Recessive cancer genes engage in negative genetic interactions with their functional paralogs

Paralog dispensability shapes homozygous deletion patterns in tumor genomes

Genomic instability is a hallmark of cancer, resulting in tumor genomes having large numbers of genetic aberrations, including homozygous deletions of protein coding genes. That tumor cells remain viable in the presence of such gene loss suggests high robustness to genetic perturbation. In model organisms and cancer cell lines, paralogs have been shown to contribute substantially to genetic robustness-they are generally more dispensable for growth than singletons. Here, by analyzing copy number profiles of > 10,000 tumors, we test the hypothesis that the increased dispensability of paralogs shapes tumor genome evolution. We find that genes with paralogs are more likely to be homozygously deleted and that this cannot be explained by other factors known to influence copy number variation. Furthermore, features that influence paralog dispensability in cancer cell lines correlate with paralog deletion frequency in tumors. Finally, paralogs that are broadly essential in cancer cell lines are less frequently deleted in tumors than non-essential paralogs. Overall, our results suggest that homozygous deletions of paralogs are more frequently observed in tumor genomes because paralogs are more dispensable.

3D Chromatin Architecture Re-Wiring at the CDH3/CDH1 Loci Contributes to E-Cadherin to P-Cadherin Expression Switch in Gastric Cancer

Cadherins are cell-cell adhesion molecules, fundamental for cell architecture and polarity. E-cadherin to P-cadherin switch can rescue adherens junctions in epithelial tumours. Herein, we disclose a mechanism for E-cadherin to P-cadherin switch in gastric cancers. CDH1 and CDH3 mRNA expression was obtained from 42 gastric tumours' RNA-seq data. CRISPR-Cas9 was used to knock out CDH1 and a putative regulatory element. CDH1-depleted and parental cells were submitted to proteomics and enrichment GO terms analysis; ATAC-seq/4C-seq with a CDH1 promoter viewpoint to assess chromatin accessibility and conformation; and RT-PCR/flow cytometry to assess CDH1/E-cadherin and CDH3/P-cadherin expression. In 42% of gastric tumours analysed, CDH1 to CDH3 switch was observed. CDH1 knockout triggered CDH1/E-cadherin complete loss and CDH3/P-cadherin expression increase at plasma membrane. This switch, likely rescuing adherens junctions, increased cell migration/proliferation, commonly observed in aggressive tumours. E- to P-cadherin switch accompanied increased CDH1 promoter interactions with CDH3-eQTL, absent in normal stomach and parental cells. CDH3-eQTL deletion promotes CDH3/CDH1 reduced expression. These data provide evidence that loss of CDH1/E-cadherin expression alters the CDH3 locus chromatin conformation, allowing a CDH1 promoter interaction with a CDH3-eQTL, and promoting CDH3/P-cadherin expression. These data highlight a novel mechanism triggering E- to P-cadherin switch in gastric cancer.

The methyltransferase domain of DNMT1 is an essential domain in acute myeloid leukemia independent of DNMT3A mutation

Aberrant DNA methylation patterns are a prominent feature of cancer. Methylation of DNA is mediated by the DNA methyltransferase (DNMT) protein family, which regulates de novo (DNMT3A and DNMT3B) and maintenance (DNMT1) methylation. Mutations in DNMT3A are observed in approximately 22% of acute myeloid leukemia (AML). We hypothesized that DNMT1 or DNMT3B could function as a synthetic lethal therapeutic strategy for DNMT3A-mutant AML. CRISPR-Cas9 tiling screens were performed to identify functional domains within DNMT1/DNMT3B that exhibited greater dependencies in DNMT3A mutant versus wild-type cell lines. Although increased sensitivity to DNMT1 mutation was observed in some DNMT3A mutant cellular models tested, the subtlety of these results prevents us from basing any conclusions on a synthetic lethal relationship between DNMT1 and DNMT3A. Our data suggests that a therapeutic window for DNMT1 methyltransferase inhibition in DNMT3A-driven AML may exist, but validation in more biologically relevant models is required.

Paralogous synthetic lethality underlies genetic dependencies of the cancer-mutated gene STAG2

STAG2, a component of the mitotically essential cohesin complex, is highly mutated in several different tumour types, including glioblastoma and bladder cancer. Whereas cohesin has roles in many cancer-related pathways, such as chromosome instability, DNA repair and gene expression, the complex nature of cohesin function has made it difficult to determine how STAG2 loss might either promote tumorigenesis or be leveraged therapeutically across divergent cancer types. Here, we have performed whole-genome CRISPR-Cas9 screens for STAG2-dependent genetic interactions in three distinct cellular backgrounds. Surprisingly, STAG1, the paralog of STAG2, was the only negative genetic interaction that was shared across all three backgrounds. We also uncovered a paralogous synthetic lethal mechanism behind a genetic interaction between STAG2 and the iron regulatory gene IREB2 Finally, investigation of an unusually strong context-dependent genetic interaction in HAP1 cells revealed factors that could be important for alleviating cohesin loading stress. Together, our results reveal new facets of STAG2 and cohesin function across a variety of genetic contexts.

The differential expression patterns and co-expression networks of paralogs as an indicator of the TNM stages of lung adenocarcinoma and squamous cell carcinoma

Cancers constitute a severe threat to human health. Elucidating the association between the expression patterns of the paralogous genes and transcription factors (TF) and the progression of cancers by comprehensively investigating the expression patterns and co-expression networks will contribute to the in-depth understanding of the pathogenesis of cancers. Here, we identified the paralogous gene pairs and systematically analyzed the expression patterns of these paralogs and the known TFs to elucidate the associations with Tumor, Node, Metastasis (TNM) staging information across ten cancers. We found that the expression of ~60% paralogs was cancer-dependent, and more than 50% of the differentially expressed TFs pairs showed positive expression correlations. The down-regulation patterns of paralogs and TFs were closely associated with the M and N developmental stages of lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC). Our results will help to understand the roles of paralogs and TFs in cancer progression and to screen prognostic biomarkers for early cancer diagnosis.

Rapid, Paralog-Sensitive CNV Analysis of 2457 Human Genomes Using QuicK-mer2

Gene duplication is a major mechanism for the evolution of gene novelty, and copy-number variation makes a major contribution to inter-individual genetic diversity. However, most approaches for studying copy-number variation rely upon uniquely mapping reads to a genome reference and are unable to distinguish among duplicated sequences. Specialized approaches to interrogate specific paralogs are comparatively slow and have a high degree of computational complexity, limiting their effective application to emerging population-scale data sets. We present QuicK-mer2, a self-contained, mapping-free approach that enables the rapid construction of paralog-specific copy-number maps from short-read sequence data. This approach is based on the tabulation of unique k-mer sequences from short-read data sets, and is able to analyze a 20X coverage human genome in approximately 20 min. We applied our approach to newly released sequence data from the 1000 Genomes Project, constructed paralog-specific copy-number maps from 2457 unrelated individuals, and uncovered copy-number variation of paralogous genes. We identify nine genes where none of the analyzed samples have a copy number of two, 92 genes where the majority of samples have a copy number other than two, and describe rare copy number variation effecting multiple genes at the APOBEC3 locus.

Paralog buffering contributes to the variable essentiality of genes in cancer cell lines

What makes a gene essential for cellular survival? In model organisms, such as budding yeast, systematic gene deletion studies have revealed that paralog genes are less likely to be essential than singleton genes and that this can partially be attributed to the ability of paralogs to buffer each other's loss. However, the essentiality of a gene is not a fixed property and can vary significantly across different genetic backgrounds. It is unclear to what extent paralogs contribute to this variation, as most studies have analyzed genes identified as essential in a single genetic background. Here, using gene essentiality profiles of 558 genetically heterogeneous tumor cell lines, we analyze the contribution of paralogy to variable essentiality. We find that, compared to singleton genes, paralogs are less frequently essential and that this is more evident when considering genes with multiple paralogs or with highly sequence-similar paralogs. In addition, we find that paralogs derived from whole genome duplication exhibit more variable essentiality than those derived from small-scale duplications. We provide evidence that in 13–17% of cases the variable essentiality of paralogs can be attributed to buffering relationships between paralog pairs, as evidenced by synthetic lethality. Paralog pairs derived from whole genome duplication and pairs that function in protein complexes are significantly more likely to display such synthetic lethal relationships. Overall we find that many of the observations made using a single strain of budding yeast can be extended to understand patterns of essentiality in genetically heterogeneous cancer cell lines.

Somewhat surprisingly, the majority of human genes can be mutated or deleted in individual cell lines without killing the cells. This observation raises a number of questions—which genes can be lost and why? Here we address these questions by analyzing data on which genes are essential for the growth of over 500 cancer cell lines. In general we find that paralog genes are essential in fewer cell lines than genes that are not paralogs. Paralogs are genes that have been duplicated at some point in evolutionary history, resulting in our genome having two copies of the same gene—a paralog pair. These paralog pairs are a potential source of redundancy, similar to a car having a spare tire. If this is the case, one might expect that losing one gene from a paralog pair could be tolerated by cells, due to the existence of a 'backup gene', but losing both members would cause cells to die. By analyzing the cancer cell lines we estimate this to be the case for 13–17% of paralog pairs, and that this provides an explanation for why some genes are essential in some cell lines but not others.

Genome-scale analysis identifies paralog lethality as a vulnerability of chromosome 1p loss in cancer

Functional redundancy shared by paralog genes may afford protection against genetic perturbations, but it can also result in genetic vulnerabilities due to mutual interdependency1-5. Here, we surveyed genome-scale short hairpin RNA and CRISPR screening data on hundreds of cancer cell lines and identified MAGOH and MAGOHB, core members of the splicing-dependent exon junction complex, as top-ranked paralog dependencies6-8. MAGOHB is the top gene dependency in cells with hemizygous MAGOH deletion, a pervasive genetic event that frequently occurs due to chromosome 1p loss. Inhibition of MAGOHB in a MAGOH-deleted context compromises viability by globally perturbing alternative splicing and RNA surveillance. Dependency on IPO13, an importin-β receptor that mediates nuclear import of the MAGOH/B-Y14 heterodimer9, is highly correlated with dependency on both MAGOH and MAGOHB. Both MAGOHB and IPO13 represent dependencies in murine xenografts with hemizygous MAGOH deletion. Our results identify MAGOH and MAGOHB as reciprocal paralog dependencies across cancer types and suggest a rationale for targeting the MAGOHB-IPO13 axis in cancers with chromosome 1p deletion.

Computational Approaches to Identify Genetic Interactions for Cancer Therapeutics

The development of improved cancer therapies is frequently cited as an urgent unmet medical need. Here we describe how genetic interactions are being therapeutically exploited to identify novel targeted treatments for cancer. We discuss the current methodologies that use 'omics data to identify genetic interactions, in particular focusing on synthetic sickness lethality (SSL) and synthetic dosage lethality (SDL). We describe the experimental and computational approaches undertaken both in humans and model organisms to identify these interactions. Finally we discuss some of the identified targets with licensed drugs, inhibitors in clinical trials or with compounds under development.

Defining a Cancer Dependency Map

Most human epithelial tumors harbor numerous alterations, making it difficult to predict which genes are required for tumor survival. To systematically identify cancer dependencies, we analyzed 501 genome-scale loss-of-function screens performed in diverse human cancer cell lines. We developed DEMETER, an analytical framework that segregates on- from off-target effects of RNAi. 769 genes were differentially required in subsets of these cell lines at a threshold of six SDs from the mean. We found predictive models for 426 dependencies (55%) by nonlinear regression modeling considering 66,646 molecular features. Many dependencies fall into a limited number of classes, and unexpectedly, in 82% of models, the top biomarkers were expression based. We demonstrated the basis behind one such predictive model linking hypermethylation of the UBB ubiquitin gene to a dependency on UBC. Together, these observations provide a foundation for a cancer dependency map that facilitates the prioritization of therapeutic targets.

Defining a Cancer Dependency Map

Most human epithelial tumors harbor numerous alterations, making it difficult to predict which genes are required for tumor survival. To systematically identify cancer dependencies, we analyzed 501 genome-scale loss-of-function screens performed in diverse human cancer cell lines. We developed DEMETER, an analytical framework that segregates on- from off-target effects of RNAi. 769 genes were differentially required in subsets of these cell lines at a threshold of six SDs from the mean. We found predictive models for 426 dependencies (55%) by nonlinear regression modeling considering 66,646 molecular features. Many dependencies fall into a limited number of classes, and unexpectedly, in 82% of models, the top biomarkers were expression based. We demonstrated the basis behind one such predictive model linking hypermethylation of the UBB ubiquitin gene to a dependency on UBC. Together, these observations provide a foundation for a cancer dependency map that facilitates the prioritization of therapeutic targets.

Synthetic lethal interaction between the tumour suppressor STAG2 and its paralog STAG1

Cohesin is a multi-protein complex that tethers sister chromatids during mitosis and mediates DNA repair, genome compartmentalisation and regulation of gene expression. Cohesin subunits frequently acquire cancer loss-of-function alterations and act as tumour suppressors in several tumour types. This has led to increased interest in cohesin as potential target in anti-cancer therapy. Here we show that the loss-of-function of STAG2, a core component of cohesin and an emerging tumour suppressor, leads to synthetic dependency of mutated cancer cells on its paralog STAG1. STAG1 and STAG2 share high sequence identity, encode mutually exclusive cohesin subunits and retain partially overlapping functions. We inhibited STAG1 and STAG2 in several cancer cell lines where the two genes have variable mutation and copy number status. In all cases, we observed that the simultaneous blocking of STAG1 and STAG2 significantly reduces cell proliferation. We further confirmed the synthetic lethal interaction developing a vector-free CRISPR system to induce STAG1/STAG2 double gene knockout. We provide strong evidence that STAG1 is a promising therapeutic target in cancers with inactivating alterations of STAG2.

Genetic Redundancy, Functional Compensation, and Cancer Vulnerability

Cancer genomes acquire somatic alterations that largely differ between and within cancer types. Several of these alterations inactivate genes that are normally functional with no deleterious consequences on cancer cells due to genetic redundancy. Here we discuss how this leads to cancer synthetic dependencies that can be exploited in therapy.

NCG 5.0: updates of a manually curated repository of cancer genes and associated properties from cancer mutational screenings

The Network of Cancer Genes (NCG, http://ncg.kcl.ac.uk/) is a manually curated repository of cancer genes derived from the scientific literature. Due to the increasing amount of cancer genomic data, we have introduced a more robust procedure to extract cancer genes from published cancer mutational screenings and two curators independently reviewed each publication. NCG release 5.0 (August 2015) collects 1571 cancer genes from 175 published studies that describe 188 mutational screenings of 13 315 cancer samples from 49 cancer types and 24 primary sites. In addition to collecting cancer genes, NCG also provides information on the experimental validation that supports the role of these genes in cancer and annotates their properties (duplicability, evolutionary origin, expression profile, function and interactions with proteins and miRNAs).

Therapeutic opportunities within the DNA damage response

The DNA damage response (DDR) is essential for maintaining the genomic integrity of the cell, and its disruption is one of the hallmarks of cancer. Classically, defects in the DDR have been exploited therapeutically in the treatment of cancer with radiation therapies or genotoxic chemotherapies. More recently, protein components of the DDR systems have been identified as promising avenues for targeted cancer therapeutics. Here, we present an in-depth analysis of the function, role in cancer and therapeutic potential of 450 expert-curated human DDR genes. We discuss the DDR drugs that have been approved by the US Food and Drug Administration (FDA) or that are under clinical investigation. We examine large-scale genomic and expression data for 15 cancers to identify deregulated components of the DDR, and we apply systematic computational analysis to identify DDR proteins that are amenable to modulation by small molecules, highlighting potential novel therapeutic targets.

Building high-resolution synthetic lethal networks: a 'Google map' of the cancer cell

The most commonly used therapies for cancer involve delivering high doses of radiation or toxic chemicals to the patient that also cause substantial damage to normal tissue. To overcome this, researchers have recently resorted to a basic biological concept called 'synthetic lethality' (SL) that takes advantage of interactions between gene pairs. The identification of SL interactions is of considerable therapeutic interest because if a particular gene is SL with a tumor-causing mutation, then the targeting that gene carries therapeutic advantages. Mapping these interactions in the context of human cancer cells could hold the key to effective, targeted cancer treatments. In this review, we cover the recent advances that aim to identify these SL interactions using unbiased genetic screens.