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Published on: February 27, 2015
Inferring predominant pathways in cellular models of breast cancer using limited sample proteomic profiling
Yogesh M Kulkarni1, Vivian Suarez, David J Klinke
1Department of Chemical Engineering, West Virginia University College of Engineering and Mineral Resources, West Virginia University, Morgantown, WV 26506, USA.
Background:
Molecularly targeted drugs inhibit aberrant signaling within oncogenic pathways. Identifying the predominant pathways at work within a tumor is a key step towards tailoring therapies to the patient. Clinical samples pose significant challenges for proteomic profiling, an attractive approach for identifying predominant pathways. The objective of this study was to determine if information obtained from a limited sample (i.e., a single gel replicate) can provide insight into the predominant pathways in two well-characterized breast cancer models.
Methods:
A comparative proteomic analysis of total cell lysates was obtained from two cellular models of breast cancer, BT474 (HER2+/ER+) and SKBR3 (HER2+/ER-), using two-dimensional electrophoresis and MALDI-TOF mass spectrometry. Protein interaction networks and canonical pathways were extracted from the Ingenuity Pathway Knowledgebase (IPK) based on association with the observed pattern of differentially expressed proteins.
Results:
Of the 304 spots that were picked, 167 protein spots were identified. A threshold of 1.5-fold was used to select 62 proteins used in the analysis. IPK analysis suggested that metabolic pathways were highly associated with protein expression in SKBR3 cells while cell motility pathways were highly associated with BT474 cells. Inferred protein networks were confirmed by observing an up-regulation of IGF-1R and profilin in BT474 and up-regulation of Ras and enolase in SKBR3 using western blot.
Conclusion:
When interpreted in the context of prior information, our results suggest that the overall patterns of differential protein expression obtained from limited samples can still aid in clinical decision making by providing an estimate of the predominant pathways that underpin cellular phenotype.
Insights
Proteomic analysis of limited breast cancer samples can identify key signaling pathways. This approach aids in tailoring molecularly targeted therapies for improved clinical decision-making.
Area of Science:
- Oncology
- Proteomics
- Bioinformatics
Background:
- Molecularly targeted drugs require identification of aberrant oncogenic signaling pathways.
- Clinical sample proteomic profiling is challenging but crucial for personalized therapy.
- This study investigates pathway identification from limited clinical samples.
Purpose of the Study:
- To assess if limited proteomic data from a single gel replicate can reveal predominant pathways in breast cancer models.
- To evaluate the utility of proteomic profiling for guiding targeted therapy selection.
Main Methods:
- Comparative proteomic analysis of BT474 (HER2+/ER+) and SKBR3 (HER2+/ER-) breast cancer cell lines.
- Two-dimensional electrophoresis and MALDI-TOF mass spectrometry were employed.
- Ingenuity Pathway Knowledgebase (IPK) was used to analyze protein networks and canonical pathways.
Main Results:
- 167 out of 304 protein spots were identified, with 62 proteins meeting the 1.5-fold change threshold.
- SKBR3 cells showed high association with metabolic pathways.
- BT474 cells were highly associated with cell motility pathways, confirmed by western blot validation.
Conclusions:
- Proteomic patterns from limited samples can estimate predominant pathways underpinning cellular phenotype.
- This approach can assist in clinical decision-making for targeted therapy.
- Limited sample proteomic analysis is a viable strategy for personalized oncology.
