Bioinformatic and statistical analysis.

Protein interaction networks were constructed using Cytoscape 3.4. Briefly, identified proteins were represented as nodes in the network. The size of each node relates to the normalized relative quantification value as defined in “Label free relative protein quantitation (LFQ)”: protein node shape was set to “circle”; the length and width (diameter) of the circle were formatted by the continuous mapping function of the software to represent the normalized relative quantification value. Physical protein-protein interaction networks were predicted using STRING v10.0 (http://string.db.org/) with high confidence (parameter value 0.70). Predicted protein-protein interactions were represented as Edges/Links connecting protein nodes; the thickness of each edge represented the statistical significance of the string prediction (Supplemental Tables 6 and 7). Different colors were assigned to protein function complexes, with blue for transcription factors, green for coactivators, and red for corepressors.

Myeloid commitment and monocyte and granulocyte terminal differentiation genes were identified by applying the Comparative Marker Selection (V10) tool in Morpheus (https://software.broadinstitute.org/morpheus/) (95), an algorithm for identifying genes that discriminate between classes of samples, to a public database of gene expression at different stages of hematopoiesis ({"type":"entrez-geo","attrs":{"text":"GSE24759","term_id":"24759"}}GSE24759; ref. 51), to identify probes that significantly discriminated (500 probes in each direction) between CMPs/GMPs versus HSCs/monocytes/granulocytes (commitment genes), monocytes versus HSCs/CMPs/GMPs (monocyte terminal differentiation genes) and granulocytes versus HSCs/CMPs/GMPs (granulocyte terminal differentiation genes). Statistical significance was determined by the 1,000 permutations test and a P value cutoff of ≤0.02. Expression of the commitment and monocyte differentiation genes was correlated with PU.1 expression in the same samples (Pearson’s correlation coefficient).

Expression of myeloid master transcription factors and myeloid differentiation programs in AML cells versus the normal hematopoietic hierarchy were compared using an integrated dataset, BloodPool, that we assembled and built as described previously (48, 49).

GEO database numbers for analyzed ChIP-Seq reads were (52, 96) as follows: {"type":"entrez-geo","attrs":{"text":"GSM538017","term_id":"538017"}}GSM538017 (Pu.1 ChIP-Seq in bone marrow macrophages), {"type":"entrez-geo","attrs":{"text":"GSM537983","term_id":"537983"}}GSM537983 (Pu.1 ChIP-Seq in peritoneal macrophages), and {"type":"entrez-geo","attrs":{"text":"GSM1692857","term_id":"1692857"}}GSM1692857 (Pu.1 ChIP-Seq in hematopoietic progenitors). Aligned ChIP-Seq reads were imported, analyzed, and visualized using EaSeq (97). All values were normalized to reads per million per 1 kbp.