The Multiple Faces of the MRN Complex: Roles in Medulloblastoma and Beyond

Hypomorphic mutations in MRN complex genes are frequently found in cancer, supporting their role as oncosuppressors. However, unlike canonical oncosuppressors, MRN proteins are often overexpressed in tumor tissues, where they actively work to counteract DSBs induced by both oncogene-dependent RS and radio-chemotherapy. Moreover, at the same time, MRN genes are also essential genes, since the constitutive KO of each component leads to embryonic lethality. Therefore, even though it is paradoxical, MRN genes may work as oncosuppressive, oncopromoting, and essential genes. In this review, we discussed how alterations in the MRN complex impact the physiopathology of cancer, in light of our recent discoveries on the gene-dosage-dependent effect of NBS1 in Medulloblastoma. These updates aim to understand whether MRN complex can be realistically used as a prognostic/predictive marker and/or as a therapeutic target for the treatment of cancer patients in the future.

A gene dosage-dependent effect unveils NBS1 as both a haploinsufficient tumour suppressor and an essential gene for SHH-medulloblastoma

OBJECTIVES

Inherited or somatic mutations in the MRE11, RAD50 and NBN genes increase the incidence of tumours, including medulloblastoma (MB). On the other hand, MRE11, RAD50 and NBS1 protein components of the MRN complex are often overexpressed and sometimes essential in cancer. In order to solve the apparent conundrum about the oncosuppressive or oncopromoting role of the MRN complex, we explored the functions of NBS1 in a MB prone animal model.

MATERIALS AND METHODS

We generated and analysed mono- or biallelic deletion of the Nbn gene in the context of the SmoA1 transgenic mouse, a SHH-dependent MB prone animal model. We used normal and tumour tissue from these animal models, primary granule cell progenitors (GCPs) from genetically modified animals, and NBS1-depleted primary MB cells, to uncover the effects of NBS1-depletion by RNA-Seq, by biochemical characterization of the SHH-pathway and the DNA damage response (DDR) as well as on the growth and clonogenic properties of GCPs.

RESULTS

We found that monoallelic Nbn deletion increases SmoA1-dependent MB incidence. In addition to a defective DDR, Nbn^+/- GCPs show increased clonogenicity compared to Nbn^+/+ GCPs, dependent on an enhanced Notch signalling. In contrast, full Nbn^KO impairs MB development both in SmoA1 mice and in a SHH-driven tumour allograft.

CONCLUSIONS

Our study indicates that Nbn is haploinsufficient for SHH-MB development while full Nbn^KO is epistatic on SHH-driven MB development, thus revealing a gene dosage-dependent effect of Nbn inactivation on SHH-MB development.

A combination of PARP and CHK1 inhibitors efficiently antagonizes MYCN-driven tumors

MYCN drives aggressive behavior and refractoriness to chemotherapy, in several tumors. Since MYCN inactivation in clinical settings is not achievable, alternative vulnerabilities of MYCN-driven tumors need to be explored to identify more effective and less toxic therapies. We previously demonstrated that PARP inhibitors enhance MYCN-induced replication stress and promote mitotic catastrophe, counteracted by CHK1. Here, we showed that PARP and CHK1 inhibitors synergized to induce death in neuroblastoma cells and in primary cultures of SHH-dependent medulloblastoma, their combination being more effective in MYCN amplified and MYCN overexpressing cells compared to MYCN non-amplified cells. Although the MYCN amplified IMR-32 cell line carrying the p.Val2716Ala ATM mutation showed the highest sensitivity to the drug combination, this was not related to ATM status, as indicated by CRISPR/Cas9-based correction of the mutation. Suboptimal doses of the CHK1 inhibitor MK-8776 plus the PARP inhibitor olaparib led to a MYCN-dependent accumulation of DNA damage and cell death in vitro and significantly reduced the growth of four in vivo models of MYCN-driven tumors, without major toxicities. Our data highlight the combination of PARP and CHK1 inhibitors as a new potential chemo-free strategy to treat MYCN-driven tumors, which might be promptly translated into clinical trials.

Clinical Multigene Panel Sequencing Identifies Distinct Mutational Association Patterns in Metastatic Colorectal Cancer

Extensive molecular characterization of human colorectal cancer (CRC) via Next Generation Sequencing (NGS) indicated that genetic or epigenetic dysregulation of a relevant, but limited, number of molecular pathways typically occurs in this tumor. The molecular picture of the disease is significantly complicated by the frequent occurrence of individually rare genetic aberrations, which expand tumor heterogeneity. Inter- and intratumor molecular heterogeneity is very likely responsible for the remarkable individual variability in the response to conventional and target-driven first-line therapies, in metastatic CRC (mCRC) patients, whose median overall survival remains unsatisfactory. Implementation of an extensive molecular characterization of mCRC in the clinical routine does not yet appear feasible on a large scale, while multigene panel sequencing of most commonly mutated oncogene/oncosuppressor hotspots is more easily achievable. Here, we report that clinical multigene panel sequencing performed for anti-EGFR therapy predictive purposes in 639 formalin-fixed paraffin-embedded (FFPE) mCRC specimens revealed previously unknown pairwise mutation associations and a high proportion of cases carrying actionable gene mutations. Most importantly, a simple principal component analysis directed the delineation of a new molecular stratification of mCRC patients in eight groups characterized by non-random, specific mutational association patterns (MAPs), aggregating samples with similar biology. These data were validated on a The Cancer Genome Atlas (TCGA) CRC dataset. The proposed stratification may provide great opportunities to direct more informed therapeutic decisions in the majority of mCRC cases.

Identification of novel BRCA1 large genomic rearrangements by a computational algorithm of amplicon-based Next-Generation Sequencing data

Background

Genetic testing for BRCA1/2 germline mutations in hereditary breast/ovarian cancer patients requires screening for single nucleotide variants, small insertions/deletions and large genomic rearrangements (LGRs). These studies have long been run by Sanger sequencing and multiplex ligation-dependent probe amplification (MLPA). The recent introduction of next-generation sequencing (NGS) platforms dramatically improved the speed and the efficiency of DNA testing for nucleotide variants, while the possibility to correctly detect LGRs by this mean is still debated. The purpose of this study was to establish whether and to which extent the development of an analytical algorithm could help us translating NGS sequencing via an Ion Torrent PGM platform into a tool suitable to identify LGRs in hereditary breast-ovarian cancer patients.

Methods

We first used NGS data of a group of three patients (training set), previously screened in our laboratory by conventional methods, to develop an algorithm for the calculation of the dosage quotient (DQ) to be compared with the Ion Reporter (IR) analysis. Then, we tested the optimized pipeline with a consecutive cohort of 85 uncharacterized probands (validation set) also subjected to MLPA analysis. Characterization of the breakpoints of three novel BRCA1 LGRs was obtained via long-range PCR and direct sequencing of the DNA products.

Results

In our cohort, the newly defined DQ-based algorithm detected 3/3 BRCA1 LGRs, demonstrating 100% sensitivity and 100% negative predictive value (NPV) (95% CI [87.6–99.9]) compared to 2/3 cases detected by IR (66.7% sensitivity and 98.2% NPV (95% CI [85.6–99.9])). Interestingly, DQ and IR shared 12 positive results, but exons deletion calls matched only in five cases, two of which confirmed by MLPA. The breakpoints of the 3 novel BRCA1 deletions, involving exons 16–17, 21–22 and 20, have been characterized.

Conclusions

Our study defined a DQ-based algorithm to identify BRCA1 LGRs using NGS data. Whether confirmed on larger data sets, this tool could guide the selection of samples to be subjected to MLPA analysis, leading to significant savings in time and money.