Biology subjects

Paradiso, V.

Publications and source records attributed to Paradiso, V..

3 recordsLinked to original sources

bioRxiv2020

GATA3 and MDM2 are synthetic lethal in estrogen receptor-positive breast cancers

Synthetic lethal interactions, where the simultaneous but not individual inactivation of two genes is lethal to the cell, have been successfully exploited to treat cancer. GATA3 is frequently mutated in estrogen receptor (ER)-positive breast cancers and its deficiency defines a subset of patients with poor response to hormonal therapy and poor prognosis. However, GATA3 is not targetable. Here we show that GATA3 and MDM2 are synthetically lethal in ER-positive breast cancer. Depletion and pharmacological inhibition of MDM2 induce apoptosis in GATA3-deficient models in vitro, in vivo and in patient-derived organoids (PDOs) harboring GATA3 somatic mutation. The synthetic lethality requires p53 and acts via the PI3K/Akt/mTOR pathway. Our results present MDM2 as a novel therapeutic target in the substantial cohort of ER-positive, GATA3-mutant breast cancer patients. With MDM2 inhibitors widely available, our findings can be rapidly translated into clinical trials to evaluate in-patient efficacy.

cancer biology↗

bioRxiv2020

The Genomic Landscape of Prostate Cancer Brain Metastases

Lethal prostate cancer commonly metastasizes to bone, lymph nodes, and visceral organs but with more effective therapies, there is an increased frequency of metastases to the brain. Little is known about the genomic drivers of prostate cancer brain metastases (PCBM). To address this, we conducted a comprehensive multi-regional, genomic, and targeted transcriptomic analysis of PCBM from 28 patients. We compared whole-exome and targeted RNA sequencing with matched primary tumors when available (n = 10) and with publicly available genomic data from non-brain prostate cancer metastases (n = 416). In addition to common alterations in TP53, AR, RB1, and PTEN, we identified highly significant enrichment of mutations in NF1 (25% cases (6/28), q = 0.049, 95% CI = 2.38 - 26.52, OR = 8.37) and RICTOR (17.9% cases (5/28), q = 0.01, 95% CI = 6.74 - 480.15, OR = 43.7) in PCBM compared to non-brain prostate cancer metastases, suggesting possible activation of the druggable pathways RAS/RAF/MEK/ERK and PI3K/AKT/mTOR, respectively. Compared to non-brain prostate cancer metastases, PCBM were almost three times as likely to harbor DNA homologous repair (HR) alterations (42.9% cases (12/28), p =0.016, 95% CI = 1.17 - 6.64, OR = 2.8). When considering the combination of somatic mutations, copy number alteration, and Large-scale State Transitions, 64.3% of patients (18/28) were affected. HR alterations may be critical drivers of brain metastasis that potentially provide cancer cells a survival advantage during re-establishment in a special microenvironment. We demonstrate that PCBM have genomic dependencies that may be exploitable through clinical interventions including PARP inhibition.

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↗