Thol, Felicitas

PURPOSE Chronic myelomonocytic leukemia (CMML) is a rare myeloid neoplasm characterized by clinical heterogeneity and is associated with poor outcomes. To date, limited molecular information has been incorporated into disease classification and risk stratification. We aimed to integrate genomic features into the clinical decision-making process for CMML. PATIENTS AND METHODS We analyzed a retrospective cohort of 3013 patients with CMML (training set) and a prospective population of 516 patients (validation set). Using an innovative framework for multimodal data analysis, we developed molecular-based disease taxonomy and prognostication. RESULTS Unsupervised clustering identified nine entities with distinct genomic features and outcomes ( P < .001), including splicing machinery, transcription factors, signal transduction and tyrosine kinase pathways aberrations, and high-risk molecular signatures. Notably, 15% of patients showed molecular/clinical overlap with other myeloid neoplasms. We integrated molecular and clinical information to build the international CMML Prognostic Scoring System (iCPSS), incorporating mutations in nine genes together with hematologic parameters and cytogenetic abnormalities. The iCPSS identified five groups with distinct probability of overall and leukemia-free survival in both training and validation cohorts ( P < .001), outperforming existing prognostic models. Importantly, 55% of patients were reassigned to higher or lower risk groups by the iCPSS. Decision analysis demonstrated that iCPSS could refine the optimal timing of allogeneic transplantation at the individual level; compared with conventional prognostic tools, iCPSS-based decision modeling changed transplantation strategy in 31% of cases, resulting in a significant gain-in-life expectancy for eligible patient population ( P < .001). A federated learning platform was implemented to enable continuous, privacy-preserving model update across multiple centers. CONCLUSION Molecular information improves CMML classification and prognostication, supports more effective clinical decision making, and potentially refines the design of clinical trials.

Background. Study of myeloid neoplasms (MN) has been rapidly transformed by genome characterization. Gene mutations have been reported to influence disease phenotype and risk of disease progression, and the evaluation of mutational status can provide valuable insights to improve the decision-making process. In this international study, we evaluated the clinical impact of mutational screening in patients with CMML, which is characterized by significant clinical and genomic heterogeneity, including a substantial proportion of patients with poor outcomes and unmet needs. Currently, only limited molecular information has been incorporated into CMML classification and prognostication. Methods. We retrospectively collected 3182 CMML patients. We used MOSAIC, an AI-based framework for multimodal analysis in rare cancers (PMID: 38875514) to develop innovative, molecular-based tools for classification and prognostication. An independent validation of the results on 516 prospectively collected patients was performed. Results. Bayesian Networs and Hierarchical Dirichlet Processes were used to identify genomic associations and define a CMML molecular classification. We identified 11 clusters with distinct clinical and genomic features, including splicing machinery, signal transduction and tyrosine kinase pathways aberrations, and high-risk molecular signatures (i.e., AML-like and TP53 mutations). Up to 15% of patients showed clear genomic overlap with other MN. Importantly, extensive multi-color flow cytometry on T lymphocytes, NK and myeloid cells (BD FACSymphony A5 Cell Analyzer) revealed specific immunologic and inflammation signatures associated with molecular subgroups. We then integrated molecular and clinical information to build an international CMML Prognostic Scoring System (iCPSS). We assessed the performance of different statistical and AI-based methods; L1-penalized Cox Model resulted as the best performing method. Selected features included hematological parameters (WBC, Hb, PLT and marrow blasts), cytogenetic abnormalities, and mutations in 10 genes (ASXL1, DNMT3A, EZH2, NRAS, RUNX1, SETBP1, STAG2, TET2, TP53, U2AF1). By Bayesian thresholds optimization, we identified 5 risk classes (i.e., very-low, low, intermediate, high and very-high) with median overall survival (OS) ranging from 99 to 9 months (P&lt;.001), and median leukemia-free survival (LFS) ranging from 210 to 18 months (P&lt;.001). iCPSS provided better patient discrimination across all clinical endpoints compared to currently available prognostic tools, with a Concordance Index (CI) for OS of 0.75 versus 0.62-0.64, respectively. Notably, compared to available scoring systems, up to 40% of patients were reassigned to higher or lower risk classes by the iCPSS. External validation on an independent prospective cohort confirmed that the performance of iCPSS is superior to existing prognostic models (CI for OS 0.71 vs 0.54-0.61, respectively) To demonstrate the clinical utility of iCPSS, we focused on 753 patients who underwent allogeneic stem cell transplantation (HSCT). In multivariable analysis, iCPSS stratified the probability of OS post-HSCT (P&lt;.001) and identified groups of patients with different probabilities of disease relapse, ranging from 9% to 62% (P&lt;.001). We employed a clinical- and genomic-based decision support system (PMID: 38723212) to determine the optimal timing of HSCT. Preliminary analyses indicated that higher-risk patients according to iCPSS (i.e., intermediate, high, and very high) benefit from an immediate HSCT strategy. Lower-risk patients (i.e., very low and low) had life expectancy maximized with delayed HSCT. Modeling decision analysis using iCPSS compared to conventional scores, resulted in a change in transplantation policy for a significant proportion of patients (up to 25%). Conclusion. Molecular information significantly improves the classification of CMML patients, providing a basis for refining diagnostic boundaries with other MN and for a more rational inclusion of patients in clinical trials. The iCPSS demonstrated superior performance over currently available scoring systems and improved HSCT decision making process.

Background: Heterozygous somatic mutations in the genes encoding RNA splicing factors SF3B1, U2AF1, SRSF2 or ZRSR2 induce aberrant splicing in cancer cells and are among the most common mutations in patients with MDS, AML or CMML. H3B-8800 is an orally available small molecule that binds to the SF3b complex and induces alternative splicing changes in cells. Because splicing factor mutant cells depend on residual wild-type function of splicing factors for survival, we hypothesized that H3B-8800 would induce preferential cell killing of mutant cells by further perturbing splicing to synthetic lethality. In pre-clinical models, H3B-8800 preferentially kills spliceosome mutant cells and induces antitumor activity in xenograft leukemia models with core spliceosome mutations (Seiler M et al Nature Medicine 2018). Therefore, we conducted a phase I clinical trial (NCT02841540) of H3B-8800 in patients with MDS, AML or CMML. Here we describe the safety profile and clinical outcomes of the dose escalation cohorts in this phase I trial. Methods: This phase I trial explored the safety, pharmacokinetics (PK), and pharmacodynamics (PD) of H3B-8800 in patients with myeloid cancers. Dose escalation cohorts, employing a standard 3+3 design, examined 2 different once daily dosing regimens (schedule I: 5 days on/9 days off; schedule II: 21 days on/7 days off) in a 28-day cycle, with stratification based on lower-risk (LR) versus higher-risk (HR) myeloid neoplasms. Results: As of June 16th, 2019, 84 patients were enrolled at 24 centers in the US and Europe. Dose ranged from 1-40 mg among 65 patients on schedule I, while 19 patients were enrolled with dose ranging from 7-20 mg on schedule II. The patient population included AML (n=38), CMML (n=4), HR-MDS (n=20), LR-MDS (n=21) and 1 MDS with unknown risk level. Most patients (88%) had spliceosome mutations of interest. The most common mutations were in SRSF2 (p.P95H in 17, p.P95L in 9, p.P95_R102Del in 4), SF3B1 (p.K700E in 11, p.R625C in 4), and U2AF1 (p.Q157P in 6, p.S34F in 4). Patients remained on treatment from 7 to 819 days; 25 patients (30%) had time on treatment greater than 180 days, 20% more than 1 year and 2% over 2 years. The median therapy duration for LR-CMML/MDS, HR-CMML/MDS, and AML patients were 216, 62, and 47 days respectively. Most observed treatment related treatment-emergent adverse events (TEAEs) were Grade 1 or 2. The most common treatment-related (as judged by the investigator, &gt;10% frequency) TEAEs in the patients treated on schedule I were diarrhea (75%), nausea (37%), fatigue (28%) and vomiting (27%). The most common treatment-related TEAEs in the patients treated on schedule II were diarrhea (68%), vomiting (42%), QTc prolongation (21%), nausea (16%), and fatigue (16%). The most common dose limiting toxicity was prolongation of the QTcF interval &gt;500 msec (n=2, 40 mg on schedule I and n=1, 20 mg on schedule II, all ≥Grade 3) and bradycardia without other arrhythmias (n=1, 14 mg on schedule II, ≥Grade 3). No ophthalmic AEs were observed; 1 patient (LR-MDS) experienced durable marrow aplasia. The maximum tolerated dose (MTD) has not been confirmed for either Schedule I or Schedule II. PK analysis indicates that H3B-8800 is rapidly absorbed and exhibits dose-proportional increase in plasma exposure. Consistent dose-dependent target engagement (i.e., alteration in mature mRNA transcripts) was observed in blood mononuclear cells from patients enrolled in the 2 mg up to 40 mg dose cohorts on both schedules. Despite this splicing modulation, no objective complete responses (CR) or partial responses (PR) meeting International Working Group criteria were observed. One patient with CMML had a durable platelet response that began in Cycle 1 and persisted through Cycle 13. Nine red blood cell (RBC) transfusion-dependent patients with MDS or CMML and 2 patients with AML did not require RBC transfusions for ≥8 weeks (up to 28 weeks) while on study. One platelet transfusion-dependent patient with LR-MDS did not require platelet transfusions for ≥8 weeks. Conclusion: Results from this first-in-human multicenter prospective clinical trial of a splicing modulator in myeloid neoplasms demonstrate dose-dependent target engagement and predictable PK profile of H3B-8800, and safety even with prolonged dosing. Although no objective CR or PR were achieved, decreased RBC or platelet transfusion requirements were observed in 12 (14%) of enrolled patients. Disclosures Steensma: H3 Biosciences: Other: Research funding to institution, not investigator.; Pfizer: Consultancy; Aprea: Research Funding; Stemline: Consultancy; Arrowhead: Equity Ownership; Summer Road: Consultancy; Astex: Consultancy; Onconova: Consultancy. Wermke:Novartis: Honoraria, Research Funding. Greenberg:Notable Labs: Research Funding; Celgene: Research Funding; Genentech: Research Funding; H3 Biotech: Research Funding; Aprea: Research Funding; Novartis: Membership on an entity's Board of Directors or advisory committees. Font:Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Pfizer: Membership on an entity's Board of Directors or advisory committees; Novartis: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Komrokji:Agios: Consultancy; JAZZ: Speakers Bureau; DSI: Consultancy; JAZZ: Consultancy; Incyte: Consultancy; Novartis: Speakers Bureau; celgene: Consultancy; pfizer: Consultancy. Yang:Agios: Consultancy; AstraZeneca: Research Funding. Brunner:Astra Zeneca: Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Forty Seven Inc: Membership on an entity's Board of Directors or advisory committees; Jazz Pharma: Membership on an entity's Board of Directors or advisory committees; Novartis: Research Funding. Ades:Agios: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees; Jazz: Membership on an entity's Board of Directors or advisory committees; Takeda: Membership on an entity's Board of Directors or advisory committees; Novartis: Membership on an entity's Board of Directors or advisory committees; Silence Therapeutics: Membership on an entity's Board of Directors or advisory committees; Abbvie: Membership on an entity's Board of Directors or advisory committees; Astellas: Membership on an entity's Board of Directors or advisory committees; Amgen: Research Funding; Helsinn Healthcare: Membership on an entity's Board of Directors or advisory committees. Al-Kali:Astex Pharmaceuticals, Inc.: Research Funding. Coombs:H3 Biomedicine: Research Funding. Foran:Agios: Honoraria, Research Funding. Garcia-Manero:Helsinn: Research Funding; Novartis: Research Funding; AbbVie: Research Funding; Celgene: Consultancy, Research Funding; Amphivena: Consultancy, Research Funding; Astex: Consultancy, Research Funding; Onconova: Research Funding; H3 Biomedicine: Research Funding; Merck: Research Funding. Micol:AbbVie: Consultancy; Jazz Pharmaceuticals: Consultancy. Perez De Oteyza:Celgene: Speakers Bureau. Wang:Abbvie: Other: Advisory role; Kite: Other: Advisory role; Jazz: Other: Advisory role; Astellas: Other: Advisory role, Speakers Bureau; celyad: Other: Advisory role; Pfizer: Other: Advisory role, Speakers Bureau; Stemline: Other: Advisory role, Speakers Bureau; Daiichi: Other: Advisory role; Amgen: Other: Advisory role; Agios: Other: Advisory role. Watts:Pfizer: Membership on an entity's Board of Directors or advisory committees; Jazz Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Takeda: Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees. Buonamici:H3 Biomedicine: Employment. Kim:H3 Biomedicine: Employment. Gourineni:H3 Biomedicine: Employment. Marino:H3 Biomedicine: Employment. Rioux:H3 Biomedicine: Employment. Schindler:H3 Biomedicine: Employment. Smith:H3 Biomedicine: Employment. Yao:H3 Biomedicine: Employment. Yuan:Eisai: Employment. Yu:H3 Biomedicine: Employment. Platzbecker:Abbvie: Consultancy, Honoraria; Novartis: Consultancy, Honoraria, Research Funding; Celgene: Consultancy, Honoraria, Research Funding. OffLabel Disclosure: H3B-8800 (experimental, unapproved)