Biology subjects

Piscuoglio, S.

Publications and source records attributed to Piscuoglio, S..

23 records · Page 2Linked to original sources

bioRxiv2020

Role of Specialized mSWI/SNF Complexes in Prostate Cancer Lineage Plasticity

Advanced prostate cancer initially responds to hormonal treatment, but ultimately becomes resistant and requires more potent therapies. One mechanism of resistance observed in [~]10% of these patients is through lineage plasticity, which manifests in a partial or complete small cell or neuroendocrine prostate cancer (NEPC) phenotype. Here, we investigate the role of the mammalian SWI/SNF (mSWI/SNF) chromatin remodeling complex in NEPC. Using large patient datasets, patient-derived organoids and cancer cell lines, we identify mSWI/SNF subunits that are deregulated in NEPC and demonstrate that SMARCA4 (BRG1) overexpression is associated with aggressive disease. We also show that SWI/SNF complexes interact with different lineage-specific factors in NEPC compared to prostate adenocarcinoma. These data suggest a role for mSWI/SNF complexes in therapy-related lineage plasticity, which may be relevant for other solid tumors.

cancer biology↗

bioRxiv2019

APSiC: Analysis of Perturbation Screens for the Identification of Novel Cancer Genes

BackgroundSystematic perturbation screens provide comprehensive resources for the elucidation of cancer driver genes, including rarely mutated genes that are missed by approaches focused on frequently mutated genes and driver genes for which the basis for oncogenicity is non-genetic. The perturbation of many genes in relatively few cell lines in such functional screens necessitates the development of specialized computational tools with sufficient statistical power. ResultsHere we developed APSiC (Analysis of Perturbation Screens for identifying novel Cancer genes) that can identify genetic and non-genetic drivers even with a limited number of samples. Applying APSiC to the large-scale deep shRNA screen Project DRIVE, APSiC identified well-known, pan-cancer genetic drivers, novel putative genetic drivers known to be dysregulated in specific cancer types and the context dependency of mRNA-splicing between cancer types. Additionally, APSiC discovered a median of 28 and 35 putative non-genetic oncogenes and tumor suppressor genes, respectively, for individual cancer types, including genes involved in genome stability maintenance and cell cycle. We functionally demonstrated that LRRC4B, a putative non-genetic tumor suppressor gene that has not previously been associated with carcinogenesis, suppresses proliferation by delaying cell cycle and modulates apoptosis in breast cancer. ConclusionWe demonstrate APSiC is a robust statistical framework for discovery of novel cancer genes through analysis of large-scale perturbation screens. The analysis of DRIVE using APSiC is provided as a web portal and represents a valuable resource for the discovery of novel cancer genes.

genomics↗

bioRxiv2019

Discovery of synthetic lethal interactions from large-scale pan-cancer perturbation screens

Despite the progress in precision oncology, development of cancer therapies is limited by the dearth of suitable drug targets1. Novel candidate drug targets can be identified based on the concept of synthetic lethality (SL), which refers to pairs of genes for which an aberration in either gene alone is non-lethal, but co-occurrence of the aberrations is lethal to the cell. We developed SLIdR (Synthetic Lethal Identification in R), a statistical framework for identifying SL pairs from large-scale perturbation screens. SLIdR successfully predicts SL pairs even with small sample sizes while minimizing the number of false positive targets. We applied SLIdR to Project DRIVE data2 and found both established and novel pan-cancer and cancer type-specific SL pairs. We identified and experimentally validated a novel SL interaction between AXIN1 and URI1 in hepatocellular carcinoma, thus corroborating the potential of SLIdR to identify new SL-based drug targets.

bioinformatics↗

bioRxiv2019

AGO1x prevents dsRNA-induced interferon signaling to promote breast cancer cell proliferation

Initially reported for viral RNA, elongation of polypeptide chains beyond the stop codon (translational readthrough (TR)) also occurs on eukaryotic transcripts. TR diversifies the proteome and can modulate protein levels 1-6. Here we report that AGO1x, a conserved TR isoform of Argonaute 1, is generated in highly proliferative breast cancer cells, where it curbs accumulation of double stranded RNAs, the induction of the interferon response and apoptosis. In contrast to other mammalian Argonaute protein family members with primarily cytoplasmic functions, AGO1x localizes to the nucleus, in the vicinity of nucleoli. We identify a novel interaction of AGO1x with the Polyribonucleotide Nucleotidyltransferase 1, depletion of either protein leading to dsRNA accumulation and impaired cell proliferation. Our study thus uncovers a novel function of an Argonaute protein outside of the miRNA effector pathway, in buffering dsRNA-induced interferon responses. As AGO1x expression is tightly linked to breast cancer cell proliferation, our study suggests a new direction for limiting tumor growth.

cell biology↗

bioRxiv2018

Reliability of Whole-Exome Sequencing for Assessing Intratumor Genetic Heterogeneity

Multi-region sequencing is used to detect intratumor genetic heterogeneity (ITGH) in tumors. To assess whether genuine ITGH can be distinguished from sequencing artifacts, we whole-exome sequenced (WES) three anatomically distinct regions of the same tumor with technical replicates to estimate technical noise. Somatic variants were detected with three different WES pipelines and subsequently validated by high-depth amplicon sequencing. The cancer-only pipeline was unreliable, with about 69% of the identified somatic variants being false positive. Even with matched normal DNA where 82% of the somatic variants were detected reliably, only 36%-78% were found consistently in technical replicate pairs. Overall 34%-80% of the discordant somatic variants, which could be interpreted as ITGH, were found to constitute technical noise. Excluding mutations affecting low mappability regions or occurring in certain mutational contexts was found to reduce artifacts, yet detection of subclonal mutations by WES in the absence of orthogonal validation remains unreliable.

genomics↗