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Functional genomics identifies new synergistic therapies for retinoblastoma.

ABSTRACT: Local intravitreal or intra-arterial chemotherapy has improved therapeutic success for the pediatric cancer retinoblastoma (RB), but toxicity remains a major caveat. RB initiates primarily with RB1 loss or, rarely, MYCN amplification, but the critical downstream networks are incompletely understood. We set out to uncover perturbed molecular hubs, identify synergistic drug combinations to target these vulnerabilities, and expose and overcome drug resistance. We applied dynamic transcriptomic analysis to identify network hubs perturbed in RB versus normal fetal retina, and performed in vivo RNAi screens in RB1^null and RB1^wt;MYCN^amp orthotopic xenografts to pinpoint essential hubs. We employed in vitro and in vivo studies to validate hits, define mechanism, develop new therapeutic modalities, and understand drug resistance. We identified BRCA1 and RAD51 as essential for RB cell survival. Their oncogenic activity was independent of BRCA1 functions in centrosome, heterochromatin, or ROS regulation, and instead linked to DNA repair. RAD51 depletion or inhibition with the small molecule inhibitor, B02, killed RB cells in a Chk1/Chk2/p53-dependent manner. B02 further synergized with clinically relevant topotecan (TPT) to engage this pathway, activating p53-BAX mediated killing of RB but not human retinal progenitor cells. Paradoxically, a B02/TPT-resistant tumor exhibited more DNA damage than sensitive RB cells. Resistance reflected dominance of the p53-p21 axis, which mediated cell cycle arrest instead of death. Deleting p21 or applying the BCL2/BCL2L1 inhibitor Navitoclax re-engaged the p53-BAX axis, and synergized with B02, TPT or both to override resistance. These data expose new synergistic therapies to trigger p53-induced killing in diverse RB subtypes.

SUBMITTER: Aubry A

PROVIDER: S-EPMC7391301 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:A whole genome screen was used to assay every gene of Escherichia coli strain BW25113 to identify genes involved in susceptibility to the monobactam (beta-lactam) antibiotic aztreonam. The methodology has been called TraDIS-Xpress, and is a version of TraDIS or Tn-seq. A transposon mutant library consisting of several hundred thousand mutants was constructed using a Tn5-derived transposon incorporating an inducible outward transcribing promoter. All the mutants were grown in LB broth cultures supplemented with aztreonam at 2 x, 1 x, 0.5 x and 0.25 x MIC with induction of the transposon promoter using 0.2 mM IPTG or 1 mM IPTG or without induction. Following growth, mutants with increased susceptibility show reduced numbers and those with reduced susceptibility show increased numbers. Each condition was performed in duplicate. The methodology enable genes to be assayed by insertional inactivation or by changes in expression. Expression changes result from altered transcription from upstream transposon insertions transcribing into the gene, or downstream insertions transcribing into the gene in the reverse direction leading to RNA interference through the generation of reverse and complementary RNA. Thus, essential genes into which transposon insertions are not tolerated may be assayed also by changes in numbers of upstream or downstream insertion mutants. Changes to high throughput sequencing protocols permit the generation of nucleotide sequence reads from the known transposon sequences into the surrounding insertion site for all the mutants in the mixture simultaneously. Matching the sequence of the reads to the genome nucleotide sequence of E. coli BW25113 then allows the precise locations of all the transposon insertion sites of all the mutants to be mapped simultaneously. The relative changes in mutants between control (without) and selective condition (with aztreonam) then indicates which genes are involved in susceptibility. The numbers of sequence reads that match is reflected by the number of mutants, and so the degree of susceptibility can also be estimated.

Project description:BackgroundIdentifying functions for all gene products in all sequenced organisms is a central challenge of the post-genomic era. However, at least 30-50% of the proteins encoded by any given genome are of unknown or vaguely known function, and a large number are wrongly annotated. Many of these 'unknown' proteins are common to prokaryotes and plants. We set out to predict and experimentally test the functions of such proteins. Our approach to functional prediction integrates comparative genomics based mainly on microbial genomes with functional genomic data from model microorganisms and post-genomic data from plants. This approach bridges the gap between automated homology-based annotations and the classical gene discovery efforts of experimentalists, and is more powerful than purely computational approaches to identifying gene-function associations.ResultsAmong Arabidopsis genes, we focused on those (2,325 in total) that (i) are unique or belong to families with no more than three members, (ii) occur in prokaryotes, and (iii) have unknown or poorly known functions. Computer-assisted selection of promising targets for deeper analysis was based on homology-independent characteristics associated in the SEED database with the prokaryotic members of each family. In-depth comparative genomic analysis was performed for 360 top candidate families. From this pool, 78 families were connected to general areas of metabolism and, of these families, specific functional predictions were made for 41. Twenty-one predicted functions have been experimentally tested or are currently under investigation by our group in at least one prokaryotic organism (nine of them have been validated, four invalidated, and eight are in progress). Ten additional predictions have been independently validated by other groups. Discovering the function of very widespread but hitherto enigmatic proteins such as the YrdC or YgfZ families illustrates the power of our approach.ConclusionsOur approach correctly predicted functions for 19 uncharacterized protein families from plants and prokaryotes; none of these functions had previously been correctly predicted by computational methods. The resulting annotations could be propagated with confidence to over six thousand homologous proteins encoded in over 900 bacterial, archaeal, and eukaryotic genomes currently available in public databases.

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Functional genomics identifies new synergistic therapies for retinoblastoma.

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