This study presents a method for multiclass cancer diagnosis using tumor gene expression signatures. Researchers analyzed 218 tumor samples and 90 normal tissue samples using oligonucleotide microarray gene expression analysis to evaluate the accuracy of a multiclass classifier based on a support vector machine (SVM) algorithm. The overall classification accuracy was 78%, far exceeding the accuracy of random classification (9%). The results show that molecular classification can accurately distinguish between different cancer types, even when traditional clinical and histopathological methods are insufficient. The study highlights the challenges of cancer diagnosis, including incomplete or misleading clinical information and the wide spectrum of cancer morphology. Molecular diagnostics offer a promising alternative, but the identification of characteristic molecular markers for most solid tumors has been limited. The researchers developed a gene expression database containing the expression profiles of 218 tumor samples representing 14 common human cancer classes. Using an innovative analytical method, they demonstrated that accurate multiclass cancer classification is possible, suggesting the feasibility of molecular cancer diagnosis through comparison with a comprehensive gene expression catalog. The study used a supervised learning approach, training a classifier to recognize distinctions among 14 clinically defined tumor classes based on gene expression patterns. The SVM algorithm was used to create a hyperplane that best separates samples from two classes. The classifier was tested on a set of 144 training samples and an independent test set of 54 tumor samples, achieving an overall accuracy of 78%. The results indicate that molecularly complex tumors can be distinguished despite the presence of varying proportions of nonneoplastic elements in clinical specimens. The study also identified genes that are highly correlated with each of the 14 tumor classes, providing potential markers for cancer diagnosis. The results suggest that gene expression-based approaches may be useful for the diagnosis of clinically problematic metastases of unknown primary origin. The findings also indicate that poorly differentiated tumors have fundamentally distinct gene expression patterns compared to their well-differentiated counterparts, with significant implications for the future management of patients with these cancers. The study demonstrates the feasibility of accurate, multiclass molecular cancer classification and suggests a strategy for future clinical implementation of molecular cancer diagnostics. The results highlight the importance of comprehensive gene expression databases and the need for further research to fully explore the limitations of gene expression-based multiclass classification. The study also emphasizes the potential of molecular characteristics of tumor samples for improving cancer diagnosis and treatment outcomes.This study presents a method for multiclass cancer diagnosis using tumor gene expression signatures. Researchers analyzed 218 tumor samples and 90 normal tissue samples using oligonucleotide microarray gene expression analysis to evaluate the accuracy of a multiclass classifier based on a support vector machine (SVM) algorithm. The overall classification accuracy was 78%, far exceeding the accuracy of random classification (9%). The results show that molecular classification can accurately distinguish between different cancer types, even when traditional clinical and histopathological methods are insufficient. The study highlights the challenges of cancer diagnosis, including incomplete or misleading clinical information and the wide spectrum of cancer morphology. Molecular diagnostics offer a promising alternative, but the identification of characteristic molecular markers for most solid tumors has been limited. The researchers developed a gene expression database containing the expression profiles of 218 tumor samples representing 14 common human cancer classes. Using an innovative analytical method, they demonstrated that accurate multiclass cancer classification is possible, suggesting the feasibility of molecular cancer diagnosis through comparison with a comprehensive gene expression catalog. The study used a supervised learning approach, training a classifier to recognize distinctions among 14 clinically defined tumor classes based on gene expression patterns. The SVM algorithm was used to create a hyperplane that best separates samples from two classes. The classifier was tested on a set of 144 training samples and an independent test set of 54 tumor samples, achieving an overall accuracy of 78%. The results indicate that molecularly complex tumors can be distinguished despite the presence of varying proportions of nonneoplastic elements in clinical specimens. The study also identified genes that are highly correlated with each of the 14 tumor classes, providing potential markers for cancer diagnosis. The results suggest that gene expression-based approaches may be useful for the diagnosis of clinically problematic metastases of unknown primary origin. The findings also indicate that poorly differentiated tumors have fundamentally distinct gene expression patterns compared to their well-differentiated counterparts, with significant implications for the future management of patients with these cancers. The study demonstrates the feasibility of accurate, multiclass molecular cancer classification and suggests a strategy for future clinical implementation of molecular cancer diagnostics. The results highlight the importance of comprehensive gene expression databases and the need for further research to fully explore the limitations of gene expression-based multiclass classification. The study also emphasizes the potential of molecular characteristics of tumor samples for improving cancer diagnosis and treatment outcomes.