Contenuto

Uno studio della Fondazione IRCCS Istituto Nazionale dei Tumori mette in luce le difficoltà nell'assegnare le pazienti con tumore della mammella ai sottotipi di tumore definiti con l'utilizzo dei microarray

articolo sul JNCI

Articolo

Lara Lusa, Lisa M. McShane, James F. Reid, Loris De Cecco, Federico Ambrogi, Elia Biganzoli, Manuela Gariboldi, Marco A. Pierotti

Challenges in Projecting Clustering Results Across Gene Expression – Profiling Datasets

J Natl Cancer Inst 2007;99: 1715 – 23

Abstract

Background
Gene expression microarray studies for several types of cancer have been reported to identify previously unknown subtypes of tumors. For breast cancer, a molecular classification consisting of five subtypes based on gene expression microarray data has been proposed. These subtypes have been reported to exist across several breast cancer microarray studies, and they have demonstrated some association with clinical outcome. A classification rule based on the method of centroids has been proposed for identifying the subtypes in new collections of breast cancer samples; the method is based on the similarity of the new profiles to the mean expression profile of the previously identified subtypes.

Methods
Previously identified centroids of five breast cancer subtypes were used to assign 99 breast cancer samples, including a subset of 65 estrogen receptor–positive (ER+) samples, to five breast cancer subtypes based on microarray data for the samples. The effect of mean centering the genes (i.e., transforming the expression of each gene so that its mean expression is equal to 0) on subtype assignment by method of centroids was assessed. Further studies of the effect of mean centering and of class prevalence in the test set on the accuracy of method of centroids classifications of ER status were carried out using training and test sets for which ER status had been independently determined by ligand-binding assay and for which the proportion of ER+ and ER- samples were systematically varied.

Results
When all 99 samples were considered, mean centering before application of the method of centroids appeared to be helpful for correctly assigning samples to subtypes, as evidenced by the expression of genes that had previously been used as markers to identify the subtypes. However, when only the 65 ER+ samples were considered for classification, many samples appeared to be misclassified, as evidenced by an unexpected distribution of ER+ samples among the resultant subtypes. When genes were mean centered before classification of samples for ER status, the accuracy of the ER subgroup assignments was highly dependent on the proportion of ER+ samples in the test set; this effect of subtype prevalence was not seen when gene expression data were not mean centered.

Conclusions
Simple corrections such as mean centering of genes aimed at microarray platform or batch effect correction can have undesirable consequences because patient population effects can easily be confused with these assay-related effects. Careful thought should be given to the comparability of the patient populations before attempting to force data comparability for purposes of assigning subtypes to independent subjects.

Commento all'articolo

Utilizzando la tecnologia dei microarray (strumenti capaci di misurare simultaneamente l'espressione di decine di migliaia di geni per ogni campione) diversi laboratori hanno identificato dei sottotipi del tumore della mammella che potrebbero rivelarsi importanti dal punto di vista clinico, poichè associati ad una diversa prognosi e quindi potenzialmente utili per la scelta del tipo di trattamento da utilizzare.

La rivista Journal of the National Cancer Insitute ha pubblicato un articolo dei ricercatori della Fondazione INT in cui si mettono in luce le difficoltà nell'assegnare le pazienti con tumore della mammella ai sottotipi. Utilizzando il metodo proposto negli studi precedenti per determinare il sottotipo di nuove pazienti, questo lavoro identifica l'esistenza di molti fattori che influenzano l'accuratezza dell'assegnazione delle pazienti ai diversi sottotipi e che aumentano la possibilità di classificare erratamente le pazienti. Il lavoro sottolinea la necessità di utilizzare metodi più robusti per la quantificazione dell'espressione genica per trasformare questi risultati promettenti in strumenti clinicamente utili.

Ultimo aggiornamento: mercoledì 14 novembre 2007