Cancer-associated FBXW7 loss is synthetic lethal with pharmacological targeting of CDC7.

The F-box and WD repeat domain containing 7 (FBXW7) tumour suppressor gene encodes a substrate-recognition subunit of Skp, cullin, F-box (SCF)-containing complexes. The tumour-suppressive role of FBXW7 is ascribed to its ability to drive ubiquitination and degradation of oncoproteins. Despite this molecular understanding, therapeutic approaches that target defective FBXW7 have not been identified. Using genome-wide clustered regularly interspaced short palindromic repeats (CRISPR)-Cas9 screens, focussed RNA-interference screens and whole and phospho-proteome mass spectrometry profiling in multiple FBXW7 wild-type and defective isogenic cell lines, we identified a number of FBXW7 synthetic lethal targets, including proteins involved in the response to replication fork stress and proteins involved in replication origin firing, such as cell division cycle 7-related protein kinase (CDC7) and its substrate, DNA replication complex GINS protein SLD5 (GINS4). The CDC7 synthetic lethal effect was confirmed using small-molecule inhibitors. Mechanistically, FBXW7/CDC7 synthetic lethality is dependent upon the replication factor telomere-associated protein RIF1 (RIF1), with RIF1 silencing reversing the FBXW7-selective effects of CDC7 inhibition. The delineation of FBXW7 synthetic lethal effects we describe here could serve as the starting point for subsequent drug discovery and/or development in this area.

MND1 and PSMC3IP control PARP inhibitor sensitivity in mitotic cells.

The PSMC3IP-MND1 heterodimer promotes meiotic D loop formation before DNA strand exchange. In genome-scale CRISPR-Cas9 mutagenesis and interference screens in mitotic cells, depletion of PSMC3IP or MND1 causes sensitivity to poly (ADP-Ribose) polymerase inhibitors (PARPi) used in cancer treatment. PSMC3IP or MND1 depletion also causes ionizing radiation sensitivity. These effects are independent of PSMC3IP/MND1's role in mitotic alternative lengthening of telomeres. PSMC3IP- or MND1-depleted cells accumulate toxic RAD51 foci in response to DNA damage, show impaired homology-directed DNA repair, and become PARPi sensitive, even in cells lacking both BRCA1 and TP53BP1. Epistasis between PSMC3IP-MND1 and BRCA1/BRCA2 defects suggest that abrogated D loop formation is the cause of PARPi sensitivity. Wild-type PSMC3IP reverses PARPi sensitivity, whereas a PSMC3IP p.Glu201del mutant associated with D loop defects and ovarian dysgenesis does not. These observations suggest that meiotic proteins such as MND1 and PSMC3IP have a greater role in mitotic DNA repair.

Resistance to DNA repair inhibitors in cancer.

The DNA damage response (DDR) represents a complex network of proteins which detect and repair DNA damage, thereby maintaining the integrity of the genome and preventing the transmission of mutations and rearranged chromosomes to daughter cells. Faults in the DDR are a known driver and hallmark of cancer. Furthermore, inhibition of DDR enzymes can be used to treat the disease. This is exemplified by PARP inhibitors (PARPi) used to treat cancers with defects in the homologous recombination DDR pathway. A series of novel DDR targets are now also under pre-clinical or clinical investigation, including inhibitors of ATR kinase, WRN helicase or the DNA polymerase/helicase Polθ (Pol-Theta). Drug resistance is a common phenomenon that impairs the overall effectiveness of cancer treatments and there is already some understanding of how resistance to PARPi occurs. Here, we discuss how an understanding of PARPi resistance could inform how resistance to new drugs targeting the DDR emerges. We also discuss potential strategies that could limit the impact of these therapy resistance mechanisms in cancer.

Functional annotation of the 2q35 breast cancer risk locus implicates a structural variant in influencing activity of a long-range enhancer element.

A combination of genetic and functional approaches has identified three independent breast cancer risk loci at 2q35. A recent fine-scale mapping analysis to refine these associations resulted in 1 (signal 1), 5 (signal 2), and 42 (signal 3) credible causal variants at these loci. We used publicly available in silico DNase I and ChIP-seq data with in vitro reporter gene and CRISPR assays to annotate signals 2 and 3. We identified putative regulatory elements that enhanced cell-type-specific transcription from the IGFBP5 promoter at both signals (30- to 40-fold increased expression by the putative regulatory element at signal 2, 2- to 3-fold by the putative regulatory element at signal 3). We further identified one of the five credible causal variants at signal 2, a 1.4 kb deletion (esv3594306), as the likely causal variant; the deletion allele of this variant was associated with an average additional increase in IGFBP5 expression of 1.3-fold (MCF-7) and 2.2-fold (T-47D). We propose a model in which the deletion allele of esv3594306 juxtaposes two transcription factor binding regions (annotated by estrogen receptor alpha ChIP-seq peaks) to generate a single extended regulatory element. This regulatory element increases cell-type-specific expression of the tumor suppressor gene IGFBP5 and, thereby, reduces risk of estrogen receptor-positive breast cancer (odds ratio = 0.77, 95% CI 0.74-0.81, p = 3.1 × 10-31).

Identifying high-confidence capture Hi-C interactions using CHiCANE.

The ability to identify regulatory interactions that mediate gene expression changes through distal elements, such as risk loci, is transforming our understanding of how genomes are spatially organized and regulated. Capture Hi-C (CHi-C) is a powerful tool to delineate such regulatory interactions. However, primary analysis and downstream interpretation of CHi-C profiles remains challenging and relies on disparate tools with ad-hoc input/output formats and specific assumptions for statistical modeling. Here we present a data processing and interaction calling toolkit (CHiCANE), specialized for the analysis and meaningful interpretation of CHi-C assays. In this protocol, we demonstrate applications of CHiCANE to region capture Hi-C (rCHi-C) and promoter capture Hi-C (pCHi-C) libraries, followed by quality assessment of interaction peaks, as well as downstream analysis specific to rCHi-C and pCHi-C to aid functional interpretation. For a typical rCHi-C/pCHi-C dataset this protocol takes up to 3 d for users with a moderate understanding of R programming and statistical concepts, although this is dependent on dataset size and compute power available. CHiCANE is freely available at https://cran.r-project.org/web/packages/chicane .

Capture Hi-C identifies putative target genes at 33 breast cancer risk loci.

Genome-wide association studies (GWAS) have identified approximately 100 breast cancer risk loci. Translating these findings into a greater understanding of the mechanisms that influence disease risk requires identification of the genes or non-coding RNAs that mediate these associations. Here, we use Capture Hi-C (CHi-C) to annotate 63 loci; we identify 110 putative target genes at 33 loci. To assess the support for these target genes in other data sources we test for associations between levels of expression and SNP genotype (eQTLs), disease-specific survival (DSS), and compare them with somatically mutated cancer genes. 22 putative target genes are eQTLs, 32 are associated with DSS and 14 are somatically mutated in breast, or other, cancers. Identifying the target genes at GWAS risk loci will lead to a greater understanding of the mechanisms that influence breast cancer risk and prognosis.