We developed a selectable marker rendering human cells resistant to Diphtheria Toxin (DT). The marker (DT(R)) consists of a primary microRNA sequence engineered to downregulate the ubiquitous DPH2 gene, a key enzyme for the biosynthesis of the DT target diphthamide. DT(R) expression in human cells invariably rendered them resistant to DT in vitro, without altering basal cell growth. DT(R)-based selection efficiency and stability were comparable to those of established drug-resistance markers. As mice are insensitive to DT, DT(R)-based selection can be also applied in vivo. Direct injection of a GFP-DT(R) lentiviral vector into human cancer cell-line xenografts and patient-derived tumorgrafts implanted in mice, followed by systemic DT administration, yielded tumors entirely composed of permanently transduced cells and detectable by imaging systems. This approach enabled high-efficiency in vivo selection of xenografted human tumor tissues expressing ectopic transgenes, a hitherto unmet need for functional and morphological studies in laboratory animals.

We propose a new multi-network-based strategy to integrate different layers of genomic information and use them in a coordinate way to identify driving cancer genes. The multi-networks that we consider combine transcription factor co-targeting, microRNA co-targeting, protein-protein interaction and gene co-expression networks. The rationale behind this choice is that gene co-expression and protein-protein interactions require a tight coregulation of the partners and that such a fine tuned regulation can be obtained only combining both the transcriptional and post-transcriptional layers of regulation. To extract the relevant biological information from the multi-network we studied its partition into communities. To this end we applied a consensus clustering algorithm based on state of art community detection methods. Even if our procedure is valid in principle for any pathology in this work we concentrate on gastric, lung, pancreas and colorectal cancer and identified from the enrichment analysis of the multi-network communities a set of candidate driver cancer genes. Some of them were already known oncogenes while a few are new. The combination of the different layers of information allowed us to extract from the multi-network indications on the regulatory pattern and functional role of both the already known and the new candidate driver genes.

Stratification of breast cancer (BC) into molecular subtypes by multigene expression assays is of demonstrated clinical utility. In principle, global RNA-sequencing (RNA-seq) should enable reconstructing existing transcriptional classifications of BC samples. Yet, it is not clear whether adaptation to RNA-seq of classifiers originally developed using PCR or microarrays, or reconstruction through machine learning (ML) is preferable. Hence, we focused on robustness and portability of PAM50, a nearest-centroid classifier developed on microarray data to identify five BC "intrinsic subtypes". We found that standard PAM50 is profoundly affected by the composition of the sample cohort used for reference construction, and we propose a strategy, named AWCA, to mitigate this issue, improving classification robustness, with over 90% of concordance, and prognostic ability; we also show that AWCA-based PAM50 can even be applied as single-sample method. Furthermore, we explored five supervised learners to build robust, single-sample intrinsic subtype callers via RNA-seq. From our ML-based survey, regularized multiclass logistic regression (mLR) displayed the best performance, further increased by ad-hoc gene selection on the global transcriptome. On external test sets, mLR classifications reached 90% concordance with PAM50-based calls, without need of reference sample; mLR proven robustness and prognostic ability make it an equally valuable single-sample method to strengthen BC subtyping.