Lanino, Luca

Introduction The International Prognostic Scoring System (IPSS) and its revised version (IPSS-R) were developed using datasets from patients (pts) with untreated MDS. The cytogenetic risk groups used in IPSS-R were expanded to 5 groups from the 3 used in IPSS. Among the new groups introduced in IPSS-R is the “very complex” karyotype (VCK) where ≥4 karyotypic abnormalities are seen compared to “complex karyotype (CK)” with only 3 abnormalities. However, the prognostic impact of VCK vs CK, as well as individual cytogenetic abnormalities and TP53 mutation (TP53mut) status within the CK and VCK subgroups in pts with MDS treated with hypomethylating agents (HMAs) has not been well studied. Hence, we analyzed these questions in a large cohort of HMA-treated MDS pts in the icMDS VALIDATE database. Methods The icMDS VALIDATE database contains MDS pts treated with frontline HMA monotherapy or HMA-based combinations from 22 specialized international centers. Pts with unknown HMA type, date of HMA initiation, missing age, missing sex, missing cytogenetic data, bone marrow or peripheral blood blasts ≥20%, or missing survival status were excluded. We performed time-to-event analyses using the Kaplan-Meier method and compared groups by the log-rank test. We measured overall survival (OS) from time of HMA initiation. VALIDATE protocol was exempted, acknowledged, or approved by all centers' IRBs. VALIDATE is supported by an independent grant from Abbvie. Results A total of 1940 pts were included. Median age was 70 (range: 18-99) years and 66% were men. According to IPSS-R cytogenetic risk groups, 1.9% (37) were very good, 43.4% (841) good, 15.5% (301) intermediate, 11.6% (226) poor, and 27.6% (535) very poor. By HMA type, 71% of pts were treated with azacitidine, 14% with decitabine, 6% with HMA and venetoclax, and 9% with another HMA combination. Approximately 28% of pts underwent allogeneic hematopoietic stem cell transplant (alloHSCT). The IPSS-R cytogenetic risk groups had different OS (p<0.001)with median OS (mOS; months [mo], 95% CI) as follows: very good (43.4, 34.7-72.5), good (41.7, 36.4-46.8), intermediate (24.5, 23.1-32.4), poor (24.2, 19.2-28.5), and very poor (13.6, 12.5-14.5). The difference remained significant (p=0.001) after censoring at time of alloHSCT with mOS as follows: very good (40.4, 23.2-64.3), good (24.3, 22.2-26.3), intermediate (19.3, 15.1-22.2), poor (12.2, 10.3-14.8), and very poor (9.1, 8.2-19.7). Interestingly, the difference between mOS for intermediate and poor risk cytogenetics became more apparent after censoring at time of alloHSCT. Next, we compared the outcomes of pts with CK and VCK to non-complex karyotype (NCK) MDS. The mOS for pts with VCK MDS (13.6 mo, 12.5-14.5) was worse compared to pts with CK (19.9 mo, 19.9-16.8; p=0.004) and NCK (36.1 mo, 32.7-37.8; p<0.001) MDS. Among the 1458 pts with available molecular data, more pts with VCK (75%, 330) MDS had a concurrent TP53mut compared to pts with CK (40%, 21; p<0.001) and NCK (7%, 64 p<0.001) MDS. Considering only patients with TP53mut MDS, mOS for pts with VCK (12.2, 11.1-13.9) was worse compared with NCK (24.2, 16-1-53.1; p<0.001) but not with CK (19.9, 9.5-NR; p=0.9); the difference between CK and NCK was not significant (p=0.16). Among pts with TP53 wildtype (TP53WT) MDS, VCK (15.0, 13.2-18.2) had a similar mOS to TP53WT CK (18.8, 12.6-41.1; p=0.19) while both groups had worse mOS compared to TP53WT NCK (33.4, 30.1-37.0) (p=0.01). In a survival analysis of 616 pts with MDS with ≥3 cytogenetic abnormalities, -17/del(17p) (12.8 mo; p=0.009), inv(3)/t(3q)/del(3q) (10.9 mo; p=0.006), and del(7q) (12.1mo; p=0.024), though not -7 (12.6mo; p=0.23), were associated with lower mOS compared to the pts with MDS without these abnormalities (14.3mo). Conclusions Using one of the largest databases of HMA-treated MDS pts, we show that IPSS-R cytogenetic risk categories associate with significantly different survivals among HMA-treated MDS pts, even after censoring at time of alloHSCT. Importantly, VCK MDS was associated with worse mOS compared to CK and NCK MDS but not in pts with a concurrent TP53mut. Among pts with MDS with ≥3 chromosomal abnormalities, having -17/del(17p), inv(3)/t(3q)/del(3q), or del(7q) was associated with worse survival.

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.

Introduction: Response assessment is used to evaluate effectiveness of hypomethylating agent (HMA)-based therapy in myelodysplastic syndromes/neoplasms (MDS) in clinical practice and trials. Recently, the clinical response criteria (IWG 2023) and the clinical-molecular risk assessment (IPSS-M) in MDS were published. However, it remains unknown whether the IWG 2023 response criteria have an independent impact on overall survival (OS) when adjusting for baseline IPSS-M risk. Additionally, the association of IWG 2023 response criteria with OS in patients (pts) undergoing allogeneic stem cell transplantation (allo-HCT) versus those who do not is unknown. Lastly, it is unclear if optimal time-to-transplant impacts OS in MDS pts who achieve a IWG 2023 defined remission with HMA therapy. We examined these questions in a large population of MDS pts within the multi-institutional international consortium for MDS (icMDS) VALIDATE database. Methods: High-risk MDS pts with IPSS ≥1.5 or IPSS-R >3.5 treated with HMA-based therapy were included in the study. Baseline IPSS-M risk was stratified into very low/low/moderate low (VL+L+ML), moderate-high/high (MH+H), and very high (VH). Response to treatment was evaluated using the IWG 2023 criteria and grouped into: composite complete response (cCR) and no cCR. cCR included: complete response (CR), CR equivalent (CRequ), CR bilineage (CRbi), CR unilineage (CRuni), and CR with partial hematologic recovery (CRh). We analyzed the differential impact of achieving cCR on HMA-based therapy prior to transplant on OS in pts who either did or did not undergo allo-HCT, while adjusting for their baseline IPSS-M risk in Cox regression analysis. Optimal time-to-transplant was determined based on age and IPSS-M category as described by Tentori et al. (JCO 2024). Pts proceeding to allo-HCT were stratified on whether they underwent transplant within the optimal time-to-transplant timeframe after MDS diagnosis or not. VALIDATE protocol was exempted, acknowledged, or approved by all centers' IRBs. VALIDATE is supported by an independent grant from AbbVie. Results: We analyzed 762 MDS pts. The median age at MDS diagnosis was 68 years (range: 19-95 years) and 64% of pts were male. Distribution of the baseline IPSS-M risk was: VL+L+ML in 110 (14%) pts, MH+H in 347 (46%) pts, VH in 305 (40%) pts. Pts received azacitidine monotherapy, decitabine monotherapy, and HMA-based combination therapy (e.g. HMA + venetoclax) in 52%, 21%, and 28% of cases, respectively. A total of 282/762 (37%) pts achieved cCR as their best response to HMA-based therapy. The median OS for all pts was 18.7 months (95% CI: 16.6-21.3 months). Pts who did not undergo allo-HCT (n=436, 57%) had an improved median OS if they achieved cCR when compared to pts in the no cCR group in all IPSS-M risk groups (VL+L+ML: 23.5 months vs. 10.7 months; MH+H: 20.8 months vs. 11.6 months; VH: 13.8 months vs. 7.5 months). Cox analysis demonstrated an independent effect of cCR on OS in these pts when adjusted for baseline IPSS-M risk (cCR vs. no cCR: HR 0.60, 95% CI 0.48-0.76, p<0.001). In contrast, in pts who subsequently received an allo-HCT (n=326, 43%), achieving cCR with HMA prior to allo-HCT did not provide an additional OS benefit, when adjusted for baseline IPSS-M risk (cCR vs. no cCR: HR 1.00, 95% CI 0.73-1.38, p=0.986). The median time-to-transplant was 6.7 months and 34% of transplanted MDS pts received their allo-HCT during the optimal timeframe. Among pts who achieved cCR with HMA-based therapy prior to allo-HCT, optimal time-to-transplant was associated with significantly improved OS (optimal vs. non-optimal timing: HR 0.55; p=0.033). Conclusion: Baseline IPSS-M risk and allo-HCT had an independent impact on OS, while IWG 2023 defined cCR had an independent effect on OS only in non-transplanted MDS pts. While this retrospective analysis occurred at large academic centers with specific expertise in the management of MDS, our data may have an impact on clinical practice: (i) failure to achieve cCR on HMA therapy prior to transplant should not impact the decision to proceed to allo-HCT, (ii) upfront allo-HCT without prior HMA therapy might also be an option if the clinical situation and availability of donors allow, and (iii) allo-HCT should ideally be performed during the timeframe that is considered optimal as defined by pt age and baseline IPSS-M.

Background. The inclusion of gene mutations and chromosomal abnormalities in the 2022 WHO and ICC Classifications of MDS has enhanced diagnostic precision and is expected to improve clinical decision-making process. Although these two systems share similarities, clinically relevant discrepancies still exist and potentially cause inconsistency in their adoption in a clinical setting. In this study on behalf of the International Consortium for MDS (icMDS), we adopted a data-driven approach to provide a harmonization roadmap between the 2022 WHO and ICC classification for MDS. A modified Delphi Process consensus approach is currently ongoing among icMDS experts to finalize a harmonized MDS classification scheme. Methods. We analyzed retrospective international cohorts of patients with a diagnosis of MDS (n=7017) and AML (n=1002) according to WHO 2016 criteria. Hierarchical Dirichlet Processes were applied to define clusters capturing broad dependencies among all gene mutations and cytogenetic abnormalities. To investigate the features of importance and their impact on the clustering process, we employed the SHapley Additive exPlanations approach (SHAP). This allowed to define harmonized labels for each clinical entity. The clinical relevance of the unsupervised clustering was assessed through the analysis of phenotypic features and outcomes among each group. ( Blood 2022;140: 9828-9830) Results. Patients' characteristics are summarized in Table 1. We identified 9 clusters, defined by specific genomic features. The cluster of highest hierarchical importance was characterized by biallelic inactivation of TP53 (biTP53). According to SHAP analysis, bi TP53 was defined as 2 or more TP53 mutations, or 1 mutation with copy number loss or cnLOH. Most patients assigned to bi TP53 cluster had TP53 VAF>10% (77.9%) and complex karyotype (70.1%). Assignment to bi TP53 cluster was irrespective of blast count. Patients with monoallelic TP53 mutation segregated into other clusters. Hierarchically, the second cluster included patients with del(5q). SHAP analysis highlighted 5q deletion alone, or with one other chromosomal abnormality other than -7, and absence of bi TP53, as the most relevant features. Most of these patients had blast counts <5% (88.1%). The third distinct cluster included patients with SF3B1 mutations (in the absence of concurrent del(7q), abn3q26.2, complex karyotype or RUNX1 mutation). Most patients with MDS and SF3B1 mutation had <5% blasts (94.2%). Common co-mutated variants in the SF3B1 cluster included mutant DNMT3A (25.2%) and TET2 (38.3%). Morphologically defined MDS cases (i.e., not meeting criteria for bi TP53, del(5q) or SF3B1) were preferentially assigned to the following additional clusters: SF3B1 and concurrent higher-risk mutations (e.g., RUNX1 and ASXL1); SRSF2 and concomitant TET2 mutations; U2AF1 mutations with del(20q), del(7q) or -7; SRSF2 with TET2 mutations and co-mutational patterns including RUNX1 and ASXL1; and AML-like genomic signatures. Our analyses suggest that morphologically defined MDS is characterized by a large heterogeneity in terms of mutation profiles, not entirely captured by the presence of unilineage versus multilineage dysplasia, percentage of bone marrow blasts, and presence of hypocellularity and fibrosis. To better investigate the continuum between high risk MDS (i.e., patients with ≥10% blasts) and AML, an exploratory comparison was made using a cohort of AML (defined according to WHO 2016) patients analyzed using the same statistical methods. Only a partial overlap in genetic signatures was observed between MDS with ≥10% blasts and AML. However, similarities were observed between the AML-like MDS clusters (characterized by mutant NPM1, bZIP CEBPA, and Core Binding Factor abnormalities) and AML clusters defined by the same genetic signature, thus supporting the classification of these entities as AML, irrespective of blast count. Conclusion. Our study demonstrated the utility of a data-driven approach based on advanced statistical methods to generate a harmonized classification for MDS. Table 2 shows a provisional, hierarchical classification algorithm. Further refinement of entity labels and classification criteria is the subject of the ongoing modified Delphi Process consensus approach among icMDS experts.