Clonal hematopoiesis-derived therapy-related myeloid neoplasms after autologous hematopoietic stem cell transplant for lymphoid and non-lymphoid disorders.

Therapy-related myeloid neoplasms (tMN) are complications of cytotoxic therapies. Risk of tMN is high in recipients of autologous hematopoietic stem cell transplantation (aHSCT). Acquisition of genomic mutations represents a key pathogenic driver but the origins, timing and dynamics, particularly in the context of preexisting or emergent clonal hematopoiesis (CH), have not been sufficiently clarified. We studied a cohort of 1507 patients undergoing aHSCT and a cohort of 263 patients who developed tMN without aHSCT to determine clinico-molecular features unique to post-aHSCT tMN. We show that tMN occurs in up to 2.3% of patients at median of 2.6 years post-AHSCT. Age ≥ 60 years, male sex, radiotherapy, high treatment burden ( ≥ 3 lines of chemotherapy), and graft cellularity increased the risk of tMN. Time to evolution and overall survival were shorter in post-aHSCT tMN vs. other tMN, and the earlier group's mutational pattern was enriched in PPM1D and TP53 lesions. Preexisting CH increased the risk of adverse outcomes including post-aHSCT tMN. Particularly, antecedent lesions affecting PPM1D and TP53 predicted tMN evolution post-transplant. Notably, CH-derived tMN had worse outcomes than non CH-derived tMN. As such, screening for CH before aHSCT may inform individual patients' prognostic outcomes and influence their prospective treatment plans. Presented in part as an oral abstract at the 2022 American Society of Hematology Annual Meeting, New Orleans, LA, 2022.

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.

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.