Thrombolysis and Mechanical Thrombectomy for Acute Ischemic Stroke in Pregnancy: A Case Report.

Objective: Intravenous (IV) recombinant tissue plasminogen activator (rt-PA) and mechanical thrombectomy (MT) are effective treatments for acute ischemic stroke (AIS). However, the treatment for AIS in pregnancy is not established because no clinical trials have included pregnant patients. We present a case of middle cerebral artery (MCA) M2 segment occlusion in pregnancy treated with IV thrombolysis and endovascular therapy. Case Presentation: A 36-year-old woman being 6 weeks pregnant presented with right-sided hemiparesis and aphasia. MRI showed a high-intensity area on diffusion-weighted imaging of the left parietal lobe, and MRA showed left MCA M2 segment occlusion. She underwent IV rt-PA and MT and achieved thrombolysis in cerebral infarction 2b revascularization without complications. The protein S concentration was lower than that in the physiological changes during pregnancy. She was diagnosed with embolic stroke related to coagulopathy in pregnancy, and she underwent anticoagulation. At the 3-month follow-up, the modified Rankin Scale was 0. She miscarried at 4 months, and the fetal death was presumed to be obstetric cause. Conclusion: IV rt-PA and MT may be effective and safe treatments for pregnant patients. Estimated fetal radiation exposure during MT is low and is presumed not to affect fetal development. We should mitigate the radiation dose and reduce the dose of iodinated contrast agents, particularly in pregnant patients.

Mechanical Thrombectomy for Acute Middle Cerebral Artery Occlusion Caused by Tumor Embolism: A Case Report.

Objective: We report a case of acute middle cerebral artery (MCA) occlusion caused by tumor embolism. Case Presentation: A 64-year-old man with lung cancer presented with sudden onset left-sided hemiparesis and sensory disturbance. Diffusion-weighted imaging (DWI) revealed hyper-intense foci in the right MCA territory and magnetic resonance angiography (MRA) demonstrated right MCA M2 segment occlusion. Mechanical thrombectomy (MT) was performed with Thrombolysis in Cerebral Infarction 2B recanalization. On histopathology, thrombus composed of fibrin and squamous cell carcinoma was observed. We diagnosed him with tumor embolism from lung cancer that invaded the pulmonary vein and the left atrium. Conclusion: Tumor cells may be confirmed by pathological examination regardless of the morphology of the embolus. Pathological examination of the cerebral embolus is useful for the accurate diagnosis of ischemic stroke subtypes.

ALPHLARD-NT: Bayesian Method for Human Leukocyte Antigen Genotyping and Mutation Calling through Simultaneous Analysis of Normal and Tumor Whole-Genome Sequence Data.

Human leukocyte antigen (HLA) genes provide useful information on the relationship between cancer and the immune system. Despite the ease of obtaining these data through next-generation sequencing methods, interpretation of these relationships remains challenging owing to the complexity of HLA genes. To resolve this issue, we developed a Bayesian method, ALPHLARD-NT, to identify HLA germline and somatic mutations as well as HLA genotypes from whole-exome sequencing (WES) and whole-genome sequencing (WGS) data. ALPHLARD-NT showed 99.2% accuracy for WGS-based HLA genotyping and detected five HLA somatic mutations in 25 colon cancer cases. In addition, ALPHLARD-NT identified 88 HLA somatic mutations, including recurrent mutations and a novel HLA-B type, from WES data of 343 colon adenocarcinoma cases. These results demonstrate the potential of ALPHLARD-NT for conducting an accurate analysis of HLA genes even from low-coverage data sets. This method can become an essential tool for comprehensive analyses of HLA genes from WES and WGS data, helping to advance understanding of immune regulation in cancer as well as providing guidance for novel immunotherapy strategies.

A Bayesian model integration for mutation calling through data partitioning.

MOTIVATION: Detection of somatic mutations from tumor and matched normal sequencing data has become among the most important analysis methods in cancer research. Some existing mutation callers have focused on additional information, e.g. heterozygous single-nucleotide polymorphisms (SNPs) nearby mutation candidates or overlapping paired-end read information. However, existing methods cannot take multiple information sources into account simultaneously. Existing Bayesian hierarchical model-based methods construct two generative models, the tumor model and error model, and limited information sources have been modeled. RESULTS: We proposed a Bayesian model integration framework named as partitioning-based model integration. In this framework, through introducing partitions for paired-end reads based on given information sources, we integrate existing generative models and utilize multiple information sources. Based on that, we constructed a novel Bayesian hierarchical model-based method named as OHVarfinDer. In both the tumor model and error model, we introduced partitions for a set of paired-end reads that cover a mutation candidate position, and applied a different generative model for each category of paired-end reads. We demonstrated that our method can utilize both heterozygous SNP information and overlapping paired-end read information effectively in simulation datasets and real datasets. AVAILABILITY AND IMPLEMENTATION: https://github.com/takumorizo/OHVarfinDer. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

OVarCall: Bayesian Mutation Calling Method Utilizing Overlapping Paired-End Reads.

Detection of somatic mutations from tumor and matched normal sequencing data has become a standard approach in cancer research. Although a number of mutation callers have been developed, it is still difficult to detect mutations with low allele frequency even in exome sequencing. We expect that overlapping paired-end read information is effective for this purpose, but no mutation caller has modeled overlapping information statistically in a proper form in exome sequence data. Here, we develop a Bayesian hierarchical method, OVar- Call (https://github.com/takumorizo/OVarCall), where overlapping paired-end read information improves the accuracy of low allele frequency mutation detection. Firstly, we construct two generative models: one is for reads with somatic variants generated from tumor cells and the other is for reads that does not have somatic variants but potentially includes sequence errors. Secondly, we calculate marginal likelihood for each model using a variational Bayesian algorithm to compute Bayes factor for the detection of somatic mutations. We empirically evaluated the performance of OVarCall and confirmed its better performance than other existing methods.