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Oncogenomics

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Oncogenomics is a sub-field of genomics that characterizes cancer-associated genes. It focuses on genomic, epigenomic and transcript alterations in cancer.

Cancer is a genetic disease caused by accumulation of DNA mutations leading to unrestrained cell proliferation and neoplasm formation. The goal of oncogenomics is to identify new oncogenes or tumor suppressor genes that may provide new insights into cancer diagnosis, predicting clinical outcome of cancers and new targets for cancer therapies. The success of targeted cancer therapies such as Gleevec, Herceptin and Avastin raised the hope for oncogenomics to elucidate new targets for cancer treatment.[1]

Overall goals of oncogenomics

Besides understanding the underlying genetic mechanisms that initiate or drive cancer progression, oncogenomics targets personalized cancer treatment. Cancer develops due to DNA mutations that accumulate randomly. Identifying and targeting the mutations in an individual patient may lead to increased treatment efficacy.

The completion of the Human Genome Project facilitated the field of oncogenomics and increased the abilities of researchers to find oncogenes. Sequencing technologies have been applied to the study of oncogenomics.

History

The genomics era began in the 1990s, with the generation of DNA sequences of many organisms. In the 21st century, the completion of the Human Genome Project enabled the study of functional genomics and examining tumor genomes. Cancer is a main focus.

Access to whole cancer genome sequencing is important to cancer (or cancer genome) research because:

  • Mutations are the immediate cause of cancer and define the tumor phenotype.
  • Access to cancerous and normal tissue samples from the same patient and the fact that most cancer mutations represent somatic events, allow the identification of cancer-specific mutations.
  • Cancer mutations are cumulative and sometimes are related to disease stage. Metastasis and drug resistance are distinguishable.[2]

Whole genome sequencing

The first cancer genome was sequenced in 2008.[2] This study sequenced a typical acute myeloid leukaemia (AML) genome and its normal counterpart genome obtained from the same patient. The comparison revealed ten mutated genes. Two were already thought to contribute to tumor progression: an internal tandem duplication of the FLT3 receptor tyrosine kinase gene, which activates kinase signaling and is associated with a poor prognosis and a four base insertion in exon 12 of the NPM1 gene (NPMc). These mutations are found in 25-30% of AML tumors and are thought to contribute to disease progression rather than to cause it directly.

The remaining 8 were new mutations and all were single base changes: Four were in families that are strongly associated with cancer pathogenesis (PTPRT, CDH24, PCLKC and SLC15A1). The other four had no previous association with cancer pathogenesis. They did have potential functions in metabolic pathways that suggested mechanisms by which they could act to promote cancer (KNDC1, GPR124, EB12, GRINC1B)

These genes are involved in pathways known to contribute to cancer pathogenesis, but before this study most would not have been candidates for targeted gene therapy. This analysis validated the approach of whole cancer genome sequencing in identifying somatic mutations and the importance of parallel sequencing of normal and tumor cell genomes.[3]

In 2011, the genome of an exceptional bladder cancer patient whose tumor had been eliminated by the drug everolimus was sequenced, revealing mutations in two genes, TSC1 and NF2. The mutations disregulated mTOR, the protein inhibited by everolimus, allowing it to reproduce without limit. As a result in 2015, the Exceptional Responders Initiative was created at the National Cancer Institute. The initiative allows such exceptional patients (who have responded positively for at least six months to a cancer drug that usually fails) to have their genomes sequenced to identify the relevant mutations. Once identified, other patients could be screened for those mutations and then be given the drug. In 2016 To that end, a nationwide cancer drug trial began in 2015, involving up to twenty-four hundred centers. Patients with appropriate mutations are matched with one of more than forty drugs.[4]

In 2014 the Center for Molecular Oncology rolled out the MSK-IMPACT test, a screening tool that looks for mutations in 341 cancer-associated genes. By 2015 more than five thousand patients had been screened. Patients with appropriate mutations are eligible to enroll in clinical trials that provide targeted therapy.[4]

Technologies

Current technologies being used in Oncogenomics.

Genomics technologies include:

Genome sequencing

Further information: Cancer genome sequencing
  • DNA sequencing: Pyrosequencing-based sequencers offer a relatively low-cost method to generate sequence data.[1][5][6]
  • Array Comparative Genome Hybridization: This technique measures the DNA copy number differences between normal and cancer genomes. It uses the fluorescence intensity from fluorescent-labeled samples, which are hybridized to known probes on a microarray.[7][8]
  • Representational oligonucleotide microarray analysis: Detects copy number variation using amplified restriction-digested genomic fragments that are hybridized to human oligonucleotides, achieving a resolution between 30 and 35 kbit/s.[9]
  • Digital Karyotyping: Detects copy number variation using genomics tags obtained via restriction enzyme digests. These tags are then linked to into ditags, concatenated, cloned, sequenced and mapped back to the reference genome to evaluate tag density.[10][11]
  • Bacterial Artificial Chromosome (BAC)-end sequencing (end-sequence profiling): Identifies chromosomal breakpoints by generating a BAC library from a cancer genome and sequencing their ends. The BAC clones that contain chromosome aberrations have end sequences that do not map to a similar region of the reference genome, thus identifying a chromosomal breakpoint.[12]

Transcriptomes

  • Microarrays: Assess transcript abundance. Useful in classification, prognosis, raise the possibility of differential treatment approaches and aid identification of mutations in the proteins' coding regions.[13][14] The relative abundance of alternative transcripts has become an important feature of cancer research. Particular alternative transcript forms correlate with specific cancer types.[15]

Bioinformatics and functional analysis of oncogenes

Bioinformatics technologies allow the statistical analysis of genomic data. The functional characteristics of oncogenes has yet to be established. Potential functions include their transformational capabilities relating to tumour formation and specific roles at each stage of cancer development.

Operomics

Operomics aims to integrate genomics, transcriptomics and proteomics to understand the molecular mechanisms that underlie the cancer development.[16]

Comparative oncogenomics

Comparative oncogenomics uses cross-species comparisons to identify oncogenes. This research involves studying cancer genomes, transcriptomes and proteomes in model organisms such as mice, identifying potential oncogenes and referring back to human cancer samples to see whether homologues of these oncogenes are important in causing human cancers.[17] Genetic alterations in mouse models are similar to those found in human cancers. These models are generated by methods including retroviral insertion mutagenesis or graft transplantation of cancerous cells.

Synthetic lethality

Synthetic lethality is the knockout of a nonessential gene as an indirect way of killing a mutated oncogene that depends on it. Some oncogenes are essential for survival of all cells (not only cancer cells). Thus, drugs that knock out these oncogenes (and thereby kill cancer cells) may also damage normal cells, inducing significant illness. However, other genes may be essential to cancer cells but not to healthy cells.

One approach is to individually knock out each gene in a genome and observe the effect on normal and cancerous cells.[18][19] If the knockout of an otherwise nonessential gene has little or no effect on healthy cells, but is lethal to cancerous cells containing a mutated oncogene, then the system-wide suppression of the suppressed gene can destroy cancerous cells while leaving healthy ones relatively undamaged. The technique was used to identify PARP-1 inhibitors to treat BRCA1/BRCA2-associated cancers.[20][21] In this case, the combined presence of PARP-1 inhibition and of the cancer-associated mutations in BRCA genes is lethal only to the cancerous cells.

Databases for cancer research

See also: Databases for oncogenomic research

The Cancer Genome Project is an initiative to map out all somatic mutations in cancer. The project systematically sequences the exons and flanking splice junctions of the genomes of primary tumors and cancerous cell lines. COSMIC software displays the data generated from these experiments. As of February 2008, the CGP had identified 4,746 genes and 2,985 mutations in 1,848 tumours.

The Cancer Genome Anatomy Project includes information of research on cancer genomes, transcriptomes and proteomes.

Progenetix is an oncogenomic reference database, presenting cytogenetic and molecular-cytogenetic tumor data.

Oncomine has compiled data from cancer transcriptome profiles.

The integrative oncogenomics database IntOGen and the Gitools datasets integrate multidimensional human oncogenomic data classified by tumor type. The first version of IntOGen focused on the role of deregulated gene expression and CNV in cancer.[22] A later version emphasized mutational cancer driver genes across 28 tumor types,.[23][24] All releases of IntOGen data are made available at the IntOGen database.

The International Cancer Genome Consortium is the biggest project to collect human cancer genome data. The data is accessible through the ICGC website. The BioExpress® Oncology Suite contains gene expression data from primary, metastatic and benign tumor samples and normal samples, including matched adjacent controls. The suite includes hematological malignancy samples for many well-known cancers.

Specific databases for model animals include the Retrovirus Tagged Cancer Gene Database (RTCGD) that compiled research on retroviral and transposon insertional mutagenesis in mouse tumors.

Gene families

Mutational analysis of entire gene families revealed that genes of the same family have similar functions, as predicted by similar coding sequences and protein domains. Two such classes are the kinase family, involved in adding phosphate groups to proteins and the phosphatase family, involved with removing phosphate groups from proteins.[25] These families were first examined because of their apparent role in transducing cellular signals of cell growth or death. In particular, more than 50% of colorectal cancers carry a mutation in a kinase or phosphatase gene. Phosphatidylinositold 3-kinases (PIK3CA) gene encodes for lipid kinases that commonly contain mutations in colorectal, breast, gastric, lung and various other cancers.[26][27] Drug therapies can inhibit PIK3CA. Another example is the BRAF gene, one of the first to be implicated in melanomas.[28] BRAF encodes a serine/threonine kinase that is involved in the RAS-RAF-MAPK growth signaling pathway. Mutations in BRAF cause constitutive phosphorylation and activity in 59% of melanomas. Before BRAF, the genetic mechanism of melanoma development was unknown and therefore prognosis for patients was poor.[29]

Mitochondrial DNA

Mitochondrial DNA (mtDNA) mutations are linked the formation of tumors. Four types of mtDNA mutations have been identified:[30]

Point mutations

Point mutations have been observed in the coding and non-coding region of the mtDNA contained in cancer cells. In individuals with bladder, head/neck and lung cancers, the point mutations within the coding region show signs of resembling each other. This suggests that when a healthy cell transforms into a tumor cell (a neoplastic transformation) the mitochondria seem to become homogenous. Abundant point mutations located within the non-coding region, D-loop, of the cancerous mitochondria suggest that mutations within this region might be an important characteristic in some cancers.[30]

Deletions

This type of mutation is sporadically detected due to its small size ( < 1kb). The appearance of certain specific mtDNA mutations (264-bp deletion and 66-bp deletion in the complex 1 subunit gene ND1) in multiple types of cancer provide some evidence that small mtDNA deletions might appear at the beginning of tumorigenesis. It also suggests that the amount of mitochondria containing these deletions increases as the tumor progresses. An exception is a relatively large deletion that appears in many cancers (known as the "common deletion"), but more mtDNA large scale deletions have been found in normal cells compared to tumor cells. This may be due to a seemingly adaptive process of tumor cells to eliminate any mitochondria that contain these large scale deletions (the "common deletion" is > 4kb).[30]

Insertions

Two small mtDNA insertions of ~260 and ~520 bp can be present in breast cancer, gastric cancer, hepatocellular carcinoma (HCC) and colon cancer and in normal cells. No correlation between these insertions and cancer are established.[31]

Copy number mutations

The characterization of mtDNA via real-time polymerase chain reaction assays shows the presence of quantitative alteration of mtDNA copy number in many cancers. Increase in copy number is expected to occur because of oxidative stress. On the other hand, decrease is thought to be caused by somatic point mutations in the replication origin site of the H-strand and/or the D310 homopolymeric c-stretch in the D-loop region, mutations in the p53 (tumor suppressor gene) mediated pathway and/or inefficient enzyme activity due to POLG mutations. Any increase/decrease in copy number then remains constant within tumor cells. The fact that the amount of mtDNA is constant in tumor cells suggests that the amount of mtDNA is controlled by a much more complicated system in tumor cells, rather than simply altered as a consequence of abnormal cell proliferation. The role of mtDNA content in human cancers apparently varies for particular tumor types or sites.[30]

Mutations in mitochondrial DNA in various cancers
Cancer Type Location of Point mutations Nucleotide Position of Deletions Increase of mtDNA copy # Decrease of mtDNA copy #
D-Loop mRNAs tRNAs rRNAs
Bladder[32] X X X 15,642-15,662
Breast[33][34][35][36] X X X X 8470-13,447 and 8482-13459 X
Head and neck[33][37][38] X X X X 8470-13,447 and 8482-13459 X
Oral[39] X X 8470-13,447 and 8482-13459
Hepatocellular carcinoma (HCC)[40][41] X X X X 306-556 and 3894-3960 X
Esophageal[42] X X X 8470-13,447 and 8482-13459 X
Gastric[43][44][45] X X X 298-348 X
Prostate[46][47] X X 8470-13,447 and 8482-13459 X

57.7% (500/867) contained somatic point putations and of the 1172 mutations surveyed 37.8% (443/1127) were located in the D-loop control region, 13.1% (154/1172) were located in the tRNA or rRNA genes and 49.1% (575/1127) were found in the mRNA genes needed for producing complexes required for mitochondrial respiration.

Diagnostic applications

Some anticancer drugs target mtDNA and have shown positive results in killing tumor cells. Research has used mitochondrial mutations as biomarkers for cancer cell therapy. It is easier to target mutation within mitochondrial DNA versus nuclear DNA because the mitochondrial genome is much smaller and easier to screen for specific mutations. MtDNA content alterations found in blood samples might be able to serve as a screening marker for predicting future cancer susceptibility as well as tracking malignant tumor progression. Along with these potential helpful characteristics of mtDNA, it is not under the control of the cell cycle and is important for maintaining ATP generation and mitochondrial homeostasis. These characteristics make targeting mtDNA a practical therapeutic strategy.[30]

Cancer biomarkers

Several biomarkers can be useful in cancer staging, prognosis and treatment. They can range from single-nucleotide polymorphisms (SNPs), chromosomal aberrations, changes in DNA copy number, microsatellite instability, promoter region methylation, or even high or low protein levels.[48]

References

  1. ^ a b Strausberg R.L.; Simpson, Andrew J.G.; Old, Lloyd J.; Riggins, Gregory J.; et al. (2004). ", (2004) Oncogenomics and the development of new cancer therapies". Nature. 429 (6990): 469–474. Bibcode:2004Natur.429..469S. doi:10.1038/nature02627. PMID 15164073.
  2. ^ a b Strausberg, R L; Simpson, A J G (22 December 2009). "Whole-genome cancer analysis as an approach to deeper understanding of tumour biology". British Journal of Cancer. 102 (2): 243–248. doi:10.1038/sj.bjc.6605497. PMC 2816661. PMID 20029419.
  3. ^ Ley, Timothy J.; Mardis, Elaine R.; Ding, Li; Fulton, Bob; McLellan, Michael D.; Chen, Ken; Dooling, David; Dunford-Shore, Brian H.; McGrath, Sean; Hickenbotham, Matthew; Cook, Lisa; Abbott, Rachel; Larson, David E.; Koboldt, Dan C.; Pohl, Craig; Smith, Scott; Hawkins, Amy; Abbott, Scott; Locke, Devin; Hillier, LaDeana W.; Miner, Tracie; Fulton, Lucinda; Magrini, Vincent; Wylie, Todd; Glasscock, Jarret; Conyers, Joshua; Sander, Nathan; Shi, Xiaoqi; Osborne, John R.; Minx, Patrick; Gordon, David; Chinwalla, Asif; Zhao, Yu; Ries, Rhonda E.; Payton, Jacqueline E.; Westervelt, Peter; Tomasson, Michael H.; Watson, Mark; Baty, Jack; Ivanovich, Jennifer; Heath, Sharon; Shannon, William D.; Nagarajan, Rakesh; Walter, Matthew J.; Link, Daniel C.; Graubert, Timothy A.; DiPersio, John F.; Wilson, Richard K. (2008). "DNA sequencing of a cytogenetically normal acute myeloid leukaemia genome". Nature. 456 (7218): 66–72. Bibcode:2008Natur.456...66L. doi:10.1038/nature07485. PMC 2603574. PMID 18987736.
  4. ^ a b Peikoff, Kira (2015-10-16). "What Miraculous Recoveries Tell Us About Beating Cancer". Popular Mechanics. Retrieved 2016-04-28.
  5. ^ Bardelli A., Velculescu V.E. (2005). "Mutational analysis of gene families in human cancer". Current Opinion in Genetics & Development. 15 (1): 5–12. doi:10.1016/j.gde.2004.12.009.
  6. ^ Benvenuti S., Arena S., Bardelli A. (2005). "Identification of cancer genes by mutational profiling of tumor genomes". FEBS Letters. 579 (8): 1884–1890. doi:10.1016/j.febslet.2005.02.015. PMID 15763568.{{cite journal}}: CS1 maint: multiple names: authors list (link)
  7. ^ Shih I.M., Wang T.L. (2005). "Apply innovative technologies to explore cancer genome". Current Opinion in Oncology. 17 (1): 33–38. doi:10.1097/01.cco.0000147382.97085.e4. PMID 15608510.
  8. ^ Greshock J, et al. (2004). ", "1-Mb resolution array-based comparative genomic hybridization using a BAC clone set optimized for cancer gene analysis". Genome Research. 14 (1): 179–187. doi:10.1101/gr.1847304. PMC 314295. PMID 14672980.
  9. ^ Lucito R, et al. (2003). ", "Representational oligonucleotide microarray analysis: A high-resolution method to detect genome copy number variation". Genome Research. 13 (10): 2291–2305. doi:10.1101/gr.1349003. PMC 403708. PMID 12975311.
  10. ^ Hu M., Yao J., Polyak K. (2006). "Methylation-specific digital karyotyping". Nature Protocols. 1 (3): 1621–1636. doi:10.1038/nprot.2006.278. PMID 17406428.{{cite journal}}: CS1 maint: multiple names: authors list (link)
  11. ^ Korner H.; Epanchintsev, Alexey; Berking, Carola; Schuler-Thurner, Beatrice; Speicher, Michael R.; Menssen, Antje; Hermeking, Heiko; et al. (2007). ", Digital karyotyping reveals frequent inactivation of the Dystrophin/DMD gene in malignant melanoma". Cell Cycle. 6 (2): 189–198. doi:10.4161/cc.6.2.3733. PMID 17314512.
  12. ^ Volik S.; Zhao, S.; Chin, K.; Brebner, J. H.; Herndon, D. R.; Tao, Q.; Kowbel, D.; Huang, G.; et al. (2003). ", End-sequence profiling: Sequence-based analysis of aberrant genomes". Proceedings of the National Academy of Sciences of the United States of America. 100 (13): 7696–7701. Bibcode:2003PNAS..100.7696V. doi:10.1073/pnas.1232418100. PMC 164650. PMID 12788976. {{cite journal}}: Explicit use of et al. in: |author= (help)
  13. ^ Van , de Vijver M.J.; et al. (2002). ", "A gene-expression signature as a predictor of survival in breast cancer". New England Journal of Medicine. 347 (25): 1999–2009. doi:10.1056/NEJMoa021967. PMID 12490681.
  14. ^ Van , Veer L.J.; et al. (2002). ", "Expression profiling predicts outcome in breast cancer". Breast Cancer Research. 5 (1): 57–58. doi:10.1186/bcr562. PMC 154139. PMID 12559048.{{cite journal}}: CS1 maint: unflagged free DOI (link)
  15. ^ Xu Q., Lee C. (2003). "Discovery of novel splice forms and functional analysis of cancer-specific alternative splicing in human expressed sequences". Nucleic Acids Research. 31 (19): 5635–5643. doi:10.1093/nar/gkg786. PMC 206480. PMID 14500827.
  16. ^ Hanash S.M. (2000). "Operomics: Molecular analysis of tissues from DNA to RNA to protein". Clinical Chemistry and Laboratory Medicine. 38 (9): 805–813. doi:10.1515/CCLM.2000.116.
  17. ^ Peeper D., Berns A. (2006). "Cross-species onocogenomics in cancer gene idenfification". Cell. 125 (7): 1230–1233. doi:10.1016/j.cell.2006.06.018. PMID 16814709.
  18. ^ Kaelin W.G. (2005). "The concept of synthetic lethality in the context of anticancer therapy". Nature Reviews Cancer. 5 (9): 689–698. doi:10.1038/nrc1691. PMID 16110319.
  19. ^ O'Connor M.J., Martin N.M.B., Smith G.C.M. (2007). "Targeted cancer therapies based on the inhibition of DNA strand break repair". Oncogene. 26 (56): 7816–7824. doi:10.1038/sj.onc.1210879. PMID 18066095.{{cite journal}}: CS1 maint: multiple names: authors list (link)
  20. ^ Farmer H., McCabe N., Lord C.J.; McCabe; Lord; Tutt; Johnson; Richardson; Santarosa; Dillon; Hickson; Knights; Martin; Jackson; Smith; Ashworth; et al. (2005). "Targeting the DNA repair defect in BRCA mutant cells as a therapeutic strategy". Nature. 434 (7035): 917–921. Bibcode:2005Natur.434..917F. doi:10.1038/nature03445. PMID 15829967.{{cite journal}}: CS1 maint: multiple names: authors list (link)
  21. ^ Bryant H.E.; Schultz, Niklas; Thomas, Huw D.; Parker, Kayan M.; Flower, Dan; Lopez, Elena; Kyle, Suzanne; Meuth, Mark; Curtin, Nicola J.; Helleday, Thomas; et al. (2005). ", "Specific killing of BRCA2-deficient tumours with inhibitors of poly(ADP-ribose) polymerase". Nature. 434 (7035): 913–917. Bibcode:2005Natur.434..913B. doi:10.1038/nature03443. PMID 15829966.
  22. ^ Gundem G.; Perez-Llamas, Christian; Jene-Sanz, Alba; Kedzierska, Anna; Islam, Abul; Deu-Pons, Jordi; Furney, Simon J; Lopez-Bigas, Nuria; et al. (2010). ", "IntOGen: integration and data mining of multidimensional oncogenomic data". Nature Methods. 300 (5621): 92–93. doi:10.1038/nmeth0210-92.
  23. ^ Gonzalez-Perez A.; Perez-Llamas, Christian; Deu-Pons, Jordi; Tamborero, David; Schroeder, Michael P; Jene-Sanz, Alba; Santos, Alberto; Lopez-Bigas, Nuria; et al. (2013). ", "IntOGen-mutations identifies cancer drivers across tumor types". Nature Methods. 10 (2542): 1081–1082. doi:10.1038/nmeth.2642.
  24. ^ Rubio-Perez C.; Tamborero, David; Schroeder, Michael P; Antolin, Albert A; Deu-Pons, Jordi; Perez-Llamas, Christian; Mestres, Jordi; Gonzalez-Perez, Abel; Lopez-Bigas, Nuria; et al. (2015). ", "In Silico Prescription of Anticancer Drugs to Cohorts of 28 Tumor Types Reveals Targeting Opportunities". Cancer Cell. 27 (3): 382–396. doi:10.1016/j.ccell.2015.02.007.
  25. ^ Blume-Jensen P., Hunter T. (2001). "Oncogenic kinase signalling". Nature. 411 (6835): 355–365. doi:10.1038/35077225. PMID 11357143.
  26. ^ Bardelli A, et al. (2003). ", Mutational analysis of the tyrosine kinome in colorectal cancers". Science. 300 (5621): 949–949. doi:10.1126/science.1082596. PMID 12738854.
  27. ^ Samuels Y, et al. (2004). ", "High frequency of mutations of the PIK3CA gene in human cancers". Science. 304 (5670): 554–554. doi:10.1126/science.1096502. PMID 15016963.
  28. ^ Davies H.; Bignell, Graham R.; Cox, Charles; Stephens, Philip; Edkins, Sarah; Clegg, Sheila; Teague, Jon; Woffendin, Hayley; Garnett, Mathew J.; Bottomley, William; Davis, Neil; Dicks, Ed; Ewing, Rebecca; Floyd, Yvonne; Gray, Kristian; Hall, Sarah; Hawes, Rachel; Hughes, Jaime; Kosmidou, Vivian; Menzies, Andrew; Mould, Catherine; Parker, Adrian; Stevens, Claire; Watt, Stephen; Hooper, Steven; Wilson, Rebecca; Jayatilake, Hiran; Gusterson, Barry A.; Cooper, Colin; et al. (2002). ", Mutations of the BRAF gene in human cancer". Nature. 417 (6892): 949–954. doi:10.1038/nature00766. PMID 12068308. {{cite journal}}: Explicit use of et al. in: |author= (help)
  29. ^ Danson S., Lorigan P. (2005). "Improving outcomes in advanced malignant melanoma - Update on systemic therapy". Drugs. 65 (6): 733–743. doi:10.2165/00003495-200565060-00002. PMID 15819587.
  30. ^ a b c d e Yu, Man (2012). "Somatic Mitochondrial DNA Mutations in Human Cancers". Advances in Clinical Chemistry. Advances in Clinical Chemistry. 57: 99–138. doi:10.1016/B978-0-12-394384-2.00004-8. ISBN 9780123943842. PMID 22870588.
  31. ^ Hung, W.Y.; J.C. Lin; L.M. Lee; et al. (2008). "Tandem duplication/triplication correlated with poly-cytosine stretch variation in human mitochondrial DNA D-loop region". Mutagenesis. 23 (2): 137–142. doi:10.1093/mutage/gen002. PMID 18252697.
  32. ^ Fliss, M. S.; Usadel, H.; Caballero, O. L.; et al. (2000). "Facile detection of mitochondrial DNA mutations in tumors and bodily fluids". Science. 287 (5460): 2017–2019. Bibcode:2000Sci...287.2017F. doi:10.1126/science.287.5460.2017. PMID 10720328.
  33. ^ a b Dani, M.A.; S.U. Dani; S.P. Lima; et al. (2004). "Less ΔmtDNA4977 than normal in various types of tumors suggests that cancer cells are essentially free of this mutation". Genet. Mol. Res. 3 (3): 395–409. PMID 15614730.
  34. ^ Ye, C.; X.O. Shu; W. Wen; et al. (2008). "Quantitative analysis of mitochondrial DNA 4977-bp deletion in sporadic breast cancer and benign breast diseases". Breast Cancer Res. Treat. 108 (3): 427–434. doi:10.1007/s10549-007-9613-9. PMID 17541740.
  35. ^ Tseng, L.M.; P.H. Yin; C.W. Chi; et al. (2006). "Mitochondrial DNA mutations and mitochondrial DNA depletion in breast cancer". Genes Chromosomes Cancer. 45 (7): 629–638. doi:10.1002/gcc.20326. PMID 16568452.
  36. ^ Zhu, W.; W. Qin; P. Bradley; A. Wessel; C.L. Puckett; E.R. Sauter (2005). "Mitochondrial DNA mutations in breast cancer tissue and in matched nipple aspirate fluid". Carcinogenesis. 26 (1): 145–152. doi:10.1093/carcin/bgh282. PMID 15375011.
  37. ^ Zhou, S.; Kachhap, S.; Sun, W.; et al. (2007). "Frequency and phenotypic implications of mitochondrial DNA mutations in human squamous cell cancers of the head and neck". Proc. Natl. Acad. Sci. USA. 104 (18): 7540–7545. Bibcode:2007PNAS..104.7540Z. doi:10.1073/pnas.0610818104. PMC 1863503. PMID 17456604. {{cite journal}}: Explicit use of et al. in: |author10= (help)
  38. ^ Poetsch, M.; A. Petersmann; E. Lignitz; B. Kleist (2004). "Relationship between mitochondrial DNA instability, mitochondrial DNA large deletions, and nuclear microsatellite instability in head and neck squamous cell carcinomas". Diagn. Mol. Pathol. 13 (1): 26–32. doi:10.1097/00019606-200403000-00005. PMID 15163006.
  39. ^ Tan, D.J.; J. Chang; W.L. Chen; et al. (2004). "Somatic mitochondrial DNA mutations in oral cancer of betel quid chewers". Ann. N. Y. Acad. Sci. 1011: 310–316. Bibcode:2004NYASA1011..310T. doi:10.1196/annals.1293.030. PMID 15126307.
  40. ^ Lee, H.C.; S.H. Li; J.C. Lin; C.C. Wu; D.C. Yeh; Y.H. Wei (2004). "Somatic mutations in the D-loop and decrease in the copy number of mitochondrial DNA in human hepatocellular carcinoma". Mutation Research. 547 (1–2): 71–78. doi:10.1016/j.mrfmmm.2003.12.011. PMID 15013701.
  41. ^ Yin, P.H.; C.C. Wu; J.C. Lin; C.W. Chi; Y.H. Wei; H.C. Lee (2010). "Somatic mutations of mitochondrial genome in hepatocellular carcinoma". Mitochondrion. 10 (2): 174–182. doi:10.1016/j.mito.2009.12.147. PMID 20006738.
  42. ^ Tan, D.J.; J. Chang; L.L. Liu; et al. (2006). "Significance of somatic mutations and content alteration of mitochondrial DNA in esophageal cancer". BMC Cancer. 6: 93. doi:10.1186/1471-2407-6-93. PMC 1459869. PMID 16620376.{{cite journal}}: CS1 maint: unflagged free DOI (link)
  43. ^ Kassauei, K.; N. Habbe; M.E. Mullendore; C.A. Karikari; A. Maitra; G. Feldmann (2006). "Mitochondrial DNA mutations in pancreatic cancer". Int. J. Gastrointest. Cancer. 37 (2–3): 57–64. doi:10.1007/s12029-007-0008-2. PMID 17827523.
  44. ^ Hung, W.Y.; C.W. Wu; P.H. Yin; et al. (2010). "Somatic mutations in mitochondrial genome and their potential roles in the progression of human gastric cancer". Biochim. Biophys. Acta. 1800 (3): 264–270. doi:10.5772/8630. ISBN 978-953-307-086-5. PMID 19527772.
  45. ^ Wu, C.W.; P.H. Yin; W.Y. Hung; et al. (2005). "Mitochondrial DNA mutations and mitochondrial DNA depletion in gastric cancer". Genes Chromosomes Cancer. 44 (1): 19–28. doi:10.1002/gcc.20213. PMID 15892105.
  46. ^ Yu, J.J.; T. Yan (2010). "Effect of mtDNA mutation on tumor malignant degree in patients with prostate cancer". Aging Male. 13 (3): 159–165. doi:10.3109/13685530903536668. PMID 20136572.
  47. ^ Gomez-Zaera, M.; J. Abril; L. Gonzalez; et al. (2006). "Identification of somatic and germline mitochondrial DNA sequence variants in prostate cancer patients". Mutation Research. 595 (1–2): 42–51. doi:10.1016/j.mrfmmm.2005.10.012. PMID 16472830.
  48. ^ Ludwig, Joseph A.; Weinstein, John N. (20 October 2005). "Biomarkers in Cancer Staging, Prognosis and Treatment Selection". Nature Reviews Cancer. 5 (11): 845–856. doi:10.1038/nrc1739. PMID 16239904.
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