Project description:Transcriptome profiling of Acute Myeloid Leukemia samples. This dataset includes patients with diagnosis of de novo or secondary AML who experienced non-HLA loss disease relapse after allo-HCT, and for whom paired pre- and post-transplant viable leukemic samples were available.
Project description:Transcriptome profiling of Acute Myeloid Leukemia samples. This dataset includes patients with diagnosis of de novo or secondary AML who experienced non-HLA loss disease relapse after allo-HCT, and for whom paired pre- and post-transplant viable leukemic samples were available.
Project description:Transcriptome profiling of Acute Myeloid Leukemia samples. This dataset includes patients with diagnosis of de novo or secondary AML who experienced non-HLA loss disease relapse after allo-HCT, and for whom paired pre- and post-transplant viable leukemic samples were available.
Project description:Genotype profiling of Acute Myeloid Leukemia samples. This dataset includes patients with diagnosis of de novo or secondary AML who experienced non-HLA loss disease relapse after allo-HCT, and for whom paired pre- and post-transplant viable leukemic samples were available.
Project description:To understand relapse mechanisms related to AML patients treated with venetoclax plus azacitidine therapy, we performed CITE-seq on paired diagnosis and relapse specimens from an AML patient treated with the therapy.
Project description:35 paired samples from initial diagnosis and first marrow relapse. Genes and pathways differentiating diagnosis and relapse were identified. Potential therapeutic targets were also identified. Keywords: paired
Project description:35 paired samples from initial diagnosis and first marrow relapse. Genes and pathways differentiating diagnosis and relapse were identified. Potential therapeutic targets were also identified. Experiment Overall Design: 35 patients with samples from initial diagnosis and first marrow relapse.
Project description:The treatment landscape of AML is evolving with promising therapies entering clinical translation, yet patient responses remain heterogeneous and biomarkers for tailoring treatment are lacking. To understand how disease heterogeneity links with therapy response, we determined the leukemia cell hierarchy make-up from bulk transcriptomes of over 1000 patients through deconvolution using single-cell reference profiles of leukemia stem, progenitor, and mature cell types. Leukemia hierarchy composition was associated with functional, genomic, and clinical properties and converged into four overall classes, spanning Primitive, Mature, GMP, and Intermediate. Critically, variation in hierarchy composition along the Primitive vs GMP or Primitive vs Mature axes were associated with response to chemotherapy or drug sensitivity profiles of targeted therapies, respectively. A 7-gene biomarker derived from the Primitive vs Mature axis was predictive of patient response to 105 investigational drugs. Thus, hierarchy composition constitutes a novel framework for understanding disease biology and advancing precision medicine in AML.