DNA double-strand break repair in cancer: A path to achieving precision medicine

Abstract

The assessment of DNA damage can be a significant diagnostic for precision medicine. DNA double strand break (DSBs) pathways in cancer are the primary targets in a majority of anticancer therapies, yet the molecular vulnerabilities that underlie each tumor can vary widely making the application of precision medicine challenging. Identifying and understanding these interindividual vulnerabilities enables the design of targeted DSB inhibitors along with evolving precision medicine approaches to selectively kill cancer cells with minimal side effects. A major challenge however, is defining exactly how to target unique differences in DSB repair pathway mechanisms. This review comprises a brief overview of the DSB repair mechanisms in cancer and includes results obtained with revolutionary advances such as CRISPR/Cas9 and machine learning/artificial intelligence, which are rapidly advancing not only our understanding of determinants of DSB repair choice, but also how it can be used to advance precision medicine. Scientific innovation in the methods used to diagnose and treat cancer is converging with advances in basic science and translational research. This revolution will continue to be a critical driver of precision medicine that will enable precise targeting of unique individual mechanisms. This review aims to lay the foundation for achieving this goal.

Keywords: Artificial intelligence; DSB repair; Homologous recombination; Machine learning; Nonhomologous end joining; Precision medicine; Risk prediction.

Figure 1.. DSB repair pathway choice.

Balancing DSB repair through multiple mechanisms throughout the cell cycle. (A) cNHEJ and alt-EJ can occur at any phase of the cell cycle while HR and SSA are restricted to the S and G2 phase. (B) cNHEJ is initiated with Ku70/80 heterodimer recognizing and protecting the broken DNA ends, then recruits additional proteins to trim the ends and ligate gaps. Without the protection from Ku70/80, broken ends are subjected to resection. When there are limited amounts of resection, mediated by PARP1, the alt-EJ pathway is taken, resulting in annealing at microhomologies which can potentially cause multiple indels. With extensive end resection, HR and SSAare initiated by the MRN complex to make single stranded regions that allow recruitment of DNA repair proteins to mediate strand invasion and annealing at long homologies, respectively. The SSA pathway results in large deletions. Created with Biorender.com.

Figure 2.. BRCA mutations identified across cancer studies.

Cancer genomic portal provides a vast amount of information that enables visualization and analysis of molecular profiles and clinical features. Data from 184 studies representing more than 485,604 patient samples can be quickly assessed to determine BRCA mutations identified across all cancers. The somatic mutation frequencies for BRCA1 and BRCA2 are 1.8% and 3.0% respectively. These mutations are grouped between missense (green circles), truncating (black circles), inframe (deletion/insertion) (brown), fusion (purple) or other (pink) and span the entire protein sequence, including known protein domains (x-axis) while also indicating the frequency of each mutation. Image created in cBioPortal (Cerami et al., Cancer Discov. 2012 ; Gao et al., Sci. Signal. 2013) and modified with Biorender.com.

Figure 3.. A path to precision medicine.

Traditional gene-disease associations, as identified by GWAS data, existed solely based on the presence of genetic mutations. However, with the large amount of data that has and continues to be collected from the multiple -omics (epigenomics, transcriptomics, proteomics, interactomics, and metabolomics) researchers and clinicians can gain a more precise understanding of how individual genetic mutations correlate to distinct disease phenotypes. To actualize on the promise of precision medicine, an integrated approach from gene association to phenotype investigation must be taken. Computational modeling can be used to predict consequences of specific gene mutations at the protein level (i.e. altered conformation, protein-protein interactions etc.). These findings could then be experimentally validated in vitro and/or in vivo to provide a deeper insight into the molecular mechanism(s) that may be altered and how these affect the distinct phenotype observed as a result of each mutation. The big data that can be produced at each step along this path can be integrated and applied to algorithms of machine learning to better predict individualized disease risk, drug sensitivity, and prognosis.