Zhou, Shouhao

e19049 Background: Acala and zanu are 2nd generation Bruton tyrosine kinase inhibitors (BTKis) that have demonstrated improved safety profiles vs 1st generation BTKi ibrutinib in clinical trials. While head-to-head comparison of zanu vs acala is lacking, a recent meta-analysis of clinical trials provided a comprehensive, indirect comparison of the AE profiles in B-cell malignancies (Hwang, Hemasphere. 2023;7(S3):1134). This study aims to evaluate costs and impacts on quality of life (QoL) for zanu vs acala using their AE profiles. Methods: A health economic model was developed to determine cost and QoL associated with AE management for zanu and acala. The incidence rates ( IRs) of all grade and grade ≥3 AEs of interest (n=21, including bleeding events, hypertension, atrial fibrillation, cytopenias, infections, headache, arthralgia, diarrhea) were taken from the meta-analysis. Additional model inputs, such as disutility, average duration of AE, and unit cost of each AE were sourced from published literature and publicly available cost databases in consultation with clinical experts. The model was developed from the US perspective, considering Medicare costs, inflated to 2023 USD. Model outcomes were AE management cost (= IR * unit cost) and quality-adjusted life years (QALYs) lost due to AE (= IR * Disutility * Duration/365.25). Results: In the base-case scenario (considering all AEs), treating a hypothetical cohort of 1000 patients (pts) with zanu instead of acala yielded cost savings of $124K and 3.7 QALY gains (i.e., 3.7 years extra in full health). Subgroup analysis for severe (grade ≥3) and non-severe AEs (grade 1-2) demonstrated similar trends. A sensitivity analysis limited to AEs significantly different between zanu and acala (n=13) yielded consistent results. Additionally, a probabilistic sensitivity analysis, conducted with 1000 iterations to account for uncertainty in model parameters, confirmed robustness of the model, indicating stable conclusions across a wide range of parameter uncertainties. Conclusions: This analysis suggested that zanu is cost-saving and associated with added health benefits compared to acala in terms of AE management in pts with B-cell malignancies. Should these results derived from meta-analysis of clinical trial data be applicable to real-world pt populations across different indications, the potential cost savings could be considerable. These results could be further improved if the differences in efficacy of zanu vs acala are also incorporated. [Table: see text]

AbstractMeta‐analysis allows researchers to combine evidence from multiple studies, making it a powerful tool for synthesizing information on the safety profiles of new medical interventions. There is a critical need to identify subgroups at high risk of experiencing treatment‐related toxicities. However, this remains quite challenging from a statistical perspective as there are a variety of clinical risk factors that may be relevant for different types of adverse events, and adverse events of interest may be rare or incompletely reported. We frame this challenge as a variable selection problem and propose a Bayesian hierarchical model which incorporates a horseshoe prior on the interaction terms to identify high‐risk groups. Our proposed model is motivated by a meta‐analysis of adverse events in cancer immunotherapy, and our results uncover key factors driving the risk of specific types of treatment‐related adverse events.

Abstract Mantle cell lymphoma (MCL) is an incurable rare subtype of non-Hodgkin lymphoma and is subject to relapse and therapeutic resistance. Molecular aberrations in MCL affect pathogenesis, prognosis, and therapeutic response. In this systematic review, we searched 3 databases and selected 32 articles that described mutations in MCL patients. We then conducted a meta-analysis using a Bayesian multiregression model to analyze patient-level data in 2127 MCL patients, including prevalence of mutations. In tumor or bone marrow samples taken at diagnosis or baseline, ATM was the most frequently mutated gene (43.5%) followed by TP53 (26.8%), CDKN2A (23.9%), and CCND1 (20.2%). Aberrations were also detected in IGH (38.4%) and MYC (20.8%), primarily through cytogenetic methods. Other common baseline mutations were NSD2 (15.0%), KMT2A (8.9%), S1PR1 (8.6%), and CARD11 (8.5%). Our data also show a change in mutational status from baseline samples to samples at disease progression and present mutations of interest in MCL that should be considered for future analysis. The genes with the highest mutational frequency difference (>5%) are TP53, ATM, KMT2A, MAP3K14, BTK, TRAF2, CHD2, TLR2, ARID2, RIMS2, NOTCH2, TET2, SPEN, NSD2, CARD11, CCND1, SP140, CDKN2A, and S1PR1. These findings provide a summary of the mutational landscape of MCL. The genes with the highest change in mutation frequency should be included in targeted next-generation sequencing panels for future studies. These findings also highlight the need for analysis of serial samples in MCL. Patient-level data of prevalent mutations in MCL provide additional evidence emphasizing molecular variability in advancing precision medicine initiatives in MCL.

Background The objectives of this study were to characterize the risk of death (1) from the primary cancer vs competing cause of death; and (2) from various causes of death vs the general poplation. The relative risk of death after a pediatric cancer diagnosis versus the general population and the risk of death from a primary cancer diagnosis versus competing causes of death. Methods This retrospective, population‐based study used the Surveillance, Epidemiology, and End Results database (1980‐2015) and included patients aged 0 to 19 years at the time of diagnosis. Observed deaths were calculated; the risk of death versus the general population was assessed with standardized mortality ratios (SMRs). Competing risk models for the cause of death were performed. Results There were 58,356 patients who were diagnosed, and the mortality rate was 22.8%. To assess causes of death, 6996 patients who died during the study period were included (45,580 total person‐years at risk): 5128 (73%) died of their primary cancer, and 1868 (27%) died of a competing cause. Among all patients, the rate of death from the index cancer was higher than the rate of death from another cause within the first 5 years after diagnosis. The risk of death from a nonprimary cancer began to supersede the rate of death from the primary cancer 10 years after diagnosis for patients with germ cell tumors, lymphomas, and sarcomas. SMRs for the primary cancer were highest within the first 5 years after diagnosis for all cancers (SMRs, 100‐50,000; P < .0001). The risk of death from competing causes (heart disease, suicide, and sepsis) was elevated (SMR, >100; P < .001). The risk of dying of heart disease was high, especially for patients with astrocytomas (SMR, 47.84; 95% confidence interval [CI], 27.87‐76.59) and neuroblastomas (SMR, 98.59; 95% CI, 47.28‐181.32). The risk of dying of suicide was high in most patients, particularly for those with osteosarcomas (SMR, 111.40; 95% CI, 2.82‐620.69), Hodgkin lymphomas (SMR, 62.35; 95% CI, 34.89‐102.83), and gonadal germ cell tumors (SMR, 28.97; 95% CI, 12.51‐57.09). Conclusions The cause of death for patients with gonadal germ cell tumors, lymphomas, and sarcomas is more commonly a secondary cancer or noncancerous cause than the primary disease; their risk of death from competing causes (heart disease, suicide, and sepsis) rises throughout life.

7556 Background: The frontline treatment for advanced follicular lymphoma has evolved with the introduction of maintenance therapy, bendamustine (Benda), obinutuzumab (G), and lenalidomide (Len). We conducted a network meta-analysis of phase 3 randomized controlled trials (RCTs) to identify the regimens with superior efficacy. Methods: Data were extracted from 7 RCTs (FOLL05, StiL NHL1, BRIGHT, PRIMA, GALLIUM, StiL NHL7, and RELEVANCE). Progression-free survival (PFS) was compared between 11 regimens with different immunochemotherapy and maintenance strategies. To incorporate direct and indirect comparisons, random-effects Bayesian network meta-analyses were conducted after adjusting for study-wise variation. The posterior inference was derived based on Markov chain Monte Carlo methods and implemented using JAGS v4.3.0. Pairwise comparison of hazard ratios (HRs) and 95% credible intervals (CIs) were calculated. Results: PFS HRs of other regimens compared to the reference regimen are summarized in the Table. Compared to Rituximab(R)-Benda, R-CHOP had inferior PFS, R-CHOP-R, G-CHOP-G, and R-Len-R had similar PFS, while R-Benda-R, R-Benda-R4 and G-Benda-G had better PFS. Compared to R-CHOP-R, G-CHOP-G and R-Len-R had similar PFS, while R-Benda-R, R-Benda-R4 and G-Benda-G had better PFS. In addition, the PFS for G-Benda-G was similar to R-Benda-R4 (HR 0.94, 95% CI 0.78-1.09) but better than R-Benda-R (HR 0.82, 95% CI 0.75-0.97). Conclusions: Compared with the commonly used R-Benda and R-CHOP-R regimens, G-CHOP-G, R-Benda-R and R-Benda-R4 had better PFS, while the chemotherapy-free regimen R-Len-R had similar PFS. [Table: see text]

Abstract Aims This observational study characterized cardiovascular disease (CVD) mortality risk for multiple cancer sites, with respect to the following: (i) continuous calendar year, (ii) age at diagnosis, and (iii) follow-up time after diagnosis. Methods and results The Surveillance, Epidemiology, and End Results program was used to compare the US general population to 3 234 256 US cancer survivors (1973–2012). Standardized mortality ratios (SMRs) were calculated using coded cause of death from CVDs (heart disease, hypertension, cerebrovascular disease, atherosclerosis, and aortic aneurysm/dissection). Analyses were adjusted by age, race, and sex. Among 28 cancer types, 1 228 328 patients (38.0%) died from cancer and 365 689 patients (11.3%) died from CVDs. Among CVDs, 76.3% of deaths were due to heart disease. In eight cancer sites, CVD mortality risk surpassed index-cancer mortality risk in at least one calendar year. Cardiovascular disease mortality risk was highest in survivors diagnosed at &lt;35 years of age. Further, CVD mortality risk is highest (SMR 3.93, 95% confidence interval 3.89–3.97) within the first year after cancer diagnosis, and CVD mortality risk remains elevated throughout follow-up compared to the general population. Conclusion The majority of deaths from CVD occur in patients diagnosed with breast, prostate, or bladder cancer. We observed that from the point of cancer diagnosis forward into survivorship cancer patients (all sites) are at elevated risk of dying from CVDs compared to the general US population. In endometrial cancer, the first year after diagnosis poses a very high risk of dying from CVDs, supporting early involvement of cardiologists in such patients.