Biology subjects

Chang, A. C.-C.

Publications and source records attributed to Chang, A. C.-C..

4 recordsLinked to original sources

bioRxiv2026

Novel transplantable mouse cell line model recapitulates invasive lobular breast carcinoma (ILC) phenotype and immune microenvironment.

Invasive lobular breast carcinoma (ILC) is the most common special histological subtype of breast cancer, which accounts for 10-15% of all cases. To study the phenotype characteristics, metastatic growth kinetic and immune microenvironment of ILC, we developed an orthotopically transplantable cell line model from the spontaneous mammary fat pad tumor of CDH1-PTEN dual knockout C57BL/6 mouse with Cre-loxP system, designated CPT6. CPT6 recapitulates single-file growth pattern of human ILC, with pleomorphic features and a high mitotic index. RNA sequencing together with whole exome sequencing reveals a luminal A subtype with targetable driver mutations such as Kras G12C. As a novel orthotopically transplantable ILC model in immune competent mice, CPT6 shows robust in vivo growth and metastatic rate, and has moderate immunogenicity which appears to be T-cell independent. We also profiled the immune microenvironment of CPT6, revealing a myeloid-rich environment with dominant M2-macrophage population, which is concordant with human ILC. In summary, this model recapitulates human ILC phenotype and represents a valuable preclinical platform for evaluating immunotherapy and other therapeutic strategies for invasive lobular breast carcinoma.

cancer biology↗

bioRxiv2026

Benchmarking scRNA-seq Copy Number Inference: A Comprehensive Evaluation and Practitioner Guide

Accurately inferring copy number variation (CNV) from scRNA-seq data is critical for identifying malignant cells, reconstructing tumor subclonal architecture, and uncovering the genomic drivers that dictate cancer cell biology. However, the performance of existing tools varies significantly, and current benchmarks lack the breadth of datasets and methods necessary to provide definitive guidance. We present a comprehensive benchmark of 12 CNV inference methods across 28 real datasets (>100,000 cells) and diverse synthetic datasets. By evaluating methods based on malignant cell classification accuracy, CNV inference accuracy, scalability, and robustness, we establish a definitive practitioners guideline: allele-aware methods like Numbat excel when high-quality allelic inference can be achieved, whereas expression-centric tools such as Clonalscope, CopyKAT, inferCNV, and SCEVAN remain reliable when raw sequencing data are unavailable. Our study provides both a practical decision-making framework for researchers and a public repository of standardized CNV profiles to catalyze further methodological innovation.

cancer biology↗

bioRxiv2025

CITEgeist: Cellular Indexing of Transcriptomes and Epitopes for Guided Exploration of Intrinsic Spatial Trends

BackgroundDysplastic tissue architecture in estrogen receptor-positive (ER+) breast cancer across therapy-naive and therapy-exposed cancer tissues presents unique challenges in the analysis of spatial transcriptomics. Many tools for deconvolution are developed on well-structured tissue architectures such as the 10x Genomics mouse hippocampus dataset. Spatial transcriptomics analysis could offer valuable insights into treatment response, but faces limitations in cellular resolution. MethodsTo address this problem, we developed CITEgeist, a computational tool for spatial transcriptomic deconvolution using integrated proteomics data from the same slide. Visium Antibody Capture technology was applied alongside our novel algorithm to analyze the tumor microenvironment. We demonstrate the reliability of our method using pre- and post-treatment samples from six breast cancer cases. ResultsOur approach revealed previously undetectable cellular interactions within the tumor microenvironment. By taking an interoperable approach to software development and grounding our algorithm in interpretable variables, we demonstrate how CITEgeist deconvolution is not only accurate but robust enough to be directly used as input in external analytical tools developed by other research teams. We then applied this approach to a set of specimens from a prospective trial our group ran and further validated the findings in a series of in vitro experiments as a demonstrated use case of the utility, necessity, and flexibility of CITEgeist; and the potential of our method to rapidly translate novel clinical samples to new biological insights. ConclusionsCITEgeist addresses a critical technical gap in spatial multi-omics analysis through an integrated, multi-disciplinary approach. This work demonstrates the value of combining clinical, translational, and computational expertise to identify novel mechanisms of treatment resistance, potentially transforming therapeutic strategies for resistant disease.

cancer biology↗

bioRxiv2021

Identification of Cancer-Associated Fibroblasts in Glioblastoma and Defining Their Pro-tumoral Effects

Despite their identification in some cancers, pro-tumoral cancer-associated fibroblasts (CAFs) were presumed absent in glioblastoma given the lack of brain fibroblasts. Serial trypsinization of primary glioblastoma cultures yielded cells with CAF morphology, CAF transcriptomic profile, and mesenchymal lineage in single-cell RNA-seq. Glioblastoma CAFs were attracted to glioblastoma stem cells (GSCs) and CAFs enriched GSCs. We created a resource of inferred crosstalk by mapping expression of receptors to their cognate ligands, identifying PDGF-{beta} and TGF-{beta} as mediators of GSC effects on CAFs, and osteopontin and hepatocyte growth factor as mediators of CAF-induced GSC enrichment. Glioblastoma CAFs also induced M2 macrophage polarization by producing the EDA fibronectin variant. Glioblastoma CAFs were enriched in the subventricular zone which houses neural stem cells that produce GSCs. Including CAFs in GSC-derived xenografts induced in vivo growth. These findings are among the first to identify glioblastoma CAFs and their GSC interactions, making them an intriguing target.

cancer biology↗