Functional and Structural Analysis of C-Terminal BRCA1 Missense Variants

<div><p>Germline inactivating mutations in <i>BRCA1</i> and <i>BRCA2</i> genes are responsible for Hereditary Breast and Ovarian Cancer Syndrome (HBOCS). Genetic testing of these genes is available, although approximately 15% of tests identify variants of uncertain significance (VUS). Classification of these variants into pathogenic or non-pathogenic type is an important challenge in genetic diagnosis and counseling. The aim of the present study is to functionally assess a set of 7 missense VUS (Q1409L, S1473P, E1586G, R1589H, Y1703S, W1718L and G1770V) located in the C-terminal region of BRCA1 by combining <i>in silico</i> prediction tools and structural analysis with a transcription activation (TA) assay. The <i>in silico</i> prediction programs gave discrepant results making its interpretation difficult. Structural analysis of the three variants located in the BRCT domains (Y1703S, W1718L and G1770V) reveals significant alterations of BRCT structure. The TA assay shows that variants Y1703S, W1718L and G1770V dramatically compromise the transcriptional activity of BRCA1, while variants Q1409L, S1473P, E1586G and R1589H behave like wild-type BRCA1. In conclusion, our results suggest that variants Y1703S, W1718L and G1770V can be classified as likely pathogenic <i>BRCA1</i> mutations.</p> </div

<i>In silico</i> predictions for pathogenicity of the variants presented in this study.

<p>Each predictor yields its own type of results, usually quantitative and categorical. We have chosen the most representative of each.</p>1<p>Predictors PON-P and Condel integrate results from other predictors run in their own servers; as PolyPhen-2 versions and their results differ if they are run in the PolyPhen-2 website or the other 2, all of them are shown for comparison. ²Classification of variants in different classes according to Align-GVGD algorithm (Align-Grantham Variation Grantham Deviation; <a href="http://agvgd.iarc.fr" target="_blank">http://agvgd.iarc.fr</a>), C65 means “Most likely to interfere with function, C0 means “least likely” (possible classes are: C65> C55>C45>C35>C25>C15>C0).</p><p>Abbreviations: Prob.-Probability; Pred.-Prediction; Funct.-Functional; Accur.-Accuracy; ΔΔG–ΔΔG value (kcal/mol); Del-Deleterious; N-Neutral; Dest- Destabilizing; P-Pathogenic; UV-Unknown Variant.</p

Results of functional assay of the studied missense variants.

<p>Percentage of transcriptional activity was expressed as percentage relative to the wild-type construct. Experiments were performed in triplicate and normalized against the <i>Renilla</i> luciferase internal control. Results are shown in bars: gray for controls and black for variants.</p

Structural analysis of missense variants lying in the BRCA1 BRCT domain.

<p>Representation of the structure of the BRCT tandem repeat from human BRCA1 in complex with a binding phosphopeptide (with the typical binding motif pSer-X-X-Phe, PDB ID 1t2v): in blue, the N-terminal repeat (BRCT-N); in red, the C-terminal repeat (BRCT-C); in green, the linker region between the two; in cyan, the phosphopeptide with the phosphoserine and the key binding residue Phe(+3); in, magenta the three mutated residues. Y1703 (A) forms a hydrogen bond with H1746 and is adjacent to K1702, which binds directly to the pSer residue in the phosphopeptide. W1718 (B) fills the internal core and stabilizes the BRCT-N domain. G1770 (C) gives the loop connecting the β1 strand with α1-helix in the BRCT-C repeat the necessary flexibility to form a tight turn, bringing the residues N1774 and M1775 close to the phosphopeptide for interaction with the Phe(+3) key residue.</p

Illustration depicting the location of the C-terminal BRCA missense variants and negative controls.

<p>This Figure shows the position of missense variants analyzed in the present study (black arrows) and the negative controls (closed gray arrows, underlined) within the vector used in the TA experiments.</p

Tumors defective in homologous recombination rely on oxidative metabolism: relevance to treatments with PARP inhibitors.

Mitochondrial metabolism and the generation of reactive oxygen species (ROS) contribute to the acquisition of DNA mutations and genomic instability in cancer. How genomic instability influences the metabolic capacity of cancer cells is nevertheless poorly understood. Here, we show that homologous recombination-defective (HRD) cancers rely on oxidative metabolism to supply NAD+ and ATP for poly(ADP-ribose) polymerase (PARP)-dependent DNA repair mechanisms. Studies in breast and ovarian cancer HRD models depict a metabolic shift that includes enhanced expression of the oxidative phosphorylation (OXPHOS) pathway and its key components and a decline in the glycolytic Warburg phenotype. Hence, HRD cells are more sensitive to metformin and NAD+ concentration changes. On the other hand, shifting from an OXPHOS to a highly glycolytic metabolism interferes with the sensitivity to PARP inhibitors (PARPi) in these HRD cells. This feature is associated with a weak response to PARP inhibition in patient-derived xenografts, emerging as a new mechanism to determine PARPi sensitivity. This study shows a mechanistic link between two major cancer hallmarks, which in turn suggests novel possibilities for specifically treating HRD cancers with OXPHOS inhibitors

Cancer network activity associated with therapeutic response and synergism

Cancer patients often show no or only modest benefit from a given therapy. This major problem in oncology is generally attributed to the lack of specific predictive biomarkers, yet a global measure of cancer cell activity may support a comprehensive mechanistic understanding of therapy efficacy. We reasoned that network analysis of omic data could help to achieve this goal. A measure of "cancer network activity" (CNA) was implemented based on a previously defined network feature of communicability. The network nodes and edges corresponded to human proteins and experimentally identified interactions, respectively. The edges were weighted proportionally to the expression of the genes encoding for the corresponding proteins and relative to the number of direct interactors. The gene expression data corresponded to the basal conditions of 595 human cancer cell lines. Therapeutic responses corresponded to the impairment of cell viability measured by the half maximal inhibitory concentration (IC) of 130 drugs approved or under clinical development. Gene ontology, signaling pathway, and transcription factor-binding annotations were taken from public repositories. Predicted synergies were assessed by determining the viability of four breast cancer cell lines and by applying two different analytical methods. The effects of drug classes were associated with CNAs formed by different cell lines. CNAs also differentiate target families and effector pathways. Proteins that occupy a central position in the network largely contribute to CNA. Known key cancer-associated biological processes, signaling pathways, and master regulators also contribute to CNA. Moreover, the major cancer drivers frequently mediate CNA and therapeutic differences. Cell-based assays centered on these differences and using uncorrelated drug effects reveals novel synergistic combinations for the treatment of breast cancer dependent on PI3K-mTOR signaling. Cancer therapeutic responses can be predicted on the basis of a systems-level analysis of molecular interactions and gene expression. Fundamental cancer processes, pathways, and drivers contribute to this feature, which can also be exploited to predict precise synergistic drug combinations. The online version of this article (doi:10.1186/s13073-016-0340-x) contains supplementary material, which is available to authorized users