Outcome Prediction in CLL Patients Based on a Disease-Specific Oligonucleotide Microarray.

Chronic Lymphocytic Leukemia (CLL) is characterized by a extreme variability in the clinical course; some patients survive only a few months whereas others have stable disease for more than 10 years. Individual outcome prediction in CLL is still a challenge, in spite of the existence of multiple newly described prognostic factors: cytogenetic alterations, mutational IgVH status, and expression of surrogate markers such as ZAP-70. The accuracy of outcome prediction in CLL may improve thanks to the inclusion of new additional biological variables identified by high-throughput molecular techniques. This more reliable risk-stratification of patients could allow to assign a risk-adapted individualized therapy. With this aim, we have analysed with oligonucleotide microarrays three different series of CLL patients in a three-step process.

In a first-step, RNA extracted from tumour samples of a series of 26 CLL patients, was hybridised using commercial 22K oligonucleotide chips (Agilent Technologies). A total of 135 genes whose expression was associated with changes in survival probability were identified. An additional review of published data yielded a total number of 497 genes whose expression has been related with outcome in CLL. A CLL specific chip was then built in a second-step including, besides these 632 genes, control genes until a total of 1023 genes. This comprehensive CLL-chip was then hybridised with RNA extracted from peripheral-blood samples in a new series of consecutively diagnosed 100 CLL patients. Cox’s univariate analysis was performed to look for molecular variables associated with Progression Free Survival (PFS) as endpoint. FDR significance levels were also used when appropriate, for adjusting the associated p-value. Expression of 94 genes was significantly associated with PFS. Profile patterns of the 94 resulting genes were clustered, and allow to identify 12 clusters. Based on biological function, we selected a representing gene of each cluster, to perform a multivariate Cox’s analysis. This approach generated a model of 4 genes, implicated in BCR signalling, signal transduction and transcription regulation, that distinguishes four quartiles of CLL patients with PFS at 5 years of 95%, 70%, 44% and 8% respectively.

These results have been validated in a new independent series of 60 consecutively diagnosed cases of CLL. If confirmed in additional studies, the availability of this CLL-specific chip could allow a more accurate prediction of the clinical course in CLL patients, this giving support to a stratification of patients into different risk-groups