Chi‐Ping Day

The paper identifies transient isoform remodeling as a key mechanism in early MAPKi resistance in melanoma, showing it is distinct from gene expression changes and linked to splicing regulators and developmental pathways.

STITCH pairs size-independent tangent-PCA contour shape modes with expression factors by canonical correlation, recovering keratinocyte, fibroblast and melanoma shape-expression links in Xenium and CosMx data.

This paper develops HEiST, a deep learning method to infer spatial transcriptomes from H&E slides, identifies two prognostic Spatiotypes in HNSC, and shows they predict treatment response better than existing biomarkers.

This study identifies TAZ deficiency as synthetically lethal with BRAF inhibitors in anaplastic thyroid cancer, revealing a mechanism of resistance and a potential therapeutic target.

Wakhan reconstructs chromosome-scale copy number profiles of tumor genomes using long-read sequencing, showing better consistency with data than existing methods.

SpacePhenotyper transfers predictive gene expression-phenotype relationships from bulk data to spatial transcriptomics data to analyze clinical phenotypes in spatial regions of tumors.

The paper uses long-read sequencing to analyze genomic and epigenomic changes in melanoma subclones, revealing parallel evolutionary paths and lineage-specific methylation patterns.

This paper introduces TIME_ACT, a transcriptomic signature predicting immune checkpoint blockade response across cancers, validated using histopathology and multiple cohorts.

Severus detects and characterizes somatic structural variations in tumor genomes using long reads, outperforming existing methods in accuracy and revealing clinically relevant rearrangements.

This study evaluates SIRT1 inhibition's effect on granulosa cell tumor growth and metastasis using in vitro and in vivo models, finding reduced tumor progression with EX-527 treatment.

This paper introduces LogiCAR designer, a computational method using single-cell transcriptomics to identify logic-gated antigen combinations for CAR therapy, showing improved efficacy and safety compared to existing approaches.

This study identifies distinct metastatic phenotypes in cutaneous melanoma using clinico-genomic data and machine learning, linking specific mutations and clinical factors to organ-specific metastasis.

The paper models gene expression adaptation in tumor subclones using stochastic processes and finds non-genetic mechanisms drive subclonal evolution and treatment response.

The paper identifies resistance-deactivated interactions (RDIs) as predictive biomarkers for ICB response in melanoma by analyzing transcriptomics data from multiple cohorts.

This study develops mouse models of brain metastatic melanoma to identify immune and molecular factors influencing response to immunotherapy.

TranNet identifies predictors of tumor progression by analyzing gene expression differences between tumor and adjacent normal tissues using TCGA data.

Trisicell infers subclonal expansion patterns and therapeutic impact of intratumor heterogeneity from scRNAseq data, revealing correlations between mutational composition and tumor aggressiveness.

PhISCS-BnB computes the most likely perfect tumor phylogeny from single-cell sequencing data, offering optimality guarantees and significant speed improvements over existing methods.