Kumar, Shaji

AbstractOur prior studies identified the prognostic significance of quantifying cPCs by multiparametric flow cytometry (MFC) in newly diagnosed multiple myeloma (NDMM) patients. We evaluated if a similar quantification of cPCs could add prognostic value to the current R‐ISS classification of 556 consecutive NDMM patients seen at the Mayo Clinic, Rochester from 2009 to 2017. Those patients that had ≥5 cPCs/μL and either R‐ISS stage I or stage II disease were re‐classified as R‐ISS IIB stage for the purposes of this study. The median time to next therapy (TTNT) and overall survival (OS) for patients with ≥5 cPCs/μL at diagnosis was as follows: R‐ISS I (N = 110) ‐ 40 months and not reached; R‐ISS II (N = 69) ‐ 30 and 72 months; R‐ISS IIB (N = 96) ‐ 21 and 45 months and R‐ISS III (N = 281) ‐ 20 and 47 months respectively. Finally, ≥ 5 cPCs/μL retained its adverse prognostic significance in a multivariable model for TTNT and OS. Hence, quantifying cPCs by MFC can potentially enhance the R‐ISS classification of a subset of NDMM patients with stage I and II disease by identifying those patients with a worse than expected survival outcome.

AbstractHistological transformation in Waldenström macroglobulinemia (WM) is an uncommon complication, with limited data, particularly regarding the impact of MYD88 L265P mutation on transformation. We examined risk factors and outcomes associated with transformation in WM, highlighting the role of MYD88 L265P mutation. Patients with WM seen at Mayo Clinic, Rochester, USA and University Hospital of Reims, France, between 01/01/1996 and December 31, 2017 were included; 50 (4.3%) of 1147 patients transformed to a high‐grade lymphoma, with median time‐to‐transformation of 4.5 (range 0‐21) years in the transformed cohort. The MYD88 L265P mutation status was known in 435/1147 (38%) patients (406 with non‐transformed WM and 29 patients in transformed cohort). On multivariate analysis, MYD88 WT status alone was an independent predictor of transformation (odds ratio, 7[95%CI: 2.1‐23]; P = .003). Additionally, the MYD88 WT status was independently associated with shorter time‐to‐transformation (HR 7.9 [95%CI: 2.3‐27; P = .001]), with a 5‐year transformation rate of 16% for MYD88 WT vs 2.8% with MYD88 L265P mutated patients. Patients with transformation demonstrated a significant increase in risk of death compared to patients who did not transform (HR 5.075; 95%CI: 3.8‐6.8; P < .001). In conclusion, the MYD88 WT status is an independent predictor of transformation and associated with a shorter time‐to‐transformation. Additionally, transformation conferred an inferior overall survival in patients with WM.

Abstract Background: Normalization of serum free light chain ratio (FLCr) following achievement of a complete response (CR) to therapy denotes a stringent CR in multiple myeloma, and is associated with improved overall survival (OS). However, its value in patients achieving less than a CR is not clear. We hypothesized that patients in whom FLCr normalizes with the initial anti-myeloma therapy will have an improved outcome, even in the absence of a CR. Methods: We analyzed the medical records of patients with MM who were evaluated at Mayo Clinic, Rochester, MN between January 2004 and December 2011, seen within 90 days of diagnosis. We selected patients with measurable disease on serum protein electrophoresis, defined as a serum M-spike ≥1 g/dl at the time of diagnosis. Then, we excluded all patients with negative immunofixation for monoclonal M-protein in serum and urine at their first response as well as those who did not have serum FLC results available from the time of their first best response. M protein measurements were collected serially from the time of diagnosis until the time of the first relapse or last follow up date if no relapse was recorded. Progression-free survival (PFS) and OS were analyzed using the Kaplan-Meier method. Results: Out of the 1354 MM patients seen at our institution during this period, a total of 453 patients were included in our study. The median age of the cohort at the time of diagnosis was 65 (range, 29-91); 49% were older than 65 years and 284 (63%) were male. The estimated median follow up for the whole cohort was 66 months (95% CI; 61, 71); 241 patients were alive with a median follow up of 4.7 years (range, 0.4 to 10). Three hundred and seventy three (82%) patients had relapsed following the initial therapy with a median time to progression (TTP) of 19.5 months (95% CI; 18, 22) and PFS of 19 months (95% CI; 17, 20). The median OS for the entire cohort is 67 months (95% CI; 59, 73). The median time to the best first response was 6.9 months (range: 0.3-83 months). At the time of the best first response 154 patients (34%) had normal FLCr. Patients with normal FLCr had a longer PFS (29 vs. 16 months, P< .0001) and OS (91 vs. 58 months, P= .0002). We then examined the impact of FLCr normalization on survival outcomes according to their IMWG response categories. We found that FLCr normalization had a favorable impact on PFS in each of the three groups [very good partial response (VGPR), Partial response (PR), and Stable disease (SD)], However, when looking at the OS, only patients with PR had a better OS associated with a normal sFLC κ/λ ratio (Table 1). Conclusions: Obtaining a normal sFLC ratio appears to confer a more favorable prognosis irrespective of the depth of the response, suggesting an important role for serial-sFLC measurements in disease monitoring even in patients who achieve only a PR to therapy as their best response. This supports the inclusion of sFLC at all levels of response included in the current International Myeloma Working Group (IMWG) criteria. Abstract 3427. Table 1 Effect of FLCr normalization according to IMWG response categories SD PR VGPR Normal FLCr Abnormal FLCr p-value Normal FLCr Abnormal FLCr p-value Normal FLCr Abnormal FLCr p-value Number of patients 7 70 62 157 85 60 PFS, month, median (95% CI) 27 (14, 36) 7 (5, 12) < .02 26 (20, 36) 17 (13, 20) .0005 29 (23, 32) 21 (18, 26) .03 OS, months, median (95% CI) 68 (49, 68) 41 (31, 61) > .1 NR (65, NR) 61 (50, NR) .004 74 (67, 107) 73 (59, NR) > .9 • Abbreviations: NR; statistically not reached •• Progressive disease (PD) was not included because none of the 12 patients achieved a normal FLCr. Disclosures No relevant conflicts of interest to declare.

Abstract Background: Multiple Myeloma (MM) patients have a substantially reduced Health related Quality of Life (HQOL) at diagnosis (dx) due to disease related symptoms like bone pain (58%) and fatigue (32%). HQOL monitoring is becoming increasingly important, owing to improved survival and the impact of treatment-related toxicity. As a result HQOL assessments are increasingly being used in clinical trials, but the literature regarding the relationship between HQOL and outcomes in MM is sparse. We used the Mayo Clinic “Hematology Patient Reported Symptom Screen” (HPRSS) to assess the impact of HQOL on outcomes in newly dx patients with MM. Methods:We retrospectively reviewed charts of 453 patients with newly dx MM seen at Mayo Clinic, Rochester from 2009 to 2014. All patients who visit Mayo clinic hematology clinic complete a 3-point questionnaire (HPRSS) on pain, fatigue and quality of life (QOL) (scored on a scale of 0-10; with 0 being the least fatigue or pain and 10 being the best QOL). Pain, fatigue and QOL scores documented at the time of dx and at 6 months were collected. JMP version 10 was used for the data analysis. Results: The median age at diagnosis was 67 years (range, 33-95); 60% were male. The estimated median OS for the cohort was 21 m (95% CI19, 23); 387 (85%) of the patients were alive at last follow up and the median OS for the population was not reached. The median (IQR) scores for pain, fatigue and QOL were 4 (2, 6), 3 (1, 5), and 7 (5, 9), respectively. First, we examined the relationship between each of the scores (dichotomized at the median) and the baseline characteristics. Higher fatigue scores were associated with lower Hb and serum albumin and higher beta 2 microglobulin, LDH and marrow plasmacytosis. Higher pain scores were seen in female patients, and associated with higher B2M, serum calcium and lytic bone lesions. Lower QOL scores were associated with lower absolute lymphocyte count and serum albumin levels. Next, we examined the relationship between the scores and the OS from diagnosis. The overall survival was inferior for patients with higher pain scores, fatigue scores and lower QOL scores (Figure). In a multivariable analysis, fatigue scores were most strongly associated with survival outcome. Patients with adverse scores in two or more of pain, fatigue and QOL had significantly inferior outcomes (Figure). In univariate analysis, age, B2M, LDH, FISH high risk and the presence of two or more adverse scores were all significantly associated with poorer OS. In a multivariable analysis, age, LDH and the presence >=2 adverse scores were all associated with shorter OS. Of the 453 patients included in the study, 222 patients had pain, fatigue and QOL scores at 6 month from diagnosis. At 6 months, the median (IQR) scores for pain, fatigue and QOL were 2 (1, 5), 4 (2, 5), and 7 (5, 9), respectively; suggesting improved pain, worsened fatigue and static QOL scores. Improvement in the scores by at least one point was seen in 50%, 49% and 38% for the fatigue, pain and QOL scores. While the pain and fatigue scores at 6 months correlated inversely with OS, improvements in the scores in any of the three were not associated with any improved outcomes. Conclusion: A simple, patient reported scoring system for pain, fatigue, and overall perceived QOL at the time of diagnosis is a powerful predictor of survival outcomes in patients with newly diagnosed MM and should be considered routinely in clinical practice. The results of these patient reported measures can be utilized to develop risk-adapted trials in patients with MM. Figure 1 Figure 1. Disclosures No relevant conflicts of interest to declare.

Abstract Background: Isatuximab (SAR650984) is a humanized IgG1 monoclonal antibody that binds selectively to a unique epitope on the human CD38 receptor. In the dose escalation part of the Phase I portion of the trial (NCT01749969), isatuximab ≥10 mg/kg IV given every other week (q2w) or 10 mg/kg weekly (qw) induced responses in 6/19 recipients (ORR 32%). Responses were noted at doses as low as 1 mg/kg q2w. The maximum tolerated dose (up to 20 mg/kg IV qw) was not reached. The most common treatment-emergent adverse events (TEAEs) were fatigue and nausea; few serious grade 3/4 TEAEs were reported (pneumonia 6%). The most common category of drug-related TEAE involved infusion-associated reactions (IARs) occurring in half of the patients, with grade 3/4 IARs in <5% of the patients. Based on promising Phase I activity, a Phase II dose finding study was added to investigate single-agent isatuximab in patients with relapsed or refractory myeloma (RRMM). Methods: In Phase II, eligible patients were originally randomly assigned (1:1:1) to one of 3 treatment groups: 3 mg/kg q2w, 10 mg/kg q2w, and 10 mg/kg q2w x 4 doses then q4w. A 4th treatment group was enrolled at a higher isatuximab dose with an optimized schedule of 20 mg/kg qw x 4 doses then q2w. Patient assignment was stratified by prior anti-myeloma therapy (Stratum 1: prior treatment with pomalidomide (POM) and/or carfilzomib (CAR); Stratum 2: no prior POM or CAR). Patients continued therapy until disease progression, unacceptable AEs or physician/patient decision. Eligible patients had measurable disease (serum M-protein >1 g/dL or urine M-protein ≥200 mg/24 h, or serum free light chain ≥10 mg/dL), prior exposure to ≥3 lines of anti-myeloma therapy or were double-refractory to immunomodulatory drugs (IMiDs) and proteasome inhibitors (PIs), prior treatment with an alkylating agent, and adequate baseline bone marrow reserve and organ function. Enrollment of 24 patients was planned for each treatment group. An interim efficacy analysis is planned when all patients have completed ≥2 disease assessments, with central review of these data currently ongoing. Enrollment has now been completed and all patients who completed at least 1 cycle of isatuximab at the cutoff date (5/29/15) are included in this preliminary analysis. Results: Patient characteristics (n=96): median age 62.5 yrs (range 38-85); median time from initial diagnosis to study treatment 5.85 yrs (1.2-24.1); 76% had IgA/G MM; 24% had light-chain only disease; and 22%, 22%, and 44% had Stage I, II, and III disease, respectively. The median number of prior lines of therapy was 5 (2-14) with 100%, 99%, and 98% having prior exposure to PIs, IMiDs, and alkylator-containing therapy, respectively. Overall, 78% of patients had previously received POM and/or CAR; 55% had been exposed to both of these agents. At data cut-off, 36.5% of patients remained on therapy and 53.1%, 6.3%, and 4.2% had discontinued therapy due to disease progression, AEs, or other reason, respectively. The median number of cycles received was 3 (1-9), with a median duration of treatment of 11.7 wks. Approximately 17% of patients required at least 1 dose delay and 7.3% of isatuximab infusions were interrupted. TEAEs occurred in 93% of patients, with 51% being grade ≥3. Drug-related TEAEs occurred in 61% of patients (grade ≥3, 10%). The most common drug-related TEAEs (>5% of patients) were nausea (14.6%), chills (14.6%), dyspnea (13.5%), chest discomfort (10.4%), flushing (7.3%), headache (7.3%), cough (6.3%), and vomiting (5.2%). The majority of the symptoms occurred within 24 h of isatuximab infusion and were attributed to an IAR. IARs occurred in 50% of patients, were predominantly grade 1/2 (grade ≥3, 3.1%), and were mostly limited to Cycle 1 (2 patients had IARs in Cycle 2). Pneumonia (6.3%) and sepsis (5.2%) were the most common serious TEAEs. Six deaths have occurred within 30 days of last dose, 4 due to disease progression, and 2 due to serious AEs unrelated to study therapy (cardiopulmonary arrest and cerebral hemorrhage). Conclusion: Isatuximab administered to heavily pre-treated patients with RRMM appears well tolerated. IARs are common in Cycle 1, but typically do not recur. Results by treatment arm, including interim analysis response data, will be presented. Disclosures Martin: Sanofi: Research Funding. Richter:Takeda: Speakers Bureau; Celgene: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Janssen: Speakers Bureau; Amgen: Speakers Bureau; Novartis: Speakers Bureau. Vij:Millennium: Honoraria, Speakers Bureau; Onyx: Consultancy, Honoraria, Research Funding, Speakers Bureau; Celgene: Consultancy, Honoraria, Research Funding, Speakers Bureau; BMS: Consultancy; Takeda: Consultancy, Research Funding; Novartis: Consultancy; Sanofi: Consultancy; Janssen: Consultancy; Merck: Consultancy. Zonder:Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; BMS, Amgen, Prothena, Seattle Genetics: Membership on an entity's Board of Directors or advisory committees. Kaufman:Spectrum: Consultancy; Merck: Research Funding; Onyx: Consultancy, Research Funding; Novartis: Consultancy; Celgene: Consultancy, Research Funding; Millenium: Consultancy. Bensinger:Novartis: Membership on an entity's Board of Directors or advisory committees, Research Funding; Acetylon: Research Funding; Sanofi: Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Onyx: Research Funding, Speakers Bureau; Millenium: Research Funding; BMS: Membership on an entity's Board of Directors or advisory committees, Research Funding. Dimopoulos:Novartis: Honoraria; Amgen: Honoraria; Genesis: Honoraria; Janssen: Honoraria; Celgene: Honoraria; Onyx: Honoraria; Janssen-Cilag: Honoraria. San Miguel:Millennium, Celgene, Novartis, Janssen, Onyx, BMS, MSD: Membership on an entity's Board of Directors or advisory committees. Zimmerman:Onyx: Honoraria; Celgene: Honoraria, Speakers Bureau; Millennium: Honoraria, Speakers Bureau; Amgen: Honoraria, Speakers Bureau. Hari:Celgene: Consultancy; Takeda: Consultancy; BMS: Consultancy; Janssen: Consultancy; Novartis: Consultancy; Spectrum: Consultancy; Sanofi: Consultancy. Gasparetto:Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Onyx: Honoraria; Millennium: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Kumar:Skyline, Noxxon: Honoraria; Celgene, Millennium, Onyx, Janssen, Noxxon, Sanofi, BMS, Skyline: Consultancy; Celgene, Millennium, Onyx, Novartis, Janssen, Sanofi: Research Funding. Hsu:Sanofi: Employment. Charpentier:Sanofi: Employment. Strickland:Alexion Pharmaceuticals: Other: Advisory Board Particpation; Amgen: Other: Advisory Board Particpation; Daiichi-Sankyo: Other: Advisory Board Particpation; Boehringer-Ingelheim: Other: Advisory Board Particpation; Sunesis Pharmaceuticals: Other: Steering Committee and Advisory Board Participation. Mikhael:Sanofi: Research Funding; Onyx: Research Funding; Celgene: Research Funding; AbbVie: Research Funding.

Abstract Background: Cytogenetic evaluation using fluorescence in situ hybridization (FISH) at the time of diagnosis is essential for initial risk stratification in multiple myeloma. The presence of specific cytogenetic abnormalities is known to confer a poor prognosis, less is known about the cumulative effect of multiple cytogenetic high-risk abnormalities. We aimed to evaluate the prognostic implications of the presence of multiple cytogenetic high-risk abnormalities at the time of diagnosis. Methods: We studied 226 patients who were diagnosed with multiple myeloma between July 2004 and July 2014 at Mayo Clinic Rochester, underwent FISH evaluation within six months of diagnosis, and presented with cytogenetic high-risk abnormalities. High-risk cytogenetics were defined as t(4;14), t(14;16), t(14;20), del(17p), or gain(1q). Bone marrow aspirates were evaluated for deletions, monosomies, trisomies, and tetrasomies using chromosome- or centromere-specific FISH probes. IGH rearrangements were evaluated using an IGH break-apart probe and evaluating up to five potential partners (FGFR3, CCND1, CCND3, MAF, and MAFB). Kaplan-Meier overall survival estimates were calculated and the log-rank test was used to compare overall survival in patients with single and multiple cytogenetic high-risk abnormalities. A multivariable-adjusted Cox regression model was used to assess the effect of multiple cytogenetic high-risk abnormalities on overall survival adjusting for age, sex, and Revised International Staging System (R-ISS) stage. P-values below 0.05 were considered statistically significant. Results: The median age at diagnosis was 65 years (32 - 90), 129 (57%) of the patients were male. The median overall survival was 3.5 years (3.1 - 4.9) for the entire cohort (n = 226), 4.0 years (3.3 - 5.1) for those with one cytogenetic high-risk abnormality (n = 182, 80%), and 2.6 years (1.7 - 3.1) for those with two cytogenetic high-risk abnormalities (n = 44, 20%). There were no patients with more than two cytogenetic high-risk abnormalities. Ninety-eight patients (45%) had a high-risk translocation, 77 (35%) had del(17p), 39 (18%) had a high-risk translocation plus del(17p), and 5 (2%) had gain(1q) plus either a high-risk translocation or del(17p). Figure 1 shows the Kaplan-Meier overall survival estimates stratified by the number of cytogenetic high-risk abnormalities (n = 226). The presence of two cytogenetic high-risk abnormalities (compared to one) was of prognostic significance after adjusting for age, sex, and R-ISS stage (HR 2.01, 95% CI 1.27 - 3.19, p = 0.003, n = 205). Conclusions: Approximately one in five patients with newly diagnosed high-risk multiple myeloma presented with two high-risk abnormalities at the time of diagnosis. These patients experienced inferior overall survival suggesting a cumulative effect of multiple cytogenetic high-risk abnormalities. The relatively low number of observed gain(1q) was likely related to the fact that not all patients were evaluated for that abnormality. Therefore the presented hazard ratio represents a conservative effect estimate and may underestimate the true effect. Figure 1 Figure 1. Disclosures Dispenzieri: GSK: Membership on an entity's Board of Directors or advisory committees; Jannsen: Research Funding; Alnylam: Research Funding; Celgene: Research Funding; Takeda: Membership on an entity's Board of Directors or advisory committees, Research Funding; Prothena: Membership on an entity's Board of Directors or advisory committees; pfizer: Research Funding. Kapoor:Takeda: Research Funding; Celgene: Research Funding; Amgen: Research Funding. Kumar:Janssen: Consultancy, Research Funding; BMS: Consultancy; AbbVie: Research Funding; Millennium: Consultancy, Research Funding; Onyx: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; Sanofi: Consultancy, Research Funding; Skyline: Honoraria, Membership on an entity's Board of Directors or advisory committees; Array BioPharma: Consultancy, Research Funding; Noxxon Pharma: Consultancy, Research Funding; Kesios: Consultancy; Glycomimetics: Consultancy.

Introduction: The clinical presentation and frequency of organ involvement are quite distinct in patients with IgM amyloidosis compared to patients with non-IgM amyloidosis, with less heart involvement and more nerve, soft tissue and lung involvement in IgM patients. It has been observed that immunoglobulin light chain variable region (IGVL) gene and gene family of the plasma cell clone impact phenotypic manifestations in AL amyloidosis, particularly organ tropism. (Kourelis et al. Blood. 2017;129). Given the variability in clinical features amongst patients with IgM vs. non IgM AL amyloidosis, we hypothesized that IGVL gene usage would be different between these two groups. Methods: Patients with newly diagnosed IgM and non-IgM amyloidosis seen at our institution from 01/2006 to 12/2015 were identified. IGVL gene usage was assessed by liquid chromatography tandem mass spectrometry (LC/MS/MS) as previously described. (Vrana et al Blood 2009;Dasari et al J of Proteome Res; Kourelis et al. Blood. 2017). Mass spectrometry data for IGVL analysis was obtained from clinically available data. LC/MS/MS was performed on available archival specimens when such data was not available clinically. Results: Patients with newly diagnosed amyloidosis who had sufficient mass spectrometry data on fat aspirate or tissue biopsy for IGVL gene usage identification were included (IgM AL, N=44 and Non-IgM AL, N=391). Clinical Features: As expected, differences were noted in baseline characteristics of patients with IgM vs. non-IgM AL amyloidosis. Patients with IgM amyloidosis were older (68 vs. 64 years, p=0.04) and more likely to have kappa as the involved light chain (34% vs. 25%, p=0.2), though the difference did not reach statistical significance. IgM amyloidosis patients had lower difference in involved and uninvolved free light chains (dFLC) (15.5 vs. 25.8 mg/dL, p=0.01) and lower NTProBNP levels (median: 1987 vs. 2655 pg/mL, p=0.02) compared with non-IgM amyloid patients. Presence of t(11;14) was less common by FISH (30% vs. 48%, p=0.1), though the difference did not reach statistical significance. Patterns of organ involvement were different, with heart involvement being less frequent in IgM patients (64% vs. 76%), while peripheral nerve (30% vs. 18%, p=0.05), soft tissue (41% vs. 21%, p=0.005) and lung involvement (7% vs. 3%, p=0.1) were more common in patients with IgM vs. non-IgM AL amyloidosis. There was no difference in liver (9% vs. 15%), kidney (50% vs. 51%) GI (23% vs. 25%) or autonomic nervous system involvement (18% vs. 14%). IGVL Gene Usage: As shown in Figure 1, IGVL usage differed across the two groups. In the lambda group, LV2-08 (14% vs. 2%, p<0.001) and LV2-14 (36% vs. 10%, p<0.001) were more common in IgM amyloid patients, while LV1-44 (0% vs. 10%, p=0.02) and LV6-57 (2% vs. 18%, p=0.004) were less common in the IgM group. There was also a trend towards less frequent involvement of LV3-01 (2% vs. 12%, p=0.07) in IgM amyloid patients. In the kappa group, KV4-01 (11% vs. 4%, p=0.04) was more common in patients with IgM amyloidosis. Correlation Between IGVL Gene usage and Clinical Features: IGVL gene usage correlated with disease characteristics and differences in the clinical presentation of patients across the two groups. LV2-14 was more commonly seen in IgM amyloid patients (above). It has been previously associated with a higher frequency of peripheral nerve involvement (Kourelis et al. Blood 2017), which was more frequent in IgM group. LV2-14 is also associated with low dFLC levels, which were noted to be lower in the IgM AL cohort. LV1-44 has been associated with higher likelihood of cardiac involvement. LV1-44 was seen less frequently in the IgM amyloid patients, who also had lower rates of cardiac involvement. LV6-57 has been historically associated with higher rates of t(11;14). It was less common in IgM AL patients, who also had lower rates of t(11;14). Conclusions: IGVL gene usage differed significantly in patients with IgM vs. non-IgM amyloidosis, correlating with differences in disease characteristics and organ involvement. This is the first study to our knowledge to report IGVL gene usage differences in IgM vs. non-IgM AL amyloidosis. Differences in IGVL gene usage may explain the distinct clinical presentation seen in IgM amyloidosis, the biology of which has so far remained poorly understood. *SS and SD contributed equally Disclosures Dasari: The Binding Site: Patents & Royalties: US Patent Rights on Mass Spectroscopy Licensing agreement with The Binding Site, Research Funding. Dispenzieri:Alnylam: Research Funding; Celgene: Research Funding; Takeda: Research Funding; Pfizer: Research Funding; Janssen: Consultancy; Akcea: Consultancy; Intellia: Consultancy. Murray:The Binding Site: Patents & Royalties: US Patent Rights on Mass Spectroscopy Licensing agreement with The Binding Site, Research Funding. Kumar:Janssen: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; Takeda: Research Funding. Gertz:Prothena: Honoraria; Alnylam: Honoraria; Ionis: Honoraria; Janssen: Honoraria; Spectrum: Honoraria, Research Funding; Celgene: Honoraria.

High-dose therapy (HDT) and autologous stem-cell transplantation (ASCT) has historically been an essential part of multiple myeloma (MM) management since early studies demonstrated its efficacy in relapsed disease, and subsequent phase III trials demonstrated better responses and improved survival with this modality compared with standard chemotherapy. With further advances in the MM treatment landscape, including the development of potent novel agents, there has been an increasing debate around various aspects of ASCT, including the optimal timing, role of single versus tandem ASCT, and the practice of consolidation and maintenance therapy post-ASCT. Routine incorporation of the novel agents at each of the treatment phases, induction, consolidation when used, and maintenance has led to better responses as reflected by increasing rates of minimal residual disease (MRD) negativity, longer progression-free survival (PFS) with improvement in overall survival (OS) and in some of the trials. The phase III trials over the last decade have provided significant clarity on the current approach, and have raised important questions regarding the applicability of this modality in all patients. This review aims to summarize the latest literature in the field and discusses how these findings impact the practice of ASCT today.

BACKGROUND: pPCL is a rare and aggressive form of multiple myeloma (MM) with poor survival outcomes compared to that experienced by MM patients. It has been traditionally defined by the presence of an absolute plasma cell count of > 2000/microL or >20% circulating plasma cells (cPCs) in the peripheral blood. However, several recent studies have validated the optimal cutoff for defining pPCL to be >5% cPCs on a peripheral blood smear due to equally poor outcomes in this less restrictive cohort. Hence, given the rarity of this disease entity, we evaluated the clinical outcomes and cytogenetic features of patients diagnosed with pPCL at our institution with the new proposed definition of pPCL by >5% cPCs on peripheral blood smear that were treated with novel agent induction therapies. METHODS: We evaluated all patients with pPCL diagnosed between 2000 - 2018 evaluated at Mayo Clinic in Rochester, Minnesota. Data regarding these patients were extracted from a prospectively maintained database and from the review of the electronic medical records. Patients were categorized as having high risk cytogenetics if any of the following abnormalities were present: del 17p, t(4;14), t(14;16) or t(14;20). Chi-square tests and Fisher exact tests were used to compare differences between the sub-groups of interest. Survival analysis was performed by the Kaplan-Meier method and differences assessed using the log rank test. RESULTS: This cohort consisted of 71 patients with pPCL with a median age of 62 years (range: 34-91) of which 33 (46%) were male. The median follow up was 46 months (95% CI: 41 - 90). The median bone marrow plasma cell content was 84% (Range: 10 - 100) and the median plasma cell percentage on the peripheral smear was 23% (range: 5 - 93). There were 48 (68%) patients with >20% cPCs in their peripheral blood smear. Only 23 (32%) patients had concurrent information on the number of cPCs by multiparametric flow cytometry at diagnosis and the median number of cPCs detected was 32,807 per 150,000 events analyzed (Range: 354 - 132,256). There were 15/54 (28%) patients who presented with a creatinine of 2 mg/dL or greater and 17/55 (31%) pts who presented with hypercalcemia (11 mg/dL or greater). Data for ISS classification was available in 45 patients and is as follows: Stage 1- 3 (7%) patients, Stage 2- 10 (22%) patients and Stage 3- 32 (71%) patients. Data on the presence of primary cytogenetic abnormalities were available in 60 (85%) patients of which 29 (49%) had high risk cytogenetics. The distribution of primary cytogenetic abnormalities among this cohort was as follows: t(11;14) - 27 (45%), t(4;14) - 5 (8%), t(14;16) - 8 (13%), t(14;20)- 3(5%) and del 17p- 15(25%). All patients received novel agent induction therapy with 40 (57%) of them having received at least one autologous stem cell transplant and 1 patient undergoing an allogeneic stem cell transplant. The median overall survival (OS) for all pPCL patients in this cohort was 23 months (95% CI: 19 - 38). The OS was similar in patient with 5-19% cPCs vs. >20% cPCs (24 vs. 23 months, P = 0.999). However, when the entire cohort was stratified by cytogenetic risk, the median OS was 51 months for standard risk vs. 19 months for high risk (P = 0.005) (FIGURE). There were only 17 (30%) patients in this cohort who were alive for 48 months or longer since diagnosis (i.e. twice the median OS of this cohort) and they were more likely to not have high risk cytogenetics at diagnosis (P = 0.05). CONCLUSION: In this analysis of one of the largest contemporary cohorts of pPCL patients, the outcomes of patients diagnosed with pPCL remain poor despite the use of novel agent induction therapy. However, some patients appear to do better than expected and this phenomenon may be influenced by the absence of high risk cytogenetics. Disclosures Kumar: Celgene: Consultancy, Research Funding; Janssen: Consultancy, Research Funding; Takeda: Research Funding. Dispenzieri:Akcea: Consultancy; Intellia: Consultancy; Janssen: Consultancy; Pfizer: Research Funding; Takeda: Research Funding; Celgene: Research Funding; Alnylam: Research Funding. Dingli:Karyopharm: Research Funding; Rigel: Consultancy; Millenium: Consultancy; Janssen: Consultancy; alexion: Consultancy. Lacy:Celgene: Research Funding. Kapoor:Glaxo Smith Kline: Research Funding; Sanofi: Consultancy, Research Funding; Amgen: Research Funding; Takeda: Honoraria, Research Funding; Cellectar: Consultancy; Celgene: Honoraria; Janssen: Research Funding. Russell:Imanis: Equity Ownership. Leung:Omeros: Research Funding; Takeda: Research Funding; Prothena: Membership on an entity's Board of Directors or advisory committees; Aduro: Membership on an entity's Board of Directors or advisory committees. Gertz:Celgene: Consultancy; Prothena Biosciences Inc: Consultancy; Appellis: Consultancy; Amgen: Consultancy; Springer Publishing: Patents & Royalties; Amyloidosis Foundation: Research Funding; International Waldenstrom Foundation: Research Funding; Janssen: Consultancy; Ionis/Akcea: Consultancy; Alnylam: Consultancy; Annexon: Consultancy; Abbvie: Other: personal fees for Data Safety Monitoring board; Research to Practice: Consultancy; Teva: Speakers Bureau; Medscape: Consultancy, Speakers Bureau; Physicians Education Resource: Consultancy; Pharmacyclics: Membership on an entity's Board of Directors or advisory committees; Proclara: Membership on an entity's Board of Directors or advisory committees; Spectrum: Consultancy, Research Funding; i3Health: Other: Development of educational programs and materials; Johnson and Johnson: Speakers Bureau; DAVA oncology: Speakers Bureau.