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Megalencephaly syndromes: exome pipeline strategies for detecting low-level mosaic mutations.

ABSTRACT: Two megalencephaly (MEG) syndromes, megalencephaly-capillary malformation (MCAP) and megalencephaly-polymicrogyriapolydactyly-hydrocephalus (MPPH), have recently been defined on the basis of physical and neuroimaging features. Subsequently, exome sequencing of ten MEG cases identified de-novo postzygotic mutations in PIK3CA which cause MCAP and de-novo mutations in AKT and PIK3R2 which cause MPPH. Here we present findings from exome sequencing three unrelated megalencephaly patients which identified a causal PIK3CA mutation in two cases and a causal PIK3R2 mutation in the third case. However, our patient with the PIK3R2 mutation which is considered to cause MPPH has a marked bifrontal band heterotopia which is a feature of MCAP. Furthermore, one of our patients with a PIK3CA mutation lacks syndactyly/polydactyly which is a characteristic of MCAP. These findings suggest that the overlap between MCAP and MPPH may be greater than the available studies suggest. In addition, the PIK3CA mutation in one of our patients could not be detected using standard exome analysis because the mutation was observed at a low frequency consistent with somatic mosaicism. We have therefore investigated several alternative methods of exome analysis and demonstrate that alteration of the initial allele frequency spectrum (AFS), used as a prior for variant calling in samtools, had the greatest power to detect variants with low mutant allele frequencies in our 3 MEG exomes and in simulated data. We therefore recommend non-default settings of the AFS in combination with stringent quality control when searching for causal mutation(s) that could have low levels of mutant reads due to post-zygotic mutation.

SUBMITTER: Tapper WJ

PROVIDER: S-EPMC3908952 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Circulating hybrid cells (CHCs) are a newly discovered, tumor-derived cell population identified in the peripheral blood of cancer patients and are thought to contribute to tumor metastasis. However, identifying CHCs by immunofluorescence (IF) imaging of patient peripheral blood mononuclear cells (PBMCs) is a time-consuming and subjective process that currently relies on manual annotation by laboratory technicians. Additionally, while IF is relatively easy to apply to tissue sections, its application on PBMC smears presents challenges due to the presence of biological and technical artifacts. To address these challenges, we present a robust image analysis pipeline to automate the detection and analyses of CHCs in IF images. The pipeline incorporates quality control to optimize specimen preparation protocols and remove unwanted artifacts, leverages a β-variational autoencoder (VAE) to learn meaningful latent representations of single-cell images and employs a support vector machine (SVM) classifier to achieve human-level CHC detection. We created a rigorously labeled IF CHC dataset including 9 patients and 2 disease sites with the assistance of 10 annotators to evaluate the pipeline. We examined annotator variation and bias in CHC detection and then provided guidelines to optimize the accuracy of CHC annotation. We found that all annotators agreed on CHC identification for only 65% of the cells in the dataset and had a tendency to underestimate CHC counts for regions of interest (ROI) containing relatively large amounts of cells (>50,000) when using conventional enumeration methods. On the other hand, our proposed approach is unbiased to ROI size. The SVM classifier trained on the β-VAE encodings achieved an F1 score of 0.80, matching the average performance of annotators. Our pipeline enables researchers to explore the role of CHCs in cancer progression and assess their potential as a clinical biomarker for metastasis. Further, we demonstrate that the pipeline can identify discrete cellular phenotypes among PBMCs, highlighting its utility beyond CHCs.

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Megalencephaly syndromes: exome pipeline strategies for detecting low-level mosaic mutations.

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