Multi-Omic Analyses of TP53-Mutated Acute Myeloid Leukemia Identify Prognostic Metabolic Signatures

Introduction

Somatic TP53 mutations and 17p deletions resulting in the genomic loss of TP53 occur in 8% to 10% of de novo AML and are associated with chemotherapy resistance, relapse, and poor outcome. Increasingly, TP53 is being recognized as a guardian of immune integrity. We have previously demonstrated that TP53 abnormalities are linked to elevated PD-L1 and FoxP3 expression, as well as bone marrow (BM) infiltration by dysfunctional natural killer-like CD8⁺ T cells. In this study, we employ a systems-level approach to interrogate HL-60 TP53-isogenic AML cell lines as well as primary bone marrow (BM) samples from patients with TP53-m AML.

Methods

To assess the impact of the three most common mis-sense mutations within the p53 protein’s DNA-binding domain on gene expression, we pursued lentiviral transduction of parental TP53^null HL-60 cells with either a custom TP53-wt construct or the R175H, R248Q and R273H hotspot mutations (GenScript Biotech), followed by RNA-seq analysis. We then carried out proteomic profiling on both the secretome and whole AML cell lysates. We merged legacy sc-RNA-seq data (Penter al et. 2023; Lasry et al. 2023; van Galen et al. 2019) with newly generated sc-RNA-seq data from 4 patients with TP53-m AML, resulting in 139,610 high-quality transcriptomes from 70 patients, 13 of which had TP53-m AML. After batch correction, we re-annotated the integrated dataset based on a curated atlas of BM hematopoiesis (263,159 single-cell transcriptomes spanning 55 cellular states; BoneMarrowMap; Zeng et al. 2023).

Results

Upon comparing TP53^null HL-60 with isogenic TP53-wt and TP53-m AML cells, we observed large transcriptional differences unique to each TP53 hotspot mutation. TP53^R175H AML cells exhibited the greatest divergence from their TP53-wt counterpart, including elevated expression of inflammatory response genes, CXCL8, HSPA8 (known to interact with TP53 and associated with inferior outcomes in TCGA solid tumors), and genes involved in metabolic processes (ANXA1, DUSP1) and DNA binding (FOSB). Weighted gene coexpression network analysis (WGCNA) pinpointed 26 modules of interconnected genes. The top-ranking module by false discovery rate encompassed 3,847 genes significantly enriched in gene ontology terms related to protein and DNA metabolism. Congruent with these observations, secretome analyses of TP53^R175H AML cells revealed an enrichment of aminoacid and RNA metabolism pathways. Next, we curated a compendium of gene sets capturing glycolysis, oxidative phosphorylation [OXPHOS], lipid metabolism, cellular senescence and AML stemness, and we calculated ssGSEA scores from bulk RNA-seq data. In contrast to TP53-wt and TP53^R248Q AML cells, which displayed enrichment in cellular senescence, heme and fatty acid metabolism, HL-60 cells harboring the TP53^R175H and TP53^R273H hotspot mutations showed enhanced OXPHOS and a higher prevalence of primitive leukemia stem and progenitor states. To assess the clinical utility of these findings, we built a LASSO-based Cox regression model for feature selection from bulk RNA-seq data from 444 diagnostic Beat-AML2 BM samples. Thirteen metabolic (fatty acid and cholesterol biosynthesis; OXPHOS, production of reactive oxygen species), stemness and senescence-related scores from our compendium predicted overall survival (OS), irrespective of TP53 mutational status. Furthermore, patients with high senescence and OXPHOS scores experienced significantly worse outcomes (median OS = 0.88 months compared to median OS = not reached in patients with low gene expression scores; log-rank P = 0.00014), suggesting that the TP53 pathway may be dysfunctional in a high proportion of patients with TP53-wt AML.

Finally, we computed single-cell gene set scores using our integrated AML atlas and we found that mitochondrial oxidative metabolism genes were expressed by cell clusters annotated as pro-erythroblasts, CFU-E and basophilic erythroblasts, which displayed high predicted pseudotime values, indicating large transcriptional changes. This finding is in keeping with recent reports supporting an erythroid-biased differentiation trajectory in TP53-m secondary AML (Rodriguez-Meira A, 2023).

Conclusions

Our study offers a conceptual framework for exploiting metabolic dependencies with prognostic significance in TP53-m AML. Targeting cancer metabolism could improve outcomes in this heterogeneous patient population.