Citations

Abstract Therapeutic strategies that target novel pathways are urgently needed for patients with relapsed/refractory multiple myeloma (RRMM). Ibrutinib is an oral covalent inhibitor of Bruton tyrosine kinase, which is overexpressed in MM cells. This phase 1 dose‐escalation study examined various doses of ibrutinib in combination with standard doses of lenalidomide (25 mg) and dexamethasone (40 mg) using a standard 3 + 3 design in RRMM patients. The primary objective was to determine the maximum tolerated dose (MTD) of ibrutinib in combination with lenalidomide and dexamethasone. Patients ( n = 15) had received a median of 4 prior regimens, 53% were triple‐class exposed, 33% were penta‐exposed, and 54% were lenalidomide‐refractory. The MTD of ibrutinib was 840 mg ( n = 6) and only 1 dose‐limiting toxicity; a grade 3 rash possibly related to ibrutinib was noted. The most common ≥ grade 3 adverse events were rash in 2 (13%), lymphopenia in 2 (13%), leukopenia, neutropenia, thrombocytopenia, and anemia all occurring in 3 (20%) patients each. One patient achieved a partial response for an overall response rate of 7%. The clinical benefit rate was 80%. The median time to progression was 3.8 months. Ibrutinib, lenalidomide and dexamethasone appears to be a safe and well‐tolerated regimen with reasonable efficacy in heavily pretreated RRMM patients.

Abstract Background: There is an urgent need to improve clinical trial (CT) enrollment of breast cancer (BC) patients (pts), especially from unrepresented minorities. In this regard, our study aims to better understand the characteristics and factors associated with CT enrollment to inform strategies to enhance CT accrual rates. Method: We included 1,308,767 female pts from the National Cancer Data Base diagnosed from 2010 to 2017 with known ER, PR, and HER2 status. Demographic, socioeconomic, Charlson-Deyo Score (CDS) and BC specific data were compared between CT versus non-CT enrolled pts for initial therapy. Comparisons were assessed by Chi-square test. Logistic regression was utilized to determine associations between characteristics and CT enrollment. Odds ratios (OR) and p-values are reported for each group. Results: A total of 1797 pts (0.14%) were enrolled in CT for initial BC therapy. Focusing on the CT group, majority of pts were non-Hispanic White (W;(78.2%), followed by non-Hispanic black (AA;12.2%); Hispanic (H; 5.6%); and non-Hispanic Asian (A; 4%). Most pts (29.3%) were in 50-59 age quartile. TNBC pts formed 22% of CT pts and non-TNBC were 78%. Most pts were from academic centers (54%), followed by comprehensive community cancer (CCC; 23.5%), community cancer (3.9%) and integrated network cancer (INC; 5.6%) programs. Most had private insurance (70%) versus government insurance (27%) and others (3%). Stage IV pts were only 10% of the CT group while most pts (41%) had Stage II BC. CDS of 0 was noted in 90% pts while 1.3% pts had CDS of 2 or more. Most CT pts traveled < 50 miles from treatment center (88.5%), and only 1.2% CT pts traveled 200 or more miles. Most CT pts resided in areas with highest literacy rate (30%) while 15% were from areas with lowest literacy rate. In multivariable model, non-white pts were less likely than Whites to be enrolled in CT (OR 0.81, p<0.05). The odds of CT enrollment in 40-49, 50-59 and 60-69 age quartiles were 3.5, 2.7 and 2.3 times respectively that of pts 70 years or older (p<0.001). TNBC pts were 1.9 times more likely to be enrolled in CT than non-TNBC pts (p<0.001). Academic center pts were 3 times more likely (p<0.001) while INC program pts were less likely than CCC pts to be enrolled (OR 0.7, p=0.006). Pts with government insurance and other insurance were less likely to enroll in CT (OR 0.74, and 0.55, p <0.001 and p=0.003, respectively) compared to private insurance. The odds of a patient with stage II, III, IV BC enrolling in CT was about 2, 3.5 and 4 times higher than a stage I pt (p -<0.001). Pts with CDS 1 (OR 0.8, p=0.025) or 2 or more (OR 0.5, p=0.002) were less likely than CDS 0 pts to be enrolled. Pts who traveled 50-99 or 100-199 miles from the treatment center conducting CT, were 1.9 (p<0.001) and 1.5 times (p<0.05) more likely to be enrolled while those with > 200 miles were less likely (OR 0.75, p=0.442). The group of pts who belonged to a residential area with the highest literacy rate (<6.3% of no high school diploma) were 1.4 times more likely to accrue in CT (p<0.001) than those with high school diploma rate between 6.3 and 10.8%. Compared to metro areas, pts from rural areas were less likely to be enrolled (OR 0.5, p=0.04) as was a lower median income < $40,227 (OR 0.77, p= 0.016).Conclusion: CT enrollment is poor for initial therapy of BC treatment. We identify several modifiable factors including sociodemographics which are associated with BC pts not getting treated on CT. There are other factors that are non-modifiable, e.g., disease stage but may point to the need for more CT development in these areas. Addressing these disparities would lead to improved CT enrollment and overall advancement in BC management landscape. Citation Format: Pooja P Advani, Aaron Spaulding, Alexander Hochwald, Mizba Baksh, David Hodge, Saranya Chumsri, Shanada Monestime, Sikander Ailawadhi. Clinical trial enrollment for initial therapy of breast cancer: Exploring factors associated with poor accrual and significant disparities [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P2-11-15.

AbstractExtramedullary multiple myeloma is seen in advanced and aggressive disease and occurs due to plasma cell infiltration of sites other than the bone marrow. Myelomatous ascites or pleural effusion is seen in less than 1% of cases and can be differentiated from infectious etiologies based on fluid cytology.

Abstract Background: Concern exists that the clinical trial populations differ from respective cancer populations in terms of their age distribution affecting the generalizability of the results, especially the underrepresented minorities. We hypothesized that the clinical trials that do not report race are likely to suffer from a higher degree of age disparity as well and the issue should be addressed in conjunction with each other. Methods: Food and Drug Administration (FDA) drug approvals from 7/2007-6/2019 were reviewed to identify oncology approvals, and trials with age details were selected. The primary outcome was the weighted mean difference in age between the clinical trial population and realworld population for various cancers. The secondary outcomes were the prevalence of race reporting and association of age and race reporting with each other. Group comparisons for proportions were performed using the χ2 test while continuous variables were compared using multiple analysis of variance (MANOVA) test or students t-test as appropriate. P values < .05 were considered significant. Results: Of the 261 trials, the race was reported in 223 (85.4%) of the trials while 38 trials (14.6%) had no mention of race. Race reporting improved minimally over time – 29 (85.3%) in 2007-2010 vs. 49 (80.3%) in 2011-2014 vs. 145 (85.4%) during 2015-2019 (p-value=0.41). No significant association between type of cancer and race reporting was noted – 147 (86.5%) of solid cancer trials vs. 76 (83.5%) of hematological cancer trials reported race (p-value=0.52). Age discrepancy between the clinical trial population and the real-world population was present for several cancer types. The discrepancy was higher for studies that did not report race, mean difference - 8.8 years 4 (95% CI -12.6 to -5.0 years) vs. studies that did report the race, mean difference -5.1 years, (95% CI -6.4 to -3.7 years), p-value=0.04. Conclusion: The study demonstrates that a significant number of clinical trials leading to cancer drug approvals suffer from racial and age disparity when compared to real-world populations and that the two factors may be interrelated. We recommend continued efforts to recruit diverse populations. In the meantime, reporting these characteristics for these crucial trials should be mandated. Citation Format: Thejus Jayakrishnan, Sonikpreet Aulakh, Mizba Baksh, Kianna Nguyen, Meghna Ailawadhi, Ayesha Samreen, Ricardo Parrondo, Taimur Sher, Vivek Roy, Rami Manochakian, Aneel Paulus, Asher Chanan-Khan, Sikander Ailawadhi. Disparity in race and age reporting in landmark cancer clinical trials: Underrepresentation of the traditionally underserved U.S. population [abstract]. In: Proceedings of the AACR Virtual Conference: 14th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2021 Oct 6-8. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2022;31(1 Suppl):Abstract nr PO-104.

AbstractWaldenström macroglobulinemia (WM)/lymphoplasmacytic lymphoma (LPL) is often differentiated from myeloma based on the presence of lytic bone lesions (LBL). However, WM/LPL can present with LBL, and management is poorly understood. We describe a case of an 81‐year‐old woman with LPL who presented with LBL and was successfully treated with chemoimmunotherapy.

Extramedullary multiple myeloma is seen in advanced and aggressive disease and occurs due to plasma cell infiltration of sites other than the bone marrow. Myelomatous ascites or pleural effusion is seen in less than 1 % of cases and can be differentiated from infectious etiologies based on fluid cytology.

Abstract Introduction: Treatment resistance remains a challenge that most patients (pts) with MM will encounter in their disease course. With each subsequent line of therapy (LOT), pt outcomes and health-related quality of life (HRQoL) worsen. Pts with MM who are refractory to immunomodulatory drugs, proteasome inhibitors (PIs), and anti-CD38 antibodies (triple-class refractory; TCR) have a poor prognosis and the holistic burden is not well understood. To capture these features, the ongoing prospective Connect ® MM Disease Registry was used to report treatment patterns and outcomes in pts with TCR MM. Methods: Data from September 28, 2009 to February 4, 2021 were used. Eligible pts were ≥18 years of age at first documented diagnosis of MM, which became TCR during their disease course. MM was defined as refractory to therapies based on the International Myeloma Working Group Criteria. Index date was defined as first record of TCR during the disease course. Data analyzed included pt characteristics, treatment patterns pre- and post-index, overall survival (OS), healthcare resource utilization (HCRU), and HRQoL. General HRQoL was captured by EQ-5D-3L utility index (range −0.11-1, higher score indicated better HRQoL), EQ-5D visual analog scale (VAS) score (range 0-100, "worst imaginable" to "best imaginable" health status), and FACT-G total score (range 0-108). Disease-specific HRQoL was measured by FACT-MM total score (range 0-164), FACT-MM Trial Outcome Index (TOI) (range 0-112), and the Brief Pain Inventory (BPI) average pain item (range 0-10, "no pain" to "worst pain you can imagine"). Higher scores indicated better HRQoL for all FACT-related index scores. Cohort-level clinically meaningful change in HRQoL during post-index year 1 was defined as 0.08 for the EQ-5D utility index, 7 for the EQ-5D VAS, and 6-8 points for the FACT-G total score. Validated thresholds were unavailable for other index scores. All statistical analyses were descriptive. Results: This analysis included 240 pts identified from the Registry. Mean age at index date was 68.1 (standard deviation [SD] 10.4) years. Mean time from initial MM diagnosis was 64.1 (SD 26.9) months. 63.3% of pts had prior stem cell transplantation. Mean number of prior LOTs was 4 (SD 1.7). 64.6% of pts had received a subsequent MM LOT since index date. Median follow-up time post-index date was 4.6 months. Treatment pattern analysis of post-index treated pts (Table A) revealed that retreatment with an immunomodulatory drug, PI, or anti-CD38 agent post-index date was common, led by carfilzomib (47.1%) and pomalidomide (40.0%). However, no therapeutic combination was preferred in this pt population. Regarding their index therapy (first post-index LOT), 70.3% and 22.6% were on triplet and quadruplet combination therapy; 3.2% initiated belantamab mafodotin, selinexor, melflufen, and/or idecabtagene vicleucel; mean duration of treatment was 5.6 (SD 6.1) months. For all pts, median OS was 8.9 months; median OS for those who received < 4 prior LOTs (43.8%) versus those who received 4 or more prior LOTs (56.2%) was 10.0 and 8.0 months, respectively. Regarding HCRU during the post-index follow-up, all-cause and MM-related hospitalizations were observed in 49.6% and 23.8% of all eligible pts, respectively. Among these pts, annualized mean number of all-cause/MM-related hospitalization and length of hospital stay were estimated as 8.5 (SD 18.2)/11.0 (SD 25.2) and 64.0 days (SD 80.0)/ 69.2 days (SD 83.7), respectively. Analysis of HRQoL showed a numerical decline in HRQoL relative to index during the 1-year post-index period across all studied HRQoL instruments. There was a clinically meaningful decline in HRQoL from baseline following the index date based on the EQ-5D utility index, EQ-5D VAS, and FACT-G total score (Table B). Conclusion: Pts with TCR MM experienced poor survival, substantial hospitalizations, and a clinically meaningful decline in HRQoL. 35.4% of pts did not receive any new LOTs since the disease became TCR. Among those who did receive subsequent LOTs, re-treatment with an immunomodulatory drug, PI, or anti-CD38 antibody therapy occurred, showing a lack of novel treatment options. This study suggests novel tolerable and efficacious therapeutic agents are needed to address the burden of illness in pts with MM. Figure 1 Figure 1. Disclosures Tang: Bristol-Myers Squibb: Current Employment, Current equity holder in publicly-traded company. Hari: Amgen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other, Research Funding, Speakers Bureau; Takeda: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other, Research Funding, Speakers Bureau; Celgene-BMS: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other, Research Funding, Speakers Bureau; Adaptive Biotech: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Oncopeptides: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Sanofi: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Karyopharm: Consultancy; GSK: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other, Research Funding, Speakers Bureau; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Other, Research Funding, Speakers Bureau; Millenium: Membership on an entity's Board of Directors or advisory committees, Research Funding. Ramasamy: Pfizer: Honoraria, Membership on an entity's Board of Directors or advisory committees; Adaptive Biotechnologies: Honoraria, Membership on an entity's Board of Directors or advisory committees; GSK: Honoraria, Membership on an entity's Board of Directors or advisory committees; Oncopeptides: Honoraria, Membership on an entity's Board of Directors or advisory committees; Abbvie: Honoraria, Membership on an entity's Board of Directors or advisory committees; Amgen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene - Bristol Myers Squibb: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding. Weisel: Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Bristol Myers Squibb: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Amgen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Takeda: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Oncopeptides: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Adaptive Biotechnologies: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; GSK: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Janssen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Karyopharm: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Roche: Honoraria; Sanofi: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Abbvie: Consultancy; Novartis: Honoraria; Pfizer: Honoraria. Joshi: Bristol Myers Squibb: Current Employment. Liu: Bristol Myers Squibb: Current Employment. Che: Bristol Myers Squibb: Current Employment. Hernandez: Bristol Myers Squibb: Current Employment, Current equity holder in publicly-traded company. Abonour: Jensen: Honoraria, Research Funding; Celgene-BMS: Membership on an entity's Board of Directors or advisory committees, Research Funding; GSK: Consultancy, Honoraria, Research Funding; Takeda: Research Funding. Hardin: Bristol Myers Squibb: Membership on an entity's Board of Directors or advisory committees. Rifkin: Sanofi: Membership on an entity's Board of Directors or advisory committees; McKesson: Current Employment, Current equity holder in publicly-traded company; Karyopharm: Membership on an entity's Board of Directors or advisory committees; Fresenius-Kabi: Membership on an entity's Board of Directors or advisory committees; Coherus: Membership on an entity's Board of Directors or advisory committees; Bristol Myers Squibb (Celgene): Membership on an entity's Board of Directors or advisory committees; Takeda: Membership on an entity's Board of Directors or advisory committees; Amgen: Membership on an entity's Board of Directors or advisory committees. Ailawadhi: BeiGene, Ltd.: Consultancy; Bristol Myers Squibb: Consultancy, Research Funding; Janssen: Consultancy, Research Funding; Amgen: Consultancy, Research Funding; Pharmacyclics: Consultancy, Research Funding; Ascentage: Research Funding; Medimmune: Research Funding; Cellectar: Research Funding; Xencor: Research Funding; GSK: Consultancy, Research Funding; Takeda: Consultancy; Genentech: Consultancy; AbbVie: Consultancy; Karyopharm: Consultancy; Sanofi: Consultancy; Oncopeptides: Consultancy. Lee: Legend Biotech: Consultancy; Sanofi: Consultancy; Oncopetides: Consultancy; Bristol Myers Squibb: Consultancy; Amgen: Consultancy, Research Funding; Celgene: Consultancy; Janssen: Consultancy, Research Funding; Takeda Pharmaceuticals: Consultancy, Research Funding; Karyopharm: Consultancy; Regeneron: Research Funding; Genentech: Consultancy; GlaxoSmithKline: Consultancy, Research Funding. Terebelo: Pharmacylics: Speakers Bureau; Celgene-BMS: Consultancy; Takeda: Speakers Bureau; Janssen: Speakers Bureau. Durie: Amgen, Celgene/Bristol-Myers Squibb, Janssen, and Takeda: Consultancy; Amgen: Other: fees from non-CME/CE services . Narang: Bristol Myers Squibb: Consultancy; TG Pharma: Consultancy. Toomey: Bristol Myers Squibb: Consultancy. Gasparetto: Oncopeptide: Consultancy, Honoraria, Speakers Bureau; Janssen: Consultancy; Sanofi: Consultancy, Honoraria, Speakers Bureau; Karyopharm: Consultancy, Honoraria, Speakers Bureau; Gsk: Consultancy, Honoraria, Speakers Bureau. Wagner: Celgene-BMS: Consultancy, Membership on an entity's Board of Directors or advisory committees; Athenex Inc: Consultancy; Eli Lilly, Johnson & Johnson: Other: Spouse, previously held individual stocks;. Jagannath: Janssen Pharmaceuticals: Consultancy; Bristol Myers Squibb: Consultancy; Karyopharm Therapeutics: Consultancy; Legend Biotech: Consultancy; Sanofi: Consultancy; Takeda: Consultancy.

Abstract Background There has been an increase in overall utilization as well as improvement in overall survival (OS) after hematopoietic cell transplantation (HCT) for most hematologic disorders over time. It is not known if these changes have impacted racial/ethnic minorities equally. In this study, we describe changes in the volume and rate of autologous (auto) and allogeneic (allo) HCT and examine trends in survival in different racial/ethnic groups from 2009 to 2018. Methods Data were obtained from the Center for International Blood and Marrow Transplant Research (CIBMTR) which collects patient data on most autologous and allogeneic HCT recipients in the US. We compared the number of auto and allo transplants in 4 different racial/ethnic groups: Non-Hispanic Whites (NHWs), African Americans (AAs), Hispanics and 'Others' across five 2-year cohorts from 2009 to 2018. 'Others' included Asians, Pacific Islanders and Native Americans as well as non-US residents. Since numbers in individual categories were too low to be meaningful, they were grouped together as 'Others'. Multi race individuals and those with either race or ethnicity missing were excluded. Cox proportional hazards models were used to examine trends in overall mortality after auto HCT in adult patients (for NHL, HD, and MM), and allo HCT among adult and pediatric patients (for AML, ALL, lymphoma including chronic lymphocytic leukemia, and MDS/MPD) in the 4 different racial ethnic groups over 5 study time periods. These models were adjusted for sociodemographic and clinical variables (age, sex, Karnofsky/Lansky score, HCT-comorbidity index, disease, disease status, geographic region, insurance, marital status, education, distance to the transplant center, median income derived from patient's ZIP code). Graft type, donor, conditioning, GVHD prophylaxis, donor-recipient CMV match and donor-recipient sex match were included for allo HCT models. Results A total of 80,080 (59,666 NHWs, 10,683 AAs, 7,110 Hispanics) patients received an auto HCT and 60,412 (44,272 NHWs, 4,964 AAs, 7,826 Hispanics) received an allo HCTs from 2009 to 2018. The volume of auto and allo transplants increased overall, as well as within each race/ethnicity group, over the study periods. In addition, there was a significant change in the proportion of patients in each racial/ethnic group undergoing an auto HCT, with a lower proportion of NHWs and increased proportion of minorities over time (from 78%, 12% and 8% in 2009 -2010 to 71%, 15% and 10% in 2017-2018 for NHWs, AAs and Hispanics respectively; p<0.001). Similar trends were observed for allo HCT: from 76%, 8% and 12% in 2009-2010 to 71%, 9% and 14% in 2017-2018 of all allo HCTs for NHWs, AAs and Hispanics respectively; p<0.001. While there was an increase in the rate of transplants over the years across all racial/ethnic groups, the rate was higher in AAs and Hispanics as compared to NHWs (Table 1). There was no significant difference in overall mortality amongst the three main racial ethnic groups, though there was decrease in mortality over time for auto HCT and adult allo HCT patients. In the allo HCT pediatric group, AAs had higher overall mortality as compared to NHWs across all time periods. (Table 2) Figure 1 shows temporal trends in two-year survival by race and ethnicity. Conclusions This large population-based study shows an improvement in the rates and survival of auto and allo HCT in adults over time. Changes in the proportion of different racial/ethnic groups undergoing HCT are higher than rate of change of population growth (3% decline for NHWs, no change in AAs and 2% increase in Hispanics from 2010 to 2019 per William H. Frey analysis of census population estimates released June 25, 2020). Progress is reflected in higher rates of increase in racial/ ethnic minority groups and comparable survival across the racial ethnic groups in our cohort as compared to two prior registry studies (Baker et al. JCO 2005 and Baker et al. BBMT 2009) for most HCT patients. However, in pediatric patients undergoing allo HCT, the gap in survival between NHWs and AAs has continued to persist over the years. With increasing diversity of the US population, we need to strengthen our efforts for improving access to, and outcomes of HCT to narrow the gap for all patients requiring HCT, irrespective of their sociodemographic characteristics. Figure 1 Figure 1. Disclosures Wood: Koneksa Health: Consultancy, Current equity holder in publicly-traded company; Teladoc: Consultancy; Pfizer: Research Funding. Ailawadhi: Karyopharm: Consultancy; AbbVie: Consultancy; Genentech: Consultancy; Takeda: Consultancy; GSK: Consultancy, Research Funding; Xencor: Research Funding; Cellectar: Research Funding; Medimmune: Research Funding; Ascentage: Research Funding; Pharmacyclics: Consultancy, Research Funding; Amgen: Consultancy, Research Funding; Janssen: Consultancy, Research Funding; Bristol Myers Squibb: Consultancy, Research Funding; BeiGene, Ltd.: Consultancy; Sanofi: Consultancy; Oncopeptides: Consultancy. Saber: Govt. COI: Other.

Abstract Background: trALL is an important secondary primary malignancy (SPM) that has recently been appreciated. While patients (pts) with MM have an 8%-17% risk of developing an SPM while on lenalidomide (R) maintenance following autologous stem cell transplant (ASCT), trALL has been rarely observed. Little is known about the characteristics of trALL in pts with MM compared to pts who had other malignancies prior to the development of trALL. We define tALL as any prior exposure to cytotoxic chemotherapy and/or radiation for another malignancy, and herein, we report a comparative analysis of characteristics and outcomes of pts with trALL with and without MM from the Mayo Clinic Cancer Center (MCCC). Methods: We performed a systematic search in the 3-site MCCC registry and included all pts diagnosed and received at least 1 cycle of ALL-directed therapy and/or underwent allogeneic transplantation (AlloHCT) for ALL between 2007-2020. All cases of trALL in this series are B-ALL. Comparisons of characteristics between trALL patients with and without MM were made using a Wilcoxon rank sum test (continuous variables) or Fisher's exact test (categorical variables). Time-to-event variables were compared using unadjusted Cox proportional hazards regression models. Results: 70 trALL pts (14 MM [20%], 56 non-MM [80%]) were identified during the study period. The median age at trALL diagnosis was 63.9 (range 47-71) for MM pts and 63.1 (range 18.2-84) for non-MM pts. Median follow-up from trALL diagnosis was 1.42 years (range 0.02-10.6 years). The most common prior non-MM malignancies among tALL pts were breast (28.6%), lymphoma (17.9%), myeloid (17.9%), and GU/GYN (14.3%). Amongst the 14 MM pts, 100% (n=10) had ISS-I disease, 8 (57%) had IgG K MM, 1 (n=10) had high risk cytogenetics, 8 (57.1%) received R-based induction , 4 (28.6%) received CyBorD induction, 4(28.6%) received VRD induction, 3 (21.4%) received RD induction. 10 (71.4%) pts were actively on R when they developed trALL, 8 (57.1%) pts received prior ASCT. The median time to develop trALL following ASCT for MM was 46.3 months (m), (95% CI 20.4-67.6), 12 (85.7%) pts received R maintenance with a median duration of 53m (95% CI 16.9-121). The median time to develop trALL after initiation of R maintenance was 61.2m (95% CI 16.9-123.4). The most common trALL induction regimen received was HyperCVAD which was used in 71.4% of MM pts and 51.8% of non-MM pts. In comparison to non-MM pts, MM patients had a significantly lower WBC at diagnosis (Median: 2.0 vs. 5.8, P=0.005),no t(9;22) BCR/ABL1 cytogenetics (0.0% vs. 35.7%, P=0.008), a higher frequency of hypodiploid/near-triploid (Ho-Tri) cytogenetics (42.9% vs. 12.5%, P=0.009), and a lower frequency of prior radiation therapy (7.1% vs. 44.6%, P=0.012). MM pts had a higher frequency of AlloHCT for trALL (71.4% vs. 42.9%, P=0.075) and were more commonly in complete response (CR) at transplant (100.0% vs. 70.8%, P=0.078). No trALL in MM pts were Ph-like ALL. There was a statistically significant difference in time to development of trALL between previous malignancy locations (P=0.018), with GU/GYN (Median: 9 years) having the longest time and myeloid (Median: 3 years) having the shortest time; MM was 6 years. In the overall pt cohort, 56 (80%) pts achieved a complete response after induction and 60% (n=25) were minimal residual disease (MRD)+ after induction. Pts with MM had a significantly lower likelihood of being MRD+ after induction (OR=0.09, P=0.015). Overall, 24.3% of pts relapsed after induction therapy for trALL; 39 (55.5%) died after trALL diagnosis including 43% (n=35) who died after AlloHCT. Although pts with MM tended to have better outcomes compared to non-MM trALL pts in terms of overall survival after trALL diagnosis (Figure 1A), survival after AlloHCT (Figure 1B), relapse rate after trALL diagnosis and non-relapse mortality after AlloHCT, none of these findings were statistically significant in this small cohort (Figure 1C). Conclusion: trALL is a unique SPM that can occur in pts with MM. It is associated with Ho-Tri cytogenetics and is BCR/ABL1 negative. The majority of MM pts that developed trALL had undergone prior ASCT and were on R maintenance when they developed trALL. Despite being more likely to have adverse cytogenetics features, MM-associated trALL has similar survival compared to non-MM-associated trALL which speaks to the poor survival outcomes of this SPM. Further research on trALL in MM pts is merited. Figure 1 Figure 1. Disclosures Litzow: Amgen: Research Funding; Pluristem: Research Funding; AbbVie: Research Funding; Actinium: Research Funding; Astellas: Research Funding; Omeros: Other: Advisory Board; Jazz: Other: Advisory Board; Biosight: Other: Data monitoring committee. Bergsagel: Janssen: Consultancy. Fonseca: Scientific Advisory Board: Adaptive Biotechnologies: Membership on an entity's Board of Directors or advisory committees; Caris Life Sciences: Membership on an entity's Board of Directors or advisory committees; AbbVie: Consultancy; Patent: Prognosticaton of myeloma via FISH: Patents & Royalties; Bayer: Consultancy; Takeda: Consultancy; Celgene: Consultancy; Amgen: Consultancy; BMS: Consultancy; Mayo Clinic in Arizona: Current Employment; Juno: Consultancy; Kite: Consultancy; Aduro: Consultancy; OncoTracker: Consultancy, Membership on an entity's Board of Directors or advisory committees; GSK: Consultancy; Novartis: Consultancy; Merck: Consultancy; Sanofi: Consultancy; Pharmacyclics: Consultancy; Janssen: Consultancy. Dispenzieri: Takeda: Research Funding; Alnylam: Research Funding; Pfizer: Research Funding; Oncopeptides: Consultancy; Sorrento Therapeutics: Consultancy; Janssen: Consultancy, Research Funding. Murthy: CRISPR Therapeutics: Research Funding. Ailawadhi: GSK: Consultancy, Research Funding; Sanofi: Consultancy; Pharmacyclics: Consultancy, Research Funding; Takeda: Consultancy; Medimmune: Research Funding; Xencor: Research Funding; Genentech: Consultancy; Janssen: Consultancy, Research Funding; Amgen: Consultancy, Research Funding; Cellectar: Research Funding; Beigene: Consultancy; BMS: Consultancy, Research Funding; AbbVie: Consultancy; Karyopharm: Consultancy; Ascentage: Research Funding. Foran: actinium: Research Funding; taiho: Honoraria; bms: Honoraria; servier: Honoraria; pfizer: Honoraria; novartis: Honoraria; OncLive: Honoraria; abbvie: Research Funding; boehringer ingelheim: Research Funding; takeda: Research Funding; trillium: Research Funding; aptose: Research Funding; revolution medicine: Honoraria; syros: Honoraria; sanofi aventis: Honoraria; certara: Honoraria; gamida: Honoraria; kura: Research Funding; h3bioscience: Research Funding; aprea: Research Funding; sellas: Research Funding; stemline: Research Funding.