Identification of protein clusters predictive of response to chemotherapy in breast cancer patients.
ABSTRACT An attempt for the identification of potential biomarkers predictive of response to chemotherapy (CHT) in breast cancer patients has been performed by the use of two-dimensional electrophoresis and mass spectrometry analysis. Since growth and progression of tumor cells depend also on stromal factors in the microenvironment, we choose to investigate the proteins secreted in Tumor Interstitial Fluid (TIF) and in Normal Interstitial Fluids (NIF). One-hundred and twenty-two proteins have been analyzed and a comparison was also made between the proteomic profile of responders versus nonresponders to CHT. At baseline, proteins isolated in TIF and NIF of all the 28 patients show significant differences in expression. Two clusters of proteins, differentially expressed in TIF with respect to NIF were found. Most significant is the decreased expression in TIF of CRYAB. In the protein metabolism group, also FIBB was found decreased. Some proteins involved in energy pathways were overexpressed (PGAM-1, ALDO A, PGK1, G3Pcn), while some other were down-regulated (CAH2, G3Pdx, PRDX6, TPIS). The same trend was observed for signal transduction proteins, with 14-3-3-Z overexpressed, and ANXA2 and PEBP 1 down-regulated. Moreover, an analysis has been conducted comparing protein expression in interstitial fluids of responders and nonresponders, irrespective of TIF or NIF source. This analysis lead us to identify two clusters of proteins with a modified expression, which might be predictive of response to CHT. In responders, an increase in expression of LDHA, G3Pdx, PGK1sx (energy pathways), VIME (cell growth and maintenance) and 14-3-3-Z (signal transduction), coupled with a decreased expression of TPIS, CAH 2, G3Psx, PGK 1dx (energy pathways), TBB5 (cell growth and maintenance), LDHB and FIBB (protein metabolism), was found. We observed that CHT modifies the expression of these cluster proteins since, after treatment, their expression in TIF of responder is generally decreased. Patients not responding to CHT show an unchanged expression pattern in TIF, with the exception of protein 14-3-3-Z, which is overexpressed, and a decreased expression in NIF of several cluster proteins. In conclusion, the identification of protein clusters associated with response to CHT might be important for predicting the efficacy of a specific antineoplastic drug and for the development of less empiric strategies in choosing the therapy to be prescribed to the single patient.
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ABSTRACT: Colorectal cancer (CRC) is a major health problem. Biomarkers associated with molecular changes in cancer cells can aid early detection, diagnosis, prognosis, therapy selection, and disease monitoring. Tumor tissue secretomes are a rich source of candidate biomarkers. To identify CRC protein biomarkers, secretomes of four pairs of human CRC tissue and patient-matched normal colon tissue samples, and secretomes of five CRC cell lines were analyzed by GeLC-MS/MS. Subsequent data analysis was based on label-free spectral counting, Ingenuity Pathway Analysis, Secretome/SignalP, STRING and Cytoscape. Resulting in 2703 protein identifications in the tissue secretomes, of which 409 proteins were significantly more present in CRC samples than in controls. Biomarker selection of 76 candidates was based on consistent and abundant over-representation in cancer- compared to control-secretomes, and presumed neoplastic origin. Overlap analysis with previously obtained datasets revealed 21 biomarkers suited for early detection of CRC. Immunohistochemistry confirmed overexpression in CRC of one candidate marker (MCM5). In conclusion, a human reference dataset of 76 candidate biomarkers was identified for which we illustrate that combination with existing pre-clinical datasets allows pre-selection of biomarkers for blood- or stool-based assays to support clinical management of CRC. Further dedicated validation studies are required to demonstrate their clinical applicability. Tissue secretome proteomes are a rich source of candidate biomarkers. Several secretome proteome datasets have been obtained from pre-clinical in vitro and in vivo colorectal cancer (CRC) model systems, yielding promising CRC biomarkers obtained under well-defined experimentally controlled conditions. However, which of these biomarker proteins are actually secreted by human CRC samples was not known. To our knowledge, this is the first study that directly compares secretome proteomes from clinically relevant human CRC tissues to patient-matched normal colon tissues. We identified 76 human CRC protein biomarkers that may facilitate blood-based or stool-based assay development to support clinical management of CRC. Overlap analysis with datasets from well-defined pre-clinical studies helps to determine what clinical application suits these human CRC biomarkers best, i.e. early detection, diagnosis, prognosis, therapy selection, and/or disease monitoring of CRC. This is demonstrated for a CRC mouse model dataset, revealing 21 human CRC biomarkers suited for early detection of CRC.Journal of proteomics 01/2014; · 5.07 Impact Factor
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ABSTRACT: Post-surgery therapies are given to early-stage breast cancer patients due to the possibility of residual micrometastasis, and optimized by clincopathological parameters such as tumor stage, and hormone receptor/lymph node status. However, current efficacy of post-surgery therapies is unsatisfactory, and may be varied according to unidentified patient genetic factors. Increases of breast cancer occurrence and recurrence have been associated with dyslipidemia, which can attribute to other known risk factors of breast cancer including obesity, diabetes and metabolic syndrome. Thus we reasoned that dyslipidemia-associated nucleotide polymorphisms (SNPs) on the APOA1/C3/A5 gene cluster may predict breast cancer risk and tumor progression. We analyzed the distribution of 5 selected APOA1/C3/A5 SNPs in recruited Taiwanese breast cancer patients (n=223) and healthy controls (n=162). The association of SNP (APOA1 rs670) showing correlation with breast cancer with baseline and follow-up parameters was further examined. APOA1 rs670 A allele carriage was higher in breast cancer patients than controls (59.64% vs. 48.77%, p=0.038). The rs670 A allele carrying patients showed less favorable baseline phenotype with positive lymph nodes (G/A: OR=3.32, 95%CI=1.77-6.20, p<0.001; A/A: OR=2.58, 95%CI=1.05-6.32, p=0.039) and negative hormone receptor expression (A/A: OR=4.85, 95%CI=1.83-12.83, p=0.001) in comparison to G/G carriers. Moreover, rs670 A/A carrying patients had higher risks in both tumor recurrence (HR=3.12, 95%CI=1.29-7.56, p=0.012) and mortality (HR=4.36, 95%CI=1.52-12.47, p=0.006) than patients with no A alleles after adjustments for associated baseline parameters. Furthermore, the prognostic effect of rs670 A/A carriage was most evident in lymph node-negative patients, conferring to the highest risks of recurrence (HR=4.98, 95%CI=1.40-17.70, p=0.013) and mortality (HR=9.87, 95%CI=1.60-60.81, p=0.014) than patients with no A alleles. APOA1 rs670 A/A carriage showed poor post-surgery prognosis in Taiwanese lymph node-negative breast cancer patients, whose prognosis were considered better and adjuvant treatment might be less stringent according to currently available assessment protocols. Our findings suggest that APOA1 rs670 indicate a post-surgery risk of breast cancer disease progression, and that carriers of this SNP may benefit from more advanced disease monitoring and therapy regimens than the current regular standards. Furthermore, control of lipid homeostasis might protect APOA1 rs670 minor allele carriers from breast cancer occurrence and progression.BMC Cancer 07/2013; 13(1):330. · 3.32 Impact Factor
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ABSTRACT: The identification of biomarkers predictive of neoadjuvant chemotherapy response in breast cancer patients would be an important advancement in personalized cancer therapy. In this study, we hypothesized that due to similarities between radiation- and chemotherapy-induced cellular response mechanisms, radiation-responsive genes may be useful in predicting response to neoadjuvant chemotherapy. Murine p53 null breast cancer cell lines representative of the luminal, basal-like and claudin-low human breast cancer subtypes were irradiated to identify radiation-responsive genes across subtypes. These murine tumor radiation-induced genes were then converted to their human orthologs, and subsequently tested as a predictor of pathologic complete response (pCR), which was validated on two independent published neoadjuvant chemotherapy datasets of genomic data with chemotherapy response. A radiation-induced gene signature consisting of 30 genes was identified on a training set of 337 human primary breast cancer tumor samples that was prognostic for survival. Mean expression of this signature was calculated for individual samples on two independent published datasets and was found to be significantly predictive of pCR. Multivariate logistic regression analysis in both independent datasets showed that this 30 gene signature added significant predictive information independent of that provided by standard clinical predictors and other gene expression-based predictors of pCR. This study provides new information for radiation-induced biology, as well as information regarding response to neoadjuvant chemotherapy and a possible means of improving the prediction of pCR.Radiation Research 02/2014; · 2.70 Impact Factor