Nabhan, Chadi

Abstract Background: Breast cancer (BC) in males represents a rare clinical entity, accounting about 1% of all breast malignancies diagnosed every year. Emerging evidence suggest that a gender medicine approach is critical in the research and treatment of cancer, based on the possibility that sex hormones, molecular differences, immune system, and other factors might play a crucial role in disease management. Here, we characterized molecular and immune differences between male BC (MaBC) and female BC (FeBC) across BC subtypes. Methods: 10,728 BC samples (male, n=137; female, n=10591) were analysed by next-generation sequencing (592, NextSeq; WES, NovaSeq), Whole Transcriptome Sequencing (WTS; NovaSeq) (Caris Life Sciences, Phoenix, AZ). Tumor mutational burden (TMB) totaled somatic mutations per tumor (high ≥10 mt/MB). Microsatellite-instability (MSI) was tested by IHC and NGS. Immune cell fractions were calculated by deconvolution of WTS: Quantiseq. Real world treatment-associated survival was extracted from insurance claims and calculated from sample collection to last contact using Kaplan-Meier estimates. Statistical significance was determined using chi-square and Mann-Whitney U test with p-values adjusted for multiple comparisons (q < 0.05). Results: MaBC represented higher HR+/HER2+ (6.61% vs 4.76%), HR+/HER2- (80.17% vs 60.66%) and lower HR-/HER2+ (1.65% vs 3.5%), TNBC (11.57% vs 31.08%) molecular subtypes compared to FeBC. Compared to HR+/HER2+ FeBC, MaBC had higher frequency of CHEK2, RAD51B, TSC1 mutation (Table 1) and RARα (100% vs 13.61%), DAXX (10% vs 0.16%) copy number alteration (all p < 0.05). HR+/HER2- MaBC had higher frequency of BRCA2, FOXA1, CREBBP, FLCN mutation (Table 1), and FGF3 (21.62% vs 14.31%), MRE11 (1.75% vs 0.23%), MEN1 (1.75% vs 0.13%) copy number alteration, but lower frequency of TP53, CHD1, ESR1 and AKT1 mutation (Table 1) (all p < 0.05). HR-/HER2+ MaBC had higher frequency of BRCA1 (p < 0.05) mutation (Table 1). Male TNBC had higher frequency of CDKN2A, KMT2D, STK11, ASXL1, MYC, NF2, PPM1D, WT1, EPHA2, AMER1, ARHGAP35 mutation (Table 1) and BCL6 (4.35% vs 0.05%) copy number alteration, but lower frequency of TP53 mutation (all p < 0.05) (Table 1). MaBC had higher AR protein expression (82.5% vs 60%) but lower frequency of fusion variant-AR (0.7% vs 3.9%) (all p < 0.05) compared to FeBC. There was no difference in TMB high (7.1% vs 8.4%, p = 0.5) and dMMR/MSI-H (1.8% vs 0.8%, p = 0.1). Analysis of inferred immune cell infiltrates showed that MaBC had increased immune cell infiltration of B cells (5.8% vs 5.3%) and M2 Mφ (4.9% vs 4.3%), but decreased infiltration of DC (2% vs 2.6%) (all p < 0.05). MaBC had increased expression of MHC class II gene HLA-DQB2 (FC: 1.3), but decreased expression of immune-related genes CD274 (FC: 1.2), IDO (FC: 1.4), IL1A (FC: 1.3) and IL12A (FC: 1.5) (all p < 0.05). Correlative analyses with survival will be presented at the meeting. Conclusions: These data indicate that MaBC has a differential mutational frequency, copy number alteration, immune gene expression and immune cell infiltration and overall survival compared to their FeBC counterparts. A better understanding of these sex-based differences with additional research may help inform disease outcomes, provide a rationale for tailored therapeutic approaches and design future treatments. Table 1. Mutation frequency in male and female breast cancer Citation Format: Dario Trapani, Sachin Deshmukh, Sharon Wu, Joanne Xiu, Phil Walker, Pooja Advani, Nancy Lin, Giuseppe Curigliano, Stephanie Graff, Chadi Nabhan, George Sledge Jr, Sara Tolaney, Jose Leone. Molecular and immunological landscape of sex-based differences in breast cancer: a distinct disease in men [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PS13-09.

216 Background: PCa harbors recurrent FOXA1 alterations, yet distinct alteration types may drive divergent consequences. Here, we established several classes of FOXA1 alterations and evaluated their clinical outcomes, molecular correlates, and immunologic landscape in a large cohort of primary and metastatic PCa samples from Caris Life Sciences. Methods: We interrogated the Caris DNA (592-gene/whole exome) and RNA (whole transcriptome) NGS databases. FOXA1 alterations were annotated based on amino acid (a.a.) location and prior functional studies (Table). Real-world overall survival (OS) was determined from insurance claims data using Kaplan-Meier estimates. Gene set enrichment analyses (GSEA) was used to estimate signaling pathway activity. Immune-cell fractions were inferred from bulk RNA-seq using quanTIseq. All comparisons are made relative to FOXA1 WT cases. Results: The overall prevalence of FOXA1 alterations was 16%, with no enrichment in metastatic PCa samples (15.5%). Class 2 mutations were enriched in histologic NEPC, were associated with a classical NEPC mRNA signature, and showed poor survival with ADT (HR 5.6, 95% CI: 2.5–12.5, p<0.001). Conversely, metastatic PCa patients with Class 1B mutations had numerically improved survival with ADT (HR 0.76, 95% CI: 0.50–1.14, p=0.18). All FOXA1 alterations (except Class 2) resulted in increased FOXA1 transcripts, with 1B and 1C having the greatest effects (q<0.0001). Class 0 and 3 alterations had 2–5 fold higher frequency of TMB-high and MSI-high status (q<0.05). Conversely, Class 1B exhibited lower TMB and markedly fewer TP53 mutations (35.7% vs 20.3%, q<0.001) and TMPRSS2-ERG fusions (34.5% vs 4.6%, q<0.001). Class 1B was depleted of AR transcriptional signatures (q=0.01) but enriched of MYC signaling in GSEA analysis (FDR<0.001), and showed reduced expression of B7-H3. On immune-cell deconvolution analysis, Class 1B showed reduced dendritic cells, but increased B cells and neutrophils (DC: 1.96% vs 0.01%, B cell: 4.27% vs 5.29%, neutrophils: 5.05% vs 8.00%, each q<0.001). Conclusions: Different FOXA1 alterations exhibit divergent molecular and clinical features, and should not be interpreted in aggregate. In particular, Class 1B mutations are associated with a unique molecular and immunological landscape with potentially better outcomes to ADT, while Class 2 mutations are associated with NEPC phenotype with inferior ADT sensitivity. [Table: see text]

SPOP mutations drive opposing molecular and immune landscapes in prostate and endometrial cancers, suggesting tailored therapies

Abstract The management of chronic lymphocytic leukemia (CLL) has undergone unprecedented changes over the last decade. Modern targeted therapies are incorporated into clinical practice. Unfortunately, patients have begun to develop resistance or intolerance to multiple classes. Symptomatic patients previously treated with a BTK inhibitor (BTKi) and venetoclax represent a new and rapidly growing unmet need in CLL. Here, we define unmet needs in a modern treatment context. We also critically review the literature for PI3K inhibitors and chemoimmunotherapy and lack of data to support their utility following BTKis and venetoclax. Finally, we suggest opportunities to ensure the continued innovation for patients with CLL.

AbstractHealth‐related quality of life (HRQoL) in patients with chronic lymphocytic leukemia (CLL) is important in guiding treatment decisions. However, the impact of CLL treatment initiation on HRQoL is unclear. We assessed HRQoL using the FACT‐Leu and EQ‐5D‐3L questionnaires in the Connect® CLL Registry, a large, US‐based, multicenter, prospective observational study of CLL patients enrolled between 2010 and 2014, prior to the introduction of novel therapies. Among 889 patients initiating first‐line therapy with chemoimmunotherapy or rituximab monotherapy, questionnaire completion rates were 95.7% and 95.8% at enrollment, and 70.8% and 69.4% at 12 months, for FACT‐Leu Total and EQ‐5D‐3L, respectively. For 849 patients completing all five FACT‐Leu components, average total scores were 135.7 at enrollment and 141.6 at 12 months. Among 526 patients with FACT‐Leu Total scores at enrollment and 12 months, clinically meaningful (≥11‐point) improvements or reductions were observed in 179 (34.0%) and 88 (16.7%) patients, respectively. Mean EQ‐5D‐3L index scores were 0.87 at enrollment and 12 months. Among 513 patients completing EQ‐5D‐3L at enrollment and 12 months, clinically meaningful (≥0.06‐point) improvements or reductions were observed in 125 (24.4%) and 116 (22.6%) patients, respectively. In the Connect® CLL Registry, HRQoL remained stable or slightly improved after 12 months of follow‐up.

Abstract Optimal treatment of chronic lymphocytic leukemia (CLL) remains unclear. The Connect CLL Registry, a United States–based multicenter prospective observational cohort study, enrolled 1494 patients between 2010 and 2014 from predominantly community-based settings. Patients were grouped by line of therapy (LOT) at enrollment. With a median follow-up of 46.6 months (range, 0-63.0 months), median overall survival (OS) was not reached in LOT1, 63.0 months (95% confidence interval [CI], 46.0-63.0 months) in LOT2, and 38.0 months (95% CI, 33.0-47.0 months) in LOT≥3. Bendamustine and rituximab (BR; 33.5%); fludarabine, cyclophosphamide, and rituximab (FCR; 21.4%); and rituximab monotherapy (18.5%) were the most common regimens across LOTs. Median event-free survival (EFS) was similar in patients treated with BR (59.0 months) and FCR (55.0 months) in LOT1; median OS was not reached. In multivariable analysis, BR or FCR vs other treatments in LOT1 was associated with improved EFS (hazard ratio [HR], 0.60; P &lt; .0001) and OS (0.67; P = .0162). Using the Kaplan-Meier product limit, ibrutinib vs other treatments improved OS in LOT2 (HR, 0.279; P = .009), LOT3 (0.441; P = .011), and LOT≥4 (0.578; P = .043). Prognostic modeling of death at 2 years postenrollment identified 3 risk groups: low (mortality rate, 6.2%), medium (14.5%), and high (27.4%). The most frequent adverse events across LOTs were pneumonia (11.6%) and febrile neutropenia (6.2%). These data suggest that advantages of LOT1 FCR over BR seen in clinical trials may not translate to community practice, whereas receiving novel LOT2 agents improved outcomes. This trial was registered at www.clinicaltrials.gov as NCT01081015.

Abstract Introduction: CLL pts who qualify for participation in clinical trials are often thought of as a highly selected population. Therefore whether safety and efficacy data from these trials can be generalized to all CLL pts treated in the community setting is unknown. Prospective, observational studies can provide significant insight into pt and disease characteristics as well as practice patterns in different clinical settings. Here we report on baseline demographics and clinical characteristics for 1495 pts who participated in the Connect CLL observational study and were followed prospectively. Methods: Connect®CLL is a non study-specific intervention treatment registry which collects data on the management and outcomes of CLL pts in clinical, academic and community practices in the US. Clinical information was prospectively collected for pts treated in community (n=1312), academic (n=155), or government (n=28) centers in the US between March 2010 – Jan 2014. Pts eligible for treatment or on treatment not more than 2 months were eligible for enrollment. Here we report on the baseline demographics and clinical characteristics of a large CLL cohort stratified by AG, LOT and type of treatment center. Results: In total,1,495 pts were accrued with a median age at enrollment of 69 (range 22–99). Pts were Caucasian (92.2%), African American (6.9%), and male (63.7%). Median duration from diagnosis until initial therapy was 3.7 years (range 0-32). Age of enrolled pts was as follows; 527 pts age <65 years (AG1; 35%); 511 age 65–75 (AG2; 34%); and 457 age ≥75 (AG3; 31%) at the time of enrollment. Eight hundred and ninety-four pts (60%) were treated with front-line therapy (LOT1); 259 pts (17%) treated with second-line therapy (LOT2); and 342 pts (23%) treated beyond second-line therapy (LOT≥3). Patient age: Pt age was associated with significant variation in several clinical variables: Charlson comorbidity index (CCI) score ≥3 (AG 1-3 35/46/52%; p<0.0001), ECOG PS = 0 (AG1-3 60, 48, 33%, p<0.0001) and mean creatinine clearance (CrCl) decreased (AG 1-3 101/74/53 mL/min; p<0.0001). Line of therapy: Differences in baseline characteristics were observed between pts enrolled at LOT1 (previously untreated) and LOT2/≥3 (relapsed). Median age of pts accrued at LOT1 was 68 vs. 70 years for pts enrolled at LOT2/≥3 (p=0.007). ECOG PS = 0 was more frequent at LOT1 (50%) vs. LOT2/≥3 (42%) (p=0.0057). CCI ≥3 at LOT1 was 41% vs 48% at LOT2/≥3 (p=0.0072). When adjusted for differences in age, logistic regression also demonstrated significant differences in performance status (PS=0; p=0.02) and CCI (p=0.03) stratified by LOT. Treating institution: Baseline characteristics also differed by type of treating institution. Median age was 63 vs 70 at academic vs non-academic (p<0.0001). Pts treated at academic sites also had lower CCI (CCI≤2; 72.9% vs 54.2%, academic vs non-academic) and higher median CrCl (86.4 vs 71.4) (p=0.0017). In this registry, only 16% of patients under age 75 had CCI <= 2, CrCl >= 70 mL/min, ECOG 0-1; 18% of patients on LOT1 and 12% of pts on LOT=2 would satisfy these criteria. They represent 36% of eligible pts from academic treating institutions and 13% from community/government centers. Only 20% of patients on LOT1 would satisfy enrollment criteria for CLL8 (FCR vs. FC; Hallek; Lancet 2010) CLL 10 (BR vs FCR; NCT00769522). which did not have enrollment restrictions for age. Conclusions:Baseline characteristics of pts with CLL vary by age, LOT, and type of treatment center. In this large prospective observational study, our data confirms that older pts typically have a worse ECOG PS, more medical comorbidities, more advanced disease when initiating therapy and a lower probability of being treated at an academic medical center. Based on inclusion criteria used for entry in either CLL10 or CLL8, fewer than 20% of pts satisfy age, renal clearance, and fitness profile to be considered suitable for aggressive chemoimmunotherapy in the front line setting. This observation supports the notion that current clinical trial data might not always be applicable to the general CLL population seen in the community and argues for designing clinical trials that are more generalizable to the broader pt population. Disclosures Sharman: Gilead: Research Funding; Calistoga: Honoraria; Phamacyclics: Honoraria, Research Funding; Celgene: Consultancy, Research Funding; TG therapeutics: Research Funding; Roche: Research Funding. Mato:Millennium: Speakers Bureau; Celgene: Speakers Bureau; Genentech: Speakers Bureau. Kay:Celgene: Research Funding. Kipps:Celgene: Honoraria, 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: 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. Weiss:Celgene: Consultancy; Phamacyclics: Consultancy; Gilead: Consultancy. Nabhan:Celgene: Honoraria, Research Funding. Flinn:Celgene: Research Funding. Grinblatt:Celgene: Honoraria, Speakers Bureau. Kozloff:Celgene: Consultancy. Farber:Alexion: Equity Ownership, Speakers Bureau; Celgene: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Gilead: Speakers Bureau; Janssen/Pharmacyclics: Speakers Bureau; Seattle Genetics: Speakers Bureau; Genentech: Membership on an entity's Board of Directors or advisory committees. Sullivan:Celgene: Employment, Equity Ownership. Street:Celgene: Employment, Equity Ownership. Swern:Celgene: Employment, Equity Ownership. Flowers:TG Therapeutics: Research Funding; Phamacyclics: Research Funding; Acerta: Research Funding; Abbvie: Research Funding; Infinity: Research Funding; Lymphoma Research Foundation: Membership on an entity's Board of Directors or advisory committees; Janssen: Research Funding; Spectrum: Research Funding; Gilead: Research Funding; Genentech: Unpaid consultancy Other; Millennium: Research Funding, Unpaid consultancy, Unpaid consultancy Other; Seattle Genetics: Consultancy; Prescription Solutions: Consultancy; Celgene: Unpaid consultancy Other.

Abstract Introduction: Rituximab (R) improves survival in CLL when added to intensive chemotherapy (such as fludarabine and cyclophosphamide [FCR]), and has been shown to improve progression-free survival when given as maintenance therapy following first- or second-line chemoimmunotherapy. However, early phase 2 studies demonstrated only modest activity of R monotherapy (R mono), particularly at standard dose, and its use as a single agent for pts with CLL has remained controversial. Here we describe the characteristics and outcomes of pts with CLL receiving R mono in a real-world setting. Methods: The Connect CLL registry (NCT01081015), a multicenter, prospective observational cohort study, enrolled 1,494 pts with CLL between 2010-2014 from 179 community, 17 academic, and 3 government sites throughout the USA. Pts were ≥ 18 years and were enrolled ≤ 2 months after initiating any line of therapy (LOT). Pts were treated according to individual physician/patient decision making, and adherence to IWCLL standards cannot be determined. Bone marrow biopsy and CT scans were at the physician's discretion; responses were physician assessed and not centrally reviewed. For this analysis, pts were stratified by first (LOT1) or subsequent LOT (LOT≥2), and by treatment with R mono or other treatments (Tx). Kaplan-Meier methods were used to estimate event-free survival (EFS; event defined as death, progression/relapse, or transformation). A log-rank test was used to evaluate differences in EFS. A multivariate analysis of EFS in LOT1 pts was performed using the Cox regression model. Results: Of the pts enrolled, 105 (11.8%) pts in LOT1 and 81 (13.4%) pts in LOT≥2 received R mono. Of the pts receiving other Tx, 620/784 pts (79%) in LOT1 and 333/524 pts (64%) in LOT≥2 received R in combination. In LOT1, pts receiving R mono were older than pts receiving other Tx (74 vs 67 years; P < 0.0001); in LOT≥2 median ages were comparable (71 vs 70 years). Pts receiving R mono were more likely to have Rai stage 0-1 CLL than pts receiving other Tx, both in LOT1 (63.9% vs 51.8%; P = 0.05) and LOT≥2 (70.4% vs 49.9%; P = 0.005). The proportion of pts receiving R mono vs other Tx with a CCI score of ≥ 4 was similar in LOT1 (25.7% vs 22.8%) and LOT≥2 (32.1% vs 28.4%). Median absolute lymphocyte count at enrollment was lower in pts on R mono vs other Tx in LOT1 (27.7 vs 46.1 x 109/L) but similar in LOT≥2 (35.6 vs 30.2 x 109/L). A lower proportion of pts receiving R mono had FISH/cytogenetics performed at enrollment than pts receiving other Tx in LOT1 (48.6% vs 67.0%; P = 0.0002) and LOT≥2 (30.9% vs 49.6%; P = 0.002). The most common Tx regimens in LOT1 were FCR (25.9%), bendamustine plus R ([BR] 21.0%), R mono (11.8%), fludarabine plus R ([FR] 6.3%), and chlorambucil (4.6%); and in LOT≥2, BR (27.6%), R mono (13.4%), FCR (8.6%), bendamustine (7.4%), FR (4.1%), and ofatumumab (4.0%). The most common reason to initiate treatment in any LOT was bone marrow failure (LOT1, 44.8% vs 39.3%; LOT≥2, 37.0% vs 31.5%; R mono vs other Tx). Duration of therapy was shorter for pts receiving R mono vs other Tx in LOT1 (1.4 vs 4.1 months) and LOT≥2 (1.6 vs 3.3 months). In LOT1, responses were lower in pts on R mono vs other Tx: overall response rate (ORR) was 38.1% vs 64.2% (complete response [CR], 16.2% vs 41.2%; P < 0.0001). In LOT≥2, ORR was 25.9% vs 24.6% for pts on R mono vs other Tx (CR, 9.9% vs 11.5%; P = 0.68). Partial response did not differ significantly between pts receiving R mono vs other Tx in LOT1 and LOT≥2. Pts receiving R mono in LOT1 had inferior median EFS vs other Tx (34 vs 50 months; log-rank P = 0.04); however, after adjusting for factors such as ECOG status and del(17p) status, there was no difference in EFS (HR 0.932; P = 0.79). In pts in LOT≥2, median EFS was similar for R mono and other Tx (15 months for both groups; P = 0.93). Conclusions: Pts receiving R mono as frontline CLL therapy were older than pts receiving other Tx. They also had lower stage disease and lower lymphocyte counts, a shorter duration of treatment, and inferior response rates. Pts receiving R mono in relapsed CLL, more closely approximated pts receiving other Tx in terms of age. Despite the shorter duration of therapy for pts receiving R mono in LOT1 and LOT≥2, both groups had similar ORR and EFS in LOT≥2. These data reinforce the idea that R mono is inadequate as frontline therapy in progressive CLL, and demonstrate the opportunity for improvement in relapsed/refractory disease with novel agents relative to traditional chemoimmunotherapy approaches. Disclosures Sharman: Celgene: Research Funding; Gilead: Research Funding; Pharmacyclics: Research Funding; Acerta: Research Funding; TG Therapeutics: Research Funding; Seattle Genetics: Research Funding. Davids:Pharmacyclics: Membership on an entity's Board of Directors or advisory committees, Research Funding; Genentech: 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; TG Therapeutics: Membership on an entity's Board of Directors or advisory committees, Research Funding; AbbVie, Janssen, Gilead: Consultancy; Celgene Corporation: Consultancy. Farber:Seattle Genetics: Research Funding. Grinblatt:Celgene Corporation: Consultancy, Speakers Bureau. Lamanna:Gilead: Research Funding; AbbVie: Research Funding; Genentech: Research Funding; Infinity: Research Funding; Pronai: Research Funding; Celgene Corporation: Research Funding. Mato:TG Therapeutics: Consultancy; Abbvie: Research Funding; Acerta Pharma: Research Funding; Gilead Sciences: Research Funding; ProNAi: Research Funding; TG Therapeutics: Research Funding; Theradex: Research Funding; Pharmacyclics: Consultancy; Gilead Sciences: Consultancy; Abbvie: Consultancy. Nabhan:Astellas: Research Funding; Seattle Genetics: Research Funding; Cardinal Health: Consultancy; Infinity: Consultancy; Abbvie: Consultancy; Genentech: Consultancy, Research Funding; Celgene Corporation: Consultancy, Research Funding. Sullivan:Celgene Corporation: Employment, Equity Ownership. Flick:Celgene Corporation: Employment, Equity Ownership. Kiselev:Celgene Corporation: Employment, Equity Ownership. Bhushan:Celgene Corporation: Employment, Equity Ownership. Swern:Celgene: Employment, Equity Ownership. Flowers:Seattle Genetics: Research Funding; Optum Rx, Seattle Genetics, Genentech/Roche: Consultancy; Celgene Corporation: Consultancy, Honoraria; Spectrum, Janssen, Infinity, AbbVie, Acerta, Pharmacyclics, TG Therapeutics: Research Funding; Millennium: Consultancy, Research Funding; Gilead: Consultancy, Research Funding.

Abstract Introduction: Given the availability of several active treatment (tx) options for CLL, providers frequently make tx recommendations based on their interpretation of objective data from consensus criteria and clinical trials. There are limited data describing patient (pt) experience, values, and insights when encountering multiple tx choices. The CLL Society, a pt-driven, physician-curated nonprofit organization focused on the unmet needs of the CLL community, sought to explore how CLL pts make tx decisions. Methods: A 64 question survey was distributed online and in paper to CLL pts from Oct-Dec 2017. The survey was developed by CLL experts. The research was IRB-approved. Chi square was used for statistical comparison; all other analyses are descriptive in nature. Results: 1147 pts from 48 states completed the survey. Median age was 65 (range 28-86), 46% male, 96% Caucasian. Who Influences Tx: Of the 79% of pts who have seen a CLL expert, defined as a provider focused on CLL at an academic/research center, 95% rated their provider as very/extremely influential. 83% of pts rated their own opinions as influential, and 59% rated the opinion of a general hematologist/oncologist (gen heme/onc), a provider who sees a variety of cancer types, as influential. Only 27% let their provider unilaterally decide on tx. Of these pts, 19% trusted their provider to select the best tx and 5% didn't have enough understanding to be involved in the decision. 64% were influenced by outside sources of information, such as family members, live CLL support groups, or online sources including pt experts and forums. Factors in Deciding Tx: Of the 67% pts surveyed who have been treated, 73% were offered > 1 tx choice by their provider. In deciding on a specific tx, 94% of respondents stated that it was very/extremely important to know there were tx options beyond their current regimen. The most important factors in selecting tx were response rate (91%), overall survival (88%), progression-free survival (86%), long-term side effects (82%), ability to achieve undetectable MRD (80%), cost/insurance coverage (66%), and location of tx (50%) with significant differences between subgroups (Table 1). Tx was impacted by cost or insurance issues for 16% (n=104). Of these 104 pts, cost/insurance issues resulted in tx delay for 34 and change in tx plan for 24 pts. 16 pts reported that an appeal led to coverage and 16 pts received financial assistance. Pts' willingness to accept potentially curable but high-risk tx was heavily influenced by availability of alternate options. For example, 18% of pts would be willing to consider cytotoxic chemotherapy if alternate options were available vs. 72% if no options were available. Similar patterns were seen with CAR-T tx (28% vs. 79%) and bone marrow transplantation (BMT) (15% vs. 66%) with significant differences between subgroups (Table 2). 82% were willing to take a life-long tx for long-term control without potential for cure. Willingness to accept long-term tx was significantly higher in treated vs. untreated pts (84% vs. 77%, p=.006) and men vs. women (84% vs. 78%, p=.008). Clinical Trial Education: Of those treated by a gen heme/onc, 31% received no education on clinical trials. Only 15% reported good understanding of trial opportunities vs. 52% treated by a CLL expert (p<.001). 9% of pts treated by gen heme/onc were offered clinical trial participation vs. 47% treated by a CLL expert (p<0.001). Pts who declined or would have declined participation cited reasons including preference for "proven" tx (38%), distance from trial site (29%), fear (20%), frequent imaging (20%), overall lack of understanding about trials (15%), and lack of information (14%). Summary: Most respondents want to be and are involved in tx decisions. While providers are influential in decision making, CLL pts also rely on outside sources. Gender, age, and tx status influence which factors drive decision making. Pts select tx based on response rate, survival, side effects, and ability to achieve deep response and want a plan in place for sequencing tx. The majority are willing to consider long-term tx without cure. Few want chemotherapy, CAR-T, or BMT unless they have no other options. A significant opportunity for improvement in education on clinical trial opportunities was identified. This description of pt experience, values, and preferences enriches the informed consent process and may lead to more tailored and patient-centered care. Disclosures Kennard: AbbVie, Gilead, Verastem: Consultancy. Furman:TG Therapeutics: Consultancy; Loxo Oncology: Consultancy; Sunesis: Consultancy; Verastem: Consultancy; Incyte: Consultancy, Other: DSMB; Genentech: Consultancy; Acerta: Consultancy, Research Funding; Gilead: Consultancy; Janssen: Consultancy; Pharmacyclics LLC, an AbbVie Company: Consultancy; AbbVie: Consultancy. Pagel:Pharmacyclics, an AbbVie Company: Consultancy; Gilead: Consultancy. Davids:Sunesis: Membership on an entity's Board of Directors or advisory committees; Roche: Consultancy; Astra-Zeneca: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; BMS: Research Funding; Genentech: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Astra-Zeneca: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; MEI Pharma: Consultancy, Research Funding; Surface Oncology: Research Funding; Gilead: Membership on an entity's Board of Directors or advisory committees; Celgene: Consultancy; Merck: Consultancy; Merck: Consultancy; MEI Pharma: Consultancy, Research Funding; TG Therapeutics: Membership on an entity's Board of Directors or advisory committees, Research Funding; Astra-Zeneca: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; MEI Pharma: Consultancy, Research Funding; Abbvie: Consultancy, Membership on an entity's Board of Directors or advisory committees; Celgene: Consultancy; Sunesis: Membership on an entity's Board of Directors or advisory committees; Abbvie: Consultancy, Membership on an entity's Board of Directors or advisory committees; TG Therapeutics: Membership on an entity's Board of Directors or advisory committees, Research Funding; Genentech: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Pharmacyclics: Consultancy, 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; Verastem: Consultancy, Research Funding; Surface Oncology: Research Funding; Gilead: Membership on an entity's Board of Directors or advisory committees; Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees; Pharmacyclics: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Abbvie: Consultancy, Membership on an entity's Board of Directors or advisory committees; Celgene: Consultancy; Roche: Consultancy; Pharmacyclics: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees; BMS: Research Funding; Verastem: Consultancy, Research Funding; BMS: Research Funding; Genentech: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Surface Oncology: Research Funding; Verastem: Consultancy, Research Funding; TG Therapeutics: Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees; Sunesis: Membership on an entity's Board of Directors or advisory committees; Merck: Consultancy; Roche: Consultancy. Nabhan:Cardinal Health: Employment, Equity Ownership. Kay:Acerta: Research Funding; 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; Morpho-sys: 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; Celgene: 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; Cytomx Therapeutics: Membership on an entity's Board of Directors or advisory committees. Siddiqi:Juno Therapeutics: Other: Steering committee. Brander:Genentech: Consultancy, Honoraria, Other: Institutional research funding for non investigator initiated clinical trial, Research Funding; Pharmacyclics, an AbbVie Company: Consultancy, Honoraria, Research Funding; AbbVie: Consultancy, Honoraria, Other: Institutional research funding for non investigator initiated clinical trial, Research Funding; Teva: Consultancy, Honoraria; Novartis: Consultancy, Other: DSMB; TG Therapeutics: Consultancy, Honoraria, Other: Institutional research funding for non investigator initiated clinical trial, Research Funding; BeiGene: Other: Institutional research funding for non investigator initiated clinical trial, Research Funding; Acerta: Other: Institutional research funding for non investigator initiated clinical trial, Research Funding; DTRM: Other: Institutional research funding for non investigator initiated clinical trial, Research Funding. Wierda:AbbVie, Inc: Research Funding; Genentech: Research Funding. Hill:Abbvie: Honoraria, Membership on an entity's Board of Directors or advisory committees; Pfizer: Honoraria, Membership on an entity's Board of Directors or advisory committees; Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees; Seattle Genetics: Honoraria, Membership on an entity's Board of Directors or advisory committees; Genentech: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Pfizer: Honoraria, Membership on an entity's Board of Directors or advisory committees; Seattle Genetics: Honoraria, Membership on an entity's Board of Directors or advisory committees; Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees; Pharmacyclics: Honoraria, Membership on an entity's Board of Directors or advisory committees; Amgen: Research Funding; Abbvie: Honoraria, Membership on an entity's Board of Directors or advisory committees; Pharmacyclics: Honoraria, Membership on an entity's Board of Directors or advisory committees. Cheson:AbbVie, Roche/Genentech, Pharmacyclics, Acerta, TG Therapeutics: Consultancy. Choi:Pharmacyclics: Consultancy, Research Funding, Speakers Bureau; Gilead: Speakers Bureau; Rigel: Consultancy; Genentech: Speakers Bureau; AbbVie, Inc: Consultancy, Speakers Bureau. Mato:AstraZeneca: Consultancy; Prime Oncology: Honoraria; Portola: Research Funding; Johnson & Johnson: Consultancy; Medscape: Honoraria; Celgene: Consultancy; TG Therapeutics: Consultancy, Research Funding; Acerta: Research Funding; Pharmacyclics, an AbbVie Company: Consultancy, Research Funding; AbbVie: Consultancy, Research Funding; Regeneron: Research Funding.

Abstract Abstract 4607 Background: Extramedullary Chronic Lymphocytic Leukemia (EM-CLL) is reported in the literature as case reports and series. The prognosis, patient characteristics, and treatment approaches to EM-CLL have not been fully analyzed in a comprehensive fashion. We therefore conducted a systematic review to better understand EM-CLL and how it might affect patients' clinical course and overall survival. Methods: A comprehensive PubMed search for English-language articles published between 1975 and 2010 in peer-reviewed journals was performed. EM-CLL was defined as CLL identified pathologically in these reports outside the bone marrow. Search terms included “EM-CLL, CLL outside the bone marrow, and CLL with each system [central nervous system (CNS), genitourinary (GU/GYN), gastrointestinal (GI), cardiac, pulmonary, head and neck (H+N), skin, and vascular]. Data regarding location, sex, age at diagnosis, systemic CLL disease setting (relapsed vs. initial diagnosis), laboratory studies/prognostic factors (when available), treatment for CLL (yes or no), use of chemotherapy (yes or no), use of radiation (yes or no), use of rituximab (R), use of purine analogues, outcome (alive or deceased), and follow-up were collected. A z-test was used for assessing proportion differences for gender, stage (Rai 0, 1, 2 vs. Rai 3, 4), and chemotherapy (yes or no). Univariate analyses were performed via the Chi-square test. A backward stepwise logistic regression analysis was performed to identify whether disease location, disease setting, age, and sex influence overall survival (OS). No other multivariate analyses were performed due to large percent of missing data for different variables of interest. A two-tailed P level of 0.05 was considered statistically significant in all analyses; which were performed using SPSS for windows, version 19.0 (SPSS Inc.) and StarStat (DataStar, Inc.). Results: A total of 192 cases were identified [63 (33%) skin, 51 (27%) CNS, 26 (14%) GI, 20 (10%) GU/GYN, 10 (5%) lung, 10 (5%) ocular, 6 (3%) heart, 5 (3%) H+N, and 1(0.5%) vascular]. Median age at detection of EM-CLL was 62 (range, 25–89); significantly more men were diagnosed with EM-CLL versus women (61% vs. 35%; z = 3.51, P <.001); more patients were at early stage (Rai 0, 1, 2) versus advanced (Rai 3 and 4) at time of EM-CLL but this difference was not statistically significant (22% vs. 18%; z = .43, P =.665). More patients with EM-CLL were treated with chemotherapy for their underlying systemic CLL than not (65% vs. 15%, z = 6.25, P <.001); very few patients received radiotherapy which was delivered for their EM-CLL (n=35; 18%) but treatment information was missing from approximately 20% of cases. Our analysis showed that there was no significant difference with regard to number of patients treated with chemotherapy before and after the contemporary chemoimmunotherapy era (defined here as before and after the year 2000) (87% vs. 78%, p =.171). As far as systemic therapy for CLL, 12% of pts received R and 14% received purine analogues. Tissue sites such as CNS (45%), ocular (33%), H+N (25%), and skin (19%) were significantly more likely to receive XRT (p <.001) but no significant differences between the sites were reported for chemotherapy (p =.106), R (p =.237), and purine analogues (p =.090) usage. OS data was missing in 56 cases (29%), 79 were alive (41%), and 54 (28%) were dead at the time of case reporting. Backward stepwise logistic regression analysis, which included data for 102 (53%) patients showed that EM-CLL detected in patients with systemic relapse had worse OS compared to those with EM-CLL at time of initial CLL diagnosis (p =.027), while no significant effect on OS was found for tissue site (p =.085), age (above or below 70, p =.796), and sex (p =.791). All other variables were not significant. Conclusions: To our knowledge, this analysis represents the largest collection of EM-CLL series examined comprehensively. EM-CLL is not an uncommon presentation in the spectrum of CLL/SLL with skin and CNS being the most commonly affected organs (33% and 27% respectively). Patients who develop EM-CLL in the relapsed setting have significantly worse OS compared to those who manifest EM-CLL at the time of initial diagnosis. We propose that this subset of CLL needs a prospective and robust accumulation of prognostic and clinical data as it will be critical in defining how extramedullary presentation affects the biology and natural course of CLL. Disclosures: No relevant conflicts of interest to declare.