Brooks A. Benard

EcoNet improves immunotherapy response prediction by modeling tumor microenvironment signaling networks, outperforming existing methods in AUC and stratifying ccRCC patients by clinical outcome.

The paper integrates mouse lymph node tumor data with human melanoma tumors to identify transcriptional programs and ecotypes associated with metastasis.

PhenoMapR maps bulk expression phenotypes to single-cell and spatial transcriptomics data to identify biologically relevant cellular populations and spatial niches.

The paper identifies spatially distinct epithelial states in PDAC that correlate with immunosuppressive myeloid niches and poor prognosis.

The paper identifies and isolates human AML leukemia stem cells using single-cell multi-omics and establishes a prognostic gene signature for clinical relevance.

The paper updates the PRECOG resource by adding pediatric and immunotherapy datasets, enabling analysis of gene expression associations with clinical outcomes using statistical models and deconvolution.

The paper identifies ZMAT3 and CDKN1A as key hubs in the p53 tumor suppression network through integrative multiomic analyses in a mouse model.

The paper identifies genomic subtypes of multiple myeloma using the MMRF CoMMpass study and finds that 25.5% of patients transition to a high-risk expression subtype at progression, which expresses immunotherapy targets.