A Prognostic Model for Acute Myeloid Leukemia Based on IL-2/STAT5 Pathway-Related Genes

Unraveling genotype-phenotype associations and predictive modeling of outcome in acute myeloid leukemia

Acute myeloid leukemia (AML) comprises 32% of adult leukemia cases, with a 5-year survival rate of only 20-30%. Here, the immunophenotypic landscape of this heterogeneous malignancy is explored in a single-center cohort using a novel quantitative computational pipeline. For 122 patients who underwent induction treatment with intensive chemotherapy, leukemic cells were identified at diagnosis, computationally preprocessed, and quantitatively subtyped. Computational analysis provided a broad characterization of inter- and intra-patient heterogeneity, which would have been harder to achieve with manual bivariate gating. Statistical testing discovered associations between CD34, CD117, and HLA-DR expression patterns and genetic abnormalities. We found the presence of CD34+ cell populations at diagnosis to be associated with a shorter time to relapse. Moreover, CD34- CD117+ cell populations were associated with a longer time to AML-related mortality. Machine learning (ML) models were developed to predict 2-year survival, European LeukemiaNet (ELN) risk category, and inv(16) or NPM1mut, based on computationally quantified leukemic cell populations and limited clinical data, both readily available at diagnosis. We used explainable artificial intelligence (AI) to identify the key clinical characteristics and leukemic cell populations important for our ML models when making these predictions. Our findings highlight the importance of developing objective computational pipelines integrating immunophenotypic and genetic information in the risk stratification of AML.

Identification of hub genes and potential molecular mechanisms related to drug sensitivity in acute myeloid leukemia based on machine learning

Background: Acute myeloid leukemia (AML) is the most common form of leukemia among adults and is characterized by uncontrolled proliferation and clonal expansion of hematopoietic cells. There has been a significant improvement in the treatment of younger patients, however, prognosis in the elderly AML patients remains poor. Methods: We used computational methods and machine learning (ML) techniques to identify and explore the differential high-risk genes (DHRGs) in AML. The DHRGs were explored through multiple in silico approaches including genomic and functional analysis, survival analysis, immune infiltration, miRNA co-expression and stemness features analyses to reveal their prognostic importance in AML. Furthermore, using different ML algorithms, prognostic models were constructed and validated using the DHRGs. At the end molecular docking studies were performed to identify potential drug candidates targeting the selected DHRGs. Results: We identified a total of 80 DHRGs by comparing the differentially expressed genes derived between AML patients and normal controls and high-risk AML genes identified by Cox regression. Genetic and epigenetic alteration analyses of the DHRGs revealed a significant association of their copy number variations and methylation status with overall survival (OS) of AML patients. Out of the 137 models constructed using different ML algorithms, the combination of Ridge and plsRcox maintained the highest mean C-index and was used to build the final model. When AML patients were classified into low- and high-risk groups based on DHRGs, the low-risk group had significantly longer OS in the AML training and validation cohorts. Furthermore, immune infiltration, miRNA coexpression, stemness feature and hallmark pathway analyses revealed significant differences in the prognosis of the low- and high-risk AML groups. Drug sensitivity and molecular docking studies revealed top 5 drugs, including carboplatin and austocystin-D that may significantly affect the DHRGs in AML. Conclusion: The findings from the current study identified a set of high-risk genes that may be used as prognostic and therapeutic markers for AML patients. In addition, significant use of the ML algorithms in constructing and validating the prognostic models in AML was demonstrated. Although our study used extensive bioinformatics and machine learning methods to identify the hub genes in AML, their experimental validations using knock-out/-in methods would strengthen our findings.

A Novel Prognostic Model for Acute Myeloid Leukemia Based on Gene Set Variation Analysis

Acute myeloid leukemia (AML) is a malignant hematological malignancy with a poor prognosis. Risk stratification of patients with AML is mainly based on the characteristics of cytogenetics and molecular genetics; however, patients with favorable genetics may have a poor prognosis. Here, we focused on the activity changes of immunologic and hallmark gene sets in the AML population. Based on the enrichment score of gene sets by gene set variation analysis (GSVA), we identified three AML subtypes by the nonnegative matrix factorization (NMF) algorithm in the TCGA cohort. AML patients in subgroup 1 had worse overall survival (OS) than subgroups 2 and 3 ( $P<0.001$ ). The median overall survival (mOS) of subgroups 1–3 was 0.4, 2.2, and 1.7 years, respectively. Clinical characteristics, including age and FAB classification, were significantly different among each subgroup. Using the least absolute shrinkage and selection operator (LASSO) regression method, we discovered three prognostic gene sets and established the final prognostic model based on them. Patients in the high-risk group had significantly shorter OS than those in the low-risk group in the TCGA cohort ( $P<0.001$ ) with mOS of 2.2 and 0.7 years in the low- and high-risk groups, respectively. The results were further validated in the GSE146173 and GSE12417 cohorts. We further identified the key genes of prognostic gene sets using a protein-protein interaction network. In conclusion, the study established and validated a novel prognostic model for risk stratification in AML, which provides a new perspective for accurate prognosis assessment.

A Novel Prognostic Model for Acute Myeloid Leukemia Based on Gene Set Variation Analysis

Acute myeloid leukemia (AML) is a malignant hematological malignancy with a poor prognosis. Risk stratification of patients with AML is mainly based on the characteristics of cytogenetics and molecular genetics; however, patients with favorable genetics may have a poor prognosis. Here, we focused on the activity changes of immunologic and hallmark gene sets in the AML population. Based on the enrichment score of gene sets by gene set variation analysis (GSVA), we identified three AML subtypes by the nonnegative matrix factorization (NMF) algorithm in the TCGA cohort. AML patients in subgroup 1 had worse overall survival (OS) than subgroups 2 and 3 ( $P<0.001$ ). The median overall survival (mOS) of subgroups 1–3 was 0.4, 2.2, and 1.7 years, respectively. Clinical characteristics, including age and FAB classification, were significantly different among each subgroup. Using the least absolute shrinkage and selection operator (LASSO) regression method, we discovered three prognostic gene sets and established the final prognostic model based on them. Patients in the high-risk group had significantly shorter OS than those in the low-risk group in the TCGA cohort ( $P<0.001$ ) with mOS of 2.2 and 0.7 years in the low- and high-risk groups, respectively. The results were further validated in the GSE146173 and GSE12417 cohorts. We further identified the key genes of prognostic gene sets using a protein-protein interaction network. In conclusion, the study established and validated a novel prognostic model for risk stratification in AML, which provides a new perspective for accurate prognosis assessment.

Establishment of tumor protein p53 mutation-based prognostic signatures for acute myeloid leukemia

PURPOSE

The tumor protein p53 gene (TP53) mutations are associated with poor prognosis of patients with acute myeloid leukemia (AML). This study aimed to establish TP53 mutation-based prognostic risk signatures.

PATIENTS AND METHODS

The transcriptomes and clinical characteristics of AML patients were acquired from The Cancer Genome Atlas database, including 11 TP53-mutant samples and 114 TP53-wildtype samples. Differentially expressed mRNAs and long non-coding RNAs (lncRNA) in TP53-mutant samples were identified. Weighted gene correlation network analysis was performed to generate survival-associated co-expression modules. LASSO regression analysis was conducted to build mRNA- and lncRNA-based prognostic risk signatures. Kaplan-Meier curve analysis and multivariate regression analysis were carried out to assess the prognostic values of the risk signatures. Receiver operating characteristic (ROC) analysis was performed to evaluate the accuracy of the signatures.

RESULTS

Based on the co-expression modules, a 5-mRNA risk signature and a 13-lncRNA risk signature were constructed to predict the overall survival for AML patients. Kaplan-Meier curves revealed that the high-risk patients had significantly shorter overall survival than the low-risk patients. ROC analysis yielded 1-, 3-, and 5-year AUCs of 0.681, 0.783, and 0.827 for mRNA signature and 0.85, 0.835, and 0.908 for lncRNA signature. Multivariate regression analysis revealed that both mRNA (HR = 1.45, P< 0.001) and lncRNA (HR = 1.19, P< 0.001) risk scores were independent prognostic factors for AML patients.

CONCLUSION

We provided a potential patients stratification tool for AML prognosis prediction and management, which established by effective TP53 mutation-related gene signatures.