Anand G. Patel

This paper develops an AI framework to interpret single-cell RNA-seq data from pediatric solid tumors, identifying tumor-specific surface antigen pairs for targeted therapies.

This study integrates single-cell and spatial transcriptomic data across species to identify conserved and distinct tumor ecosystems in osteosarcoma, revealing canine models as more faithful and highlighting ECM signaling in metastatic lesions.

The paper identifies distinct transcriptional programs in Ewing sarcoma driven by EWS::FLI1 fusions and shows their variability across tumors and cell lines.

This paper establishes a diverse collection of preclinical models for pediatric solid tumors and demonstrates their utility in precision medicine trials and drug testing.

This paper creates a spatial cell atlas of neuroblastoma, revealing tumor heterogeneity through multi-omics data and identifying spatial compartmentalization and signaling patterns.

This paper introduces scIDUC, a computational framework that predicts drug response at the single-cell level by integrating single-cell transcriptomics with pan-cancer cell line data, showing high accuracy in identifying therapeutic vulnerabilities.

The paper develops a toolbox for scRNA-Seq and snRNA-Seq of fresh and frozen human tumors, testing it across eight tumor types and optimizing protocols for different tissue characteristics.