Biology subjects

Tsuchihara, K.

Publications and source records attributed to Tsuchihara, K..

6 recordsLinked to original sources

bioRxiv2026

SPATNIC enables H&E-grounded cancer-cell identification across spatial and single-cell transcriptomic modalities in colorectal cancer

Tumors contain diverse non-cancer cells that constitute the tumor microenvironment; thus, identifying which cells are cancerous is a foundational and critical first step in cancer research. However, to our knowledge, no transcriptome-based classifier has been trained using pathologist-defined annotations from matched H&E images at single-cell resolution. Moreover, few tools can be shared across diverse platforms. Here, we present SPATNIC, a variational autoencoder that classifies individual cells as cancer or normal, trained on Xenium cells labeled by a pathologist review of matched H&E images. It matches or exceeds the performance of existing tools in primary colorectal tumors and liver and lung metastases. SPATNIC generalizes zero-shot across Visium, Visium HD, CosMx, Xenium, MERSCOPE, and single-cell RNA sequencing. In public single-cell atlases, it identified a rare POU2F3 tuft-like cancer stem cell population missed by existing annotations and associated with worse survival. In rectal cancer profiled before and after chemoradiotherapy, it separated cancer from normal epithelium in tissue, thus enabling per-patient screening of antibody-drug conjugate (ADC) targets.

cancer biology↗

bioRxiv2026

Catalog of gut microbiota alterations associated with anticancer therapies across multiple cancer types

BackgroundAnticancer therapies can alter the gut microbiota and may affect gut bacteria associated with treatment response. However, most longitudinal studies have focused on specific cancer types or individual treatment regimens, and systematic analyses across diverse cancer therapies remain limited. We analyzed longitudinal fecal microbiota profiles using 16S ribosomal RNA gene amplicon sequencing in the pan-cancer SCRUM-Japan MONSTAR-SCREEN cohort. We included 528 paired pre- and post-treatment fecal samples from 264 patients with advanced solid tumors across 18 cancer types and characterized the gut microbiota alterations associated with 22 anticancer drugs and related clinical factors. ResultsAcross the cohort, Shannon diversity did not significantly change after treatment (mean, 3.81 vs. 3.78; P = 0.58), and pre- and post-treatment samples exhibited no clear separation in ordination space. However, within-patient analysis detected a subtle but significant longitudinal microbiota shift (paired PERMANOVA, P = 0.0001), highlighting the importance of accounting for paired sampling. Clustering of genus-level compositional alterations revealed patient groups with distinct degrees of microbiota alteration, with the largest shifts associated with antibiotic exposure, transition from normal stool to diarrhea, and specific treatment regimens. Multivariable regression analysis of 22 anticancer drugs identified drug-bacteria associations and demonstrated that drugs with similar mechanisms of action, including epidermal growth factor receptor (EGFR) inhibitors and immune checkpoint inhibitors, exhibited similar microbiota change profiles. Targeted analyses highlighted concordant reductions in the Christensenellaceae R-7 group among EGFR inhibitor-exposed patients and depletion of Faecalibacterium among immune checkpoint inhibitor-exposed patients. ConclusionsThis study provides a cross-cancer catalog of microbiota alterations associated with anticancer therapies and highlights therapy-related shifts in the gut ecosystem, including patterns shared by drugs with similar mechanisms of action.

microbiology↗

bioRxiv2026

Single-cell spatial multiomics identifies POSTN+ CAFs mediating chemoradiotherapy resistance in rectal cancer

Neoadjuvant chemoradiotherapy (CRT) is standard for locally advanced rectal cancer (LARC), yet many patients retain residual disease. To resolve CRT-associated remodeling of the tumor microenvironment, we generated a multimodal spatial atlas from serial sections of paired pretreatment and post-treatment specimens from 24 patients using Xenium single-cell spatial transcriptomics and PhenoCycler multiplex proteomics, profiling 2.8 million cells; matched Visium HD datasets were generated on adjacent serial sections. Resistance was most strongly associated with fibroblast and myeloid programs adjacent to residual tumor. We identify a periostin (POSTN)-expressing CAF subset selectively enriched around residual tumor cells in non-responders, displaying a myofibroblastic phenotype and activating extracellular matrix remodeling, noncanonical WNT signaling, and immunosuppressive pathways. Tumor cells neighboring POSTN+ CAFs show consistent epithelial-mesenchymal transition signatures. Together, this atlas enables interrogation of CRT-induced spatial remodeling and nominates POSTN+ CAFs as key mediators and targets of CRT resistance, with direct relevance to CRT-based combination strategies.

cancer biology↗

bioRxiv2026

SpatialCompassV (SCOMV): De novo cell and gene spatial pattern classification and spatially differential gene identification

Spatial omics technologies enable the detection of gene expression together with spatial information in tissues. However, many existing analytical methods rely on prior biological knowledge or predefined annotations, while being limited in their ability to systematically characterize spatial distribution patterns. Here, we developed SpatialCompassV (SCOMV), a computational tool that clusters genes and cell types based on vectorial relationships between transcript locations and regions of interest, such as tumors. This tool quantifies the spatial positioning of genes and cells relative to a defined reference region by encoding their distance and direction into structured feature representations. SCOMV captured tumor-associated spatial patterns and enabled the unsupervised classification of genes into internal, peripheral, partially peripheral, and ubiquitous distribution types in breast and lung cancer spatial transcriptomic datasets of Xenium. Notably, SCOMV detected immune cell-related signatures that were preferentially localized in CAF-low regions. Extending the analysis to multiple regions of interest further enabled malignant state discrimination. Moreover, SCOMV identifies genes that differ not only in gene expression levels, but also in spatial distribution patterns, which we termed spatially differential genes (spatially DEGs).

bioinformatics↗

bioRxiv2024

Spatial domain analysis to estimate spatiotemporal pathological mechanisms in microenvironment with single-cell spatial omics data

Single-cell spatial omics analysis requires consideration of biological functions and mechanisms in a microenvironment. However, microenvironment analysis using bioinformatic methods is limited by the need to detect histological morphology. In this study, we developed SpatialKNife (SKNY), an image-processing-based toolkit that detects spatial domains that potentially reflect histology and extends these domains to the microenvironment. The SKNY algorithm identified tumour spatial domains from spatial transcriptomic data of breast cancer, followed by clustering of these domains, trajectory estimation, and spatial extension to the tumour microenvironment (TME). The results of the trajectory estimation were consistent with the known mechanisms of cancer progression. We observed endothelial cell and macrophage infiltration into the TME at mid-stage progression. Our results suggest that analysis using the spatial domain as a unit reflects pathological mechanisms in the TME. This approach may be applicable to the biological estimation of diverse microenvironments.

bioinformatics↗

bioRxiv2019

Long read sequencing reveals a novel class of structural aberrations in cancers: identification and characterization of cancerous local amplifications

Here we report identification of a new class of local structural aberrations in lung cancers. The whole-genome sequencing of cell lines using a long read sequencer, PromethION, demonstrated that typical cancerous mutations, such as point mutations, large deletions and gene fusions can be detected also on this platform. Unexpectedly, we revealed unique structural aberrations consisting of complex combinations of local duplications, inversions and micro deletions. We further analyzed and found that these mutations also occur in vivo, even in key cancer-related genes. These mutations may elucidate the molecular etiology of patients for whom causative cancerous events and therapeutic strategies remain elusive.

genomics↗