Capturing the unpredictability of stem cells.

A new mathematical model that can be applied to both single-cell and bulk DNA sequencing data sheds light on the processes governing population dynamics in stem cells.

Molecular and clinical analyses of PHF6 mutant myeloid neoplasia provide their pathogenesis and therapeutic targeting.

PHF6 mutations (PHF6MT) are identified in various myeloid neoplasms (MN). However, little is known about the precise function and consequences of PHF6 in MN. Here we show three main findings in our comprehensive genomic and proteomic study. Firstly, we show a different pattern of genes correlating with PHF6MT in male and female cases. When analyzing male and female cases separately, in only male cases, RUNX1 and U2AF1 are co-mutated with PHF6. In contrast, female cases reveal co-occurrence of ASXL1 mutations and X-chromosome deletions with PHF6MT. Next, proteomics analysis reveals a direct interaction between PHF6 and RUNX1. Both proteins co-localize in active enhancer regions that define the context of lineage differentiation. Finally, we demonstrate a negative prognostic role of PHF6MT, especially in association with RUNX1. The negative effects on survival are additive as PHF6MT cases with RUNX1 mutations have worse outcomes when compared to cases carrying single mutation or wild-type.

Splenectomy outcomes in immune cytopenias: Treatment outcomes and determinants of response.

BACKGROUND: Splenectomy is commonly used to treat refractory immune-mediated cytopenia, but there are no established factors that are associated with response to the procedure. OBJECTIVES: A cohort study was conducted to evaluate the hematologic and surgical outcomes of splenectomy in adult patients with immune cytopenias and identify preoperative factors associated with response. METHODS: Data from the Cleveland Clinic Foundation for 1824 patients aged over 18 who underwent splenectomy from 2002 to 2020 were analyzed. RESULTS: The study found that the most common indications for splenectomy were immune thrombocytopenic purpura (ITP) and autoimmune hemolytic anemia, with a median age of 55 years and median time from diagnosis to splenectomy of 11 months. Hematologic response rates were 74% overall, with relapse in 12% of cases. Postsplenectomy discordant diagnoses were present in 13% of patients, associated with higher relapse rates. Surgery-related complications occurred in 12% of cases, whereas only 3% of patients died from disease complications. On univariate analysis, preoperative factors associated with splenectomy treatment failure were ≥3 lines of pharmacologic treatment, whereas isolated thrombocytopenia, primary ITP, and age ≤40 years had a strong association with response. The multivariable regression confirmed that treatment failure with multiple lines of medical therapy was associated with the failure to respond to splenectomy. CONCLUSION: Overall, the study demonstrates that splenectomy is an effective treatment option for immune-mediated cytopenias with a low complication rate.

nSEA: n-Node Subnetwork Enumeration Algorithm Identifies Lower Grade Glioma Subtypes with Altered Subnetworks and Distinct Prognostics.

Advances in molecular characterization have reshaped our understanding of low-grade glioma (LGG) subtypes, emphasizing the need for comprehensive classification beyond histology. Lever-aging this, we present a novel approach, network-based Subnetwork Enumeration, and Analysis (nSEA), to identify distinct LGG patient groups based on dysregulated molecular pathways. Using gene expression profiles from 516 patients and a protein-protein interaction network we generated 25 million sub-networks. Through our unsupervised bottom-up approach, we selected 92 subnetworks that categorized LGG patients into five groups. Notably, a new LGG patient group with a lack of mutations in EGFR, NF1, and PTEN emerged as a previously unidentified patient subgroup with unique clinical features and subnetwork states. Validation of the patient groups on an independent dataset demonstrated the robustness of our approach and revealed consistent survival traits across different patient populations. This study offers a comprehensive molecular classification of LGG, providing insights beyond traditional genetic markers. By integrating network analysis with patient clustering, we unveil a previously overlooked patient subgroup with potential implications for prognosis and treatment strategies. Our approach sheds light on the synergistic nature of driver genes and highlights the biological relevance of the identified subnetworks. With broad implications for glioma research, our findings pave the way for further investigations into the mechanistic underpinnings of LGG subtypes and their clinical relevance.Availability: Source code and supplementary data are available at https://github.com/bebeklab/nSEA.

Genomics of deletion 7 and 7q in myeloid neoplasm: from pathogenic culprits to potential synthetic lethal therapeutic targets.

Complete or partial deletions of chromosome 7 (-7/del7q) belong to the most frequent chromosomal abnormalities in myeloid neoplasm (MN) and are associated with a poor prognosis. The disease biology of -7/del7q and the genes responsible for the leukemogenic properties have not been completely elucidated. Chromosomal deletions may create clonal vulnerabilities due to haploinsufficient (HI) genes contained in the deleted regions. Therefore, HI genes are potential targets of synthetic lethal strategies. Through the most comprehensive multimodal analysis of more than 600 -7/del7q MN samples, we elucidated the disease biology and qualified a list of most consistently deleted and HI genes. Among them, 27 potentially synthetic lethal target genes were identified with the following properties: (i) unaffected genes by hemizygous/homozygous LOF mutations; (ii) prenatal lethality in knockout mice; and (iii) vulnerability of leukemia cells by CRISPR and shRNA knockout screens. In -7/del7q cells, we also identified 26 up or down-regulated genes mapping on other chromosomes as downstream pathways or compensation mechanisms. Our findings shed light on the pathogenesis of -7/del7q MNs, while 27 potential synthetic lethal target genes and 26 differential expressed genes allow for a therapeutic window of -7/del7q.

Novel scheme for defining the clinical implications of TP53 mutations in myeloid neoplasia.

BACKGROUND: TP53 mutations (TP53MT) occur in diverse genomic configurations. Particularly, biallelic inactivation is associated with poor overall survival in cancer. Lesions affecting only one allele might not be directly leukemogenic, questioning the presence of cryptic biallelic subclones in cases with dismal prognosis. METHODS: We have collected clinical and molecular data of 7400 patients with myeloid neoplasms and applied a novel model by identifying an optimal VAF cutoff using a statistically robust strategy of sampling-based regression on survival data to accurately classify the TP53 allelic configuration and assess prognosis more precisely. RESULTS: Overall, TP53MT were found in 1010 patients. Following the traditional criteria, 36% of the cases were classified as single hits, while 64% exhibited double hits genomic configuration. Using a newly developed molecular algorithm, we found that 579 (57%) patients had unequivocally biallelic, 239 (24%) likely contained biallelic, and 192 (19%) had most likely monoallelic TP53MT. Interestingly, our method was able to upstage 192 out of 352 (54.5%) traditionally single hit lesions into a probable biallelic category. Such classification was further substantiated by a survival-based model built after re-categorization. Among cases traditionally considered monoallelic, the overall survival of those with probable monoallelic mutations was similar to the one of wild-type patients and was better than that of patients with a biallelic configuration. As a result, patients with certain biallelic hits, regardless of the disease subtype (AML or MDS), had a similar prognosis. Similar results were observed when the model was applied to an external cohort. In addition, single-cell DNA studies unveiled the biallelic nature of previously considered monoallelic cases. CONCLUSION: Our novel approach more accurately resolves TP53 genomic configuration and uncovers genetic mosaicism for the use in the clinical setting to improve prognostic evaluation.

Applicability of 2022 classifications of acute myeloid leukemia in the real-world setting.

The increasing knowledge of molecular genetics of acute myeloid leukemia (AML) necessitated the update of previous diagnostic and prognostic schemes, which resulted in the development of the World Health Organization (WHO), the International Consensus Classification (ICC), and the new European LeukemiaNet (ELN) recommendations in 2022. We aimed to provide a real-world application of the new models, unravel differences and similarities, and test their implementation in clinical AML diagnosis. A total of 1001 patients diagnosed with AML were reclassified based on the new schemes. The overall diagnostic changes between the WHO 2016 and the WHO 2022 and ICC classifications were 22.8% and 23.7%, respectively, with a 13.1% difference in patients' distribution between ICC and WHO 2022. The 2022 ICC "not otherwise specified" and WHO "defined by differentiation" AML category sizes shrank when compared with that in WHO 2016 (24.1% and 26.8% respectively, vs 38.7%), particularly because of an expansion of the myelodysplasia (MDS)-related group. Of 397 patients with a MDS-related AML according to the ICC, 55.9% were defined by the presence of a MDS-related karyotype. The overall restratification between ELN 2017 and ELN 2022 was 12.9%. The 2022 AML classifications led to a significant improvement of diagnostic schemes. In the real-world setting, conventional cytogenetics, usually rapidly available and less expensive than molecular characterization, stratified 56% of secondary AML, still maintaining a powerful diagnostic role. Considering the similarities between WHO and ICC diagnostic schemes, a tentative scheme to generate a unified model is desirable.

Single-Cell Next-Generation Sequencing to Monitor Hematopoietic Stem-Cell Transplantation: Current Applications and Future Perspectives.

Acute myeloid leukemia (AML) and myelodysplastic syndrome (MDS) are genetically complex and diverse diseases. Such complexity makes challenging the monitoring of response to treatment. Measurable residual disease (MRD) assessment is a powerful tool for monitoring response and guiding therapeutic interventions. This is accomplished through targeted next-generation sequencing (NGS), as well as polymerase chain reaction and multiparameter flow cytometry, to detect genomic aberrations at a previously challenging leukemic cell concentration. A major shortcoming of NGS techniques is the inability to discriminate nonleukemic clonal hematopoiesis. In addition, risk assessment and prognostication become more complicated after hematopoietic stem-cell transplantation (HSCT) due to genotypic drift. To address this, newer sequencing techniques have been developed, leading to more prospective and randomized clinical trials aiming to demonstrate the prognostic utility of single-cell next-generation sequencing in predicting patient outcomes following HSCT. This review discusses the use of single-cell DNA genomics in MRD assessment for AML/MDS, with an emphasis on the HSCT time period, including the challenges with current technologies. We also touch on the potential benefits of single-cell RNA sequencing and analysis of accessible chromatin, which generate high-dimensional data at the cellular resolution for investigational purposes, but not currently used in the clinical setting.

Molecular patterns identify distinct subclasses of myeloid neoplasia.

Genomic mutations drive the pathogenesis of myelodysplastic syndromes and acute myeloid leukemia. While morphological and clinical features have dominated the classical criteria for diagnosis and classification, incorporation of molecular data can illuminate functional pathobiology. Here we show that unsupervised machine learning can identify functional objective molecular clusters, irrespective of anamnestic clinico-morphological features, despite the complexity of the molecular alterations in myeloid neoplasia. Our approach reflects disease evolution, informed classification, prognostication, and molecular interactions. We apply machine learning methods on 3588 patients with myelodysplastic syndromes and secondary acute myeloid leukemia to identify 14 molecularly distinct clusters. Remarkably, our model shows clinical implications in terms of overall survival and response to treatment even after adjusting to the molecular international prognostic scoring system (IPSS-M). In addition, the model is validated on an external cohort of 412 patients. Our subclassification model is available via a web-based open-access resource ( https://drmz.shinyapps.io/mds_latent ).

Novel Scheme for Defining the Clinical Implications of TP53 Mutations in Myeloid Neoplasia.

Background: TP53 mutations ( TP53 MT ) occur in diverse genomic configurations. Particularly, biallelic inactivation is associated with poor overall survival in cancer. Lesions affecting only one allele might not be directly leukemogenic, questioning the presence of cryptic biallelic subclones in cases with dismal prognosis. Methods: We have collected clinical and molecular data of 7400 patients with myeloid neoplasms and applied a novel model to properly resolve the allelic configuration of TP53 MT and assess prognosis more precisely. Results: Overall, TP53 MT were found in 1010 patients. Following the traditional criteria, 36% of cases were classified as single hits while 64% exhibited double hits genomic configuration. Using a newly developed molecular algorithm, we found that 579 (57%) patients had unequivocally biallelic, 239 (24%) likely contained biallelic, and 192 (19%) had most likely monoallelic TP53 MT . Such classification was further substantiated by a survival-based model built after re-categorization. Among cases traditionally considered monoallelic, the overall survival of those with probable monoallelic mutations was similar to the one of wild-type patients and was better than that of patients with a biallelic configuration. As a result, patients with certain biallelic hits, regardless of the disease subtype (AML or MDS), had a similar prognosis. Similar results were observed when the model was applied to an external cohort. These results were recapitulated by single-cell DNA studies, which unveiled the biallelic nature of previously considered monoallelic cases. Conclusion: Our novel approach more accurately resolves TP53 genomic configuration and uncovers genetic mosaicism for the use in the clinical setting to improve prognostic evaluation.

A multimodal analysis of genomic and RNA splicing features in myeloid malignancies.

RNA splicing dysfunctions are more widespread than what is believed by only estimating the effects resulting by splicing factor mutations (SFMT) in myeloid neoplasia (MN). The genetic complexity of MN is amenable to machine learning (ML) strategies. We applied an integrative ML approach to identify co-varying features by combining genomic lesions (mutations, deletions, and copy number), exon-inclusion ratio as measure of RNA splicing (percent spliced in, PSI), and gene expression (GE) of 1,258 MN and 63 normal controls. We identified 15 clusters based on mutations, GE, and PSI. Different PSI levels were present at various extents regardless of SFMT suggesting that changes in RNA splicing were not strictly related to SFMT. Combination of PSI and GE further distinguished the features and identified PSI similarities and differences, common pathways, and expression signatures across clusters. Thus, multimodal features can resolve the complex architecture of MN and help identifying convergent molecular and transcriptomic pathways amenable to therapies.

Frequency-dependent ecological interactions increase the prevalence, and shape the distribution, of pre-existing drug resistance.

The evolution of resistance remains one of the primary challenges for modern medicine from infectious diseases to cancers. Many of these resistance-conferring mutations often carry a substantial fitness cost in the absence of treatment. As a result, we would expect these mutants to undergo purifying selection and be rapidly driven to extinction. Nevertheless, pre-existing resistance is frequently observed from drug-resistant malaria to targeted cancer therapies in non-small cell lung cancer (NSCLC) and melanoma. Solutions to this apparent paradox have taken several forms from spatial rescue to simple mutation supply arguments. Recently, in an evolved resistant NSCLC cell line, we found that frequency-dependent ecological interactions between ancestor and resistant mutant ameliorate the cost of resistance in the absence of treatment. Here, we hypothesize that frequency-dependent ecological interactions in general play a major role in the prevalence of pre-existing resistance. We combine numerical simulations with robust analytical approximations to provide a rigorous mathematical framework for studying the effects of frequency-dependent ecological interactions on the evolutionary dynamics of pre-existing resistance. First, we find that ecological interactions significantly expand the parameter regime under which we expect to observe pre-existing resistance. Next, even when positive ecological interactions between mutants and ancestors are rare, these clones provide the primary mode of evolved resistance because their positive interaction leads to significantly longer extinction times. We then find that even in the case where mutation supply alone is sufficient to predict pre-existing resistance, frequency-dependent ecological forces still contribute a strong evolutionary pressure that selects for increasingly positive ecological effects. Finally, we genetically engineer several of the most common clinically observed resistance mechanisms to targeted therapies in NSCLC, a treatment notorious for pre-existing resistance. We find that each engineered mutant displays a positive ecological interaction with their ancestor, as predicted our theoretical work. As a whole, these results suggest that frequency-dependent ecological effects may provide the primary mode by which pre-existing resistance emerges.

Clinical and Molecular Determinants of Clonal Evolution in Aplastic Anemia and Paroxysmal Nocturnal Hemoglobinuria.

PURPOSE: Secondary myeloid neoplasms (sMNs) remain the most serious long-term complications in patients with aplastic anemia (AA) and paroxysmal nocturnal hemoglobinuria (PNH). However, sMNs lack specific predictors, dedicated surveillance measures, and early therapeutic interventions. PATIENTS AND METHODS: We studied a multicenter, retrospective cohort of 1,008 patients (median follow-up 8.6 years) with AA and PNH to assess clinical and molecular determinants of clonal evolution. RESULTS: Although none of the patients transplanted upfront (n = 117) developed clonal complications (either sMN or secondary PNH), the 10-year cumulative incidence of sMN in nontransplanted cases was 11.6%. In severe AA, older age at presentation and lack of response to immunosuppressive therapy were independently associated with increased risk of sMN, whereas untreated patients had the highest risk among nonsevere cases. The elapsed time from AA to sMN was 4.5 years. sMN developed in 94 patients. The 5-year overall survival reached 40% and was independently associated with bone marrow blasts at sMN onset. Myelodysplastic syndrome with high-risk phenotypes, del7/7q, and ASXL1, SETBP1, RUNX1, and RAS pathway gene mutations were the most frequent characteristics. Cross-sectional studies of clonal dynamics from baseline to evolution revealed that PIGA/human leukocyte antigen lesions decreased over time, being replaced by clones with myeloid hits. PIGA and BCOR/L1 mutation carriers had a lower risk of sMN progression, whereas myeloid driver lesions marked the group with a higher risk. CONCLUSION: The risk of sMN in AA is associated with disease severity, lack of response to treatment, and patients' age. sMNs display high-risk morphological, karyotypic, and molecular features. The landscape of acquired somatic mutations is complex and incompletely understood and should be considered with caution in medical management.

Stability of scRNA-Seq Analysis Workflows is Susceptible to Preprocessing and is Mitigated by Regularized or Supervised Approaches.

Background: Statistical methods developed to address various questions in single-cell datasets show increased variability to different parameter regimes. In order to delineate further the robustness of commonly utilized methods for single-cell RNA-Seq, we aimed to comprehensively review scRNA-Seq analysis workflows in the setting of dimension reduction, clustering, and trajectory inference. Methods: We utilized datasets with temporal single-cell transcriptomics profiles from public repositories. Combining multiple methods at each level of the workflow, we have performed over 6k analysis and evaluated the results of clustering and pseudotime estimation using adjusted rand index and rank correlation metrics. We have further integrated neural network methods to assess whether models with increased complexity can show increased bias/variance trade-off. Results: Combinatorial workflows showed that utilizing non-linear dimension reduction techniques such as t-SNE and UMAP are sensitive to initial preprocessing steps hence clustering results on dimension reduced space of single-cell datasets should be utilized carefully. Similarly, pseudotime estimation methods that depend on previous non-linear dimension reduction steps can result in highly variable trajectories. In contrast, methods that avoid non-linearity such as WOT can result in repeatable inferences of temporal gene expression dynamics. Furthermore, imputation methods do not improve clustering or trajectory inference results substantially in terms of repeatability. In contrast, the selection of the normalization method shows an increased effect on downstream analysis where ScTransform reduces variability overall.

Measuring competitive exclusion in non-small cell lung cancer.

In this study, we experimentally measure the frequency-dependent interactions between a gefitinib-resistant non-small cell lung cancer population and its sensitive ancestor via the evolutionary game assay. We show that cost of resistance is insufficient to accurately predict competitive exclusion and that frequency-dependent growth rate measurements are required. Using frequency-dependent growth rate data, we then show that gefitinib treatment results in competitive exclusion of the ancestor, while the absence of treatment results in a likely, but not guaranteed, exclusion of the resistant strain. Then, using simulations, we demonstrate that incorporating ecological growth effects can influence the predicted extinction time. In addition, we show that higher drug concentrations may not lead to the optimal reduction in tumor burden. Together, these results highlight the potential importance of frequency-dependent growth rate data for understanding competing populations, both in the laboratory and as we translate adaptive therapy regimens to the clinic.

Personalized Risk Schemes and Machine Learning to Empower Genomic Prognostication Models in Myelodysplastic Syndromes.

Myelodysplastic syndromes (MDS) are characterized by variable clinical manifestations and outcomes. Several prognostic systems relying on clinical factors and cytogenetic abnormalities have been developed to help stratify MDS patients into different risk categories of distinct prognoses and therapeutic implications. The current abundance of molecular information poses the challenges of precisely defining patients' molecular profiles and their incorporation in clinically established diagnostic and prognostic schemes. Perhaps the prognostic power of the current systems can be boosted by incorporating molecular features. Machine learning (ML) algorithms can be helpful in developing more precise prognostication models that integrate complex genomic interactions at a higher dimensional level. These techniques can potentially generate automated diagnostic and prognostic models and assist in advancing personalized therapies. This review highlights the current prognostication models used in MDS while shedding light on the latest achievements in ML-based research.