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Deconvolution of Bulk Gene Expression Profiles with Single-Cell Transcriptomics to Develop a Cell Type Composition-Based Prognostic Model for Acute Myeloid Leukemia.

ABSTRACT: Acute myeloid leukemia (AML) is one of the malignant hematologic cancers with rapid progress and poor prognosis. Most AML prognostic stratifications focused on genetic abnormalities. However, none of them was established based on the cell type compositions (CTCs) of peripheral blood or bone marrow aspirates from patients at diagnosis. Here we sought to develop a novel prognostic model for AML in adults based on the CTCs. First, we applied the CIBERSORT algorithm to estimate the CTCs for patients from two public datasets (GSE6891 and TCGA-LAML) using a custom gene expression signature reference constructed by an AML single-cell RNA sequencing dataset (GSE116256). Then, a CTC-based prognostic model was established using least absolute shrinkage and selection operator Cox regression, termed CTC score. The constructed prognostic model CTC score comprised 3 cell types, GMP-like, HSC-like, and T. Compared with the low-CTC-score group, the high-CTC-score group showed a 1.57-fold [95% confidence interval (CI), 1.23 to 2.00; p = 0.0002] and a 2.32-fold (95% CI, 1.53 to 3.51; p < 0.0001) higher overall mortality risk in the training set (GSE6891) and validation set (TCGA-LAML), respectively. When adjusting for age at diagnosis, cytogenetic risk, and karyotype, the CTC score remained statistically significant in both the training set [hazard ratio (HR) = 2.25; 95% CI, 1.20 to 4.24; p = 0.0119] and the validation set (HR = 7.97; 95% CI, 2.95 to 21.56; p < 0.0001]. We further compared the performance of the CTC score with two gene expression-based prognostic scores: the 17-gene leukemic stem cell score (LSC17 score) and the AML prognostic score (APS). It turned out that the CTC score achieved comparable performance at 1-, 2-, 3-, and 5-years timepoints and provided independent and additional prognostic information different from the LSC17 score and APS. In conclusion, the CTC score could serve as a powerful prognostic marker for AML and has great potential to assist clinicians to formulate individualized treatment plans.

SUBMITTER: Dai C

PROVIDER: S-EPMC8633313 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BACKGROUND:To facilitate the investigation of the pathogenic roles played by various immune cells in complex tissues such as tumors, a few computational methods for deconvoluting bulk gene expression profiles to predict cell composition have been created. However, available methods were usually developed along with a set of reference gene expression profiles consisting of imbalanced replicates across different cell types. Therefore, the objective of this study was to create a new deconvolution method equipped with a new set of reference gene expression profiles that incorporate more microarray replicates of the immune cells that have been frequently implicated in the poor prognosis of cancers, such as T helper cells, regulatory T cells and macrophage M1/M2 cells. METHODS:Our deconvolution method was developed by choosing ?-support vector regression (?-SVR) as the core algorithm assigned with a loss function subject to the L1-norm penalty. To construct the reference gene expression signature matrix for regression, a subset of differentially expressed genes were chosen from 148 microarray-based gene expression profiles for 9 types of immune cells by using ANOVA and minimizing condition number. Agreement analyses including mean absolute percentage errors and Bland-Altman plots were carried out to compare the performances of our method and CIBERSORT. RESULTS:In silico cell mixtures, simulated bulk tissues, and real human samples with known immune-cell fractions were used as the test datasets for benchmarking. Our method outperformed CIBERSORT in the benchmarks using in silico breast tissue-immune cell mixtures in the proportions of 30:70 and 50:50, and in the benchmark using 164 human PBMC samples. Our results suggest that the performance of our method was at least comparable to that of a state-of-the-art tool, CIBERSORT. CONCLUSIONS:We developed a new cell composition deconvolution method and the implementation was entirely based on the publicly available R and Python packages. In addition, we compiled a new set of reference gene expression profiles, which might allow for a more robust prediction of the immune cell fractions from the expression profiles of cell mixtures. The source code of our method could be downloaded from https://github.com/holiday01/deconvolution-to-estimate-immune-cell-subsets.

Project description:Background Acute myeloid leukemia (AML) is a heterogeneous and aggressive blood cancer that results from diverse genetic aberrations in the hematopoietic stem or progenitor cells (HSPCs) leading to the expansion of blasts in the hematopoietic system. The heterogeneity and evolution of cancer blasts can render therapeutic interventions ineffective in a yet poorly understood patient-specific manner. In this study, we investigated the clonal heterogeneity of diagnosis (Dx) and relapse (Re) pairs at genetic and transcriptional levels, and unveiled the underlying pathways and genes contributing to recurrence. Methods Whole-exome sequencing was used to detect somatic mutations and large copy number variations (CNVs). Single cell RNA-seq was performed to investigate the clonal heterogeneity between Dx-Re pairs and amongst patients. Results scRNA-seq analysis revealed extensive expression differences between patients and Dx-Re pairs, even for those with the same -presumed- initiating events. Transcriptional differences between and within patients are associated with clonal composition and evolution, with the most striking differences in patients that gained large-scale copy number variations at relapse. These differences appear to have significant molecular implications, exemplified by a DNMT3A/FLT3-ITD patient where the leukemia switched from an AP-1 regulated clone at Dx to a mTOR signaling driven clone at Re. The two distinct AML1-ETO pairs share genes related to hematopoietic stem cell maintenance and cell migration suggesting that the Re leukemic stem cell-like (LSC-like) cells evolved from the Dx cells. Conclusions In summary, the single cell RNA data underpinned the tumor heterogeneity not only amongst patient blasts with similar initiating mutations but also between each Dx-Re pair. Our results suggest alternatively and currently unappreciated and unexplored mechanisms leading to therapeutic resistance and AML recurrence. Supplementary Information The online version contains supplementary material available at 10.1186/s12943-022-01635-4.

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Deconvolution of Bulk Gene Expression Profiles with Single-Cell Transcriptomics to Develop a Cell Type Composition-Based Prognostic Model for Acute Myeloid Leukemia.

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