Cancer Biology

Recent studies from multiple labs including ours have demonstrated the importance of extrachromosomal circular DNA (eccDNA) from yeast to humans (Shibata et al., 2012; Dillon et al., 2015; Møller et al., 2016; Kumar et al., 2017; Turner et al., 2017; Kim et al., 2020). More recently, it has been found that cancer cells obtain a selective advantage by amplifying oncogenes on eccDNA, which drives genomic instability (Wu et al., 2019; Kim et al., 2020). Previously, we have purified circular DNA and enriched the population using rolling circle amplification followed by high-throughput sequencing for the identification of eccDNA based on the unique junctional sequence. However, eccDNA identification by rolling circle amplification is biased toward small circles. Here, we report a rolling circle-independent method to detect eccDNA in human cancer cells. We demonstrate a sensitive and robust step-by-step workflow for finding novel eccDNAs using ATAC-seq (Assay for Transposase-Accessible Chromatin using sequencing) combined with a Circle_finder bioinformatics algorithm to predict the eccDNAs, followed by its validation using two independent methods, inverse PCR and metaphase FISH (Fluorescence in situ Hybridization).

Cancer cell lines serve as invaluable model systems for cancer biology research and help in evaluating the efficacy of new therapeutic agents. However, cell line contamination and misidentification have become one of the most pressing problems affecting biomedical research. Available methods of cell line authentication suffer from limited access, time-consuming and often costly for many researchers, hence a new and cost-effective approach for cell line authentication is needed. In this regard, we developed a new method called CeL-ID for cell line authentication using genomic variants as a byproduct derived from RNA-seq data. CeL-ID was trained and tested on publicly available more than 900 RNA-seq dataset derived from the Cancer Cell Line Encyclopedia (CCLE) project; including most frequently used adult and pediatric cancer cell lines. We generated cell line specific variant profiles from RNA-seq data using our in-house pipeline followed by pair-wise variant profile comparison between cell lines using allele frequencies and depth of coverage values of the entire variant set. Comparative analysis of variant profiles revealed that they differ significantly from cell line to cell line whereas identical, synonymous and derivative cell lines share high variant identity and their allelic fractions are highly correlated, which is the basis of this cell line authentication protocol. Additionally, CeL-ID also includes a method to estimate the possible cross-contamination using a linear mixture model with any possible CCLE cells in case no perfect match was detected.

Homologous recombination deficiency, mainly resulted from BRCA1 or BRCA2 inactivation (so called BRCAness), is found in breast and ovarian cancers. Detection of actual inactivation of BRCA1/2 in a tumor is important for patients’ treatment and follow-up as it may help predicting response to DNA damaging agents and give indication Homologous recombination deficiency, mainly resulted from BRCA1 or BRCA2 inactivation (so called BRCAness), is found in breast and ovarian cancers. Detection of actual inactivation of BRCA1/2 in a tumor is important for pat for genetic testing. This protocol describes how to detect impairment of homologous recombination based on the tumor genomic profile measured by SNP-array. The proposed signature of BRCAness is related to the number of large-scale chromosomal breaks in a tumor genome calculated after filtering and smoothing small-scale alterations. The procedure strongly relies on good quality SNP-arrays preprocessed to absolute copy number and allelic content (allele-specific copy number) profiles. This genomic signature of homologous recombination deficiency was shown to be highly reliable in predicting BRCA1/2 inactivation in triple-negative breast carcinoma (97% accuracy; for more details, see Popova et al., 2012) and predictive of survival in ovarian carcinoma (unpublished data). Authors are grateful to Dominique Stoppa-Lyonnet, Anne Vincent-Salomon, Thierry Dubois, and Xavier Sastre-Garau for their contributions. (Patent was deposited: Reference number EP12305648.3, June 7, 2012)