We deal with the problem of gene selection when genes must be selected group-wise, where the groups, defined a priori and representing functional families, may overlap. We propose a new optimization procedure for solving the regularization problem proposed in [4], where the group lasso penalty is generalized to overlapping groups. While in [4] the proposed implementation requires replication of genes belonging to more than one group, our iterative procedure, provides a scalable alternative with no need for data duplication. This scalability property allows avoiding the otherwise necessary pre-processing for dimensionality reduction, which is at risk of discarding relevant biological information, and leads to improved prediction performances and higher selection stability.