Proteome-Wide Assessment of Clustering of Missense Variants in Neurodevelopmental Disorders Versus Cancer

Abstract

Missense de novo variants (DNVs) and missense somatic variants contribute to neurodevelopmental disorders (NDDs) and cancer, respectively. Proteins with statistical enrichment based on analyses of these variants exhibit convergence in the differing NDD and cancer phenotypes. Herein, the question of why some of the same proteins are identified in both phenotypes is examined through investigation of clustering of missense variation at the protein level. Our hypothesis is that missense variation is present in different protein locations in the two phenotypes leading to the distinct phenotypic outcomes. We tested this hypothesis in 1D protein space using our software CLUMP. Furthermore, we newly developed 3D-CLUMP that uses 3D protein structures to spatially test clustering of missense variation for proteome-wide significance. We examined missense DNVs in 39,883 parent-child sequenced trios with NDDs and missense somatic variants from 10,543 sequenced tumors covering five TCGA cancer types and two COSMIC pan-cancer aggregates of tissue types. There were 57 proteins with proteome-wide significant missense variation clustering in NDDs when compared to cancers and 79 proteins with proteome-wide significant missense clustering in cancers compared to NDDs. While our main objective was to identify differences in patterns of missense variation, we also identified a novel NDD protein BLTP2. Overall, our study is innovative, provides new insights into differential missense variation in NDDs and cancer at the protein-level, and contributes necessary information toward building a framework for thinking about prognostic and therapeutic aspects of these proteins.

Figure 1:. Schematic of Examples of the CLUMP and 3D-CLUMP Methods.

A) Two proteins are shown: one where there is more clustering in NDDs (Top) and one where there is more clustering in cancer (Bottom). B) Our AlphaFold prediction for NP_002065.1 (used as an example only) is shown in this image where variants are placed to exemplify more clustering in NDDs (Left) and more clustering in cancer (Right).

Figure 2:. Variant Data Types in This Study.

A) NDD data consisted of 39,883 parent-child sequenced trios (the lightning bolt is used to exemplify DNVs which, by definition, are only found in children). B) Cancer data consisted of 10,543 individuals from the TCGA and COSMIC databases.

Figure 3:. Chicago Plots for the 3D-CLUMP Results in the COSMIC Datasets.

A) Chicago plot for 3D-CLUMP results in the NDD versus COSMIC CNS analyses. B) Chicago plot for 3D-CLUMP results in the NDD versus COSMIC GI analyses. For both A and B proteins that exhibit significant clustering in NDDs are shown on the top above the significance line and proteins that exhibit significant clustering in cancer are shown on the bottom below the significance line. Proteins are placed based on the genomic coordinates of the genes that encode them and all significant proteins are labeled on the plots.

Figure 4:. Examples of Proteins with Proteome-Wide Significant Clustering of Missense Variants.

Subfigures A to E are significant in NDDs and subfigures F to H are significant in cancer. Red are variants seen in individuals with NDDs. Numbers are shown next to some residues to indicate the number of individuals. Blue are seen in individuals with cancer. Black are seen in both. The intensity of the color is scaled by the number of individuals with missense variants at the residue. A) NDD versus BRCA SMARCA2 (NP_620614), B) NDD versus CNS PPP2R5D (NP_851307), C) NDD versus COAD TRAF7 (NP_115647), D) NDD versus LUAD KIF1A (NP_004312), E) NDD versus COAD GRIN1 (NP_067544), F) NDD versus CNS PIK3CA (NP_006209), G) NDD versus GI GNAS (NP_000507), H) NDD versus PRAD SPOP (NP_003554).

Figure 5:. Discovery is Greater with 3D Structures.

Shown are proteins exhibiting proteome-wide significance for clustering in either NDDs or the specified cancer type using the 3D-CLUMP and/or CLUMP methods.

Figure 6:. Protein-Protein Interaction Enrichment of Proteins with Significant Missense Clustering.

A) PPI network of proteins with proteome-wide significant clustering in NDDs (number of nodes = 57, number of edges = 123, expected number of edges = 41, p < 1 × 10⁻¹⁶). B) PPI network of proteins with proteome-wide significant clustering in cancer (number of nodes = 78, number of edges = 51, expected number of edges = 33, p = 2.5 × 10⁻³).

Figure 7:. Comparison to Known NDD and Known Cancer Genes.

A) Shown is the distribution of protein results in our study and comparison to 379 known NDD genes. *TRIP12 is significant in NDDs in comparison to LUAD and is significant in COSMIC CNS comparison to NDDs. B) Shown is the distribution of protein results in our study and comparison to 299 known Cancer genes

Figure 8:. Discovery of BLTP2.

A) Shown are missense DNVs observed in individuals with NDDs on the BLTP2 protein structure (NP_001350756.1). This protein had seven amino acid changes with three at amino acid position 1487 and one each at amino acid positions 605, 705, 1253, and 1483. B) The Arginine at position 1487 is highly conserved across several species. C) The Arginine to Glutamine missense variant is significantly enriched in NDDs (Fisher’s Exact Test p = 2.96 × 10⁻³, OR = 29.4). Another missense variant (Arginine to Tryptophan) was identified at this amino acid position in an independent NDD cohort (SPARK) and is also enriched in NDDs (Fisher’s Exact Test p = 8.30 × 10⁻⁴, OR = 15.2)