Hou, Hsin‐An

ABSTRACT Myelodysplastic syndrome (MDS) and chronic myelomonocytic leukemia (CMML) are clonal myeloid neoplasms shaped by genetic lesions and immune dysregulation, both of which contribute to disease progression and poor outcomes. Existing prognostic systems, such as IPSS‐R and IPSS‐M, do not incorporate immune alterations. We assessed the biological and clinical relevance of an immune dysfunction signature (IDS) across multi‐center MDS and CMML cohorts. IDS scores, derived from bulk transcriptomic data, were significantly associated with inferior leukemia‐free and overall survival. In multivariable analyses, IDS retained independent prognostic value alongside IPSS‐R, IPSS‐M, and bi‐allelic TP53 inactivation. Incorporation of IDS into existing models improved prognostic discrimination and time‐dependent predictive accuracy. Longitudinal analyses revealed that rising IDS scores paralleled disease progression and acute transformation, whereas declining scores were observed in remission. Biologically, IDS‐high cases demonstrated reduced cytotoxic T‐cell activity, expansion of regulatory T cells, enrichment of primitive progenitor signals, and increased expression of checkpoint pathway genes. These findings were validated across multiple independent MDS cohorts and consistently reproduced in CMML, where IDS also stratified risk and tracked disease evolution. Finally, integration with drug response signatures from the BeatAMLv2 cohort suggested potential therapeutic vulnerability of IDS‐high cases to multikinase and NF‐κB pathway inhibitors. These results establish IDS as a robust and dynamic biomarker in MDS and CMML, with applications in refined risk stratification, longitudinal disease monitoring, and guiding personalized therapeutic strategies targeting immune dysfunction. Trial Registration: #201709072RINC, #202109078RINB, #202207050RINB

Abstract Introduction The 2022 WHO/ICC classifications of myeloid neoplasms (MNs) incorporated genomics into the existing clinicopathologic framework, defining new (mono)genetic entities. However, this iterative approach resulted in a mix of pathologically and genomically-defined entities, reliant on arbitrary thresholds and historically-derived boundaries between entities. We hypothesised that integrating genomics with other features, de novo from first principles, might better incorporate modern molecular knowledge to define the biological continuum, evolutionary links and overlapping features across MN. Methods The discovery cohort comprised 6484 thoroughly annotated pts. Contemporary diagnoses included 1817 AML, 1695 MDS, 737 MPN, and 1101 MDS/MPN. Non-malignant pts (AA/CHIP/CCUS/ICUS) were included to explore beyond current neoplastic boundaries. 1407 pts had ≥1 follow-up sample/s. Validation was performed in an independent international cohort (n=2012). An unsupervised random forest algorithm was used to integrate status (and VAF) for 28 mutations, karyotypes, CBC features, sex and age (total parameters 144). Features were selected for potential to be independent of BM sampling. Proximity-based multidimensional scaling/PCA were used to visualize latent structure and identify variance-driving features. Results The model projected pts onto a continuous 3-dimensional space. We first applied clustering seeking 1st-order structure, revealing 4 major clusters (C1–C4). These were further subdivided into 58 distinct subclusters (SCs) by an unsupervised iterative clustering approach. All SCs were enriched for distinctive features, with diverse associations linking genomics to clinicopathological/outcome features. Notably, contemporary diagnoses were distributed widely throughout this landscape, with 9/58 SCs containing cases spanning multiple WHO/ICC MN categories. C1 displayed the most internal heterogeneity, with admixed AML, MDS and MDS/MPN pts. It naturally subdivided down to 4th-order subclustering. Ten SCs were dominated by splicing factor mutations. For example, SF3B1MT segregated into 3 SCs, alone or with high-VAF TET2/ASXL1 or DNMT3A mutations. A separate TET2/SRSF2 SC associated with elevated monocyte%, reflecting a discrete CMML-phenotype genomic entity. NPM1MT formed a distinct SC with FLT3-ITD, distinguished further by VAFs of FLT3, TET2, and NRAS. Current genomically-defined AML subtypes separated in the 4th subclustering. C2 encompassed classical MPN driver mutations across 6 SCs, including a distinct CALRMT group and others defined by variable JAK2 VAFs, co-mutation patterns and other CBC features: eg high-VAF SF3B1MT correlated with PB blast%, and concurrent TET2MT with higher Hb/monocyte counts. C3 was characterized by TP53 alterations, with 5 SCs differing by TP53MT burden, 17p lesions, karyotypic complexity and age. Two SCs, one marked by high TP53 VAF+complex karyotype, the other by ch17 lesions, were associated with the worst OS in the whole cohort. C4 contained most known non-malignant pts, dividing into 4 SCs: 2 largely comprising CCUS and AA, while 2 reflect overlaps/convergence between AA and MDS and MPN, respectively. Repurposing single-cell pseudotime analysis suggested multiple disease origins, with various progression trajectories converging on C3. Projecting them onto the model longitudinal samples moved along these derived paths, consistent with their known progression/remission changes, validating the model's biological relevance and predictive utility. Remarkably, the 58 SCs displayed distinct risks of clonal progression and survival. To maximize potential clinical utility we created a pan-MN prognostic score integrating these features. It performed robustly (c-index 0.84), matching/exceeding existing prognostic tools and validated in an independent dataset. Conclusion Integrating clinicopathologic and genomic data we developed ab initio a continuum model of MNs that transcends historical diagnostic boundaries, captures disease evolution and stratifies clinical outcomes. It may better reflect the core biology and natural histories of specific cases, placing more rational boundaries between discrete entities. Potential advantages extend to treatment selection/trial design (in the era of molecularly-targeted therapies) and personalised prognostication throughout each disease course. Expanded validation is needed to resolve complexity in some areas (notably in C2/C4) and validate/refine this prototype framework.

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<.001), and median leukemia-free survival (LFS) ranging from 210 to 18 months (P<.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<.001) and identified groups of patients with different probabilities of disease relapse, ranging from 9% to 62% (P<.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.

PURPOSE Allogeneic hematopoietic cell transplantation (HCT) improves outcomes for patients with AML harboring an internal tandem duplication mutation of FLT3 ( FLT3-ITD) AML. These patients are routinely treated with a FLT3 inhibitor after HCT, but there is limited evidence to support this. Accordingly, we conducted a randomized trial of post-HCT maintenance with the FLT3 inhibitor gilteritinib (ClinicalTrials.gov identifier: NCT02997202 ) to determine if all such patients benefit or if detection of measurable residual disease (MRD) could identify those who might benefit. METHODS Adults with FLT3-ITD AML in first remission underwent HCT and were randomly assigned to placebo or 120 mg once daily gilteritinib for 24 months after HCT. The primary end point was relapse-free survival (RFS). Secondary end points included overall survival (OS) and the effect of MRD pre- and post-HCT on RFS and OS. RESULTS Three hundred fifty-six participants were randomly assigned post-HCT to receive gilteritinib or placebo. Although RFS was higher in the gilteritinib arm, the difference was not statistically significant (hazard ratio [HR], 0.679 [95% CI, 0.459 to 1.005]; two-sided P = .0518). However, 50.5% of participants had MRD detectable pre- or post-HCT, and, in a prespecified subgroup analysis, gilteritinib was beneficial in this population (HR, 0.515 [95% CI, 0.316 to 0.838]; P = .0065). Those without detectable MRD showed no benefit (HR, 1.213 [95% CI, 0.616 to 2.387]; P = .575). CONCLUSION Although the overall improvement in RFS was not statistically significant, RFS was higher for participants with detectable FLT3-ITD MRD pre- or post-HCT who received gilteritinib treatment. To our knowledge, these data are among the first to support the effectiveness of MRD-based post-HCT therapy.

SummaryLeukaemic stem cell (LSC) gene expression has recently been linked to prognosis in patients with acute myeloid leukaemia (17‐gene LSC score, LSC‐17) and myelodysplastic syndromes. Although chronic myelomonocytic leukaemia (CMML) is regarded as a stem cell disorder, the clinical and biological impact of LSCs on CMML patients remains elusive. Making use of multiple independent validation cohorts, we here describe a concise three‐gene expression signature (LSC‐3, derived from the LSC‐17 score) as an independent and robust prognostic factor for leukaemia‐free and overall survival in CMML. We propose that LSC‐3 could be used to supplement existing risk stratification systems, to improve prognostic performance and guide management decisions.

MYC gene amplification and somatic mutations are frequent in both adult and pediatric AML although how MYC drives and contributes to the development and maintenance of AML has not been resolved. Transcription factor EB (TFEB) is a master regulator of genes that control autophagy and lysosome biogenesis, a central catabolic recycling pathway that regulates cell survival. Given the oncogenic effects of MYC in AML and that the induction of autophagy compromises AML cell growth and survival, we tested if the oncogenic effect of MYC depends on its suppression of TFEB transcription programs in AML. In support of this hypothesis, inducible MYC expression in K562 and THP-1 leukemia cells was sufficient to suppress expression of TFEB and its target genes. Further and conversely, MYC knockdown in NB4 AML cells provoked increased expression of TFEB mRNA and protein, as well as increased expression of TFEB target genes. Notably, dose response studies demonstrated that expression of TFEBS211A, constitutively nuclear form of TFEB that is refractory to control by mTORC1 signaling, dramatically impairs proliferation of HL60, OCI-AML2 and OCI-AML3 AML cells. In addition, induction of TFEBS211A provoked rampant apoptosis. Of important, overexpression of TFEBS211A in HL-60 and OCI-AML3 cells was also sufficient to promote monocytic and granulocytic differentiation, as judged by morphological changes and the acquisition of mature monocytic and granulocytic markers including CD11b, Gr1, and CD15. To identify TFEB targets that might contribute to myeloid/granulocytic differentiation, we performed RNA-seq analysis of HL60 leukemia cells engineered to inducibly express the TFEBS211A transgene. Using a cut-off of fold change>4 with q<0.01, a total of 1152 genes were differentially regulated following the induction of TFEBS211A in HL60 cells. As expected, this included the robust induction of nearly all TFEB target genes associated with the autophagy-lysosome pathway, but also of STAT1, KLF4, KLF6, CEBPB, CSF1 and GATA2 genes that are necessary and/or sufficient to provoke terminal monocytic and granulocytic differentiation of AML cells. Surprisingly, among genes induced by TFEB in HL60 cells is IDH1, which catalyzes the production of α-ketoglutarate (α-KG), a required substrate of the TET family of dioxygenases that convert 5-methylcytosine (5mC) to 5-hydroxymethylcytosine (5hmC). In particular, TFEBS211A expression provoked increases in levels of α-KG and significant increases in global levels of 5hmC in genomic DNA of HL60 leukemia cells both ex vivo and in vivo. Furthermore, TFEB-mediated induction of IDH1/2 mRNA and protein, and of IDH1 promoter activity, was antagonized by inducible expression of MYC. To assess the global effects of TFEB on 5mC/5hmC landscapes, we performed paired reduced representation bisulfite (BS)- and oxidation bisulfite (oxBS)-sequencing. As predicted, TFEBS211A induced both loss and gains of 5mC, but there were more losses (n = 722) than gains (n = 459) across all 22 chromosomes. Quite remarkably, and consistent with TFEB-provoked increases of 5hmC signals, TFEBS211A exclusively induced 5hmC gains (in a total of 863 genes), and 37% and 36% of these 5hmC gains occurred in promoter regions and CpG islands, and across all 22 chromosomes. Comparison of BS- and oxBS-seq versus RNA-seq analyses of HL60 cells expressing TFEB revealed significant changes in mRNA levels and concomitant differential changes in 5mC and 5hmC marks in KLF4, KLF6, STAT3, TP73, andFOXO1 that have pivotal roles in controlling myeloid cell differentiation and death. Collectively, these findings demonstrate that MYC suppresses TFEB expression and function in AML cells, and that TFEB functions as a tumor suppressor that provokes AML cell differentiation and death. Strikingly, these responses rely on epigenetic control, where TFEB directly induces the transcription of IDH1 and IDH2 to provoke global hydroxylation of 5-methylcytosine and the expression of genes that drive terminal differentiation and apoptosis. Thus, a MYC-TFEB-IDH1/2-TET2 circuit controls AML cell fate. Disclosures Murphy: Merck: Research Funding; Puma Biotech: Research Funding. Ballabio:CASMA Therapeutics: Other: Co-Founder. Kaufmann:Takeda Pharmaceuticals: Research Funding.

Abstract MYC oncoproteins regulate transcription of genes directing cell proliferation, metabolism, and tumorigenesis. A variety of alterations drive MYC expression in acute myeloid leukemia (AML), and enforced MYC expression in hematopoietic progenitors is sufficient to induce AML. Here we report that AML and myeloid progenitor cell growth and survival rely on MYC-directed suppression of Transcription Factor EB (TFEB), a master regulator of the autophagy–lysosome pathway. Notably, although originally identified as an oncogene, TFEB functions as a tumor suppressor in AML, where it provokes AML cell differentiation and death. These responses reflect TFEB control of myeloid epigenetic programs by inducing expression of isocitrate dehydrogenase-1 (IDH1) and IDH2, resulting in global hydroxylation of 5-methycytosine. Finally, activating the TFEB–IDH1/IDH2–TET2 axis is revealed as a targetable vulnerability in AML. Thus, epigenetic control by an MYC–TFEB circuit dictates myeloid cell fate and is essential for maintenance of AML. Significance: Alterations in epigenetic control are a hallmark of AML. This study establishes that a MYC–TFEB circuit controls AML differentiation and epigenetic programs by inducing IDH1/IDH2 and hydroxylation of 5-methylcytosine, that TFEB functions as a tumor suppressor in AML, and that this circuit is a targetable vulnerability in AML. See related commentary by Wu and Eisenman, p. 116.