The Proteogenomic Subtypes of Acute Myeloid Leukemia

Acute myeloid leukemia (AML) is an aggressive blood cancer with poor prognosis. We report a comprehensive proteogenomic analysis of bone-marrow biopsies from 252 uniformly treated AML patients to elucidate the molecular pathophysiology of AML in order to inform future diagnostic and therapeutic approaches. In addition to in-depth quantitative proteomics, our analysis includes cytogenetic profiling and DNA/RNA sequencing. We identify five proteomic AML subtypes, each reflecting specific biological features spanning genomic boundaries. Two of these proteomic subtypes correlate with patient outcome, but none are exclusively associated with specific genomic aberrations. Remarkably, one subtype (Mito-AML), which is only captured in the proteome, is characterized by high expression of mitochondrial proteins and confers poor outcome, with reduced remission rate and shorter overall survival upon treatment with intensive induction chemotherapy. Functional analyses reveal that Mito-AML is metabolically wired towards stronger complex I dependent respiration and is more responsive to treatment with the BCL2 inhibitor venetoclax.

Type: Other
Archiver: European Genome-Phenome Archive (EGA)

5 Datasets 1 Publication

Click on a Dataset ID in the table below to learn more, and to find out who to contact about access to these data

Dataset ID	Description	Technology	Samples
EGAD00001008484	Whole transcriptome RNA-sequencing of purified bone marrow blasts of 136 de novo, treatment naive AML patients. For further details, we refer to the manuscript "The Proteogenomic Landscape of AML" by Jayavelu, Wolf, Buettner et al. mRNA extraction and whole transcriptome sequencing For transcriptome analysis the TruSeq Total Stranded RNA kit was used, starting with 250ng of total RNA, to generate RNA libraries following the manufacturer’s recommendations (Illumina, San Diego, CA, USA). 100bp paired-end reads were sequenced on the NovaSeq 6000 (Illumina) with a median of 57 mio. reads per sample. RNA Data Analysis Data quality control was performed with FastQC v0.11.9. Reads were aligned to the human reference genome (Ensembl GRCh38 release 82) using STAR v2.6.1. Gene count tables were generated while mapping, using Gencode v31 annotations. All downstream analyses were carried out using R v4.0 and BioConductor v3.12 (Huber et al., 2015; R Core Team, 2020). Size-factor based normalization was performed using DESeq2 v1.28.1(Love et al., 2014).	Illumina NovaSeq 6000	177
EGAD00001008485	Sequencing data from a targeted myeloid DNA-Panelsequnencing at the MLL Dx, Munich lab. Targeted sequencing was performed using the Nextera DNA Flex library preparation kit, starting with 100ng of genomic DNA (Illumina, San Diego, CA, USA). The target regions were enriched by a custom xGen Lockdown panel using a hybridization capture workflow (IDT Integrated DNA Technologies, Coralville, IA, USA). All libraries were sequenced with 100bp paired-end reads on a NovaSeq6000 (Illumina) with a mean coverage of 3206x. Somatic variant calling was performed with Pisces and a sensitivity cut off of 2%. Large deletions and medium-sized insertions, as they are for example found in CALR and FLT3, were called with Pindel. Variant annotation considered the publicly available data bases Cosmic (v91), ClinVar (2020-03), gnomAd (non-cancer, v2.1.1), dbNSFP (v3.5) and UMD TP53 (2017_R2). Variants that are described as somatic, protein truncating or affecting splice sites were considered as mutations while variants with no or discrepant data base information were considered as variant of uncertain significance.	Illumina NovaSeq 6000	15
EGAD00001008488	To infer the proteomic Mito signature in the LSC subcompartiment, myeloid blasts for 10 patients from the discovery cohort were FACS-sorted into CD34-GPR56+NKG2DLigands- (CD34-), alias 61dc5fb798e2520001702c03 CD34+GPR56+NKG2DLigands- (CD34+), alias 61dc5fb798e2520001702c03 Detailed gating strategy will be described in Donato, Correia, Andresen and Trumpp et al., (manuscript in preparation)	NextSeq 550	1
EGAD00001008501	Targeted myeloid DNA-Panelsequencing from purified bone marrow blasts of 104 treatment naive AML patients from the discovery cohort. For more details, we refere to Jayavelu, Wolf, Buettner et al. Libraries were prepared from 40 ng DNA using the QIASeq Human Myeloid Neoplasms Panel (Qiagen) according to the manufacturer’s protocol. Samples were tagged with the QIAseq 96-Unique Dual Index Set A for Illumina platforms (Qiagen) to yield unique combinations of i5 and i7 barcodes for each sample. Sample fragment size distribution and concentration was estimated using the Agilent High Sensitivity DNA kit on a 2100 Bioanalyzer (Agilent). Samples were pooled in an equimolar fashion, denatured, and diluted to 1.5 pM according to Illumina’s recommendations. The diluted library was sequenced on a NextSeq 500 benchtop sequencer (Illumina) using NextSeq High Output cartridges. Demultiplexing was performed using the BaseSpace cloud platform (Illumina).	NextSeq 500	19
EGAD00001008506	Profiling of co-mutations was done by targeted resequencing using the TruSight Myeloid assay (Illumina, Chesterford, UK) covering 54 genes recurrently mutated in AML: BCOR, BCORL1, CDKN2A, CEBPA, CUX1, DNMT3A, ETV6, EZH2, IKZF1, KDM6A, PHF6, RAD21, RUNX1, STAG2, ZRSR2, ABL1, ASXL1, ATRX, BRAF, CALR, CBL, CBLB, CBLC, CDKN2A, CSF3R, FBXW7, FLT3, GATA1, GATA2, GNAS, HRAS, IDH1, IDH2, JAK2, JAK3, KIT, KRAS, MLL, MPL, MYD88, NOTCH1, NPM1, NRAS, PDGFRA, PTEN, PTPN11, SETBP1, SF3B1, SMC1A, SMC3, SRSF2, TET2, TP53, U2AF1 and WT1. For each reaction, 50 ng of genomic DNA was used. Library preparation was done as recommended by the manufacturer (TruSight Myeloid Sequencing Panel Reference Guide 15054779 v02, Illumina). Samples were sequenced paired-end (150 bp PE) on NextSeq- (Illumina) or (300 bp PE) MiSeq-NGS platforms, with a median coverage of 3076 reads (range 824–30565). Sequence data alignment of demultiplexed FastQ files, variant calling and filtering was done using the Sequence Pilot software package (JSI medical systems GmbH, Ettenheim, Germany) with default settings and a 5% variant allele frequency (VAF) mutation calling cut-off. Human genome build HG19 was used as reference genome for mapping algorithms.		-

Publications	Citations
Preleukemic single-cell landscapes reveal mutation-specific mechanisms and gene programs predictive of AML patient outcomes. Isobe T, Kucinski I, Barile M, Wang X, Hannah R, Bastos HP, Chabra S, Vijayabaskar MS, Sturgess KHM, Williams MJ, Giotopoulos G, Marando L, Li J, Rak J, Gozdecka M, Prins D, Shepherd MS, Watcham S, Green AR, Kent DG, Vassiliou GS, Huntly BJP, Wilson NK, Göttgens B. Cell Genom 3: 2023 100426	0

Publications

Citations

Preleukemic single-cell landscapes reveal mutation-specific mechanisms and gene programs predictive of AML patient outcomes.
Isobe T, Kucinski I, Barile M, Wang X, Hannah R, Bastos HP, Chabra S, Vijayabaskar MS, Sturgess KHM, Williams MJ, Giotopoulos G, Marando L, Li J, Rak J, Gozdecka M, Prins D, Shepherd MS, Watcham S, Green AR, Kent DG, Vassiliou GS, Huntly BJP, Wilson NK, Göttgens B. Cell Genom 3: 2023 100426