The impact of nonsense-mediated mRNA decay on genetic disease, gene editing and cancer immunotherapy

Abstract

Premature termination codons (PTCs) can result in the production of truncated proteins or the degradation of messenger RNAs by nonsense-mediated mRNA decay (NMD). Which of these outcomes occurs can alter the effect of a mutation, with the engagement of NMD being dependent on a series of rules. Here, by applying these rules genome-wide to obtain a resource called NMDetective, we explore the impact of NMD on genetic disease and approaches to therapy. First, human genetic diseases differ in whether NMD typically aggravates or alleviates the effects of PTCs. Second, failure to trigger NMD is a cause of ineffective gene inactivation by CRISPR-Cas9 gene editing. Finally, NMD is a determinant of the efficacy of cancer immunotherapy, with only frameshifted transcripts that escape NMD predicting a response. These results demonstrate the importance of incorporating the rules of NMD into clinical decision-making. Moreover, they suggest that inhibiting NMD may be effective in enhancing cancer immunotherapy.

Extended Data Fig.1. The distribution of genome-wide NMD efficacy scores and of NMD rules in all genes with more than 20 disease-associated PTC variants.

a, the distribution of NMDetective-A scores over all genes in hg38 reveals three global clusters of inefficient, intermediate-efficiency and efficient NMD. b, genes in which there is an excess of PTCs in NMD-evading regions (left barplot) and genes where there is a dearth of PTCs is NMD-evading regions (right barplot). The proportion of PTCs in different NMD-evading regions is shown as colored segments in the bar chart. The relative portion of the protein-coding mRNA sequence that is covered by the NMD rules is shown as a black vertical stripe. c, a schematic of a gene that illustrates how PTCs that trigger or evade NMD can lead to different outcomes in protein expression.

Extended Data Fig.2. The sequence context of nonsense variants is not different between different types of NMD regions.

a, the trinucleotide spectrum of nonsense variants in ExAC is consistent across gene regions that trigger or evade NMD, b. spectrum of variants shows high Pearson correlations between the different types of NMD regions. c, the baseline NMD-evasion rule coverage for population genomic data, obtained from nonsense variants simulated from the trinucleotide context of whole-genome population variants at different VAF ranges, exhibits a consistent distribution at different VAF ranges. Observed nonsense variants are increasingly enriched towards NMD-evading regions with an increasing VAF, compared to the simulated baseline at same VAFs. Odds ratios significant at P<0.01 (Fisher’s exact test) are shown, comparing the distribution of simulated versus observed nonsense mutations.

Extended Data Fig.3. Disease genes with a significant enrichment of PTC variants that do or do not trigger NMD, with and without normalization to local density of missense mutations.

a-b, significant enrichment of genes at FDR<0.05 after normalization to the number of ClinVar missense variants observed in the same NMD regions. c-d, genes significant at an FDR<25% are shown (see Fig. 2d-e for a list at FDR<5%). Log₂ odds ratios are for ClinVar frequencies of NMD-evading frameshifting indel and nonsense variants versus NMD-detected frameshifting indel and nonsense variants regions of a gene, normalized to the length of the NMD-evading versus NMD-detected regions. FDRs are by Fisher’s exact test, two-tailed, Benjamini-Hochberg adjusted. a-d, log2 odds ratios are shown separately for the four rules, for each rule which is significant in a particular gene.

Extended Data Fig.4. Effect of NMD rules observed in CRISPR assays.

a, sgRNAs targeted to gene regions that evade NMD show a smaller enrichment when selecting for cells that do not express the targeted protein. Fold differences in sgRNA abundance are pooled per rule and shown for all proteins in a and additionally broken down by protein in c. P values are by Mann-Whitney U test, two-sided. b, Models that discriminate essential from non-essential genes based on the fold-depletion of sgRNAs are more accurate for sgRNAs that target gene regions that trigger NMD than for sgRNAs targeted to different NMD-evading regions.

Extended Data Fig.5. Relevance of NMD rules for CRISPR sgRNA design.

a, fitness loss upon targeting a non-essential gene (left) versus an essential gene (right) using a sgRNA directed at gene sections which are covered by various NMD-evasion rules. b-e, distribution of loci targeted by sgRNAs that are NMD-detected or NMD-evading (according to the individual NMD rules) for genome-wide CRISPR libraries (b, c) or by sgRNA design tools (d, e).

Extended Data Fig.6. CRISPR screening data support canonical and non-canonical determinants of NMD efficacy.

a, the non-canonical long-exon NMD evasion rule has similar effects as the canonical last-exon NMD evasion rule in terms of attenuated loss of fitness when targeting an essential gene (Methods). b-e, minor non-canonical NMD determinants, which are not included in the NMDetective-B model, but are included in the comprehensive NMDetective-A model. This includes: distance to downstream splice site in long exons (b), for the start-proximal rule, existence of a downstream in-frame AUG codon, presumably facilitating translation re-initiation (c), distance to the wild-type stop codon (d), and the effect of mRNA turnover on the observed NMD efficacy (e).

Extended Data Fig.7. Tumor infiltration by immune cells is associated with a high burden of NMD-evading frameshifting indels.

a-b, Individual immune markers for the TCGA samples were estimated using gene expression data. FDR is by two-sided Mann-Whitney U test, Benjamini-Hochberg adjusted. In panel b, only tests significant at FDR<25% are shown.

Extended Data Fig.8. Evidence that NMD activity is a determinant of immune reactivity of tumors.

a, in the TCGA kidney cancer cohorts (KIRC, KICH and KIRP), a cancer type where indel burden is known to be particularly strongly associated with immunogenicity, higher relative burden of NMD-evading frameshifts was associated with longer survival (p=0.011 for pooled data from both panels, by log-rank test) without application of immunotherapy. Patients were separated based on the number of frameshifting indels that do not trigger NMD being higher than the number that trigger NMD (cyan) and those patients where the converse is true (red). b, in the TCGA UCEC cohort of uterine corpus endometrial carcinoma, where the key NMD gene UPF1 is commonly mutated, the predicted higher impact of UPF1 mutations is associated with multiple gene-expression based markers of lymphocyte, but not macrophage, infiltration. Patients with more than one UPF1 mutation were assigned to the group of the mutation with the higher VEP score. P values by Mann-Whitney U test.

Extended Data Fig.9. NMD rules improve predictions of response to immunotherapy across multiple cancer types.

a, assigning NMD rules to frameshift mutations based on the location of the first downstream PTC in the new reading frame also shows that the burden of frameshifts that cannot trigger NMD is higher in patients that respond to immunotherapy. P values are by a one-tailed Mann-Whitney U test. b, standardized regression coefficients and the 95% confidence interval in a logistic regression model that predicts responders versus non-responders. c, pseudo-R² for sequential addition of features to a joint model. The null model includes only the study (dataset) as a covariate. d, precision-recall curves for logistic regression models with three different sets of features: a tumor mutation burden (TMB) baseline, another baseline where TMB and all frameshifting indels are considered together, and the full model that considers TMB and NMD-evading and NMD-detected frameshifting indels separately. P values are by Chi-squared test. AUPRC, area under the precision-recall curve.

Figure 1. NMDetective catalogues the effects of all possible PTCs in the human genome.

a, an overview of the data used to create the NMDetective-A and -B resources. b, accuracy of predictions by NMDetective evaluated on an independent set of frameshifting indel mutations. c, the NMDetective-B decision tree model. The number of PTCs in the training set assigned to each group is shown as n. d, coverage of the gene coding regions with NMD rules.

Figure 2. Disease phenotypes arising from germline PTCs are modulated by NMD.

a, signatures of negative selection on NMD-detected variants in population genomic data. b, genes where NMD is predicted to aggravate the phenotype, c. genes where NMD is predicted to alleviate the phenotype. b-c. genes significant at an FDR<5% are shown (see Extended Data Fig. 3c-d for a more permissive list at FDR<25%). Log₂ odds ratios are for ClinVar frequencies of frameshifting indel and nonsense variants in NMD-evading versus NMD-detected regions of a gene, normalized to the length of the regions. FDRs are by a Fisher’s exact test, two-tailed, Benjamini-Hochberg adjusted. d, NMD rules significantly improve predictions of PTC pathogenicity, e. variable importance in the PTC pathogenicity predictor, * significant at p<0.001 by Chi-Square-test.

Figure 3. NMD rules determine the outcome of CRISPR-Cas9 gene editing.

a, a decrease in protein expression due to tiling sgRNAs placed along the length of human and mouse genes (y axis quantifies the sgRNA fold difference between a low-expressing versus high-expressing set of cells) reveals, overall, similar associations with the non-canonical start-proximal NMD rule to the canonical NMD last-exon rule. The CD13 gene demonstrates the effect of the non-canonical long-exon rule. Shaded regions are 95% confidence interval of the loess fit to protein expression. Pearson correlation coefficients and two-sided tests for association were computed by comparing the loess fit to the NMDetective-A NMD efficacy scores. b, evading NMD attenuates the loss of fitness (y axis) due to knockout of essential genes. Data for non-essential genes are in Extended Data Fig. 5a. P values are by Mann-Whitney U test. The knock-out efficiency compares the reduction of sgRNAs in NMD evading regions to the reduction in regions that trigger NMD. c, a ‘saturation genome editing’ CRISPR experiment shows strongly reduced mRNA levels for nonsense mutations in BRCA1, except for those introduced into regions covered by the start-proximal (top) and last-exon NMD evasion rules (bottom panel).

Figure 4. Efficacy of immunotherapy is predicted by the burden of NMD-evading frameshifting indels but not other indels.

a, across five studies, responders to immune checkpoint blockade are enriched for a high burden of NMD-evading (top panels) but not for NMD-detected (bottom panels) frameshifting indels. P values are by Mann-Whitney U test (one-tailed, testing positive association of responders with higher burden). b, enrichment for NMD-evading frameshifting indels in responders versus non-responders is observed for all four NMD rules. Error bars are 95% confidence intervals, c, coverage of NMD-evading frameshifting indels by the individual NMD rules, observed in exomes of immunotherapy responders.