Citations

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.

While the cure rate of pediatric acute lymphoblastic leukemia (ALL) now exceeds ~90% with contemporary combination chemotherapy, the prognosis for adults with ALL remains significantly inferior with long-term overall survival ranging from 50% to 70%. Recent studies have uncovered marked differences in ALL genomics between children and adults, with some high-risk subtypes becoming more prevalent with age. However, the underlying biology of age-related disparities in ALL is not fully understood, especially with regard to differences in leukemia sensitivity to chemotherapy. To address this knowledge gap, we performed ex vivo drug sensitivity profiling (i.e., pharmacotyping) of 21 anti-leukemia agents on primary B-ALL diagnostic samples from 767 pediatric (age, 0-18 years) and 309 adult (19-84 years) patients. Drug sensitivity was measured as LC 50: the concentration of drug required to kill 50% of the leukemia cells (PMID: 36604538). A total of 7,975 unique LC 50 values were experimentally determined. RNA-seq was used for subtype classification and gene expression analysis. Among 21 drugs, seven showed significant differences in overall LC 50 between children and adults ( P<0.05 after Bonferroni correction): children displayed higher sensitivity to asparaginase, prednisolone, mercaptopurine, daunorubicin, and inotuzumab, while adults showed higher sensitivity to dasatinib and nelarabine. In multivariate models adjusting for 23 ALL molecular subtypes, only mercaptopurine remained significantly associated with age ( P=1.5×10 -5), suggesting that age-related differences in drug sensitivity can be primarily attributed to the variation in ALL subtypes between children and adults. For mercaptopurine, within KMT2A, CRLF2, and DUX4 subtypes, pediatric samples consistently showed a lower LC 50 than adults carrying the same genomic abnormality ( P=0.032, 0.0045, and 0.02, respectively). To explore intra-subtype heterogeneity, we performed unsupervised clustering using gene expression data for each of these three subtypes. Remarkably, within each of these subtypes, we identified two clusters with distinct transcriptomic profiles that were also largely segregated by age group, i.e., an adult-dominated cluster (C-a) and a pediatric-dominated cluster (C-p). In the KMT2A subtype, cases in C-a exhibited an over-representation of the KMT2A:: AFF1 fusion, and resistance to mercaptopurine ( P=0.029), prednisolone ( P=0.0039), vincristine ( P=0.046) and cytarabine ( P=0.0037). Within CRLF2 ALL, cases in C-a were associated with the presence of BCR:: ABL1-like signature and IGH:: CRLF2 rearrangements, and were more resistant to mercaptopurine ( P=0.0073) and prednisolone ( P=0.00036) compared to those in C-p. For DUX4 ALL, C-a was characterized by an under-representation of ERG deletions and resistance to mercaptopurine ( P=0.0056) and prednisolone ( P=0.0031), compared to C-p within DUX4. To explore the clinical relevance of this heterogeneity, we analyzed the in vivo treatment response of KMT2A (N=35), CRLF2 (N=59) and DUX4 (N=118) B-ALL enrolled in six frontline ALL trials. Compared to cases in C-p (usually drug-sensitive), those in C-a (usually drug-resistant) consistently had significantly poorer initial treatment responses as measured by persistent end-of-induction minimal residual disease (≥0.01%) in KMT2A (58% vs 9%; P=0.0063), CRLF2 (74% vs 41%; P=0.030), and DUX4 (66% vs 40%; P=0.0058) ALL. In conclusion, these studies have revealed important new insights into the pharmacogenomic basis of age-related differences in B-ALL treatment response. These results indicate that both inter- and intra-subtype heterogeneity contribute to inferior prognosis in adults with ALL, but also point to therapeutic opportunities to improve their outcomes.