Luciane Tsukamoto Kagohara

The paper calibrates a spatial QSP model using CODEX data to predict TME states and identify biomarkers for HCC immunotherapy response.

CODAvision provides a user-friendly interface for rapid, customizable medical image segmentation using deep learning, enabling non-experts to train and evaluate models across diverse imaging modalities.

PICTographPlus infers clone-resolved transcriptional programs from bulk RNA-seq by integrating clonal phylogenies and proportions, achieving high accuracy in recovering clone-level expression and localizing pathway changes.

This study shows antigen-presenting cancer-associated fibroblasts (apCAFs) in murine pancreatic tumors influence regulatory T cell phenotype and function through CXCL9 and CCL22, affecting immune checkpoint blockade response.

STAPLE automates spatial transcriptomics analysis by integrating multiple tools into a modular framework and provides AI-driven interpretation of results.

The paper develops a 3D multi-omic pipeline using deep learning to analyze chemotherapy response in pancreatic cancer, identifying molecular signatures of resistance and fibroblast-cancer interactions.

The paper introduces dominoSignal, a software tool that statistically tests differential cell signaling from single-cell data to infer changes in cell-cell communication networks.

This study integrates single-cell and spatial transcriptomics to identify prognostic fibroblast subpopulations in thyroid cancer progression.

The paper identifies multi-omic phenotypes in pancreatic precursor lesions to distinguish progressive from indolent precancers using integrative transcriptomic, genomic, and spatial data.

The paper presents a 3D multi-omic framework to detect and characterize ovarian cancer precursors in fallopian tubes, identifying TP53 mutations in microscopic lesions.

The paper introduces a framework for mathematical modeling of multicellular systems using interpretable cell behavior rules derived from biological knowledge and multi-omics data.

PIVOT is an open-source tool for nonlinear registration of multi-omic spatial data, demonstrating improved throughput compared to existing workflows.

The paper investigates how cancer-associated fibroblasts (CAF) influence tumor cell behavior in pancreatic ductal adenocarcinoma by driving EMT and altering cell subtypes, with IL-8 playing a key modulatory role.

This study examines TLS in HCC treated with neoadjuvant immunotherapy, finding TLS correlate with better outcomes and show distinct morphological and functional features in tumor regression.

This study uses spatial multi-omics to identify TLS-associated immune niches in PDAC that correlate with improved survival in immunotherapy responders.

The paper develops a spatial quantitative systems pharmacology model for virtual clinical trials in HCC, validated with spatial multi-omics data from a neoadjuvant immunotherapy trial.

The paper characterizes a novel syngeneic murine breast cancer model (NT2.5-LM) that exhibits rapid and widespread metastases, enabling study of metastatic progression and therapeutic responses.

This paper introduces SpaceMarkers, a method to infer molecular changes from cell-cell interactions in spatial transcriptomics data, applied to tumor-immune interactions in metastasis and immunotherapy.

CancerInSilico simulates bulk and single-cell gene expression data from mathematical models to enable benchmarking of time course analysis methods with known ground truth.

The paper uses integrated time course omics analysis to distinguish immediate therapeutic response from acquired resistance in head and neck cancer.

SEVA identifies differential heterogeneity in gene isoform usage between cancer subtypes using RNA-seq data.