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Publisher's version (útgefin grein), Circular RNAs (circRNAs) are a class of RNAs that is under increasing scrutiny, although their functional roles are debated. We analyzed RNA-seq data of 348 primary breast cancers and developed a method to identify circRNAs that does not rely on unmapped reads or known splice junctions. We identified 95,843 circRNAs, of which 20,441 were found recurrently. Of the circRNAs that match exon boundaries of the same gene, 668 showed a poor or even negative (R < 0.2) correlation with the expression level of the linear gene. In silico analysis showed only a minority (8.5%) of circRNAs could be explained by known splicing events. Both these observations suggest that specific regulatory processes for circRNAs exist. We confirmed the presence of circRNAs of CNOT2, CREBBP, and RERE in an independent pool of primary breast cancers. We identified circRNA profiles associated with subgroups of breast cancers and with biological and clinical features, such as amount of tumor lymphocytic infiltrate and proliferation index. siRNA-mediated knockdown of circCNOT2 was shown to significantly reduce viability of the breast cancer cell lines MCF-7 and BT-474, further underlining the biological relevance of circRNAs. Furthermore, we found that circular, and not linear, CNOT2 levels are predictive for progression-free survival time to aromatase inhibitor (AI) therapy in advanced breast cancer patients, and found that circCNOT2 is detectable in cell-free RNA from plasma. We showed that circRNAs are abundantly present, show characteristics of being specifically regulated, are associated with clinical and biological properties, and thus are relevant in breast cancer., We thank the Erasmus MC Cancer Computational Biology Center for giving access to their IT infrastructure and the software that was used for the computations and data analysis in this study. We thank Sandra Albassam for her help with the first versions of the script to identify circular regions. We thank Maurice P.H.M. Jansen, Jean C. Helmijr, Inge de Kruijff, and Manouk K. Bos for their help in evaluating plasma samples that were gathered in the EU-FP7 CareMore (nr 601760) project. We thank for technical support Miriam Ragle Aure and Anita Langerød of the Oslo University Hospital, Norway; Ewan Birney of the European Bioinformatics Institute, UK; and Stefania Tommasi of the IRCCS Istituto Tumori “Giovanni Paolo II,” Bari, Italy. We thank the Oslo Breast Cancer Research Consortium (OSBREAC), Norway (https://www.ous-research.no/home/kgjebsen/home/14105) for contributing patient samples and Sabine Linn and Marleen Kok of The Netherlands Cancer Institute for contributing samples for the AI cohort. Finally, we thank all members of the ICGC Breast Cancer Working Group. This work has been funded through the ICGC Breast Cancer Working group by the Breast Cancer Somatic Genetics Study (a European research project funded by the European Community’s Seventh Framework Programme (FP7/2010-2014) under the grant agreement number 242006) and the Triple Negative project funded by the Wellcome Trust (grant reference 077012/Z/05/Z). F.G.R.-G. and S.M. were funded by BASIS. J.A.F. was funded through an ERC Advanced Grant (ERC-2012-AdG-322737) and ERC Proof-of-Concept Grant (ERC-2017-PoC-767854). K.U. was funded by the Daniel den Hoed Foundation. S.N.-Z. is a Wellcome Beit Fellow and personally funded by a Wellcome Trust Intermediate Fellowship (WT100183MA). A.L.R. is partially supported by the Dana-Farber/Harvard Cancer Center SPORE in Breast Cancer (NIH/NCI 5 P50 CA16 8504-02). A.M.S. was supported by Cancer Genomics Netherlands (CGC.nl) through a grant from the Netherlands Organization of Scientific research (NWO). M. Smid was supported by the EU-FP7-DDR response project. C.D. was supported by a grant from the Breast Cancer Research Foundation. J.E. was funded by The Icelandic Centre for Research (RANNIS).

Drug resistance is an almost inevitable consequence of cancer therapy and ultimately proves fatal for the majority of patients. In many cases this is the consequence of specific gene mutations that have the potential to be targeted to re-sensitize the tumor. The ability to uniformly saturate the genome with point mutations without chromosome or nucleotide sequence context bias would open the door to identify all putative drug resistance mutations in cancer models. Here we describe such a method for elucidating drug resistance mechanisms using genome-wide chemical mutagenesis allied to next-generation sequencing. We show that chemically mutagenizing the genome of cancer cells dramatically increases the number of drug-resistant clones and allows the detection of both known and novel drug resistance mutations. We have developed an efficient computational process that allows for the rapid identification of involved pathways and druggable targets. Such a priori knowledge would greatly empower serial monitoring strategies for drug resistance in the clinic as well as the development of trials for drug resistant patients.

Cancer genomes are complex, carrying thousands of somatic mutations including base substitutions, insertions and deletions, rearrangements, and copy number changes that have been acquired over decades. Recently, technologies have been introduced that allow generation of high-resolution, comprehensive catalogs of somatic alterations in cancer genomes. However, analyses of these data sets generally do not indicate the order in which mutations have occurred, or the resulting karyotype. Here, we introduce a mathematical framework that begins to address this problem. By using samples with accurate data sets, we can reconstruct relatively complex temporal sequences of rearrangements and provide an assembly of genomic segments into digital karyotypes. For cancer genes mutated in rearranged regions, this information can provide a chronological examination of the selective events that have taken place.

For decades, cytogenetic studies have demonstrated that somatically acquired structural rearrangements of the genome are a common feature of most classes of human cancer. However, the characteristics of these rearrangements at sequence-level resolution have thus far been subject to very limited description. One process that is dependent upon somatic genome rearrangement is gene amplification, a mechanism often exploited by cancer cells to increase copy number and hence expression of dominantly acting cancer genes. The mechanisms underlying gene amplification are complex but must involve chromosome breakage and rejoining. We sequenced 133 different genomic rearrangements identified within four cancer amplicons involving the frequently amplified cancer genes MYC, MYCN, and ERBB2. The observed architectures of rearrangement were diverse and highly distinctive, with evidence for sister chromatid breakage–fusion–bridge cycles, formation and reinsertion of double minutes, and the presence of bizarre clusters of small genomic fragments. There were characteristic features of sequences at the breakage–fusion junctions, indicating roles for nonhomologous end joining and homologous recombination-mediated repair mechanisms together with nontemplated DNA synthesis. Evidence was also found for sequence-dependent variation in susceptibility of the genome to somatic rearrangement. The results therefore provide insights into the DNA breakage and repair processes operative in somatic genome rearrangement and illustrate how the evolutionary histories of individual cancers can be reconstructed from large-scale cancer genome sequencing.

Mutation is associated with developmental and hereditary disorders, aging, and cancer. While we understand some mutational processes operative in human disease, most remain mysterious. We used Caenorhabditis elegans whole-genome sequencing to model mutational signatures, analyzing 183 worm populations across 17 DNA repair-deficient backgrounds propagated for 20 generations or exposed to carcinogens. The baseline mutation rate in C. elegans was approximately one per genome per generation, not overtly altered across several DNA repair deficiencies over 20 generations. Telomere erosion led to complex chromosomal rearrangements initiated by breakage–fusion–bridge cycles and completed by simultaneously acquired, localized clusters of breakpoints. Aflatoxin B1 induced substitutions of guanines in a GpC context, as observed in aflatoxin-induced liver cancers. Mutational burden increased with impaired nucleotide excision repair. Cisplatin and mechlorethamine, DNA crosslinking agents, caused dose- and genotype-dependent signatures among indels, substitutions, and rearrangements. Strikingly, both agents induced clustered rearrangements resembling “chromoanasynthesis,” a replication-based mutational signature seen in constitutional genomic disorders, suggesting that interstrand crosslinks may play a pathogenic role in such events. Cisplatin mutagenicity was most pronounced in xpf-1 mutants, suggesting that this gene critically protects cells against platinum chemotherapy. Thus, experimental model systems combined with genome sequencing can recapture and mechanistically explain mutational signatures associated with human disease.

A comprehensive understanding of cancer is predicated upon knowledge of the structure of malignant genomes underlying its many variant forms and the molecular mechanisms giving rise to them. It is well established that solid tumor genomes accumulate a large number of genome rearrangements during tumorigenesis. End Sequence Profiling (ESP) maps and clones genome breakpoints associated with all types of genome rearrangements elucidating the structural organization of tumor genomes. Here we extend the ESP methodology in several directions using the breast cancer cell line MCF-7. First, targeted ESP is applied to multiple amplified loci, revealing a complex process of rearrangement and coamplification in these regions reminiscent of breakage/fusion/bridge cycles. Second, genome breakpoints identified by ESP are confirmed using a combination of DNA sequencing and PCR. Third, in vitro functional studies assign biological function to a rearranged tumor BAC clone, demonstrating that it encodes antiapoptotic activity. Finally, ESP is extended to the transcriptome identifying four novel fusion transcripts and providing evidence that expression of fusion genes may be common in tumors. These results demonstrate the distinct advantages of ESP including: (1) the ability to detect all types of rearrangements and copy number changes; (2) straightforward integration of ESP data with the annotated genome sequence; (3) immortalization of the genome; (4) ability to generate tumor-specific reagents for in vitro and in vivo functional studies. Given these properties, ESP could play an important role in a tumor genome project.

Genomic copy number alterations are a feature of many human diseases including cancer. We have evaluated the effectiveness of an oligonucleotide array, originally designed to detect single-nucleotide polymorphisms, to assess DNA copy number. We first showed that fluorescent signal from the oligonucleotide array varies in proportion to both decreases and increases in copy number. Subsequently we applied the system to a series of 20 cancer cell lines. All of the putative homozygous deletions (10) and high-level amplifications (12; putative copy number >4) tested were confirmed by PCR (either qPCR or normal PCR) analysis. Low-level copy number changes for two of the lines under analysis were compared with BAC array CGH; 77% (n = 44) of the autosomal chromosomes used in the comparison showed consistent patterns of LOH (loss of heterozygosity) and low-level amplification. Of the remaining 10 comparisons that were discordant, eight were caused by low SNP densities and failed in both lines. The studies demonstrate that combining the genotype and copy number analyses gives greater insight into the underlying genetic alterations in cancer cells with identification of complex events including loss and reduplication of loci.