Cerhan, James R.

Introduction Primary mediastinal large B cell lymphoma (PMBCL) is a rare non-Hodgkin lymphoma (NHL) with a female predominance; often presenting with a large anterior mediastinal mass. Though PMBCL has clinical and molecular features overlapping with Hodgkin lymphoma, it is a distinct entity defined by the World Health Organization classification. PMBCL is heterogeneously treated, and most patients receive front line therapy with either rituximab, cyclophosphamide, doxorubicin, vincristine, prednisone (R-CHOP) with radiotherapy (RT), or the more intensive etoposide, prednisone, vincristine, cyclophosphamide, doxorubicin with rituximab (EPOCH-R) regimen. Diagnosis of PMBCL is made using clinicopathologic criteria and radiographic imaging, however gene expression profiling (GEP) studies reveal a characteristic genotypic signature distinct from diffuse large B cell lymphoma (DLBCL). Molecular classification of PMBCL using the Lymph3Cx assay from formalin-fixed paraffin-embedded tissue (FFPE) is feasible, reproducible, and highly concordant in a training and validation cohort (Mottok et al. Blood 2018). Using a multicenter cohort of patients, we sought to estimate the rate of mis-match among patients with a clinical diagnosis of PMBCL using Lymph3Cx, and describe treatment selections and outcomes for each group. Methods Patients were identified from a cohort of patients with newly diagnosed NHL from the University of Iowa and Mayo Clinic Molecular Epidemiology Resource, and the Lymphoma Epidemiology of Outcomes cohort. Patients were enrolled between 2002-2019, and included if they had clinically defined PMBCL. FFPE was retrieved from hematopathology archives of participating academic centers. All diagnoses of PMBCL were based on expert hematopathology review at the time of therapy, and all cases underwent classification by GEP using the Lymph3Cx assay. Lymph3Cx was performed in the clinical lab at the Mayo Clinic in Arizona: Contiguous unstained sections were deparaffinized and macrodissected to enrich for tumor content before RNA isolation;100-200 ng of total RNA was used in an nCounter Elements XT, hybridized, and processed the following day using the nCounter FLEX system. Raw counts were processed through the Lymph3Cx algorithm and results reported as probability of PMBCL (≥0.90 as PMBCL, ≤0.10 as DLBCL all other results "Unclear PMBCL/DLBCL") (A. Mottok et al, Blood, 2018). For cases classified as DLBCL, the Lymph2Cx cell-of-origin classifier results was reported (Scott et al, JCO, 2016). Time to event endpoints were described with Kaplan-Meier plots by groups defined by mismatch status and compared with a logrank test. Binary outcomes will be presented with 90% exact confidence intervals. Results Fifty patients were identified. Median age was 35 years (range 19-70). Sixty four percent were women. Median follow up was 47 months. Treatments included R-CHOP (44%), EPOCH-R (44%), and MACOP-B [methotrexate with leucovorin rescue, doxorubicin, cyclophosphamide, vincristine, prednisone, and bleomycin] (6%), other (4%). Ten patients (20%) had events (defined as progression or death). Three patients in the entire cohort (6%) died. The Kaplan-Meier estimated survival at 47 months (median follow-up) is 92%. The Lymph3Cx assay yielded gene expression data of sufficient quality in 47/50 cases (94%, 90% CI=85.2, 98.3%). Of 47 cases clinically identified as PMBCL, 5 unclear were DLBCL/PMBCL and 1 was Germinal Center B cell subtype of DLBCL. Among these 6 patients, 4 received R-EPOCH (66%), 1 received R-CHOP (16.6%). One patient had missing treatment data. One patient had an event requiring subsequent therapy; all patients remain alive. Conclusions Using 47 patients with PMBCL defined by histology, clinical and radiographic findings, and molecular features, we demonstrate high concordance between clinical phenotype and molecular genotype of PMBCL by Lymph3Cx. Among the 6 patients not classified as PMBCL, most received R-EPOCH. Differences in outcome by mis-match status await longer follow-up and further accrual of subjects to our data base. Our data suggest molecular genotyping may have a role in mediastinal presentations of large cell lymphoma to optimize treatment decision making. Disclosures Maurer: Nanostring: Research Funding; Pfizer: Membership on an entity's Board of Directors or advisory committees; Celgene / BMS: Research Funding; Morphosys: Membership on an entity's Board of Directors or advisory committees; Kite: Membership on an entity's Board of Directors or advisory committees. Cerhan:BMS/Celgene: Research Funding; NanoString: Research Funding. Flowers:AbbVie: Consultancy, Research Funding; Kite: Research Funding; Burroughs Wellcome Fund: Research Funding; Genentech, Inc./F. Hoffmann-La Roche Ltd: Consultancy, Research Funding; Denovo Biopharma: Consultancy; Celgene: Consultancy, Research Funding; Cancer Prevention and Research Institute of Texas: Research Funding; TG Therapeutics: Research Funding; Eastern Cooperative Oncology Group: Research Funding; V Foundation: Research Funding; Bayer: Consultancy; National Cancer Institute: Research Funding; Millennium/Takeda: Consultancy, Research Funding; Gilead: Consultancy, Research Funding; Acerta: Research Funding; Spectrum: Consultancy; Pharmacyclics/Janssen: Consultancy; Karyopharm: Consultancy; OptumRx: Consultancy; Leukemia and Lymphoma Society: Membership on an entity's Board of Directors or advisory committees; BeiGene: Consultancy. Friedberg:Acerta Pharma - A member of the AstraZeneca Group, Bayer HealthCare Pharmaceuticals.: Other; Astellas: Consultancy; Bayer: Consultancy; Kite Pharmaceuticals: Research Funding; Portola Pharmaceuticals: Consultancy; Roche: Other: Travel expenses; Seattle Genetics: Research Funding.

Background The importance of the immune system in modulating the trajectory of lymphoma outcomes has been increasingly recognized. We recently showed that CD4+ cells are associated with clinical outcomes in a prospective cohort of almost 500 patients with follicular lymphoma (FL). Specifically, we showed that the absence of CD4+ cells inside follicles was independently associated with increased risk of early clinical failure. These data suggest that the composition, as well as the spatial distribution of immune cells within the tumor microenvironment (TME), play an important role in FL. To further define the architecture of the TME in FL we analyzed a FL tumor section using the Co-Detection by Indexing (CODEX) multiplex immunofluorescence system. Methods An 8-micron section from a formalin-fixed paraffin-embedded block containing a lymph node specimen from a patient with FL was stained with a cocktail of 15 CODEX antibodies. Five regions of interest (ROIs) were imaged using a 20X air objective. Images underwent single-cell segmentation using a Unet neural network, trained on manually segmented cells (Fig 1A). Cell type assignment was done after scaling marker expression and clustering using Phenograph. Each ROI was manually masked to indicate areas inside follicles (IF) and outside follicles (OF). Relative and absolute frequencies of cell types were calculated for each region. Cellular contacts were measured as number and types of cell-cell contacts within two cellular diameters. To identify proximity communities, we clustered cells based on number and type of neighboring masks using Phenograph. The number of cell types and cellular communities were calculated inside and outside follicles after adjustment for total IF and OF areas. The significance of cell contact was measured using a random permutation test. Results We identified 13 unique cell subsets (11 immune, 1 endothelial, 1 unclassified) in the TME of our FL section (Fig. 1A). The unique phenotype of each subset was confirmed using a dimensionality reduction tool (t-SNE). The global composition of the TME varied minimally across ROIs and consisted primarily of B cells, T cells, and macrophages subsets - in decreasing order of frequency. Higher spatial heterogeneity across ROIs was observed in the frequency of T cell subsets in comparison to B cells subsets. Inspecting the spatial distribution of T cell subsets (Fig. 1B), we observed that cytotoxic T cells were primarily located in OF areas, whereas CD4+ T cells were found in both IF and OF areas. Notably, the majority of CD4+ T cells inside the follicles expressed CD45RO (memory phenotype), while most of the CD4+ T cells outside the follicles did not. Statistical analysis of the spatial distribution of CD4+ memory T cell subsets confirmed a significant increase in their frequency inside follicles compared to outside (20.4% vs 11.2%, p < 0.001; Fig. 1D). Cell-cell contact analysis (Fig 1C) showed increased homotypic contact for all cell types. We also found a higher frequency of heterotypic contact between Ki-67+CD4+ memory T cells and Ki-67+ B cells. Pairwise analysis showed these findings were statistically significant, indicating these cells are organized in niches rather than randomly distributed across image. Analysis of cellular communities (Fig. 1C) identified 13 niches, named according to the most frequent type of cell-cell contact. All CD4+ memory T cell subsets were found to belong to the same neighborhood (CD4 Memory community). Analysis of the spatial distribution of this community confirmed that these niches were more frequently located inside follicles rather than outside (26.3±4% vs 0.004%, p < 0.001, Fig. 1D). Conclusions Analysis of the TME using CODEX provides insights on the complex composition and unique architecture of this FL case. Cells were organized in a pattern characterized by (1) high degree of homotypic contact and (2) increased heterotypic interaction between activated B cells and activated CD4+ memory T cells. Spatial analysis of both individual cell subsets and cellular neighborhoods demonstrate a statistically significant increase in CD4+ memory T cells inside malignant follicles. This emerging knowledge about the specific immune-architecture of FL adds mechanistic details to our initial observation around the prognostic value of the TME in this disease. These data support future studies using modulation of the TME as a therapeutic target in FL. Figure 1 Disclosures Galkin: BostonGene: Current Employment, Patents & Royalties. Svekolkin:BostonGene: Current Employment, Current equity holder in private company, Patents & Royalties. Postovalova:BostonGene: Current Employment, Current equity holder in private company. Bagaev:BostonGene: Current Employment, Current equity holder in private company, Patents & Royalties. Ovcharov:BostonGene: Current Employment, Current equity holder in private company, Patents & Royalties. Varlamova:BostonGene: Current Employment, Current equity holder in private company, Patents & Royalties. Novak:Celgene/BMS: Research Funding. Witzig:AbbVie: Consultancy; MorphSys: Consultancy; Incyte: Consultancy; Acerta: Research Funding; Karyopharm Therapeutics: Research Funding; Immune Design: Research Funding; Spectrum: Consultancy; Celgene: Consultancy, Research Funding. Nowakowski:Nanostrings: Research Funding; Seattle Genetics: Consultancy; Curis: Consultancy; Ryvu: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other; Kymera: Consultancy; Denovo: Consultancy; Kite: Consultancy; Celgene/BMS: Consultancy, Research Funding; Roche: Consultancy, Research Funding; MorphoSys: Consultancy, Research Funding. Cerhan:BMS/Celgene: Research Funding; NanoString: Research Funding. Ansell:Trillium: Research Funding; Takeda: Research Funding; Regeneron: Research Funding; Affimed: Research Funding; Seattle Genetics: Research Funding; Bristol Myers Squibb: Research Funding; AI Therapeutics: Research Funding; ADC Therapeutics: Research Funding.

Abstract Immune dysfunction is a hallmark of CLL/MBL; ~25% individuals with newly diagnosed CLL have hypogammaglobulinemia at diagnosis. In this study of newly diagnosed CLL/MBL, we sought to a) investigate the association between serum immunoglobulins and specific mutations identified by next generation sequencing, and b) determine if serum immunoglobulin levels predict TFT, independent of the CLL-International Prognostic Index (CLL-IPI) and tumor mutational load (TML). We used the Mayo Clinic CLL Resource to identify newly diagnosed CLL/MBL individuals consented within 2 years of diagnosis. Baseline clinical characteristics and the CLL-IPI score was ascertained on all individuals. DNA was extracted from peripheral blood CD5+/CD19+ clonal B-cells; and the entire coding regions of 60 somatically recurring mutated CLL driver genes were enriched using a customized Agilent SureSelect gene panel and sequenced in Illumina Hiseq4000. Serum immunoglobulins were quantitated by radial immunodiffusion using Immunoplates. The relationship between serum immunoglobulins was assessed: a) with individual gene mutations (mutated vs. not); and b) using the TML score (using a value of 0, 1, and 2 or greater [2+] mutated genes). The impact of serum immunoglobulin levels on TFT was analyzed for all individuals. Of 356 individuals identified, 80 (22%) had MBL, median age was 63 years (28-87), 261 (73%) were male, and the CLL-IPI distribution was: low risk (n=149, 42%), intermediate risk (n=112, 32%), and high/very high risk (n=94, 26%). We observed 43%, 39%, and 18% individuals with 0, 1, or 2+ mutated genes, respectively. The most commonly mutated genes were NOTCH1 (13%), SF3B1 (13%), TP53 (8%), ATM (8%), BIRC3 (7%), and NFKBIE (7%). Using standard cutoffs, 135 (38%) individuals had low serum IgG, 73 (21%) had low serum IgA, and 154 (44%) had low serum IgM at diagnosis. There were no differences in individual gene mutation frequencies between individuals with low and normal serum IgG. In contrast, individuals with low serum IgA were more likely to have mutations in NOTCH1 (25% vs 10%) and ATM (14% vs 7%) compared to those with normal IgA, and individuals with low serum IgM were more likely to have mutations in NOTCH1 (19% vs 9%) and BIRC3 (11% vs 4%) compared to those with normal IgM (all p-values <0.05). There were no significant differences in individuals with low serum IgG based on TML scores of 0, 1, and 2+ (31%, 35%, 40%, respectively, p=0.28). However, individuals with low serum IgA and low serum IgM were more likely to have a higher TML (8%, 20%, 36% for IgA, p<0.0001; and 35%, 45%, 54% for IgM, p<0.0001, for TML scores of 0, 1, and 2+, respectively). After a median follow-up of 6.9 years, 157 individuals required CLL therapy. After adjusting for CLL-IPI and TML, low serum IgG, low serum IgA, and low serum IgM (hazard ratio [HR]: 1.8, 2.3, and 2.4, respectively, all p-values <0.05) were independently associated with a shorter TFT. Our study shows that individuals with CLL/MBL who harbor mutations in NOTCH1, ATM and BIRC3 are more likely to have low serum IgA and IgM at diagnosis. Serum IgA and IgM (but not serum IgG) levels decrease with an increase in the TML score. Finally, low serum immunoglobulins can predict TFT, independent of the CLL-IPI and TML, which has important implications for risk stratification in newly diagnosed CLL/MBL. Citation Format: Sameer A. Parikh, Cristine Allmer, Geffen Kleinstern, Nicholas J. Boddicker, Shulan Tian, Cecilia B. De Campos, Laura A. Bruins, Daniel R. o"brien, Brian F. Kabat, Kari G. Rabe, Aaron D. Norman, Huihuang Yan, Xing Li, Timothy G. Call, Wei Ding, Jose F. Leis, Saad J. Kenderian, Celine M. Vachon, James R. Cerhan, Neil E. Kay, Susan L. Slager, Esteban Braggio. Serum immunoglobulins are an independent prognostic marker of time to first therapy in newly diagnosed chronic lymphocytic leukemia (CLL) and monoclonal B-cell lymphocytosis (MBL) [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 6466.

Abstract Background Factors associated with chronic immune stimulation such as transplantation, autoimmune disorders and chronic infections have been linked to the development of lymphoma, while atopic conditions have been associated with a lower risk of lymphoma. Vaccinations, in part due to their ability to induce immune responses, have been hypothesized to play a role in lymphoma etiology, but there are few studies with mixed results and limited data on lymphoma subtypes. Methods We used a clinic-based study of 2461 newly diagnosed lymphoma cases and 2253 frequency-matched controls enrolled from 2002-2015. Participants self-reported lymphoma risk factors and history of vaccinations for hepatitis A, hepatitis B, chicken pox, yellow fever and influenza. Pathology was centrally reviewed, and included 644 chronic lymphocytic leukemia, 528 follicular (FL), 414 diffuse large B-cell (DLBCL), 170 marginal zone, 127 mantle cell, 119 T-cell, 181 Hodgkin, and 278 other lymphoma subtypes. We used polytomous logistic regression to estimate odds ratio (OR) and 95% confidence intervals (CI) of vaccinations with lymphoma risk overall and for subtypes, adjusting for age, sex, socioeconomic status, body mass, alcohol use, smoking history, recreational sun exposure, and family history of hematological malignancies. Results The median age at diagnosis for lymphoma cases was 64 years (range: 18-93) and 58% were male; the median age at enrollment for controls was 62 years (range: 18-91) and 53% were male. The most common vaccinations among controls were influenza (90%) followed by hepatitis B (41%), hepatitis A (30%), chicken pox (20%), and yellow fever (11%). Vaccination for influenza was inversely associated with lymphoma risk overall (OR=0.75, CI:0.61-0.93, P=0.01), with an inverse trend for increasing number of vaccinations (P=0.0001). In subtype analyses, Influenza vaccination was inversely associated with risk of FL (OR=0.59, CI:0.43-0.80, P=0.001), DLBCL (OR=0.64, CI:0.44-0.92, P=0.02) and other lymphoma subtypes (OR=0.66, CI:0.43-1.00, P=0.05). Vaccination for hepatitis A was inversely associated with lymphoma risk (OR=0.79, CI:0.65-0.96, P=0.02), with no significant subtype-specific associations. No evidence of association with vaccination for hepatitis B (OR=0.95, CI:0.79-1.14) and chicken pox (OR=0.83, CI:0.68-1.02) for lymphoma risk overall or for any subtypes. Although no evidence of an association with vaccination for yellow fever (OR=0.81, CI:0.62-1.06) for lymphoma risk, it was inversely associated with DLBCL risk (OR=0.42, CI:0.22-0.80). Conclusion Vaccination for influenza and hepatitis A were inversely associated with overall lymphoma risk. DLBCL was associated with vaccination for influenza and yellow fever. FL was associated with vaccination for influenza. Understanding the underlying mechanisms for a putative protective effect of certain vaccines could provide new clues to the etiology and prevention of lymphoma. Citation Format: Geffen Kleinstern, Dennis P. Robinson, Carrie A. Thompson, Melissa C. Larson, Cristine Allmer, Grzegorz S. Nowakowski, Timothy G. Call, Stephen M. Ansell, Andrew L. Feldman, Neil E. Kay, Thomas M. Habermann, Susan L. Slager, James R. Cerhan. Vaccination history and risk of lymphoma and its major subtypes [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 1054.

AbstractObjectiveTo examine the prevalence of hypogammaglobulinemia in chronic lymphocytic lymphoma (CLL) patients and to test the hypothesis that patients with hypogammaglobulinemia have a distinct clinical profile and outcome.MethodsImmunoglobulin levels (IgA, IgG, IgM, IgE) were measured in newly diagnosed, treatment naïve banked samples of 150 patients with CLL followed prospectively for outcomes. Cox regression models were used to assess the effects of clinical variables on overall survival (OS).ResultsThe median age of the selected CLL cohort was 64 years with a male predominance; 96.2% of the patients were white. Fifty‐nine deaths occurred during a median follow up of 6.8 years. Hypogammaglobulinemia in CLL was common in our cohort with 88 (58.7%, 95% CI: 50.4‐66.6%) patients having a measurable isotype deficiency. The most common Ig deficiency was IgM (44.0%). IgA deficiency or low IgE was associated with higher Rai stages as well as with higher white blood cell counts at presentation. Any immunoglobulin deficiency was not associated with overall survival.ConclusionA significant proportion of treatment‐naïve CLL patients had underlying Ig deficiencies – both in isolation and in isotype combinations. Although a deficiency of IgA or IgE was associated with more severe disease at presentation, the impact of this association was mild.

Abstract Chronic lymphocytic leukemia (CLL) is the most common adult leukemia in Western countries. It has a strong genetic basis, showing a ~ 8-fold increased risk of CLL in first-degree relatives. Genome-wide association studies (GWAS) have identified 41 risk variants across 41 loci. However, for a majority of the loci, the functional variants and the mechanisms underlying their causal roles remain undefined. Here, we examined the genetic and epigenetic features associated with 12 index variants, along with any correlated (r2 ≥ 0.5) variants, at the CLL risk loci located outside of gene promoters. Based on publicly available ChIP-seq and chromatin accessibility data as well as our own ChIP-seq data from CLL patients, we identified six candidate functional variants at six loci and at least two candidate functional variants at each of the remaining six loci. The functional variants are predominantly located within enhancers or super-enhancers, including bi-directionally transcribed enhancers, which are often restricted to immune cell types. Furthermore, we found that, at 78% of the functional variants, the alternative alleles altered the transcription factor binding motifs or histone modifications, indicating the involvement of these variants in the change of local chromatin state. Finally, the enhancers carrying functional variants physically interacted with genes enriched in the type I interferon signaling pathway, apoptosis, or TP53 network that are known to play key roles in CLL. These results support the regulatory roles for inherited noncoding variants in the pathogenesis of CLL.

8052 Background: A significant proportion of patients with FL experience an early relapse and a subsequent poor outcome. While several prognostic indices have been developed, none were designed to predict early failure. Recently, we established that lack of intrafollicular CD4+ T-cell expression predicted risk of early failure, and integrating this microenvironment biomarker with the Follicular Lymphoma International Prognostic Index, termed BioFLIPI, further improved identification of FL patients at risk of early failure ( Blood 2019;134(suppl1):121). However, the microenvironment may be influenced by the genetic composition of tumor. We investigated whether the CD4 biomarker and BioFLIPI were impacted by genetic features of the tumor as assessed by a 23-gene expression prognostic score ( Lancet Oncol 2018;19:549-61). Methods: Of the 186 cases with FL grade 1-3A treated with immunochemotherapy (IC) in our prior study, 152 had digital expression quantification of 23 selected genes (23-GEP score), which used RNA from formalin-fixed, paraffin-embedded samples. Event-free survival (EFS) was defined as time from diagnosis to progression, relapse, retreatment, or death. Early failure was defined as failing to achieve EFS at 24 months. Risk of early failure was estimated using odds ratios (ORs) and 95% confidence intervals from logistic regression models. We also used Cox regression to assess associations with continuous EFS and overall survival (OS). Results: 28% of patients failed to achieve EFS24. Lack of CD4+ intrafollicular expression (38% of patients, OR = 2.33, p = 0.024) and high risk 23-GEP score (26% of patients, OR = 3.52, p = 0.001) each predicted early failure, and in a multivariable model that included FLIPI, both CD4+ (OR = 2.26, p = 0.046) and 23-GEP score (OR = 2.26, p = 0.0.057) remained predictors. Similarly, BioFLIPI modeled as a continuous score (1-4, OR per one point increase = 2.31, p < 0.001) predicted early failure, and the association remained (OR = 2.14, p < 0.001) when the high risk 23-GEP score (OR = 2.79, p = 0.013) was included in the model. When stratified on 23-GEP score, BioFLIPI was a stronger predictor of early failure in low risk (74%, OR = 2.51, p = 0.002) relative to high risk (26%, OR = 1.55, p = 0.27) patients. Similar patterns were observed for EFS and OS. Conclusions: CD4+ T-cell infiltrate and tumor gene expression appear to be independently predictive of early failure in newly diagnosed FL patients treated with IC. Future studies should integrate and validate these measures.

8026 Background: CLL progression and CLL-related complications (infections and second malignancies) were the leading cause of death (COD) in a prospective cohort of CLL patients (Strati, BJH 2017). The CLL-IPI integrates major clinical and molecular prognostic factors and stratifies patients into 4 risk groups with distinct prognosis. It is unknown if COD differs according to CLL-IPI risk group in patients with newly diagnosed CLL. Methods: Patients diagnosed with CLL between 1/2000-12/2019 and seen within 1 year of diagnosis were identified from the Mayo Clinic CLL database. Cumulative incidences of cause-specific death were analyzed using Gray’s test, with deaths from different causes treated as competing events and deaths from unknown causes excluded. Results: 1276 patients were included in this study. The median age at diagnosis was 63 years (range 24-92), and 880 (69%) were male. Based on CLL-IPI score, 449 (35%) had low risk disease, 443 (35%) had intermediate risk disease, and 384 (30%) had high/very high risk disease. Median follow-up time for the study was 6 years; 286 deaths occurred. The COD was CLL progression in 99 (35%), infection in 16 (6%), second malignancy in 47 (16%), CLL-unrelated in 59 (21%), and unknown in 65 (23%) patients. The rates of death due to CLL progression were higher (17.3% at 5 years; 30.3% at 10 years) than the rates due to CLL-related complications (5.7% at 5 years; 12.9% at 10 years) or due to CLL-unrelated causes (8.6% at 5 years; 16.9% at 10 years) in the CLL-IPI high/very high risk group, but not the CLL-IPI low or intermediate risk group (Table). A higher CLL-IPI risk group was associated with a higher rate of death due to CLL progression ( P < 0.001), as well as a higher rate of death due to CLL-related complications ( P = 0.013), and CLL-unrelated causes ( P < 0.001). Conclusions: Causes of death in newly diagnosed CLL patients differ according to their CLL-IPI risk group. In patients with high/very high risk CLL, improving CLL disease control with novel agents seems justified. In patients with low/intermediate risk CLL, there should be increased efforts to reverse immune dysfunction to reduce infections and second malignancies. [Table: see text]

e19112 Background: We sought to study the longitudinal QOL, financial burden and perception of care (PoC) in patients with multiple myeloma (MM) or lymphoma (LYM) enrolled on CTs vs. those receiving standard care (Non-CT). Methods: QOL was evaluated using the FACT-G questionnaire (baseline, 1, 2, 3, 6 & 12 months). Financial burden was evaluated using 10 questions adapted from the MEPS survey and PoC was evaluated using 6 questions adapted from the CAHPS survey (baseline, 3, 6 & 12 months). Results: 35 (28%) of 123 enrolled patients were treated on CTs. Demographics, QOL and financial burden are shown in the Table. Baseline QOL scores were similar in CT vs non-CT groups. Age, gender and self-reported performance status were significantly associated with baseline QOL, while lines of therapy & cancer type were not. There were no significant differences in change in QOL vs. baseline in patients treated on CTs vs. non-CTs at 1, 2, 3, 6 and 12 months from baseline, including the total FACT-G score and the subdomains of functional, physical, emotional and social well-being. Within each group, there was a decline in QOL over the first 3 months, specifically physical WB, which gradually returned to baseline. Baseline answers to financial burden questions were similar, except patients on CTs reported less need for taking extended time-off from work (22% vs. 46%, p = 0.02). The only statistically significant difference in individual Qs over 1 year was ‘worry about having to pay large medical bills’ at the 6-month timepoint (CT: 0%, non-CT: 52%, p = 0.01). Using a composite score from 4 MEPS questions, financial burden over time was lower in the CT group, though the differences were not statistically significant. There were no significant differences in PoC at baseline, 3, 6 and 12 months in the two groups based on answers to each of the six questions adapted from the CAHPS survey. Conclusions: Over 1 year, patients on CTs experienced a similar QOL and perceived their care to be similar as non-CT patients. Financial burden was lower in the CT group, though differences were not statistically significant. [Table: see text]