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Biology subjects

Webber, J. T.

Publications and source records attributed to Webber, J. T..

2 recordsLinked to original sources

Kinome rewiring reveals AURKA is a molecular barrier to the efficacy of PI3K/mTOR-pathway inhibitors in breast cancer

Dysregulation of the PI3K-AKT-mTOR signaling network is a prominent feature of breast cancers. However, clinical responses to drugs targeting this pathway have been modest. We hypothesized that dynamic changes in signaling, including adaptation and feedback, limit drug efficacy. Using a quantitative chemoproteomics approach we mapped dynamic changes in the kinome in response to various agents and identified signaling changes that correlate with drug sensitivity. Measurement of dynamics across a panel of breast cancer cell lines identified that maintenance of CDK4 and AURKA activity was associated with drug resistance. We tested whether incomplete inhibition of CDK4 or AURKA was a source of therapy failure and found that inhibition of either was sufficient to sensitize most breast cancer cells to PI3K, AKT, and mTOR inhibitors. In particular, drug combinations including the AURKA inhibitor MLN8237 were highly synergistic and induced apoptosis through enhanced suppression of mTOR signaling to S6 and 4E-BP1 leading to tumor regression in vivo. This signaling map identifies survival factors whose presence limits the efficacy of target therapy and indicates that Aurora kinase co-inhibition could unlock the full potential of PI3K-AKT-mTOR pathway inhibitors in breast cancer.

cancer biology

Connecting tumor genomics with therapeutics through multi-dimensional network modules

Recent efforts have catalogued genomic, transcriptomic, epigenetic and proteomic changes in tumors, but connecting these data with effective therapeutics remains a challenge. In contrast, cancer cell lines can model therapeutic responses but only partially reflect tumor biology. Bridging this gap requires new methods of data integration to identify a common set of pathways and molecular events. Using MAGNETIC, a new method to integrate molecular profiling data using functional networks, we identify 219 gene modules in TCGA breast cancers that capture recurrent alterations, reveal new roles for H3K27 tri-methylation and accurately quantitate various cell types within the tumor microenvironment. We show that a significant portion of gene expression and methylation in tumors is poorly reproduced in cell lines due to differences in biology and microenvironment and MAGNETIC identifies therapeutic biomarkers that are robust to these differences. This work addresses a fundamental challenge in pharmacogenomics that can only be overcome by the joint analysis of patient and cell line data.

bioinformatics