Systematic analysis of hematopoietic gene expression profiles for prognostic prediction in acute myeloid leukemia

High IL2RA mRNA expression is an independent adverse prognostic biomarker in core binding factor and intermediate-risk acute myeloid leukemia

BACKGROUND

Elevated protein expressions of CD markers such as IL2RA/CD25, CXCR4/CD184, CD34 and CD56 are associated with adverse prognosis in acute myeloid leukemia (AML). However, the prognostic value of mRNA expressions of these CD markers in AML remains unclear. Through our pilot evaluation, IL2RA mRNA expression appeared to be the best candidate as a prognostic biomarker. Therefore, the aim of this study is to characterize the prognostic value of IL2RA mRNA expression and evaluate its potential to refine prognostification in AML.

METHODS

In a cohort of 239 newly diagnosed AML patients, IL2RA mRNA expression were measured by TaqMan realtime quantitative PCR. Morphological, cytogenetics and mutational analyses were also performed. In an intermediate-risk AML cohort with 66 patients, the mRNA expression of prognostic biomarkers (BAALC, CDKN1B, ERG, MECOM/EVI1, FLT3, ID1, IL2RA, MN1 and WT1) were quantified by NanoString technology. A TCGA cohort was analyzed to validate the prognostic value of IL2RA. For statistical analysis, Mann-Whitney U test, Fisher exact test, logistic regression, Kaplan-Meier and Cox regression analyses were used.

RESULTS

In AML cohort of 239 patients, high IL2RA mRNA expression independently predicted shorter relapse free survival (RFS, p < 0.001) and overall survival (OS, p < 0.001) irrespective of age, cytogenetics, FLT3-ITD or c-KIT D816V mutational status. In core binding factor (CBF) AML, high IL2RA mRNA expression correlated with FLT3-ITD status (p = 0.023). Multivariable analyses revealed that high IL2RA expression (p = 0.002), along with c-KIT D816V status (p = 0.013) significantly predicted shorter RFS, whereas only high IL2RA mRNA expression (p = 0.014) significantly predicted shorter OS in CBF AML. In intermediate-risk AML in which multiple gene expression markers were tested by NanoString, IL2RA significantly correlated with ID1 (p = 0.006), FLT3 (p = 0.007), CDKN1B (p = 0.033) and ERG (p = 0.030) expressions. IL2RA (p < 0.001) and FLT3 (p = 0.008) expressions remained significant in predicting shorter RFS, whereas ERG (p = 0.008) and IL2RA (p = 0.044) remained significant in predicting shorter OS. Similar analyses in TCGA intermediate-risk AML showed the independent prognostic role of IL2RA in predicting event free survival (p < 0.001) and OS (p < 0.001).

CONCLUSIONS

High IL2RA mRNA expression is an independent and adverse prognostic factor in AML and specifically stratifies patients to worse prognosis in both CBF and intermediate-risk AML.

The clinical and prognostic significance of FIS1, SPI1, PDCD7 and Ang2 expression levels in acute myeloid leukemia

OBJECTIVES

The marked heterogeneity of acute myeloid leukemia (AML) renders precisely predicting patient prognosis extremely difficult. Genetic alterations, fusions and mutations, may result in misexpression of key genes in AML. We aimed to investigate the expression patterns of 4 novel genes; FIS1, SPI1, PDCD7 and Ang2 to determine their potential prognostic role in AML patients.

METHODS

Bone marrow mononuclear cells were analyzed for of FIS1, SPI1, PDCD7 and Ang2 expression levels by real-time quantitative PCR as well as of FLT3/ITD and NPM1 mutations in 100 newly diagnosed cytogenetically normal (CN-AML) patients, and 100 non-malignant controls.

RESULTS

FIS1, SPI1, PDCD7 and Ang2 were significantly overexpressed in CN-AML patients (p < 0.001). Their high expression levels were significantly associated with lower complete remission (CR) rate, shorter relapse-free survival (RFS) and overall survival (OS). On multivariate analysis, high FIS1 expression showed a significant impact on CR response after induction therapy (OR = 88.777, 95% C.I: 2.85-2765.78, p = 0.011) while high PDCD7 appeared to be an independent risk factor for RFS (HR = 5.107, 95% C.I: 1.731-15.066, p = 0.003) and OS (HR = 7.353, 95% C.I: 1.859-29.079, p = 0.004) in CN-AML patients.

CONCLUSIONS

FIS1 and PDCD7 expression are considered independent risk factors and should be integrated into the current AML stratification system.

Systematic Pan-Cancer Analysis Reveals Immune Cell Interactions in the Tumor Microenvironment

With the recent advent of immunotherapy, there is a critical need to understand immune cell interactions in the tumor microenvironment in both pan-cancer and tissue-specific contexts. Multidimensional datasets have enabled systematic approaches to dissect these interactions in large numbers of patients, furthering our understanding of the patient immune response to solid tumors. Using an integrated approach, we inferred the infiltration levels of distinct immune cell subsets in 23 tumor types from The Cancer Genome Atlas. From these quantities, we constructed a coinfiltration network, revealing interactions between cytolytic cells and myeloid cells in the tumor microenvironment. By integrating patient mutation data, we found that while mutation burden was associated with immune infiltration differences between distinct tumor types, additional factors likely explained differences between tumors originating from the same tissue. We concluded this analysis by examining the prognostic value of individual immune cell subsets as well as how coinfiltration of functionally discordant cell types associated with patient survival. In multiple tumor types, we found that the protective effect of CD8+ T cell infiltration was heavily modulated by coinfiltration of macrophages and other myeloid cell types, suggesting the involvement of myeloid-derived suppressor cells in tumor development. Our findings illustrate complex interactions between different immune cell types in the tumor microenvironment and indicate these interactions play meaningful roles in patient survival. These results demonstrate the importance of personalized immune response profiles when studying the factors underlying tumor immunogenicity and immunotherapy response. Cancer Res; 77(6); 1271-82. ©2017 AACR.

In silico frameworks for systematic pre-clinical screening of potential anti-leukemia therapeutics

INTRODUCTION

Leukemia is a collection of highly heterogeneous cancers that arise from neoplastic transformation and clonal expansion of immature hematopoietic cells. Post-treatment recurrence is high, especially among elderly patients, thus necessitating more effective treatment modalities. Development of novel anti-leukemic compounds relies heavily on traditional in vitro screens which require extensive resources and time. Therefore, integration of in silico screens prior to experimental validation can improve the efficiency of pre-clinical drug development. Areas covered: This article reviews different methods and frameworks used to computationally screen for anti-leukemic agents. In particular, three approaches are discussed including molecular docking, transcriptomic integration, and network analysis. Expert opinion: Today's data deluge presents novel opportunities to develop computational tools and pipelines to screen for likely therapeutic candidates in the treatment of leukemia. Formal integration of these methodologies can accelerate and improve the efficiency of modern day anti-leukemic drug discovery and ease the economic and healthcare burden associated with it.