Patnaik, Mrinal M.

Abstract The importance of bone marrow (BM) microenvironment and its immune interactions within and their role in chronic myelomonocytic leukemia (CMML) disease progression have not been well studied. Our previous work in CMML identified type 1 helper T cell (Th1) and CD4 terminal effector cell suppression in peripheral blood from untreated patients, in comparison to age-matched normal controls (Mangaonkar A et al. Blood Adv 2020). Furthermore, clonal dendritic cell (DC) aggregates in CMML are present in approximately 20-30% patients at diagnosis, display significant cell heterogeneity (plasmacytoid DCs + myeloid DCs and other myeloid/immune cells), express indoleamine 2,3-dioxygenase-1 (IDO), associate with T regulatory cell (Treg) expansion, and transformation to AML [blast transformation or BT, Lucas N et al. Leukemia 2019, Mangaonkar A et al. Blood Adv 2020, and Mangaonkar A et al. Blood 2023 (abstract)]. The purpose of this study was to define the myeloid-immune cell interactions within the BM microenvironment critical for CMML progression to AML. To phenotypically identify cell types and their interactions within the CMML BM, we used single-cell type spatial omics through two distinct modalities on archival formalin-fixed paraffin-embedded (FFPE) BM biopsy specimens. Ten serial CMML samples at chronic phase [CP, n=5, median BM blast% 5 (2-15)] and at BT [n=5, median BM blast% 60 (25-74)] were used for both techniques. Spatial transcriptomics was conducted through GeoMx Digital Spatial Profiler (DSP) platform that uses UV-photocleavable, spatial-barcoded oligo probes to profile the whole transcriptome in defined regions of interest (ROI). CD123 (marker for DC-myeloid aggregates) immunofluorescence antibody was used to identify and segment ROI. 16-color flow cytometry was done to validate cell-specific expression. Spatial cellular phenotyping of immune cells on the same samples (serial cuts) was done through multiplex imaging by co-detection by indexing (CODEX) using 27 markers: CD31, Ki67, CD11b, CD20, CD44, Ecad, CD123, CD107a, CD8, CD11c, CD45RO, FoxP3, CD14, PDL1, CD56, CD68, CD163, PD-1, CD38, HLA-DR, CD4, IDO, CD45, PanCK, Na/K/ATPase, CD3e, and DAPI. CD8T+ cell anergy was demonstrated through CFSE cell proliferation assay. Benjamini-Hochberg correction was used to adjust for multiple hypotheses. After filtering poor quality ROIs [ROIs with total read count < 1000 were removed, ROIs with trim, stitch, alignment < 80% or saturation < 50% were removed], 3rd-quartile normalization [BiocGenerics (v. 0.48.1)] and batch correction [limma (v. 3.58.1)], we compared transcriptomics signatures between CD123+ and CD123- ROIs in all samples. Results showed that LIPA, SLC4A1, HBA2, TIMD4 and HMOX1 werethe top five genes upregulated in CD123+ areas. Among all gene transcripts, TIMD4 expression increased the most (Log2FC=0.6, P-value=8.79E-10, FC=1.52, FDR=1.68E-07) when CD123+ ROIs were compared between CMML-CP and CMML-BT serial samples, suggesting a biological relevance in disease progression. Previous work in metastatic peritoneal cancer has established that TIM-4 expressing macrophages suppress Th1/Th2 ratios and impair CD8+ T cell function (Chow A et al. Cancer Cell 2021). To confirm expression at the protein level, we used a multicolor flow cytometry panel and confirmed that TIM4 is expressed on macrophages (Lineage-negative, HLA-DR+, CD68+ cells) in independent CMML BM aspirate samples (n=3). Next, we conducted spatial phenotyping using CODEX on serial cuts from the same serial samples used for spatial transcriptomics. Anatomical co-localization of macrophages with leukemic cells, monocytes and T cells was visually inspected and confirmed. Cell-interaction analysis (after Benjamini-Hochberg correction) indicated that macrophages-monocyte, macrophage-leukemic cells, and macrophage-memory CD4+T cells were significantly (P<0.05) increased in CMML-BT versus CMML-CP. CD8T+ cell anergy was confirmed through a CFSE cell proliferation assay in untreated CMML PBMC samples. In conclusion, our data suggests that TIM4-expressing macrophages and their interactions with monocytes/monocyte precursors and immune cells within a DC aggregate create a protective leukemic stem cell niche and T cell tolerance, promoting blast transformation in CMML.

Abstract Background The expansion of hematopoietic stem and progenitor cells (HSPCs) in the bone marrow is universally associated with adverse outcomes and disease progression in all chronic myeloid neoplasms, to include chronic myelomonocytic leukemia (CMML). However, HSPCs are a heterogeneous cellular compartment and the impact of its composition on clinical outcomes remains poorly defined. Single cell RNA-sequencing (scRNA-seq) represents an optimal strategy to annotate cellular composition but no large clinically annotated scRNA-seq datasets are currently available. To address this, we have embarked to generate a clinically annotated 500 patient CMML scRNA-seq atlas from CD34+ enriched BMNCs to establish among the largest clinically and genetically annotated single cell references for any chronic myeloid malignancy. Here we report the interim analysis of 113 clinically annotated cases that demonstrates massive reshaping of HSPC composition that is independently a predictor of overall survival. Methods Single-cell RNA expression profiling was performed on 113 CD34-enriched CMML BMNC samples using 10x Genomics GEM-X Universal 3' Gene Expression v4 (4-plex) chemistry. Libraries were sequenced on NovaSeq S4 flow cells in two runs, and data were processed using Cell Ranger to generate gene expression matrices. A total of 502,659 single cells from 113 samples were processed using Seurat. The upper threshold for gene count was defined as the mean number of detected genes plus two standard deviations data normalization was performed using the log-normalization method. Batch correction and integration were conducted with Harmony to mitigate technical variation across pools. Descriptive statistics were used to tabulate clinical and genetic parameters. KM curves, log-rank test, and cox regression were used to calculate survival, test for statistical differences, and perform multivariate analysis, respectively. Results To derive high resolution cell annotations, we employed the SingleR algorithm using a large hematopoietic reference of over 300,000 single cells (Zhang, X. Nature Immunology, 2024). Using this approach a diverse set of cell types were identified that included HSCs, 23 myeloid progenitors, 18 erythroid progenitors, and 18 lymphoid progenitors. This diverse cellular repertoire displayed massive reshaping relative to the normal CD34 enriched reference including a >53x expansion of multilineage GMPs and a >100x depletion of B-cell progenitors consistent with the known myeloid skewing seen in this disease. To understand whether expansion of specific progenitors was associated with outcomes we iteratively evaluated key progenitor populations and observed that expansions of HSC (HR 0.468 p<0.005) and MEP (HR 0.485, p<0.005) were associated with improved survival while GMP (HR 1.729, p=0.029) expansions were associated with inferior survival when using a cut point derived with a maximally selected ranks statistics approach. To achieve a more comprehensive metric of HSPC composition we explored GMP ratios to include GMP/HSC ratios and GMP/MEP ratios and observed that both were associated with inferior survival in univariate and multivariate when considering clinical and genetic features such as the CPSS-molecular prognostic score and FAB classification. When considering both GMP/HSC and GMP/MEP ratios in a multivariate model only GMP/MEP ratios and CPSS molecular remained independently prognostic. As an orthogonal approach, pseudotime projections were carried out using a reference map from Setty et al., 2019 to calculate differentiation potential and branch probabilities. This approach enabled both cell identity assignment and placement of cells along the hematopoietic differentiation trajectory. Samples were classified as monocyte-biased, MEP-biased, or normal-like, as previously described (Ferrall-Fairbanks et al. 2022) and demonstrated that increased GMP ratios are associated with a monocytic biased trajectory and that this trajectory is also associated with overall survival. Conclusion This study represents an interim analysis of among the largest scRNA-seq cohorts generated in chronic myeloid malignancies. This enabled us, for the first time, to demonstrate that HSPC composition is independently prognostic even when considering modern CMML prognostic models. As this cohort continues to increase, validation of these findings and examining the impact of therapy on HPSC composition in serial samples will be prioritized.

Abstract Background Blast transformation (BT) occurs in approximately 15-30% of patients with chronic myelomonocytic leukemia (CMML) and remains a leading cause of death. Allogeneic stem cell transplantation (ASCT) is currently the only treatment modality with the potential to cure the disease or prolong survival. Optimal timing of ASCT is critical for maximizing benefit while minimizing risks. To that end, contemporary risk models have focused on the prognostic relevance of individual, as opposed to concurrent mutations. In the current study, we looked into the possibility of prognostic prominence from concurrent mutations in predicting BT in CMML. Methods CMML diagnostic criteria were according to the International Consensus Classification (Arber et al. Blood 2022; 140:1200). A machine-learning hierarchical clustering algorithm was developed and tailored for patient stratification using survival outcomes and co-expression of genomic alterations. To reduce complexity and improve interpretability, we generalized each cluster using existing rule induction algorithms, including the in Trees framework and JRip. The final output was a set of mutation-based cluster definitions, each representing a distinct patient subgroup with similar survival trajectories. Competing risk analysis and cumulative incidence functions were used in downstream evaluation to validate the clinical distinctiveness of the clusters. For all survival analysis, patients were censored at the time of ASCT. Time-to-BT was calculated from the date of diagnosis to the date of BT or ASCT or last contact. BT-free-survival was calculated from the date of diagnosis to the date of BT, ASCT, death, or last contact. Results The core study cohort included 605 patients from the Mayo Clinic, USA, and the external validation cohort 501 patients from Humanitas Cancer Center, Milan, Italy. Using the patient cohort from the Mayo Clinic, machine-learning algorithms identified five molecular clusters with 3-year blast transformation (BT) rates ranging from 0% to 100% (AUC at 3 years 0.78): the order of molecular signature assignment (probability of BT/death from another cause) was i) PHF6MUT/ASXL1WT (0%/17% at 3 years; N=32), ii) NPM1MUT OR BCORMUT/ASXL1MUT OR SETBP1MUT/NRASMUT (48%/30% at 1 year; N=24), iii) RUNX1MUT/ASXL1MUT OR SRSF2MUT/NRASMUT OR EZH2MUT/ASXL1MUT OR SETBP1MUT OR BCORMUT (31%/55% at 3 years; N=132), iv) ASXL1MUT/TET2MUT OR DNMT3AMUT OR JAK2MUT (24%/28% at 3 years; N=123), and v) all other permutations (10%/40% at 3 years; N=294) [Figure 1]. Additional analysis confirmed significant differences in survival between RUNX1MUT/ASXL1MUT vs. RUNX1MUT/ASXL1WT (p<0.01) OR RUNX1WT/ASXL1WT (p<0.01) OR RUNX1WT/ASXL1MUT (p=0.038) [Figure 2]. Similar patterns of differences in survival were also documented for NRAS/SETBP1, ASXL1/EZH2, and NRAS/SRSF2 mutation combinations (Figure 2). A subsequent Cox regression analysis confirmed independent prognostic contributions from “PHF6MUT/ASXL1WT” (HR 5.43e-10; p<0.01), NPM1MUT (HR 26.7; p<0.01), “SETBP1MUT/NRASMUT” (HR 12.7; p<0.01), BCORMUT(HR 5.8; p<0.01), “RUNX1MUT/ASXL1MUT” (HR 2.3, p<0.01), JAK2MUT (HR 2.1; p<0.01), and “ASXL1MUT/TET2MUT” (HR 1.7; p=0.02). The prognostic relevance of “SETBP1MUT/NRASMUT”, “RUNX1MUT/ASXL1MUT”, NPM1MUT, and BCORMUT was validated in the external cohort from Italy (N=501). In the Mayo Clinic cohort, presence of any of the latter mutations was associated with 1-, 3-, and 5-year BT (death from other cause) rates of 27% (21%), 44% (49%), and 44% (51%), respectively (Figure 3). The corresponding values in the absence of high risk mutations were 7% (15%), 15% (37%), and 18% (52%) [Gray's p value <0.01 for both BT and death from another cause; Figure 3]. Similarly, the 1-, 3-, and 5-year BT (death from another cause) rates in the Italian cohort were 21% (19%), 37% (47%), and 42% (54%) in the presence and 8% (14%), 20% (35%), and 24% (45%) in the absence of high-risk mutations (Gray's p value <0.01 for BT and 0.18 for death from another cause; Figure 4). Conclusions In the current study, machine-learning algorithms enabled the discovery of concurrent mutations in CMML that were shown to be prognostically more significant than their individual constituents. Such prognostic interaction might have contributed to some of the discrepancies noted in current literature regarding the prognostic relevance of certain mutations in CMML and should be accounted for in the development of future risk models.

Abstract Recent updates to the monocyte count thresholds for diagnosing chronic myelomonocytic leukemia (CMML) have recognized oligomonocytic CMML (OM-CMML) as an early stage within the CMML spectrum. However, the clinical validity of these revised criteria remains uncertain without considering biological factors and might misclassify myelodysplastic syndromes (MDS) with mild monocytosis as CMML. To characterize the genomic landscape of OM-CMML, we evaluated OM-CMML (n=249), MDS (n=304) and myelodysplastic CMML (MD-CMML, n=358) patients (pts) treated at three academic institutions. Targeted next-generation sequencing was performed on bone marrow (BM) at initial diagnosis or first referral. OM-CMML was characterized by lower frequencies of biallelic TET2 (biTET2) and RAS pathway (RASp) gene mutations (BRAF, CBL, KRAS, NF1, NRAS and PTPN11) compared to MD-CMML (p<0.001) but higher frequencies than MDS (p<0.001). BM monocyte frequencies, assessed morphologically, were higher in OM-CMML than in MDS (p<0.001) but lower than in MD-CMML (p<0.001), indicating that OM-CMML is a heterogenous group including true CMML and distinct MDS subsets. Unsupervised hierarchical clustering based on genomic alterations across all cases (n=911) identified three distinct clusters: 1) TP53 alterations (predominantly multihit TP53) and complex karyotype (TP53-complex); 2) biTET2 or TET2-SRSF2 co-mutation (biTET2/SRSF2); 3) a heterogenous group lacking these features (not otherwise specifiable [NOS]). Among OM-CMML pts, 11%, 34% and 55% belonged to these clusters, respectively. Model-based unsupervised clustering further identified 9 unique clusters that could be consolidated into 6 groups: 1) TP53-complex; 2) biTET2/SRSF2; 3) SF3B1 (without biTET2 or SRSF2); 4) SRSF2 without TET2 and frequent IDH2 mutations (SRSF2-IDH2); 5) ASXL1 and U2AF1 mutations and copy number alterations (CNA-ASXL1/U2AF1); 6) no somatic mutations (No mutation). biTET2/SRSF2 group exhibited monocytic bias with higher AMC, PB and BM monocyte % even among OM-CMML cases. This bias was not attributed to higher RAS pathway mutation frequencies, as biTET2/SRSF2 cases had similar RASp mutation frequencies but lower median variant allele frequencies for RASp mutations compared to NOS cases (9% vs. 21%, p=0.029). PB monocyte partitioning using flow cytometry (FC) in a subset of OM-CMML pts (n=50) demonstrated higher frequency of classical monocytosis (>94% CD14+/CD16- monocytes) in biTET2/SRSF2 vs other OM-CMML. To further determine if biTET2/SRSF2 genotype defines a distinct biological subgroup, we performed RNA-sequencing of BM CD34+ cells from healthy donors (n=15) and pts with OM-CMML (n=12), MD-CMML (n=14) and MDS (n=55). Unsupervised clustering based on gene expression profiles revealed that biTET2/SRSF2 clustered irrespective of their diagnostic group with greater dispersion being observed based on diagnosis. MDS and CMML with biTET2/SRSF2 had less differentially expressed genes compared to MDS and CMML with biTET2/SRSF2 relative to other MDS and CMML genotypes. Among 138 OM-CMML pts in whom progression to overt CMML was evaluable, 37 (27%) progressed to overt CMML of which 26 (70%) belonged to the biTET2/SRSF2 group. Competing risk analysis confirmed higher cumulative incidence and shorter time to CMML progression among biTET2/SRSF2 OM-CMML. Using ElasticNet regression we identified BM monocytosis and 14 genomic variables (including biTET2 and TET2-SRSF2) as predictors of overt CMML progression among OM-CMML. We developed a score-based model able to predict progression with high reliability (AUC 0.805, sensitivity 0.70, specificity 0.76). Model accuracy was confirmed by optimism-corrected bootstrap internal validation (AUC 0.668). Monocytic bias, BM classical monocytosis (>94% CD16- monocytes) and higher overt CMML progression in biTET2/SRSF2 was confirmed in a validation cohort (28, 66 and 22 pts with OM-CMML, MDS and MD-CMML, respectively). BM monocytosis of biTET2/SRSF2 was confirmed in this cohort with morphologic and FC monocyte frequency quantification having high concordance (r=0.737, p<0.001). Genomic subgroup analysis discriminated survival outcomes more reliably than diagnostic categories alone. Our findings suggest that the presence of biTET2 or TET2-SRSF2 co-mutations, combined with BM monocytosis, can more reliably identify true CMML cases within the OM-CMML spectrum. Integrating genomic determinants into the CMML diagnostic framework enhances diagnostic accuracy.

Abstract Introduction: Standardized reporting of clinical trials results for MDS is essential to improve clinical interpretation and cross-study comparison. However, reporting of patient characteristics, response criteria, and endpoints in publications is often inconsistent; there is no systemic analysis of MDS trial reporting available. We conducted a systematic review of published MDS trial manuscripts on behalf of the international consortium for MDS (icMDS) to assess variability in reporting practices. Methods We searched ClinicalTrials.gov to identify all clinical trials registered for adults (≥18 years) with MDS that reported results between 2015-2024. Clinical trials that included acute myeloid leukemia (AML) or MDS/myeloproliferative neoplasms (MPN) were included if they enrolled ≥5 MDS patients. We excluded trials without an MDS cohort; non-therapeutic trials; trials focused on transplant interventions; or those which enrolled solid malignancies or other hematologic malignancies (chronic myeloid leukemia or lymphoid diseases). Finally, trials were required to have a manuscript available on Larvol, PubMed, or Google Scholar. Trials were categorized by disease risk (lower-risk [LR] vs. higher-risk [HR]) and trial phase (early phase [EP] vs. late phase [LP]). Results We identified 502 MDS trials on ClinicalTrials.gov, of which 351 did not meet inclusion criteria and 79 lacked a manuscript. A total of 72 trials (32 EP, 40 LP) with 80 publications (34 EP, 46 LP) were analyzed. Frequently reported baseline characteristics included age (100% EP, 100% LP), disease risk (74% EP, 74% LP), Eastern Cooperative Oncology Group performance status (85% EP, 72% LP), prior treatments (71% EP, 67% LP), and RBC transfusion dependency (32% EP, 70% LP). Less frequently reported characteristics included blood counts (hemoglobin [Hb; 38% EP, 54% LP], platelet count [38% EP, 54% LP], neutrophil count [24% EP and LP]) and bone marrow blasts (29% EP, 43% LP). Although disease risk was widely reported, definitions varied: IPSS and IPSS-R were each reported in 54% of manuscripts, while IPSS-M appeared in only 3%. Cytogenetic risk was separately reported in 39% of manuscripts (38% EP, 39% LP), and mutational data in 45% (47% EP, 43% LP). Responses per IWG 2006 criteria were reported in 84% of manuscripts. The primary endpoint involved safety/tolerability in 85% of EP manuscripts, though definitions were variable; recommended phase 2 dose was the most common (26% of EP manuscripts). In LP trials, primary endpoints were risk-specific: transfusion independence (TI) in 77% of LR, overall response rate (ORR) in 28% of HR, and complete remission (CR) or overall survival (OS) in 22% of HR. In HR manuscripts, CR was reported in 100%, ORR in 73%, hematologic improvement (HI) in 48%, RBC-TI in 23%, and OS in 80%. However, seven different definitions were used for ORR: CR + marrow CR + partial remission + HI per IWG 2006 was most common (35%). Event-free survival was reported in 25%; progression-free survival and relapse-free survival in 13%; and leukemia-free survival in 5%. Early mortality rate was reported in 35% (18% EP, 56% LP) and transplant rate in 58% (50% EP, 67% LP). Only 47% of HR and 90% of LR manuscripts identified HI-eligible patients. In LR-MDS, RBC-TI was reported in 72% of manuscripts but used six definitions. The most common were RBC-TI ≥8 weeks at any time during treatment (24%), ≥8 weeks within 28 weeks of treatment (13%), ≥8 weeks with Hb increase ≥1.0 (4%), ≥12 weeks with Hb increase ≥1.5 (4%), and ≥16 weeks within 24 weeks of treatment (12%). The remainder did not report RBC-TI, did not define it, or used a custom definition not used in another trial. HI was reported in 80% of LR papers with most of those reporting HI using IWG 2006 criteria (90%). Conclusion Reporting of baseline characteristics, response criteria, and outcomes is inconsistent in MDS clinical trials, with wide variability in response definitions, including ORR and TI. This heterogeneity limits interpretability, hampers historical comparisons and may obscure efficacy signals. Our findings highlight the need for standardized reporting guidelines to ensure clarity and consistency in MDS trial publications. As a next phase of this effort, we will conduct a formal Delphi process among icMDS experts to establish consensus recommendations for minimal and optimal MDS clinical trial reporting in manuscripts by disease risk (LR/HR) and trial phase (EP/LP).

Abstract Background and Significance: Chronic myelomonocytic leukemia (CMML) is a clonal hematopoietic malignancy with overlapping dysplastic and proliferative features, a high risk of leukemic transformation, and no disease-modifying therapies. Patients with proliferative CMML often present with symptomatic monocytosis, leukocytosis, splenomegaly, and systemic inflammation, which contribute to shortened survival and impaired quality of life. Hydroxyurea (HU), the standard cytoreductive agent, provides transient control of leukocytosis but has no known disease modifying properties. In the phase 3 DACOTA trial (Itzykson et al., JCO 2022), decitabine modestly delayed leukemic transformation compared to HU but did not improve event-free or overall survival and was associated with increased early mortality. HU achieved a clinical benefit rate (CBR) of approximately 15% (excluding stable disease), highlighting the urgent need for safer and more effective therapies. Pacritinib is a potent and selective multikinase inhibitor with a kinome profile that targets key mediators of clonal proliferation and inflammatory signaling relevant to CMML. It inhibits JAK2 (IC₅₀ = 6 nM) and IRAK1 (IC₅₀ = 13.6 nM)—critical drivers of cytokine-mediated growth and monocytic skewing—while sparing JAK1, potentially reducing myelosuppressive toxicity. Pacritinib also inhibits CSF1R (IC₅₀= 39.5 nM) which is involved in monocytic differentiation. Preclinical models demonstrate that dual inhibition of JAK2 and IRAK1 suppresses CMML colony formation and prolongs survival in xenografts. Furthermore, CSF1R inhibition may reduce monocyte-lineage bias in leukemic clones. Clinically, pacritinib is FDA-approved for intermediate- or high-risk myelofibrosis with severe thrombocytopenia (platelets <50 × 10⁹/L) based on evidence of significant spleen volume and symptom reduction with limited hematologic toxicity. These mechanistic and clinical data provide a strong rationale for evaluating pacritinib in advanced proliferative CMML, a setting characterized by both cytokine-driven proliferation and treatment-limiting cytopenias. Study Design and Methods PROSPERA (NCT07033598) is a collaborative multicenter, open-label, randomized phase 2 trial. Sixty-six patients with WHO-defined CMML-1 or CMML-2 will be enrolled. Eligible patients must have proliferative features (WBC ≥13 × 10⁹/L) and advanced disease as defined by ≥5 cm palpable splenomegaly, symptom burden (Myeloproliferative Neoplasm-Symptom Assessment Form Total Symptom Score MPNSAF-TSS ≥20), or thrombocytopenia (platelets <100 × 10⁹/L). Prior JAK inhibitor therapy is not allowed. The trial will be conducted at approximately six sites across the United States and United Kingdom, with enrollment anticipated to begin by the end of 2025. Participants will be randomized 2:1 to receive either pacritinib (200 mg orally twice daily) or HU (dosed as per investigator discretion) for 48 weeks. Patients in the HU arm may cross over to pacritinib at Week 24 if, in the opinion of the treating physician, they are not deriving clinical benefit (after assessment of the primary endpoint). Randomization will be stratified by prior cytoreductive therapy (none vs. prior HU or hypomethylating agents). The primary endpoint is CBR at Week 24, defined by modified IWG MDS/MPN criteria incorporating hematologic improvement, spleen volume reduction, and symptom response. Secondary endpoints include CBR at any time, duration of response, event-free survival, leukemia-free survival, and overall survival. Correlative studies will assess treatment-related changes in clonal dynamics, cytokine signaling, and hematopoietic composition using longitudinal peripheral blood and bone marrow sampling. Single-cell transcriptomic and immunophenotypic profiling will be employed to characterize pacritinib's impact on both the malignant clone and the surrounding immune microenvironment.

Abstract Background and Significance: CMML is a myeloid neoplasm characterized by hematopoietic stem and progenitor cell (HSPCs) monocytic bias, leading to downstream monocyte expansion that plays a key role in disease maintenance and progression. Monocytes in CMML express high surface expression of the homing C-C chemokine receptor 2 (CCR2), which promotes malignant monocyte survival. Expansion of CCR2+ monocytic progenitors drives acute transformation in CMML and contributes to venetoclax resistance in AML. For these reasons, CCR2 is a potential novel therapeutic target in CMML and monocytic AML. The CyTAC platform was designed to enable targeting of cell surface antigens that demonstrate limited or ineffective binding to conventional therapeutic antibodies. A CyTAC is a heterobivalent small molecule that binds to both a target of choice and to a fully humanized monoclonal antibody. STX-0712 comprises a CCR2 CyTAC which potently binds to CCR2 on malignant cells, and a CyTAC humanized ADCC-enabling monoclonal antibody, which when combined eliminates target cells via enhanced effector-mediated killing in the presence of NK cells. We have demonstrated that treatment with STX-0712 ex vivo results in human target engagement and pharmacodynamic activity, and in vivo leads to depletion of CCR2 MO1 monocytes in a potent and dose-dependent manner in CMML and AML (ASH 2024). Therapeutic depletion of CCR2+ malignant monocytes presents a novel opportunity to develop a potential disease-modifying targeted therapeutic agent. We have designed a first-in-human, phase 1 clinical trial to evaluate the effect of CCR2 CyTAC in patients with refractory/resistant CMML, and relapsed/refractory monocytic or monocytic predominant AML. Study Design and Methods This is a phase 1, first-in-human, open-label, multicenter clinical trial based in the USA to evaluate STX-0712 as a monotherapy in patients with CMML and AML who have no remaining treatment options known to confer clinical benefit. STX-0712 will be administered as a single intravenous (IV) infusion every 3 weeks (additional regimen schedules may be explored) until evidence of disease progression, death, intolerance to study medication, or withdrawal of consent. The study consists of a dose-escalation and dose-expansion part and includes two cohorts: Cohort 1 (refractory/resistant CMML) and Cohort 2 (relapsed/refractory monocytic or monocytic predominant AML). Dose escalation will begin in participants with CMML using the Bayesian Optimal Interval (BOIN) design, with a minimum of three patients enrolled at each dose level. Backfilling of up to two cleared dose levels will be initiated once efficacious exposure levels are reached and at least one CMML participant has achieved a complete remission (CR) or a partial remission (PR), marrow response, or clinical benefit as defined by the MDS/MPN IWG 2015 criteria. In the AML cohort, backfilling may begin once at least one participant has achieved a response defined by ELN 2022 criteria. Dose expansion will occur upon completion of dose escalation in each cohort, using a Simon 2-stage adaptive design. Approximately 47 CMML and 58 AML patients may be enrolled in this phase 1 study. The primary objectives of the study are to evaluate the safety and tolerability of STX-0712 and to determine the minimal safe and biologically effective dose, recommended phase 2 dose (RP2D), and/or maximum tolerated dose (MTD), and to characterize dose-limiting toxicities (DLTs). Secondary objectives include evaluation of preliminary antitumor activity, pharmacokinetic (PK) parameters, and pharmacodynamic effects of STX-0712 on CCR2+ malignant cells in blood and bone marrow. Key exploratory endpoints include: (1) assessment of changes in CCR2+ tumor cells following STX-0712 dosing, (2) evaluation of candidate biomarkers, (3) characterization of T cells, B cells and NK cells at baseline and over the course of treatment, and (4) to explore the effects of STX-0712 on patient-reported outcomes using the CMML Symptoms Assessment Tool (MPN-SAF-TSS scoring system) and the EuroQoL 5 Dimension 5 level (EQ-5D-5L) for AML. Enrollment for Cohort 1 (CMML) began in March 2025, and the study is ongoing. Clinical Trial Information: NCT06950034

Abstract Background: TP53-mutated (TP53mut) myeloid neoplasms (MN) are a biologically and clinically distinct subgroup of blood cancers associated with extremely poor outcomes and limited response to conventional therapies. In some patients, MN are preceded by clonal cytopenia of undetermined significance (CCUS). Prior cytotoxic exposure (treatment related, TR) is an independent predictor for survival in CCUS (Li et al, Blood Adv. 2024). As TP53mut CCUS and MN are enriched in TR cases, the interaction between cytotoxic exposure and TP53mut status on the risk of progression and survival remains unclear. Methods: Adult patients with CCUS who were evaluated at the Mayo Clinic or South Australian myelodysplastic syndrome (MDS) Registry (Adelaide, Australia) were included. CCUS and MDS were defined according to the 5th edition of the WHO classification for MN (Khoury et al, 2022), except for the exclusion of cases harboring MDS-defining cytogenetics. Next-generation sequencing was performed using a targeted panel covering genes commonly mutated in MN. In cases with >1 TP53mut, VAFs were summed. Survival outcomes were assessed by Kaplan-Meier with log-rank testing. Univariate and multivariate analyses were estimated using Cox proportional hazards regression. Results: Of 320 CCUS cases, 50 (15.6%) harbored TP53mut (TP53mut CCUS), whereas 270 (84.4%) were wild-type (TP53wt CCUS). Median age at diagnosis was 71 years (range 16-99). TP53mut CCUS had a greater proportion of females (60% vs 33.7%, p<0.001) and TR (66% vs 23%, p<0.001) compared to TP53wt. Hematologic parameters including hemoglobin, red cell distribution width, mean corpuscular volume, white blood count, absolute neutrophil count, platelets, and bone marrow blasts were comparable between the two groups. Almost all TP53mut (n=48, 96%) were located in the DNA-binding domain and the median variant allele frequency (VAF) was 9% (range 2-46%). Five (10%) cases harbored 2 TP53mut. The distribution of TP53mut VAF among patients was as follows: ≤2.5% (n=2), 2.5-5% (n=10), 5-10% (n=19), 10-20% (n=11), and ≥20% (n=7). The proportion of cases with cytogenetic abnormalities was smaller in TP53mut compared to TP53wt CCUS(12% vs 25.9%, p=0.035). Both cohorts had a comparable median number of mutations: 2 (range 0-7) in TP53mut and 2 (range 0-5) in TP53wt. The most common co-mutations in TP53mut CCUS were TET2 (n=8, 17.8%), PPMD1 (n=7, 15.6%), DNMT3A (n=6, 13.3%), ASXL1 (n=6, 13.3%), and SRSF2 (n=4, 8.9%). At last follow up, the rate of progression was comparable between TP53mut and TP53wt CCUS (12% vs 22%, p=0.13). Median time to MN progression was not reached in TP53mut and 6.25 months in TP53wt CCUS (p=0.5197). A significantly higher proportion of TP53mut cases acquired complex cytogenetics compared to TP53wt cases (33% vs 2%, p=0.024) at MN progression. Of 20 deaths observed in the TP53mut cohort, non-myeloid hematologic malignancies (n=6, 30%), infections (n=5, 25%), and TP53mut MN (n=4, 20%) were the 3 most common causes. With a median follow-up of 4 years (IQR 1.2-4.8), median OS was longer in de novo (dn) cases (8.7 years for dn TP53wt; not reached for dn TP53mut) and shorter in TR cases (3.3 years for TR TP53wt; 4.8 years for TR TP53mut). Notably, TP53mut showed no independent prognostic impact in the entire cohort, or when stratified by TR vs dn CCUS. On multivariate analysis that included TP53mut status, factors independently associated with inferior OS included prior therapy (HR 2.21, 95% CI 1.41-3.45, p<0.001), anemia (HR 4.1, 95% CI 1.88-8.99, p<0.001), and thrombocytopenia (HR 2.38, 95% CI 1.43-3.95, p<0.001). Similarly, TR CCUS (HR 1.6, 95% CI 1.06-2.4, p=0.03) and anemia (HR 2.15, 95% CI 1.25-3.7, p=0.006) were independently associated with inferior progression free survival. Conclusion: TP53 mut in CCUS did not independently predict inferior survival or increase leukemic transformation. While TP53mut CCUS were less likely to harbor cytogenetic abnormalities at diagnosis, significantly more cases harbored genomic instability at leukemic transformation. The poor outcomes attributed to TP53mut CCUS were driven by prior cytotoxic exposure, cytopenia – particularly anemia, and non-leukemic causes of death. These findings suggest risk stratification in CCUS should prioritize clinical context over TP53mut status alone.

Abstract Introduction: Somatic mutations in splicing factors (SF) - SF3B1, SRSF2, U2AF1, and ZRSR2 – represent one of the most common drivers of premalignant clonal hematopoesis. While SF mutations are potent drivers of clonal expansion (Fabre et al., Nature 2022), multiple SF mutations are rarely observed in the same individual (negative epistasis). Prior studies have identified rare cases of concurrent SF mutations (<4%) and described specific hotspot combinations that escape negative epistasis (Taylor et al., Blood 2020). We conducted this study to further characterize the frequency and clonal relationship between concurrent SF mutations using a larger patient cohort. Methods: After IRB approval, we assessed 3,369 myeloid next generation sequencing (NGS) results that identified somatic variants in SF3B1, SRSF2, U2AF1, or ZRSR2, in 2,500 adult patients from 08/2015 to 07/2025. Patient demographics, ICD-9/10 codes, and complete blood counts with differentials were obtained, retaining only those datapoints assigned 30 days before or after NGS testing. To allow comparison of clone size between autosomal and the X-linked SF ZRSR2, VAFs were multiplied by two to estimate the cancer cell fraction (CCF), except for ZRSR2 mutations in males. For patients harboring concurrent SF mutations, we categorized them as co-dominant if the difference in respective CCFs was < 10% of the greater CCF. Specific SF variants were categorized into common hotspots (n=13), except for ZRSR2 variants, which do not occur in defined hotspots. Fisher's exact testing was used to assess the enrichment of SF hotspot variants in patients with concurrent SF mutations, as well as combinations of SF variants in patients with co-dominant SF mutations. Results: We identified 2,331 patients with at least 1 pathogenic SF mutation, of which 6.7% (n=156) had > 1 SF mutation (multi-SF MT). The total cohort comprised cases of AML (n=549, 24%), MDS (n=977, 42%), MDS/MPN (n=233, 10%), MPN (n=228,10%), and uncategorized ICD codes (“Other”, n=326), of which the largest category was clonal cytopenia(s). Notably, the prevalence of multi-SF cases was higher than previously described and our cohort included 5 patients with 3 SF mutations and 1 with 4 SF mutations. Of the multi-SF MT patients, we identified 42 (27%) with co-dominant CCFs among their respective SF mutations. Thirty-three multi-SF MT patients had multiple NGS sampling timepoints (median 2, range 2-4) spanning a median of 0.97 years (range 0.06-5.69 years); we found that 17 of these 33 demonstrated correlative SF CCFs over multiple timepoints, suggesting their SF mutations occurred in the same clone (“co-clonal”). Of these 17 patients, 82% had co-dominant CCFs at the single-timepoint level, whereas none of the 16 non-co-clonal demonstrated co-dominance. Thus, we used SF co-dominance as a surrogate for cases likely to be co-clonal. ZRSR2 (p=8.8x10-29), U2AF1Q157 (p=9.4x10-9), SF3B1K666 (p=1.4x10-4) and infrequent SF3B1 (“SF3B1-Other”, p=.0029) mutations were overrepresented in the multi-SF MT cohort compared to patients with a single SF mutation. Within the multi-SF MT cohort, 3 SF mutation combinations showed overrepresentation in codominant cases – SRSF2P95R/H/L+ ZRSR2 (p=0.014), SRSF2P95R/H/L+ SF3B1K666 (p=0.044), and U2AF1S34 + U2AF1Q157 (p=0.024), although not significant after multiple hypothesis correction. Dual ZRSR2 mutations were the most common combination seen in the multi-SF MT cohort but were underrepresented in codominant cases (p=0.025). Although there was no disease subtype uniquely associated with total or co-dominant multi-SF MT cases, occurrence of multiple SF mutations was more common in males (8% vs. 3% in females), an effect driven by ZRSR2 mutations. Hemoglobin and WBC were lower in multi-SF MT cases, with the lower hemoglobin strongly driven by the codominant multi-SF MT cases (median 8.3 g/dL vs 9.4 g/dL, p=0.00046). Conclusions: These findings suggest that SF mutation co-occurrence in myeloid neoplasms is more prevalent than previously estimated (~7%). Furthermore, we identified 6 individuals harboring > 2 SF mutations, which has not been previously described. We validate specific SF variant pairs that appear to exist in the same clone and are working to confirm the clonal architecture and characterize global splicing aberration in selected cases with single-cell studies to fully uncover the mechanism by which negative epistasis is overcome.

Abstract Background: Isocitrate dehydrogenase (IDH) mutations are frequently implicated in the development of acute myeloid leukemia (AML). In a recent study (Salman MY, Blood Adv 2025), it was reported that IDH2 mutation subtypes, R140 and R172, may respond differently to intensive chemotherapy. In this study, we evaluated mid-cycle and end-of-cycle bone marrow blast responses in AML patients with IDH2 R140, IDH2 R172, and IDH1 R132 mutations, aiming to determine whether these subtypes exhibit different response dynamics. Methods: We reviewed the charts of AML patients diagnosed at Mayo Clinic, Rochester, between the years 2016 - 2023 that had available next generation sequencing (NGS) data. Institutional review board approval was obtained. All data were recorded at the time of AML diagnosis. Patients without molecular data or not treated with intensive chemotherapy (7+3 or CPX-351) were excluded from this study. Bone marrow blast percentages were assessed at mid-induction (days 14-21) and end of cycle (counts recovery). Blast clearance dynamics and complete remission rates were compared across IDH mutation subgroups and correlated with outcomes. GraphPad Prism 10.0.0 was used for statistical analysis. Results: Out of 381 AML patients, 31 had IDH2 mutations (23 with R140 and 8 with R172), and 8 had IDH1 mutations (all R132). Among patients with IDH2 mutations, those with R172 had numerically higher median mid-cycle bone marrow blast percentage compared to R140 (10% vs. 4%, p = 0.37). There was no significant difference in mid-cycle blast percentages between patients with IDH1 and IDH2 mutations (4.5% vs. 4%, p = 0.8). At mid-cycle, 68% of patients with IDH2 R140 had blast counts <5%, compared to 38% of those with IDH2 R172, suggesting a trend for faster mid-cycle clearance in the R140 group, though not statistically significant (OR 3.57, p = 0.2). In a broader comparison, 60% of IDH2-mutated patients and 50% of IDH1-mutated patients achieved blast clearance (blasts <5%) at mid-cycle, though not statistically significant (p = 0.7). At the end of the induction, patients with IDH2 R140 had significantly lower median bone marrow blast percentages compared to those with R172mutations (2% vs. 4%, p = 0.01). Similarly, patients with IDH1 mutations showed low blast counts compared to the IDH2 group (1.5% vs. 3%, p = 0.1). A higher proportion of IDH2 R140 patients achieved blast clearance compared to those with R172 mutations (100% vs. 75%, p = 0.06), while proportion of patients who achieved blast clearance were similar between IDH1 and IDH2 patients (87.5% vs 93.1%, P = 0.5). Analysis of overall complete remission rates (including both complete remission [CR] and CR with incomplete hematologic recovery [CRi]) within the IDH2-mutated group showed a higher rate in patients with the R140 mutation compared to those with R172 (87% vs. 75%), though the difference was not statistically significant (p = 0.5). When comparing IDH1- and IDH2-mutated patients, overall complete remission rates were 75% and 84%, respectively, with no significant difference observed (p = 0.6). Kaplan–Meier analysis of time to end-of-induction blast clearance revealed no statistically significant difference between IDH2 R140 and R172 patients (median time to clearance: 31 vs. 37 days, p = 0.3). Similarly, no difference was observed between IDH1 and IDH2-mutated patients (33 vs. 31 days, p = 0.9). Overall survival analyses showed no significant difference between IDH2 R140 and R172 groups (median 66.8 months vs not reached, P = 0.4). Likewise, no difference was observed between IDH1 and IDH2 patients (44.4 months vs not reached, P = 0.7), indicating similar overall survival across mutation subtypes. Conclusion: IDH2-R140 was associated with more effective blast cytoreduction following intensive induction compared to IDH2-R172. Although not statistically significant, IDH2 R172consistently showed trends toward higher blast percentages, delayed response, and lower overall complete remission rates. These findings align with the recent study reporting significantly slower blast reduction and delayed response in R172-mutated AML. Also, no significant differences were found between IDH1- and IDH2-mutated patients in terms of response kinetics or overall survival. Together, these findings highlight potential biological differences among IDH mutation subtypes that may impact treatment response and require further investigation in larger prospective cohorts.