A scientific treatment approach for acute mast cell leukemia: using a strategy based on next-generation sequencing data

BACKGROUND: Mast cell leukemia (MCL) is the most aggressive form of systemic mastocytosis disorders. Owing to its rarity, neither pathogenesis nor standard treatment is established for this orphan disease. Hence, we tried to treat a patient with MCL based on the exome and transcriptome sequencing results of the patient's own DNA and RNA. METHODS: First, tumor DNA and RNA were extracted from bone marrow at the time of diagnosis. Germline DNA was extracted from the patient's saliva 45 days after induction chemotherapy and used as a control. Then, we performed whole-exome sequencing (WES) using the DNA and whole transcriptome sequencing (WTS) using the RNA. Single nucleotide variants (SNVs) were called using MuTect and GATK. Samtools, FusionMap, and Gene Set Enrichment Analysis were utilized to analyze WTS results. RESULTS: WES and WTS results revealed mutation in KIT S476I. Fusion analysis was performed using WTS data, which suggested a possible RARα-B2M fusion. When RNA expression analysis was performed using WTS data, upregulation of PIK3/AKT pathway, downstream of KIT and mTOR, was observed. Based on our WES and WTS results, we first administered all-trans retinoic acid, then dasatinib, and finally, an mTOR inhibitor. CONCLUSION: We present a case of orphan disease where we used a targeted approach using WES and WTS data of the patient. Even though our treatment was not successful, use of our approach warrants further validation.

Application of Structural Equation Models to Genome-wide Association Analysis

Genome-wise association studies (GWASs) have become popular approaches to identify genetic variants associated with human biological traits. In this study, we applied Structural Equation Models (SEMs) in order to model complex relationships between genetic networks and traits as risk factors. SEMs allow us to achieve a better understanding of biological mechanisms through identifying greater numbers of genes and pathways that are associated with a set of traits and the relationship among them. For efficient SEM analysis for GWASs, we developed a procedure, comprised of four stages. In the first stage, we conducted single-SNP analysis using regression models, where age, sex, and recruited area were included as adjusting covariates. In the second stage, Fisher's combination test was conducted for each gene to detect significant genes using p-values obtained from the single-SNP analysis. In the third stage, Fisher's exact test was adopted to determine which biological pathways were enriched with significant SNPs. Finally, based on a pathway that was associated with the four traits in common, a SEM was fit to model a causal relationship among the genetic factors and traits. We applied our SEM model to GWAS data with four central obesity related traits: suprailiac and subscapular measures for upper body fat, BMI, and hypertension. Study subjects were collected from two Korean cohort regions. After quality control, 327,872 SNPs for 8842 individuals were included in the analysis. After comparing two SEMs, we concluded that suprailiac and subscapular measures may indirectly affect hypertension susceptibility by influencing BMI. In conclusion, our analysis demonstrates that SEMs provide a better understanding of biological mechanisms by identifying greater numbers of genes and pathways.