In silico analysis of several frequent SLX4 mutations appearing in human cancers.

SLX4 is an interactor and activator of structure-specific exonuclease that helps resolve tangled recombination intermediates arising at stalled replication forks. It is one of the many factors that assist with homologous recombination, the major mechanism for restarting replication. SLX4 mutations have been reported in many cancers but a pan cancer map of all the mutations has not been undertaken. Here, using data from the Catalogue of Somatic Mutations in Cancers (COSMIC), we show that mutations occur in almost every cancer and many of them truncate the protein which should severely alter the function of the enzyme. We identified a frequent R1779W point mutation that occurs in the SLX4 domain required for heterodimerization with its partner, SLX1. In silico protein structure analysis of this mutation shows that it significantly alters the protein structure and is likely to destabilize the interaction with SLX1. Although this brief communication is limited to only in silico analysis, it identifies certain high frequency SLX4 mutations in human cancers that would warrant further in vivo studies. Additionally, these mutations may be potentially actionable for drug therapies.

KDM4B mutations in human cancers.

Homologous recombination (HR) is essential for repair of DNA double-strand breaks (DSBs) and restart of stalled or collapsed replication forks. Most cancers are characterized by mutations in components of the DSB repair pathways. Redundant DSB repair pathways exist in eukaryotes from yeast to humans and recent evidence has shown that complete loss of HR function appears to be lethal. Recent evidence has also shown that cancer cells with mutations in one DSB repair pathway can be killed by inhibiting one or more parallel pathways, a strategy that is currently aggressively explored as a cancer therapy. KDM4B is a histone demethylase with pleiotropic functions, which participates in preparing DSBs for repair by contributing to chromatin remodeling. In this report we carried out a pan-cancer analysis of KDM4B mutations with the goal of understanding their distribution and interaction with other DSB genes. We find that although KDM4B mutations co-occur with DSB repair genes, most KDM4B mutations are not drivers or pathogenic. A sequence conservation analysis from yeast to humans shows that highly conserved residues are resistant to mutation. Finally, all mutations occur in a heterozygous state. A single mutation, R986L, was predicted to significantly affect protein structure using computational modeling. This analysis suggests that KDM4B makes contributions to DSB repair but is not a key player.

Detection of driver mutations and genomic signatures in endometrial cancers using artificial intelligence algorithms.

Analyzed endometrial cancer (EC) genomes have allowed for the identification of molecular signatures, which enable the classification, and sometimes prognostication, of these cancers. Artificial intelligence algorithms have facilitated the partitioning of mutations into driver and passenger based on a variety of parameters, including gene function and frequency of mutation. Here, we undertook an evaluation of EC cancer genomes deposited on the Catalogue of Somatic Mutations in Cancers (COSMIC), with the goal to classify all mutations as either driver or passenger. Our analysis showed that approximately 2.5% of all mutations are driver and cause cellular transformation and immortalization. We also characterized nucleotide level mutation signatures, gross chromosomal re-arrangements, and gene expression profiles. We observed that endometrial cancers show distinct nucleotide substitution and chromosomal re-arrangement signatures compared to other cancers. We also identified high expression levels of the CLDN18 claudin gene, which is involved in growth, survival, metastasis and proliferation. We then used in silico protein structure analysis to examine the effect of certain previously uncharacterized driver mutations on protein structure. We found that certain mutations in CTNNB1 and TP53 increase protein stability, which may contribute to cellular transformation. While our analysis retrieved previously classified mutations and genomic alterations, which is to be expected, this study also identified new signatures. Additionally, we show that artificial intelligence algorithms can be effectively leveraged to accurately predict key drivers of cancer. This analysis will expand our understanding of ECs and improve the molecular toolbox for classification, diagnosis, or potential treatment of these cancers.