Abstract:BackgroundGenomic gains and losses are a result of genomic instability in many types of cancers. BRCA1- and BRCA2-mutated breast cancers are associated with increased amounts of chromosomal aberrations, presumably due their functions in genome repair. Some of these genomic aberrations may harbor genes whose absence or overexpression may give rise to cellular growth advantage. So far, it has not been easy to identify the driver genes underlying gains and losses. A powerful approach to identify these driver gene… Show more
“…Subsequently, it has been found that the total number of CNAs, also referred to as the chromosomal instability (CIN) score or the genomic instability index, differs between BRCA-associated familial cancers and sporadic tumors. Mainly BRCA1-mutated breast tumors were observed to have the highest number of CNAs compared to other tumors [198][199][200]. Furthermore, the type of CNAs differed between familial and sporadic tumors, with a higher frequency of large deletions being present in the BRCA-mutated breast cancers [199].…”
Section: Dna Copy Number Aberrationsmentioning
confidence: 96%
“…It has been shown in multiple studies that BRCA1-and BRCA2-mutated breast cancers display characteristic DNA copy number gains and losses [165,[198][199][200]. Subsequently, it has been found that the total number of CNAs, also referred to as the chromosomal instability (CIN) score or the genomic instability index, differs between BRCA-associated familial cancers and sporadic tumors.…”
Section: Dna Copy Number Aberrationsmentioning
confidence: 99%
“…Furthermore, the type of CNAs differed between familial and sporadic tumors, with a higher frequency of large deletions being present in the BRCA-mutated breast cancers [199]. Interestingly, high numbers of CNAs were also found in sporadic ER-negative/TN/basal-like breast cancers [42,[199][200][201] and these tumors clustered with the BRCA1-mutated group [42,199]. Similarly, the number of CNAs in ER-positive breast cancers was found to be highest in luminal B tumors, which co-clustered with BRCA2-mutated breast cancers [42,201].…”
Breast and ovarian cancer are among the most common malignancies diagnosed in women worldwide. Together, they account for the majority of cancer-related deaths in women. These cancer types share a number of features, including their association with hereditary cancer syndromes caused by heterozygous germline mutations in BRCA1 or BRCA2. BRCA-associated breast and ovarian cancers are hallmarked by genomic instability and high sensitivity to DNA double-strand break (DSB) inducing agents due to loss of error-free DSB repair via homologous recombination (HR). Recently, poly(ADP-ribose) polymerase inhibitors, a new class of drugs that selectively target HR-deficient tumor cells, have been shown to be highly active in BRCA-associated breast and ovarian cancers. This finding has renewed interest in hallmarks of HR deficiency and the use of other DSB-inducing agents, such as platinum salts or bifunctional alkylators, in breast and ovarian cancer patients. In this review we discuss the similarities between breast and ovarian cancer, the hallmarks of genomic instability in BRCA-mutated and BRCA-like breast and ovarian cancers, and the efforts to search for predictive markers of HR deficiency in order to individualize therapy in breast and ovarian cancer.
“…Subsequently, it has been found that the total number of CNAs, also referred to as the chromosomal instability (CIN) score or the genomic instability index, differs between BRCA-associated familial cancers and sporadic tumors. Mainly BRCA1-mutated breast tumors were observed to have the highest number of CNAs compared to other tumors [198][199][200]. Furthermore, the type of CNAs differed between familial and sporadic tumors, with a higher frequency of large deletions being present in the BRCA-mutated breast cancers [199].…”
Section: Dna Copy Number Aberrationsmentioning
confidence: 96%
“…It has been shown in multiple studies that BRCA1-and BRCA2-mutated breast cancers display characteristic DNA copy number gains and losses [165,[198][199][200]. Subsequently, it has been found that the total number of CNAs, also referred to as the chromosomal instability (CIN) score or the genomic instability index, differs between BRCA-associated familial cancers and sporadic tumors.…”
Section: Dna Copy Number Aberrationsmentioning
confidence: 99%
“…Furthermore, the type of CNAs differed between familial and sporadic tumors, with a higher frequency of large deletions being present in the BRCA-mutated breast cancers [199]. Interestingly, high numbers of CNAs were also found in sporadic ER-negative/TN/basal-like breast cancers [42,[199][200][201] and these tumors clustered with the BRCA1-mutated group [42,199]. Similarly, the number of CNAs in ER-positive breast cancers was found to be highest in luminal B tumors, which co-clustered with BRCA2-mutated breast cancers [42,201].…”
Breast and ovarian cancer are among the most common malignancies diagnosed in women worldwide. Together, they account for the majority of cancer-related deaths in women. These cancer types share a number of features, including their association with hereditary cancer syndromes caused by heterozygous germline mutations in BRCA1 or BRCA2. BRCA-associated breast and ovarian cancers are hallmarked by genomic instability and high sensitivity to DNA double-strand break (DSB) inducing agents due to loss of error-free DSB repair via homologous recombination (HR). Recently, poly(ADP-ribose) polymerase inhibitors, a new class of drugs that selectively target HR-deficient tumor cells, have been shown to be highly active in BRCA-associated breast and ovarian cancers. This finding has renewed interest in hallmarks of HR deficiency and the use of other DSB-inducing agents, such as platinum salts or bifunctional alkylators, in breast and ovarian cancer patients. In this review we discuss the similarities between breast and ovarian cancer, the hallmarks of genomic instability in BRCA-mutated and BRCA-like breast and ovarian cancers, and the efforts to search for predictive markers of HR deficiency in order to individualize therapy in breast and ovarian cancer.
“…In a recent cross species comparison study [6] our algorithm was used successfully to compare mouse to human aCGH data, showing the wide range of datasets our method can be applied to. For a more in depth analysis and comparison of KC-SMARTR to other methods we made use of a publicly available aCGH dataset [7] consisting of copy number profiles of cell lines derived from B- and T-cell lymphoma's.…”
BackgroundMost approaches used to find recurrent or differential DNA Copy Number Alterations (CNA) in array Comparative Genomic Hybridization (aCGH) data from groups of tumour samples depend on the discretization of the aCGH data to gain, loss or no-change states. This causes loss of valuable biological information in tumour samples, which are frequently heterogeneous. We have previously developed an algorithm, KC-SMART, that bases its estimate of the magnitude of the CNA at a given genomic location on kernel convolution (Klijn et al., 2008). This accounts for the intensity of the probe signal, its local genomic environment and the signal distribution across multiple samples.ResultsHere we extend the approach to allow comparative analyses of two groups of samples and introduce the R implementation of these two approaches. The comparative module allows for a supervised analysis to be performed, to enable the identification of regions that are differentially aberrated between two user-defined classes.We analyzed data from a series of B- and T-cell lymphomas and were able to retrieve all positive control regions (VDJ regions) in addition to a number of new regions. A t-test employing segmented data, that we implemented, was also able to locate all the positive control regions and a number of new regions but these regions were highly fragmented.ConclusionsKC-SMARTR offers recurrent CNA and class specific CNA detection, at different genomic scales, in a single package without the need for additional segmentation. It is memory efficient and runs on a wide range of machines. Most importantly, it does not rely on data discretization and therefore maximally exploits the biological information in the aCGH data.The program is freely available from the Bioconductor website http://www.bioconductor.org/ under the terms of the GNU General Public License.
“…As such, whole-exome and whole-genome sequencing approaches have been used to characterize the mutational landscapes of Kras -mutant mouse skin squamous cell carcinoma [71] and Egfr -, Myc - and Kras -driven lung cancers [72,73]. Similarly, whole-exome sequencing, RNA sequencing and DNA copy-number-based approaches have identified several driver genes in mouse models of Brca1 - and Brca2 -deficient breast cancer [74,75]. Figure 3.De novo driver gene identification in mice.…”
Section: Identifying Cancer Drivers In Mouse Modelsmentioning
Cancer is a complex disease in which cells progressively accumulate mutations disrupting their cellular processes. A fraction of these mutations drive tumourigenesis by affecting oncogenes or tumour suppressor genes, but many mutations are passengers with no clear contribution to tumour development. The advancement of DNA and RNA sequencing technologies has enabled in-depth analysis of thousands of human tumours from various tissues to perform systematic characterization of their (epi)genomes and transcriptomes in order to identify (epi)genetic changes associated with cancer. Combined with considerable progress in algorithmic development, this expansion in scale has resulted in the identification of many cancer-associated mutations, genes and pathways that are considered to be potential drivers of tumour development. However, it remains challenging to systematically identify drivers affected by complex genomic rearrangements and drivers residing in non-coding regions of the genome or in complex amplicons or deletions of copy-number driven tumours. Furthermore, functional characterization is challenging in the human context due to the lack of genetically tractable experimental model systems in which the effects of mutations can be studied in the context of their tumour microenvironment. In this respect, mouse models of human cancer provide unique opportunities for pinpointing novel driver genes and their detailed characterization. In this review, we provide an overview of approaches for complementing human studies with data from mouse models. We also discuss state-of-the-art technological developments for cancer gene discovery and validation in mice.
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