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Background: B-cell maturation antigen (BCMA) directed chimeric antigen receptor (CAR-) T cells, show high response rates in patients with highly refractory and heavily treated myeloma, and have become a standard treatment in this setting. The durability of these responses, however, has been limited with few long term remissions. One approach to potentially improve CAR T outcome is to direct these cells to alternative targets such as CD38 almost universally expressed on malignant plasma cells. In this open-label phase I multi-institutional study we explored the efficacy and safety of CAR2 anti-CD38 A2 CAR-T cells in patients with RRMM. Methods: The SOR-CART-MM-001 study was a multicenter, open-label, “3+3” dose-escalation, phase 1b trial of a single administration of a fully human antibody with lambda light chain generated by transduction of activated T cells with an anti-CD38 A2 CAR expressing retroviral vector. A single dose of CAR2 Anti-CD38 CAR-T cells was administered 2 days post lymphodepletion with single-agent cyclophosphamide 1.5 g/m 2. The primary endpoint was determination of the maximum tolerated dose (MTD) assessed by the occurrence of treatment-emergent dose-limiting toxicities during the 28-day treatment period in the dose-escalation phase of the study. Dosing was divided into three cohorts ranging from 1 × 10 5 cells/kg (Cohort 1) to 1 × 10 7 cells/kg (Cohort 3). Circulating CAR-T cells were measured at different timepoints post infusion. Key eligibility criteria included ≥3 lines of therapy with prior proteasome inhibitor, immunomodulatory IMID drug or daratumumab exposure, measurable and progressive MM per International Myeloma Working Group criteria and ECOG ≤ 2. Results: Between March 2018 and March 2022, 9 patients were enrolled and assigned to each of the three Cohorts. Patient demographics and baseline characteristics are described in Table 1. Hematologic toxicity was the most common treatment emergent adverse event experienced (6/9, 66.7%), all Grade 3 or Grade 4. All cytopenias recovered in 21 days in subjects treated with no more than MTD. Two subjects within Cohort 3 exhibited mild cytokine release syndrome (CRS) (Grade 1 and 2) with one of these two subjects also developing mild neurotoxicity (Grade 2). No CRS or neurotoxicity was observed at either of the lower dose levels. Two serious adverse events (SAEs) were reported: Klebsiella sepsis 60 days after treatment that recovered in 4 days after antibiotics and muscle weakness at 24 days after treatment that received within 1 day without treatment and neither were deemed related to the study medication. The first 2 patients in Cohort 3 met dose limiting toxicity (DLT) criteria after treatment due to grade 3 hematological toxicity and the next lower dose 1 x 10 6 cells/kg (Cohort 2) became the MTD. Pharmacokinetic data showed the percentage of the detectable circulating CAR-T cells in any subject at any time point was low with many showing no detectable cells. 4 deaths were reported after the end of the study period, and none were considered related to the investigated CAR-T cells (3 due to progression and one due to COVID-19). Overall, a total of 3 patients had a partial response (PR) and 3 patients had stable disease (SD) across 3 dose levels. PR constituted the best response in the study and the durations of objective clinical responses were each less than 3 months. These treatment responses are summarized in Table 2. Conclusion: This phase I trial demonstrated that CAR2 anti-CD38 A2 CAR-T cells were generally well tolerated at the Cohort 2 (MTD) dose and demonstrated objective though short responses even in patients progressing after daratumumab and belantamab mafodotin. More clinical studies are needed to optimize the efficacy and tolerability of CAR-T cell therapies for patients with RRMM. This study was completed, and no more patients will be enrolled. DART-RRMM-101 is a phase 1b, open-label, dose-escalation study ongoing to assess allogeneic Anti-CD38 A2 Dimeric Antigen Receptor T Cells in RRMM (ClinicalTrials: NCT05007418).

Background: QOL can be affected by disease burden, side effects of treatment, and psychosocial effects of living with cancer. Although MZL is considered incurable, we previously found that patients diagnosed from 2002-2015 had a good QOL both at baseline (BL) and the first decade after diagnosis, regardless of treatment choice (Bommier et al., submitted). Here, we evaluate whether these findings can be extended to the 2015-2020 treatment era and a more diverse US cohort. Methods: Adults with newly diagnosed MZL from 8 academic centers were prospectively enrolled within 6 months of diagnosis from 2015-2020 in the LEO cohort (NCT02736357). Participants were actively followed for disease progression/relapse, retreatment and death. QOL was measured at BL, and at 1, 2, 3 and 5 years after diagnosis using the Functional Assessment of Cancer Therapy-Lymphoma (FACT-Lym) scale (range 0-164), which consists of the FACT-General (FACT-G, range 0-104) plus a lymphoma-specific scale (range 0-60). Total FACT-G measures 4 QOL domains (range): physical (0-28), social/family (0-28), emotional (0-24), and functional (0-28) well-being (WB). We also used the Trial Outcome Index (TOI; range 0-112), which consists of FACT-G physical and functional WB plus the lymphoma-specific scale and is commonly used as an endpoint in treatment trials. All MZL patients with both BL and at least one follow-up QOL questionnaire were eligible for analysis. Patients were grouped by initial management: active surveillance (observation), local/antibiotic therapy (radiation, surgery, or anti- H. pylori therapy), or systemic therapy (chemotherapy, targeted agents, or antibody therapy). Change in QOL scores over time was analyzed using mixed models for repeated measurements, and initial treatment groups were compared. Results: Of 384 patients, 60% were female, 91% were White, 6.6% were Black/African American, and 13% were Hispanic (all races). The median age was 64 years (range 19-94). For initial management, 28% (N=106) were observed, 31% (N=118) received local/antibiotic therapy, and 42% (N=160) received systemic therapy. Across treatment groups, there were no significant differences by age, gender, race, or performance status, while there were differences by Hispanic ethnicity (p=0.006), Ann Arbor stage (p<0.001) and MALT-IPI score (p<0.001) (Table). At BL, patients receiving systemic therapy had lower physical WB (p=0.008), emotional WB (p=0.04), functional WB (p=0.03), FACT-G total score (p=0.008), lymphoma subscale (p<0.001), FACT-Lym (p=0.001) and TOI (p<0.001) compared to observation and local/antibiotic therapy (Table). While FACT-G total scores were largely stable through 5 years after diagnosis, social/family WB declined from BL for patients initially receiving either local/antibiotic therapy (mean at 1-year -1.4, p<0.05; 2-year -2.1, p<0.05; 3-year -1.6, p<0.05; 5-year-2.9, p<0.05) or systemic therapy (mean at 2-year -1.2 , p<0.05; 3-year -1.4, p<0.05; and 5-year -1.2, p<0.05), while this was stable for observed patients. In patients receiving initial systemic therapy, there was a transient improvement in the lymphoma-specific scale (mean +1.7, p<0.05) and TOI (mean +3.7, p<0.05). Otherwise, QOL remained stable over time and global QoL (FACT-G, FACT-Lym), TOI, Lymphoma-specific scale (Figure), and the other FACT-G subscales (physical, emotional, or functional WB) did not differ by initial treatment strategy. Conclusions: In the diverse LEO cohort from the recent treatment era, we confirm our prior findings for newly diagnosed MZL patients that QOL is globally stable over the first 5 years after diagnosis; emotional WB is not associated with initial treatment type, providing evidence that being initially observed does not negatively impact emotional health; and there is a decline in social/family WB (support from friends and family, family acceptance of illness, family communication, close to partner), which supports the need to consider interventions for patients to address these unmet needs. In addition, this study extends these findings to the lymphoma-specific scale, FACT-Lym, and TOI, which all showing largely stable trends in the 5 years after diagnosis and suggest that recent MZL treatment strategies, including systemic therapies, neither impair nor improve QOL.

Introduction: Next-generation sequencing (NGS) represents a promising modality for measurable residual disease (MRD) testing in patients with acute myeloid leukemia (AML), but systematic efforts are required to define the appropriate targets, test characteristics, and clinical implications. We recently reported that detection of persistent NPM1 or FLT3 internal tandem duplication (ITD) mutations in adults with AML in first complete remission (CR1) prior to allogeneic hematopoietic cell transplantation (alloHCT) is associated with increased relapse and death compared with those testing negative (JAMA 2023, PMID: 36881031). FLT3 tyrosine kinase domain (TKD) variants are present in 7-10% of adult patients with AML but the utility of this target for AML MRD testing in CR1 prior to alloHCT was previously unknown. Methods: Patients aged 18 or older who underwent alloHCT for AML in CR1 between 2013 and 2019 reported to have a FLT3-TKD variant at diagnosis and a pre-conditioning remission blood sample available in the CIBMTR biorepository were eligible for this study. Single-amplicon NGS (SA-NGS) library generation utilizing UMI-tagged primers targeting the D835 and I836 codons of FLT3 was performed on 400ng genomic DNA sequenced with unique dual indices, with error-corrected variant calling. Primary outcomes were overall survival (OS) and cumulative incidence of relapse (CIR). Secondary outcomes were relapse-free survival (RFS) and non-relapse mortality (NRM), with NRM being considered as a competing risk. Results: A total of351 patients met inclusion criteria for this study, and 342 (97.4%) had sufficient DNA for SA-NGS and were analyzed for clinical outcomes. Of these patients, 81 (23.7%) experienced relapse at a median of 5 months post-alloHCT. Using the current ELN-recommended variant allele fraction (VAF) threshold of 0.1% for NGS-based AML MRD, 14 patients (4.1%) tested positive. Patients with FLT3-TKD MRD VAF≥0.1% experienced higher rates of relapse (73.8% vs. 20.5%, p < 0.0001) and decreased OS (11.4% vs. 66.8%, p < 0.001) compared to VAF<0.1%. The highly sensitive SA-NGS method allowed for exploration to determine if a deeper VAF threshold of 0.01% (previously validated for NPM1 and FLT3-ITD in the Pre-MEASURE study) was prognostic for FLT3-TKD as an MRD target. This led to an additional 20 patients being reclassified as positive (n = 34 with VAF above 0.01%, =9.9%). However, there was no significant difference in CIR (33.6% vs. 19.7%, p = 0.089) or OS (65.2% vs. 66.9%, p = 0.267) between patients with 0.01%≤VAF<0.1% and VAF<0.01% ( Figure). Out of 28 SA-NGS positive calls, 6 could not be validated by orthogonal digital droplet PCR testing, and interestingly, none of those 6 patients experienced relapse. Site-reported pre-transplant remission multiparametric flow cytometry (MFC) was available for 335 patients (98%). Results of MFC were not associated with differences in CIR (HR 1.2, 95% CI 0.52 - 2.76, p = 0.67) or OS (HR 0.78, 95% CI 0.34 - 1.77, p = 0.551).In multivariable analysis, FLT3-TKD NGS-MRD VAF≥0.1% remained prognostic for CIR (HR 6.1, 95% CI 3.1 - 12.0, p < 0.001) and OS (HR 3.2, 95% CI 1.7 - 5.9, p < 0.001). Conclusions: Detectable persistence of FLT3-TKD variants in blood from AML patients in first remission prior to first alloHCT is rare, but at a VAF level of 0.1% was strongly associated with increased relapse and death after transplant compared to those testing negative. In contrast to FLT3-ITD NGS-MRD, no evidence was found to support lowering the NGS-MRD VAF threshold from 0.1% to 0.01% for FLT3-TKD. Alternative methodology may further improve NGS-MRD test performance. Site-reported MFC prior to alloHCT did not stratify for post-transplant relapse or survival. Optimal NGS-MRD testing for patients with AML will likely require a multi-target approach and testing for persistence of FLT3-TKD MRD is shown here to represent an important component of this strategy.

Introduction: Detection of measurable residual disease (MRD) prior to allogeneic hematopoietic cell transplant (alloHCT) in patients with acute myeloid leukemia (AML) in remission is associated with increased relapse and inferior survival after transplant. We recently reported that next-generation sequencing (NGS) detection of mutated NPM1 or FLT3-ITD in first complete remission (CR1) blood prior to alloHCT is strongly associated with increased relapse and death (PMID: 36881031). However, the prognostic implications of detecting persistence of other common AML-associated mutations at this treatment landmark remain incompletely defined. Around 20% of patients diagnosed with AML have variants in isocitrate dehydrogenase ( IDH) genes (~7% for IDH1) and clinical outcomes are known to differ due to co-occurrence of other mutations, IDH subtypes, treatment strategies, and patient factors. Studies have examined the relationship between residual IDH1 variants ( IDH1m) in CR and subsequent clinical outcomes, but definitive standardized evidence to inform decision-making for the clinical utility of IDH1m as a target for AML MRD testing was not previously available. Methods: Adult patients undergoing alloHCT for IDH1 mutated AML in CR1 at CIBMTR sites in the USA between 2013-2019 were eligible for the study if remission blood sample collected within 100 days before alloHCT and outcome data were available. Ultrasensitive error-corrected NGS (NGS-MRD) targeting IDH1, NPM1, and FLT3 genes was performed on DNA from pre-conditioning blood to identify residual variants. Detected IDH1m were validated by digital droplet PCR (ddPCR). The day of transplant was considered as day 0. Median follow-up time was calculated for censored patients. Overall survival (OS) was estimated using Kaplan-Meier (log-rank tests) and Cox proportional hazards models (forward selection); cumulative incidence of relapse was examined using Fine-Gray regression models with non-relapse mortality as a competing risk. Results: A total of 148 patients were included in the study, with a median follow-up time of 24 months. Relapse was observed in 37 (25%) patients, with the majority occurring within 1 year after alloHCT (n = 28, 76%). Pre-transplant flow cytometry results were reported by the transplant centers for 97% of patients (n = 144), but no statistically significant differences in clinical outcomes were observed between the positive and negative groups (OS: p = 0.3; relapse: p = 0.07). 53 patients (36%) tested positive for IDH1m persistence in CR1, with a 100% validation rate by ddPCR for variants with an orthogonal assay available. Logistic regression indicated that IDH1m persistence in remission was more likely to occur in patients older than 40 years (OR: 1.02 with every 1-year increase after 40, p = 0.04). Clinical outcomes showed no statistically significant difference between the pre-alloHCT IDH1m positive and negative groups (Figure 1A), and this remained true after further subgroup analysis by age. The variant allele fraction (VAF) of the residual IDH1m ranged from 0.09% to 48.5% (median: 1.5%), and no differences in clinical outcomes were observed when stratifying by high (≥2.5%) or low VAF (<2.5%). Among patients with co-mutated NPM1 and/or FLT3-ITD at baseline (n=69), those with residual mutated NPM1 and/or FLT3-ITD in pre-transplant remission had significantly higher rates of relapse compared to those with residual IDH1m only or NGS-negative groups (p = 0.01, Figure 1B). The cohort excluding patients with co-mutated NPM1 and/or FLT3-ITD did not show any significant clinical outcome differences based on IDH1m persistence. Conditioning intensity did not show an association with clinical outcomes, and multivariable analysis for overall survival and relapse did not identify residual IDH1m as an important marker. Conclusion: Detection of persistent IDH1 variants in the blood of adult patients with AML in CR1 prior to first alloHCT is common, but not associated with increased relapse or death after transplant compared with those testing negative. For those patients with IDH1 mutated AML co-mutated with either NPM1 and/or FLT3-ITD, detection of persistent NPM1 and/or FLT3-ITD was associated with higher rates of relapse. These findings, from the largest study to date, do not support the detection of isolated IDH1 variants in CR1 blood prior to alloHCT as evidence of AML MRD or increased post-transplant relapse risk.

Introduction: In many cases, treatment decisions for multiple myeloma patients must be made in the absence of high quality randomized controlled clinical trials. As a result, many clinicians lean on prognostic models to drive treatment selection. The most widely used of these models incorporate a limited number of patient related factors that reflect tumor burden, cytogenetic features, or gene expression profiling. These prediction scores are derived from regression models that incorporate these variables at the time of diagnosis. They do not account for variables that change dynamically over time, do not explore the relationship between treatment selection and progression, and imperfectly predict survival outcomes. We designed a prognostic model based on an ensemble machine learning platform to predict progression in NDMM using Extreme Gradient Boosting Machine (XGBoost) combined with accelerated failure time modeling for survival analysis. Methods: We utilized a large retrospective data set containing treatment and response information for 1127 patients with newly diagnosed multiple myeloma (NDMM) treated at the Cleveland Clinic Foundation between 2000 and 2023. Following data preprocessing based on data completeness, 953 patient records were included in our training, testing, and cross validation datasets. For the purposes of our analysis, the initial dataset was randomly split into training (70%), testing (15%) and validation (15%) subsets at the patient level. We also defined the lower and upper bounds for each subset, which is critical due to the right-censored nature of survival data. Hyperparameters for XGBoost were optimized using Bayesian search, minimizing the negative log-likelihood. A random forest method was applied for the imputation of missing data (MissForest). We then trained the XGBoost model using GPU acceleration for enhanced computational efficiency. Finally, a log-rank test on predicted survival times was used to test the model's performance on patients with and without known progression. Results: Our preprocessed data set had 953 patients with a mean age at disease onset of 65 years and a slight male predominance (55%). Approximately 27% of patients harbored high cytogenetic risk disease and 3.5% of the cohort presented with extramedullary disease a diagnosis. At a median follow up of 35 months, 47% of patients had experienced disease progression or death. Induction therapy included an immunomodulatory drug in 40% of patients and proteasome inhibitors in 22%. Frontline autologous stem cell transplantation followed induction in 28% of patients. Median progression free survival in the overall data set was 44 months. These features were consistent across the randomly assigned training, testing, and validation cohorts. Following data preprocessing, 34 independent clinical and genomic variables including selection of first line treatment were assessed as model inputs each available record. Under optimized parameters, the model was trained with a maximum tree depth = 7 and a learning rate ~ 0.22 for the desired output of progression free survival (PFS). We then queried PFS for both the training and validation sets and divided patients into those known to have progressed and those without a progression event. Survival analysis was undertaken using predicted PFS values for patients known to have progressed and those who remained progression free. For the validation data set, our model successfully discerned between progressed and non-progressed cases (log-rank test statistic = 22.07, p < 0.005) (Figure 1). Discussion: We present a machine learning approach based on regularized gradient boosting that accurately discerns between patients who experience progression and those who remain progression free at a median ~35 months of follow-up in a large retrospective data set in patients presenting with NDMM. Further elaboration of our model will allow for the incorporation of large amounts data to predict survival outcomes on the basis of dynamic variables such as depth of response and treatment selection. With the ability to parse outcomes among multiple myeloma patients at high resolution, future clinicians and clinical trialists may be able to overcome limitations in trial design and patient-level therapy selection for both newly diagnosed and relapsed patients.

Background: Understanding the symptoms and quality of life (QOL) over time of adults with follicular lymphoma (FL) is important for treatment decision-making and clinical management. However, there are limited population-level data on the long-term QOL of adults with FL. We aim to describe the real-world, long-term QOL of adults with newly diagnosed FL up to 5/6 years after diagnosis. Methods: We used the Mayo Clinic/Iowa Molecular Epidemiology Resource (MER) and the multi-institutional Lymphoma Epidemiology of Outcomes (LEO) to identify adults with grade 1-3A FL who completed QOL surveys at baseline and follow-up. Participants with lymphoma were prospectively enrolled within 9 months of diagnosis in the MER cohort from 2002-2015 and within 6 months of diagnosis in the multi-institutional LEO cohort study across 8 cancer centers from 2015-2020. The LEO/MER cohort studies systematically collected information on disease status, QOL, health behaviors, and functional assessment. Treatments, disease relapses, and deaths were verified by medical record review. Treating physicians determined clinical management. QOL was measured using the Functional Assessment of Cancer Therapy-General (FACT-G) at years 1, 2, 3, and 5/6 post-diagnosis. A higher FACT-G total score (range 0-108) indicated better QOL across 4 subscales (range): physical (0-28), social/family (0-28), emotional (0-24), and functional (0-28) well-being. We categorized participants based on their frontline management at time of diagnosis: “observation” (surveillance), “treatment” (systemic treatment with immunotherapy +/- chemotherapy), and “local” (radiation) groups. We employed a generalized linear mixed model to evaluate and compare the changes in QOL scores (as a continuous variable) from baseline for the observation, treatment, and local groups, adjusting for sex, race/ethnicity, age, FLIPI risk, and cohort (i.e., LEO or MER). We also evaluated QOL changes by FLIPI risk. Results: Our study included 1,544 participants with FL and QOL data. At the time of enrollment, median age was 61 years (range 19-91), 88% were non-Hispanic White, and 49% were female. Based on initial management, 529 (34%) were in the observation group, 880 (57%) were in the treatment group, and 135 (9%) were in the local group. FLIPI risk was high for 17% in the observation group, 29.3% in the treatment group, and 3% in the local group. The mean (standard deviation) baseline FACT-G total score was lowest in the treatment group with the following baseline scores: 86 (13) for the observation group, 83 (14) for the treatment group, and 88 (12) for the local group (Table 1). The observation and local groups reported a greater worsening of their baseline FACT-G total score vs the treatment group at both the 2-year (-1.2, -0.8 vs +1.8, respectively) and 5/6 year (-5.4, -6 vs. -1.6, respectively) timepoints (Figure 1). This appeared to be driven by worsened physical and functional well-being in the observation (-1.3, -2.2, respectively) and local (-0.9, -2.4, respectively) groups vs the treatment group (0, -0.3, respectively). These differences were statistically significant between the observation vs treatment groups. There were no statistically significant differences in the other subscales, i.e., social/family and emotional well-being, by frontline management. Social/family well-being decreased across all groups by 5/6 years (-2.3 in observation, -2.2 in treatment, and -3 in local, p<0.05). Regardless of FLIPI risk, the total FACT-G scores decreased within each FLIPI risk group at 5/6 years (-4.4 for low, -3.8 for intermediate, and -4.9 for high, p<0.05). This was primarily due to worsened social/family well-being (-2.4 for low, -2.2 for intermediate, and -3 for high, p<0.05) at 5/6 years. There was no statistically significant difference in QOL between FLIPI risk groups. Conclusion: This is one of the first and largest studies with real-world longitudinal QOL data for FL. Our study suggests that frontline systemic treatment initially improved QOL and resulted in a lower degree of QOL decline over time. Social/family well-being decreased over time regardless of frontline management or FLIPI risk. Further research is warranted to explore the impact of timing and specific systemic treatments on QOL, incorporate the patient/caregiver experience to address social/family well-being, and identify the clinical significance of our findings.