Cancer Profiling

Much of my current work centers on the identification of determinants that drive the response of cancer cells to clinically relevant perturbations. I focus my work on two types of agents key to the treatment of cancer: small molecule kinase inhibitors—both FDA approved drugs and tool compounds—predominantly targeting signaling pathways commonly dysregulated in cancer patients and soluble growth factors of the tumor microenvironment that play key roles in cell growth, migration, invasion, and death. As model system I am using a panel of 45 breast cancer cell lines that have been shown to represent much of the breadth of clinical disease. 

Profiling the receptor tyrosine kinase layer.

We profiled signaling pathway activity in a collection of breast cancer cell lines before and after stimulation with ligands, which revealed the variability in network activity among cells of known genotype and molecular subtype. We found that the abundance and activity of signaling proteins in unstimulated cells (basal profile) as well as the activity of proteins in stimulated cells (signaling profile) varied within each subtype.

Molecular determinants of growth factor responses.

Our profiling data set shows that breast cancers are diverse with respect to ligand sensitivity and signaling biochemistry. Surprisingly, triple Negative Breast Cancers (which express low levels of Her2/ErbB2, progesterone, and estrogen receptors) are the most broadly responsive to growth factors and Her2/ErbB2-amplified cells the least. The ratio of ERK to AKT activation varies with ligand and subtype, with a systematic bias in factor of ERK in HR+ cells. We developed a heuristic that explains immediate-early ligand responses in terms of receptor abundance, ERK/AKT bias and an “indirect negative regulation” by Her2/ErbB2. Our analysis shows that the responses to growth factors are highly diverse across breast cancer cell lines, even within the same subtype and are driven primarily by abundance of relevant receptor tyrosine kinases. Identification of the molecular determinants of growth factor signaling in breast cancer lines can lead to a better understanding of the interaction between tumors and their microenvironment.

Prediction of drug responses.

We constructed models that significantly predicted sensitivity to 23 different targeted therapeutics. This analysis identified key proteins that could serve as biomarkers of drug sensitivity. For example, one model showed that the response to the growth factor receptor ligand heregulin effectively predicted the sensitivity of cells to drugs targeting the cell survival pathway mediated by PI3K and Akt; whereas the abundance of Akt or the mutational status of the enzymes in the pathway did not. Thus, basal and signaling protein profiles may yield new biomarkers and enable the identification of appropriate therapies in cancers characterized by similar functional dysregulation of signaling networks.

Follow up studies.

The approach to determine the behavior of cancer cell lines by looking at the receptor tyrosine kinase layer and signal response pathways has proven successful and I am now following up multiple hypotheses generated by this approach. In collaboration with the Gygi lab we are collecting much more extensive data and the cellular proteome and activity state and we are currently relating this information to cell type specific drug responses with a particular focus on triple negative breast cancer. In addition, we are pursuing the interaction between high levels of ErbB2 and other receptor tyrosine kinases that may be responsible for the high sensitivity of HER2 amplified breast cancer to inhibitors targeting ErbB2 directly. We are also expanding our dataset by measuring the drug sensitivity of over 30 cell lines and multiple PDX-derived lines in more detail.

Niepel M et al., Analysis of growth factor signaling in genetically diverse breast cancer lines, BMC Biology, 2014

Niepel M et al., Profiles of Basal and Stimulated Receptor Signaling Networks Predict Drug Response in Breast Cancer Lines, Science Signaling, 2013