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Biomedical subjects

Chandandeep Nagi

Publications and source records attributed to Chandandeep Nagi.

2 recordsLinked to original sources

Rat somatic genome editing enables ER+ breast cancer modeling.

Genetically engineered mouse models have advanced cancer research but often fail to capture key features of certain human tumors. Rats, with distinct physiology and tumor biology, offer a powerful alternative, yet their use has been constrained by technical barriers to genome editing. Here, we report efficient somatic genome editing in rats, enabling both Indel and substitution mutations. We then apply this approach to model estrogen receptor (ER)-positive breast cancer, which accounts for ~70% of human cases but remains poorly represented in mice. The resulting rat tumors reproduce hallmarks of human ER+ breast cancer, including ductal histology, hormone responsiveness, and immune-microenvironmental features. By contrast, identical genetic alterations in mice failed to yield ER+ tumors, underscoring critical species differences in tumorigenesis. Together, this work establishes a versatile platform for rapid generation of clinically relevant rat tumor models, opening new avenues to dissect tumor biology, therapeutic response, and immune interactions in previously inaccessible cancer subtypes.

Journal Article

Coalescing single-cell genomes and transcriptomes to decode breast cancer progression.

Understanding epithelial lineages of breast cancer and genotype-phenotype relationships requires direct measurements of the genome and transcriptome of the same single cells at scale. To achieve this, we developed wellDR-seq, a high-genomic-resolution, high-throughput method to simultaneously profile the genome and transcriptome of thousands of single cells. We profiled 33,646 single cells from 12 estrogen-receptor-positive breast cancers and identified ancestral subclones in multiple patients that showed a luminal hormone-responsive lineage, indicating a potential cell of origin. In contrast to bulk studies, wellDR-seq enabled the study of subclone-level gene-dosage relationships, which showed near-linear correlations in large chromosomal segments and extensive variation at the single-gene level. We identified dosage-sensitive and dosage-insensitive genes, including many breast cancer genes as well as sporadic copy-number aberrations in non-cancer cells. Overall, these data reveal complex relationships between copy number and gene expression in single cells, improving our understanding of breast cancer progression.

Breast Neoplasms