PURPOSE: The absence of tumor-associated antigens (TAA) which might elicit an immune response is one reason for the disappointing results of therapeutical vaccines in cancer patients. Moreover, impaired expression of MHC class-I and components involved in antigen processing, such as TAP-1, -2, LMP-2, -7, and MECL-1, may lead to tumor escape from immune recognition. Expression profiling of TAA is one approach towards the design of well-defined and individualized anti-tumor vaccines. METHODS: Quantitative polymerase chain reaction (qRT-PCR) is the method of choice to characterize immunologically relevant properties of individual tumors. However, the application of qRT-PCR as a surrogate parameter for the expression of TAAs depends upon the assumption that the level of an mRNA species correlates with the cellular level of the protein it encodes. Therefore, we additionally analyzed TAA expression by immunofluorescence and T cell recognition. RESULTS: In the present study we were unable to confirm that impaired TAP-1 or -2 (transporter associated with antigen processing) expression characterized at the mRNA level is an appropriate surrogate parameter for down-regulated MHC class-I expression in breast cancer. In addition, we analyzed the expression pattern of TAAs in breast and ovarian cancer cell lines. Besides the well-known over-expression of HER-2/neu, CEA, and MUC-1, multiple antigens of the MAGE-family were frequently co-expressed. We investigated whether detection of TAAs by qRT-PCR correlates with monoclonal antibody staining, and which method could predict T cell recognition. We demonstrated a correlation between tumor cell lysis by HLA-A*0201-restricted, MUC-1-specific CTL and threshold levels of MUC-1-specific mRNA. CONCLUSION: MUC-1 is an example that TAA profiling by RT-PCR and flow cytometry can fail to correlate with each other and are of limited value in the prediction of T cell recognition.
PURPOSE: The absence of tumor-associated antigens (TAA) which might elicit an immune response is one reason for the disappointing results of therapeutical vaccines in cancerpatients. Moreover, impaired expression of MHC class-I and components involved in antigen processing, such as TAP-1, -2, LMP-2, -7, and MECL-1, may lead to tumor escape from immune recognition. Expression profiling of TAA is one approach towards the design of well-defined and individualized anti-tumor vaccines. METHODS: Quantitative polymerase chain reaction (qRT-PCR) is the method of choice to characterize immunologically relevant properties of individual tumors. However, the application of qRT-PCR as a surrogate parameter for the expression of TAAs depends upon the assumption that the level of an mRNA species correlates with the cellular level of the protein it encodes. Therefore, we additionally analyzed TAA expression by immunofluorescence and T cell recognition. RESULTS: In the present study we were unable to confirm that impaired TAP-1 or -2 (transporter associated with antigen processing) expression characterized at the mRNA level is an appropriate surrogate parameter for down-regulated MHC class-I expression in breast cancer. In addition, we analyzed the expression pattern of TAAs in breast and ovarian cancer cell lines. Besides the well-known over-expression of HER-2/neu, CEA, and MUC-1, multiple antigens of the MAGE-family were frequently co-expressed. We investigated whether detection of TAAs by qRT-PCR correlates with monoclonal antibody staining, and which method could predict T cell recognition. We demonstrated a correlation between tumor cell lysis by HLA-A*0201-restricted, MUC-1-specific CTL and threshold levels of MUC-1-specific mRNA. CONCLUSION:MUC-1 is an example that TAA profiling by RT-PCR and flow cytometry can fail to correlate with each other and are of limited value in the prediction of T cell recognition.
Authors: M Vitale; R Rezzani; L Rodella; G Zauli; P Grigolato; M Cadei; D J Hicklin; S Ferrone Journal: Cancer Res Date: 1998-02-15 Impact factor: 12.701
Authors: P de Cremoux; J M Extra; M G Denis; J Y Pierga; E Bourstyn; C Nos; K B Clough; E Boudou; E C Martin; A Müller; P Pouillart; H Magdelénat Journal: Clin Cancer Res Date: 2000-08 Impact factor: 12.531
Authors: G F Hofbauer; C Schaefer; C Noppen; R Böni; J Kamarashev; F O Nestle; G C Spagnoli; R Dummer Journal: Am J Pathol Date: 1997-12 Impact factor: 4.307
Authors: J L Schultze; A A Cardoso; G J Freeman; M J Seamon; J Daley; G S Pinkus; J G Gribben; L M Nadler Journal: Proc Natl Acad Sci U S A Date: 1995-08-29 Impact factor: 11.205
Authors: Medea Neek; Jo Anne Tucker; Tae Il Kim; Nicholas M Molino; Edward L Nelson; Szu-Wen Wang Journal: Biomaterials Date: 2017-11-20 Impact factor: 12.479
Authors: L Vassilev; T Ranki; T Joensuu; E Jäger; J Karbach; C Wahle; K Partanen; K Kairemo; T Alanko; R Turkki; N Linder; J Lundin; A Ristimäki; M Kankainen; A Hemminki; C Backman; K Dienel; M von Euler; E Haavisto; T Hakonen; J Juhila; M Jäderberg; P Priha; A Vuolanto; S Pesonen Journal: Oncoimmunology Date: 2015-04-01 Impact factor: 8.110