Paludo, Jonas

Management of PD-1 blockade resistance in metastatic melanoma (MM) remains challenging. Immunotherapy or chemotherapy alone provides limited benefit in this setting. Chemo-immunotherapy (CIT) has demonstrated favorable efficacy and safety profiles in lung cancer. Our pre-clinical study showed that in MM patients who have failed PD-1 blockade, the addition of chemotherapy increases CX3CR1+ therapy-responsive CD8+ T-cells with enhanced anti-tumor activity, resulting in improved clinical response. Here, we examined the clinical outcomes of CIT in MM patients after PD-1 blockade failure and the treatment-related changes in CX3CR1+ therapy-responsive CD8+ T-cells. We reviewed MM patients seen between January 2012 and June 2018 who failed anti-PD-1-based therapy and received subsequent CIT, immune checkpoint inhibitors (ICI) or chemotherapy alone. Overall survival (OS), objective response rate (ORR), event-free survival (EFS), and toxicities were assessed. Among 60 patients, 33 received CIT upon disease progression on PD-1 blockade. At a median follow-up of 3.9 years, the CIT group had a median OS of 3.5 years [95% confidence interval (CI) 1.7–NR] vs. 1.8 years (95% CI 0.9–2; P = 0.002) for those who received subsequent ICI (n = 9) or chemotherapy alone (n = 18), with ORR of 59% vs. 15% (P = 0.0003), respectively. The median EFS was 7.6 months (95% CI 6–10) following CIT vs. 3.4 months (95% CI 2.8–4.1; P = 0.0005) following ICI or chemotherapy alone. Therapy-responsive CX3CR1+CD8+ T-cells showed dynamic increase with successful CIT. CIT showed favorable clinical outcomes and acceptable safety profile in PD-1 blockade-resistant patients. CX3CR1+CD8+ therapy-responsive T-cells can be potentially used for monitoring disease response to CIT.

e19523 Background: Previous studies have shown a disparity in outcomes of MM between clinical trials and real-world practice. This could be partly due to clinical trial exclusion of patients who need urgent treatment due to high burden of disease or significant illness. In this study, we aim to analyze the impact of time to initiation of systemic therapy for MM on overall survival (OS). Methods: We included MM patients reported to National Cancer Data Base (2004-2014). We excluded 2,860 patients with suspected smoldering MM due to initial treatment beyond 120 days. Relative risk of delayed time to systemic therapy was assessed by logistic regression model. Kaplan-Meier method and Cox multivariate analysis were used to evaluate the association between time to therapy and OS. Results: A total of 53, 519 MM patients were included. Median time to systemic therapy in our cohort was 18 days (range 0-120). Of the entire cohort, 24% patients received systemic therapy for MM < 7 days from diagnosis, and 32% received it after 30 days. Factors associated with time to systemic therapy > 30 days were Black race (OR 1.12, CI 1.07-1.16), female sex (OR 1.10, CI 1.06-1.14), lower Charlson comorbidity score (OR 1.36, CI 1.28-1.45), and academic facility (OR 1.18, CI 1.13-1.24). The median OS for patients who initiated therapy > 30 days after diagnosis was longer than those who received it within 7 days (37 vs. 29 month, p < 0.001). Similarly, on multivariate analysis, patients who received treatment > 30 days after diagnosis were found to have a lower mortality as compared to those who initiated treatment < 7 days (HR 0.81, CI 0.79-0.83; Table). Conclusions: In our study, time to initiation of systemic therapy was an independent prognostic factor in MM. This could be related to the high-risk nature of disease of patients treated within a short duration of diagnosis. These findings have implications for future clinical trial design, which should allow for a pragmatic approach toward enrollment of these high-risk patients to improve external validity. [Table: see text]

Introduction: With the expanding use of CAR-T cell therapy, which is associated with serious adverse effects (AEs), there is a need to characterize the patient's experience over time to guide patient/provider education, and help optimize symptom management. This study reports on longitudinal evaluation of patient-reported quality of life (QOL) and symptom burden of CAR-T cell therapy compared with established forms of cellular therapy i.e autologous stem cell transplant (autoSCT) and allogeneic SCT (alloSCT). Methods: Patients with hematologic malignancies were prospectively recruited in three cohorts: CAR-T, autoSCT and alloSCT. The primary endpoint was change in QOL from baseline, using the FACT-G questionnaire. Secondary endpoints were patient-reported AEs (PRO-AEs) using 7 items from the PRO-CTCAE and assessment of cognition/memory using the NeuroQOLv2 questionnaire. PRO-CTCAE data was graded using a composite score (combining frequency, severity, and interference) and rates, using a method adjusting for pre-existing baseline symptoms, were compared using Fisher's exact test. We also evaluated the time profile of PRO-AEs using the Toxicity over Time (ToxT) approach, a longitudinal approach to AE analysis (Thanarajasingam Lancet Onc 2016). Patients completed questionnaires at baseline, week 2 and monthly thereafter. Results: From 07/2018 to 06/2019, 93 patients were recruited (CAR-T: 20; autoSCT: 37; alloSCT: 36). At data cut-off, week 2 and months 1, 2 and 3 data were available in 74, 62, 46 and 35 patients, respectively. There was no difference in patient age across the 3 groups (median age 63, range 23-77; p=0.26). Baseline QOL by FACT-G total score (mean=83.4, SD=14.7; p=0.77), side effect bother by FACT-G GP5 (66/93 [71%] a little bit or less; p=0.72), activities and function (70/93 [75%] fairly normal activities or no limitations; p=0.68) and cognition by NeuroQOL t-score (mean=52.2, SD=8.13; p=0.39) were similar across 3 groups at baseline. The CAR-T group experienced significantly less worsening in QOL (FACT-G) than both autoSCT and alloSCT groups (Fig. 1a). Worsening in overall QOL nadired at week 2, after which QOL gradually returned to baseline in all groups. When comparing changes from baseline in overall QOL, statistically significant differences between groups were evident at week 2 (CAR-T vs autoSCT p&lt;0.001; vs alloSCT p&lt;0.001), month 1 (CAR-T vs autoSCT p=0.02; vs alloSCT p=0.003), month 2 (CAR-T vs autoSCT p=0.02; vs alloSCT p=0.001) and month 3 (CAR-T vs autoSCT p=NS; vs alloSCT p=0.03). Results for physical (Fig. 1b) and functional well-being (WB) were similar, with significantly less QOL worsening at week 2 in CAR-T vs autoSCT and alloSCT groups. The most common PRO-AEs (Table 1) in the CAR-T group were decreased appetite (59%), diarrhea (53%) and fatigue (44%). There was no statistically significant difference in the PRO-AEs between CAR-T and autoSCT groups. However, patients undergoing alloSCT had significantly higher proportion of PRO-AEs vs the CAR-T group, except for neuropathy and sad feelings. AutoSCT and alloSCT groups had significant worsening of FACT-G side effect bother (GP5) at week 2, which was significantly different from that of the CAR-T group, following which the side effect bother gradually returned to baseline. (Fig. 1c) This was the first application of ToxT to PRO-CTCAE data (graphs not shown). Bar charts of maximum grade frequency and stream plots of mean grade over time demonstrate that mouth sores, fatigue, diarrhea and decreased appetite peak at week 2 and improve by month 3, and are of lesser severity in the CAR-T vs autoSCT and alloSCT groups. The trajectory of maximum grade across all PRO-AEs was similar by group as overall side effect burden by FACT-G GP5. Heatmap visualization demonstrated significant intra-patient variability and allowed inspection of data completeness. No difference in cognition and memory was observed across the three groups over the first three months. (Fig. 1d) Conclusion: This study is the first to our knowledge to provide comprehensive PRO data comparing QOL, patient-reported AEs and cognition in CAR-T cell therapy vs. auto- and alloSCT, and the first application of ToxT to PRO-CTCAE data. Short-term QOL, including physical and functional WB domains was better in the CAR-T group vs. SCT groups. These data can serve as a guide for patient education and symptom management in CAR-T cell therapy. Co-senior authors: SKK & ACD Disclosures Paludo: Celgene: Research Funding; Celgene: Research Funding; Verily Life Sciences: Research Funding; Verily Life Sciences: Research Funding. Gertz:Johnson and Johnson: Speakers Bureau; Celgene: Consultancy; Appellis: Consultancy; Amyloidosis Foundation: Research Funding; DAVA oncology: Speakers Bureau; Physicians Education Resource: Consultancy; Amgen: Consultancy; Janssen: Consultancy; Abbvie: Other: personal fees for Data Safety Monitoring board; Medscape: Consultancy, Speakers Bureau; Prothena Biosciences Inc: Consultancy; Teva: Speakers Bureau; Research to Practice: Consultancy; Annexon: Consultancy; Alnylam: Consultancy; International Waldenstrom Foundation: Research Funding; Ionis/Akcea: Consultancy; Spectrum: Consultancy, Research Funding; i3Health: Other: Development of educational programs and materials; Pharmacyclics: Membership on an entity's Board of Directors or advisory committees; Proclara: Membership on an entity's Board of Directors or advisory committees; Springer Publishing: Patents & Royalties. Dispenzieri:Akcea: Consultancy; Intellia: Consultancy; Janssen: Consultancy; Pfizer: Research Funding; Takeda: Research Funding; Celgene: Research Funding; Alnylam: Research Funding. Ansell:Trillium: Research Funding; LAM Therapeutics: Research Funding; Mayo Clinic Rochester: Employment; LAM Therapeutics: Research Funding; Seattle Genetics: Research Funding; Affimed: Research Funding; Regeneron: Research Funding; Trillium: Research Funding; LAM Therapeutics: Research Funding; Seattle Genetics: Research Funding; Regeneron: Research Funding; Seattle Genetics: Research Funding; Mayo Clinic Rochester: Employment; Regeneron: Research Funding; Bristol-Myers Squibb: Research Funding; LAM Therapeutics: Research Funding; Bristol-Myers Squibb: Research Funding; Trillium: Research Funding; LAM Therapeutics: Research Funding; Regeneron: Research Funding; Mayo Clinic Rochester: Employment; Seattle Genetics: Research Funding; Affimed: Research Funding; Bristol-Myers Squibb: Research Funding; Bristol-Myers Squibb: Research Funding; Bristol-Myers Squibb: Research Funding; Regeneron: Research Funding; LAM Therapeutics: Research Funding; LAM Therapeutics: Research Funding; Trillium: Research Funding; Seattle Genetics: Research Funding; Regeneron: Research Funding; Regeneron: Research Funding; Regeneron: Research Funding; Trillium: Research Funding; Seattle Genetics: Research Funding; Seattle Genetics: Research Funding; Trillium: Research Funding; Trillium: Research Funding; Trillium: Research Funding; Mayo Clinic Rochester: Employment; Mayo Clinic Rochester: Employment; Regeneron: Research Funding; Affimed: Research Funding; Trillium: Research Funding; Mayo Clinic Rochester: Employment; Affimed: Research Funding; Mayo Clinic Rochester: Employment; Seattle Genetics: Research Funding; Mayo Clinic Rochester: Employment; Mayo Clinic Rochester: Employment; Affimed: Research Funding; Seattle Genetics: Research Funding; Bristol-Myers Squibb: Research Funding; LAM Therapeutics: Research Funding. Bennani:Bristol-Myers Squibb: Research Funding; Bristol-Myers Squibb: Research Funding; Adicet Bio: Other: Advisory board; Adicet Bio: Other: Advisory board; Purdue Pharma: Other: Advisory board; Purdue Pharma: Other: Advisory board; Seattle Genetics: Other: Advisory board; Seattle Genetics: Other: Advisory board; Seattle Genetics: Other: Advisory board; Kite Pharma: Other: Advisory board; Adicet Bio: Other: Advisory board; Kite Pharma: Other: Advisory board; Bristol-Myers Squibb: Research Funding; Kite Pharma: Other: Advisory board; Purdue Pharma: Other: Advisory board. Kumar:Takeda: Research Funding; Celgene: Consultancy, Research Funding; Janssen: Consultancy, Research Funding.

BACKGROUND: Axi-cel is an autologous anti-CD19 chimeric antigen receptor (CAR) T-cell therapy that is approved for treatment of relapsed/refractory (R/R) large B-cell lymphoma and is associated with high response rates and durable remissions. Recent data show that axi-cel is effective across various adverse prognostic features, namely cell of origin, disease bulkiness, and extranodal disease, among others. Hypoalbuminemia is a known adverse prognostic factor in lymphomas. It is unknown if axi-cel overcomes the adverse prognostic feature of hypoalbuminemia in R/R large B-cell or transformed follicular lymphoma. METHODS: We conducted a retrospective analysis of patients treated with axi-cel across three Mayo Clinic campuses (Rochester, Jacksonville, and Phoenix) from 06/01/2018 until 04/01/2019. The primary objective of this analysis was to assess the impact of hypoalbuminemia (defined at day 0, prior to infusion) on outcome after axi-cel therapy. RESULTS: A total of 50 (male=37, 74%) patients (pts), median age of 53 (26-67) years received axi-cel. The median number of prior lines of therapy was 3 (2-8) (Table 1). Two pts had no available serum albumin levels at time of axi-cel infusion. Seven (15%) of 48 pts had serum albumin levels lower than 3.5 g/dL (median= 3.3 g/dL (range 2.6-3.4)) and the median follow up of survivors was 7.6 (1.9-14.3) months. The best overall response rate (ORR) and complete remission (CR) rates in these pts were 57% and 57%, respectively. One (14%) patient had stable disease and 2 (29%) had disease progression. The median overall survival (OS) for pts with hypoalbuminemia was not reached. On the other hand, 41 (85%) pts had a normal serum albumin level (median=4.0 (range 3.5-5.1) g/dL) and the median follow up for survivors was 6.3 months. The best objective response rate (ORR) and complete remission (CR) rates in these pts were 82% and 44%, respectively. The median OS for pts with normal serum albumin was 14 (95%CI=6.3-29.6) months. There was no significant difference at 6-months and 1-year OS between pts with hypoalbuminemia vs. those with normal baseline serum albumin levels [6-month=100% vs. 79%(95%CI=64-93%); 1-year (100% vs. 54% (95%CI=26-82%), p=0.17] (Figure 1). All grades cytokine release syndrome (CRS) was diagnosed in all 7 pts with hypoalbuminemia (100%) and in 38 of 41 (92%) pts without hypoalbuminemia. There was no difference in the median duration of CRS between pts with or without hypoalbuminemia [6 (range 1-11) days vs 5 (range 1-19) days, p=0.89]. Neurotoxicity (all grades) was observed in 5 (71%) pts with hypoalbuminemia compared 26 (63%) with normal albumin levels. There was no statistically significant difference in median duration of neurotoxicity between pts with hypoalbuminemia and those with normal baseline albumin levels [9 (range 1-10) days vs. 3 (range 0-25) days, p= 0.72]. CONCLUSIONS: Hypoalbuminemia does not have a significant impact on the outcomes of axi-cel therapy, including the incidence of CRS or neurotoxicity. These results need to be validated in a large collaborative multicenter study. Further investigation is needed to assess the prognostic impact of severe hypoalbuminemia (<3g/dL) on axi-cel therapy. Disclosures Ansell: Mayo Clinic Rochester: Employment; Seattle Genetics: Research Funding; Trillium: Research Funding; Trillium: Research Funding; Mayo Clinic Rochester: Employment; Bristol-Myers Squibb: Research Funding; Bristol-Myers Squibb: Research Funding; Affimed: Research Funding; Regeneron: Research Funding; Affimed: Research Funding; Bristol-Myers Squibb: Research Funding; Mayo Clinic Rochester: Employment; LAM Therapeutics: Research Funding; Affimed: Research Funding; Bristol-Myers Squibb: Research Funding; Trillium: Research Funding; Regeneron: Research Funding; Trillium: Research Funding; Affimed: Research Funding; LAM Therapeutics: Research Funding; Mayo Clinic Rochester: Employment; LAM Therapeutics: Research Funding; LAM Therapeutics: Research Funding; Bristol-Myers Squibb: Research Funding; Mayo Clinic Rochester: Employment; Mayo Clinic Rochester: Employment; Affimed: Research Funding; Regeneron: Research Funding; LAM Therapeutics: Research Funding; Seattle Genetics: Research Funding; Trillium: Research Funding; Affimed: Research Funding; Trillium: Research Funding; Bristol-Myers Squibb: Research Funding; LAM Therapeutics: Research Funding; Mayo Clinic Rochester: Employment; Trillium: Research Funding; Regeneron: Research Funding; LAM Therapeutics: Research Funding; Bristol-Myers Squibb: Research Funding; Mayo Clinic Rochester: Employment; LAM Therapeutics: Research Funding; Seattle Genetics: Research Funding; Trillium: Research Funding; Trillium: Research Funding; Seattle Genetics: Research Funding; Regeneron: Research Funding; Bristol-Myers Squibb: Research Funding; Mayo Clinic Rochester: Employment; Bristol-Myers Squibb: Research Funding; Seattle Genetics: Research Funding; LAM Therapeutics: Research Funding; Regeneron: Research Funding; Affimed: Research Funding; Seattle Genetics: Research Funding; Regeneron: Research Funding; Seattle Genetics: Research Funding; Regeneron: Research Funding; Seattle Genetics: Research Funding; Regeneron: Research Funding; Seattle Genetics: Research Funding; Affimed: Research Funding; Affimed: Research Funding. Bennani:Seattle Genetics: Other: Advisory board; Kite Pharma: Other: Advisory board; Seattle Genetics: Other: Advisory board; Adicet Bio: Other: Advisory board; Seattle Genetics: Other: Advisory board; Purdue Pharma: Other: Advisory board; Adicet Bio: Other: Advisory board; Purdue Pharma: Other: Advisory board; Purdue Pharma: Other: Advisory board; Adicet Bio: Other: Advisory board; Bristol-Myers Squibb: Research Funding; Bristol-Myers Squibb: Research Funding; Kite Pharma: Other: Advisory board; Bristol-Myers Squibb: Research Funding; Kite Pharma: Other: Advisory board. Paludo:Verily Life Sciences: Research Funding; Celgene: Research Funding; Verily Life Sciences: Research Funding; Celgene: Research Funding. Tun:DTRM Biopharma: Research Funding; Mundi-pharma: Research Funding; BMS: Research Funding; Celgene: Research Funding; Curis: Research Funding; TG Therapeutics: Research Funding. Foran:Agios: Honoraria, Research Funding. Kharfan-Dabaja:Daiichi Sankyo: Consultancy; Pharmacyclics: Consultancy.

Background: Central nervous system (CNS) involvement by peripheral T cell lymphoma (PTCL) is a rare condition. Among primary CNS lymphomas, only 2% are secondary to PTCL, while the risk of CNS relapse in all cases of PTCL is estimated at 2% to 6%. Little is known about the presentation and outcomes of PTCL patients with CNS involvement given the rarity of this entity. In this study, we describe patient characteristics, histology, and clinical course of patients with CNS involvement by PTCL. Methods: The Mayo Clinic Lymphoma Database was used to identify PTCL patients with primary or secondary CNS involvement seen at our institution between 2000 and 2018. A total of 12 patients were identified and their medical records were reviewed for patient and disease characteristics, CNS-directed treatment modality, and outcomes. The Kaplan-Meier method was used for time-to-event analysis. Results: The median age at CNS diagnosis was 63 years (range 41 to 76) and 11 (93%) patients were male. The histological diagnoses were PTCL, NOS in 9 (75%) patients, enteropathy-associated T-cell lymphoma in 2 (17%) patients, and angioimmunoblastic T-cell lymphoma in 1 (8%) patient. Five patients presented with primary T-cell CNS lymphoma (all with a PTCL, NOS histology), while the remaining 7 (58%) patients also had systemic involvement. All patients presented with neurologic symptoms at the time of CNS involvement diagnosis including: focal motor deficits in 6 patients (unilateral upper extremity weakness, gait impairments, and hemiparesis), cognitive decline in 5 patients (memory impairments, reduced attention, and confusion), headache in 4 patients, and seizure in 3 patients. The CNS disease location included the brain parenchyma in 9 (75%) patients, leptomeninges in 1 (8%) patient, and lumbar plexus in 1 (8%) patient. One patient (8%) had positive CSF finding only without radiologic evidence of involvement. CSF analysis was performed in 11 patients. Elevated protein levels were noted in 3 (27%) patients, malignant cells in 2 (18%), and no clear abnormalities in the remaining 6 (55%) patients. Concomitant bone marrow involvement was seen in only 1 patient. Elevated LDH was seen in 2 patients. The a median LDH was 195 U/L (range 139 to 4,360) The most common CNS-directed therapies were: high-dose methotrexate (MTX)-based regimens in 8 (67%) patients, including high-dose MTX in combination with temozolomide (n=2), or cytarabine and thiotepa (n=2). Intrathecal MTX, temozolomide and dexamethasone, lenalidomide, high-dose steroids, and surgical resection were the treatment modality used for one patient each. At a median follow up of 18 months, eight (75%) out of 12 patients were not alive at the time of last follow up. The median overall survival (OS) from diagnosis was 16 months (95% CI: 2.8-173). The median progression free survival (PFS) from initiation of CNS-directed therapy was 9 months (95% CI: 1.6-33) (figure). Four patients had a PFS longer than 12 months. These 4 patients were treated with: temozolomide/dexamethasone, high-dose MTX, lenalidomide, and high-dose MTX followed by cytarabine/thiothepa. Conclusion: CNS involvement by T-cell lymphoma is a rare complication that carries a poor prognosis. Early onset of neurologic symptoms should trigger prompt investigation of CNS involvement. Despite the short OS and PFS, some patients may achieve a relatively longer disease free interval. Disclosures Bennani: Adicet Bio: Other: Advisory board; Seattle Genetics: Other: Advisory board; Purdue Pharma: Other: Advisory board; Seattle Genetics: Other: Advisory board; Seattle Genetics: Other: Advisory board; Bristol-Myers Squibb: Research Funding; Bristol-Myers Squibb: Research Funding; Kite Pharma: Other: Advisory board; Kite Pharma: Other: Advisory board; Bristol-Myers Squibb: Research Funding; Adicet Bio: Other: Advisory board; Purdue Pharma: Other: Advisory board; Purdue Pharma: Other: Advisory board; Adicet Bio: Other: Advisory board; Kite Pharma: Other: Advisory board. Cerhan:Celgene: Research Funding; NanoString: Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees. Nowakowski:Celgene: Consultancy, Research Funding; Bayer: Consultancy, Research Funding; Curis: Research Funding; F. Hoffmann-La Roche Ltd: Research Funding; Genentech, Inc.: Research Funding; MorphoSys: Consultancy, Research Funding; NanoString: Research Funding; Selvita: Membership on an entity's Board of Directors or advisory committees. Ansell:Mayo Clinic Rochester: Employment; Affimed: Research Funding; Bristol-Myers Squibb: Research Funding; Bristol-Myers Squibb: Research Funding; Regeneron: Research Funding; Trillium: Research Funding; Seattle Genetics: Research Funding; Regeneron: Research Funding; LAM Therapeutics: Research Funding; Trillium: Research Funding; Mayo Clinic Rochester: Employment; Affimed: Research Funding; LAM Therapeutics: Research Funding; Seattle Genetics: Research Funding. Paludo:Celgene: Research Funding; Verily Life Sciences: Research Funding; Verily Life Sciences: Research Funding; Celgene: Research Funding.

Introduction Neutropenic fever following high-dose chemotherapy and autologous stem cell transplantation (ASCT) is a common (incidence 63-100%) and potentially life-threatening complication. Recommended time to antibiotic (TTA) administration is within 1 hr of fever onset with delays associated with significant morbidity, prolonged hospitalization, and mortality. Standard of care guidelines emphasize patient self-monitoring for fever, with instructions to seek immediate medical attention if body temperature (temp) reaches 100.4°F or higher. In this study, we evaluated if a novel wearable, continuous temp monitor, tPatch, could reliably estimate core body temp and detect fever in an outpatient setting following ASCT. Additionally, we gathered preliminary data to explore early detection and prediction of clinically relevant temp rise in this clinical setting. Methods Patients (N = 86) with hematologic malignancies (62% multiple myeloma) who underwent high-dose chemotherapy followed by ASCT at Mayo Clinic, MN were prospectively enrolled between June 2018 and March 2019. Patients (82% male) wore an axilla-placed tPatch continuously for 7 days in an outpatient setting during the post ASCT period and were asked to record self-measured oral temp in 3-4 hr intervals daily using a standardized thermometer after appropriate training . Patients followed standard of care procedures with daily clinic assessment of temp, blood counts, and vital signs. An optional patient questionnaire was given at end-of-study. A model was trained using both patient- and clinic-assessed oral temp measures to estimate core temp from 2 sensors on the tPatch device. Core temp estimates and trends were then compared to patient- and clinic-assessed measurements. Fever was defined as a temperature ≥100.4°F for at least 1 hr. Results When compared to all oral temp reads, the tPatch estimated core temp within 0.03 ± 0.7℉. Among the 86 patients, clinic-assessed fever incidence was 29.4% while tPatch-assessed incidence was 58.8%. Using all clinic-recorded temp readings as "ground truth," the sensitivity and specificity of the tPatch algorithm in detecting fevers were 88% and 86%, respectively, while patient self fever detection sensitivity was 62% and specificity 93%. With "fever episode" defined as a temp ≥100.4°F for at least 1 hr, tPatch detected 9.6 times the number of fever episodes vs. clinic reads. The average lead time of tPatch detection of clinic-recorded fevers was 3.7 hours. In 25% of all intervals between clinic temp readings, either tPatch or patients detected at least 1 fever episode. The tPatch was well-tolerated, the only adverse events reported were grade 1 skin irritation and discomfort in 4 (5%) patients. Of 65 patients who completed the survey, 95% reported the tPatch as "quite" or "somewhat" comfortable and 94% stated no difficulty in using the tPatch. Exploration of tPatch temp trends over various time intervals for use in fever prediction is ongoing. Conclusions Patient self-monitoring of temp has low sensitivity and is not feasible for long intervals of time (e.g., overnight). Continuous temperature monitoring by a wearable device overcomes these challenges and has the potential to improve early detection and consequently shorten time to antibiotic initiation. A follow-up randomized study is planned to assess the clinical benefits of continuous temp monitoring through patient and clinician alerts triggering early clinical intervention for febrile neutropenia. Figure Disclosures Vera-Aguilera: Verily Life Sciences: Research Funding. Haji-Abolhassani:Verily Life Sciences: Employment. Kulig:Verily Life Sciences: Employment. Heitz:Verily Life Sciences: Employment. Paludo:Verily Life Sciences: Research Funding; Verily Life Sciences: Research Funding; Celgene: Research Funding; Celgene: Research Funding. Ghoreyshi:Verily Life Sciences: Employment. Scheevel:Verily Life Sciences: Research Funding. Schimke:Verily Life Sciences: Research Funding. Markovic:Verily Life Sciences: Research Funding.

Introduction Waldenstrom macroglobulinemia (WM) is a rare, incurable, indolent non-Hodgkin lymphoma that is preceded by an IgM monoclonal gammopathy of undetermined significance (IgM MGUS). In recent years, better understanding of the pathobiology of WM and IgM MGUS has strengthened the hypothesis that they may represent different stages of the same disease. Studying the relationship between these two conditions is the essential first step to understanding the mechanisms driving the malignant transformation from IgM MGUS to WM. Recently long non-coding RNAs (lncRNAs) have garnered significant recognition for their roles in the regulation of gene expression and cellular function. lncRNA can regulate gene expression of neighboring protein-coding genes through multiple mechanisms such as by recruiting chromatin modifiers and increasing accessibility of genes to transcription proteins, or by directly binding to enhancers or promoters. Here we present the first analysis of the lncRNA expression profile in patients with WM compared to IgM MGUS. Methods Twenty-one patients with untreated WM (n=14) and IgM MGUS (n=7) seen at Mayo Clinic were included in this analysis. Malignant cells were isolated from bone marrow samples using CD19 and CD38 positive selection. Total RNA was extracted and sequencing was performed using the Illumina HiSeq4000 NGS platform. Differentially expressed lncRNAs in WM compared to IgM MGUS were defined as log2 fold change (FC) > 1.0 (upregulated) or < -1.0 (downregulated) and a false discovery rate (FDR) < 0.05. The nearest gene to each lncRNA was also identified and their gene expression was detected. Using the Ingenuity Pathway Analysis software and taking into account the transcriptional expression and known function of these genes, a correlation between cell function and disease analysis was performed. Results A total of 119 lncRNAs were differently expressed between WM and IgM MGUS (17 transcripts were upregulated in WM, while 102 were downregulated in WM). Sixteen unique genes were identified as the nearest gene to 17 upregulated lncRNAs, while 18 genes were identified as the nearest genes to the top 18 downregulated lncRNAs in WM compared to IgM MGUS. Several of the upregulated lncRNA in WM were in proximity to known oncogenes. The well-known oncogenic lncRNA PVT1 was found to be upregulated in WM compared to IgM MGUS (log2 FC 1.35, FRD 0.04). PVT1 is overexpressed in several malignancies and associated with inhibition of apoptosis and increased cell proliferation. PVT1 is in close proximity to MYC and co-expression of PVT1-MYC has been described in multiple cancers. PVT1 also potentiate MYC amplification by increasing MYC protein stability, therefore promoting malignant cell growth. The lncRNA TCB1D27, also upregulated in WM compared to IgM MGUS, is located in close proximity to the oncogene TNFRSF13B (TACI), which was found to be overexpressed in WM patients (log2 FC 1.61, FDR <0.001). TACI is a receptor for BLyS/BAFF, which modulates the development of normal B-cells and plays a role in various B-cell malignancies. In fact, BLyS/BAFF level is known to be increased in WM and to promote survival and proliferation of WM B cells. The upregulated lncRNA RP11-25K21.1 in WM is located in close proximity to the oncogene FCGR2B (CD32B), which was also upregulated in WM compared to IgM MGUS (log2 FC 1.59, FDR 0.001). Overexpression of CD32B was previously shown to be associated with inferior outcomes in DLBCL and FL patients, presumably secondary to an increased internalization and clearance of rituximab due to increased CD32B expression on malignant cells. Several other upregulated lncRNAs in WM compared to IgM MGUS are also in proximity of other oncogenes such as PCED1B, POLD3, CLLU1, and SMAD7. Finally, we considered the expression of the nearest genes and their reported cell function and association with diseases. Several genes that are known to have a role in the cell proliferation and/or neoplasm development were noted to be upregulated in WM (figure 1). Conclusion We present the first report of lncRNAs expression in WM and IgM MGUS in addition to a concurrent analysis of the gene expression of the nearest protein-coding genes. Several known oncogenes are in proximity of overexpressed lncRNAs in WM compared to IgM MGUS that could indicate potential implications in the pathobiology of these diseases and the mechanism driving the malignant transformation to WM. Disclosures Paludo: Celgene: Research Funding; Celgene: Research Funding; Verily Life Sciences: Research Funding; Verily Life Sciences: Research Funding. Dasari:The Binding Site: Patents & Royalties: US Patent Rights on Mass Spectroscopy Licensing agreement with The Binding Site, Research Funding. Kapoor:Celgene: Honoraria; Cellectar: Consultancy; Janssen: Research Funding; Sanofi: Consultancy, Research Funding; Amgen: Research Funding; Takeda: Honoraria, Research Funding; Glaxo Smith Kline: Research Funding. Ailawadhi:Amgen: Consultancy, Research Funding; Pharmacyclics: Research Funding; Takeda: Consultancy; Janssen: Consultancy, Research Funding; Cellectar: Research Funding; Celgene: Consultancy. Gertz:Amyloidosis Foundation: Research Funding; i3Health: Other: Development of educational programs and materials; Medscape: Consultancy, Speakers Bureau; Ionis/Akcea: Consultancy; Celgene: Consultancy; Appellis: Consultancy; Amgen: Consultancy; Teva: Speakers Bureau; Alnylam: Consultancy; Prothena Biosciences Inc: Consultancy; Janssen: Consultancy; Spectrum: Consultancy, Research Funding; Annexon: Consultancy; Physicians Education Resource: Consultancy; Abbvie: Other: personal fees for Data Safety Monitoring board; Research to Practice: Consultancy; Johnson and Johnson: Speakers Bureau; DAVA oncology: Speakers Bureau; Pharmacyclics: Membership on an entity's Board of Directors or advisory committees; Proclara: Membership on an entity's Board of Directors or advisory committees; Springer Publishing: Patents & Royalties; International Waldenstrom Foundation: Research Funding. Novak:Celgene Coorperation: Research Funding. Ansell:Affimed: Research Funding; Regeneron: Research Funding; Mayo Clinic Rochester: Employment; Seattle Genetics: Research Funding; Bristol-Myers Squibb: Research Funding; Bristol-Myers Squibb: Research Funding; Affimed: Research Funding; LAM Therapeutics: Research Funding; Bristol-Myers Squibb: Research Funding; Seattle Genetics: Research Funding; Affimed: Research Funding; Affimed: Research Funding; Mayo Clinic Rochester: Employment; Bristol-Myers Squibb: Research Funding; Mayo Clinic Rochester: Employment; Seattle Genetics: Research Funding; Trillium: Research Funding; Mayo Clinic Rochester: Employment; Bristol-Myers Squibb: Research Funding; Bristol-Myers Squibb: Research Funding; Trillium: Research Funding; Regeneron: Research Funding; Seattle Genetics: Research Funding; Mayo Clinic Rochester: Employment; Trillium: Research Funding; Trillium: Research Funding; Mayo Clinic Rochester: Employment; Regeneron: Research Funding; Regeneron: Research Funding; LAM Therapeutics: Research Funding; Mayo Clinic Rochester: Employment; Seattle Genetics: Research Funding; LAM Therapeutics: Research Funding; Seattle Genetics: Research Funding; Mayo Clinic Rochester: Employment; LAM Therapeutics: Research Funding; LAM Therapeutics: Research Funding; Trillium: Research Funding; Affimed: Research Funding; Trillium: Research Funding; Regeneron: Research Funding; Affimed: Research Funding; Affimed: Research Funding; Bristol-Myers Squibb: Research Funding; LAM Therapeutics: Research Funding; Trillium: Research Funding; LAM Therapeutics: Research Funding; Regeneron: Research Funding; LAM Therapeutics: Research Funding; Trillium: Research Funding; Mayo Clinic Rochester: Employment; Regeneron: Research Funding; Bristol-Myers Squibb: Research Funding; Affimed: Research Funding; Seattle Genetics: Research Funding; Affimed: Research Funding; Regeneron: Research Funding; Seattle Genetics: Research Funding; Bristol-Myers Squibb: Research Funding; LAM Therapeutics: Research Funding; Regeneron: Research Funding; Seattle Genetics: Research Funding; Trillium: Research Funding.

Background: Two-year follow-up from ZUMA-1 trial for axicabtagene ciloleucel (axi-cel) CD19 chimeric antigen receptor T-cell (CART) in aggressive non-Hodgkin lymphoma (NHL) demonstrated that patients (pts) achieving complete remission (CR) as their best response have the longest progression free survival (PFS), while PFS remained poor for others. Majority of the best response are achieved by month 3 post-infusion. Moreover, a subset of pts who do not achieve CR by month 1 may still achieve CR while others will progress within months. Having a clinically available test to predict for durable response can assist clinicians in selecting pts who can be observed for continued response vs. those who may need intervention earlier. Peak CAR-T expansion has been correlated with best clinical response to axi-cel. However there is no clinically available test to quantify CART cells post treatment. Given the prolonged lymphotoxic effect of the lymphodepletion (LD) chemotherapy administered before CART infusion, we hypothesize that the peak expansion of absolute lymphocyte count (ALC, ALCpeak) after infusion likely corresponds to CART expansion. We examined for correlation between ALCpeak and clinical response at month 3 post-infusion for pts who received axi-cel. Methods: Data was collected from pts who received axi-cel between June 2016 and March 2019 at Mayo Clinic Rochester (NCT02348216, NCT03153462, and standard of care). All infused pts were included in the intention to treat analysis for response and event-free survival (EFS). Lugano 2014 criteria were used for clinical response. EFS was defined as the date of infusion until progression or death due to any cause and evaluated using Kaplan Meier curves with log-rank test. Comparison between subgroups was investigated using Fisher's exact test and Wilcoxon test for categorical and continuous variables, respectively. Statistical analysis performed using JMP Pro 14.1. Results: Of the thirty-six NHL pts treated with axi-cel, the median age was 56 yrs (range 26-65); 78% (28) were male; 44% (16) had DLBCL; the median number of prior therapies was 3 (2-6), 47% (17) had stem cell transplant. Nineteen (53%) pts received bridging therapy after leukapheresis. All pts received fludarabine and cyclophosphamide for LD chemotherapy and achieved ALC nadir by the day of CART infusion (median 0.03 x 10^9/L, range 0.01-0.11). As CART expansion were found to peak within the first two weeks in ZUMA-1 trial, we examined ALC trend over 15 days post infusion and found that ALCpeak generally happened within the same timeframe (median ALCpeak day: 9, range 6-15). At 3 months post infusion, 33.3% (12/36) of pts achieved CR, 8.3% (3/36) pts were in partial remission (PR). No correlation was found between CR response and clinical characteristics: age >60 years, stage III/IV, B symptoms, elevated LDH, IPI score 3-4, number of previous treatments, use of bridging therapy, peak CRP and ferritin. However, the ALCpeak was significantly different between CR and non-CR pts (median, range: 1.09, 0.35-2.55; 0.67, 0.15-1.03; p=0.04). Using receiver operator curve (ROC)-derived area under the curve (AUC) estimates, ALCpeak 1.08 x 10^9/L was identified as the best threshold to discriminate patients at different likelihood to achieve CR at month 3. Pts achieving ALCpeak ≥ 1.08 showed a strong increased likelihood of achieving CR at month 3 (OR 2.8; 95% CI, 1.12-6.99; P=0.02). There was no statistical difference between the ALCpeak high and low groups in terms of use of bridging therapy, occurrence of any grade or higher grade cytokine release syndrome or neurotoxicity, or the use of tocilizumab or steroid. Interestingly, for pts who had changes in clinical response from month 1 to month 3, all 3 pts who converted to CR by month 3 had ALCpeak≥1.08, while the 13 of the 15 pts who had progressed by month 3 had ALCpeak<1.08. In addition, EFS was significantly different between pts who had ALCpeak above and below 1.08 (6 months EFS rate 75% vs. 25%, respectively, Figure 1). Conclusions: We found that the ALC expansion occurred in the first 15 days post axi-cel infusion, consistent with the known time frame for CART expansion. Higher ALCpeak level in this timeframe correlated with higher likelihood of achieving CR, and is associated with conversion to CR. This study is limited by the small sample size and limited follow-up, but the results are promising for further prospective investigation in a larger cohort of patients. Disclosures Paludo: Verily Life Sciences: Research Funding; Verily Life Sciences: Research Funding; Celgene: Research Funding; Celgene: Research Funding. Ansell:Mayo Clinic Rochester: Employment; Bristol-Myers Squibb: Research Funding; Seattle Genetics: Research Funding; LAM Therapeutics: Research Funding; Regeneron: Research Funding; LAM Therapeutics: Research Funding; Mayo Clinic Rochester: Employment; Seattle Genetics: Research Funding; Affimed: Research Funding; Affimed: Research Funding; Trillium: Research Funding; Mayo Clinic Rochester: Employment; Trillium: Research Funding; Seattle Genetics: Research Funding; Seattle Genetics: Research Funding; Seattle Genetics: Research Funding; Mayo Clinic Rochester: Employment; Trillium: Research Funding; Mayo Clinic Rochester: Employment; Trillium: Research Funding; Trillium: Research Funding; Mayo Clinic Rochester: Employment; Seattle Genetics: Research Funding; Regeneron: Research Funding; Regeneron: Research Funding; Bristol-Myers Squibb: Research Funding; Affimed: Research Funding; Affimed: Research Funding; Mayo Clinic Rochester: Employment; Seattle Genetics: Research Funding; LAM Therapeutics: Research Funding; Bristol-Myers Squibb: Research Funding; Bristol-Myers Squibb: Research Funding; Regeneron: Research Funding; LAM Therapeutics: Research Funding; Affimed: Research Funding; Seattle Genetics: Research Funding; Bristol-Myers Squibb: Research Funding; Trillium: Research Funding; Regeneron: Research Funding; Affimed: Research Funding; LAM Therapeutics: Research Funding; Bristol-Myers Squibb: Research Funding; Trillium: Research Funding; Mayo Clinic Rochester: Employment; LAM Therapeutics: Research Funding; Regeneron: Research Funding; LAM Therapeutics: Research Funding; Affimed: Research Funding; Trillium: Research Funding; Regeneron: Research Funding; Trillium: Research Funding; LAM Therapeutics: Research Funding; Affimed: Research Funding; Regeneron: Research Funding; Bristol-Myers Squibb: Research Funding; LAM Therapeutics: Research Funding; Affimed: Research Funding; Bristol-Myers Squibb: Research Funding; Mayo Clinic Rochester: Employment; Regeneron: Research Funding; Bristol-Myers Squibb: Research Funding; Seattle Genetics: Research Funding. Bennani:Purdue Pharma: Other: Advisory board; Purdue Pharma: Other: Advisory board; Seattle Genetics: Other: Advisory board; Adicet Bio: Other: Advisory board; Bristol-Myers Squibb: Research Funding; Kite Pharma: Other: Advisory board; Kite Pharma: Other: Advisory board; Adicet Bio: Other: Advisory board; Seattle Genetics: Other: Advisory board; Adicet Bio: Other: Advisory board; Bristol-Myers Squibb: Research Funding; Kite Pharma: Other: Advisory board; Bristol-Myers Squibb: Research Funding; Seattle Genetics: Other: Advisory board; Purdue Pharma: Other: Advisory board. Dingli:alexion: Consultancy; Janssen: Consultancy; Millenium: Consultancy; Karyopharm: Research Funding; Rigel: Consultancy. Kapoor:Celgene: Honoraria; Janssen: Research Funding; Cellectar: Consultancy; Sanofi: Consultancy, Research Funding; Amgen: Research Funding; Takeda: Honoraria, Research Funding; Glaxo Smith Kline: Research Funding. Kenderian:Novartis: Patents & Royalties, Research Funding; Tolero: Research Funding; Humanigen: Other: Scientific advisory board , Patents & Royalties, Research Funding; Lentigen: Research Funding; Morphosys: Research Funding; Kite/Gilead: Research Funding. Kumar:Takeda: Research Funding; Janssen: Consultancy, Research Funding; Celgene: Consultancy, Research Funding.

Abstract Introduction Waldenstrom macroglobulinemia (WM) is a rare disease accounting for only 1-2% of all hematologic malignancies and with an overall annual, age-adjusted incidence of approximately 3.5 to 5.5 cases per million-person years. With approximately 13,000 active hematologist/oncologists in the US, most of who are in the community oncology setting, it's estimated that a hematologist/oncologist will only diagnose a WM case approximately every 8 years. Therefore, the clinical experience of a general hematologist/oncologist in the management of WM is likely very limited compared to more prevalent malignances. Previous studies in the setting of rare cancers suggest a correlation between higher volume of care and improved outcomes. Therefore, we explored the volume-outcome relationship in WM using the National Cancer Data Base (NCDB). Methods Patient-level data from the NCDB, a nationwide, facility-based, database covering approximately 70% of all newly diagnosed cancer cases in the US, was queried for all new WM cases diagnosed between 2004 and 2014. Only patients requiring treatment were included. Treatment facilities were divided into quartiles based on the average annual volume of newly diagnosed cases of WM seen. Cox regression was used to analyze the association between facility WM volume and survival, adjusted by demographics (sex, age, race), socioeconomic status (income, education, insurance type), geography (area of residence, treatment facility type, travel distance), comorbidity factors (Charlson-Deyo score), and year of diagnosis. Time-to-event analysis was calculated from frontline therapy initiation date using the Kaplan-Meier method and the log-rank test. Results A total of 3,732 patients with WM treated in 831 facilities were included. The median age at diagnosis was 70 years and 75% of the patients were treated within 20 miles from their residency zip code. Patient characteristics per treatment facility volume quartile are shown in table 1. The median annual facility volume was 1 new WM patient/year (range 0.1 to 21). The median follow-up from frontline treatment was 5 years. The unadjusted median OS by facility volume was: Q1: 6.5 years (95% CI: 5.7-7.4), Q2: 7 years (95% CI: 6.3-8.2), Q3: 8.2 years (95% CI: 7.1-8.9), and Q4: NR (95% CI: 8.5-NR), p<0.0001 (figure 1). The estimated 5-year OS by facility volume was: Q1: 56%, Q2: 61%, Q3: 64%, Q4: 71%, p<0.0001. Multivariate analysis including all variables showed in Table 1 demonstrated that facility volume was independently associated with all-cause mortality. Compared to patients treated at Q4 facilities, patients treated at lower-quartiles facilities had a higher incremental risk of death (Q3 hazard ratio [HR], 1.07 [95% CI: 0.88 to 1.29] p=0.46; Q2 HR, 1.34 [95% CI: 1.11 to 1.63] p=0.002; Q1 HR, 1.52 [95% CI: 1.23 to 1.88] p=<0.0001). Conclusion Our results suggest that a volume-outcome relationship exits in WM as patients treated initially at higher-volume facilities had a lower risk of mortality. Although differences in the underlying disease biology, referral patterns after initial therapy, or cumulative treating-physician experience could not be assessed, these potential biases would only underestimate the magnitude of the volume-outcome relationship reported. Disclosures Ansell: Celldex: Research Funding; Trillium: Research Funding; Takeda: Research Funding; Regeneron: Research Funding; Pfizer: Research Funding; Affimed: Research Funding; Merck & Co: Research Funding; Bristol-Myers Squibb: Research Funding; Seattle Genetics: Research Funding; LAM Therapeutics: Research Funding. Gertz:Medscape: Consultancy; janssen: Consultancy; Alnylam: Honoraria; Ionis: Honoraria; Physicians Education Resource: Consultancy; Prothena: Honoraria; Teva: Consultancy; Research to Practice: Consultancy; spectrum: Consultancy, Honoraria; celgene: Consultancy; Amgen: Consultancy; Abbvie: Consultancy; annexon: Consultancy; Apellis: Consultancy. Kapoor:Takeda: Research Funding; Celgene: Research Funding. Ailawadhi:Amgen: Consultancy; Janssen: Consultancy; Pharmacyclics: Research Funding; Celgene: Consultancy; Takeda: Consultancy. Reeder:Affimed: Research Funding. Witzig:Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding. Dispenzieri:Celgene, Takeda, Prothena, Jannsen, Pfizer, Alnylam, GSK: Research Funding. Lacy:Celgene: Research Funding. Kumar:Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; AbbVie: 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, Research Funding; KITE: Membership on an entity's Board of Directors or advisory committees, Research Funding.

Abstract Background Limited data exist with respect to outcome and optimal therapeutic strategies in patients with central nervous system (CNS) involvement in multiple myeloma (MM). We present a single center experience of patients with myelomatous CNS involvement in newly diagnosed and relapsed / refractory MM. Methods Of 4060 patients with MM seen at Mayo Clinic, Rochester, MN between January, 1st 1998 and December, 31st 2012, 26 patients had identifiable CNS involvement confirmed by biopsy / presence of plasma cells in the cerebrospinal fluid (CSF) and/or identification of intraparenchymal or meningeal involvement by imaging (MRI/CT). Patients were risk stratified by mSMART (Mayo Stratification for Myeloma And Risk-adapted Therapy) criteria. Overall survival (OS) from diagnosis and the time of CNS involvement was calculated using the Kaplan-Meier method. Results Myelomatous CNS involvement occurred in 26 (0.6%) patients with MM of whom 3 were newly diagnosed. The median time to detection of CNS involvement was 24 months (range: 0-125) from diagnosis of MM. Median age at CNS myeloma diagnosis was 58 years (range 37-80) and 73% were male. Abnormally high LDH was observed in 11 out of 22 patients at the time of CNS involvement. Lumbar puncture was performed on 16 patients, 13 (81%) of whom had plasma cells in the CSF detected by cytology/flow-cytometry. Two patients, without evidence of plasma cells, had abnormally high CSF protein and 1 had normal CSF analysis. Of the 25 patients who underwent MRI, 16 (64%) had at least one of the following significant abnormalities; intraparenchymal disease (37%), leptomeningeal enhancement (68%) or direct extension of MM (31%). The neurological symptoms at or after diagnosis of CNS myeloma included headache (38%), cranial nerve palsy (38%), visual disturbance (38%), gait disturbance (35%), paresthesias (35%), limb weakness (31%), confusion (30%), nausea/vomiting (15%), dysarthria (12%), seizures (8%) and urinary incontinence (4%). Fluorescent in-situ hybridization (FISH n=7), cytogenetics (n=8) and/or plasma cell labeling index (PCLI n=12) were available in 18 patients prior to the diagnosis of CNS disease. Ten (56%) out of those 18 patients had high-risk features at least by one criterion. At the time of CNS involvement, six additional patients demonstrated high risk features [5 by PCLI (≥ 3%) and 1 by FISH]. Overall, 16 out of 26 (62%) patients were classified as high risk by mSMART criteria prior to or at the time of CNS involvement. Four (27%) out of 15 had a deletion p53 or monosomy 17 chromosomal abnormality. Median OS was 42 (95% CI, 19-55) months from the diagnosis of MM and 3 months (95% CI: 1-7.9) from the time of CNS involvement (Figure). OS of patients with high risk features was significantly worse (27 months) compared to standard risk disease (67 months, p=0.02) from diagnosis of MM. Eighteen patients received radiation therapy for CNS myeloma. Five of those patients also received intrathecal (MTX and ARA-C) therapy. One patient received intrathecal and systemic chemotherapy. Four patients did not receive any treatment. Novel agents, including bortezomib (23%), thalidomide (15%), and pomalidomide (4%) were administered to the patients post diagnosis of CNS disease. Six (23%) patients underwent autologous stem cell transplant post diagnosis of CNS disease with a median OS of 19 months (95% CI: 10-122 months) from the time of CNS involvement. Conclusion CNS involvement is a rare complication of MM that portends a poor survival outcome. It is likely that the rates in this study are an underestimate of the true incidence due the perceived futility of CNS directed therapy in this disease. Among those recognized, high-risk genetic and markers of increased proliferative activity, including deletion p53 / monosomy 17 and elevated PCLI (≥ 3%) appear to cluster in this cohort. Current therapeutic approaches are largely ineffective in managing this aggressive subset of myeloma patients. Disclosures: No relevant conflicts of interest to declare.