based on few selected, cancer-specific genes. PCA3 is the best single CaP predictor: it is highly specific, but the sensitivity is relatively low [30]. Moreover, the sensitivity did not improve even when two more prostate-related genes were added [24,25]. A novel and reliable method based on a robust approach Our work provides a reliable method for the molecular diagnosis of CaP that takes advantage of a relatively simple method. The 7- gene model here presented is based on the determination of genes which are known to be expressed in both benign and cancer tissue, and subjected to high individual variation. Therefore, the novelty of our approach consist in achieving efficient molecular classification of CaP with regard to final pathological diagnosis without using genes that are specifically highly up- or down-regulated in benign versus cancer tissue. In fact, our work is based on the detection and analysis of the expression of genes which are long time known to be physiologically expressed as well as essential for the biology of prostate cells. For instance, aliphatic polyamines are produced at very high levels by prostate epithelial cells and then released in prostate fluid in mM amounts [31–33], although the precise role of these compounds is still unknown. Detection of polyamines in urine was already attempted long time ago for the diagnosis of CaP [34]. To achieve a reliable detection of our gene signature in cancer tissue specimens and to implement practical use we had to address several technical issues. In absence of any dedicated specific tool or instrument, the development of a ‘‘sandwich’’ procedure was necessary for obtaining morphological and molecular data from the same tissue specimens (Figure 1). Statistical analysis In previous publications, working with both animal [14] and human [3,4] CaP models we have demonstrated strictly related shifts in the expression of all genes studied. Nevertheless, the REST analysis showed that the relative expression of 7 genes besides CLU did not reach statistical significance in malignancy. This was somehow unexpected. The difficulty of pursuing a molecular approach to CaP diagnosis in the real clinical setting is evident. But this result is explained by the fact that polyamine genes expression are known to be characterized by high individual variation in the tissue specimens analysed. Nevertheless, this did not jeopardize our analysis, because the molecular classification of tissue specimens here presented takes into consideration the whole gene signature, detecting a significant difference in the entire gene expression profile regardless of individual variations. The final result is that the integrated pattern of expression is significantly different in CaP versus benign controls. This novel approach has been successful using the NN classifier, a statistical method known to perform well specifically for cancer classification even when compared with other sophisticated methods [35,36]. NN is a nonparametric method that belongs to the class of ‘‘robust’’ classifiers [37]. NN is well known to render a good performance where the boundary between the two classes to be discriminated is not clear-cut from the geometric point of view. This condition often happens in clinical studies in which individual variations may be much higher as compared to laboratory samples obtained from in vitro experiments, providing ‘‘fuzzy’’ boundaries which might result from complex gene expression profiles. NN is virtually not sensible to limited sampling of the classes to be discriminated because of the very limited number of parameters (namely, the choice of the distance function and the number of neighbours). Nevertheless, NN renders very good classification performances also compared to other multiparametric well known classifiers like Neural Networks or Support Vector Machines, methods that achieve optimal performance only when large datasets are available. Gene Signature To date, the best approach to reach a final evaluation on the performance of even a ‘‘robust’’ statistical classifier such as NN is to reuse the collected data both for the training and the validation of the chosen classifier: this procedure is commonly referred to as cross validation (well described in many "classical" statistical books [37]). In N-fold cross validation, the dataset is randomly divided into N parts and each part is used as a validation set using the other as training set. Under these working conditions the classifier performance varies for each random realization. We applied this procedure both with a 5- fold (not shown) and a 10-fold cross validation, and obtained similar performances. Because of the above considerations, the result obtained by NN+10-fold cross validation procedure can be considered as very reliable even with a small-size dataset. The same statistical approach has been used already to analyze and validate gene expression signatures in cancer research [38,39]. Under these conditions, the best classification performance was obtained with a 7-gene model (H3, GAS1, SSAT, CLU, AdoMetDC, OAZ and ODC). The concordance between molecular and final pathological classification had an accuracy (8065)% with a sensitivity of (8166)% and a specificity of (7867)%. This result demonstrates that diagnosis of CaP by molecular data alone is feasible. The gene signature detects very few cancer cells: the cancer field effect Notably, our method showed a very high sensitivity working on a collection of CaP tissue specimens in which a significant portion of the total, namely 7/31, were considered CaP on the basis of having only 5% of cancer cells. This accounts for only 1 mg of cancer tissue present in a total of 20 mg. A rationale consequence of this result is that molecular events detected in pre-malignant tissue or in tissues adjacent to cancer have provided diagnostic information, supporting the hypothesis that a molecular approach for CaP diagnosis would be a more sensitive and powerful tool than morphological examination. This result is consistent with the ‘‘cancer field effect’’, as recently hypothesized. According to this idea, transformed cells extend beyond morphologically evident tumour. These events would precede development of malignancy, because normal appearing prostate tissue can undergo genetic changes in response to, or in expectation of, morphologic cancer [40]. Recently, field effects based on epigenetic events [41], nuclear matrix alterations [42], androgen receptor immunoreactivity in the stroma surrounding cancer lesion [43] have been discovered. Using oligonucleotide microarrays, another confirmation derived by a study in which the expression profiles of primary prostate cancer, adjacent normal tissue and normal tissue from tumour free donors were compared [44]. This scenario reinforce the requirement for objective molecular biological markers of the aggressive phenotype to resolve uncertainties with respect to identification of those precursor lesions which are most likely to progress to invasive and metastatic prostate cancer. The most likely explanation for misclassification between molecular and morphological data obtained in our study is that very early molecular changes may precede alteration of morphology, and therefore these conditions would not overlap (or be associated with) the pathological response. Molecular changes preceding changes in phenotype would be detected by the molecular method for diagnosis, increasing its sensitivity and explaining why 7 specimens (Table 1, bold) were seen as cancer by the gene signature method, while the pathological classification was benign. In this regard, higher sensitivity of the method would result in reducing the number of samples that need to be taken, with a clear advantage for the patient. It is known that sensitivity of biopsy is a potential problem, explaining why saturation biopsies have to be taken from patients. The procedure of saturating the prostate with biopsies according to volume to maintain a constant density of probing was developed in order to improve CaP PLoS ONE | www.plosone.org 5 October 2008 | Volume 3 | Issue 10 | e3617