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The treatment of relapsed/refractory multiple myeloma (MM) has evolved to include several new options. These include new combinations with second generation proteasome inhibitors (PI); second generation immunomodulators, monoclonal antibodies, CAR T cells, bispecific antibodies, selinexor, venetoclax, and many others. Most patients with MM undergo several cycles of remissions and relapse, and therefore need multiple lines of combination therapies. Selecting treatment options for relapsed/refractory MM requires consideration of resistance status to specific classes, and patient-specific factors such as age and other comorbidities should be considered. The NCCN Guidelines for MM provide a framework on which to base decisions regarding workup, treatment, and follow-up of newly diagnosed and previously treated MM. This manuscript outlines the recommendations from NCCN Guidelines for MM specific to relapsed/refractory disease.

PURPOSE: This study aimed to analyze organ system-based causes and non-organ system-based mechanisms of death (COD, MOD) in people with myelomeningocele (MMC), comparing urological to other COD. METHODS: A retrospective review was performed of 16 institutions in Canada/United States of non-random convenience sample of people with MMC (born > = 1972) using non-parametric statistics. RESULTS: Of 293 deaths (89% shunted hydrocephalus), 12% occurred in infancy, 35% in childhood, and 53% in adulthood (documented COD: 74%). For 261 shunted individuals, leading COD were neurological (21%) and pulmonary (17%), and leading MOD were infections (34%, including shunt infections: 4%) and non-infectious shunt malfunctions (14%). For 32 unshunted individuals, leading COD were pulmonary (34%) and cardiovascular (13%), and leading MOD were infections (38%) and non-infectious pulmonary (16%). COD and MOD varied by shunt status and age (p < = 0.04), not ambulation or birthyear (p > = 0.16). Urology-related deaths (urosepsis, renal failure, hematuria, bladder perforation/cancer: 10%) were more likely in females (p = 0.01), independent of age, shunt, or ambulatory status (p > = 0.40). COD/MOD were independent of bladder augmentation (p = >0.11). Unexplained deaths while asleep (4%) were independent of age, shunt status, and epilepsy (p >= 0.47). CONCLUSION: COD varied by shunt status. Leading MOD were infectious. Urology-related deaths (10%) were independent of shunt status; 26% of COD were unknown. Life-long multidisciplinary care and accurate mortality documentation are needed.

Background: First-line therapies for chronic-phase (CP) CML include the tyrosine kinase inhibitors(TKIs) dasatinib, nilotinib, bosutinib or imatinib, while ponatinib and asciminib are indicated only in later lines. There are typically high response rates to first-line therapy in CP-CML, with responses to second- and third-line therapy lower and highly dependent on the specific TKI. Our goal in this study is to evaluate the role of ponatinib in the treatment of patients with CML using data from routine clinical practice. Study design and methods: Data will come from the H. Jean Khoury Cure CML Consortium (HJKC3) CML registry, a North American multi-site research database. Participating sites include the HJKC3 members (curecml.org). Patient inclusion criteria include a diagnosis CML in chronic phase, receiving second line therapy after initial therapy with a second generation TKI (dasatinib, nilotinib, or bosutinib) or receiving third or later line TKI therapy for CML. Participating patients provide consent to collect their data both retrospectively and prospectively for 1 year. Clinical data, including toxicities and complications of TKI therapy, are abstracted from the electronic health record. Participating patients are invited to complete a questionnaire to assess health-related quality of life. Data are managed using REDCap, a secure web application. Study Objectives: The primary objective is to compare the outcome of patients treated with ponatinib vs. other TKIs as second or later line therapy. Secondary objectives are to evaluate patient-reported outcomes (PRO) for CML treated with second or later line therapy and to determine if there are any patient or disease characteristics that can predict outcomes. Statistical plan for clinical outcomes: Descriptive statistics will be used to compare baseline demographic characteristics. Categorical variables will be compared using the Chi-square test. Continuous variables will be compared using ANOVA. Overall survival will be calculated from the time of diagnosis to death from any cause and patients will be censored if they were alive at the last follow-up. Survival probabilities will be computed using the Kaplan-Meier method and compared with the log-ranktest. Cox regression analyses will be used to determine significant factors that influence survival, and hazard ratios with 95% confidence intervals will be presented. Event free survival (EFS) and transformation free survival (TFS) will be calculated for patients. For EFS, loss of complete hematologic remission, loss of major cytogenetic response, progression to accelerated or blast phase, or death from any cause at any time will be considered an event. For TFS, transformation to accelerated or blast phase while on therapy or death on study will be considered an event. Statistical plan for PRO: Different PRO domains will be analyzed separately (for example, depression separate from fatigue, etc.). Descriptive statistics for each PRO measure will be reported and compared to previously published studies. Differences by treatment type will be evaluated.

Background and significance: There are typically high response rates to first line TKI therapies in chronic phase chronic myeloid leukemia (CP-CML). However, responses in later lines (second line and beyond) are diminished and highly dependent on the specific tyrosine kinase inhibitors (TKIs) used. Asciminib received FDA approval in October 2021 for the treatment of adult patients with Philadelphia chromosome-positive (Ph+) CML-CP who were previously treated with two or more TKIs, and in patients with Ph+ CML-CP with the T315I mutation. Our goal in this study is to evaluate the role of asciminib therapy for patients with CML beyond clinical studies, in routine clinical practice. Study Design and methods: Data for this study will be abstracted from the HJKC3 (H. Jean Khoury Cure CML Consortium) CML registry project (HJKC3-0005); the HJKC3 CML Registry is a collaborative academic multi-site registry. Patients will be consented to allow collection of both retrospective and prospective data for the HJKC3 CML Registry. The data forms and surveys will be built and managed through the Research Electronic Data Capture (REDCap), a secure web application managed by the Clinical & Translational Science Institute. Participating sites include members of the HJKC3 (curecml.org). This study will be an opportunity to evaluate asciminib usage in ‘real-world’ academic practices across the United States. This observational study will include cohorts of patients diagnosed with CP-CML previously treated with two or more tyrosine kinase inhibitors and/or receiving asciminib and/or those with a T315I mutation, not currently enrolled in a randomized controlled trial. The HJKC3 CML Registry intends to collect clinical and patient reported outcome (PRO) data. After obtaining patient consent, patients will be invited to complete PRO assessments. The study will attempt to collect PRO data regardless of disease or treatment status. PRO assessments will be primarily administered online, though study staff will offer alternatives (e.g., phone) for participants without internet access. Study subjects will be emailed a link to access a secure platform hosted by one of the study sites, Medical College of Wisconsin (MCW), using the REDCap platform. Study Objectives: The primary objective of the study is to describe characteristics of patients who were previously treated with two or more TKIs and treated with asciminib or with any other TKIs. The secondary objectives are to describe the clinical outcomes and PRO of those patients. Exploratory objectives include description of characteristics, treatment outcome and PRO for patients with T315I mutation. Statistical plan: The sample size of approximately 225 participants during the 5-year recruitment period is based on the available clinical expertise and ongoing recruitment data in the HJKC3 CML registry in lieu of statistical power analysis. We estimate that 150 patients in the HJKC3 CML registry will be treated with other TKI and 75 treated with asciminib. Among a total of 225 patients, we estimate that 75 patients with the T315I mutation will be enrolled in the registry. No formal statistical inference will be drawn to compare the outcomes between these two groups. Bayesian predictive probability (PP) will be utilized for the exploratory analysis of Major Molecular Response (MMR). Survival probabilities will be computed using the Kaplan-Meier method, and the p-values from the log-rank test will be provided. Different PRO domains of the patient's symptoms and functioning will be analyzed separately (e.g., depression separate from fatigue, separate from GI symptoms, etc.) since combining them into a summary score may dilute the effects of the individual (and not necessarily related) components and mask actual change. The main objective for the analyses of PRO data from the registry is to describe any differences by treatment type in patients treated with asciminib and other TKIs.

Despite notable advancements in treating chronic lymphocytic leukemia (CLL) with targeted therapies, a significant clinical challenge arises as a subset of CLL patients progress and develop Richter's transformation (RT), an aggressive form of lymphoma. While immune cell dysfunction and T cell defects are well-known characteristics of CLL, the alterations in immune cell subsets during CLL progression to RT remain largely unexplored. While CLL patients typically fail to respond to anti-PD1 pembrolizumab, a positive response has been observed in 20-40% of RT patients. Previously, our study involving three RT patients (1 responder and 2 progressors) revealed enriched regulatory T cells (Tregs) in the responders compared to progressors after pembrolizumab treatment (Iyer et al, ASH2020). Hence, we propose that dynamic changes in T cell subsets at baseline may be associated with response to pembrolizumab treatment. To this end, we performed single-cell RNA sequencing on sorted T cells (CD3 +) from PBMCs derived from 20 patients (responders (RES), n=5; stable disease (SD), n=5; progressive disease/progressors (PD), n=10). A total of 408,627 cells were captured, and an average of 1,393 genes were detected per cell per patient. Using a graph-based clustering approach (Seurat V4.3), we identified 19 clusters based on marker genes and transcriptional signatures and identified enriched pathways using Enrichr analysis. Our results showed that RES had an increased Th17 subset at baseline compared to PD (15% vs. 5%, p=0.01) and reduced Th2 T cells compared to SD (p=0.004) and PD (p=0.05). The Th17/Th2 ratio was higher in RES at baseline (Th17/Th2 R: 1.24, PD: 0.21, SD: 0.25, p0.05) and post-pembrolizumab (Th17/Th2 RES: 1.31, PD: 0.34, SD: 0.30, p0.05). Furthermore, RES had an increased fraction of proliferating CD4 T cells at baseline (4% RES vs. 0.4% SD and 0.4% PD, p=0.02) and post-treatment (1.9% RES vs. 0.8% PD and 0.34% SD, p=0.02). Although we observed an increasing trend in the fraction of Tregs in the RES at baseline [(7.6% RES vs.6.1% PD and 6.5% SD, p=0.4; ns) and post-treatment (10.3% RES vs. 8.3% PD and 5.8% SD, p= 0.2; ns] compared to PD and SD, the difference was not significant. Similarly, among CD8+ T cells, we observed an increasing trend in CD8+ effector T cell fraction in the responders at baseline (29.8% RES vs. 21% PD and 15.6% SD, p=0.9, ns) and post-treatment (30.1% RES vs. 21.2% PD and 19.9% SD, p=0.44, ns). However, the difference was not statistically significant. These findings highlight the dynamic changes in T cell subsets associated with pembrolizumab response. In terms of transcriptional changes in Tregs following pembrolizumab treatment among RES, PD, and SD, we observed dynamic changes on a few Treg marker genes, including TIGIT and TOX. TIGIT and TOX expression drastically decreased after treatment in RES (p &lt; 0.0001), while expression did not change in PD and SD. There were no significant changes in the expression of FOXP3 or CD27 in RES, PD, and SD before and after the treatment. In CD8 effector T cells, GZMK, GZMA (granzymes), and PRF1 (perforin) are marker genes. We observed higher expression of GZMK and GZMA in RES (p &lt;0.0001) at the baseline compared to SD and PD. In the proliferating CD4 T cells, we observed increased expression of IDH2 (Isocitrate dehydrogenase 2-key enzyme of TCA cycle) in RES at the baseline, as compared to PD and SD, indicating increased TCA cycle activity in the proliferating T cells. Pathway analysis of T cell clusters using the Enrichr database showed TNFA-IFNG-MYC targets are enriched in CD8 + cells, IL2-STAT5 signaling is upregulated in Th17, TREGs, and E2F cell cycle associated pathways enriched in CD4 + proliferation cluster. In conclusion, our study unveils significant baseline differences in CD4 subsets between RES and PD/SD subjects and dynamic changes in T cell subsets and transcriptional signatures. These findings offer potential predictors of pembrolizumab response in RT, such as increased Th17/Th2 ratio, enriched proliferating CD4 + T cells, and elevated TIGIT, TOX gene expression in Tregs, GZMK, and GZMA in CD8 effector cells. This deeper understanding of the immunological landscape in RT patients sheds light on the interplay between T cell subsets and treatment response in RT, paving the way for personalized therapeutic strategies. Further validation and exploration can lead to improved outcomes and tailored interventions for RT.

INTRODUCTION Richter transformation (RT) is the histologic transformation of CLL/SLL into an aggressive lymphoma, most commonly diffuse large B-cell lymphoma (DLBCL). We previously reported an incidence of 0.5% per year of RT in patients with newly diagnosed CLL and an incidence of 1% per year following initiation of therapy (Parikh, BJH, 2013). It is unknown whether these incidences have changed in the novel agent era. METHODS We identified patients with previously untreated CLL/SLL in the Mayo Clinic CLL Database who were seen within 12 months of diagnosis. Only patients with biopsy-proven DLBCL with histopathology confirmation at Mayo Clinic were included. Cumulative incidence methodology was used to display the time to development of RT with death as a competing risk (both from time of initial CLL/SLL diagnosis, and from start of CLL directed therapy). We defined the time period prior to February 2014 (FDA approval of ibrutinib for CLL) as the pre-novel agent era and the time period starting February 2014 as the novel agent era. Using Cox regression analysis, we compared the incidence of RT, both after initial CLL diagnosis and after initiation of CLL therapy, between patients diagnosed with CLL in the pre-novel agent era versus the novel agent era. RESULTS Between 1/2000 and 4/2023, 3347 patients were seen at Mayo Clinic. The median age at diagnosis was 65 years (range 21-97 years), and 66% patients were male. Baseline characteristics of patients at the time of CLL and RT diagnoses between the two treatment eras were similar, including distance in miles of primary residence from Mayo Clinic ( Table 1). The median follow-up for the entire cohort was 5.6 years (9.5 years for the pre-novel agent era cohort and 3.2 years for the novel agent era cohort). A total of 82 patients were diagnosed with RT (69 in the pre-novel agent era and 13 in the novel agent era). Overall, the 1-, 5- and 10-year cumulative incidence rates for RT from the time of CLL/SLL diagnosis were 0.6% (95% CI: 0.4-0.9%), 1.8% (95% CI: 1.3-2.3%), and 3.0% (95% CI: 2.4-3.8%), respectively. Among the patients who received CLL/SLL therapy (n=1382), the 1-, 5-, and 10-year incidence rates for RT from the time of treatment start were 0.3% (95% CI: 0.1-0.8%), 3.3% (95% CI: 2.4-4.5%), and 4.9% (95% CI: 3.7-6.5%), respectively. Among 1981 patients diagnosed with CLL/SLL in the pre-novel agent era, the 1- and 5-year incidence rates for RT from the time of diagnosis were 0.6% (95% CI: 0.4-1.1%) and 2.1% (95% CI: 1.5-2.8%), respectively. Among 1366 patients diagnosed with CLL/SLL in the novel agent era, the 1- and 5-year incidence rates following CLL/SLL diagnosis were 0.5% (95% CI: 0.2-1.1%) and 1.1% (95% CI: 0.6-2.2%), respectively. The RT incidence from time of CLL/SLL diagnosis was not significantly different between the pre-novel agent and novel agent era patients (hazard ratio [HR] 1.6, 95%CI 0.9-3.0, p=0.13). Patients who received CLL/SLL treatment in the pre-novel agent era (n=926) and novel agent era (n=456) had subsequent 1-year incidence rates of RT of 0.3% (95% CI: 0.1-1.0%) and 0.2% (95% CI: 0.0-1.8%), respectively. The 5-year incidence rates for these groups were 3.7% (95% CI: 2.6-5.2%) and 1.9% (95% CI: 0.7-5.0%), respectively. Although an increased risk of RT after CLL/SLL directed therapy was observed in the pre-novel agent era compared to the novel agent era (HR 2.4, 95%CI: 0.9-6.1, p=0.07; Figure 1), this did not cross the threshold of statistial significance. Among patients diagnosed in the pre-novel agent era who developed RT (n=69), CLL treatment prior to RT included: none, n=19 (28%); chemoimmunotherapy only, n=36 (52%); and novel agent, n=14 (20%; includes 11 treated with prior chemoimmunotherapy also). In contrast, among patients diagnosed in the novel agent era who developed RT (n=13), CLL treatment prior to RT included: none, n=8 (62%); and novel agent, n=5 (38%). CONCLUSIONS Patients who received CLL/SLL directed treatment appear to have a lower risk of RT in the novel agent era. This may be driven by the fact that none of the patients who developed RT in the novel agent era received chemoimmunotherapy. Whether novel agent use for CLL/SLL suppresses the development of RT or avoids the risk associated with cytotoxic chemotherapy-induced clonal evolution requires further study. Although limited by length of follow-up, our findings challenge the notion that RT incidence will increase in parallel with CLL patients living longer with novel agent treatments.