Biology subjects

Miyata, J.

Publications and source records attributed to Miyata, J..

6 recordsLinked to original sources

bioRxiv2026

A single chromosome 3p break initiates clear cell renal cell carcinoma evolution

Clear cell renal cell carcinoma (ccRCC) is initiated by chromosome 3p loss, yet chromosome losses impose a profound fitness burden on normal cells. How renal epithelial cells tolerate this deleterious aneuploidy during early tumorigenesis remains unclear. Analysis of 949 ccRCC genomes reveals two major classes of chromosome 3p alterations: simple deletions and complex rearrangements surrounding a terminal breakpoint - a pattern we term breakpoint-confined chromothripsis. We modeled both alterations in non-transformed human renal proximal tubule epithelial cells by introducing a single DNA double-strand break on chromosome 3p. Despite an initial fitness disadvantage, chromosome 3p loss drives adaptive genomic evolution that recapitulates recurrent ccRCC-associated aneuploidies, including 5q gain and 14q loss. These alterations alleviate the fitness constraints of 3p loss and promote metabolic reprogramming, clonal expansion, and malignant transformation, producing tumors with features of ccRCC. Thus, a single chromosome break initiates the evolutionary trajectory of ccRCC by creating a fitness bottleneck that selects for recurrent aneuploidies.

cancer biology↗

bioRxiv2026

A Visually Interpretable Histopathology-Based Immune Model Predicts T-effector Biology and Response to Immune checkpoint inhibition in Clear Cell Renal Cell Carcinoma Clinical Trial and Contemporary Real-World Datasets

Immune checkpoint inhibitors (ICI) are central to the treatment of metastatic clear cell renal cell carcinoma (ccRCC), yet only a subset of patients derive durable benefit, and clinically deployable predictive biomarkers remain an unmet need. RNA-based T-effector signatures capture cytotoxic immune biology and have been associated with ICI response in clinical trial cohorts; however, their clinical implementation is limited by the marked spatial heterogeneity of ccRCC, as well as cost, long turnaround time, sample quality requirements, and limited accessibility. Here, we developed a visually interpretable deep learning (DL) model that predicts a T-cell-enriched immune score directly from hematoxylin and eosin (H&E)-stained whole-slide images. To overcome the inability of H&E morphology alone to distinguish lymphocyte subsets, we trained the model using multimodal spatial supervision from CD8, PAX8, and ERG IHC, which respectively identified cytotoxic T-cell-rich regions, tumor cells, and endothelial cells, thereby constraining immune predictions to relevant tumor microenvironmental niches. The resulting H&E DL Immune score was validated by pathologist review, comparison with held-out CD8 IHC annotations, and independent datasets. The H&E DL Immune score correlated with T-effector RNA scores across independent institutional and IMmotion150 clinical trial cohorts (spearman correlations of 0.726; p=5.90x10-15 and 0.706; p=4.04x10-19). As a proof of principle, the score was used to characterize associations with key biological features across large cohorts, including sarcomatoid differentiation, BAP1 and PBRM1 mutation status, and additional transcriptomic signatures. In IMmotion150 clinical trial cohort, a median-dichotomized H&E DL Immune score, similar to RNA-based T-effector score, was significantly associated with clinical benefit from atezulumab therapy. In contemporary institutional cohorts of patients treated with frontline ipilimumab plus nivolumab or in initial 3 lines of nivolumab monotherapy, patients in the top quartile of H&E DL Immune score had significantly longer progression-free survival. Collectively, these findings support a scalable and interpretable H&E-based biomarker that captures T-effector biology and can help identify patients with ccRCC more likely to benefit from ICIs.

bioinformatics↗

bioRxiv2021

Face processing of social cognition in patients with first episode psychosis: Its deficits and association with the right subcallosal anterior cingulate cortex

The clinical importance of social cognition is well acknowledged in patients with psychosis, in particular those with first episode psychosis (FEP). Nevertheless, its brain substrates and circuitries remain elusive, lacking precise analysis between multimodal brain characteristics and behavioral sub-dimensions within social cognition. In the present study, we examined face processing of social cognition in 79 FEP patients and 80 healthy controls (HCs). We looked for a possible correlation between face processing and multimodal MRI characteristics such as resting-state functional connectivity (rsFC) and brain volume. We observed worse recognition accuracy, longer recognition response time, and longer memory response time in FEP patients when compared with HCs. Of these, memory response time was selectively correlated with specific rsFCs, which included the right subcallosal sub-region of BA24 in the ACC (scACC), only in FEP patients. The volume of this region was also correlated with memory response time in FEP patients. The scACC is functionally and structurally important in FEP-associated abnormalities of face processing measures in social cognition.

neuroscience↗

bioRxiv2020

Three-dimensional convolutional autoencoder extracts features of structural brain images with a diagnostic label-free approach: Application to schizophrenia datasets

There has been increasing interest in performing psychiatric brain imaging studies using deep learning. However, most studies in this field disregard three-dimensional (3D) spatial information and targeted disease discrimination, without considering the genetic and clinical heterogeneity of psychiatric disorders. The purpose of this study was to investigate the efficacy of a 3D convolutional autoencoder (CAE) for extracting features related to psychiatric disorders without diagnostic labels. The network was trained using a Kyoto University dataset including 82 patients with schizophrenia (SZ) and 90 healthy subjects (HS), and was evaluated using Center for Biomedical Research Excellence (COBRE) datasets including 71 SZ patients and 71 HS. The proposed 3D-CAEs were successfully reconstructed into high-resolution 3D structural magnetic resonance imaging (MRI) scans with sufficiently low errors. In addition, the features extracted using 3D-CAE retained the relevant clinical information. We explored the appropriate hyper parameter range of 3D-CAE, and it was suggested that a model with eight convolution layers might be relevant to extract features for predicting the dose of medication and symptom severity in schizophrenia.

neuroscience↗

bioRxiv2019

Atypical spatial frequency dependence of visual metacognition among schizophrenia patients

Although altered early stages of visual processing have been reported among schizophrenia patients, how such atypical visual processing may affect higher-level cognition remains largely unknown. Here we tested the hypothesis that metacognitive performance may be atypically modulated by spatial frequency (SF) of visual stimuli among individuals with schizophrenia, given their altered magnocellular function. To study the effect of SF on metacognitive performance, we asked patients and controls to perform a visual detection task on gratings with different SFs and report confidence, and analyzed the data using the signal detection theoretic measure meta-d. Control subjects showed better metacognitive performance after yes- (stimulus-presence) than after no- (stimulus-absence) responses ( yes-response advantage) for high SF (HSF) stimuli but not for low SF (LSF) stimuli. The patients, to the contrary, showed a yes-response advantage not only for HSF but also for LSF stimuli, indicating atypical SF dependency of metacognition. An fMRI experiment using the same task revealed that the dorsolateral prefrontal cortex (DLPFC), known to be crucial for metacognition, shows activity mirroring the behavioral results: decoding accuracy of perceptual confidence in DLPFC was significantly higher for HSF than for LSF stimuli in controls, whereas this decoding accuracy was independent of SF in patients. While individuals without schizophrenia may flexibly adapt metacognitive computations across SF ranges, patients may employ a different mechanism that is independent of SF. Because visual stimuli of low SF have been linked to top-down processing in predictive coding, this may reflect atypical functioning in these processes in schizophrenia. HighlightsO_LIVisual metacognition of controls was dependent on spatial frequency. C_LIO_LIVisual metacognition of schizophrenia patients was independent of spatial frequency. C_LIO_LIPatients and controls differently rely on the dorsolateral prefrontal cortex. C_LIO_LISensory inputs may reach metacognitive circuits in an atypical manner among patients. C_LI

neuroscience↗

bioRxiv2018

Overlapping but asymmetrical relationships between schizophrenia and autism revealed by brain connectivity

(Abstract included 248 words)Although the relationship between schizophrenia spectrum disorder (SSD) and autism spectrum disorder (ASD) has long been debated, it has not yet been fully elucidated. To address this issue, we took advantage of dual (ASD and SSD) classifiers that discriminate patients from their controls based on resting state brain functional connectivity. An SSD classifier using sophisticated machine-learning algorithms that automatically selected SSD- specific functional connections was applied to Japanese datasets including adult patients with SSD in a chronic stage. We demonstrated good performance of the SSD classification for independent validation cohorts. The generalizability was tested by USA and European cohorts in a chronic stage, and one USA cohort including first episode schizophrenia. The specificity was tested by two adult Japanese cohorts of ASD and major depressive disorder, and one European cohort of attention-deficit hyperactivity disorder. The weighted linear summation of the classifiers functional connections constituted the biological dimensions representing neural liability to the disorders. Our previously developed robust ASD classifier constituted the ASD dimension. Distributions of individuals with SSD, ASD and healthy controls were examined on the SSD and ASD biological dimensions. The SSD and ASD populations exhibited overlapping but asymmetrical patterns on the two biological dimensions. That is, the SSD population showed increased liability on the ASD dimension, but not vice versa. Furthermore, the two dimensions were correlated within the ASD population but not the SSD population. Using the two biological dimensions based on resting-state functional connectivity enabled us to quantify and visualize the relationships between SSD and ASD.

neuroscience↗