Abstract
To determine the signaling pathways leading from Met activation to metastasis and poor prognosis, we measured the kinetic gene alterations in breast cancer cell lines in response to HGF/SF. Using a network inference tool we analyzed the putative protein-protein interaction pathways leading from Met to these genes and studied their specificity to Met and prognostic potential. We identified a Met kinetic signature consisting of 131 genes. The signature correlates with Met activation and with response to anti-Met therapy (p<0.005) in in-vitro models. It also identifies breast cancer patients who are at high risk to develop an aggressive disease in six large published breast cancer patient cohorts (p<0.01, N>1000). Moreover, we have identified novel putative Met pathways, which correlate with Met activity and patient prognosis. This signature may facilitate personalized therapy by identifying patients who will respond to anti-Met therapy. Moreover, this novel approach may be applied for other tyrosine kinases and other malignancies.
Highlights
Met is the tyrosine kinase receptor (TKR) for Hepatocyte Growth Factor/Scatter Factor (HGF/SF)
Using a protein-protein interaction network analysis tool, we demonstrated the association between Met and its signature genes and identified novel putative Met signaling pathways, which correlate with Met activity as well as with breast cancer patient prognosis
Our main contributions are: (i) using data derived from a cellular model of TKR activation we identify novel signaling pathways that are specific to the TKR (Met) and correlate with patient survival (ii) we demonstrate the utility of the kinetic signature in determining tyrosine kinase activity in vivo and in predicting response to anti-Met therapy in cellular models, potentially serving to personalize anti-Met therapy
Summary
Met is the tyrosine kinase receptor (TKR) for Hepatocyte Growth Factor/Scatter Factor (HGF/SF). Overexpression of HGF/SF and Met in breast carcinoma [8,9,10] correlates with triple-negative and basal type tumors [11,12], and are strong independent predictors of decreased survival [9,13,14,15], including stage-I patients [16,17,18,19]. Met overexpression is found in approximately 20% of breast cancer patients [9,14]. Targeting HGF/SF-Met pathway is becoming an attractive approach for developing anti-cancer agents [20]. A handful of cDNA array based Met signatures were published [24,25,26], one of which, a signature based on Met +/2 mouse hepatocytes [24], correlates with metastasis and prognosis, but was never validated against large breast cancer patient data sets
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