BACKGROUND AND AIMS: Despite improved techniques, the determination of tumor origin in poorly differentiated adenocarcinomas still remains a challenge for the pathologist. Here we report the use of protein profiling combined with principal component analysis to improve diagnostic decision-making in tumor samples, in which standard pathologic investigations cannot present reliable results. MATERIALS AND METHODS: A poorly differentiated adenocarcinoma of unknown origin located in the pelvis, infiltrating the sigmoid colon as well as the ovary, served as a model to evaluate our proteomic approach. Firstly, we characterized the protein expression profiles from eight advanced colon and seven ovarian adenocarcinomas using two-dimensional gel electrophoresis (2-DE). Qualitative and quantitative patterns were recorded and compared to the tumor of unknown origin. Based on these protein profiles, match sets from the different tumors were created. Finally, a multivariate principal component analysis was applied to the entire 2-DE data to disclose differences in protein patterns between the different tumors. RESULTS: Over 89% of the unknown tumor sample spots could be matched with the colon standard gel, whereas only 63% of the spots could be matched with the ovarian standard. In addition, principal component analysis impressively displayed the clustering of the unknown case within the colon cancer samples, whereas this case did not cluster at all within the group of ovarian adenocarcinomas. CONCLUSION: These results show that 2-DE protein expression profiling combined with principal component analysis is a sensitive method for diagnosing undifferentiated adenocarcinomas of unknown origin. The described approach can contribute greatly to diagnostic decision-making and, with further technical improvements and a higher throughput, become a powerful tool in the armentarium of the pathologist.
BACKGROUND AND AIMS: Despite improved techniques, the determination of tumor origin in poorly differentiated adenocarcinomas still remains a challenge for the pathologist. Here we report the use of protein profiling combined with principal component analysis to improve diagnostic decision-making in tumor samples, in which standard pathologic investigations cannot present reliable results. MATERIALS AND METHODS: A poorly differentiated adenocarcinoma of unknown origin located in the pelvis, infiltrating the sigmoid colon as well as the ovary, served as a model to evaluate our proteomic approach. Firstly, we characterized the protein expression profiles from eight advanced colon and seven ovarian adenocarcinomas using two-dimensional gel electrophoresis (2-DE). Qualitative and quantitative patterns were recorded and compared to the tumor of unknown origin. Based on these protein profiles, match sets from the different tumors were created. Finally, a multivariate principal component analysis was applied to the entire 2-DE data to disclose differences in protein patterns between the different tumors. RESULTS: Over 89% of the unknown tumor sample spots could be matched with the colon standard gel, whereas only 63% of the spots could be matched with the ovarian standard. In addition, principal component analysis impressively displayed the clustering of the unknown case within the colon cancer samples, whereas this case did not cluster at all within the group of ovarian adenocarcinomas. CONCLUSION: These results show that 2-DE protein expression profiling combined with principal component analysis is a sensitive method for diagnosing undifferentiated adenocarcinomas of unknown origin. The described approach can contribute greatly to diagnostic decision-making and, with further technical improvements and a higher throughput, become a powerful tool in the armentarium of the pathologist.
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