Search bioRxiv⌕ Search

Biology subjects

Kong, E. K.

Publications and source records attributed to Kong, E. K..

2 recordsLinked to original sources

A prognostic matrix code defines functional glioblastoma phenotypes and niches.

Interactions among tumor, immune and vascular niches play major roles in driving glioblastoma (GBM) malignancy and treatment responses. The composition, heterogeneity, and localization of extracellular core matrix proteins (CMPs) that mediate such interactions, however, are not well understood. Here, we characterize functional and clinical relevance of genes encoding CMPs in GBM at bulk, single cell, and spatial anatomical resolution. We identify a "matrix code" for genes encoding CMPs whose expression levels categorize GBM tumors into matrisome-high and matrisome-low groups that correlate with worse and better survival, respectively, of patients. The matrisome enrichment is associated with specific driver oncogenic alterations, mesenchymal state, infiltration of pro-tumor immune cells and immune checkpoint gene expression. Anatomical and single cell transcriptome analyses indicate that matrisome gene expression is enriched in vascular and leading edge/infiltrative anatomic structures that are known to harbor glioma stem cells driving GBM progression. Finally, we identified a 17-gene matrisome signature that retains and further refines the prognostic value of genes encoding CMPs and, importantly, potentially predicts responses to PD1 blockade in clinical trials for GBM. The matrisome gene expression profiles may provide biomarkers of functionally relevant GBM niches that contribute to mesenchymal-immune cross talk and patient stratification to optimize treatment responses.

cancer biology↗

Mapping the functional interactions at the tumor-immune checkpoint interface

It is challenging to identify the tumor-immune system interactions that modulate immune states and immunotherapy responses due to the prohibitively complex space of possible interactions. Our statistical analysis framework, ImogiMap quantifies tumor-immune interactions based on their synergistic co-associations with immune-associated phenotypes. ImogiMap-based analyses recapitulated known interactions modulating immunotherapy responses and nominated the CD86/CD70 axis as an immunotherapy target that overlaps with IFNG overexpression and patient survival in endometrial carcinoma.

bioinformatics↗