PATRI, a Genomics Data Integration Tool for Biomarker Discovery.

The availability of genomic datasets in association with clinical, phenotypic, and drug sensitivity information represents an invaluable source for potential therapeutic applications, supporting the identification of new drug sensitivity biomarkers and pharmacological targets. Drug discovery and precision oncology can largely benefit from the integration of treatment molecular discriminants obtained from cell line models and clinical tumor samples; however this task demands comprehensive analysis approaches for the discovery of underlying data connections. Here we introduce PATRI (Platform for the Analysis of TRanslational Integrated data), a standalone tool accessible through a user-friendly graphical interface, conceived for the identification of treatment sensitivity biomarkers from user-provided genomics data, associated with information on sample characteristics. PATRI streamlines a translational analysis workflow: first, baseline genomics signatures are statistically identified, differentiating treatment sensitive from resistant preclinical models; then, these signatures are used for the prediction of treatment sensitivity in clinical samples, via random forest categorization of clinical genomics datasets and statistical evaluation of the relative phenotypic features. The same workflow can also be applied across distinct clinical datasets. The ease of use of the PATRI tool is illustrated with validation analysis examples, performed with sensitivity data for drug treatments with known molecular discriminants.

The E3 ubiquitin ligase WWP1 sustains the growth of acute myeloid leukaemia.

The E3 ubiquitin ligase (E3) WWP1 is an oncogenic factor implicated in the maintenance of different types of epithelial cancers. The role of WW domain-containing E3 ubiquitin protein ligase 1 (WWP1) in haematological neoplasms remains unknown. Acute myeloid leukaemia (AML) is characterized by the expansion of malignant myeloid cells blocked at different stages of differentiation. Here we report that the expression of WWP1 is significantly augmented in a large cohort of primary AML patients and in AML cell lines, compared with haematopoietic cells from healthy donors. We show that WWP1 inactivation severely impairs the growth of primary AML blasts and cell lines in vitro. In vivo, we observed a reduced leukaemogenic potential of WWP1-depleted AML cells upon transplantation into immunocompromised mice. Mechanistically, WWP1 inactivation induces the accumulation of its protein substrate p27Kip1, which ultimately contributes to G0/G1 cell cycle arrest of AML blasts. In addition, WWP1 depletion triggers the autophagy signalling and reduces survival of leukaemic cells. Collectively, our findings provide molecular insights into the anti-cancer potential of WWP1 inhibition, suggesting that this E3 is a promising biomarker and druggable target in AML.

PML-RARA-associated cooperating mutations belong to a transcriptional network that is deregulated in myeloid leukemias.

It has been shown that individual acute myeloid leukemia (AML) patients are characterized by one of few initiating DNA mutations and 5-10 cooperating mutations not yet defined among hundreds identified by massive sequencing of AML genomes. We report an in vivo insertional-mutagenesis screen for genes cooperating with one AML initiating mutations (PML-RARA, oncogene of acute promyelocytic leukemia, APL), which allowed identification of hundreds of genetic cooperators. The cooperators are mutated at low frequency in APL or AML patients but are always abnormally expressed in a cohort of 182 APLs and AMLs analyzed. These deregulations appear non-randomly distributed and present in all samples, regardless of their associated genomic mutations. Reverse-engineering approaches showed that these cooperators belong to a single transcriptional gene network, enriched in genes mutated in AMLs, where perturbation of single genes modifies expression of others. Their gene-ontology analysis showed enrichment of genes directly involved in cell proliferation control. Therefore, the pool of PML-RARA cooperating mutations appears large and heterogeneous, but functionally equivalent and deregulated in the majority of APLs and AMLs. Our data suggest that the high heterogeneity of DNA mutations in APLs and AMLs can be reduced to patterns of gene expression deregulation of a single 'mutated' gene network.