk compared to unaffected relatives (OR= 2.19, 95% CI: 1.20 - 4.13, p=0.013) but not with MBL risk (OR=1.34, 95% CI: 0.54 - 3.11, p=0.51). Using the CLL driver genes, we found mutations in 119 (47%) CLL, 20 (27%) MBL, and 70 (29%) unaffected relatives. Mutations in CLL driver genes were associated with CLL risk compared to unaffected relatives (OR = 2.51, 95% CI: 1.64 - 3.87, p<0.0001), but there was no evidence for increased MBL risk (OR = 1.00). These results held when we subset the analysis to the Mayo only data where the DNA was extracted from buccal cells. This study further supports the association between mutations in CHIP related genes and risk of hematologic malignancies, in particular CLL. This is the first report of the prevalence of CHIP in MBLs. Although MBL and CHIP represent clonal population of blood cells, our data suggest the clones are different. Mutations in CLL driver genes are also associated with CLL risk but show little to no evidence with increased MBL risk. Interestingly, our unaffected relatives of CLL cases also had a high frequency of CLL driver mutations. Future studies are needed to understand these acquired mutations (both in CHIP- and CLL driver- related genes) in the unaffected to MBL to CLL continuum. Disclosures Parikh: Abbvie: Honoraria, Research Funding; Pharmacyclics: Honoraria, Research Funding; MorphoSys: Research Funding; AstraZeneca: Honoraria, Research Funding; Gilead: Honoraria; Janssen: Research Funding. Ding:Merck: Research Funding. Cerhan:Jannsen: Other: Scientific Advisory Board; Celgene: Research Funding; Nanostring: Research Funding. Shanafelt:Jansen: Research Funding; Pharmacyclics: Research Funding; Genentech: Research Funding; GlaxoSmithKline: Research Funding. Kay:Morpho-sys: Membership on an entity's Board of Directors or advisory committees; Infinity Pharm: Membership on an entity's Board of Directors or advisory committees; Cytomx Therapeutics: Membership on an entity's Board of Directors or advisory committees; Janssen: Membership on an entity's Board of Directors or advisory committees; Agios Pharm: Membership on an entity's Board of Directors or advisory committees; Pharmacyclics: Membership on an entity's Board of Directors or advisory committees, Research Funding; Acerta: Research Funding; Tolero Pharmaceuticals: 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; Celgene: Membership on an entity's Board of Directors or advisory committees.

Abstract Background MBL is a precursor state to CLL, with a prevalence of ~5-8% in the general population and 15-18% in CLL families. To date, 41 single nucleotide polymorphisms (SNPs) have been found to be associated with CLL. We previously showed that a PRS of the weighted average of the number of risk alleles of these SNPs is associated with CLL risk using cases and controls from the International Lymphoma Epidemiology (InterLymph) Consortium. We validated this score in an independent sample of CLL cases and controls from the Genetic Epidemiology of CLL (GEC) Consortium, a cohort of families with ≥2 members with CLL. In the CLL families we also reported an association between the PRS and MBL risk, where 93% of MBLs were low-count MBL. Here we evaluate the PRS in an independent sample ascertained from the Mayo Clinic Biobank. Methods The Mayo Clinic Biobank is a large-scale bio-repository of adult patients assembled to provide a wide array of health-related research studies. Biobank participants (N=2530) were screened for MBL using a highly-sensitive, 8-color (CD38, CD45, Kappa, Lambda, CD19, CD23, CD5 and CD20) flow-cytometry assay on stored cryopreserved peripheral blood mononucleotide cells. Low-count and high-count MBL were defined as those individuals who had <85% and ≥85 clonal B-cells out of total B-cells, respectively. Individuals without MBL ("controls") were frequency matched to MBLs based on age and sex, after excluding non-Caucasian and non-interpretable flow results. Participants were genotyped using the Illumina OmniExpress array. Standard genotyping quality-control metrics were applied. We computed the PRS as previously published and categorized it by quintiles based on values previously used with InterLymph controls. The middle quintile served as the reference category. We used logistic regression to estimate odds ratios (ORs) and 95% confidence intervals (CI), adjusting for age at consent, sex, and socioeconomic status. In addition, we compared the median PRS by low-count MBL and high count MBL using the Mann-Whitney U test. Results We identified 276 MBLs overall and 2109 controls; 97% (N=267) of MBLs had low-count MBL and only 3% (N=9) had high-count MBL. We then selected 844 controls frequency matched to the MBL cases on age and sex. Family history of leukemia/lymphoma was 11.3% among MBLs overall and 8.8% among controls. The median PRS was 7.88 and 7.46 (Figure) among MBLs overall and controls, respectively. The continuous PRS had a 1.75-fold increased risk for MBL overall (CI:1.5-2.1, P=2.65x10-12). Compared to the middle quintile, the highest quintile had 2.2-fold increased risk for MBL overall (CI:1.5-3.3, P=1.46x10-4) and the lowest quintile had 0.6-fold decreased risk (CI:0.4-0.96, P=0.03). The median PRS for low-count and high count MBL were 7.86 and 8.04, respectively. The continuous PRS had a 1.75-fold increased risk for low-count MBL (CI:1.5-2.1, P=4.81x10-12) and a 1.89-fold increased risk for high-count MBL (CI:0.8-4.2, P=0.13), however, the effect is not statistically significant between high-count and low-count MBL (P=0.89). Conclusion In this independent sample, we validated our previous findings that the CLL PRS is associated with MBL risk. Although the sample size of high-count MBL was limited (N=9), there is evidence of a weak trend towards a higher PRS compared to low-count MBL and controls Larger sample size of high-count MBL is warranted in order to stratify MBL risk by low-count and high-count MBL. These results may help identify individuals at higher risk of MBL including low-count and high-count MBL, beyond the risk associated with age and family history of CLL. Figure: Polygenic risk score distribution by control and MBL status Figure. Figure. Disclosures Parikh: Janssen: Research Funding; Pharmacyclics: Honoraria, Research Funding; Gilead: Honoraria; Abbvie: Honoraria, Research Funding; AstraZeneca: Honoraria, Research Funding; MorphoSys: Research Funding. Cerhan:Nanostring: Research Funding; Celgene: Research Funding; Jannsen: Other: Scientific Advisory Board. Kay:Janssen: Membership on an entity's Board of Directors or advisory committees; Acerta: Research Funding; Infinity Pharm: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees; Agios Pharm: Membership on an entity's Board of Directors or advisory committees; Pharmacyclics: Membership on an entity's Board of Directors or advisory committees, Research Funding; Cytomx Therapeutics: Membership on an entity's Board of Directors or advisory committees; Gilead: Membership on an entity's Board of Directors or advisory committees; Tolero Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees, Research Funding; Morpho-sys: Membership on an entity's Board of Directors or advisory committees. Shanafelt:Genentech: Research Funding; Pharmacyclics: Research Funding; GlaxoSmithKline: Research Funding; Jansen: Research Funding.