Clonal architecture predicts clinical outcomes and drug sensitivity in acute myeloid leukemia.
1800; 12 (1): 7244
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.
View details for DOI 10.1038/s41467-021-27472-5
View details for PubMedID 34903734
IL-6 blockade reverses bone marrow failure induced by human acute myeloid leukemia.
Science translational medicine
2020; 12 (538)
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.
View details for DOI 10.1126/scitranslmed.aax5104
View details for PubMedID 32269167
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 refractorycases 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.
View details for PubMedID 30728456
Predicting response to new drugs in AML from simulation modelling: Value of the BEAT AML project as a validation resource.
View details for PubMedID 30827724