Abstract

Functional redundancy shared by paralog genes may afford protection against genetic perturbations, but it can also result in genetic vulnerabilities due to mutual interdependency1,2,3,4,5. 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 dependencies6,7,8. 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.

Main

The systematic integration of data from genomic characterization and genetic screening of cancer cell lines can identify gene dependencies induced by specific somatic alterations and inform the development of targeted therapeutics. For example, several studies have shown that inactivation of specific driver or passenger genes may confer dependency on functionally redundant paralogs2,3,10,11,12,13. Paralog dependencies have also emerged as important targets in recent genome-scale functional genomic screens4,5, underscoring the importance of further characterizing this class of cancer vulnerabilities.

To systematically identify paralog dependencies that may represent attractive cancer targets, we analyzed data from pooled, genome-scale short hairpin RNA (shRNA) screening of 501 cancer cell lines5,14. We determined the correlation between a dependency on a gene5 and loss of function of its paralog across 10,287 paralog pairs (Supplementary Fig. 1; Supplementary Note). We identified 167 genes for which dependency was significantly correlated with loss of a paralog (1.6% of paralog test pairs at q <0.05), including many previously reported paralog dependencies (for example, ARID1B dependency with ARID1A inactivation10, SMARCA2 dependency with SMARCA4 inactivation11, UBC dependency with UBB inactivation5, and FERMT1 dependency with FERMT2 inactivation5). However, of these 167 paralog dependency pairs, only 7 were ‘symmetric’, in which dependency for each of the genes in the pair was significantly correlated with inactivation of its partner paralog (Fig. 1a,b; Supplementary Table 1). A similar analysis of data from genome-scale CRISPR screening of 341 cell lines15 identified 125 significant paralog dependencies (1.4% of paralog test pairs at q <0.05), of which 7 pairs were symmetric (Supplementary Table 2; Supplementary Note). Paralog genes arise via ancestral duplication events and may functionally diverge over time1,16. Symmetric paralog pairs likely share complete functional redundancy, making them particularly attractive targets for ‘collateral lethality’ strategies2. An enrichment for RNA-splicing related genes was noted among symmetric, but not asymmetric, paralog pairs in the shRNA and CRISPR screening datasets (Supplementary Table 3), suggesting that redundant essentiality may be exploited to target splicing-related pathways.

Fig. 1: Hemizygous MAGOH deletion confers MAGOHB dependency.
Fig. 1

a, Analysis of paralog dependencies in genome-scale screening of cancer cell lines (shRNA, 501 lines; CRISPR-Cas9, 341 lines). b, q-value/q-value plot showing significance of pairwise correlation between a gene’s dependency score and inactivation of its paralog. q-value 1, significance for dependency on the paralog labeled first with inactivation of the paralog labeled second. q-value 2, significance for dependency on the paralog labeled second with inactivation of the paralog labeled first. ‘Symmetric’ paralogs are in the upper right quadrant (q1 <0.05 and q2 <0.05). Plots show n = 1,970 paralog pairs for shRNA data and n = 1,593 pairs for CRISPR data. One-sided P value from two-class comparison was calculated via moderated t-statistic and adjusted for multiple comparisons using the Benjamini–Hochberg FDR. c, PARIS analysis to identify gene dependencies correlated with hemizygous MAGOH loss. Mutual information metric (RNMI) is plotted against FDR for gene dependencies positively correlated with MAGOH deletion. d, Cell viability in cell lines with (left) and without (right) hemizygous MAGOH loss upon MAGOHB suppression using a doxycycline-inducible shRNA against MAGOHB. Error bars show mean ± s.d., n = 3 replicates from a representative experiment repeated at least twice in each cell line; P value by two-tailed, two-sample t-test. Dox, doxycycline; NS, not significant. e, Colony formation in cell lines with (H1437, H460) or without (H1373) hemizygous MAGOH loss upon MAGOHB suppression using a doxycycline-inducible shRNA against MAGOHB. Photographs show representative wells from an experiment conducted in triplicate (quantification in Supplementary Fig. 4); experiment was repeated at least twice in each cell line. f, Cell viability measured on MAGOHB knockdown in ChagoK1 cells with or without reconstitution of MAGOH-V5. Error bars show mean ± s.d., n = 5 replicates from a representative experiment repeated at least twice; P value by two-tailed, two-sample t-test. g, Frequency of hemizygous MAGOH deletion across TCGA cohorts. Total frequency of MAGOH loss is indicated by a light blue bar; frequency of MAGOH loss occurring as a result of chromosome 1p deletion is indicated by a dark blue bar. Top panel shows total number of arm-level copy number events in each tumor type.

One symmetric paralog pair was shared between the shRNA and CRISPR datasets: MAGOH-MAGOHB; a second pair, FUBP1-KHSRP, was highly significant for symmetry in the shRNA data and borderline significant in the CRISPR dataset (q1 = 0.0547) (Fig. 1a,b; Supplementary Fig. 1; Supplementary Tables 1 and 2)15. We focus here on validation of the former pair. MAGOH and MAGOHB encode core members of the exon–junction complex (EJC), a multiprotein complex that is deposited on messenger RNAs at the time of splicing and that mediates diverse downstream processes including mRNA transport, stability, and nonsense-mediated decay (NMD)6,17.

Using both shRNA and CRISPR technologies, we individually validated MAGOHB dependency in the setting of MAGOH loss, as well as MAGOH dependency in the setting of MAGOHB loss. Furthermore, in a cell line without hemizygous deletion of either paralog, knockdown of either MAGOH or MAGOHB individually was tolerated, but the combination was lethal (Supplementary Fig. 2). We noted that MAGOHB dependency in the setting of MAGOH inactivation was particularly pronounced based on (1) effect size (log-fold difference in MAGOHB dependency between MAGOH-inactivated and non-MAGOH-inactivated cell lines) and (2) MAGOHB scoring as a robust 6σ differential dependency (having a dependency score in some cell lines greater than six standard deviations below its mean dependency score across all cell lines) in both the RNA interference (RNAi) and CRISPR screening data. We therefore sought to further characterize MAGOHB dependency in the setting of MAGOH loss.

MAGOHB was the top differential dependency in cells with hemizygous deletion of MAGOH (Fig. 1c; Supplementary Tables 4 and 5; Supplementary Note) and dependency on MAGOHB was predicted by low expression of MAGOH, consistent with the notion that hemizygous deletion of MAGOH leads to its decreased expression (Supplementary Fig. 3). shRNA-mediated knockdown of MAGOHB led to a decrease in cell viability and colony-forming capacity in three MAGOH-deleted cell lines, but not in control cell lines euploid for MAGOH (Fig. 1d,e; Supplementary Fig. 4). Ectopic expression of MAGOH in an MAGOH-deleted cell line fully rescued MAGOHB dependency, indicating that MAGOHB dependency in MAGOH-deleted cells is solely due to MAGOH loss, and consistent with complete functional redundancy between these paralogs8 (Fig. 1f; Supplementary Fig. 4). CRISPR/Cas9-mediated deletion of MAGOH in a cell line with two copies of MAGOH also conferred MAGOHB dependency (Supplementary Figs. 5 and 6).

To assess the clinical contexts in which these dependencies might be exploited, we next surveyed the frequency of MAGOH and MAGOHB loss in tumor cohorts from The Cancer Genome Atlas (TCGA). We observed pervasive hemizygous MAGOH loss across tumor types (frequency of 21% (1,675 of 8,009) in the entire TCGA dataset, and >50% in multiple tumor types). Moreover, MAGOH deletion most frequently occurs as a result of arm-level deletion of chromosome 1p across human tumors (Fig. 1g; Supplementary Table 6). We confirmed that chromosome 1p-deletion status correlates with MAGOHB dependency in the genome-scale CRISPR screening data (Supplementary Fig. 7). In the context of neuroblastoma—where 1p deletion is a hallmark event in a subset of tumors18MAGOHB knockdown was lethal in a 1p-deleted, but not a 1p-neutral, cell line (Supplementary Fig. 7). MAGOHB is located on chromosome 12p, an arm also recurrently lost across tumor types, albeit with markedly lower frequency than chromosome 1p (Supplementary Fig. 8). Analysis of genome-scale CRISPR screening data confirmed a reciprocal dependency on MAGOH in the setting of chromosome 12p deletion. Interestingly, we also observed mutual exclusivity between chromosome 1p and chromosome 12p codeletion in many tumor types, suggesting that concurrent loss of both MAGOH and MAGOHB may be poorly tolerated (Supplementary Fig. 8). We conclude that MAGOH and MAGOHB represent potential vulnerabilities in large, genetically defined subsets of tumors.

MAGOH and MAGOHB constitute core components of the EJC8; EJC deposition at exon–exon junctions allows transcripts containing premature termination codons to be identified and targeted for degradation via NMD6,17. We therefore hypothesized that MAGOHB inhibition in the setting of decreased MAGOH dosage may compromise cell viability by perturbing RNA splicing and RNA surveillance. To evaluate the global transcriptomic consequences of MAGOHB inhibition, we performed RNA sequencing on hemizygous MAGOH-deleted ChagoK1 cells in the presence or absence of MAGOHB knockdown, with or without ectopic re-expression of MAGOH. We observed an increased expression of NMD biotype transcripts on MAGOHB knockdown in ChagoK1 cells (Fig. 2a, left). In contrast, MAGOHB knockdown in MAGOH-reconstituted ChagoK1 cells was well tolerated without a notable shift in NMD biotype transcript distribution (Fig. 2a, right). We next sought to determine whether the upregulation of NMD isoforms on MAGOHB knockdown in ChagoK1 cells was occurring at the expense of other transcript biotypes. Among genes that had significantly upregulated NMD isoform(s) on MAGOHB knockdown, we observed a significant proportional decrease in coding isoform expression in ChagoK1 cells but not MAGOH-reconstituted ChagoK1 cells (Fig. 2b, compare left and right). To investigate whether particular splice event classes were driving this redistribution of isoform types, we quantified the proportion of differentially spliced events of each class that were more common in either the absence (Fig. 2c, red) or presence (Fig. 2c, blue) of MAGOHB knockdown in either ChagoK1 cells or MAGOH-reconstituted ChagoK1 cells. As compared with MAGOHB knockdown in MAGOH-reconstituted ChagoK1 cells, MAGOHB knockdown in ChagoK1 cells resulted in reduced cassette exon inclusion and increased intron retention (Fig. 2c). Therefore, many global transcriptomic effects of MAGOH/B insufficiency appear attributable to alterations in these two splice event types, indicative of a defect in exon definition/recognition.

Fig. 2: RNA splicing is globally altered upon MAGOHB suppression in cells with MAGOH loss, leading to the upregulation of NMD substrates.
Fig. 2

a, Differentially expressed transcripts in ChagoK1 cells (left) and in MAGOH-V5 reconstituted ChagoK1 cells (right) on MAGOHB knockdown (KD). Transcripts annotated as NMD substrates shown in red. Significance determined by a Wald test and adjusted for multiple comparisons using the Benjamini–Hochberg FDR. n = 3 replicates were used in all conditions. b, Density distribution of proportional expression levels among coding isoforms corresponding to genes whose NMD isoforms are upregulated upon MAGOHB knockdown in ChagoK1 cells (left) or MAGOH-V5 reconstituted ChagoK1 cells (right). X axis shows expression level of coding isoform(s) proportional to all expressed transcripts for a given gene. Y axis shows density. c, Global changes in patterns of splice site usage upon MAGOHB knockdown in ChagoK1 cells (left) or MAGOH-V5 reconstituted ChagoK1 cells (right). Splice event classes are shown in the schematic at top; solid lines denote ‘inclusion’ event and dotted lines denote the ‘alternative’ event for each class. Bar graphs denote the proportion of significant differentially spliced events that show greater inclusion in either the absence (red) or presence (blue) of MAGOHB knockdown in either ChagoK1 cells (left) or MAGOH-V5 reconstituted ChagoK1 cells (right). d, Significantly enriched gene ontology (GO) classes for genes (n = 17) that show upregulation of NMD isoform(s) and concomitant downregulation of coding isoform(s). Significance was determined by a binomial test and adjusted for multiple comparisons using the Bonferroni correction. e, Left panel shows Sashimi plots around an activated exon within the 3′UTR of the HNRNPDL gene (inclusion of which creates an NMD-substrate transcript) in either the absence or presence of MAGOHB knockdown in either ChagoK1 cells or MAGOH-V5 reconstituted ChagoK1 cells. Numbers reflect junction spanning reads averaged over three replicates for each condition. Right panel, top left: transcript abundances for various isoforms of the HNRNPDL gene (labeled by the last six digits of the ENSEMBL ID) in either the absence (gray) or presence (red) of MAGOHB knockdown in either ChagoK1 cells or MAGOH-V5 reconstituted ChagoK1 cells. Right panel, top right: isoform abundances grouped by predicted coding protein length in each condition. aa, amino acid; tpm, transcripts per million. Right panel, bottom: western blot showing increased HNRNPDL protein levels on MAGOHB knockdown in ChagoK1 cells but not MAGOH-V5 reconstituted ChagoK1 cells. Representative of a similar experiment repeated three times.

We identified 22 instances in which there was both a significant absolute upregulation of an NMD isoform (beta >1 in differential expression analysis using Kallisto19) and corresponding downregulation of at least one protein coding isoform (beta <–1) (Supplementary Table 7). These genes were significantly enriched for pathways involved in mRNA splicing and mRNA processing (Supplementary Table 8; Fig. 2d). Notably, among the seven splicing-related genes driving this enrichment were four genes (SRSF2, SRSF7, HNRNPDL, HNRNPH1) reported to auto-regulate their expression via alternative splicing-NMD (AS-NMD) loops20,21. Such AS-NMD loops, many of which are in splicing-related genes, involve ultraconserved, regulated alternative splicing events that induce NMD substrates, thus maintaining homeostatic control of gene expression20,21. We observed perturbations in isoform distributions of HNRNPDL and other splicing-related genes on MAGOHB knockdown in ChagoK1 cells, but not in MAGOH-reconstituted ChagoK1 cells (Fig. 2e; Supplementary Figs. 9 and 10; Supplementary Note). Altered RNA isoform abundance accompanied by changes in the levels of functional protein, either via disruption of AS-NMD loops or through other mechanisms, could have deleterious direct and indirect consequences on cellular splicing.

Given these transcriptomic consequences of MAGOH/MAGOHB insufficiency, we next sought to determine whether MAGOH loss unveils a broader dependency on splicing/NMD-related complexes. We performed immunoprecipitation and mass spectrometry to identify MAGOH- and MAGOHB-associated binding partners and found that these interactors, which include many splicing-related genes, were enriched for gene dependencies correlated with both MAGOH and MAGOHB dependencies. However, these dependencies were weaker than the reciprocal MAGOH/B paralog dependencies driven by redundant essentiality (Supplementary Fig. 11; Supplementary Tables 911; Supplementary Note).

MAGOH and MAGOHB share near-identity at the protein level and functional and crystallographic studies do not necessarily show domains easily amenable to targeting by small molecules22. To identify other more tractable targets that might indirectly affect MAGOH/MAGOHB function, we interrogated the genome-scale shRNA screening data for gene dependencies highly correlated with either MAGOH or MAGOHB dependency. IPO13 emerged as the top, outlier-correlated gene dependency to both MAGOH and MAGOHB (Fig. 3a; Supplementary Table 12). IPO13 is a bidirectional karyopherin responsible for nuclear import of the MAGOH/B-Y14 heterodimer, a function critical for recycling of the EJC; it is also located on chromosome 1p in proximity to MAGOH, and the two genes are frequently codeleted17 (Fig. 3b; Supplementary Table 13).

Fig. 3: IPO13 dependency is correlated with MAGOH and MAGOHB dependencies and is rescued by MAGOH reconstitution.
Fig. 3

a, Plot of gene dependencies (n = 6,300) correlated with MAGOH dependency versus those correlated with MAGOHB dependency. Axes reflect Z-scored Pearson correlation of each dependency with either MAGOH dependency (x axis) or MAGOHB dependency (y axis). MAGOH and MAGOHB self-correlations are not shown. b, Heatmap of IPO13 dependency scores across 243 screened cell lines. Black bars denote cell lines that share dependency on IPO13 and either MAGOH, MAGOHB, or both (1); cell lines that carry a hemizygous deletion in IPO13 (2); MAGOH (3); and MAGOHB (4). c, Cell viability measured on shRNA-mediated IPO13 knockdown in cell lines without (HCC1359, left) and with (H1437, right) MAGOH loss. Error bars show mean ± s.d., n = 4 replicates per cell line; P value by two-tailed, two-sample t-test. d, Colony formation in MAGOH-deleted H460 cells on IPO13 knockdown in either the absence (top) or presence (bottom) of MAGOH-V5 reconstitution. Photographs show representative wells from an experiment conducted in triplicate (quantification in Supplementary Fig. 12); experiment was repeated three times. e, Fold change in expression of the NMD substrates SC1.6 and SC1.7 of the SRSF2 gene in H460 cells on IPO13 knockdown (IPO13-sh2) in either the presence (red) or absence (blue) of MAGOH-V5 reconstitution. Data normalized to expression in the shGFP condition. Error bars show mean ± s.d., n = 3 technical replicates per condition.

We observed a selective IPO13 dependency in MAGOH-deleted cell lines compared to non-deleted cell lines (Fig. 3c) and found that dependency on IPO13 in MAGOH-deleted H460 and H1437 cells was partially attenuated by MAGOH re-expression (Fig. 3d; Supplementary Fig. 12; Supplementary Note). Knockdown of IPO13 in MAGOH-deleted cells led to cytoplasmic accumulation of MAGOH/MAGOHB and subsequent upregulation of the NMD-substrates SC1.6 and SC1.7, an effect that was rescued by MAGOH re-expression (Fig. 3e; Supplementary Fig. 13). This suggests that IPO13 dependency in MAGOH- and MAGOHB-deleted cells is mediated in part by defective shuttling of MAGOH/B, resulting in mis-splicing and impaired RNA surveillance. Haploinsufficiency of IPO13, as occurs when MAGOH and IPO13 are codeleted, may also contribute to IPO13 dependency in some contexts.

Finally, we sought to validate MAGOHB as a target in vivo. We formed xenografts from H1437 cells (which carry a hemizygous deletion in MAGOH) transduced with a lentiviral vector encoding a doxycycline-inducible shRNA against MAGOHB. Xenograft growth was significantly impaired on MAGOHB knockdown. (Fig. 4a,b). To next assess this dependency using a more therapeutically tractable system, we used tumor-penetrating nanocomplexes (TPNCs) capable of targeted delivery of short interfering RNAs (siRNAs) to the cytosol of tumor cells23. The TPNCs were decorated with the tumor-homing peptide iRGD, which allows for targeted delivery of siRNAs to tumor cells expressing surface NRP1/αvβ3; both receptors are expressed on H1437 cells (Fig. 4c). H1437 xenograft growth was significantly impaired on intratumoral injection of si-MAGOHB- and si-IPO13-containing TPNCs, but not TPNCs containing control siRNA against GFP (Fig. 4d,e; Supplementary Fig. 14). This finding was confirmed using a second TPNC system using a distinct tumor-homing peptide, Lyp-1 (Supplementary Fig. 14). Additionally, tumors treated with si-MAGOHB-containing TPNCs displayed higher levels of cleaved caspase-3, indicating that targeting MAGOHB in a MAGOH-hemizygous context triggers apoptotic cell death (Supplementary Fig. 14). Thus, MAGOHB and IPO13 represent potential in vivo targets in a MAGOH-deleted context, and this paralog vulnerability may be exploited by antisense or RNAi-based approaches.

Fig. 4: MAGOHB and IPO13 are in vivo dependencies in MAGOH-deleted xenografts.
Fig. 4

a,b, Schematic (a) and growth curves (b) of H1437 xenografts in nude mice on MAGOHB suppression. H1437 cells were transduced with lentivirus expressing a doxycycline-inducible shRNA against MAGOHB and injected into the flanks of nude mice. Once palpable tumors had formed (7 days), mice were randomized to either normal chow or chow supplemented with doxycycline. Tumor volume over time is plotted in each arm. Lines show mean ± s.e.m. for n = 6 tumors per arm. P values listed for significant (<0.05) time points by two-tailed, two-sample t-test; P also <0.0001 by two-way analysis of variance (ANOVA) between +Dox and −Dox curves. c, Surface expression for NRP-1 and αVβ3 (co-receptors for iRGD-containing nanocomplexes) in H1437 cells, as assessed by flow cytometry. Repeated twice with similar results. d,e, Schematic (d) and growth curves (e) of H1437 xenografts in nude mice on MAGOHB or IPO13 suppression using an siRNA-carrying TPNC. Following palpable tumor formation, mice received intratumoral injections of TPNC containing siRNA against GFP (control), MAGOHB, or IPO13. Lines show mean ± s.e.m. for n = 10 tumors per arm; P value by two-way ANOVA.

Hemizygous chromosome arm loss is one of the commonest features of cancer genomes24,25 and rational therapeutic targeting of this class of somatic events would therefore be attractive. Prior studies have identified several candidate targets unmasked by genomic loss4,5,26,27. Here, we integrate genomic characterization and genome-scale functional screening of cancer cell lines to systematically extend such studies. We identify a set of robust paralog dependencies that may provide the foundation for future target validation efforts and show that hemizygous loss of the MAGOH gene on chromosome 1p confers novel vulnerabilities on MAGOHB and IPO13, perhaps due to decreased nuclear reserve of MAGOH/MAGOHB (Supplementary Fig. 15). Insufficient MAGOH/MAGOHB dosage perturbs splicing and RNA surveillance and adds to growing evidence implicating splicing as a cancer dependency27,28,29. Therapeutic approaches to targeting MAGOH-deleted cells may involve either direct MAGOHB transcript suppression (such as through antisense/RNAi approaches), targeted MAGOHB protein degradation, or indirect suppression of MAGOH/MAGOHB activity via inhibition of IPO13. Antisense/RNAi-based approaches may be well suited to the exploitation of paralog dependencies, as they may allow for selective targeting of paralogs that show greater variability on the nucleotide level than on the protein level. Targeted protein degradation approaches have also recently proven to be a promising means to target conventionally ‘undruggable’ genes30,31,32,33, including RNA splicing factors34. In the case of IPO13 dependency, small-molecule inhibitors of other importin family members have been described, raising the possibility that IPO13 can be selectively targeted using a similar strategy35,36,37. As hemizygous loss of chromosome 1p is extremely common across multiple tumor types, these or other approaches to targeting this pathway may have future biomarker-driven therapeutic applications. More broadly, our work can be generalized to cancers with other chromosome arm deletions and underscores the power of intersecting comprehensive molecular characterization and functional genomic studies of cancer cell lines.

URLs

Broad RNAi Consortium, http://www.broadinstitute.org/rnai/public; LentiCRISPRv2 Cloning Protocol, http://genome-engineering.org/gecko/wp-content/uploads/2013/12/lentiCRISPRv2-and-lentiGuide-oligo-cloning-protocol.pdf; Lentiviral Production Protocol, http://portals.broadinstitute.org/gpp/public/resources/protocols; Bash script for expectation maximization algorithm, http://www.lagelab.org/resources; HUGO Gene Nomenclature Committee, http://www.genenames.org/cgi-bin/genefamilies; rMATS2Sashimiplot, https://github.com/Xinglab/rmats2sashimiplot; ENSEMBL Biomart, http://www.ensembl.org/biomart DepMap Portal, http://depmap.org; MassIVE, http://massive.ucsd.edu; NCBI GEO, https://www.ncbi.nlm.nih.gov/geo/; GO website, www.geneontology.org.

Methods

Cell culture

Cell line stocks used for validation experiments were obtained either from the Cancer Cell Line Encyclopedia (CCLE) repository at the Broad Institute or from M.M.’s laboratory, with original sources being either the American Type Culture Collection, the European Collection of Authenticated Cell Cultures, the Health Science Research Resources Bank, the Korean Cell Line Bank, or academic laboratories. Cell line identity was verified by either short tandem repeat profiling or Affymetrix SNP profiling. Cells were cultured in media specified by the source repository, supplemented with 100 international units ml−1 penicillin, 100 μg ml−1 streptomycin, 2 mM L-glutamine, and 100 μg ml−1 Normocin (Invivogen). Mycoplasma testing was performed in source repository before creation of frozen stocks and repeated periodically if lines were persistently maintained in culture.

Lentiviral constructs and transduction of cell lines

For overexpression experiments, ORFs were expressed from within the pLX304-Blast-V5 vector38 (Addgene no. 25890, Blasticidin resistance) using pLX304-eGFP as an overexpression negative control. Ectopic expression of untagged MAGOH was performed using either pLX304 (with stop codon introduced before V5 tag) or Gateway-compatible, hygromycin-resistant, doxycycline-inducible overexpression vector with complementary DNA expression driven from Tet-regulated cytomegalovirus promoter, created by modification of prior similar vectors39,40. MAGOH-reconstitution experiments were performed with V5-tagged MAGOH with the exception of those shown in Supplementary Fig. 4f, which were performed using an untagged construct. For shRNA experiments, constitutive shRNAs were expressed from the pLKO.1 vector41 (Addgene no. 10878, puromycin resistance) using an shRNA targeting GFP (shGFP) as a negative control. shRNA constructs were obtained from the RNAi Consortium shRNA collection (see URLs). Inducible shRNAs were cloned into a Gateway-compatible doxycycline-inducible lentiviral shRNA expression system (G418 resistance), as described39. Single guide RNAs (sgRNAs) were cloned into lentiCRISPRv2 (Addgene no. 52961) as described (see URLs). shRNA and sgRNA target sequences are listed in Supplementary Table 14.

Lentivirus was produced in HEK293T cells as per the ‘low throughput viral production’ protocol on the RNAi Consortium Portal (see URLs). Cells were transduced with lentivirus by spin-infection (2250 rpm for 30 minutes) in the presence of 8 μg ml−1 polybrene, followed by antibiotic selection beginning 24 hours thereafter. Following completion of antibiotic selection, cells were seeded for downstream assays as described.

Gene knockdown or knockout was confirmed by quantitative PCR (qPCR) with reverse transcription for MAGOH/B as the paralogs cannot be distinguished on western blotting. Gene overexpression was confirmed by western blotting.

Generation of MAGOH-knockout cell lines

For generation of MAGOH-knockout cell lines, Heya8 cells were transiently transfected with either a non-targeting guide (sgGFP) or MAGOH sgRNA expressed from within plentiCRISPRv2. Following 72 hours of selection with puromycin, the bulk resistant population was sorted at single-cell density into 96-well plates using a MoFlo Astrios Cell Sorter (Beckman Coulter). Clonal cell lines were expanded and assessed for MAGOH knockout by qPCR.

Western blotting

Whole-cell extracts for immunoblotting were prepared by incubating cells on ice in RIPA lysis buffer (Thermo Fisher Scientific) plus protease inhibitors (cOmplete, Mini, EDTA-free, Roche) for 20 minutes. Following centrifugation (>16,000g for 15 minutes), protein lysates were quantitated using the Pierce BCA Protein Assay Kit (Thermo Fisher Scientific). Lysates were separated by SDS–PAGE and transferred to nitrocellulose membranes using the iBlot2 system (Life Technologies). Two-color immunoblotting was performed using the LI-COR platform (LI-COR Biosciences) with IRDye 800CW and IRDye680RD secondary antibodies (mouse, IRDye 680LT Donkey anti-Mouse IgG (925-68022) used at 1:10,000; rabbit, IRDye 800CW Goat anti-Rabbit IgG (926-32211) used at 1:10,000). Imaging was performed on an Odyssey CLx Infrared Imaging System. Loading control and experimental protein were probed on the same membrane in all cases. For clarity, loading control is cropped and shown below experimental condition in all panels regardless of relative molecular weights of the two proteins.

Primary antibodies and dilutions used were as follows. HNRNPDL: Thermo Fisher Scientific (PA5-35896), 1:2,000. Vinculin: Sigma-Aldrich (V9264), 1:4,000. MAGOH: Santa Cruz Biotechnology (sc-271365), 1:250 or Abcam (ab38768) (1:500). Actin: Cell Signaling Technology (D6A8, monoclonal antibody no. 8457), 1:1,000 or Cell Signaling Technology (8H10D10, monoclonal antibody no. 3700), 1:2,000. PSPC1: Santa Cruz Biotechnology (sc-374181), 1:100. HNRNPH1: Bethyl Laboratories (A300-511A), 1:1,000.

qPCR

RNA isolation was performed using the RNeasy Mini Kit (Qiagen). cDNA preparation was performed using Superscript III cDNA synthesis kit (Thermo Fisher Scientific). PCR reactions were prepared using TaqMan Gene Expression Mastermix (Thermo Fisher Scientific) and PrimeTime qPCR probe-based assays (IDT) using HPRT1 as an internal normalization control. TaqMan assay identity catalog numbers are as provided in Supplementary Table 14. Real-time qPCR was performed on a QuantStudio 6 Flex Real-Time PCR System (Applied Biosystems) and results were quantitated using the ΔΔCt method. For quantification of SC35 NMD substrates, qPCR was performed using a Power SYBR Green PCR Master Mix (Thermo Fisher Scientific) and primer sequences for SC1.6 and SC1.7 as described8 using β-actin as an internal normalization control.

Cell viability and colony formation assays

For cell viability assays, cells were seeded in 96-well plates in 100 μl medium after lentiviral transduction and completion of antibiotic selection. For inducible hairpin experiments, equal numbers of cells were seeded for both ‘−Dox’ and ‘+Dox’ conditions and medium was supplemented with 100 ng ml−1 doxycycline in the ‘+Dox’ condition. Cells were seeded in 96-well plate format (range 1,000–8,000 per well, depending on the cell line). At 7−10 days after cell seeding, cell viability was assessed using the Cell Titer-Glo luminescent cell viability assay (Promega) using either an EnVision Multilabel Reader (PerkinElmer) or a Spectramax M5 plate reader (Molecular Devices).

For colony formation assays, cells were seeded in 12-well plates at a density of 2,000–8,000 cells per well after lentiviral transduction and completion of antibiotic selection. Cells were cultured for 10–20 days. Colonies were fixed in 4% formaldehyde and stained with 0.5% crystal violet. Cells were photographed using a Leica microscope. Colonies were then destained using 10% acetic acid and crystal violet staining was quantified by measuring absorbance at 595 nm using a Spectramax M5 instrument (Molecular Devices).

Immunofluorescence

For immunofluorescence assays, 200,000 cells were plated on SecureSlip silicone supported coverglasses (Sigma Aldrich) in 6-well plates that had been precoated for 60 minutes with 0.01 mg ml−1 human fibronectin (Calbiochem) in PBS. The following day, cells were fixed in 4% paraformaldehyde diluted in PBS for 15 minutes at room temperature. Cells were permeabilized with 0.2% Triton X-100 in PBS for 10 minutes. Blocking was performed in 2.5% normal goat serum blocking solution (Vector Laboratories). Cells were incubated in primary MAGOH/MAGOHB antibody (Abcam, ab38768, rabbit, 1:200) for 1 hour at room temperature. A Cy-3 conjugated anti-rabbit secondary (Abcam, ab97075, 1:200) and DAPI (Life Technologies, 1:1,000) were then used for 1 hour at room temperature. Cells were mounted and imaged on an Axio Observer fluorescent microscope (Zeiss) using AxioVision software (Zeiss). Nuclear/cytoplasmic ratio was quantified by Image J. Nuclear outlines were determined based on DAPI signal. Cytoplasmic signal was defined as signal in the whole cell minus signal within the nuclear area.

Mouse experiments

Studies involving mice were approved by the MIT Committee on Animal Care. Mouse strain used was NCR-nude (Charles River Laboratories), female, 4–5 weeks of age. For inducible shRNA xenograft experiments, NCR-nude mice were subcutaneously injected into bilateral flanks with 3.5 × 106 H1437 cells transduced with lentivirus expressing a doxycycline-inducible shRNA against MAGOHB (MAGOHB-sh2). Cells were resuspended in 100 μl 30% matrigel in PBS. At 7 days post injection, after tumor implantation, mice were randomized to match tumor size between two groups, and one group was started on a diet containing 200 mg doxycycline per kg (Bio-Serv). Tumor volumes were measured twice weekly using a digital caliper.

For TPNC experiments, xenografts were produced as above using 2.5 × 106 cells per tumor. TPNCs were prepared by complexing siRNA with tandem peptide at a 1:20 (LyP1) or 1:15 (iRGD) molar ratio (siRNA/peptide) in water. For intratumoral injections, 0.2 nmol siRNA was injected every 1–2 days in 20 μl TPNC solution. Myr-TP-LyP1 (myr-GWTLNSAGYLLGKINLKALAALAKKILGGGG-K(5TAMRA)-CGNKRTRGC (C-C bridge)) and Palm-TP-iRGD (palm-GWTLNSAGYLLGKINLKALAALAKKILGGGG-CRGDKGPDC (C-C bridge)) were synthesized by CPC Scientific. siRNAs were purchased from GE Dharmacon. MAGOHB siRNA target sequence was as in MAGOHB-sh2; IPO13 siRNA target sequence was as in IPO13-sh3 (see Supplementary Table 14 for sequences). Surface expression of p32 or NRP-1 was evaluated by flow cytometry using anti-p32 antibody at 1:1,000 dilution (AB2991, EMD Millipore), anti-alpha V beta 3-PE conjugated antibody at 1:100 dilution (FAB3050P, R&D Systems), anti-Neuropilin1 antibody at 1:1,000 dilution (AB9600, Millipore), or matched isotype control, and visualized with AlexaFluor 647-labeled secondary antibody (p32 and NRP-1) or conjugated PE (αvβ3).

Immunostaining was performed as previously described42. Briefly, six tumors from each condition (randomly selected) were extracted and fixed in 10% formalin overnight and stored at 4 °C before being embedded in paraffin, sectioned, and stained. Tumor sections were stained with primary antibody to Cleaved Caspase-3 (Asp175) (5A1E) Rabbit monoclonal antibody no. 9664 (Cell Signaling Technology, 1:1,000) and HRP-conjugated anti-rabbit secondary antibody (RABBIT-ON-RODENT HRP-POLYMER from BioCare Medical, cat. no. RMR622) on a ThermoScientific IHC Autostainer 360 and visualized with DAB chromogen. For cleaved caspase-3 quantification, fraction of cross-sectional area staining positive for cleaved caspase-3 was quantified in the six randomly selected tumors from each group that were stained, using ImageJ.

Immunoprecipitation and mass spectrometry

Immunoprecipitation

For immunoprecipitation experiments, 293 T cells were either untransduced (control) or transduced with pLX304-Blast-V5 (Addgene no. 25890) expressing MAGOH-V5 or MAGOHB-V5. Following antibiotic selection to derive stably transduced cell populations, immunoprecipitation was carried out using the Pierce Class Magnetic IP Kit (no. 88804) and anti-V5 magnetic beads (MBLI no. M167-11) using a starting amount of 2 mg protein and 50 μl beads. Lysis buffer was pH 7.4, 0.025 M Tris, 0.15 M NaCl, 0.001 M EDTA, 1% NP40, and 5% glycerol. Immunoprecipitation was carried out overnight at 4 °C. Samples were washed twice in sample buffer, followed by twice in PBS, before mass spectrometry. Efficient immunoprecipitation was confirmed by western blotting before proceeding with mass spectrometry.

Protein digestion and labeling with tandem mass tag (TMT) isobaric mass tags

The beads from immunopurification were washed once with IP lysis buffer, then three times with PBS. The four different lysates of each replicate were resuspended in 90 μl digestion buffer (2 M urea, 50 mM Tris HCl) and then 2 μg sequencing grade trypsin was added, followed by 1 hour of shaking at 700 rpm. The supernatant was removed and placed in a fresh tube. The beads were then washed twice with 50 μl digestion buffer and combined with the supernatant. The combined supernatants were reduced (2 μl 500 mM dithiothreitol, 30 minutes, room temperature) and alkylated (4 μl 500 mM iodoacetamide, 45 minutes, dark), and a longer overnight digestion was performed: 2 μg (4 μl) trypsin, shaken overnight. The samples were then quenched with 20 μl 10% formic acid and desalted on 10 mg Oasis cartridges.

Desalted peptides were labeled with TMT6 reagents lot QD218427 (Thermo Fisher Scientific) according to the following: 126, NoBaitCntlRep1; 127, NoBaitCntlRep2; 128, MAGOHRep1; 129, MAGOHRep2; 130, MAGOHBRep1; 131, MAGOHBRep2. Peptides were dissolved in 25 μl fresh 100 mM HEPES buffer. The labeling reagent was resuspended in 42 μl acetonitrile and 10 μl added to each sample as described below. After 1 hour incubation the reaction was stopped with 8 μl 5 mM hydroxylamine.

Protein identification with a nanoLC–MS system

Reconstituted peptides were separated on an online nanoflow EASY-nLC 1000 UHPLC system (Thermo Fisher Scientific) and analyzed on a benchtop Orbitrap Q Exactive Plus mass spectrometer (Thermo Fisher Scientific). The peptide samples were injected onto a capillary column (Picofrit with 10 μm tip opening/75 μm diameter, New Objective, PF360-75-10-N-5) packed in-house with 20 cm C18 silica material (1.9 μm ReproSil-Pur C18-AQ medium, Dr. Maisch GmbH) and heated to 50 °C in column heater sleeves (Phoenix-ST) to reduce backpressure during UHPLC separation. Injected peptides were separated at a flow rate of 200 nl min−1 with a linear 230 min gradient from 100% solvent A (3% acetonitrile, 0.1% formic acid) to 30% solvent B (90% acetonitrile, 0.1% formic acid), followed by a linear 9 min gradient from 30% solvent B to 60% solvent B and a 1 min ramp to 90% B. Each sample was run for 260 min, including sample loading and column equilibration times. The Q Exactive instrument was operated in the data-dependent mode acquiring higher-energy collisional dissociation (HCD) tandem mass spectrometry (MS/MS) scans (R = 17,500) after each MS1 scan (R = 70,000) on the 12 top most abundant ions using an MS1 ion target of 3 × 106 ions and an MS2 target of 5 × 104 ions. The maximum ion time utilized for the MS/MS scans was 120 ms; the HCD-normalized collision energy was set to 27; the dynamic exclusion time was set to 20 s; and the peptide match and isotope exclusion functions were enabled.

Database search and data processing

All mass spectra were processed using the Spectrum Mill software package v6.0 prerelease (Agilent Technologies), which includes modules developed by us for TMT6-based quantification. For peptide identification MS/MS spectra were searched against the human Uniprot database to which a set of common laboratory contaminant proteins was appended. Search parameters included ESI-QEXACTIVE-HCD scoring parameters, trypsin enzyme specificity with a maximum of two missed cleavages, 40% minimum matched peak intensity, ± 20 ppm precursor mass tolerance, ± 20 ppm product mass tolerance, and carbamidomethylation of cysteines and TMT6 labeling of lysines and peptide N termini as fixed modifications. Allowed variable modifications were oxidation of methionine, N-terminal acetylation, pyroglutamic acid (N-termQ), deamidated (N), pyro carbamidomethyl Cys (N-termC), with a precursor MH+ shift range of −18–64 Da. Identities interpreted for individual spectra were automatically designated as valid by optimizing score and delta rank1-rank2 score thresholds separately for each precursor charge state in each liquid chromatography-MS/MS while allowing a maximum target-decoy-based false-discovery rate (FDR) of 1.0% at the spectrum level.

Analysis

The expectation maximization algorithm43 was applied to the results of the peptide report (the in-house written bash script is available on the Lage Lab Resources Site (see URLs) and the peptide report can be found in the supplementary material). The list of most likely observed proteins was generated for each channel of the mass spectrometry experiment based on Swiss-Prot and TrEMBLE databases of protein sequences44. Next, ratios of intensities between channels were calculated and median normalized. Resulting data were analyzed and visualized using R. Statistical analyses were performed via moderated t-test from R package limma45 to estimate P values for each protein and the FDR corrections were applied to account for multiple hypotheses testing. Plots were created using in-house written R scripts and gplot246. RNA-binding and S ribosomal protein families were taken from the HUGO Gene Nomenclature Committee (see URLs). Proteins previously reported to be EJC/NMD complex members7 were annotated as such.

Analysis of cancer cell lines

Copy number analysis

Details regarding arm-level copy number calling are as described by Taylor and colleagues47. Briefly, to determine arm-level events (that is, 1p or 12p deletion status) in TCGA and CCLE samples, the ABSOLUTE algorithm48 was used to determine the likeliest ploidy and absolute total copy number of each genomic segment. Segments were called as amplified, deleted, or copy neutral based on copy number with reference to integer-rounded ploidy. Arm- or chromosome-level amplification/deletion status was then determined from segment data as described by Taylor and colleagues47. CCLE cell lines were fit to clusters within their corresponding TCGA tumor type to generate cell line–specific, arm-specific calls49. For CCLE data, ABSOLUTE algorithm was run on the CCLE Affymetrix SNP6.0 array data as previously reported50. For analysis of arm level and focal copy number event in TCGA data sets, 1p deletion status was determined as described above. Hemizygous MAGOH deletion was defined as the loss of one or more copies of the MAGOH gene (for example, ploidy −MAGOH copy number ≥1) using rounded tumor ploidy and MAOGH copy number calculated from the ABSOLUTE algorithm.

Genome-scale shRNA and CRISPR screening data analysis

Genome-wide shRNA screening on 501 cell lines was performed as described5. The DEMETER method, which summarizes multiple shRNAs targeting a gene into a gene-level dependency score, was used to quantify gene dependency in 17,098 unique genes5. The differential dependency set of 6,305 genes, and the 6σ dependency set of 769 genes, as defined previously5, were used for all downstream analyses. These sets represent the genes with the most significant differential dependency across cell lines and were selected based on the following criteria: (1) for each gene, there is at least one cell line with a dependency score that is two (differential set) or six (6σ set) standard deviations from the mean of scores from all genes and all cell lines, and (2) expression of the gene in the most dependent cell line is above –2 log2 reads per kilobase million.

To identify synthetic lethal relationships linked to loss of a paralog, a query was performed for each of 17,670 genes using EnsemblCompara51 via the R interface to BioMart52 to obtain a list of paralogs and their pairwise sequence identity. Pearson correlations of RNA sequencing (RNA-seq) expression values between genes in each paralog pair indicate that co-expression is limited until DNA sequence identity exceeds 25% (Supplementary Fig. 1). To increase the likelihood that the gene pairs function redundantly, pairs with less than 25% sequence identity were removed. An additional 35 genes were removed for having duplicate DEMETER scores (caused by non-unique hairpins), resulting in 3,403 genes in the DEMETER dataset with at least one paralog. Differential dependency for each of these genes was tested by grouping cell lines based on loss of the gene’s paralogs and performing a two-class comparison of the DEMETER scores using empirical Bayes moderated t-statistics implemented by the R package limma45. The binary classification of paralog loss used to group the cell lines was determined by a logic combination of the RNA-seq gene expression, protein abundance (RPPA), relative copy number, methylation fraction (RRBS), and mutation status (whole-exome sequencing, whole-genome sequencing, RNA-seq). The gene expression, RPPA, copy number, and RRBS datasets are z-scored per gene so loss of a gene is defined as having a 6σ decrease in gene expression or RPPA, or no gene expression at all (less than −3 log2 transcripts per million), 2σ decrease in copy number, 6σ increase in RRBS, or a deleterious mutation (predicted by frameshift indel or nonsense single-nucleotide variant). Genes are labeled ‘symmetric’ if loss of either gene in a pair is significantly associated with a selective dependency on its respective paralog gene with FDR <0.05.

The synthetic lethal paralog analysis was repeated using the Achilles CRISPR dataset15 consisting of 341 whole genome CRISPR/cas9 knockout screens corrected for copy number effects (one cell line, PK59, was removed from the prior set of 342 as it failed fingerprinting). Genes with variance in essentiality below 0.01 across the 341 cell lines were removed to reduce false positives, leaving 6,535 genes for paralog dependency analysis. The definition of gene loss as well as method for determining significance of differential dependency among each paralog pair is identical to the analysis using DEMETER data.

For analysis of gene dependencies correlated with MAGOH deletion (Fig. 1), the Probability Analysis by Ranked Information Score (PARIS) algorithm was run as a GenePattern module (see URLs). MAGOH-deletion status was determined by the ABSOLUTE algorithm48 as described above. Cell lines for which MAGOH absolute copy number was less than the cell line’s ploidy were considered deleted. Based on available ABSOLUTE calls, 191 lines were considered deleted and 807 lines were considered non-deleted. In total, both absolute copy number and filtered DEMETER gene-score data were available for 445 overlap cell lines.

For geneset enrichment analysis on gene dependencies correlated with MAGOH deletion, the PARIS algorithm was first run using continuous copy number data on CCLE cell lines generated using SNP arrays, as previously reported50, to generate a ranked list of gene dependencies correlated with MAGOH copy number. RNMI metric score for each gene was then used as input for preranked geneset enrichment analysis53, which was run as a GenePattern module using default parameters against the following genesets: REACTOME_NONSENSE_MEDIATED_DECAY_ENHANCED_BY_THE_EXON_JUNCTION_COMPLEX and GO_NUCLEAR_TRANSCRIBED_MRNA_CATABOLIC_PROCESS _NONSENSE_MEDIATED_DECAY

For analysis of correlated gene dependency profiles, Pearson correlations of DEMETER gene dependency scores were computed across cell lines (N = 501) for all pairs of genes that share overlap in cell lines (N = 6,300). Correlation coefficients were converted to standard scores across the full correlation matrix before evaluating the specific MAGOH and MAGOHB correlation profiles.

RNA-seq analysis

RNA-seq libraries were prepared using the Illumina strand-specific mRNA-seq Library Prep Kit (Illumina) followed by paired-end 75 bp sequencing on a NextSeq (>400 M reads per run; >33 M reads per sample). Transcript levels were quantified with kallisto19 (version 0.43.0, options: –rf-stranded, –b 30) using the GRCh38 transcriptome (ENSEMBL cDNA, release 87)54. Differential expression analysis was performed with sleuth55 (version 0.28.1). Differential expression was quantified based on the beta value, a bias estimator used by kallisto19 analogous to fold-change. Significant upregulation cut-offs were b >1, q <0.01; downregulation b < –1. Gene Ontology56 term enrichment analysis was carried out using PANTHER57; Overrepresentation Test (release 20160715) using the Gene Ontology database (release 2017-01-26), accessible via the Gene Ontology website (see URLs, last accessed 2017-01-31). Transcript biotypes were obtained from the ENSEMBL database (release 87)54. For analysis of differential alternative splicing events, reads were aligned with HiSat2 (v2.0.4, --rna-strandness RF option)58 using the prebuilt index Ensembl GRCh38 (genome_tran), and splicing events were quantified using rMATS v3.2.559. For increased stringency, rMATS output was filtered based on read support (sum of inclusion/exclusion reads ≥10 in both samples), FDR (<0.05), and inclusion level difference (|ILD| >0.1). Sashimi plots were plotted using rMats2Sashimiplot (see URLs) in grouping mode.

For estimation of protein-level effects from RNA-seq data, peptide sequences for each transcript were obtained using ENSEMBL biomart (see URLs), accessed through the R package biomaRt (version 2.26.1)52,60. For each gene, expected peptide expression was then estimated by summing over transcript per million values for all transcripts that encode peptides of the same length.

REVEALER analysis

To identify associations between MAGOH/B dependency and copy number/expression features of EJC/splicing-related genes, MAGOH or MAGOHB dependency scores across screened cell lines5 were correlated with copy number or expression features50 in EJC/splicing-related genes using the previously described method based on estimating the information coefficient61. For radial plots, the top-scoring 50 features (for copy number) or top-scoring 16 features (for expression) were plotted. A list of EJC/splicing-related genes used for this analysis was compiled by combining EJC/NMD genes in MSigDB (Reactome geneset no. M1067) and splicing factors described as being implicated in oncogenesis62.

Statistics

No statistical methods were used to predetermine sample size. Investigators were not blinded to allocation for experiments. Statistical tests applied, P values, and sample size are as listed in figure captions. For in vitro experiments, number of biologically independent replicates is as listed in figure captions. When two-sample Student’s t-tests were applied to assess significance of experimental data, unequal variance parameter was used and P values were calculated using Microsoft Excel (function t.test; heteroscedastic). Other statistical tests were performed using R (v. 3.4.1) or GraphPad Prism 7 software.

Reporting Summary

Further information on experimental design is available in the Nature Research Reporting Summary linked to this article.

Data availability

The original mass spectra may be downloaded from MassIVE (see URLs) using the identifier MSV000082292. RNA-seq data can be accessed at NCBI Gene Expression Omnibus (GSE113848) (see URLs). Code for analysis of IP-MS data is deposited in the Lage Lab website (see URLs). The authors declare that other data supporting the findings of this study are available within the paper and its supplementary information files. Other source data are available from the corresponding author on reasonable request.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

References

  1. 1.

    Diss, G. et al. Gene duplication can impart fragility, not robustness, in the yeast protein interaction network. Science 355, 630–634 (2017).

  2. 2.

    Muller, F. L., Aquilanti, E. A. & DePinho, R. A. Collateral lethality: a new therapeutic strategy in oncology. Trends Cancer 1, 161–173 (2015).

  3. 3.

    Frei, E. Gene deletion: a new target for cancer chemotherapy. Lancet 342, 662–664 (1993).

  4. 4.

    McDonald, E. R. 3rd. et al. Project DRIVE: A compendium of cancer dependencies and synthetic lethal relationships uncovered by large-scale, deep RNAi screening. Cell 170, 577–592.e10 (2017).

  5. 5.

    Tsherniak, A. et al. Defining a cancer dependency map. Cell 170, 564–576.e16 (2017).

  6. 6.

    Boehm, V. & Gehring, N. H. Exon junction complexes: supervising the gene expression assembly line. Trends Genet. 32, 724–735 (2016).

  7. 7.

    Chang, Y.-F., Imam, J. S. & Wilkinson, M. F. The nonsense-mediated decay RNA surveillance pathway. Annu. Rev. Biochem. 76, 51–74 (2007).

  8. 8.

    Singh, K. K., Wachsmuth, L., Kulozik, A. E. & Gehring, N. H. Two mammalian MAGOH genes contribute to exon junction complex composition and nonsense-mediated decay. RNA Biol. 10, 1291–1298 (2013).

  9. 9.

    Mingot, J.-M., Kostka, S., Kraft, R., Hartmann, E. & Görlich, D. Importin 13: a novel mediator of nuclear import and export. EMBO J. 20, 3685–3694 (2001).

  10. 10.

    Helming, K. C. et al. ARID1B is a specific vulnerability in ARID1A-mutant cancers. Nat. Med. 20, 251–254 (2014).

  11. 11.

    Hoffman, G. R. et al. Functional epigenetics approach identifies BRM/SMARCA2 as a critical synthetic lethal target in BRG1-deficient cancers. Proc. Natl Acad. Sci. USA 111, 3128–3133 (2014).

  12. 12.

    D'Antonio, M. et al. Recessive cancer genes engage in negative genetic interactions with their functional paralogs. Cell Rep. 5, 1519–1526 (2013).

  13. 13.

    Dey, P. et al. Genomic deletion of malic enzyme 2 confers collateral lethality in pancreatic cancer. Nature 542, 119–123 (2017).

  14. 14.

    Cowley, G. S. et al. Parallel genome-scale loss of function screens in 216 cancer cell lines for the identification of context-specific genetic dependencies. Sci. Data 1, 140035 (2014).

  15. 15.

    Meyers, R. Computational correction of copy-number effect improves specificity of CRISPR-Cas9 essentiality screens in cancer cells. Nat. Genet. 49, 1779–1784 (2017).

  16. 16.

    Veitia, R. A. Gene duplicates: agents of robustness or fragility? Trends Genet. 33, 377–379 (2017).

  17. 17.

    Bono, F. & Gehring, N. H. Assembly, disassembly and recycling. RNA Biol. 8, 24–29 (2011).

  18. 18.

    Caron, H. et al. Allelic loss of chromosome 1p as a predictor of unfavorable outcome in patients with neuroblastoma. N. Engl. J. Med. 334, 225–230 (1996).

  19. 19.

    Bray, N. L., Pimentel, H., Melsted, P. & Pachter, L. Near-optimal probabilistic RNA-seq quantification. Nat. Biotechnol. 34, 525–527 (2016).

  20. 20.

    Ni, J. Z. et al. Ultraconserved elements are associated with homeostatic control of splicing regulators by alternative splicing and nonsense-mediated decay. Genes Dev. 21, 708–718 (2007).

  21. 21.

    Lareau, L. F., Inada, M., Green, R. E., Wengrod, J. C. & Brenner, S. E. Unproductive splicing of SR genes associated with highly conserved and ultraconserved DNA elements. Nature 446, 926–929 (2007).

  22. 22.

    Lau, C.-K., Diem, M. D., Dreyfuss, G. & Van Duyne, G. D. Structure of the Y14-Magoh core of the exon junction complex. Curr. Biol. 13, 933–941 (2003).

  23. 23.

    Ren, Y. et al. Targeted tumor-penetrating siRNA nanocomplexes for credentialing the ovarian cancer oncogene ID4. Sci. Transl. Med. 4, 147ra112 (2012).

  24. 24.

    Beroukhim, R. et al. The landscape of somatic copy-number alteration across human cancers. Nature 463, 899–905 (2010).

  25. 25.

    Baudis, M. Genomic imbalances in 5918 malignant epithelial tumors: an explorative meta-analysis of chromosomal CGH data. BMC Cancer 7, 226 (2007).

  26. 26.

    Nijhawan, D. et al. Cancer vulnerabilities unveiled by genomic loss. Cell 150, 842–854 (2012).

  27. 27.

    Paolella, B. R. et al. Copy-number and gene dependency analysis reveals partial copy loss of wild-type SF3B1 as a novel cancer vulnerability. eLife 6, e23268 (2017).

  28. 28.

    Lee, S. C.-W. et al. Modulation of splicing catalysis for therapeutic targeting of leukemia with mutations in genes encoding spliceosomal proteins. Nat. Med. 22, 672–678 (2016).

  29. 29.

    Obeng, E. A. et al. Physiologic expression of Sf3b1(K700E) causes impaired erythropoiesis, aberrant splicing, and sensitivity to therapeutic spliceosome modulation. Cancer Cell 30, 404–417 (2016).

  30. 30.

    Lu, G. et al. The myeloma drug lenalidomide promotes the cereblon-dependent destruction of Ikaros proteins. Science 343, 305–309 (2014).

  31. 31.

    Krönke, J. et al. Lenalidomide causes selective degradation of IKZF1 and IKZF3 in multiple myeloma cells. Science 343, 301–305 (2014).

  32. 32.

    Hwang, S.-Y. et al. Direct targeting of β-catenin by a small molecule stimulates proteasomal degradation and suppresses oncogenic Wnt/β-catenin signaling. Cell Rep. 16, 28–36 (2016).

  33. 33.

    Kerres, N. et al. Chemically induced degradation of the oncogenic transcription factor BCL6. Cell Rep. 20, 2860–2875 (2017).

  34. 34.

    Han, T. et al. Anticancer sulfonamides target splicing by inducing RBM39 degradation via recruitment to DCAF15. Science 356, eaal3755 (2017).

  35. 35.

    Soderholm, J. F. et al. Importazole, a small molecule inhibitor of the transport receptor importin-β. ACS Chem. Biol. 6, 700–708 (2011).

  36. 36.

    Hintersteiner, M. et al. Identification of a small molecule inhibitor of importin beta mediated nuclear import by confocal on-bead screening of tagged one-bead one-compound libraries. ACS Chem. Biol. 5, 967–979 (2010).

  37. 37.

    Wagstaff, K. M., Sivakumaran, H., Heaton, S. M., Harrich, D. & Jans, D. A. Ivermectin is a specific inhibitor of importin α/β-mediated nuclear import able to inhibit replication of HIV-1 and dengue virus. Biochem. J. 443, 851–856 (2012).

  38. 38.

    Yang, X. et al. A public genome-scale lentiviral expression library of human ORFs. Nat. Methods 8, 659–661 (2011).

  39. 39.

    Lippa, M. S. et al. Expression of anti-apoptotic factors modulates Apo2L/TRAIL resistance in colon carcinoma cells. Apoptosis 12, 1465–1478 (2007).

  40. 40.

    Brown, C. Y. et al. Robust, reversible gene knockdown using a single lentiviral short hairpin RNA vector. Hum. Gene Ther. 21, 1005–1017 (2010).

  41. 41.

    Root, D. E., Hacohen, N., Hahn, W. C., Lander, E. S. & Sabatini, D. M. Genome-scale loss-of-function screening with a lentiviral RNAi library. Nat. Methods 3, 715–719 (2006).

  42. 42.

    Kwon, E. J., Dudani, J. S. & Bhatia, S. N. Ultrasensitive tumour-penetrating nanosensors of protease activity. Nat. Biomed. Eng. 1, 0054 (2017).

  43. 43.

    McLachlan, G. J. & Krishnan, T. The EM Algorithm and Extensions 2nd edn (Wiley-Interscience, Hoboken, NJ, 2008).

  44. 44.

    The UniProt Consortium. UniProt: the universal protein knowledgebase. Nucleic Acids Res. 45, D158–D169 (2017)..

  45. 45.

    Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).

  46. 46.

    Wickham, H. ggplot2 https://doi.org/10.1007/978-0-387-98141-3 (Springer, New York, 2009).

  47. 47.

    Taylor, A. M. et al. Genomic and functional approaches to understanding cancer aneuploidy. Cancer Cell 33, 676–689 (2018).

  48. 48.

    Carter, S. L. et al. Absolute quantification of somatic DNA alterations in human cancer. Nat. Biotechnol. 30, 413–421 (2012).

  49. 49.

    Pedregosa, F. et al. Scikit-learn: machine learning in Python. JMLR 12, 2825–2830 (2011).

  50. 50.

    Barretina, J. et al. The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature 483, 603–607 (2012).

  51. 51.

    Vilella, A. J. et al. EnsemblCompara GeneTrees: complete, duplication-aware phylogenetic trees in vertebrates. Genome Res. 19, 327–335 (2008).

  52. 52.

    Durinck, S., Spellman, P. T., Birney, E. & Huber, W. Mapping identifiers for the integration of genomic datasets with the R/Bioconductor package biomaRt. Nat. Protoc. 4, 1184–1191 (2009).

  53. 53.

    Subramanian, A. et al. Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).

  54. 54.

    Aken, B. L. et al. Ensembl 2017. Nucleic Acids Res. 45, D635–D642 (2017).

  55. 55.

    Pimentel, H. J., Bray, N., Puente, S., Melsted, P. & Pachter, L. Differential analysis of RNA-seq incorporating quantification uncertainty. Nat. Methods 14, 687–690 (2016).

  56. 56.

    The Gene Ontology Consortium. Gene Ontology Consortium: going forward. Nucleic Acids Res. 43, D1049–D1056 (2015).

  57. 57.

    Mi, H., Muruganujan, A., Casagrande, J. T. & Thomas, P. D. Large-scale gene function analysis with the PANTHER classification system. Nat. Protoc. 8, 1551–1566 (2013).

  58. 58.

    Kim, D., Langmead, B. & Salzberg, S. L. HISAT: a fast spliced aligner with low memory requirements. Nat. Methods 12, 357–360 (2015).

  59. 59.

    Shen, S. et al. rMATS: robust and flexible detection of differential alternative splicing from replicate RNA-seq data. Proc. Natl Acad. Sci. USA 111, E5593–E5601 (2014).

  60. 60.

    Durinck, S. et al. BioMart and Bioconductor: a powerful link between biological databases and microarray data analysis. Bioinformatics 21, 3439–3440 (2005).

  61. 61.

    Kim, J. W. et al. Characterizing genomic alterations in cancer by complementary functional associations. Nat. Biotechnol. 34, 539–546 (2016).

  62. 62.

    Sveen, A., Kilpinen, S., Ruusulehto, A., Lothe, R. A., & Skotheim, R. I. Aberrant RNA splicing in cancer; expression changes and driver mutations of splicing factor genes. Oncogene 35, 2413–2427 (2015).

Download references

Acknowledgements

S.R.V. was supported by a Young Investigator Award from the American Society of Clinical Oncology. This work was supported by a National Cancer Institute grant 1R35CA197568 and an American Cancer Society Research Professorship to M.M. P.T. was supported by NIH grants U01CA217885 and R01HG009285. W.C.H. was supported by U01CA176058. C.G.B. and S.N.B. were supported by a Koch Institute Support Grant (P30-CA14051) from the National Cancer Institute (Swanson Biotechnology Center) and a Core Center Grant (P30-ES002109) from the National Institute of Environmental Health Sciences, and the Ludwig Center for Molecular Oncology. C.G.B. was supported by the National Science Foundation Graduate Research Fellowship Program. S.N.B. is a Howard Hughes Medical Institute Investigator. P.S.C. was supported by an NIH Pathway to Independence Award (K99 CA208028). E.M. would like to thank H. Horn for help with the expectation maximization algorithm. The authors thank the Koch Institute Swanson Biotechnology Center for technical support, specifically K. Cormier in the Hope Babette Tang Histology Facility.

Author information

Author notes

  1. These authors contributed equally: Marina F. Nogueira, Colin G. Buss.

Affiliations

  1. Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA

    • Srinivas R. Viswanathan
    • , Marina F. Nogueira
    • , Ashton C. Berger
    • , Peter S. Choi
    • , Alison M. Taylor
    • , Chandra Sekhar Pedamallu
    • , William C. Hahn
    •  & Matthew Meyerson
  2. Broad Institute of MIT and Harvard, Cambridge, MA, USA

    • Srinivas R. Viswanathan
    • , Marina F. Nogueira
    • , John M. Krill-Burger
    • , Edyta Malolepsza
    • , Ashton C. Berger
    • , Peter S. Choi
    • , Juliann Shih
    • , Alison M. Taylor
    • , Benjamin Tanenbaum
    • , Andrew D. Cherniack
    • , Pablo Tamayo
    • , Craig A. Strathdee
    • , Kasper Lage
    • , Steven A. Carr
    • , Monica Schenone
    • , Sangeeta N. Bhatia
    • , Francisca Vazquez
    • , Aviad Tsherniak
    • , William C. Hahn
    •  & Matthew Meyerson
  3. Harvard Medical School, Boston, MA, USA

    • Srinivas R. Viswanathan
    • , Peter S. Choi
    • , Alison M. Taylor
    • , Sangeeta N. Bhatia
    • , William C. Hahn
    •  & Matthew Meyerson
  4. Harvard-MIT Department of Health Sciences and Technology, Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Boston, MA, USA

    • Colin G. Buss
    •  & Sangeeta N. Bhatia
  5. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA

    • Colin G. Buss
    •  & Sangeeta N. Bhatia
  6. Chemical Biology and Therapeutics Science Program, Broad Institute of Harvard and MIT, Cambridge, MA, USA

    • Mathias J. Wawer
  7. Department of Surgery, Massachusetts General Hospital, Boston, MA, USA

    • Edyta Malolepsza
    •  & Kasper Lage
  8. UCSD Moores Cancer Center and Department of Medicine, University of California, San Diego, La Jolla, CA, USA

    • Pablo Tamayo
  9. Howard Hughes Medical Institute, Chevy Chase, MD, USA

    • Sangeeta N. Bhatia
  10. Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA

    • Sangeeta N. Bhatia
  11. Department of Medicine, Brigham and Women’s Hospital, Boston, MA, USA

    • Sangeeta N. Bhatia

Authors

  1. Search for Srinivas R. Viswanathan in:

  2. Search for Marina F. Nogueira in:

  3. Search for Colin G. Buss in:

  4. Search for John M. Krill-Burger in:

  5. Search for Mathias J. Wawer in:

  6. Search for Edyta Malolepsza in:

  7. Search for Ashton C. Berger in:

  8. Search for Peter S. Choi in:

  9. Search for Juliann Shih in:

  10. Search for Alison M. Taylor in:

  11. Search for Benjamin Tanenbaum in:

  12. Search for Chandra Sekhar Pedamallu in:

  13. Search for Andrew D. Cherniack in:

  14. Search for Pablo Tamayo in:

  15. Search for Craig A. Strathdee in:

  16. Search for Kasper Lage in:

  17. Search for Steven A. Carr in:

  18. Search for Monica Schenone in:

  19. Search for Sangeeta N. Bhatia in:

  20. Search for Francisca Vazquez in:

  21. Search for Aviad Tsherniak in:

  22. Search for William C. Hahn in:

  23. Search for Matthew Meyerson in:

Contributions

S.R.V. and M.M. conceived of the research and wrote the manuscript. S.R.V. and M.F.N. performed experiments. C.G.B. performed in vivo xenograft experiments. M.J.W., S.R.V., and P.S.C. performed data analysis on RNA sequencing data. J.M.K.-B. and S.R.V. performed data analysis on shRNA and CRISPR screening data. A.C.B., A.M.T., and J.S. performed copy number analysis on TCGA and cell line data. C.A.S. assisted in generation of shRNA reagents. P.T., A.D.C., and C.S.P. performed or oversaw data analysis. B.T., K.L., S.A.C., E.M., and M.S. performed mass spectrometry or were involved in downstream data analysis. S.N.B. oversaw in vivo xenograft experiments. F.V., A.T., and W.C.H. directed shRNA and CRISPR screening efforts. M.M. directed the overall project.

Competing interests

A.C.B., A.D.C., C.A.S., and M.M. receive research support from Bayer Pharmaceuticals. M.M. is a scientific advisory board member of, consultant for, and holds equity in OrigiMed. The content of this manuscript is the subject of a pending patent application (S.V., M.M.).

Corresponding author

Correspondence to Matthew Meyerson.

Integrated supplementary information

  1. Supplementary Figure 1 MAGOH and MAGOHB score as reciprocal paralog dependencies in both genome-scale CRISPR and shRNA screening datasets.

    a, Plot of the percentage DNA sequence identity between gene pairs versus Pearson correlation for their co-expression across the CCLE cell lines (n = 93,433 gene pairs plotted). The solid line shows the mean; the shaded region shows the standard error. b, Correlation between MAOGHB expression (y axis) and MAGOH dependency in CRISPR data (x axis). Pearson’s r2 = 0.2235 (n = 336 cell lines plotted). c, Correlation between MAGOH gene dependencies in shRNA (y axis) and CRISPR (x axis) data. Pearson’s r2 = 0.1763 (n = 215 cell lines plotted). d, Correlation between MAGOH expression (y axis) and MAGOHB dependency in CRISPR data (x axis). Pearson’s r2 = 0.1113 (n = 336 cell lines plotted). e, Correlation between MAGOHB gene dependencies in shRNA (y axis) and CRISPR (x axis) data. Pearson’s r2 = 0.3068 (n = 215 cell lines plotted).

  2. Supplementary Figure 2 Validation of reciprocal MAGOH/MAGOHB dependencies.

    a, Validation of selective MAGOH and MAGOHB knockdown using doxycycline-inducible shRNAs against MAGOH or MAGOHB in Heya8 cells. Expression levels of each paralog are shown relative to the no-knockdown (–Dox) condition in each case. Data are presented as mean ± s.d., n = 4 technical replicates. b, Cell viability was measured in Heya8 cells (which do not have deletions in either MAGOH or MAGOHB) on knockdown of MAGOH, MAGOHB, or both. c, Cell viability measured on MAGOH knockdown (using Dox-inducible shRNA) in hemizygous MAGOHB-deleted Huh1 cells with or without reconstitution of MAGOHB-V5. d, Cell viability measured on MAGOH knockout (using sgRNA) in MAGOHB-deleted Huh1 cells with or without reconstitution of MAGOHB-V5. e, Validation of MAGOHB dependency in MAGOH-deleted H1437 cells using either control sgRNA (sgGFP) or two different sgRNAs against MAGOHB. For be, error bars show mean ± s.d., n = 3 replicates from a representative experiment repeated twice; P value by two-tailed, two-sample t test.

  3. Supplementary Figure 3 MAGOH and MAGOHB dependencies are associated with deletion/low expression of the partner paralog.

    Radial plots showing association of the top copy number alterations (top row) or gene expression changes (bottom row) correlated to MAGOH (left column) or MAGOHB (right column) dependency, among splicing and NMD-related genes. The radial plot depicts both the association of copy number/gene expression correlates with the target dependency profile (either MAGOH or MAGOHB dependency) and with each other. The radial distance of each feature (blue dot) from the dependency target (red) is inversely proportional to the information coefficient score between that feature and the target. The angular distances between features are based on the association matrix of information coefficient scores generated when comparing the top features with each other. If two features are close in angular distance, they have a higher information coefficient value with each other as compared to features that are father away in angular distance. MAGOH/MAGOHB dependencies are uniquely predicted by low expression or copy number loss of the partner paralog (circled in green).

  4. Supplementary Figure 4 Additional validation of MAGOHB dependency in cells with hemizygous MAGOH loss.

    a, Validation of MAGOHB knockdown with multiple MAGOHB shRNAs. H460 cells were transduced with lentivirus expressing either control shRNA (shGFP) or shRNAs against MAGOHB in the pLKO.1 vector. MAGOHB knockdown was assessed 72 h after selection using quantitative PCR. Expression levels are shown as normalized to control (shGFP) condition (dotted line). Data are presented as mean ± s.d., n = 4 technical replicates. b, Validation of selective MAGOHB dependency in MAGOH-deleted cells. Hemizygous MAGOH-deleted H460 cells or Kuramochi cells (no MAGOH deletion) were transduced with lentivirus encoding either control shRNA (shGFP) or shRNAs against MAGOHB in the pLKO.1 vector. Cell viability was assessed by CellTiterGlo 9 d after infection. Data are presented as mean ± s.d., n = 3 replicates (Kuramochi), n = 5 replicates (H460); P value by two-tailed two-sample t test. c, Quantification of colony formation assay (related to Fig. 1e). Following crystal violet staining, wells were destained and measurement of absorbance at 595 nm was performed for quantification of colony number. Data are presented as mean ± s.d., n = 3 replicates; P value by two-tailed, two-sample t test. d, Validation of ectopic MAGOH-V5 expression in ChagoK1 cells (related to Fig. 1f). Cells were transduced with lentivirus encoding V5-tagged MAGOH and western blotting was performed using an anti-MAGOH/B antibody. The experiment was repeated twice with similar results. e, Cell viability measured on MAGOHB knockdown in ChagoK1 cells with or without ectopic expression of untagged MAGOH. Data are presented as mean ± s.d., n = 6 replicates. Center wells analyzed in each condition; the experiment was repeated using two separate untagged MAGOH overexpression constructs with similar results. f, Validation of ectopic untagged MAGOH expression by western blotting in ChagoK1 cells. Results from a single experiment, repeated once in H1437 cells with similar results.

  5. Supplementary Figure 5 Knockout of MAGOH confers MAGOHB dependency.

    a, Validation of MAGOH-deleted clonal Heya8 cell lines. Heya8 cells, which are copy neutral for MAGOH, were transiently transfected with either a control sgRNA (sgGFP) or an sgRNA targeting MAGOH. Single-cell clones were derived and MAGOH expression was measured by quantitative PCR. Two single-cell clones (sgMAGOH_Clone 1 and sgMAGOH_Clone 5) demonstrated ablation of MAGOH expression as compared with control clones. Data are presented as relative MAGOH expression, normalized to expression in sgGFP_Clone 4. Points are shown from n = 2 technical replicates. b, Single-cell Heya8 clones were transduced with lentivirus encoding a doxycycline-inducible shRNA against MAGOHB. Cell viability was assessed 7 d after shRNA induction by CellTiterGlo. Data are presented as viability normalized to the –Dox condition for each cell line. Error bars show mean ± s.d., n = 3 replicates from a representative experiment repeated twice; P value by two-tailed, two-sample t test.

  6. Supplementary Figure 6 Validation of sgRNAs used for MAGOH knockout in Heya8 cells.

    a, Schematic showing the sgRNA target and PAM site, which lies near the intron 1–exon 2 junction of MAGOH. b, Sanger sequencing traces around the MAGOH sgRNA cut site, from single-cell Heya8 clones derived from cells transduced with either non-targeting guide (sgGFP) or guide against MAGOH. In both MAGOH-deleted clones (clones 1 and 5), an indel at the indicated position (black underline) occurs in a similar location and could cause a frameshift. In clone 5, mixed peaks may suggest distinct editing events on each allele. Ablation of MAGOH expression in these clones is shown in Supplementary Fig. 5.

  7. Supplementary Figure 7 MAGOHB dependency in cells with chromosome 1p loss.

    a, Distribution of MAGOHB CERES dependency scores in screened cell lines with chromosome 1p loss (–1, n = 46), 1p neutral status (0, n = 177), or 1p gain (+1, n = 14). 1p-deleted cell lines are significantly more dependent on MAGOHB than are 1p neutral cell lines; P value calculated by two-sample, one-sided Welch’s t test. Horizontal lines indicate means. b, Validation of selective MAGOHB dependency in 1p-deleted neuroblastoma cells. CHP212 (1p-deleted, left) or SKNDZ (1p-neutral, right) neuroblastoma cells were transduced with lentivirus expressing a doxycycline-inducible shRNA against MAGOHB and seeded for colony formation assay in either the absence of doxycycline or the presence of 100 ng/ml doxycycline. Colony formation was quantified by destaining of crystal violet and measurement of absorbance at 595 nm. Data are presented as mean ± s.d., n = 3 replicates; P value by two-tailed, two-sample t test.

  8. Supplementary Figure 8 MAGOH dependency in cells with chromosome 12p loss.

    a, Frequency of chromosome 12p and 1p loss across TCGA cohorts. b, Heat map of arm-level deletion events co-occurring with chromosome 1p loss across TCGA cohorts. Cohorts in which chromosome 1p co-deletion with chromosome 12p are significantly mutually exclusive (as compared with all possible 1p co-deletion events) are indicated by an asterisk. *P <0.05 by one-sided binomial test. Range of TCGA cohort sizes analyzed, n = 36 (CHOL) to n = 1,030 (BRCA). c, Distribution of MAGOH CERES dependency scores in screened cell lines with chromosome 12p loss (–1, n = 37), 12p neutral status (0, n = 169), or 12p gain (1, n = 69). 12p-deleted cell lines are significantly more dependent on MAGOH than are 12p-neutral cell lines. Horizontal lines indicate means; P value by Welch’s two-sample one-sided t test.

  9. Supplementary Figure 9 RNA sequencing reveals multiple splicing events with potential effects on AS-NMD loops on MAGOHB knockdown in ChagoK1 cells, but not MAGOH-V5-reconstituted ChagoK1 cells.

    ac, Sashimi plots around ultraconserved elements reported to regulate AS-NMD loops in either the absence (gray) or presence (red) of MAGOHB knockdown in ChagoK1 cells (left panels) or MAGOH-V5-reconstituted ChagoK1 cells (right panels) for the genes HNRNPH1 (a), SRSF7 (b), and SRSF2 (c). Schematics depict splice-site usage events that result in the creation of premature termination codons. Numbers of junction-spanning reads averaged over three replicates for each condition are as indicated.

  10. Supplementary Figure 10 Alterations in splice isoform usage on MAGOHB knockdown in a MAGOH-deleted context and predicted changes in protein levels of affected genes.

    a,b, Isoform abundances (i) and predicted expression changes on the protein level (ii) for the genes PSPC1 (a) and HNRNPH1 (b) in ChagoK1 cells and MAGOH-reconstituted ChagoK1 cells with (red) or without (gray) MAGOHB knockdown. To predict effects on protein expression, isoforms were grouped by predicted protein-coding size as described in the Methods. Protein expression levels were assessed by western blotting (iii). a (iii), Western blotting for PSPC1 shows a dominant band presumed to correspond to the 523-aa/58-kDa predicted protein isoform. Western blots are from a representative experiment repeated twice. b (iii), Western blotting for HNRNPH1 shows a dominant truncated isoform for HNRNPH1 (~35 kDa) observed on MAGOHB knockdown in a MAGOH-deleted context that does not exactly correspond in size to what is expected based on RNA-sequencing data; this truncated species has not been fully characterized. Western blots are from a representative experiment repeated three times.

  11. Supplementary Figure 11 MAGOH and MAGOHB dependencies are correlated with dependencies on other members of the MAGOH/MAGOHB interactomes.

    a, Gene set enrichment analysis reveals enrichment of nonsense-mediated decay/exon junction complex gene sets among gene dependencies (n = 6,304) correlated with MAGOH copy number. Significance was assessed by running preranked GSEA using the gene dependency list as described (Methods). b, Immunoprecipitation–mass spectrometry was performed on MAGOH-V5- or MAGOHB-V5-expressing 293T or parental 293T cells (‘control’) using an anti-V5 antibody. Fold enrichment of interacting proteins over control for MAGOH-V5 (x axis) or MAGOHB-V5 (y axis) IPs is shown. Open circles indicate interactors with log2 (fold change) enrichment > 0.5 over control. Baits are not shown on the plot. c, Volcano plot for MAGOH-V5- (left) or MAGOHB-V5- (right) interacting proteins. The color scale depicts proteins in the following classes (from light pink to maroon): RNA binding, S ribosomal, EJC/NMD pathway member. Labeled proteins highlight selected EJC/NMD members found in the IPs. Significance was determined by moderated t test to calculate P values for the n = 1,995 genes from the IP, corrected for multiple hypotheses (FDR). d, MAGOH-V5 (left) or MAGOHB-V5 (right) interactomes overlaid with gene dependencies correlated with MAGOH (left) or MAGOHB (right) dependency. Dependency–dependency correlations with z scores >1.5 or <–1.5 are colored according to the scale. Gene dependencies correlated to MAGOH dependency (z > 1.5) are enriched among MAGOH-V5 interactors (log2 FC > 0.7) (P = 1.5 × 10–4) and gene dependencies correlated to MAGOHB dependency (z > 1.5) are enriched among MAGOHB-V5 interactors (log2 FC > 0.7) (P = 0.051). P values were calculated by one-sided Fisher exact test on the population of n = 380 genes (the overlap set of IP genes with log2 FC > 0.7 and the dependency data). Labels indicate proteins with z > 1.5 and log2 FC > 1 for the MAGOH-V5 plot and z > 1.5 and log2 FC > 0.5 for the MAGOHB-V5 plot.

  12. Supplementary Figure 12 Additional validation of IPO13 dependency in MAGOH-deleted cells.

    a, IPO13 knockdown was verified in H460 cells by quantitative PCR. H460 cells were transduced with lentivirus expressing either control shRNA (shGFP) or shRNAs against IPO13 in the pLKO.1 vector. IPO13 expression levels were assessed 72 h after selection using quantitative PCR. Expression levels are shown as normalized to control (shGFP) condition. Data are presented as mean ± s.d., n = 4 technical replicates. b, Quantification of the colony formation assay (related to Fig. 3d). Following crystal violet staining, wells were destained and measurement of absorbance at 595 nm was performed for quantification of colony number; data are normalized to absorbance in the shGFP condition. Data are presented as mean ± s.d., n = 3 replicates; P value by two-tailed two-sample t test. c, Clonogenic capacity was measured in MAGOH-deleted H1437 cells on IPO13 knockdown in either the absence (left) or presence (right) of MAGOH-V5 reconstitution. Destaining and measurement of absorbance at 595 nm was performed for quantification of colonies formed; data are normalized to absorbance in the shGFP condition. Data are presented as mean ± s.d., n = 3 replicates; P value by two-tailed two-sample t test. d, Cell viability was measured in MAGOH-deleted H1437 cells on IPO13 knockdown in either the absence (left) or presence (right) of MAGOH-V5 reconstitution. Data represent relative cell viability normalized to the shGFP condition. Data are presented as mean ± s.d., n = 3 replicates; P value by two-tailed two-sample t test.

  13. Supplementary Figure 13 IPO13 knockdown in the setting of MAGOH deletion disrupts normal MAGOH/B localization and may interfere with EJC shuttling.

    a, Representative fields of immunofluoresence staining of MAGOH/B in H460 cells in either the absence (top) or presence (bottom) of siRNA-mediated IPO13 knockdown. IPO13 knockdown in the setting of MAGOH deletion leads to decreased nuclear (black arrowhead) signal in favor of cytoplasmic (green arrowhead) signal. b, Quantification of fluorescence signal (nuclear/cytoplasmic ratio) in H460 cells transfected with either control siRNA or si-IPO13-2. Quantification was performed on three random fields per condition (n = 26 cells for control; n = 30 cells for si-IPO13-2) by ImageJ. Data are presented as mean ± s.d.; P value by two-tailed two-sample t test.

  14. Supplementary Figure 14 H1437 xenografts can be targeted by TPNCs carrying siRNA against MAGOHB or IPO13.

    a, Individual tumor volumes for the xenograft experiment shown in Fig. 4b (n = 6 tumors per condition). The solid line connects the mean tumor volume at each time point. b, Individual tumor volumes for the xenograft experiment shown in Fig. 4e (n = 10 tumors per condition). Solid line overlays show mean ± s.e.m. for each condition at each time point. c, Surface expression for p32 (receptor for LyP-1-containing nanocomplexes) in H1437 cells, as assessed by flow cytometry. Results are from single experiment. d, H1437 tumor xenograft experiment performed by intra-tumoral injection of LyP-1-containing nanocompexes carrying either siGFP or siMAGOHB cargo. Thin lines show tumor growth in individual mice (n = 10 tumors per group). Solid lines show mean ± s.e.m. for each condition. e, Representative staining for cleaved caspase-3 in an siGFP-treated mouse (top) and an siMAGOHB-treated mouse (bottom) using LyP-1-based tumor-penetrating nanocomplexes. Staining was performed on n = 6 tumors per group (randomly selected). f, Quantification of staining levels in six randomly selected tumors from each group. Data are presented as mean ± s.d.; P value by two-tailed, two-sample t test.

  15. Supplementary Figure 15 A model for MAGOH/B and IPO13 dependency.

    a,b, Cells without alterations in MAGOH/B and IPO13 have adequate MAGOH/B and IPO13 to tolerate partial inhibition of any of these factors. c, However, hemizygous loss of MAGOH and/or IPO13, as occurs in chromosome 1p loss, can sensitize cells to MAGOHB or IPO13 inhibition (top panel). An increased dependency on EJC function/shuttling may also occur in other states perhaps driven by an increased need for splicing or by somatic alterations in as-yet-unknown factors (bottom panel).

Supplementary information

  1. Supplementary Text and Figures

    Supplementary Figures 1–15 and Supplementary Note

  2. Reporting Summary

  3. Supplementary Table 1

    Analysis of reciprocal paralog dependencies in genome-scale shRNA screening data

  4. Supplementary Table 2

    Analysis of reciprocal paralog dependencies in genome-scale CRISPR–Cas9 screening data

  5. Supplementary Table 3

    Gene ontology enrichment among genes found to be symmetric or asymmetric paralog dependencies in analysis of shRNA and CRISPR screening data

  6. Supplementary Table 4

    MAGOH hemizygous deletion status across CCLE cell lines used for PARIS analysis

  7. Supplementary Table 5

    Results of PARIS analysis to identify gene dependencies correlated with MAGOH deletion across CCLE cell lines

  8. Supplementary Table 6

    Significance of co-occurrence between chromosome 1p loss and hemizygous MAGOH deletion by TCGA tumor type

  9. Supplementary Table 7

    Downregulated protein-coding isoforms (b < –1) for genes that had concomitant upregulation of NMD isoforms (b > 1) upon MAGOHB knockdown in ChagoK1 cells

  10. Supplementary Table 8

    Gene ontology enrichment among genes displaying both an increase in expression level of NMD substrate isoform(s) and a proportional decrease in expression level of coding isoform(s) upon MAGOHB knockdown in ChagoK1 cells

  11. Supplementary Table 9

    Peptide report generated after IP–MS of MAGOH-V5 or MAGOHB-V5 and used as input into the expectation maximization algorithm

  12. Supplementary Table 10

    Results of the expectation maximization algorithm showing enriched interactors with MAGOH-V5

  13. Supplementary Table 11

    Results of the expectation maximization algorithm showing enriched interactors with MAGOHB-V5

  14. Supplementary Table 12

    Gene dependencies correlated to MAGOH or MAGOHB dependency

  15. Supplementary Table 13

    DEMETER score for MAGOH, MAGOHB, and IPO13 across screened cell lines as well as hemizygous deletion status (0, no deletion; 1, deletion) for each of these genes for each cell line

  16. Supplementary Table 14

    Oligonucleotide sequences used in experimental validation experiments (see Methods)

About this article

Publication history

Received

Accepted

Published

DOI

https://doi.org/10.1038/s41588-018-0155-3