Nucleic acid-triggered tumoral immunity propagates pH-selective therapeutic antibodies through tumor-driven epitope spreading.

Important roles of humoral tumor immunity are often pointed out; however, precise profiles of dominant antigens and developmental mechanisms remain elusive. We systematically investigated the humoral antigens of dominant intratumor immunoglobulin clones found in human cancers. We found that approximately half of the corresponding antigens were restricted to strongly and densely negatively charged polymers, resulting in simultaneous reactivities of the antibodies to both densely sulfated glycosaminoglycans (dsGAGs) and nucleic acids (NAs). These anti-dsGAG/NA antibodies matured and expanded via intratumoral immunological driving force of innate immunity via NAs. These human cancer-derived antibodies exhibited acidic pH-selective affinity across both antigens and showed specific reactivity to diverse spectrums of human tumor cells. The antibody-drug conjugate exerted therapeutic effects against multiple cancers in vivo by targeting cell surface dsGAG antigens. This study reveals that intratumoral immunological reactions propagate tumor-oriented immunoglobulin clones and demonstrates a new therapeutic modality for the universal treatment of human malignancies.

Somatic GJA4 gain-of-function mutation in orbital cavernous venous malformations.

Orbital cavernous venous malformation (OCVM) is a sporadic vascular anomaly of uncertain etiology characterized by abnormally dilated vascular channels. Here, we identify a somatic missense mutation, c.121G > T (p.Gly41Cys) in GJA4, which encodes a transmembrane protein that is a component of gap junctions and hemichannels in the vascular system, in OCVM tissues from 25/26 (96.2%) individuals with OCVM. GJA4 expression was detected in OCVM tissue including endothelial cells and the stroma, through immunohistochemistry. Within OCVM tissue, the mutation allele frequency was higher in endothelial cell-enriched fractions obtained using magnetic-activated cell sorting. Whole-cell voltage clamp analysis in Xenopus oocytes revealed that GJA4 c.121G > T (p.Gly41Cys) is a gain-of-function mutation that leads to the formation of a hyperactive hemichannel. Overexpression of the mutant protein in human umbilical vein endothelial cells led to a loss of cellular integrity, which was rescued by carbenoxolone, a non-specific gap junction/hemichannel inhibitor. Our data suggest that GJA4 c.121G > T (p.Gly41Cys) is a potential driver gene mutation for OCVM. We propose that hyperactive hemichannel plays a role in the development of this vascular phenotype.

Viola: a structural variant signature extractor with user-defined classifications.

SUMMARY: Here, we present Viola, a Python package that provides structural variant (SV; large scale genome DNA variations that can result in disease, e.g. cancer) signature analytical functions and utilities for custom SV classification, merging multi-SV-caller output files and SV annotation. We demonstrate that Viola can extract biologically meaningful SV signatures from publicly available SV data for cancer and we evaluate the computational time necessary for annotation of the data. AVAILABILITY AND IMPLEMENTATION: Viola is available on pip (https://pypi.org/project/Viola-SV/) and the source code is on GitHub (https://github.com/dermasugita/Viola-SV). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Functional genomics screening identifies aspartyl-tRNA synthetase as a novel prognostic marker and a therapeutic target for gastric cancers.

Efficient molecular targeting therapies for most gastric cancers (GCs) are currently lacking, despite GC being one of the most frequent and often devastating malignancies worldwide. Thus, identification of novel therapeutic targets for GC is in high demand. Recent advancements of high-throughput nucleic acid synthesis methods combined with next-generation sequencing (NGS) platforms have made it feasible to conduct functional genomics screening using large-scale pooled lentiviral libraries aimed at discovering novel cancer therapeutic targets. In this study, we performed NGS-based functional genomics screening for human GC cell lines using an originally constructed 6,399 shRNA library targeting all 2,096 human metabolism genes. Our screening identified aspartyl-tRNA synthetase (DARS) as a possible candidate for a therapeutic target for GC. In-house tissue microarrays containing 346 cases of GC combined with public datasets showed that patients with high expression levels of DARS protein exhibited more advanced clinicopathologic parameters and a worse prognosis, specifically among diffuse-type GC patients. Both in vitro and in vivo experiments concretely evidenced that DARS inhibition achieved robust growth suppression of GC cells. Moreover, RNA sequencing of GC cell lines under shRNA-mediated DARS knockdown suggested that DARS inhibition exerts its effect through the inactivation of multiple p-ERK pathways. This MAPK-related growth suppression by DARS inhibition would also be applicable to other cancers; thus, it is warranted to investigate the expression and clinical significance of DARS in a wide spectrum of malignancies. Taken together, NGS-based high-throughput pooled lentiviral screening showed DARS as a novel prognostic marker and a promising therapeutic target for GC. © 2022 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Immune repertoire profiling for disease pathobiology.

Lymphocytes consist of highly heterogeneous populations, each expressing a specific cell surface receptor corresponding to a particular antigen. Lymphocytes are both the cause and regulator of various diseases, including autoimmune/allergic diseases, lifestyle diseases, neurodegenerative diseases, and cancers. Recently, immune repertoire sequencing has attracted much attention because it helps obtain global profiles of the immune receptor sequences of infiltrating T and B cells in specimens. Immune repertoire sequencing not only helps deepen our understanding of the molecular mechanisms of immune-related pathology but also assists in discovering novel therapeutic modalities for diseases, thereby shedding colorful light on otherwise tiny monotonous cells when observed under a microscope. In this review article, we introduce and detail the background and methodology of immune repertoire sequencing and summarize recent scientific achievements in association with human diseases. Future perspectives on this genetic technique in the field of histopathological research will also be discussed.

Genomic landscape of a mouse model of diffuse-type gastric adenocarcinoma.

BACKGROUND: There is a need for a model of diffuse-type gastric cancer that captures the features of the disease, facilitates the study of its mechanisms, and aids the development of potential therapies. One such model may be Cdh1 and Trp53 double conditional knockout (DCKO) mice, which have histopathological features similar to those of human diffuse-type gastric cancer. However, a genomic profile of this mouse model has yet to be completed. METHODS: Whole-genome sequences of tumors from eight DCKO mice were analyzed and their molecular features were compared with those of human gastric adenocarcinoma. RESULTS: DCKO mice gastric cancers harbored single nucleotide variations and indel patterns comparable to those of human genomically stable gastric cancers, whereas their copy number variation fraction and ploidy were more similar to human chromosomal instability gastric cancers (perhaps due to Trp53 knockout). Copy number variations dominated changes in cancer-related genes in DCKO mice, with typical high-level amplifications observed for oncogenic drivers, e.g., Myc, Ccnd1, and Cdks, as well as gastrointestinal transcription factors, e.g., Gata4, Foxa1, and Sox9. Interestingly, frequent alterations in gastrointestinal transcription factors in DCKO mice indicated their potential role in tumorigenesis. Furthermore, mouse gastric cancer had a reproducible but smaller number of mutational signatures than human gastric cancer, including the potentially acid-related signature 17, indicating shared tumorigenic etiologies in humans and mice. CONCLUSIONS: Cdh1/Trp53 DCKO mice have similar genomic features to those found in human gastric cancer; hence, this is a suitable model for further studies of diffuse-type gastric cancer mechanisms and therapies.

The first case of gastric carcinoma with NTRK rearrangement: identification of a novel ATP1B-NTRK1 fusion.

NTRK gene rearrangements occur in a wide spectrum of tumors and are actionable events predictive of response to TRK inhibitor. We report the first case of gastric carcinoma harboring a NTRK fusion in a 79-year-old man. The tumor was composed predominantly of poorly cohesive carcinoma with focal tubular differentiation. Solid sheet-like or nested pattern of large oxyphilic cells was also noted in 10% of tumor. Pan-Trk immunohistochemistry demonstrated Trk expression with a diffuse cytoplasmic and dot-like staining only in the solid component. Extensive lymphatic invasion and multiple nodal metastases were noted and were predominated by Trk-positive component. A novel ATP1B1-NTRK1 fusion was detected by RNA-seq using fresh frozen sample. The patient died of the disease, despite surgery and chemotherapy. Although extremely rare, NTRK rearrangement does occur in gastric carcinoma and might be associated with aggressive phenotype as well as histologic features like solid growth with extensive lymphatic invasion.

Capturing the differences between humoral immunity in the normal and tumor environments from repertoire-seq of B-cell receptors using supervised machine learning.

BACKGROUND: The recent success of immunotherapy in treating tumors has attracted increasing interest in research related to the adaptive immune system in the tumor microenvironment. Recent advances in next-generation sequencing technology enabled the sequencing of whole T-cell receptors (TCRs) and B-cell receptors (BCRs)/immunoglobulins (Igs) in the tumor microenvironment. Since BCRs/Igs in tumor tissues have high affinities for tumor-specific antigens, the patterns of their amino acid sequences and other sequence-independent features such as the number of somatic hypermutations (SHMs) may differ between the normal and tumor microenvironments. However, given the high diversity of BCRs/Igs and the rarity of recurrent sequences among individuals, it is far more difficult to capture such differences in BCR/Ig sequences than in TCR sequences. The aim of this study was to explore the possibility of discriminating BCRs/Igs in tumor and in normal tissues, by capturing these differences using supervised machine learning methods applied to RNA sequences of BCRs/Igs. RESULTS: RNA sequences of BCRs/Igs were obtained from matched normal and tumor specimens from 90 gastric cancer patients. BCR/Ig-features obtained in Rep-Seq were used to classify individual BCR/Ig sequences into normal or tumor classes. Different machine learning models using various features were constructed as well as gradient boosting machine (GBM) classifier combining these models. The results demonstrated that BCR/Ig sequences between normal and tumor microenvironments exhibit their differences. Next, by using a GBM trained to classify individual BCR/Ig sequences, we tried to classify sets of BCR/Ig sequences into normal or tumor classes. As a result, an area under the curve (AUC) value of 0.826 was achieved, suggesting that BCR/Ig repertoires have distinct sequence-level features in normal and tumor tissues. CONCLUSIONS: To the best of our knowledge, this is the first study to show that BCR/Ig sequences derived from tumor and normal tissues have globally distinct patterns, and that these tissues can be effectively differentiated using BCR/Ig repertoires.

A benchmark for comparing precision medicine methods in thyroid cancer diagnosis using tissue microarrays.

Motivation: The aim of precision medicine is to harness new knowledge and technology to optimize the timing and targeting of interventions for maximal therapeutic benefit. This study explores the possibility of building AI models without precise pixel-level annotation in prediction of the tumor size, extrathyroidal extension, lymph node metastasis, cancer stage and BRAF mutation in thyroid cancer diagnosis, providing the patients' background information, histopathological and immunohistochemical tissue images. Results: A novel framework for objective evaluation of automatic patient diagnosis algorithms has been established under the auspices of the IEEE International Symposium on Biomedical Imaging 2017- A Grand Challenge for Tissue Microarray Analysis in Thyroid Cancer Diagnosis. Here, we present the datasets, methods and results of the challenge and lay down the principles for future uses of this benchmark. The main contributions of the challenge include the creation of the data repository of tissue microarrays; the creation of the clinical diagnosis classification data repository of thyroid cancer; and the definition of objective quantitative evaluation for comparison and ranking of the algorithms. With this benchmark, three automatic methods for predictions of the five clinical outcomes have been compared, and detailed quantitative evaluation results are presented in this paper. Based on the quantitative evaluation results, we believe automatic patient diagnosis is still a challenging and unsolved problem. Availability and implementation: The datasets and the evaluation software will be made available to the research community, further encouraging future developments in this field. (http://www-o.ntust.edu.tw/cvmi/ISBI2017/). Contact: cweiwang@mail.ntust.edu.tw. Supplementary information: Supplementary data are available at Bioinformatics online.

HER2 Heterogeneity Is Associated with Poor Survival in HER2-Positive Breast Cancer.

Intratumoral human epidermal growth factor receptor 2 (HER2) heterogeneity has been reported in 16⁻36% of HER2-positive breast cancer and its clinical impact is under discussion. We examined the biological effects of HER2-heterogeneity on mouse models and analyzed metastatic brains by RNA sequence analysis. A metastatic mouse model was developed using 231-Luc (triple negative cells) and 2 HER2-positive cell lines, namely, HER2-60 and HER2-90 which showed heterogeneous and monotonous HER2 expressions, respectively. Metastatic lesions developed in 3 weeks in all the mice injected with HER2-60 cells, and in 69% of the mice injected with HER2-90 and 87.5% of the mice injected with 231-Luc. The median survival days of mice injected with 231-Luc, HER2-60, and HER2-90 cells were 29 (n = 24), 24 (n = 22) and 30 (n = 13) days, respectively. RNA sequence analysis showed that CASP-1 and its related genes were significantly downregulated in metastatic brain tumors with HER2-60 cells. The low expression of caspase-1 could be a new prognostic biomarker for early relapse in HER2-positive breast cancer.

Difference in morphology and interactome profiles between orthotopic and subcutaneous gastric cancer xenograft models.

In xenograft models, orthotopic (ORT) engraftment is thought to provide a different tumor microenvironment compared with subcutaneous (SC) engraftment. We attempted to characterize the biological difference between OE19 (adenocarcinoma of the gastroesophageal junction) SC and ORT models by pathological analysis and CASTIN (CAncer-STromal INteractome) analysis, which is a novel method developed to analyze the tumor-stroma interactome framework. In SC models, SCID mice were inoculated subcutaneously with OE19 cells, and tumor tissues were sampled at 3 weeks. In ORT models, SCID mice were inoculated under the serosal membrane of the stomach wall, and tumor tissues were sampled at 3 and 6 weeks after engraftment. Results from the two models were then compared. Histopathologically, the SC tumors were well circumscribed from the adjacent tissue, with scant stroma and the formation of large ductal structures. In contrast, the ORT tumors were less circumscribed, with small ductal structures invading into abundant stroma. Then we compared the transcriptome profiles of human tumor cells with the mouse stromal cells of each model by species-specific RNA sequencing. With CASTIN analysis, we successfully identified several interactions that are known to affect the tumor microenvironment as being selectively enhanced in the ORT model. In conclusion, pathological analysis and CASTIN analysis revealed that ORT models of OE19 cells have a more invasive character and enhanced interaction with stromal cells compared with SC models.

CASTIN: a system for comprehensive analysis of cancer-stromal interactome.

BACKGROUND: Cancer microenvironment plays a vital role in cancer development and progression, and cancer-stromal interactions have been recognized as important targets for cancer therapy. However, identifying relevant and druggable cancer-stromal interactions is challenging due to the lack of quantitative methods to analyze whole cancer-stromal interactome. RESULTS: We present CASTIN (CAncer-STromal INteractome analysis), a novel framework for the evaluation of cancer-stromal interactome from RNA-Seq data using cancer xenograft models. For each ligand-receptor interaction which is derived from curated protein-protein interaction database, CASTIN summarizes gene expression profiles of cancer and stroma into three evaluation indices. These indices provide quantitative evaluation and comprehensive visualization of interactome, and thus enable to identify critical cancer-microenvironment interactions, which would be potential drug targets. We applied CASTIN to the dataset of pancreas ductal adenocarcinoma, and successfully characterized the individual cancer in terms of cancer-stromal relationships, and identified both well-known and less-characterized druggable interactions. CONCLUSIONS: CASTIN provides comprehensive view of cancer-stromal interactome and is useful to identify critical interactions which may serve as potential drug targets in cancer-microenvironment. CASTIN is available at: http://github.com/tmd-gpat/CASTIN .

Loss of YAP1 defines neuroendocrine differentiation of lung tumors.

YAP1, the main Hippo pathway effector, is a potent oncogene and is overexpressed in non-small-cell lung cancer (NSCLC); however, the YAP1 expression pattern in small-cell lung cancer (SCLC) has not yet been elucidated in detail. We report that the loss of YAP1 is a special feature of high-grade neuroendocrine lung tumors. A hierarchical cluster analysis of 15 high-grade neuroendocrine tumor cell lines containing 14 SCLC cell lines that depended on the genes of Hippo pathway molecules and neuroendocrine markers clearly classified these lines into two groups: the YAP1-negative and neuroendocrine marker-positive group (n = 11), and the YAP1-positive and neuroendocrine marker-negative group (n = 4). Among the 41 NSCLC cell lines examined, the loss of YAP1 was only observed in one cell line showing the strong expression of neuroendocrine markers. Immunostaining for YAP1, using the sections of 189 NSCLC, 41 SCLC, and 30 large cell neuroendocrine carcinoma (LCNEC) cases, revealed that the loss of YAP1 was common in SCLC (40/41, 98%) and LCNEC (18/30, 60%), but was rare in NSCLC (6/189, 3%). Among the SCLC and LCNEC cases tested, the loss of YAP1 correlated with the expression of neuroendocrine markers, and a survival analysis revealed that YAP1-negative cases were more chemosensitive than YAP1-positive cases. Chemosensitivity test for cisplatin using YAP1-positive/YAP1-negative SCLC cell lines also showed compatible results. YAP1-sh-mediated knockdown induced the neuroendocrine marker RAB3a, which suggested the possible involvement of YAP1 in the regulation of neuroendocrine differentiation. Thus, we showed that the loss of YAP1 has potential as a clinical marker for predicting neuroendocrine features and chemosensitivity.

The niche component periostin is produced by cancer-associated fibroblasts, supporting growth of gastric cancer through ERK activation.

Overexpression of periostin (POSTN), an extracellular matrix protein, has been observed in several cancers. We investigated the importance of POSTN in gastric cancer. Genome-wide gene expression analysis using publicly available microarray data sets revealed significantly high POSTN expression in cancer tissues from stage II-IV gastric cancer, compared with background normal tissues. The POSTN/vimentin mRNA expression ratio was highly associated with gene groups that regulate the cell cycle and cell proliferation. IHC showed that periglandular POSTN deposition, comprising linear deposition abutting the glandular epithelial cells in normal mucosa, disappeared during intestinal gastric cancer progression. Stromal POSTN deposition was also detected at the invasive front of intestinal-type and diffuse-type cancers. In situ hybridization confirmed POSTN mRNA in cancer-associated fibroblasts, but not in tumor cells themselves. POSTN enhanced the in vitro growth of OCUM-2MLN and OCUM-12 diffuse-type gastric cancer cell lines, accompanied by the activation of ERK. Furthermore, coinoculation of gastric cancer cells with POSTN-expressing NIH3T3 mouse fibroblast cells facilitated tumor formation. The OCUM-2MLN orthotopic inoculation model demonstrated that tumors of the gastric wall in Postn(-/-) mice were significantly smaller than those in wild-type mice. Ki-67 and p-ERK positive rates were both lower in Postn(-/-) mice. These findings suggest that POSTN produced by cancer-associated fibroblasts constitutes a growth-supportive microenvironment for gastric cancer.

High-throughput system for the thermostability analysis of proteins.

Thermal stability of proteins is a primary metric for evaluating their physical properties. Although researchers attempted to predict it using machine learning frameworks, their performance has been dependent on the quality and quantity of published data. This is due to the technical limitation that thermodynamic characterization of protein denaturation by fluorescence or calorimetry in a high-throughput manner has been challenging. Obtaining a melting curve that derives solely from the target protein requires laborious purification, making it far from practical to prepare a hundred or more samples in a single workflow. Here, we aimed to overcome this throughput limitation by leveraging the high protein secretion efficacy of Brevibacillus and consecutive treatment with plate-scale purification methodologies. By handling the entire process of expression, purification, and analysis on a per-plate basis, we enabled the direct observation of protein denaturation in 384 samples within 4 days. To demonstrate a practical application of the system, we conducted a comprehensive analysis of 186 single mutants of a single-chain variable fragment of nivolumab, harvesting the melting temperature (Tm) ranging from -9.3 up to +10.8°C compared to the wild-type sequence. Our findings will allow for data-driven stabilization in protein design and streamlining the rational approaches.

Benchmarking Deep Learning-Based Image Retrieval of Oral Tumor Histology.

INTRODUCTION:  Oral tumors necessitate a dependable computer-assisted pathological diagnosis system considering their rarity and diversity. A content-based image retrieval (CBIR) system using deep neural networks has been successfully devised for digital pathology. No CBIR system for oral pathology has been investigated because of the lack of an extensive image database and feature extractors tailored to oral pathology. MATERIALS AND METHODS: This study uses a large CBIR database constructed from 30 categories of oral tumors to compare deep learning methods as feature extractors. RESULTS: The highest average area under the receiver operating characteristic curve (AUC) was achieved by models trained on database images using self-supervised learning (SSL) methods (0.900 with SimCLR and 0.897 with TiCo). The generalizability of the models was validated using query images from the same cases taken with smartphones. When smartphone images were tested as queries, both models yielded the highest mean AUC (0.871 with SimCLR and 0.857 with TiCo). We ensured the retrieved image result would be easily observed by evaluating the top 10 mean accuracies and checking for an exact diagnostic category and its differential diagnostic categories. CONCLUSION: Training deep learning models with SSL methods using image data specific to the target site is beneficial for CBIR tasks in oral tumor histology to obtain histologically meaningful results and high performance. This result provides insight into the effective development of a CBIR system to help improve the accuracy and speed of histopathology diagnosis and advance oral tumor research in the future.

Registered multi-device/staining histology image dataset for domain-agnostic machine learning models.

Variations in color and texture of histopathology images are caused by differences in staining conditions and imaging devices between hospitals. These biases decrease the robustness of machine learning models exposed to out-of-domain data. To address this issue, we introduce a comprehensive histopathology image dataset named PathoLogy Images of Scanners and Mobile phones (PLISM). The dataset consisted of 46 human tissue types stained using 13 hematoxylin and eosin conditions and captured using 13 imaging devices. Precisely aligned image patches from different domains allowed for an accurate evaluation of color and texture properties in each domain. Variation in PLISM was assessed and found to be significantly diverse across various domains, particularly between whole-slide images and smartphones. Furthermore, we assessed the improvement in domain shift using a convolutional neural network pre-trained on PLISM. PLISM is a valuable resource that facilitates the precise evaluation of domain shifts in digital pathology and makes significant contributions towards the development of robust machine learning models that can effectively address challenges of domain shift in histological image analysis.

Association of rare variants in RNF213 with severe progression of intracranial artery stenosis in quasi-moyamoya disease.

OBJECTIVE: The genetic basis underlying the pathophysiology of quasi-moyamoya disease (qMMD) is unclear. Herein, the authors aimed to comprehensively analyze genetic variants in qMMD and investigate their association with clinical phenotypes, focusing on RNF213 and other moyamoya angiopathy (MMA)-related genes. METHODS: The authors evaluated 14 consecutive cases of qMMD, whose underlying conditions included autoimmune disease, head irradiation, meningitis/pachymeningitis, and Turner syndrome, and 9 cases of hyperthyroidism-associated MMD (hMMD). The frequencies of RNF213 p.Arg4810Lys in qMMD and hMMD were each compared to those in healthy controls and in patients with MMD. Whole-exome sequencing was performed, and rare variants (RVs) or damaging variants were analyzed in RNF213 and 36 MMA-related genes. RESULTS: The frequencies of p.Arg4810Lys were significantly higher in patients with qMMD (28.6%) and hMMD (33.3%) than in controls (1.1%; p < 0.001) and lower in the two former groups than in the MMD group (67.6%; p = 0.003 and 0.065, respectively). In qMMD, no significant clinical differences were observed based on the presence of p.Arg4810Lys. A novel RNF213 RV was identified in four cases with qMMD. These same cases also presented with significant worsening of intracranial main artery stenosis, which suggests a possible association between RNF213 RVs and the severe progression of qMMD. Among the 36 MMA-related genes, no variants correlated with specific phenotypes. CONCLUSIONS: While the clinical implications of p.Arg4810Lys in cases with qMMD were not identified, the study findings suggest a potential association between RNF213 RVs and the significant progression of intracranial artery stenosis. Genetic analysis should not focus solely on p.Arg4810Lys but instead consider a comprehensive analysis of RNF213 for more accurate clinical prognostication of qMMD.

Association of Genetic Variants with Postoperative Donor Artery Development in Moyamoya Disease: RNF213 and Other Moyamoya Angiopathy-Related Gene Analysis.

Robust postoperative bypass development is a characteristic of moyamoya disease (MMD); however, genetic factors mediating this phenomenon remain incompletely understood. Therefore, we aimed to elucidate the relationship between postoperative donor artery development and genetic variants. We retrospectively enrolled 63 patients (79 hemispheres) who underwent combined revascularization surgery. Postoperative development of the superficial temporal artery (STA), middle meningeal artery, and deep temporal artery (DTA) was assessed using the caliber-change ratio determined from magnetic resonance angiography measurements. We analyzed RNF213 and 36 other moyamoya angiopathy-related genes by whole-exome sequencing and extracted rare or damaging variants. Thirty-five participants carried RNF213 p.Arg4810Lys (all heterozygotes), whereas 5 had RNF213 rare variants (RVs). p.Arg4810Lys was significantly associated with postoperative DTA development, while age at surgery, hypertension, and hyperlipidemia were inversely associated. Multiple regression analysis revealed that age and p.Arg4810Lys held statistical significance (P = 0.044, coefficient - 0.015, 95% confidence interval (CI) - 0.029 to 0.000 and P = 0.001, coefficient 0.670, 95% CI 0.269 to 1.072, respectively). Those with RNF213 RV without p.Arg4810Lys exhibited a significant trend toward poor DTA development (P = 0.001). Hypertension demonstrated a significant positive association with STA development, which remained significant even after multiple regression analysis (P = 0.001, coefficient 0.303, 95% CI 0.123 to 0.482). Following Bonferroni correction for multiple comparisons, targeted analyses of RNF213 and 36 moyamoya angiopathy-related genes showed a significant association of only RNF213 p.Arg4810Lys with favorable DTA development (P = 0.001). A comprehensive analysis of RNF213, considering both p.Arg4810Lys and RVs, may provide a clearer prediction of postoperative DTA development.

Meet the authors: Daisuke Komura and Shumpei Ishikawa.

In this People of Data, Cell Press Community Review Scientific Editor Leia Judge talks to lead author Dr. Daisuke Komura and Principal Investigator Prof. Shumpei Ishikawa about their paper "Restaining-based annotation for cancer histology segmentation to overcome annotation-related limitations among pathologists," which was published in the February issue of Patterns, and their experiences with Cell Press Community Review.