Proteomic Portrait of Human Breast Cancer Progression Identifies Novel Prognostic Markers

Geiger, Tamar; Madden, Stephen F.; Gallagher, William M.; Cox, Juergen; Mann, Matthias

doi:10.1158/0008-5472.can-11-3711

Cited by 133 publications

(114 citation statements)

References 53 publications

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“…Together, this panel provides a view of the transformation process toward the metastatic phenotype. The elevated tumorigenic potential of these cells with increased stage has been previously shown experimentally in combination with a global proteomic analysis (35). Measurement of the growth rates of these cells showed statistically significant differences between cells from different stages (ANOVA P ¼ 9.40e-04).…”

Section: Breast Cancer Progression Is Accompanied By Reduced Prolifersupporting

confidence: 52%

Metabolic Associations of Reduced Proliferation and Oxidative Stress in Advanced Breast Cancer

et al. 2012

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Aberrant metabolism is a hallmark of cancer, but whole metabolomic flux measurements remain scarce. To bridge this gap, we developed a novel metabolic phenotypic analysis (MPA) method that infers metabolic phenotypes based on the integration of transcriptomics or proteomics data within a human genome-scale metabolic model. MPA was applied to conduct the first genome-scale study of breast cancer metabolism based on the gene expression of a large cohort of clinical samples. The modeling correctly predicted cell lines' growth rates, tumor lipid levels, and amino acid biomarkers, outperforming extant metabolic modeling methods. Experimental validation was obtained in vitro. The analysis revealed a subtype-independent "go or grow" dichotomy in breast cancer, where proliferation rates decrease as tumors evolve metastatic capability. MPA also identified a stoichiometric tradeoff that links the observed reduction in proliferation rates to the growing need to detoxify reactive oxygen species. Finally, a fundamental stoichiometric tradeoff between serine and glutamine metabolism was found, presenting a novel hallmark of estrogen receptor (ER) þ versus ER À tumor metabolism. Together, our findings greatly extend insights into core metabolic aberrations and their impact in breast cancer. Cancer Res; 72 (22); 5712-20. Ó2012 AACR.

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Section: Breast Cancer Progression Is Accompanied By Reduced Prolifersupporting

confidence: 52%

Metabolic Associations of Reduced Proliferation and Oxidative Stress in Advanced Breast Cancer

et al. 2012

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“…Research into the mechanisms involved in the transformation of normal epithelial cells into cancer cells has already determined dramatic changes in the genome (35,36), proteome (37,38), and metabolome (39, 40) of these cells. Among these changes, an alteration in the glucose metabolism (41), in the fatty acid (31,34) as well as in the lipid composition of the cells (30) has been observed.…”

Section: Alterations In Stroma and Glandular Tissue Due To Cancerizatmentioning

confidence: 99%

Spatially Resolved Metabolic Phenotyping of Breast Cancer by Desorption Electrospray Ionization Mass Spectrometry

et al. 2015

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Breast cancer is a heterogeneous disease characterized by varying responses to therapeutic agents and significant differences in long-term survival. Thus, there remains an unmet need for early diagnostic and prognostic tools and improved histologic characterization for more accurate disease stratification and personalized therapeutic intervention. This study evaluated a comprehensive metabolic phenotyping method in breast cancer tissue that uses desorption electrospray ionization mass spectrometry imaging (DESI MSI), both as a novel diagnostic tool and as a method to further characterize metabolic changes in breast cancer tissue and the tumor microenvironment. In this prospective single-center study, 126 intraoperative tissue biopsies from tumor and tumor bed from 50 patients undergoing surgical resections were subject to DESI MSI. Global DESI MSI models were able to distinguish adipose, stromal, and glandular tissue based on their metabolomic fingerprint. Tumor tissue and tumor-associated stroma showed evident changes in their fatty acid and phospholipid composition compared with normal glandular and stromal tissue. Diagnosis of breast cancer was achieved with an accuracy of 98.2% based on DESI MSI data (PPV 0.96, NVP 1, specificity 0.96, sensitivity 1). In the tumor group, correlation between metabolomic profile and tumor grade/hormone receptor status was found. Overall classification accuracy was 87.7% (PPV 0.92, NPV 0.9, specificity 0.9, sensitivity 0.92). These results demonstrate that DESI MSI may be a valuable tool in the improved diagnosis of breast cancer in the future. The identified tumor-associated metabolic changes support theories of de novo lipogenesis in tumor tissue and the role of stroma tissue in tumor growth and development and overall disease prognosis. Cancer Res; 75(9); 1828-37. Ó2015 AACR.

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“…These efforts have led to the identification of new candidate biomarkers, including at least 1 marker, CUZD1 (CUB and zona pellucida-like domain-containing 1), that has a sensitivity and a specificity superior to the currently used cancer antigen marker CA19-9 (54 ). Others have used these techniques to identify markers of taxane sensitivity in breast cancer patients receiving adjuvant paclitaxel and radiation therapy (55 ) and to identify new prognostic biomarkers (56 ).…”

Section: The Role Of Proteomics In Genomic Medicinementioning

confidence: 99%

Genomic Medicine: New Frontiers and New Challenges

2013

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BACKGROUND:The practice of personalized medicine has made large strides since the introduction of highthroughput technologies and the vast improvements in computational biotechnology. The personalizedmedicine approach to cancer management holds promise for earlier disease detection, accurate prediction of prognosis, and better treatment options; however, the early experience with personalized medicine has revealed important concerns that need to be addressed before research findings can be translated to the bedside.CONTENT: We discuss several emerging "practical" or "focused" applications of personalized medicine. Molecular testing can have an important positive impact on health and disease management in a number of ways, and the list of specific applications is evolving. This list includes improvements in risk assessment, disease prevention, identification of new diseaserelated mutations, accurate disease classification based on molecular signatures, selection of patients for enrollment in clinical trials, and development of new targeted therapies, especially for metastatic tumors that are refractory to treatment. Several challenges remain to be addressed before genomics information can be applied successfully in the routine clinical management of cancers. Further improvements and investigations are needed in data interpretation, extraction of actionable items, costeffectiveness, how to account for patient heterogeneity and ethnic variation, and how to handle the risk of "incidental findings" in genetic testing.

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Proteomic Portrait of Human Breast Cancer Progression Identifies Novel Prognostic Markers

Cited by 133 publications

References 53 publications

Metabolic Associations of Reduced Proliferation and Oxidative Stress in Advanced Breast Cancer

Metabolic Associations of Reduced Proliferation and Oxidative Stress in Advanced Breast Cancer

Spatially Resolved Metabolic Phenotyping of Breast Cancer by Desorption Electrospray Ionization Mass Spectrometry

Genomic Medicine: New Frontiers and New Challenges

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