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The Leucegene AML surfaceome atlas: immunotherapeutic target identification in AML

ABSTRACT: We exploited the extensive genomic diversity of the Leucegene cohort of primary human AML specimens to provide an overview of the human AML surfaceome. Due to high cell number requirements, surface proteomics has been underexploited in AML so far, although surface proteome analysis of AML cell lines and small cohorts of primary human AML specimens paved the way for antigen identification23-25. Herein, we compared global and surface proteomic datasets generated from primary human AML specimens and show that surfaceome analysis uniquely identifies a larger subset of cell surface proteins compared to global proteomics. We therefore built a cohort of 100 primary human AML specimens that was subjected to surface proteome analysis and served as a primary dataset for antigen identification. A significant portion of the cohort also underwent single-cell RNA sequencing, which allowed the exploration of antigen expression at the population level and the selection of AML antigens expressed by primitive blasts. These analyses led to the identification of novel AML antigens expressed by the majority of AML specimens of the cohort, of antigens overexpressed by specific AML subgroups, as well as of previously uncovered potential leukemia stem cell (LSC) markers, and represents the first large-scale surface proteomic study in AML.

INSTRUMENT(S): Q Exactive HF

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Cell Culture

SUBMITTER: Eric Bonneil

LAB HEAD: Guy Sauvageau

PROVIDER: PXD043772 | Pride | 2024-05-21

REPOSITORIES: Pride

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			Action	DRS
	Service_Roux_061018_1.raw	Raw
	Service_Roux_061018_2.raw	Raw
	Service_Roux_061018_3.raw	Raw
	Service_Roux_061018_4.raw	Raw
	Service_Roux_061018_5.raw	Raw

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Project description:Therapy-related myelodysplasia or acute myeloid leukemia (t-MDS/AML) is a lethal complication of cancer treatment. Although t-MDS/AML development is associated with known genotoxic exposures, its pathogenesis is not well understood and methods to predict risk of development of t-MDS/AML in individual cancer survivors are not available. We performed microarray analysis of gene expression in samples from patients who developed t-MDS/AML after autologous hematopoietic cell transplantation (aHCT) for Hodgkin lymphoma (HL) or non-Hodgkin lymphoma (NHL) and controls that did not develop t-MDS/AML after aHCT. CD34+ progenitor cells from peripheral blood stem cell (PBSC) samples obtained pre-aHCT from t-MDS/AML cases and matched controls, and bone marrow (BM) samples obtained at time of development of t-MDS/AML, were studied. Significant differences in gene expression were seen in PBSC obtained pre-aHCT from patients who subsequently developed t-MDS/AML compared to controls. Genetic alterations in pre-aHCT samples were related to mitochondrial function, protein synthesis, metabolic regulation and hematopoietic regulation. Progression to overt t-MDS/AML was associated with additional alterations in DNA repair and DNA-damage checkpoint genes. Altered gene expression in PBSC samples were validated in an independent group of patients. An optimal 63-gene PBSC classifier derived from the training set accurately distinguished patients who did or did not develop t-MDS/AML in the independent test set. These results indicate that genetic programs associated with t-MDS/AML are perturbed long before disease onset, and can accurately identify those at risk of developing this complication. PBSC samples obtained pre-aHCT and BM samples at the time of development of t-MDS/AML post-HCT were studied. The training set consisted of 18 patients who developed t-MDS/AML (M-bM-^@M-^]casesM-bM-^@M-^]) after aHCT, matched with 37 controls who underwent aHCT, but did not develop t-MDS/AML. One to three controls were selected per case, matched for primary diagnosis (HL/NHL), age at aHCT (M-BM-110years), and ethnicity (Caucasians, African-Americans, Hispanics, other). The length of follow-up after aHCT for controls was longer than the time to t-MDS/AML in the corresponding case. The results of the training set were validated in an independent group of 36 patients (test set) consisting of 16 cases that developed t-MDS/AML post-aHCT and 20 matched controls. In the test set, 55 PBSC samples from 18 cases and 37 matched controls were studied. BM samples from time of development of t-MDS/AML were available for 12 cases, and from 21 matched controls obtained at a comparable time from aHCT. For validation, 36 PBSC samples from 16 cases and 20 matched controls were studied. All samples had been cryopreserved as mononuclear cells. After thawing, samples were labeled with anti-CD34-APC and anti-CD45-FITC and CD34+CD45dim cells were selected using flow cytometry. Total RNA was extracted using the RNeasy kit. RNA from 1000 cells was amplified and labeled using GeneChipM-BM-. Two-Cycle Target Labeling and Control Reagents from Affymetrix. 15 M-BM-5g of cRNA each was hybridized to Affymetrix HG U133 plus 2.0 Arrays. Microarray data were analyzed using R (version 2.9) with genomic analysis packages from Bioconductor (version 2.4). Data for PBSC and BM samples were normalized separately using robust multiarray averages with consideration of GC content (GCRMA). Probesets with low expression or variability were filtered. Expression of genes represented by multiple probesets was set as the median of the probesets. Using conditional logistic model (CLM) to retain matching between cases and controls, we analyzed the magnitude of association [expressed as odds ratio (OR)] between t-MDS/AML and i) gene expression levels in PBSC at the pre-aHCT time point; ii) gene expression levels in BM at time of t-MDS/AML; and iii) change of expression of individual genes from PBSC to time of t-MDS/AML. False discovery rate (FDR) was applied to adjust for multiple testing. Gene set enrichment analysis (GSEA) was performed on ranked lists of genes differentially expressed between cases and controls. Where multiple significant gene sets were related to each other, analysis was performed to identify a subset of common enriched genes. Average gene expression was calculated for each set and heatmaps plotted to show the contrasts between cases and controls. Gene Ontology (GO) and pathway analysis was performed using DAVID 2008 and Ingenuity IPA 7.5 respectively, retaining genes with z-scores M-bM-^IM-%1.8 or M-bM-^IM-$-1.8, and M-bM-^IM-%1.5-fold change in OR between cases and controls. The association between gene expression in the PBSC product and subsequent development of t-MDS/AML identified in the training set was validated in an independent test set of 36 PBSC sample procured from patients who developed t-MDS/AML after aHCT (16 cases) or did not (20 controls). Pre-processing, normalization and filtering procedures for the test set were identical to the training set. Differential expression between cases and controls was analyzed using CLM. GSEA analysis was performed on the ranked list of differentially expressed genes. Prediction analysis of microarray (PAM) was used to derive a prognostic gene signature from the training set to classify patients as case or control. PAM uses the M-bM-^@M-^\nearest shrunken centroidM-bM-^@M-^] approach and 10-fold cross-validation to select a parsimonious gene expression signature that can classify samples with minimal misclassification. PAM was applied to genes common to both datasets. Based on the misclassification error in cross-validation, a 63-gene signature was selected for prediction using the test data.

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The Leucegene AML surfaceome atlas: immunotherapeutic target identification in AML

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