Apoptosis - associated genes and their role in predicting responses to neoadjuvant breast cancer treatment
Jazyk angličtina Země Spojené státy americké Médium print
Typ dokumentu časopisecké články, práce podpořená grantem
PubMed
22207111
PubMed Central
PMC3560664
DOI
10.12659/msm.882205
PII: 882205
Knihovny.cz E-zdroje
- MeSH
- adjuvantní chemoterapie * MeSH
- apoptóza účinky léků MeSH
- imunohistochemie MeSH
- koncové značení zlomů DNA in situ MeSH
- kvantitativní polymerázová řetězová reakce metody MeSH
- lidé středního věku MeSH
- lidé MeSH
- nádory prsu diagnóza farmakoterapie MeSH
- neoadjuvantní terapie * MeSH
- polymerázová řetězová reakce MeSH
- proteiny regulující apoptózu * genetika metabolismus MeSH
- protinádorové látky farmakologie MeSH
- shluková analýza MeSH
- stanovení celkové genové exprese metody MeSH
- Check Tag
- lidé středního věku MeSH
- lidé MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
- Názvy látek
- proteiny regulující apoptózu * MeSH
- protinádorové látky MeSH
BACKGROUND: Neoadjuvant chemotherapy is used in the treatment of breast carcinoma because it substantially reduces the size of the primary tumor and lymph node metastases. The present study investigated biomarkers that can predict a pathologic response to the therapy. MATERIAL/METHODS: The role of apoptosis in regression of the tumors after neoadjuvant chemotherapy was determined by TUNEL and anti-active caspase 3 assay. The transcriptional profile of 84 key apoptosis genes was evaluated in both pre-therapeutically obtained tumor tissue by core needle biopsy and in specimens removed by final surgery, using a pathway-specific real-time PCR assay. Obtained data were analyzed by hierarchical cluster analysis and correlation analysis. The immunohistochemical profile of each tumor was determined using the standard ABC method. RESULTS: On the basis of a hierarchical cluster analysis of 13 significantly changed genes, we divided patients into good and poor prognosis groups, which correlate well with progression-free survival. In the good prognosis group, we found a statistically significant down-regulation of the expression of MCL1 and IGF1R genes after neoadjuvant treatment. We also found a statistically significant overexpression of BCL2L10, BCL2AF1, CASP8, CASP10, CASP14, CIDEB, FADD, HRK, TNFRSF25, TNFSF8 and CD70 genes. In contrast, we found up-regulation of IGF1R after the treatment in the group with poor prognosis. CONCLUSIONS: Gene expression profiling using real-time PCR assay is a valuable research tool for the investigation of molecular markers, which reflect tumor biology and treatment response.
Zobrazit více v PubMed
Parkin DM, Bray F, Ferlay J, Pisani P. Global cancer statistics. CA Cancer J Clin. 2005;55:74–108. PubMed
Buchholz TA, Hunt KK, Whitman GJ, et al. Neoadjuvant chemotherapy for Brest carcinoma. Cancer. 2003;98:1150–60. PubMed
Jones C, Ford E, Gillett C, et al. Molecular cytogenetic identification of subgroups of grade III invasive ductal breast carcinomas with different clinical outcomes. Clin Cancer Res. 2004;10:5988–97. PubMed
Goldhirsch A, Glick JH, Gelber RD, et al. Meeting Highlights: Intemationa1 Expert Consensus on the Primary Therapy of Early Breast Cancer 2005. Ann Oncol. 2005;16:1569–83. PubMed
Eifel P, Axelson JA, Costa J, et al. NIH Consensus Development Panel: Adjuvant Therapy for Breast Cancer. J Natl Cancer Inst. 2001;93:979–89. PubMed
Oakman C, Santarpia L, Di Leo A. Breast cancer assessment tools and optimizing adjuvant therapy. Nat Rev Clin Oncol. 2010;7:725–32. PubMed
Reis-Filho JS, Westbury C, Pierga JY. The impact of expression profiling on prognostic and predictive testing in breast cancer. J Clin Pathol. 2006;59:225–31. PubMed PMC
Sotiriou C, Powles TJ, Dowsett M, et al. Gene expression profiles derived from fine needle aspiration correlate with response to systemic chemotherapy in breast cancer. Breast Cancer Res. 2002;4:R3. PubMed PMC
Pluciennik E, Krol M, Nowakowska M, et al. Breast cancer relapse prediction based on multi-gene RT-PCR algorithm. Med Sci Monit. 2010;16(3):CR132–36. PubMed
Perou CM, Sorlie T, Eisen MB, et al. Molecular portraits of human breast tumours. Nature. 2000;406:747–52. PubMed
Sorlie T, Perou CM, Tibshirani R, et al. Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc Natl Acad Sci USA. 2001;98:10869–74. PubMed PMC
Sotiriou C, Neo SY, McShane LM, et al. Breast cancer c1assification and prognosis based on gene expression profiles from a population-based study. Proc Natl Acad Sci USA. 2003;100:10393–98. PubMed PMC
van’t Veer LJ, Dai H, van de Vijver MJ, et al. Gene expression profi1ing predicts c1inical outcome of breast cancer. Nature. 2002;415:530–36. PubMed
Goldhirsch A, Ingle JN, Gelber RD, et al. Thresholds for therapies: highlights of the St Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2009. Ann Oncol. 2009;20:1319–29. PubMed PMC
Tavassoli FA, Devilee P. WHO Classification of tumours Pathology and genetics tumours of the breast and female genital organs. Lyon: IARC Press; 2004.
Normanno N, De Luca A, Carotenuto P, et al. Prognostic Applications of Gene Expression Signatures in Breast Cancer. Oncology. 2009;77(Suppl 1):2–8. PubMed
Skálová H, Dundr P, Povýšil C, et al. Altered expression of Her2/neu after neoadjuvant treatment of breast cancer. Folia Biologica. 2011;57(5):191–99. PubMed
Tvrdík D, Svatošová J, Dundr P, Povýšil C. Molecular diagnosis of synovial sarcoma: detection of SYT-SSX1/2 fusion transcripts by RT-PCR in paraffin-embedded tissue. Med Sci Monit. 2005;11(3):MT1–7. PubMed
Livak KJ, Schmittgen TD. Analysis of relative gene expression data using real-time quantitative PCR and the 2(−Delta Delta C(T)) method. Methods. 2001;25:402–8. PubMed
Li Z, Liu B, Maminishkis A, et al. Gene expression profiling in autoimmune noninfectious uveitis disease. J Immunol. 2008;181:5147–57. PubMed PMC
Duan WR, Garner DS, Williams SD, et al. Comparison of immunohistochemistry for activated caspase-3 and cleaved cytokeratin 18 with the TUNEL method for quantification of apoptosis in histological sections of PC-3 subcutaneous xenografts. J Pathol. 2003;199:221–28. PubMed
Bauer K, Parise C, Caggiano V. Use of ER/PR/HER2 subtypes in conjunction with the 2007 St Gallen Consensus Statement for early breast cancer. BMC Cancer. 2010;10:228–40. PubMed PMC
Van de Vijver M. Gene-Expression Profiling and the Future of Adjuvant Therapy. The Oncologist. 2005;10(Suppl 2):30–34. PubMed
Chang JC, Wooten EC, Tsimelzon A, et al. Gene expression profiling for the prediction of therapeutic response to docetaxel in patients with breast cancer. Lancet. 2003;362:362–69. PubMed
Kerr JF, Winterford CM, Harmon BV. Apoptosis. Its significance in cancer and cancer therapy. Cancer. 1994;73:2013–26. PubMed
Leist M, Jäättelä M. Four deaths and a funeral: From caspases to alternative mechanisms. Nat Rev Mol Cell Biol. 2001;2:589–98. PubMed
Sperandio S, de Belle I, Bredesen DE. An alternative, non-apoptotic form of programmed cell death. Proc Natl Acad Sci USA. 2000;97:14376–81. PubMed PMC
Wajant H, Gerspach J, Pfizenmaier K. Engineering death receptor ligands for cancer therapy. Cancer Lett. 2011
Daniel PT, Wieder T, Sturm I, Schulze-Osthoff K. The kiss of death: promises and failures of death receptors and ligands in cancer therapy. Leukemia. 2001;15:1022–32. PubMed
Inohara N, Ding L, Chen S, Núńez G. Harakiri, a novel regulator of cell death, encodes a protein that activates apoptosis and interacts selectively with survival-promoting proteins Bcl-2 and Bcl-X(L) EMBO J. 1997;16:1686–94. PubMed PMC
Rocha RL, Hilsenbeck SG, Jackson JG, et al. Insulin-like growth factor binding protein 3 and insulin receptor substrate 1 in breast cancer: correlation with clinical parameters and disease-free survival. Clinical Cancer Research. 1997;3:103–9. PubMed
Turner BC, Haffty BG, Narayanan L, et al. IGF-I receptor and cyclin D1 expression influence cellular radiosensitivity and local breast cancer recurrence after lumpectomy and radiation. Cancer Research. 1997;57:3079–83. PubMed
Inohara N, Koseki T, Chen S, et al. CIDE, a novel family of cell death activators with homology to the 45 kDa subunit of the DNA fragmentation factor. EMBO J. 1998;17:2526–33. PubMed PMC
Enari M, Sakahira H, Yokoyama H, et al. A caspase-activated DNase that degrades DNA during apoptosis, and its inhibitor ICAD. Nature. 1998;391:43–50. PubMed
Liu X, Zou H, Slaughter C, Wang X. DFF, a heterodimeric protein that functions downstream of caspase-3 to trigger DNA fragmentation during apoptosis. Cell. 1997;89:175–84. PubMed
Liu X, Li P, Widlak P, et al. The 40-kDa subunit of DNA fragmentation factor induces DNA fragmentation and chromatin condensation during apoptosis. Proc Natl Acad Sci USA. 1998;95:8461–66. PubMed PMC
Sakahira H, Enari M, Nagata S. Cleavage of CAD inhibitor in CAD activation and DNA degradation during apoptosis. Nature. 1998;391:96–99. PubMed