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Introduction: The utility of amyloidosis staging systems has been validated for newly diagnosed patients, but their role in restaging after starting treatment has not been explored. Given the high early mortality in this disease, restaging remote from the diagnosis may have clinical value. We designed this study to evaluate whether the currently used staging systems are prognostic when applied at 3 and 6 months from starting first-line treatment, and whether stage migration impacts survival. Methods: This is a retrospective study including patients with systemic light chain amyloidosis diagnosed between January, 1st 2006 and June, 30th 2019 and were seen in Mayo Clinic, Rochester, MN within 90 days of diagnosis; the analysis included 535 and 301 patients who had restaging data available for at least one of the staging systems at 3 and 6 months, respectively. Patients were grouped into 4 stages using the 2015 European modification of the Mayo 2004 staging system based on values of cardiac biomarkers (cardiac troponin T and NTproBNP), and using the Mayo 2012 staging system based on cardiac biomarkers and the difference in serum free light chain concentration (dFLC). Overall survival (OS) was calculated from the time of re-staging until death or last follow up. Results: Using the modified Mayo 2004 staging system at 3 months from starting first-line treatment, median OS was 11.8 [95%CI: 11.4-not reached (NR)], 10.8 (95%CI: 9.4-NR), 4.6 (95%CI: 2.8-6.7), and 1.1 (95%CI: 0.7-2.6) years in patients with stage I, II, IIIa, and IIIb, respectively. Using Mayo 2012 staging at 3 months, OS was 11.8 (95%CI: 10.9-NR), 9.0 (95%CI: 6.2-NR), 5.2 (95%CI: 3.3-6.1), and 0.8 (95%CI: 0.7-1.1) years in patients with stage I, II, III, and IV, respectively (Figure 1a &b). Using the modified Mayo 2004 staging system at 6 months from starting first-line treatment, median OS was NR (95%CI: NR-NR), 9.8 (95%CI: 6.2-NR), 5.4 (95%CI: 3.2-9.0) and 0.9 (95%CI: 0.6-3.7) years in patients with stage I, II, IIIa, and IIIb, respectively. Using Mayo 2012 staging at 6 months, OS was NR (95%CI: 8.4-NR), NR (95%CI: 7.9-NR), 4.6 (95%CI: 3.0-6.5), and 0.9 (95%CI: 0.2-1.8) years in patients with stage I, II, III, and IV, respectively (Figure 1c &d). On multivariate analysis, advanced (stage > 2) modified Mayo 2004 stage (HR: 1.6, P=0.004) and advanced (stage > 2) Mayo 2012 stage (HR: 2.4, P<0.001) at 3 months were associated with decreased survival independent of cardiac response, hematologic response, and transplantation status. Although advanced Mayo 2004 stage at 6 months was associated with decreased survival, it was not statistically significant (HR: 1.5, P=0.09). In contrast, advanced Mayo 2012 stage at 6 months was independently associated with decreased survival (HR: 2.1, P=0.02). For both systems, worsening stage at 3 months or at 6 months was associated with a worse survival than retaining the original stage. In contrast, improving stage as compared to remaining in same stage was not associated with better survival at 3 or 6 months (Table 1). When the analysis was restricted to patients who had an advanced stage at diagnosis, improving stage at 3 months was associated with longer survival compared to retaining the original stage, with both modified Mayo 2004 (10.8 vs. 3.3 years, P<0.001) and Mayo 2012 (8.1 vs. 2.6 years, P<0.001) staging systems. At 6 months, stage improvement was associated with longer survival than retaining the original stage when the modified Mayo 2004 staging system was used (NR vs. 5.0 years, P=0.02), but it was not statistically significant when the Mayo 2012 staging system was used (5.9 vs. 3.7 years, P=0.12). Conclusion: The modified Mayo 2004 and the Mayo 2012 staging systems have independent prognostic value when used for restaging after 3 and 6 months from initiation of first-line treatment. Migration to a higher stage from diagnosis predicts decreased survival, compared to retaining the same stage. Stage improvement is associated with a better OS in patients with advanced stage at diagnosis. Disclosures Dispenzieri: Alnylam, Intellia, Janssen, Takeda, Pfizer, Prothena, Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding. Kapoor:GlaxoSmithKline: Research Funding; Sanofi: Consultancy, Research Funding; Janssen: Research Funding; Celgene: Honoraria; Amgen: Research Funding; Takeda: Honoraria, Research Funding; Cellectar: Consultancy. Dingli:Rigel: Consultancy; Millenium: Consultancy; Sanofi-Genzyme: Consultancy; Bristol Myers Squibb: Research Funding; Karyopharm Therapeutics: Research Funding; Janssen: Consultancy; Alexion: Consultancy; Apellis: Consultancy. Lin:Novartis: Consultancy; Celgene: Consultancy, Research Funding; Bluebird Bio: Consultancy, Research Funding; Sorrento: Consultancy, Membership on an entity's Board of Directors or advisory committees; Gamida Cells: Consultancy; Takeda: Research Funding; Merck: Research Funding; Legend BioTech: Consultancy; Juno: Consultancy; Janssen: Consultancy, Research Funding; Vineti: Consultancy; Kite, a Gilead Company: Consultancy, Research Funding. Gertz:Alnylam: Consultancy; NCI SPORE MM: Research Funding; Ionis/Akcea: Consultancy; Amyloidosis Foundation: Research Funding; Celgene: Consultancy; Johnson and Johnson: Speakers Bureau; DAVA oncology: Speakers Bureau; Advisory Board for Pharmacyclics: Membership on an entity's Board of Directors or advisory committees; Advisory Board for Proclara: Membership on an entity's Board of Directors or advisory committees; i3Health: Consultancy; Springer Publishing: Patents & Royalties; Prothena: Consultancy; Sanofi: Consultancy; Janssen: Consultancy; Spectrum: Consultancy, Research Funding; Annexon: Consultancy; Appellis: Consultancy; Amgen: Consultancy; Medscape: Consultancy, Speakers Bureau; Physicians Education Resource: Consultancy; Data Safety Monitoring board from Abbvie: Membership on an entity's Board of Directors or advisory committees; International Waldenstrom Foundation: Research Funding. Kumar:Oncopeptides: Consultancy, Other: Independent Review Committee; IRC member; Genecentrix: Consultancy; Cellectar: Other; Genentech/Roche: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Celgene/BMS: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Kite Pharma: Consultancy, Research Funding; MedImmune: Research Funding; Carsgen: Other, Research Funding; Amgen: Consultancy, Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments, Research Funding; Merck: Consultancy, Research Funding; Janssen Oncology: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Takeda: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Karyopharm: Consultancy; AbbVie: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; BMS: Consultancy, Research Funding; Novartis: Research Funding; Adaptive Biotechnologies: Consultancy; Tenebio: Other, Research Funding; Dr. Reddy's Laboratories: Honoraria; Sanofi: Research Funding.

Background: Patients with Waldenström Macroglobulinemia (WM) tend to present at an older age. Frailty, resulting from comorbidities and reduced organ function, poses unique challenges to the management of WM in the elderly. Age above 65 years, is an established unfavorable prognostic factor in WM. However, data specifically pertaining to the very elderly patient population are scant. The impact of advanced age (≥75 years at diagnosis) on the clinical features and outcomes of patients with symptomatic/active WM were studied. Methods: Patients with active WM, evaluated at Mayo Clinic, Rochester between January 1996 and December 2018 were included in the study. The characteristics and laboratory parameters at presentation of the cohort of patients with the age ≥75 years were compared to those of the younger (age <75 years) cohort. The median follow-up and overall survival (OS) were assessed from active disease to last follow-up or death using the Kaplan Meier method. Because older age is a confounding factor for deaths from unrelated causes, the cause-specific survival (CSS) was calculated. Deaths were deemed WM-related if patients died from progressive disease, transformation with antecedent WM, concomitant light chain (AL) amyloidosis, infection associated with WM or anti-WM therapy related toxicity, while patients who died from other causes were considered to have a WM-unrelated death. The patients who died from causes unrelated to WM were censored at death for CSS analysis. The OS of the cohort of the very elderly patients was compared to that of a cohort of the general US population, matched for age, sex as well as follow-up from the calendar-year of diagnosis, to assess the degree to which WM impacts survival. Results: Among 949 consecutive patients, with a median follow-up of 8.4 years [95% Confidence Interval (CI): 7.7-9.3 years] from active disease, the median age at diagnosis was 65 years (range: 31-93 years). Of 177 patients (19%) who were ≥75 years of age at the onset of active WM, 173 (98%) received anti-WM therapy. The baseline characteristics of the 2 cohorts (patients ≥75 years of age versus <75 years) are outlined in Table 1. The patients in the age ≥75 years cohort were noted to have lower IgM levels and serum albumin, and higher serum LDH and serum β-2 microglobulin compared to the age <75 years cohort. A higher proportion of patients in the age <75 years cohort had coexisting AL amyloidosis at diagnosis. The frequency of MYD88L265P mutation was similar between the 2 cohorts (p=0.2). During the 23-year period, there was an increase in the proportion of patients ≥75 years who were diagnosed with active WM (12% during the 1996-2003 period, 18% during 2004-2010, 25% during 2011-2018; P<0.0001). In the elderly WM cohort, the most frequently used frontline treatment was rituximab monotherapy (36%), followed by rituximab-alkylating agent-based regimen (31%). Of 177 patients with age ≥75 years, 89 (50%) patients were deceased at the time of last follow-up and 58% (52/89) of all deaths in age ≥75 years were attributable to WM. By contrast, 303 (39%) out of the 772 patients in the age <75 years cohort were deceased at last follow-up, with 73% (224/303) deaths attributable to WM. The CSS for patients with age ≥75 years was 10.9 years (95% CI: 7.3-11.7 years) versus 14.7 years (95% CI: 13.2-15.7 years) for age <75 years [Risk ratio 2.1(95% CI: 1.5-2.8); p<0.0001]. At the 1 and 2 year follow-up, WM-related mortality was 6.8% and 11.9%, respectively, for patients with age ≥75 years versus 2.6% and 4.9%, respectively, for the younger cohort. The median OS of patients with age ≥75 years from the diagnosis of active WM was 5.9 years (95% CI: 4.2-7.3 years) compared to the OS of 8.3 years for an age, sex and calendar-year-matched cohort, derived from the general US population (p<0.001), Figure 1. Conclusion: Patients who are ≥75 years of age at the time of diagnosis of active WM present with certain poor prognostic features, including higher serum LDH and β-2 microglobulin and lower serum albumin compared to their younger counterparts. The impact of advanced age is underscored by a poorer WM-specific survival for the subset of patients with the age ≥75 years compared to the younger cohort. Furthermore, although active/symptomatic WM is typically an indolent malignancy, it adversely affects OS of the very elderly patients as suggested by the comparison with an age, sex and calendar-year matched cohort of the general US population. Disclosures Ansell: Bristol Myers Squibb: Research Funding; Trillium: Research Funding; Affimed: Research Funding; Regeneron: Research Funding; ADC Therapeutics: Research Funding; Seattle Genetics: Research Funding; AI Therapeutics: Research Funding; Takeda: Research Funding. Gertz:Ionis/Akcea: Other: personal fee; Alnylam: Other: personal fee; Prothena: Other: personal fee; Spectrum: Other: personal fee, Research Funding; Annexon: Other: personal fee; Appellis: Other: personal fee; Amgen: Other: personal fee; Medscape: Other: personal fee, Speakers Bureau; Physicians Education Resource: Other: personal fee; Abbvie: Other; Celgene: Other; Research to Practice: Other; Sanofi: Other; Teva: Speakers Bureau; Johnson and Johnson: Speakers Bureau; DAVA oncology: Speakers Bureau; Proclara: Other; Springer Publishing: Patents & Royalties; Aurora Bio: Other; Janssen: Other: personal fee. Nowakowski:NanoString: Research Funding; Seattle Genetics: Consultancy; Curis: Consultancy; Kymera: Consultancy; Kite: Consultancy; Ryvu: Consultancy, Membership on an entity's Board of Directors or advisory committees; MorphoSys: Consultancy, Research Funding; Celgene/BMS: Consultancy, Research Funding. Witzig:Spectrum: Consultancy; Immune Design: Research Funding; Karyopharm Therapeutics: Research Funding; Acerta: Research Funding; Incyte: Consultancy; MorphSys: Consultancy; AbbVie: Consultancy; Celgene: Consultancy, Research Funding. Dispenzieri:Pfizer: Research Funding; Janssen: Research Funding; Intellia: Research Funding; Alnylam: Research Funding; Takeda: Research Funding; Celgene: Research Funding. Dingli:Sanofi-Genzyme: Consultancy; Janssen: Consultancy; Bristol Myers Squibb: Research Funding; Rigel: Consultancy; Apellis: Consultancy; Karyopharm Therapeutics: Research Funding; Alexion: Consultancy; Millenium: Consultancy. Kumar:Sanofi: Research Funding; Kite Pharma: Consultancy, Research Funding; Cellectar: Other; Genecentrix: Consultancy; Novartis: Research Funding; Karyopharm: Consultancy; Amgen: Consultancy, Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments, Research Funding; AbbVie: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Takeda: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Carsgen: Other, Research Funding; Oncopeptides: Consultancy, Other: Independent Review Committee; IRC member; Genentech/Roche: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Merck: Consultancy, Research Funding; MedImmune: Research Funding; Adaptive Biotechnologies: Consultancy; Tenebio: Other, Research Funding; Dr. Reddy's Laboratories: Honoraria; BMS: Consultancy, Research Funding; Janssen Oncology: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Celgene/BMS: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments. Kapoor:Cellectar: Consultancy; Janssen: Research Funding; Sanofi: Consultancy, Research Funding; Amgen: Research Funding; Takeda: Honoraria, Research Funding; GlaxoSmithKline: Research Funding; Celgene: Honoraria.

70 Background: The prevalence of health insurance literacy (HIL) and financial literacy (FL) in cancer patients is not well described. Further, a better understanding of the association between these constructs and the domains of financial hardship (FH) can guide potential interventions. Methods: We examined the prevalence of HIL and FL and FH in cancer patients on active treatment at our institution. HIL and FL were assessed using Health Insurance Literacy Measure (HILM) and 5 questions from the National Financial Capability Study respectively. COST FACIT and National Health Interview Survey (NHIS) questions were used to examine overall FH and its 3 domains (material, behavioral and psychological hardship). Pearson correlation coefficient was used to describe the relation between the COST score and FH domains assessed by NHIS questions. Logistic regression analysis was performed to determine the associations of overall FH (measured by COST FACIT) with FL and HIL. Results: Among 256 patients approached, 202 completed questionnaires. Median age was 67 (range 22 to 91) years. 51% were male, 93% Whites and 5% were Hispanic. 69% had a solid tumor with 37% stage IV. Median number of lines of treatment was 4 (range 1 to 18). 21% were currently employed, 59% had ≥college degree, 41% had private insurance and 42% had monthly income≥$7000. 50% participants answered at least 4/5 FL questions correctly, indicating high FL. Being male, White, having insurance, higher education and income ≥$7000 were associated with higher FL. 72% of participants had high HIL, and they were more likely to be Non-Hispanic, married and have received more lines of treatment. FL significantly correlated with HIL (r = 0.29, p < .001). 65% patients endorsed FH based on NHIS questions (10% behavioral, 20% material, and 62% psychological hardship). 48% patients had FH based on a COST score < 28 (Median score), which correlated with material (r = -0.59, p < .001), psychological (r = -0.55, p < .001) and behavioral FH (r = -0.42, p < .001). The association between FH and HIL was not significant (OR 0.78; p = 0.08), though higher FL was associated with lower FH (OR 0.69; p = 0.02). Conclusions: Despite high FL and HIL (as compared to national standards), the prevalence of FH in our relatively affluent patient cohort was high, driven mainly by psychological hardship. We did not find strong association between the HIL and FH unlike in the reported literature. Our findings behoove us to develop tailored interventions to address unique domains of the FH in a diverse population.

Abstract Gastrointestinal complications of multiple myeloma (MM) treatment are common and include nausea, constipation, and diarrhea. However, acute gastrointestinal events like perforations are rare. We aimed to describe the characteristics and outcomes of patients with MM that had colonic perforations during their treatment. This is a retrospective study that included patients from all three Mayo Clinic sites who had MM and developed a colonic perforation. All patients were diagnosed with colonic perforations based on CT scans and were surgically treated. Patients diagnosed with AL amyloidosis, a perforated colon complicating neutropenic colitis during ASCT and those with perforation due to colonic cancer were excluded. A high dose of dexamethasone was defined as ≥40 mg dexamethasone once a week. Thirty patients met inclusion criteria. All patients received steroids at doses ≥10 mg once weekly prior to the perforation, while four (11%) were on high‐dose dexamethasone without chemotherapy. Fourteen patients were given high doses of dexamethasone. Twenty‐five patients required ostomies with all surviving surgery. Twenty‐four perforations (80%) were associated with diverticulitis. Treatment with steroids was resumed in 23 patients with no further gastrointestinal complications. The median OS was 20 months following perforation (IQR 8–59). Within the same timeframe 5854 patients were treated at Mayo Clinic for MM, making the risk of bowel perforation 0.5%. Intestinal perforations in MM are rare and, in our series, always occurred with dexamethasone ≥10 mg per week. Urgent surgery is lifesaving and resumption of anti‐myeloma treatment appears to be safe.

Abstract Purpose: Rearrangements involving the MYC protooncogene are common in newly diagnosed multiple myeloma, but their prognostic significance is still unclear. The purpose of this study was to assess the impact of MYC rearrangement on clinical characteristics, treatment response, and survival in patients with newly diagnosed multiple myeloma. Experimental Design: This is a retrospective study including 1,342 patients seen in Mayo Clinic in Rochester, MN, from January 2006 to January 2018, who had cytogenetic testing by FISH at diagnosis, including MYC testing using the break apart FISH probe (8q24.1). Results: A rearrangement involving MYC was found in 8% of patients and was associated with elevated β2-microglobulin, ≥50% bone marrow plasma cells, IgA multiple myeloma, and the cooccurrence of trisomies. There were no differences in overall response rates between patients with and without MYC rearrangement when induction chemotherapy was proteasome inhibitor (PI)-based, immunomodulatory drug (IMiD)-based or PI + IMiD-based. Overall survival was shorter in patients with MYC rearrangement compared with patients without MYC rearrangement (5.3 vs. 8.0 years, P &lt; 0.001). MYC rearrangement was associated with increased risk of death on multivariate analysis when high-risk cytogenetic abnormalities, ISS stage III, and ≥70 years of age were included (risk ratio: 1.5; P = 0.007). Conclusions: MYC rearrangement is associated with high disease burden and is an independent adverse prognostic factor in patients with newly diagnosed multiple myeloma.

Background: Parenthood during medical training is common and impacts trainee well-being. However, current graduate medical education parental health policies are often limited in scope. We explored current fellowship trainees’ knowledge of/satisfaction with current policies as well as interest in potential changes/additions to existing policies. Methods: Fellowship program directors/coordinators at a three-site academic institution were surveyed and information was collected from 2015 to 2019 regarding fellow demographics and parental health policies. We distributed an electronic survey to fellows containing Likert-type-scale questions rating knowledge/level of satisfaction with current parental health policies and interest in potential additions/modifications to current policies. Results: Thirty-five of 47 (74%) fellowship programs responded. An average of 11% of female fellows and 15% of male fellows took parental leave during the study period. Three (9%) of the programs had at least one additional parental health policy beyond institutional graduate medical education policies. In the fellow survey, 175 of 609 fellows responded (28.7%), of which 84 (48.6%) were female. Although 89.1% agreed/strongly agreed that parental health is an important part of health and well-being for fellows, only 32% were satisfied/very satisfied with current policies (no significant sex-related differences). Fellows reported the following potential interventions as important/very important: 79.2% increased (paid) maternity leave (72.7% male, 86.7% female, p = 0.02), 78% increased (paid) paternity leave (76.4% male, 81.9% female, p = 0.37), 72.3% part-time return to work (60.2% male, 84.3% female, p = 0.0005), 63% coverage for workup/management of infertility (52.3% male, 74.7% female, p = 0.002), and 79.9% on-site day care (70.7% male, 89.2% female, p = 0.003). Conclusions: Parental health includes multiple domains, not all of which are covered by current policies. Fellows feel that parental health is an important part of overall health and well-being, but most are not satisfied with current policies. Expanded access to parental leave and new policies (part-time return to work, infertility management, and on-site day care) are opportunities for innovation.

Abstract A gain in chromosome 1q (+1q) is among the most common cytogenetic abnormalities in multiple myeloma (MM). It is unclear whether +1q is independently associated with decreased overall survival (OS). The objective of this study was to evaluate the impact of +1q on clinical characteristics, treatment response, and survival outcomes. We included 1376 Mayo Clinic patients diagnosed with MM from 2005 to 2018 who underwent fluorescence in situ hybridization testing at diagnosis with a panel including the +1q probe. A gain in 1q was found in 391 patients (28%) and was associated with anemia, hypercalcemia, high tumor burden, International Staging System (ISS) stage 3, high-risk (HR) translocations, and chromosome 13 abnormalities. There was no difference in overall response or deeper responses to proteasome inhibitor (PI)–, immunomodulatory drug (iMiD)–, or PI plus IMiD–based induction. Time to next treatment was shorter in patients with +1q compared with those without +1q (19.9 vs 27.7 months; P &lt; .001). On univariate analysis, +1q was associated with increased risk of death (risk ratio [RR], 1.9; P &lt; .001), and decreased OS was seen in all treatment groups. +1q was independently associated with decreased OS on multivariate analysis when other HR cytogenetic abnormalities, ISS stage 3, and age ≥70 years were included (RR, 1.5; P &lt; .001). Gain of &gt;1 copy of 1q was not associated with worse OS compared with gain of 1 copy (4.9 vs 4.3 years; P = .21). +1q was associated with high tumor burden, advanced disease stage, and HR translocations. It is independently associated with decreased OS, even in the setting of novel therapy and transplant.

AbstractResponse assessment in light chain (AL) amyloidosis is based on serum and urine monoclonal protein studies. Newly diagnosed patients (n = 373) who achieved very good partial response or complete response (CR) to first line therapy were assessed for the survival impact of each of the monoclonal protein studies. At end of therapy (EOT), negative serum/urine immunofixation (IFE) was achieved in 61% of patients, 72% achieved normal serum free light chain ratio (sFLCR), and the median involved free light chain (iFLC) and difference between involved to uninvolved light chain (dFLC) were 17 mg/L and 5 mg/L, respectively. Overall, 46% of patients achieved a CR at EOT. At EOT, iFLC ≤20 mg/L and dFLC ≤10 mg/L were additive in survival discrimination to negative serum/urine IFE and were independent predictors of overall survival. In contrast, normalization of sFLCR did not add survival discrimination to serum/urine IFE and was not independent predictor of survival. We propose a new definition for hematological CR to include serum/urine IFE negativity plus iFLC ≤20 mg/L or dFLC ≤10 mg/L, instead of the current definition of serum/urine IFE negativity and normal sFLCR. Complete response using dFLC ≤10 mg/L had the best performance in those with significant renal dysfunction and by light chain isotype, making it the preferred partner to IFE. Validation of these results in a multicenter cohort is warranted.