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> Disorders > Cell or Molecular Dysfunction > Genomic Instability

Genomic Instability

Genomic Instability is a condition characterized by an increased rate of genetic alterations, including chromosome rearrangements, aneuploidy, and gene mutations.
This instability can lead to the development and progression of various diseases, such as cancer, neurodegenerative disorders, and aging-related conditions.
Understanding the mechanisms underlying genomic instability is crucial for the development of effective diagnostic and therapeutic strategies.
PubCompare.ai's AI-driven protocol optimization platform can enhance the accuracy of your genomic instability research by providing access to a wide range of protocols from literature, pre-prints, and patents, and leveraging AI-driven comparisons to identify the most effective protocols and products.
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Most cited protocols related to «Genomic Instability»

1
Exome-seq-based Copy Number Profiling

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Publication 2017
Alleles CDKN2A Gene Chromosomes Copy Number Polymorphism Diploid Cell Diploidy Exome Genome Genomic Instability Homozygote Loss of Heterozygosity Melanoma PTEN protein, human
2
Measuring Genome Instability Indices
Structural Complexity Score (SCS): defined as the sum of all structurally aberrant regions. Regions of intra-chromosomal gain and loss were defined relative to the modal copy number of the chromosome, and each region counted as one structural aberration. To avoid over-estimation, aberrant regions <1 MB were excluded. Numerical complexity score (NCS): the sum of all whole chromosome gains and losses (chromosomes with >75% of SNP copy number values higher or lower than the ploidy of the sample were counted as whole chromosome gains or losses respectively). Multiple copy number events affecting the same chromosome were scored separately (e.g. −2 copies = 2 chromosome losses). NCS and SCS scores were divided by 1.5 for triploid cell lines, and by 2 for tetraploid cell lines, to account for the increased likelihood of karyotypic abnormalities in polyploid genomes. Weighted genome instability index: As FACS-based DNA index measures were not available for the TCGA tumours, and information about MSI status was unavailable for a sufficient number of tumours, an alternative means of classification was required. The genome instability index (GII)33 is the percentage of SNPs across the genome present at an aberrant copy number, relative to the baseline ploidy of the sample. We adapted the GII in order to account for variation in chromosome size, so that large chromosomes do not have a greater effect on the score than small chromosomes: % aberrant SNPs for each chromosome was calculated separately and mean % aberration then calculated across all 22 chromosomes. To define a threshold for CIN− versus CIN+, the weighted GII (wGII) was calculated for the cell lines. A threshold of 0.2 accurately distinguished CIN+ from CIN−, as previously defined28 . The same threshold was then applied to the TCGA cohort of tumours.
Burrell R.A., McClelland S.E., Endesfelder D., Groth P., Weller M.C., Shaikh N., Domingo E., Kanu N., Dewhurst S.M., Gronroos E., Chew S.K., Rowan A.J., Schenk A., Sheffer M., Howell M., Kschischo M., Behrens A., Helleday T., Bartek J., Tomlinson I.P, & Swanton C. (2013). Replication stress links structural and numerical cancer chromosomal instability. Nature, 494(7438), 492-496.
Publication 2013
Cell Lines Chromosome Aberrations Chromosomes Chromosomes, Human, Pair 2 Chromosomes, Human, Pair 22 Genome Genomic Instability Neoplasms Polyploidy Single Nucleotide Polymorphism Tetraploidy Triploidy
3
Measuring Genome Instability Indices
Structural Complexity Score (SCS): defined as the sum of all structurally aberrant regions. Regions of intra-chromosomal gain and loss were defined relative to the modal copy number of the chromosome, and each region counted as one structural aberration. To avoid over-estimation, aberrant regions <1 MB were excluded. Numerical complexity score (NCS): the sum of all whole chromosome gains and losses (chromosomes with >75% of SNP copy number values higher or lower than the ploidy of the sample were counted as whole chromosome gains or losses respectively). Multiple copy number events affecting the same chromosome were scored separately (e.g. −2 copies = 2 chromosome losses). NCS and SCS scores were divided by 1.5 for triploid cell lines, and by 2 for tetraploid cell lines, to account for the increased likelihood of karyotypic abnormalities in polyploid genomes. Weighted genome instability index: As FACS-based DNA index measures were not available for the TCGA tumours, and information about MSI status was unavailable for a sufficient number of tumours, an alternative means of classification was required. The genome instability index (GII)33 is the percentage of SNPs across the genome present at an aberrant copy number, relative to the baseline ploidy of the sample. We adapted the GII in order to account for variation in chromosome size, so that large chromosomes do not have a greater effect on the score than small chromosomes: % aberrant SNPs for each chromosome was calculated separately and mean % aberration then calculated across all 22 chromosomes. To define a threshold for CIN− versus CIN+, the weighted GII (wGII) was calculated for the cell lines. A threshold of 0.2 accurately distinguished CIN+ from CIN−, as previously defined28 . The same threshold was then applied to the TCGA cohort of tumours.
Burrell R.A., McClelland S.E., Endesfelder D., Groth P., Weller M.C., Shaikh N., Domingo E., Kanu N., Dewhurst S.M., Gronroos E., Chew S.K., Rowan A.J., Schenk A., Sheffer M., Howell M., Kschischo M., Behrens A., Helleday T., Bartek J., Tomlinson I.P, & Swanton C. (2013). Replication stress links structural and numerical cancer chromosomal instability. Nature, 494(7438), 492-496.
Publication 2013
Cell Lines Chromosome Aberrations Chromosomes Chromosomes, Human, Pair 2 Chromosomes, Human, Pair 22 Genome Genomic Instability Neoplasms Polyploidy Single Nucleotide Polymorphism Tetraploidy Triploidy
4
Comprehensive Somatic Copy Number Analysis

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Publication 2019
Chromosomal Instability Chromosomes Copy Number Polymorphism Deletion Mutation Diploid Cell Down-Regulation Genes Genome Genomic Instability Germ-Line Mutation Germ Line Mutation Neoplasms Proteins Transcriptional Activation
5
Optimized Array for Genomic Analysis
V8 OLIGO is a custom-designed array with approximately 180,000 interrogating oligonucleotides, manufactured by Agilent Technologies, Inc. (Santa Clara, CA). This array contains the “best-performing” oligonucleotides (oligos) selected from Agilent’s online library (eArray; https://earray.chem.agilent.com/earray/) and has been further empirically optimized. Genomic features of the V8 OLIGO design include interrogation of all known microdeletion and microduplication syndrome regions as well as pericentromeric and subtelomeric regions and computationally predicted NAHR-mediated genomic instability regions flanked by low-copy repeats (LCR) as previously described [El-Hattab et al., 2009 (link)]. In addition, ~1,700 selected known or candidate disease genes have exonic coverage (101,644 probes in 24,319 exons; average of 4.2 probes/exon) as well as introns greater than 10 kb. The entire nuclear genome is covered with an average resolution of 30 kb, excluding LCRs and other repetitive sequences. Six hundred seventy probes interrogating the mitochondrial genome (average resolution of 25 bp) are also included. Further details are available at https://www.bcm.edu/geneticlabs/.
All genomic coordinates are based on the March 2006 assembly of the reference genome (NCBI36/hg18).
Boone P.M., Bacino C.A., Shaw C.A., Eng P.A., Hixson P.M., Pursley A.N., Kang S.H., Yang Y., Wiszniewska J., Nowakowska B.A., Gaudio D.D., Xia Z., Simpson-Patel G., Immken L.L., Gibson J.B., Tsai A.C., Bowers J.A., Reimschisel T.E., Schaaf C.P., Potocki L., Scaglia F., Gambin T., Sykulski M., Bartnik M., Derwinska K., Wisniowiecka-Kowalnik B., Lalani S.R., Probst F.J., Bi W., Beaudet A.L., Patel A., Lupski J.R., Cheung S.W, & Stankiewicz P. (2010). Detection of Clinically Relevant Exonic Copy-Number Changes by Array CGH. Human mutation, 31(12), 1326-1342.
Publication 2010
DNA Library Exons Genes Genome Genome, Mitochondrial Genomic Instability Introns Low-Copy Repeats Oligonucleotide Arrays Oligonucleotides Repetitive Region Syndrome Vincristine

Most recents protocols related to «Genomic Instability»

1
Targeting ADAR1 in High Interferon Tumors
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Example 17

Since interferon signaling is spontaneously activated in a subset of cancer cells and exposes potential therapeutic vulnerabilities, it was tested whether there is evidence for similar endogenous interferon activation in primary human tumors. An IFN-GES threshold was computed to predict ADAR dependency across the CCLE cell lines and was determined to be a z-score above 2.26 (FIG. 66, panel A). This threshold was applied to The Cancer Genome Atlas (TCGA) tumors, to identify primary cancers with similarly high interferon activation. Restricting the analysis to the 4,072 samples analyzed by TCGA with at least 70% tumor purity as estimated by the ABSOLUTE algorithm (Carter et al. (2012) Nat. Biotechnol. 30:413-421), 2.7% of TCGA tumors displayed IFN-GESs above this threshold (FIG. 66, panel B and. GSEA of amplified genes in these high purity, high interferon tumors revealed the top pathway as “Type I Interferon Receptor Binding”, comprising 17 genes that all encode type I interferons and are clustered on chromosome 9p21.3 (FIG. 67).

Furthermore, analysis of TCGA copy number data showed that the interferon gene cluster including IFN-β (IFNβI), IFN-ε (IFNE), IFN-ω (IFNWI), and all 13 subtypes of IFN-α on chromosome 9p21.3, proximal to the CDKN2A/CDKN2B tumor suppressor locus, is one of the most frequently homozygously deleted regions in the cancer genome. The interferon genes comprise 16 of the 26 most frequently deleted coding genes across 9,853 TCGA cancer specimens for which ABSOLUTE copy number data are available (FIG. 66, panels C and D). Interferon signaling and activation, both in tumors with high IFN-GESs or deletions in chromosome 9p, therefore represent a biomarker to stratify patients who benefit from interferon modulating therapies.

In summary, specific cancer cell lines have been identified with elevated IFN-β signaling triggered by an activated cytosolic DNA sensing pathway, conferring dependence on the RNA editing enzyme, ADAR1. In cells with low, basal interferon signaling, the cGAS-STING pathway is inactive and PKR levels are reduced (FIG. 68, panel A). Upon cGAS-STING activation, interferon signaling and PKR protein levels are elevated but ADAR1 is still able to suppress PKR activation (FIG. 68, panel B). However, once ADAR1 is deleted, the abundant PKR becomes activated and leads to downstream signaling and cell death (FIG. 68, panel C). This is also shown in normal cells lines (e.g. A549 and NCI-H1437) once exogenous interferon is introduced (FIG. 68, panel D). ADAR1 deficiency in cell lines with high interferon levels, whether from endogenous or exogenous sources, led to phosphorylation and activation of PKR, ATF4-mediated gene expression, and apoptosis. Recent studies have shown that cGAS activation and innate interferon signaling, induced by cytosolic DNA released from the nucleus by DNA damage and genome instability (Mackenzie et al. (2017) Nature 548:461-465; Harding et al. (2017) Nature 548:466-470), led to elevated interferon-related gene expression signatures, which have been linked to resistance to DNA damage, chemotherapy, and radiation in cancer cells (Weichselbaum et al. (2008) Proc. Natl. Acad. Sci. USA 105:18490-18495). In high-interferon tumors, blocking ADAR1 might be effective to induce PKR-mediated apoptotic pathways while upregulating type I interferon signaling, which could contribute to anti-tumor immune responses (Parker et al. (2016) Nature 16:131-144). Alternatively, in tumors without activated interferon signaling, ADAR1 inhibition can be combined with localized interferon inducers, such as STING agonists, chemotherapy, or radiation. Generation of specific small molecule inhibitors targeting ADAR1 exploits this novel vulnerability in lung and other cancers and serves to enhance innate immunity in combination with immune checkpoint inhibitors.

US11873486B2. Modulating dsRNA editing, sensing, and metabolism to increase tumor immunity and improve the efficacy of cancer immunotherapy and/or modulators of intratumoral interferon (2024-01-16). Dana-Farber Cancer Institute, Inc. [US]. Inventors: Matthew Meyerson [US], William N. Haining [US], Jeffrey Ishizuka [US], Robert Manguso [US], Hugh Gannon [US].
Patent 2024
agonists Apoptosis ATF4 protein, human Biological Markers CDKN2A Gene Cell Death Cell Lines Cell Nucleus Cells Chromogranin A Chromosome Deletion Chromosomes, Human, Pair 3 Cytosol DNA Damage Electromagnetic Radiation Enzymes Gene, Cancer Gene Clusters Gene Expression Genes Genome Genomic Instability Homo sapiens IFNAR2 protein, human Immune Checkpoint Inhibitors Immunity, Innate inhibitors Interferon-alpha Interferon Inducers interferon omega 1 Interferons Interferon Type I Lung Malignant Neoplasms Neoplasms Oncogenes Patients Pharmacotherapy Phosphorylation Proteins Psychological Inhibition Response, Immune Tumor Suppressor Genes
2
Immunogenomic Landscape Characterization
Immunogenomic features were obtained from a previous pan-cancer immune landscape project performed by Thorsson et al. (18 (link)). In brief, TNB (tumor neoantigen burden) was defined as a critical target of anti-tumor immunity and calculated by the NetMHCpan algorithm (34 (link)). HRD score was used to evaluate the deficit by summation of loss of heterozygosity (LOH), large-scale transitions (LST), and genomic instability scores (GIS) (35 (link)). The relative abundance of 22 immune cell types was estimated by the CIBERSORT algorithm (36 (link)).
He P., Liu J., Xu Q., Ma H., Niu B., Huang G, & Wu W. (2023). Development and validation of a mutation-based model to predict immunotherapeutic efficacy in NSCLC. Frontiers in Oncology, 13, 1089179.
Publication 2023
Cells Genomic Instability Loss of Heterozygosity Malignant Neoplasms Neoplasms Response, Immune Tumor Burden
3
Modeling Fragile Site-Induced Genome Instability
Fragile sites are the cause of genome instability in the model. We let fragile sites cause large‐scale chromosomal mutations with a per‐fragile site probability μf. We take into account that large‐scale mutations in Streptomyces preferentially disrupt telomeric regions (Chen et al, 2002 (link); Hopwood, 2006 (link); Hoff et al, 2018 (link); Tidjani et al, 2020 (link)) by letting fragile site‐induced mutations delete the entire chromosomal region downstream (i.e., to the right) of the genomic location of the fragile site (see Fig 1C). Effectively, this means that we model one arm of the chromosome, and that the model centromere and telomere result from the asymmetric effect of fragile site deletions. No other type of mutation has any left/right preference in the model.
Colizzi E.S., van Dijk B., Merks R.M., Rozen D.E, & Vroomans R.M. (2023). Evolution of genome fragility enables microbial division of labor. Molecular Systems Biology, 19(3), e11353.
Publication 2023
Centromere Chromosome Fragile Sites Chromosomes Gene Deletion Genome Genomic Instability Mutation Streptomyces Telomere
4
Determining Genomic Instability in LUSC
An unsupervised clustering algorithm was applied to classify the MSI status of TCGA–LUSC samples based on the expression of seven genes encoding MMR proteins (MSH2, MSH3, MSH6, MLH1, MLH3, PMS2, and PMS1). The median absolute deviation (MAD) of the data matrix was used for further cluster analysis. 1,000 time repetitions were applied for guaranteeing the stability of classification. The agglomerative hierarchical clustering algorithm was based upon Pearson’s correlation distance. The highest cluster group was set as 6 (k = 6). The heatmap of consensus matrices, cluster-consensus plot, and item-consensus plot were used for defining the ultimate MSI clusters by taking the stability and purity of clusters into consideration (Hänzelmann et al., 2013 (link)). The aforementioned steps were carried out using the ConsensusClusterPlus package.
Gene set variation analysis (GSVA) was performed to derive the MSI score based on the MMR system gene set that contained the seven genes to identify the MSI status of each sample (Chalmers et al., 2017 (link)). Genomic instability of different groups based on MSI status was characterized and compared by measuring TMB, mutant-allele tumor heterogeneity (MATH), DNA ploidy status, and aneuploidy score. TMB was calculated as the rate of somatic non-synonymous mutations per megabase of sequenced DNA. The exome size was estimated as 38 Mb (Mayakonda et al., 2018 (link)). To evaluate tumor genomic heterogeneity, MATH was calculated as the MAD and the median of variant allele frequencies of non-synonymous variants using the “inferHeterogeneity” implemented in maftools (Carter et al., 2012 (link)). DNA content is the main biologic index of tumor multiplication potentiality. Ploidy reflects the actual DNA content of cancer cells (Taylor et al., 2018 (link)). Aneuploidy reflects the imbalance and complication of DNA replication. DNA ploidy calculated using the Absolute algorithm and the aneuploidy score of TCGA–LUSC samples was directly downloaded from https://gdc.cancer.gov/about-data/publications/panimmune (Langfelder and Horvath, 2008 (link)).
Hu Z., Liu Z., Zheng J., Peng Y., Lu X., Li J., Tan K, & Cui H. (2023). Microsatellite instability-related prognostic risk score (MSI-pRS) defines a subset of lung squamous cell carcinoma (LUSC) patients with genomic instability and poor clinical outcome. Frontiers in Genetics, 14, 1061002.
Publication 2023
Alleles Aneuploidy Biopharmaceuticals Diploid Cell DNA Replication Exome Gene Expression Genes Genetic Diversity Genetic Heterogeneity Genitalia Genome Genomic Instability Malignant Neoplasms mismatch repair protein 1, human Missense Mutation MLH1 protein, human MLH3 protein, human MSH6 protein, human Neoplasms PMS2 protein, human Proteins Silent Mutation
5
Comprehensive Tumor Mutational Analysis
TMB is defined as the total number of base substitutions, insertions, and deletions per million bases in the coding region [24 (link)]. The TMB of each sample was calculated by the “tmb” function in the maftools package [21 (link)]. In addition, we recruited multiple measures, including neoantigens, SNV neoantigens, nonsilent mutation rate, and TCR Shannon from Thorsson V et al., to assess differences in overall genomic instability and immune status between the two groups [25 (link)].
Wang L., Chen K., Weng S., Xu H., Ren Y., Cheng Q., Luo P., Zhang J., Liu Z, & Han X. (2023). PI3K pathway mutation predicts an activated immune microenvironment and better immunotherapeutic efficacy in head and neck squamous cell carcinoma. World Journal of Surgical Oncology, 21, 72.
Publication 2023
Gene Deletion Genomic Instability Insertion Mutation

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More about "Genomic Instability"

Genomic instability is a condition characterized by an increased rate of genetic alterations, including chromosome rearrangements, aneuploidy, and gene mutations.
This state of heightened genetic instability can lead to the development and progression of various diseases, such as cancer, neurodegenerative disorders, and age-related conditions.
Understanding the underlying mechanisms of genomic instability is crucial for developing effective diagnostic and therapeutic strategies.
PubCompare.ai's AI-driven protocol optimization platform can enhance the accuracy of your genomic instability research by providing access to a wide range of protocols from literature, preprints, and patents.
By leveraging AI-driven comparisons, you can identify the most effective protocols and products to streamline your research workflow and unlock new insights.
Improve the accuracy of your genomic instability studies today with PubCompare.ai's powerful tools.
Explore the role of MycoAlert Mycoplasma Detection Kit, Thermo Shandon Cytospin 3, and relevant isotype controls in your genomic instability research.
Utilize X-Ten sequencing, Penicillin/streptomycin, and the DNeasy Blood and Tissue Kit to uncover the genetic alterations associated with genomic instability.
Additionally, Colcemid, the CellTiter-Glo® 3D Cell Viability Assay, and IL-4 can provide valuable insights into the cellular and molecular mechanisms underlying this condition.
Optimize your genomic instability research with PubCompare.ai's AI-driven protocol optimization platform and unlock new discoveries to advance our understanding of this complex and multifaceted condition.