A Cutting-Edge Bioinformatics Tool for Neoantigen Identification in Unpaired RNA-Seq Samples of AML

Introduction

The precise identification of neoantigens is critical for advancing immunotherapy in hematologic malignancies, particularly in aggressive myeloid neoplasms such as acute myeloid leukemia (AML). Neoantigens, arising from tumor-specific somatic mutations and complex RNA modifications such as alternative splicing and gene fusions, provide specific targets for immune activation. However, the genetic complexity of AML poses significant challenges for their identification. Traditional methods, dependent on paired tumor-normal samples and limited to simple variants, fall short in capturing the full spectrum of neoantigens. Here, we present a novel bioinformatics pipeline addressing this by identifying neoantigens from RNA-Seq data without needing paired samples and by analyzing a broader range of genetic alterations, including alternative splicing and gene fusions. This approach enables the discovery of a richer set of neoantigens, enhancing the potential for personalized immunotherapy strategies.

Objectives

To develop a novel bioinformatics tool for the precise identification of tumor neoantigens in unpaired RNA-Seq samples from patients with AML.

Methodology

We analyzed 104 AML diagnostic samples from bone marrow and peripheral blood using RNA-Seq. These patients were originally recruited in the Pethema Quiwi phase 2B trial. RNA was extracted, sequenced, and analyzed for transcript expression. We focused on detecting SNPs/INDELs, gene fusions, and alternative splicing events to predict neoepitopes. Predicted neoepitopes, ranging from 8 to 11 amino acids, were evaluated for their ability to bind MHC-I and TCR, crucial for immune response activation.

Results

Our novel bioinformatics pipeline successfully identified SNPs/INDELs, gene fusions, and splicing variants in 104 AML samples from unpaired RNA-Seq data. Neoantigens derived from SNPs/INDELs and splicing variants were consistently detected across all samples, while fusion-derived neoantigens were absent in 27 samples. On average, 23,000 neoantigens were identified from SNPs/INDELs, 19 from fusions, and 9,388 from splicing variants. Among the SNPs/INDELs, the most frequently mutated genes included BCL2 and CD58, generating neoantigens in 30 and 26 patients, respectively. Variants in BCL2, a critical regulator of apoptosis, suggest increased cell survival and resistance to apoptosis (Certo et al., 2006). Alterations in CD58, important for immune cell interactions, could contribute to immune evasion by leukemic cells (Berger et al., 1982). Splicing variants were also prominent, with alterations detected in CD27 and CD72 in 20 and 18 patients, respectively. These genes are known to play roles in immune regulation and could impair immune responses against leukemic cells (van Oers et al., 2013; Koopmans et al., 2017). Variants in GDF11 and GDF15, involved in cellular growth and senescence, were identified in 15 and 12 patients, potentially influencing cell proliferation and aging in AML (Egerman et al., 2015; Xu et al., 2006). Additionally, disruptions in NACA and NUP155, related to protein synthesis and nuclear transport, were observed in 14 and 13 patients, respectively, indicating their broad impact on cellular function and tumor biology (Garreau de Loubresse et al., 2014; Enninga et al., 1991). Fusion-derived neoantigens were identified in a subset of samples, with notable fusions involving PIM3, an oncogene, and EIF4A1, which impacts protein synthesis, detected in 10 and 8 patients, respectively. These fusions suggest enhanced oncogenic signaling and altered translational control in AML (Wang et al., 2001; Graff et al., 2008). The variability in fusion events across different leukemias underscores the need for personalized approaches in targeting these neoantigens.

Conclusion

Our novel bioinformatics pipeline successfully identifies a comprehensive range of neoantigens from unpaired RNA-Seq samples in AML patients. By encompassing SNPs/INDELs, gene fusions, and alternative splicing events, this approach overcomes traditional limitations and uncovers valuable targets for personalized immunotherapy. These findings highlight the potential for more precise and effective AML treatment strategies, underscoring the importance of advanced bioinformatics in cancer immunotherapy.