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Abstract Background: Patients with multiple myeloma (MM) that are refractory to a proteasome inhibitors (PI), immunomodulatory drug (IMiDs) and CD38/SLAMF7 directed monoclonal antibodies are commonly referred to as triple class refractory (TCR). This is an increasingly common clinical problem and here we present the outcomes, prognostic markers and response to therapy for patients with TCR disease. Methods: Consecutive patients with MM diagnosed between 01/01/2013 and 12/31/2018 and seen at Mayo Clinic, Rochester, MN were included in this study after institutional review board approval. Refractoriness to therapy was defined as progression or non-responsiveness (failure to achieve at least a minimal response) to therapy within 60 days of discontinuation of therapy, based on the International Myeloma Workshop Consensus definition. Patients meeting the definition for refractory disease for at least 1 PI (bortezomib, carfilzomib or ixazomib), 1 IMiD (lenalidomide, pomalidomide or thalidomide) and 1 monoclonal antibody (daratumumab, isatuximab or elotuzumab) were considered to have TCR disease and included in the study population. The first date when patients satisfied the above-mentioned criteria for TCR was used to calculate the follow-up, progression free survival (PFS) and OS for TCR disease. The mSMART 3.0 (www.msmart.org) classification was used to characterize high-risk and standard-risk cytogenetics features in interphase fluorescence in situ hybridization (FISH). The currently prevalent international myeloma working group (IMWG) criteria were used for response assessment. The Kaplan-Meier method was used for estimation of time-to-event endpoints. Reverse censoring was used for estimation of follow-up time. Cox regression was used for multivariate analyses. Wilcoxon test was used for comparison of continuous variables and Chi square test or Fisher's exact test were used to compare categorical variables, respectively Results: In this retrospective study, 249 consecutive patients with TCR status were identified. The median follow-up from diagnosis of MM till date of last follow-up was 6.8 years (95% CI: 6.3-7.3 years) and 1.9 years (95% CI: 1.6-2.4) from TCR MM to last follow-up. The median age at diagnosis of MM was 61 years (range 31-89) and at TCR was 65 years (range 32-93). The median number of lines of therapy for the entire cohort were 8 (range 1-17); the patients were exposed to a median of 5 (range 1-12) lines of therapy prior to TCR status. Baseline characteristics for the cohort are depicted in Table 1. The median time to development of triple refractory disease was 2.9 years (95% CI: 2.6-3.2 years) from diagnosis of MM. The time to TCR disease was shorter for high-risk cytogenetics at 2.3 years (95% CI: 2-2.7) compared to 3.4 years (95% CI: 3.1-4.1) with a hazard ratio (HR) of 1.8 (95% CI: 1.4-2.4) p<0.0001], Figure 1A. The median overall survival (OS) from TCR was 1.01 (95% CI: 0.9-1.3) years. High-risk cytogenetics at diagnosis of MM were associated with an inferior OS from TCR disease even after adjustment for age, and ISS stage [median 1.05 years (95% CI: 0.8-1.5) for high-risk vs 1.3 years (95% CI: 0.9-2.9) for standard-risk cytogenetics; hazard ratio 1.44 (95% CI: 1.01-2.05); p=0.04]. Of 249 patients, 227 had data available for therapy post TCR (initial therapy details post TCR status are depicted in Table 2) with a median PFS of 4 months (95% CI: 3.5-5) for the salvage therapy post attainment of TCR. Patients achieving a deeper response [≥partial response (PR)] were noted to have a significantly improved PFS with the initial therapy post TCR disease [median PFS 8.2 months (95% CI: 7.5-12.7)] compared to patients not achieving a PR [median PFS 2 months (95% CI: 1.6-2.7); HR 0.2 (95% CI: 0.13-0.27); p<0.0001], Figure 1B. Deeper response (≥PR) to initial therapy at TCR disease was associated with an improved PFS [HR 0.23 (95% CI: 0.16-0.34); p <0.0001] after adjusting for age at TCR status, ISS stage and cytogenetic risk categories at diagnosis. Conclusion: Patients with triple refractory MM have a guarded prognosis and demonstrate an underwhelming median PFS of 4 months and median OS of 1 year. Achievement of a deeper response to therapy at triple refractory status is associated with a prolonged PFS. High-risk cytogenetic features at diagnosis confer a negative prognosis in TCR patients. Newer therapies are the needed to sustain the improvement in outcomes for this group of patients. Figure 1 Figure 1. Disclosures Kapoor: Cellectar: Consultancy; Pharmacyclics: Consultancy; Sanofi: Consultancy; BeiGene: Consultancy; Amgen: Research Funding; Ichnos Sciences: Research Funding; Karyopharm: Consultancy; Regeneron Pharmaceuticals: Research Funding; AbbVie: Research Funding; Takeda: Research Funding; Sanofi: Research Funding; Karyopharm: Research Funding; Glaxo SmithKline: Research Funding. Dispenzieri: Pfizer: Research Funding; Sorrento Therapeutics: Consultancy; Oncopeptides: Consultancy; Alnylam: Research Funding; Takeda: Research Funding; Janssen: Consultancy, Research Funding. Gertz: Ionis Pharmaceuticals: Other: Advisory Board; Akcea Therapeutics, Alnylam Pharmaceuticals Inc, Prothena: Consultancy; AbbVie Inc, Celgene Corporation: Other: Data Safetly & Monitoring; Aurora Biopharma: Other: Stock option; Akcea Therapeutics, Ambry Genetics, Amgen Inc, Celgene Corporation, Janssen Biotech Inc, Karyopharm Therapeutics, Pfizer Inc (to Institution), Sanofi Genzyme: Honoraria. Dingli: Apellis: Consultancy; GSK: Consultancy; Sanofi: Consultancy; Janssen: Consultancy; Novartis: Research Funding; Alexion: Consultancy. Kumar: Sanofi: Research Funding; Celgene: 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; Bluebird Bio: Consultancy; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Roche-Genentech: Consultancy, Research Funding; Beigene: Consultancy; Tenebio: Research Funding; Novartis: Research Funding; Adaptive: Membership on an entity's Board of Directors or advisory committees, Research Funding; Amgen: Consultancy, Research Funding; BMS: Consultancy, Research Funding; Merck: Research Funding; Carsgen: Research Funding; Antengene: Consultancy, Honoraria; Oncopeptides: Consultancy; Abbvie: 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, Research Funding; KITE: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding.

Abstract Introduction Multiple myeloma (MM) is frequently accompanied by renal impairment (RI) in 30-40% patients at presentation (Dimopoulos et al, IMWG 2010). As per International Myeloma Working Group (IMWG), RI is defined as Creatinine clearance (CrCl) <40 mL/min. Patients with RI at diagnosis have inferior overall survival (OS), however, over the years their outcome has improved due to treatment with novel agents. We examined the baseline clinical characteristics, and outcomes in patients with a new diagnosis of multiple myeloma at our institution to identify factors associated with baseline RI and study prognostic trend over the last 2 decades. Methods We retrospectively reviewed records for MM patients with a new diagnosis of MM who were seen at Mayo Clinic, Rochester between 1/2004 and 12/2018. RI was defined as per IMWG criteria. Categorical variables were compared using chi-square test and continuous variables using Wilcoxon test. Overall Survival (OS) was estimated using Kaplan-Meier method and difference between groups was assessed using log-rank test. Results In our cohort of 2702 patients, median age at diagnosis was 65 (range, 22-96) years and 61% were males. Number of patients with CrCl <15, 15-29, 30-59 and ≥60 ml/min at diagnosis was 176 (6.5%), 204 (7.6%), 685 (25.3%) and 1637 (61%) respectively. RI was present in 557 (21%) of patients at the time of presentation. On univariate analysis, factors significantly associated with RI at diagnosis were higher age, ECOG PS ≥2, hemoglobin< 10 g/dL, calcium >11 mg/dL , LDH >222 U/L, β-2-microglobulin >5.5 mg/L, free light chain ratio >100, uric acid >8 mg/dL, higher ISS stage and cytogenetics [gain 1q, t (14;20), absence of any trisomy] as shown in table 1. However, on multivariate analysis, only elevated serum calcium >11 g/dL (OR: 11.5; 95%CI:1.78-74.9; p=0.01) and free light chain ratio>100 (OR: 4.17; 95%CI:1.29-13.4; p=0.016) had significant association with RI. The RI group had inferior OS when compared to patients with no RI at diagnosis (4.5 years vs 7.7 years, p-value <0.001). However, use of novel agents was associated with improvement in OS in patients with RI (4.9 years vs 2.6 years; p=0.0001) (Figure 1). Looking at trends over time, the rate of RI at presentation had not changed. Over the last two decades, OS for the CrCl<30 group had significantly improved in the last 5-year period (3.8 years in 2004-2014 vs not reached for 2014-2018; p= 0.0064) as shown in figure 2. Conclusion In our cohort of 2702 patient, frequency of RI at baseline was 21%, which corroborates with previous studies. Hypercalcemia and serum free light chain ratio were found to be independent factors associated with RI. RI at diagnosis is associated with inferior survival, however, use of novel agents in this population has led to improvement of patient outcomes. Figure 1 Figure 1. Disclosures Kapoor: Karyopharm: Consultancy; Cellectar: Consultancy; BeiGene: Consultancy; Pharmacyclics: Consultancy; Sanofi: Consultancy; Amgen: Research Funding; Ichnos Sciences: Research Funding; Regeneron Pharmaceuticals: Research Funding; Glaxo SmithKline: Research Funding; Karyopharm: Research Funding; Sanofi: Research Funding; Takeda: Research Funding; AbbVie: Research Funding. Gertz: Aurora Biopharma: Other: Stock option; Ionis Pharmaceuticals: Other: Advisory Board; Akcea Therapeutics, Ambry Genetics, Amgen Inc, Celgene Corporation, Janssen Biotech Inc, Karyopharm Therapeutics, Pfizer Inc (to Institution), Sanofi Genzyme: Honoraria; AbbVie Inc, Celgene Corporation: Other: Data Safetly & Monitoring; Akcea Therapeutics, Alnylam Pharmaceuticals Inc, Prothena: Consultancy. Dingli: Alexion: Consultancy; Novartis: Research Funding; Apellis: Consultancy; GSK: Consultancy; Sanofi: Consultancy; Janssen: Consultancy. Kumar: Roche-Genentech: Consultancy, Research Funding; Adaptive: Membership on an entity's Board of Directors or advisory committees, Research Funding; Beigene: Consultancy; Novartis: Research Funding; Merck: Research Funding; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Amgen: Consultancy, Research Funding; Bluebird Bio: Consultancy; Oncopeptides: Consultancy; Carsgen: Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Tenebio: Research Funding; Janssen: 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; BMS: Consultancy, Research Funding; Abbvie: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Antengene: Consultancy, Honoraria; KITE: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Sanofi: Research Funding.

Abstract Background: MASS-FIX is a novel method for detection, characterization and quantification of serum and urine monoclonal proteins in multiple myeloma (MM) which uses immunoglobulin enrichment coupled with matrix-assisted laser desorption ionization time-of-flight mass-spectrometry (MALDI-TOF). MASS-FIX has been endorsed by the International Myeloma Working Group (IMWG) as an alternative to immunofixation electrophoresis for diagnosis and disease monitoring in MM and has replaced immunofixation electrophoresis in our institution. This technique has demonstrated superior sensitivity and specificity compared to conventional gel-based methods, but its prognostic value is still unknown. We designed this study to evaluate whether serum M protein monitoring by MASS-FIX has prognostic significance in predicting disease progression in patients with MM. Methods: In the first part of the study, we included patients with MM who had a documented negative MASSFIX after first-line treatment and had serial MASS-FIX data available (n=187). We then compared the time to next treatment (TTNT) between patients who became positive by MASS-FIX and those who remained negative by last follow up. In the second part, we included patients who were positive by MASS-FIX within 90 days from diagnosis and had available serial MASS-FIX data (n=203). We compared TTNT between patients who became negative by MASS-FIX and those who remained positive by last follow up. TTNT was defined as the time of initiation of the first-line treatment to the time of initiation of second-line treatment or last follow up. Results: Part 1: We included 155 patients diagnosed with MM between January 2013 and December 2019 who had a documented negative MASSFIX with first-line treatment and had serial MASSFIX data available. After a median follow up of 3.1 (95%CI: 2.6-3.5) years, 44 (28%) patients became MASSFIX positive, while 111 (72%) still had negative MASS-FIX. The median time from treatment start to negative MASSFIX was 18.0 (IQR: 6.4-33.5) months. The median TTNT was 4.1 (95%CI: 3.2 - NR) years in patients who became MASSFIX positive and not reached (NR) (95%CI: NR-NR) in patients who were MASS-FIX negative by last follow up (P<0.001). The 2-year OS was 97% and 100% in the 2 groups, respectively (P=0.27). Part 2: We included 203 patients who had positive MASSFIX within 90 days from diagnosis and available serial MASSFIX data. The median follow-up was 1.5 (95%CI: 1.5-1.7) years. By last follow up, 39 (19%) patients became MASSFIX negative while 164 (81%) still had positive MASSFIX. The median TTNT was NR (95%CI: 2.0-NR) years in patients who developed negative MASSFIX and 2.3 (95%CI: 1.9 - NR) years in patients who were still MASS-FIX positive by last follow up (P=0.03). The 2-year OS was 97% and 94% in the 2 groups, respectively (P=0.84) Conclusion: The utilization of MASS-FIX in disease monitoring of MM patients provides prognostic information; among patients who become MASS-FIX negative after first line treatment, an earlier conversion to positive MASS-FIX predicts disease progression. Similarly, persistent MASS-FIX positivity after treatment initiation predicts earlier disease progression. Larger studies and longer follow up are needed to evaluate whether the MASS-FIX is associated with overall survival. Disclosures Murray: Mayo Clinic: Other: Has received patents for the Mass-Fix technology which has been licensed to the Binding Site with potential royalties.. Dispenzieri: Sorrento Therapeutics: Consultancy; Oncopeptides: Consultancy; Pfizer: Research Funding; Alnylam: Research Funding; Takeda: Research Funding; Janssen: Consultancy, Research Funding. Kapoor: Karyopharm: Consultancy; Cellectar: Consultancy; BeiGene: Consultancy; Pharmacyclics: Consultancy; Sanofi: Consultancy; Amgen: Research Funding; Ichnos Sciences: Research Funding; Regeneron Pharmaceuticals: Research Funding; Glaxo SmithKline: Research Funding; Karyopharm: Research Funding; Sanofi: Research Funding; Takeda: Research Funding; AbbVie: Research Funding. Gertz: Akcea Therapeutics, Alnylam Pharmaceuticals Inc, Prothena: Consultancy; Akcea Therapeutics, Ambry Genetics, Amgen Inc, Celgene Corporation, Janssen Biotech Inc, Karyopharm Therapeutics, Pfizer Inc (to Institution), Sanofi Genzyme: Honoraria; Ionis Pharmaceuticals: Other: Advisory Board; Aurora Biopharma: Other: Stock option; AbbVie Inc, Celgene Corporation: Other: Data Safetly & Monitoring. Dingli: Sanofi: Consultancy; GSK: Consultancy; Novartis: Research Funding; Apellis: Consultancy; Janssen: Consultancy; Alexion: Consultancy. Kumar: Amgen: Consultancy, Research Funding; Bluebird Bio: Consultancy; Antengene: Consultancy, Honoraria; BMS: Consultancy, Research Funding; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Beigene: Consultancy; Abbvie: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Roche-Genentech: Consultancy, Research Funding; KITE: 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; Carsgen: Research Funding; Merck: Research Funding; Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Oncopeptides: Consultancy; Novartis: Research Funding; Tenebio: Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Adaptive: Membership on an entity's Board of Directors or advisory committees, Research Funding; Sanofi: Research Funding.

Abstract Introduction: A baseline involved to uninvolved free light chain ratio (FLCr) ≥100 with involved free light chain (iFLC) ≥10 mg/dL is considered a multiple myeloma (MM)-defining event (MDE). However, prior reports have demonstrated that the presence of multimeric light chain aggregates can contribute to falsely increased FLC levels due to impaired renal light chain clearance. Therefore, we aimed to compare the disease progression risk in patients with high versus low urine monoclonal protein (uMCP) excretion in the setting of an elevated serum FLCr. Methods: We retrospectively evaluated untreated smoldering MM (SMM) and asymptomatic MM patients diagnosed between January 1, 2000 and January 10, 2020. Included patients had a baseline bone marrow plasma cell (BMPC) burden of 10-59% without concomitant end organ damage (hypercalcemia, anemia, renal failure, or lytic lesions on x-ray or cross-sectional imaging; "CRAB" MDE). Patients with a baseline FLCr ≥100 and iFLC >10 mg/dL were included if they had a 24-hour urine collection and electrophoresis. Survival analyses were performed using the Kaplan-Meier method. Progression was defined as treatment for systemic AL amyloidosis or symptomatic MM (typical "CRAB" MDE). SMM patients treated due to evolving biomarkers or "SLiM" MDE (lytic lesions on MRI, BMPC ≥60%, or FLCr ≥100) were censored. Cox proportional hazards models were used for uni- and multivariable analyses. A two-sided p-value <0.05 was considered statistically significant. Results: We included 822 patients without MDE besides elevated FLCr (n=120 with FLCr ≥100, n=702 with FLC <100). Patients with a FLC ≥100 were grouped based on 24-hour uMCP excretion (≥200 mg/24h [n=35], <200 mg/24h [n=85]). Among included patients with a baseline FLCr ≥100, the median iFLC was 79.9 (IQR 42.9-131.5) mg/dL and median FLCr was 207.1 (IQR 137.7-400.3). Patients with a FLCr ≥100 and high (≥200 mg/24h) versus low (<200 mg/24h) uMCP excretion had a median iFLC of 101 versus 66.5 mg/dL (p=0.001), median estimated glomerular filtration rate (eGFR) 74.4 versus 74.6 mL/min/1.73m 2 (p=0.505), and median BMPC burden of 30% versus 21% (p=0.088), as shown in Table 1. The 2-year risk of progression to symptomatic MM or AL amyloidosis was significantly higher in patients with uMCP excretion ≥200 versus <200 mg/24h (36.2% versus 13.5%, respectively; HR 2.79, 95% CI 1.57-4.96, p<0.001, see Figure 1). However, the progression risk was similar in patients with a baseline FLCr <100 versus those with a FLCr≥100 and uMCP <200 mg/24h (log rank p=0.127). After adjusting for baseline eGFR, BMPC burden, iFLC, and light chain isotype, the progression risk was still 2.7 times higher in patients with high versus low uMCP excretion (HR 2.66, 95% CI 1.39-5.10, p=0.003). In patients with a baseline FLCr ≥100 that had cross-sectional imaging at diagnosis or prior to progression confirming the absence of osteolytic lesions, the risk of progression remained significantly higher in patients with a high versus low uMCP excretion (n=14 versus n=53, respectively; HR 4.20, 95% CI 1.86-9.49, p<0.001). In patients with FLCr ≥100, there were a total of 52 patients with end organ damage at progression (26 [50%] patients with anemia, 8 [15%] with renal failure, 21 [40%] with lytic lesions, 6 [12%] with systemic AL amyloidosis). Among patients who progressed, 5 (25%) of 20 patients with high uMCP excretion and 3 (9.4%) of 32 patients with low uMCP excretion presented with renal failure as the MDE. In patients with baseline high versus low uMCP excretion that experienced renal failure at progression, the median serum creatinine of 2.7 (range 1.9-4.1) versus 2.5 (range 1.9-3.2) mg/dL (p=0.653), median uMCP 4964 (range 738-5910) versus 512 (range 196-734) mg/24h (p=0.052), and median iFLC 388 (range 317-1640) versus 437 (range 199-1030) mg/dL (p=1.00). Conclusions: Increased uMCP excretion in the setting of a FLCr ≥100 is an unfavourable prognostic marker and is associated with an increased risk of progression. The progression risk in patients with a FLCr ≥100 and low uMCP excretion (<200 mg/24h) is similar to patients with a baseline FLCr <100. Our findings underscore the importance of conducting a 24-hour urine assessment at diagnosis and may help refine the subset of patients with FLCr ≥100 in whom pre-emptive therapy is warranted. Figure 1 Figure 1. Disclosures Kapoor: AbbVie: Research Funding; Takeda: Research Funding; Sanofi: Research Funding; Karyopharm: Research Funding; Glaxo SmithKline: Research Funding; Regeneron Pharmaceuticals: Research Funding; Ichnos Sciences: Research Funding; Amgen: Research Funding; Karyopharm: Consultancy; Cellectar: Consultancy; BeiGene: Consultancy; Pharmacyclics: Consultancy; Sanofi: Consultancy. Gertz: Akcea Therapeutics, Ambry Genetics, Amgen Inc, Celgene Corporation, Janssen Biotech Inc, Karyopharm Therapeutics, Pfizer Inc (to Institution), Sanofi Genzyme: Honoraria; Aurora Biopharma: Other: Stock option; Akcea Therapeutics, Alnylam Pharmaceuticals Inc, Prothena: Consultancy; AbbVie Inc, Celgene Corporation: Other: Data Safetly & Monitoring; Ionis Pharmaceuticals: Other: Advisory Board. Dingli: Novartis: Research Funding; GSK: Consultancy; Alexion: Consultancy; Sanofi: Consultancy; Janssen: Consultancy; Apellis: Consultancy. Kumar: Antengene: Consultancy, Honoraria; Bluebird Bio: Consultancy; Roche-Genentech: Consultancy, Research Funding; Novartis: Research Funding; Astra-Zeneca: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; BMS: Consultancy, Research Funding; Carsgen: Research Funding; Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Merck: Research Funding; Oncopeptides: Consultancy; KITE: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Beigene: Consultancy; Tenebio: Research Funding; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Amgen: Consultancy, Research Funding; Abbvie: Consultancy, 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; Adaptive: Membership on an entity's Board of Directors or advisory committees, Research Funding; Sanofi: Research Funding.

Abstract Background: Systemic light chain (AL) amyloidosis is a plasma cell disorder characterized by multisystem deposition of misfolded immunoglobulin light chains produced by clonal plasma cells. Hematologic and organ responses with treatment have been shown to correlate with survival as early as 3 months from initiation of first-line treatment. Our group recently developed and validated a model integrating organ and hematologic responses for assessment of treatment outcomes at 6 months. Although current organ response criteria do not consider the depth of organ response, this has been shown to have prognostic utility in newly diagnosed patients. So, we designed this study to evaluate a composite hematologic and organ scoring system that also considers the depth of organ response at 1 to 6 months from initiation of first-line treatment. Methods: We included patients with AL amyloidosis who had at least one major organ involvement (cardiac, renal and/or liver involvement) and who had not started a second line treatment by 6 months from the time of initiating first-line treatment. For each patient, we calculated an organ response score and a hematologic response score at 1, 2, 3, 4, 5, and 6 months from initiation of first-line treatment. A score was assigned for each depth of hematologic response as follows: complete response=0, very good partial response=1, partial response=2, and no response/progressive disease=3). To calculate organ response, a score was assigned to each organ based on the depth of organ response (Muchtar et al. 2018): non-evaluable=0, complete response=1, very good partial response=2, partial response=3, and no response=4. The final organ response score was obtained by calculating the average of the individual involved organ scores. We then calculated the composite hematologic and organ response (HOR) score by adding the organ and hematologic responses at each interval and compared overall survival (OS) between patients with HOR score ≤ 5 (group 1) and those with score > 5 (group 2). Results: The cohort included 730 patients diagnosed with AL amyloidosis between February 10 th, 2006 and July 9 th, 2019. Median age was 63 (IQR: 56-69), and 65% were male. The involved light chain was Lambda in 75% of cases. Cardiac, renal, and liver involvement were found in 81%, 61%, and 17% of patients, respectively. Among all patients, 28% underwent autologous stem cell transplantation during their disease course. The median follow up in the entire cohort was 7.0 (95%CI: 6.4-8.0) years and OS was 3.6 (95%CI: 2.6-4.4) years. At 1 to 4 months, we observed a statistically significant difference in OS between patients with HOR score ≤5 vs. >5. However, there was no difference in OS between the 2 groups at 5 and 6 months. These results are presented in Table 1. Conclusion: A composite hematologic and organ response score that takes into consideration the depth of organ response can discriminate 2 groups of patients with distinct survival outcomes as early as 1 month from treatment initiation and maintains its predictive ability for up to 4 months. The lack of predictive ability beyond 4 months in this study may be due to limited sample size especially in group 2 as more patients achieve deeper responses with time. This approach can provide the basis for early changes in treatment but needs validation in future studies. Figure 1 Figure 1. Disclosures Dispenzieri: Janssen: Consultancy, Research Funding; Takeda: Research Funding; Sorrento Therapeutics: Consultancy; Oncopeptides: Consultancy; Pfizer: Research Funding; Alnylam: Research Funding. Gertz: Ionis Pharmaceuticals: Other: Advisory Board; Akcea Therapeutics, Ambry Genetics, Amgen Inc, Celgene Corporation, Janssen Biotech Inc, Karyopharm Therapeutics, Pfizer Inc (to Institution), Sanofi Genzyme: Honoraria; Aurora Biopharma: Other: Stock option; Akcea Therapeutics, Alnylam Pharmaceuticals Inc, Prothena: Consultancy; AbbVie Inc, Celgene Corporation: Other: Data Safetly & Monitoring. Kapoor: Ichnos Sciences: Research Funding; Karyopharm: Consultancy; Glaxo SmithKline: Research Funding; Pharmacyclics: Consultancy; Amgen: Research Funding; Cellectar: Consultancy; Sanofi: Consultancy; BeiGene: Consultancy; Regeneron Pharmaceuticals: Research Funding; Karyopharm: Research Funding; Sanofi: Research Funding; Takeda: Research Funding; AbbVie: Research Funding. Dingli: GSK: Consultancy; Sanofi: Consultancy; Janssen: Consultancy; Novartis: Research Funding; Apellis: Consultancy; Alexion: Consultancy. Kumar: Oncopeptides: Consultancy; Merck: Research Funding; Antengene: Consultancy, Honoraria; Bluebird Bio: Consultancy; Roche-Genentech: Consultancy, Research Funding; Astra-Zeneca: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Amgen: Consultancy, Research Funding; Novartis: Research Funding; Carsgen: Research Funding; Tenebio: Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; KITE: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; BMS: Consultancy, Research Funding; Abbvie: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Beigene: Consultancy; Adaptive: Membership on an entity's Board of Directors or advisory committees, Research Funding; Sanofi: Research Funding; Takeda: 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, Research Funding.

Abstract INTRODUCTION Dendritic cells (DCs) are potent antigen presenting cells that can be exploited to initiate an adaptive anti-tumoral immune response. DC vaccine clinical trials for primary glioblastoma (GBM) have reported prolonged progression-free survival without any impact on overall survival (OS). We report a radiomics approach that identifies a subpopulation of patients with prolonged OS in clinical trial MC1272. METHODS Twenty adults with primary GBM undergoing standard-of-care therapy were enrolled in MC1272. Autologous DCs were pulsed with allogenic GBM cell lines to generate vaccines that were administered for up to twelve cycles. Standard brain imaging was obtained at the initiation of treatment and two months afterwards. An independent cohort of image-localized biopsies underwent RNA sequencing followed by cellular deconvolution to estimate T cell abundance. A machine learning model was trained to predict intratumoral T cell abundance from imaging features, and the model was applied to MC1272 patient imaging. RESULTS Voxelwise predictions of T cell abundance were generated for each patient’s pre- and post-treatment images. The difference in total intratumoral T cell abundance between imaging timepoints classified patients into increasing or decreasing T cell groups. Patients whose T cells increased had longer OS (median, 21 months) than those whose T cells decreased (median, 10 months; p=0.0035). The significance remained in a Cox proportional hazards analysis that adjusted for patient age and sex (p=0.011). CONCLUSIONS A spatially-resolved radiomics model detected that an intratumoral T cell influx after DC vaccine therapy was associated with prolonged OS. The “post-treatment” imaging in this study was obtained a mere two months after DC vaccine initiation, suggesting that our radiomics model can provide an early indication of treatment responsiveness and prognosis in these patients.

Abstract Intra-tumor genetic heterogeneity is an important cause of treatment failure of GBM. Using MRI and image-localized biopsies, it is possible to train machine learning (ML) models to predict regional genetic status. However, biopsy samples are limited, making it difficult to train a robust ML model. We proposed a data-inclusive model called Weakly Supervised Ordinal Support Vector Machines (WSO-SVM) which leverages the vast amount of MRI data outside the sparsely sampled biopsy regions to augment the biopsy samples to improve ML accuracy. Our study included a unique dataset of 104 image-localized biopsies with spatially matched multiparametric MRI from 30 untreated Glioblastoma (GBM) patients. Each biopsy sample went through genetic sequencing analysis and our study focused on two GBM hallmark genes, EGFR and PDGFRA. For each gene, a biopsy sample was labeled as “altered” if the copy number of this gene was amplified or a mutation was found, and “non-altered” otherwise. From the localized region of six MRI contrasts from T1gd, T2w, diffusion and perfusion imaging, over 300 texture features were extracted. To account for biopsy sample location uncertainty, six neighboring regions of the biopsy sample including four neighbors two pixels away from the biopsy location and two neighbors on adjacent slices were also included in model training. WSO-SVM achieved 0.83 accuracy, 0.77 sensitivity, and 0.86 specificity for classifying EGFR; 0.77 accuracy, 0.74 sensitivity, and 0.79 specificity for classifying PDGFRA, based on 10-fold cross validation. Furthermore, using the trained models, we generated regional EGFR and PDGFRA alteration maps for each patient within the enhancing and non-enhancing tumoral areas. On average we found a greater proportion of enhancing tumor with co-alteration than non-enhancing tumor, while this trend was reversed when considering only one altered gene. The ratio of EGFR vs PDGFRA alterations was higher in non-enhancing tumor than enhancing tumor.

Abstract Glioblastoma (GBM) is a devastating primary brain tumor known for its heterogeneity with a median survival of 15 months. Clinical imaging remains the primary modality to assess brain tumor response, but it is nearly impossible to distinguish between tumor growth and treatment response. Ki67 is a marker of active cell proliferation that shows inter- and intra-patient heterogeneity and should change under many therapies. In this work, we assessed the utility of a semi-supervised deep learning approach for regionally predicting high-vs-low Ki67 in GBM patients based on MRI. We used both labeled and unlabeled datasets to train the model. Labeled data included 114 MRI-localized biopsies from 43 unique GBM patients with available immunohistochemistry Ki67 labels. Unlabeled data included nine repeat routine pretreatment paired scans of newly-diagnosed GBM patients acquired within three days. Data augmentation techniques were utilized to enhance the size of our data and increase generalizability. Data was split between training, validation, and testing sets using 65-15-20 percent ratios. Model inputs were 16x16x3 patches around biopsies on T1Gd and T2 MRIs for labeled data, and around randomly selected patches inside the T2 abnormal region for unlabeled data. The network was a 4-conv layered VGG-inspired architecture. Training objective was accurate prediction of Ki67 in labeled patches and consistency in predictions across repeat unlabeled patches. We measured final model accuracy on held-out test samples. Our promising preliminary results suggest potential for deep learning in deconvolving the spatial heterogeneity of proliferative GBM subpopulations. If successful, this model can provide a non-invasive readout of cell proliferation and reveal the effectiveness of a given cytotoxic therapy dynamically during the patient's routine follow up. Further, the spatial resolution of our approach provides insights into the intra-tumoral heterogeneity of response which can be related to heterogeneity in localization of therapies (e.g. radiation therapy, drug dose heterogeneity).

Abstract Choosing effective chemotherapies for intravenous delivery to brain tumors is challenging, especially given the protective nature of the blood brain barrier (BBB). Connecting drug distribution to non-invasive, pre-surgical magnetic resonance imaging (MRI) could allow for predictive insight into drug distribution. In a previous study, we found that T2Gd images were predictive of a low BBB penetrant drug (Cefazolin), and FLAIR images were predictive of a high BBB penetrant drug (Levetiracetam). While these results are promising, we further seek to explore how advanced MRI sequences might inform image-based models of drug distribution. Prior to surgery, we acquired advanced dynamic contrast enhanced (DCE) and diffusion weighted imaging (DWI) MRI sequences for eight brain tumor patients (7 gliomas and 1 metastatic adenocarcinoma) in addition to the anatomic MRIs. All resulting quantitative maps and acquired images were co-registered. Prior to incision, patients received injections of cefazolin and levetiracetam. Next, multiple blood samples and biopsies were collected during surgery. Biopsies and plasma samples were analyzed for drug concentration using liquid chromatography mass spectrometry (LCMS), and biopsy drug levels were reported as Brain-Plasma Ratio (BPR). Mean image intensity was extracted from a 15x15 voxel window surrounding the biopsy location. We performed linear regression analyses to determine which combination of images were predictive of BPR. We found that considering quantitative imaging improved our initial ability to predict BPR for both drugs. For cefazolin, the third diffusion tensor eigenvalue (L3) map was significantly correlated with BPR (p< 0.001, R2= 0.36). For levetiracetam, the best model consisted of a combination of images and maps with the L3 map and the isotropic diffusion map (P) being the most influential (p= 0.001, R2= 0.63). Advanced MRI-based modeling is a promising tool for forecasting drug distribution in brain tumors and could be of great importance for understanding efficacy and selecting therapeutic strategies.

Abstract Background Children with inflammatory bowel disease [IBD] are disproportionally affected by recurrent Clostridioides difficile infection [rCDI]. Although faecal microbiota transplantation [FMT] has been used with good efficacy in adults with IBD, little is known about outcomes associated with FMT in paediatric IBD. Methods We performed a retrospective review of FMT at 20 paediatric centres in the USA from March 2012 to March 2020. Children with and without IBD were compared with determined differences in the efficacy of FMT for rCDI. In addition, children with IBD with and without a successful outcome were compared with determined predictors of success. Safety data and IBD-specific outcomes were obtained. Results A total of 396 paediatric patients, including 148 with IBD, were included. Children with IBD were no less likely to have a successful first FMT then the non-IBD affected cohort [76% vs 81%, p = 0.17]. Among children with IBD, patients were more likely to have a successful FMT if they received FMT with fresh stool [p = 0.03], were without diarrhoea prior to FMT [p = 0.03], or had a shorter time from rCDI diagnosis until FMT [p = 0.04]. Children with a failed FMT were more likely to have clinically active IBD post-FMT [p = 0.002] and 19 [13%] patients had an IBD-related hospitalisation in the 3-month follow-up. Conclusions Based on the findings from this large US multicentre cohort, the efficacy of FMT for the treatment of rCDI did not differ in children with IBD. Failed FMT among children with IBD was possibly related to the presence of clinically active IBD.