Christine Louise Chaffer

This study identifies cytoplasmic androgen receptor (AR) localization and novel AR protein interactions in AI-resistant breast cancer, linking them to metabolic shifts and poor prognosis.

The paper uses isoform-specific single-cell Perturb-Seq to show that alternative promoters drive distinct transcriptional programs and affect drug response in breast cancer.

The paper shows that pre-existing epithelial-mesenchymal (E/M) cell states determine differential phospho-signaling responses to EGF in NSCLC cells.

This study shows that capillary constrictions reprogram melanoma cells to a tumorigenic cancer stem cell-like state via PIEZO1 mechanosensing.

AAnet identifies archetypal cell states in single-cell data to reveal tumor heterogeneity and spatial organization.

The paper uses multi-modal transcriptomic data to show basal breast cancer exhibits hybrid epithelial/mesenchymal states and higher phenotypic heterogeneity compared to luminal breast cancer.

scMMGAN integrates multi-omic data to reveal spatial patterns in triple negative breast cancer, showing improved alignment and novel gene associations.

MAGIC improves single-cell RNA-seq data by imputing missing values and revealing gene-gene interactions through a diffusion-based method.