Jelena Kusic Tisma1*, Marija Orlic Milacic2, Quang Trinh2, Rhea Ahluwalia2,3, Lincoln D. Stein2,3
1Institute of Molecular Genetics and Genetic Engineering, University of Belgrade, Vojvode Stepe 444a, Belgrade, Serbia
2Ontario Institute for Cancer Research, 661 University Avenue, Suite 510, Toronto, ON, Canada, M5G 0A3
3Department of Molecular Genetics, University of Toronto, Medical Science Building, Room 4386, 1 King’s College Circle, Toronto, ON, Canada, M5S 1A8
jkusic [at] imgge.bg.ac.rs
Abstract
The pre-replication complex (preRC) consists of 15 proteins that mark DNA replication initiation sites and regulate replication timing. Deficiency in preRC proteins results in genomic instability (re-replication) and developmental defects (Meier-Gorlin syndrome). Our aim was to assess the scope of preRC gene aberrations in cancer. Variations in preRC genes were studied using CBio Portal software and TCGA PanCancer dataset. The functional impact of detected variants was evaluated in silico by three different prediction tools: SIFT (sequence and evolutionary conservation – based), PolyPhen2 (protein sequence and structure – based) and MutPred2 (supervised learning method based on neural networks).
No mutational hotspots were observed in any of the 15 preRC genes and no mutual exclusivity between mutations in preRC genes were detected. The highest alteration incidence in preRC genes was found in endometrial carcinoma and melanoma. The majority of the variations seen in preRC genes were non-synonymous. The functional assessment has shown that 253/1215 (21%) preRC gene mutations were predicted to be pathogenic with high confidence by 2/3 computational algorithms. None of the variants reached the high confidence pathogenicity score by all 3 prediction tool. In contrast, 49% of variants were predicted to be either benign by all three tools or benign by 2/3 or 1/3 tools, with the remaining 1/3 or 2/3, respectively, classifying them as low confidence pathogenic.
These finding suggest that mutations in preRC proteins might be passenger mutations and that cancer cells can tolerate them. The future step is to see whether incidence of coding vs. noncoding preRC mutations correlates with Tumor Mutation Burden (TMB) and Genome Instability Index (GII ) of cancer.
Keywords: preRC, data mining, cBioPortal
