Structure-Based Analysis Reveals Cancer Missense Mutations Target Protein Interaction Interfaces

<div><p>Recently it has been shown that cancer mutations selectively target protein-protein interactions. We hypothesized that mutations affecting distinct protein interactions involving established cancer genes could contribute to tumor heterogeneity, and that novel mechanistic insights might be gained into tumorigenesis by investigating protein interactions under positive selection in cancer. To identify protein interactions under positive selection in cancer, we mapped over 1.2 million nonsynonymous somatic cancer mutations onto 4,896 experimentally determined protein structures and analyzed their spatial distribution. In total, 20% of mutations on the surface of known cancer genes perturbed protein-protein interactions (PPIs), and this enrichment for PPI interfaces was observed for both tumor suppressors (Odds Ratio 1.28, P-value < 10⁻⁴) and oncogenes (Odds Ratio 1.17, P-value < 10⁻³). To study this further, we constructed a bipartite network representing structurally resolved PPIs from all available human complexes in the Protein Data Bank (2,864 proteins, 3,072 PPIs). Analysis of frequently mutated cancer genes within this network revealed that tumor-suppressors, but not oncogenes, are significantly enriched with functional mutations in homo-oligomerization regions (Odds Ratio 3.68, P-Value < 10⁻⁸). We present two important examples, TP53 and beta-2-microglobulin, for which the patterns of somatic mutations at interfaces provide insights into specifically perturbed biological circuits. In patients with TP53 mutations, patient survival correlated with the specific interactions that were perturbed. Moreover, we investigated mutations at the interface of protein-nucleotide interactions and observed an unexpected number of missense mutations but not silent mutations occurring within DNA and RNA binding sites. Finally, we provide a resource of 3,072 PPI interfaces ranked according to their mutation rates. Analysis of this list highlights 282 novel candidate cancer genes that encode proteins participating in interactions that are perturbed recurrently across tumors. In summary, mutation of specific protein interactions is an important contributor to tumor heterogeneity and may have important implications for clinical outcomes.</p></div

An overview of the analyses performed.

<p><b>a)</b> A workflow describing the data processing steps from protein structures in the PDB and cancer-related somatic mutations in COSMIC and ICGC to residue-level bi-partite protein interaction networks. <b>b)</b> The percentage of residues within surface, intermediate and core regions that harbor mutations for oncogenes (n = 56) and tumor suppressors (n = 47) with 3D structures. <b>c)</b> Focusing only on surface residues, the percentage of residues within interface and non-interface regions that harbor mutations for oncogenes and tumor suppressors with 3D structures.</p

Bipartite B2M interaction networks.

<p><b>a)</b> A bipartite network of the tumor suppressor B2M, its partners and the interface residues by which they interact. <b>b)</b> A bipartite network showing only the subset of residues that were observed to harbor missense mutations in cancer patients. The size here of the residue nodes represent the number of tumors in which the residue was mutated.</p

Bipartite protein-residue interaction networks of 34 cancer genes known to form homo-oligomers displaying only those residues involved in oligomerization.

<p>Edges are colored according to functional predictions made using VEST. Red lines indicate that mutations affecting that residue were predicted to be functional (VEST > 0.75), blue lines indicate a neutral prediction (VEST < 0.25), and dashed grey lines indicate mutations could not confidently be assigned a functional or neutral label.</p

Additional file 3: Fig. S3. of Modeling of RAS complexes supports roles in cancer for less studied partners

Map of predicted HRAS-RAF1 interface residues. We have used the most favorable (lowest binding energy score) predictions for HRAS (PDB ID: 6q21A) and RAF1 (PDB ID: 4g0nB) reported by ZDOCK, pyDock, COTH and PRISM. (PNG 148 kb

Characterizing the structural location of missense mutations in tumor suppressors (TS), oncogenes (OG) and other genes.

<p>Fisher’s exact tests were performed separately for each set of genes. Shown are the odds ratios and 95% confidence intervals within each set of genes when making comparing the number of mutations located at <b>a)</b> surface versus core residues, <b>b)</b> surface interface versus surface non-interface residues.</p

Additional file 4: Fig. S4. of Modeling of RAS complexes supports roles in cancer for less studied partners

Pancancer FHOD1 gene expression distribution. This graph was produced using the gene expression data available via cBioPortal [92]. (PNG 56 kb

Additional file 2: Fig. S2. of Modeling of RAS complexes supports roles in cancer for less studied partners

Map of predicted KRAS-CALM interface residues. We have used the most favorable (lowest binding energy score) predictions for KRAS (PDB ID: 4dsoA) and CALM (PDB ID: 1zuzA) reported by ZDOCK, pyDock, COTH and PRISM. (PNG 147 kb

Lamatepec, ene.-dic. 1946, Epoca II, año X, XII, No. 136-147

Revista salvadoreña sobre temas relacionados al café, sus aspectos económicos, técnicos,cultivo,historia,Asociaciones e información variada.Esta es una publicación de la Junta Departamental de la "Asociación Cafetalera de El Salvador" y Portavoz de la Junta Departamental de la "Asociación de Ganaderos de El Salvador".— Todos los meses del año de 1946 guardados en un solo archivo. – Contenido : Enero, No. 136 (págs. 1-38) – Febrero, No. 137 (págs. 39-70) – Marzo, No. 138 (págs. 71-102) – Abril, No. (págs. 103-134) – Mayo, No. (págs. 135-166) – Junio No. (págs. 167-198) – Julio, No. 142 (págs. 199-230) – Agosto, No. 143 (págs. 231-262) – Septiembre, No. 144 (págs. 263-294) – Octubre, No. 145 (págs. 295-326) – Noviembre, No. 147 (págs. 359-388

Additional file 6: of Epigenetic aging signatures in mice livers are slowed by dwarfism, calorie restriction and rapamycin treatment

Weights of long-lived and wild-type control mice used in this study. Description of the weights (grams) of various control and long-lived mice according to their age. The minimum–maximum weight is described, along with the average for each age/treatment condition. (DOCX 54 kb