Giorgio E. M. Melloni

INSPEcT: a computational tool to infer mRNA synthesis, processing and degradation dynamics from RNA- and 4sU-seq time course experiments

MOTIVATION Cellular mRNA levels originate from the combined action of multiple regulatory processes, which can be recapitulated by the rates of pre-mRNA synthesis, pre-mRNA processing and mRNA degradation. Recent experimental and computational advances set the basis to study these intertwined levels of regulation. Nevertheless, software for the comprehensive quantification of RNA dynamics is still lacking. RESULTS INSPEcT is an R package for the integrative analysis of RNA- and 4sU-seq data to study the dynamics of transcriptional regulation. INSPEcT provides gene-level quantification of these rates, and a modeling framework to identify which of these regulatory processes are most likely to explain the observed mRNA and pre-mRNA concentrations. Software performance is tested on a synthetic dataset, instrumental to guide the choice of the modeling parameters and the experimental design. AVAILABILITY AND IMPLEMENTATION INSPEcT is submitted to Bioconductor and is currently available as Supplementary Additional File S1. CONTACT mattia.pelizzola@iit.it SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

In Vivo Genetic Screens of Patient-Derived Tumors Revealed Unexpected Frailty of the Transformed Phenotype

UNLABELLED The identification of genes maintaining cancer growth is critical to our understanding of tumorigenesis. We report the first in vivo genetic screen of patient-derived tumors, using metastatic melanomas and targeting 236 chromatin genes by expression of specific shRNA libraries. Our screens revealed unprecedented numerosity of genes indispensable for tumor growth (∼50% of tested genes) and unexpected functional heterogeneity among patients (<15% in common). Notably, these genes were not activated by somatic mutations in the same patients and are therefore distinguished from mutated cancer driver genes. We analyzed underlying molecular mechanisms of one of the identified genes, the Histone-lysine N-methyltransferase KMT2D, and showed that it promotes tumorigenesis by dysregulating a subset of transcriptional enhancers and target genes involved in cell migration. The assembly of enhancer genomic patterns by activated KMT2D was highly patient-specific, regardless of the identity of transcriptional targets, suggesting that KMT2D might be activated by distinct upstream signaling pathways. SIGNIFICANCE Drug targeting of biologically relevant cancer-associated mutations is considered a critical strategy to control cancer growth. Our functional in vivo genetic screens of patient-derived tumors showed unprecedented numerosity and interpatient heterogeneity of genes that are essential for tumor growth, but not mutated, suggesting that multiple, patient-specific signaling pathways are activated in tumors. Cancer Discov; 6(6); 650-63. ©2016 AACR.This article is highlighted in the In This Issue feature, p. 561.

DOTS-Finder: a comprehensive tool for assessing driver genes in cancer genomes.

A key challenge in the analysis of cancer genomes is the identification of driver genes from the vast number of mutations present in a cohort of patients. DOTS-Finder is a new tool that allows the detection of driver genes through the sequential application of functional and frequentist approaches, and is specifically tailored to the analysis of few tumor samples. We have identified driver genes in the genomic data of 34 tumor types derived from existing exploratory projects such as The Cancer Genome Atlas and from studies investigating the usefulness of genomic information in the clinical settings. DOTS-Finder is available athttps://cgsb.genomics.iit.it/wiki/projects/DOTS-Finder/.

Acute promyelocytic leukemias share cooperative mutations with other myeloid-leukemia subgroups.

Correction to: Blood Cancer Journal (2013) 3, e147; doi: 10.1038/bcj.2013.46; published online 13 September 2013

The hidden genomic landscape of acute myeloid leukemia: subclonal structure revealed by undetected mutations.

The analyses carried out using 2 different bioinformatics pipelines (SomaticSniper and MuTect) on the same set of genomic data from 133 acute myeloid leukemia (AML) patients, sequenced inside the Cancer Genome Atlas project, gave discrepant results. We subsequently tested these 2 variant-calling pipelines on 20 leukemia samples from our series (19 primary AMLs and 1 secondary AML). By validating many of the predicted somatic variants (variant allele frequencies ranging from 100% to 5%), we observed significantly different calling efficiencies. In particular, despite relatively high specificity, sensitivity was poor in both pipelines resulting in a high rate of false negatives. Our findings raise the possibility that landscapes of AML genomes might be more complex than previously reported and characterized by the presence of hundreds of genes mutated at low variant allele frequency, suggesting that the application of genome sequencing to the clinic requires a careful and critical evaluation. We think that improvements in technology and workflow standardization, through the generation of clear experimental and bioinformatics guidelines, are fundamental to translate the use of next-generation sequencing from research to the clinic and to transform genomic information into better diagnosis and outcomes for the patient.

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.

LowMACA: exploiting protein family analysis for the identification of rare driver mutations in cancer

BackgroundThe increasing availability of resequencing data has led to a better understanding of the most important genes in cancer development. Nevertheless, the mutational landscape of many tumor types is heterogeneous and encompasses a long tail of potential driver genes that are systematically excluded by currently available methods due to the low frequency of their mutations. We developed LowMACA (Low frequency Mutations Analysis via Consensus Alignment), a method that combines the mutations of various proteins sharing the same functional domains to identify conserved residues that harbor clustered mutations in multiple sequence alignments. LowMACA is designed to visualize and statistically assess potential driver genes through the identification of their mutational hotspots.ResultsWe analyzed the Ras superfamily exploiting the known driver mutations of the trio K-N-HRAS, identifying new putative driver mutations and genes belonging to less known members of the Rho, Rab and Rheb subfamilies. Furthermore, we applied the same concept to a list of known and candidate driver genes, and observed that low confidence genes show similar patterns of mutation compared to high confidence genes of the same protein family.ConclusionsLowMACA is a software for the identification of gain-of-function mutations in putative oncogenic families, increasing the amount of information on functional domains and their possible role in cancer. In this context LowMACA emphasizes the role of genes mutated at low frequency otherwise undetectable by classical single gene analysis.LowMACA is an R package available at http://www.bioconductor.org/packages/release/bioc/html/LowMACA.html. It is also available as a GUI standalone downloadable at: https://cgsb.genomics.iit.it/wiki/projects/LowMACA

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.

Precision Trial Drawer, a Computational Tool to Assist Planning of Genomics-Driven Trials in Oncology

PurposeTrials that accrue participants on the basis of genetic biomarkers are a powerful means of testing targeted drugs, but they are often complicated by the rarity of the biomarker-positive population. Umbrella trials circumvent this by testing multiple hypotheses to maximize accrual. However, bigger trials have higher chances of conflicting treatment allocations because of the coexistence of multiple actionable alterations; allocation strategies greatly affect the efficiency of enrollment and should be carefully planned on the basis of relative mutation frequencies, leveraging information from large sequencing projects.MethodsWe developed software named Precision Trial Drawer (PTD) to estimate parameters that are useful for designing precision trials, most importantly, the number of patients needed to molecularly screen (NNMS) and the allocation rule that maximizes patient accrual on the basis of mutation frequency, systematically assigning patients with conflicting allocations to the drug associated ...

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.