Mutation order in acute myeloid leukemia identifies uncommon patterns of evolution and illuminates phenotypic heterogeneity.

Acute myeloid leukemia (AML) has a poor prognosis and a heterogeneous mutation landscape. Although common mutations are well-studied, little research has characterized how the sequence of mutations relates to clinical features. Using published, single-cell DNA sequencing data from three institutions, we compared clonal evolution patterns in AML to patient characteristics, disease phenotype, and outcomes. Mutation trees, which represent the order of select mutations, were created for 207 patients from targeted panel sequencing data using 1 639 162 cells, 823 mutations, and 275 samples. In 224 distinct orderings of mutated genes, mutations related to DNA methylation typically preceded those related to cell signaling, but signaling-first cases did occur, and had higher peripheral cell counts, increased signaling mutation homozygosity, and younger patient age. Serial sample analysis suggested that NPM1 and DNA methylation mutations provide an advantage to signaling mutations in AML. Interestingly, WT1 mutation evolution shared features with signaling mutations, such as WT1-early being proliferative and occurring in younger individuals, trends that remained in multivariable regression. Some mutation orderings had a worse prognosis, but this was mediated by unfavorable mutations, not mutation order. These findings add a dimension to the mutation landscape of AML, identifying uncommon patterns of leukemogenesis and shedding light on heterogeneous phenotypes.

Mutation order in acute myeloid leukemia identifies uncommon patterns of evolution and illuminates phenotypic heterogeneity.

Acute myeloid leukemia (AML) has a poor prognosis and a heterogeneous mutation landscape. Although common mutations are well-studied, little research has characterized how the sequence of mutations relates to clinical features. Using published, single-cell DNA sequencing data from three institutions, we compared clonal evolution patterns in AML to patient characteristics, disease phenotype, and outcomes. Mutation trees, which represent the order of select mutations, were created for 207 patients from targeted panel sequencing data using 1 639 162 cells, 823 mutations, and 275 samples. In 224 distinct orderings of mutated genes, mutations related to DNA methylation typically preceded those related to cell signaling, but signaling-first cases did occur, and had higher peripheral cell counts, increased signaling mutation homozygosity, and younger patient age. Serial sample analysis suggested that NPM1 and DNA methylation mutations provide an advantage to signaling mutations in AML. Interestingly, WT1 mutation evolution shared features with signaling mutations, such as WT1-early being proliferative and occurring in younger individuals, trends that remained in multivariable regression. Some mutation orderings had a worse prognosis, but this was mediated by unfavorable mutations, not mutation order. These findings add a dimension to the mutation landscape of AML, identifying uncommon patterns of leukemogenesis and shedding light on heterogenous phenotypes.

Distinct Assemblies of Heterodimeric Cytokine Receptors Govern Stemness Programs in Leukemia.

Leukemia stem cells (LSC) possess distinct self-renewal and arrested differentiation properties that are responsible for disease emergence, therapy failure, and recurrence in acute myeloid leukemia (AML). Despite AML displaying extensive biological and clinical heterogeneity, LSC with high interleukin-3 receptor (IL3R) levels are a constant yet puzzling feature, as this receptor lacks tyrosine kinase activity. Here, we show that the heterodimeric IL3Rα/ßc receptor assembles into hexamers and dodecamers through a unique interface in the 3D structure, where high IL3Rα/ßc ratios bias hexamer formation. Importantly, receptor stoichiometry is clinically relevant as it varies across the individual cells in the AML hierarchy, in which high IL3Rα/ßc ratios in LSCs drive hexamer-mediated stemness programs and poor patient survival, while low ratios mediate differentiation. Our study establishes a new paradigm in which alternative cytokine receptor stoichiometries differentially regulate cell fate, a signaling mechanism that may be generalizable to other transformed cellular hierarchies and of potential therapeutic significance. SIGNIFICANCE: Stemness is a hallmark of many cancers and is largely responsible for disease emergence, progression, and relapse. Our finding that clinically significant stemness programs in AML are directly regulated by different stoichiometries of cytokine receptors represents a hitherto unexplained mechanism underlying cell-fate decisions in cancer stem cell hierarchies. This article is highlighted in the In This Issue feature, p. 1749.

Germline mutations in mitochondrial complex I reveal genetic and targetable vulnerability in IDH1-mutant acute myeloid leukaemia.

The interaction of germline variation and somatic cancer driver mutations is under-investigated. Here we describe the genomic mitochondrial landscape in adult acute myeloid leukaemia (AML) and show that rare variants affecting the nuclear- and mitochondrially-encoded complex I genes show near-mutual exclusivity with somatic driver mutations affecting isocitrate dehydrogenase 1 (IDH1), but not IDH2 suggesting a unique epistatic relationship. Whereas AML cells with rare complex I variants or mutations in IDH1 or IDH2 all display attenuated mitochondrial respiration, heightened sensitivity to complex I inhibitors including the clinical-grade inhibitor, IACS-010759, is observed only for IDH1-mutant AML. Furthermore, IDH1 mutant blasts that are resistant to the IDH1-mutant inhibitor, ivosidenib, retain sensitivity to complex I inhibition. We propose that the IDH1 mutation limits the flexibility for citrate utilization in the presence of impaired complex I activity to a degree that is not apparent in IDH2 mutant cells, exposing a mutation-specific metabolic vulnerability. This reduced metabolic plasticity explains the epistatic relationship between the germline complex I variants and oncogenic IDH1 mutation underscoring the utility of genomic data in revealing metabolic vulnerabilities with implications for therapy.

Clonal architecture predicts clinical outcomes and drug sensitivity in acute myeloid leukemia.

The impact of clonal heterogeneity on disease behavior or drug response in acute myeloid leukemia remains poorly understood. Using a cohort of 2,829 patients, we identify features of clonality associated with clinical features and drug sensitivities. High variant allele frequency for 7 mutations (including NRAS and TET2) associate with dismal prognosis; elevated GATA2 variant allele frequency correlates with better outcomes. Clinical features such as white blood cell count and blast percentage correlate with the subclonal abundance of mutations such as TP53 and IDH1. Furthermore, patients with cohesin mutations occurring before NPM1, or transcription factor mutations occurring before splicing factor mutations, show shorter survival. Surprisingly, a branched pattern of clonal evolution is associated with superior clinical outcomes. Finally, several mutations (including NRAS and IDH1) predict drug sensitivity based on their subclonal abundance. Together, these results demonstrate the importance of assessing clonal heterogeneity with implications for prognosis and actionable biomarkers for therapy.

IL-6 blockade reverses bone marrow failure induced by human acute myeloid leukemia.

Most patients with acute myeloid leukemia (AML) die from complications arising from cytopenias resulting from bone marrow (BM) failure. The common presumption among physicians is that AML-induced BM failure is primarily due to overcrowding, yet BM failure is observed even with low burden of disease. Here, we use large clinical datasets to show the lack of correlation between BM blast burden and degree of cytopenias at the time of diagnosis. We develop a splenectomized xenograft model to demonstrate that transplantation of human primary AML into immunocompromised mice recapitulates the human disease course by induction of BM failure via depletion of mouse hematopoietic stem and progenitor populations. Using unbiased approaches, we show that AML-elaborated IL-6 acts to block erythroid differentiation at the proerythroblast stage and that blocking antibodies against human IL-6 can improve AML-induced anemia and prolong overall survival, suggesting a potential therapeutic approach.

Data mining for mutation-specific targets in acute myeloid leukemia.

Three mutation-specific targeted therapies have recently been approved by the FDA for the treatment of acute myeloid leukemia (AML): midostaurin for FLT3 mutations, enasidenib for relapsed or refractory cases with IDH2 mutations, and ivosidenib for cases with an IDH1 mutation. Together, these agents offer a mutation-directed treatment approach for up to 45% of de novo adult AML cases, a welcome deluge after a prolonged drought. At the same time, a number of computational tools have recently been developed that promise to further accelerate progress in mutation-specific therapy for AML and other cancers. Technical advances together with comprehensively annotated AML tissue banks have resulted in the availability of large and complex data sets for exploration by the end-user, including (i) microarray gene expression, (ii) exome sequencing, (iii) deep sequencing data of sub-clone heterogeneity, (iv) RNA sequencing of gene expression (bulk and single cell), (v) DNA methylation and chromatin, (vi) and germline quantitative trait loci. Yet few clinicians or experimental hematologists have the time or the training to access or analyze these repositories. This review summarizes the data sets and bioinformatic tools currently available to further the discovery of mutation-specific targets with an emphasis on web-based applications that are open, accessible, user-friendly, and do not require coding experience to navigate. We show examples of how available data can be mined to identify potential targets using synthetic lethality, drug repurposing, epigenetic sub-grouping, and proteomic networks while also highlighting strengths and limitations and the need for superior models for validation.