Oncogenomics

From Wikipedia, the free encyclopedia
Jump to: navigation, search

Oncogenomics is a relatively new sub-field of genomics that applies high throughput technologies to characterize genes associated with cancer. Oncogenomics is synonymous with "cancer genomics". Cancer is a genetic disease caused by accumulation of mutations to DNA 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 initiates or drives cancer progression, one of the main goals of oncogenomics is to allow for the development of personalized cancer treatment. Cancer develops due to an accumulation of mutations in DNA. These mutations accumulate randomly, and thus, different DNA mutations and mutation combinations exist between different individuals with the same type of cancer. Thus, identifying and targeting specific mutations which have occurred in an individual patient may lead to increased efficacy of cancer therapy.

The completion of the Human Genome Project has greatly facilitated the field of oncogenomics and has increased the abilities of researchers to find cancer causing genes. In addition, the sequencing technologies now available for sequence generation and data analysis have been applied to the study of oncogenomics. With the amount of research conducted on cancer genomes and the accumulation of databases documenting the mutational changes, it has been predicted that the most important cancer-causing mutations, rearrangements, and altered expression levels will be cataloged and well characterized within the next decade. Cancer research may look either on the genomic level at DNA mutations, the epigenetic level at methylation or histone modification changes, the transcription level at altered levels of gene expression, or the protein level at altered levels of protein abundance and function in cancer cells. Oncogenomics focuses on the genomic, epigenomic, and transcript level alterations in cancer.

History[edit]

The genomics era became established with success in the 1990s, due to the generation of DNA sequences of many organisms. In the 21st century, the completion of the Human Genome Project at the Wellcome Trust Sanger Institute paved the way for many new endeavors for studying the functional genomics and examining the genomes which characterize different diseases. Cancer has been one of the main focuses.

Reasons why access to whole cancer genome sequencing is so important to cancer (or cancer genome) research:

  1. The mutations present in the cancer genome are the direct cause of disease and they define the tumor phenotype.
  2. As a result of the access to both diseased and normal tissue samples from the same patient, and the fact that most cancer genomic mutations represent somatic events, we are able to confidently identify the mutations specific to cancer.
  3. In cancer, mutations within the genome are progressive and in some cancer cases changes related to disease stage, development of metastasis, and drug resistance are distinguishable.[2]

The first cancer genome was sequenced in 2008 by Timothy J. Ley et. al.[2] This study sequenced a typical Acute Myeloid Leukaemia (AML) genome and it normal counterpart genome obtained from the same patient's skin. When comparing the two sequences these researchers discovered 10 genes which contained acquired mutations:

  • 2 of these mutations were previously thought to contribute to tumor progression, and they were:
    • an internal tandem duplication of the FLT3 receptor tyrosine kinase gene, which constitutively activates kinase signaling, and is associated with a poor prognosis
    • a four base insertion in exon 12 of the NPM1 gene (NPMc)
      • Both of these are common in AML tumors (found in about 25-30% of them), and they are both thought to contribute to the progression of the disease rather than to actually cause it directly.
  • The remaining 8 were new mutations and all were single base changes:
    • 4 of the genes were found to be in families that are strongly associated with cancer pathogenesis (PTPRT, CDH24, PCLKC, and SLC15A1)
    • the other four were not found to have any previous association with cancer pathogenesis, but they had potential functions in metabolic pathways that suggested mechanisms by which they could act to promote cancer (KNDC1, GPR124, EB12, GRINC1B)

All of these genes are involved in pathways known to contribute to cancer pathogenesis, but before this study most of these genes would not have been candidates for targeted gene therapy based on the prior understanding of cancer. Thus the results of this study were successful in showing the importance of whole cancer genome sequencing techniques in identifying somatic mutations involved in cancer. This study also showed the importance of parallel sequencing of the patient's normal genome to determine which mutations/variants were inherited or acquired. This technique is important in the identification of the true somatic mutations.[3]

Technologies[edit]

Current technologies being used in Oncogenomics.

Research examining the genomes and transcriptomes of cancer cells are currently extensively complemented by state of the art technologies.

Cancer Genomes[edit]

Further information: Cancer genome sequencing
  • High-throughput DNA sequencing technologies: The development of high-throughput DNA sequencing platforms,which utilize pyrosequencing, have greatly altered the field of genomics within only a few years. These systems allow for a relatively low-cost method to generate sequence data, and have been employed by many researchers in the oncogenomics field.[1][4][5]
  • Array Comparative Genome Hybridization: This technique measures the DNA copy number differences between genomes. This method has been used to study the gain or loss of genes in cancer genomes compared to normal genomes. It uses the fluorescence intensity from two fluorescently labeled sample DNA, which are hybridized to known probes on a microarray chip. The ratio of fluorescence intensities allows quantification of copy number changes in the cancerous genomes.[6][7]
  • Representational oligonucleotide microarray analysis: This techniques also detects copy number variation using the microarray format, using amplified restriction digested genomic fragments to represent cancerous genomes. These fragments are then hybridized to oligonucleotides of the human genome on an array, with a resolution between 30 and 35 kbit/s.[8]
  • Digital Karyotyping: Another method that provides a high resolution and high-throughput technology to quantify copy number of genes in samples. This technique involves using genomics tags which have been obtained via restriction enzyme digests on a sample of DNA. These genomic tags are then linked to into ditags, concatenated, cloned, and sequenced. These sequence tags are then mapped back to the reference genome to evaluate tag density and quantify DNA amplification or deletions of regions of genomes.[9][10]
  • Bacterial Artificial Chromosome (BAC)-end sequencing: This is another method used in oncogenomics, which identifies chromosomal breakpoints in a high-resolution manner. This technique involves generating a BAC library from a cancer genome, and sequencing the ends of these sequences. The BAC clones which contain chromosome aberrations will have end sequences that do not map to a similar region of reference genome, thus identifying a chromosomal breakpoint present in cancerous genomes. By sequencing these BACs, the breakpoints and genes involved may be identified.[11]

Cancer Transcriptomes[edit]

  • Microarrays: These have been and continue to be extremely informative in assessing transcript abundance in cancerous cells. The transcription profiles have provided different means of classification for different types of cancers, predicting prognosis of cancer, and raising the possibility of differential treatment approaches to different types of cancer. The ability to directly sequence transcriptomes of cancerous tissues with high-throughput sequencing technologies also aids in the identification of mutations which have occurred in the coding regions of the proteins[12][13]

As well, the identification of the relative abundance of alternative transcripts has become an important component of the study of cancer. It has been shown that particular alternative transcript forms are correlated with specific types of cancer. With this impact, generation of exon-array technologies which are able to quantify alternate splice forms, and other transcript sequencing technologies, have become an important part of oncogenomics.[14]

Bioinformatics and functional analysis of oncogenes[edit]

With the amounts of sequencing data and expression profiling data being generated, the development of bioinformatics technologies to statistically analyze this data is essential. As well, after the identification of these oncogenes, much research still remains to be done to analyze the functional characteristics of these genes and how they contribute to the cancer phenotype. For example, examination of transformational capabilities of discovered oncogenes are important for confirming their impact in tumour formation. In addition, in cancerous cells, many DNA mutations accumulate. It is important to identify genes which are important in the early stages of cancer progression and in cancer development. Identification of mutations in these genes will be most helpful in diagnosis and in finding new targets for cancer therapy.

Operomics[edit]

Operomics is an approach that aims to integrate genomics, transcriptomics, and proteomics in order to achieve a complete understanding of the molecular mechanisms which underlie the development of cancer.[15] This involves simultaneous molecular analysis of DNA, RNA, and protein of tumor tissue samples. With increasing advances in technologies to analyze cancer cells, operomics will be an overall goal of cancer research.

Comparative Oncogenomics[edit]

Comparative Oncogenomics is a branch of oncogenomics which uses cross-species comparisons to identify oncogenes. This research involves studying cancer genomes, transcriptomes, and proteomes in other model organisms, such as mice, identifying potential oncogenes, and referring back to human cancer samples to see whether homologues of these oncogenes are also important in causing cancer in humans.[16] Recent research has found that the genetic alterations in mouse models have been found to be exceptionally similar to those found in human cancers. This branch of oncogenomics useful in that different types of cancer may be studied in animal models. These models are generated by various methods, including retroviral insertion mutagenesis or graft transplantation of cancerous cells. Comparative oncogenomics is a powerful approach to oncogene identification.

Synthetic Lethality/Conditional Genetics[edit]

One approach to studying oncogenomics, which shows great promise in producing useful cancer therapies by taking advantage of mutational aberrations in cancer cells, is the strategic exploitation of synthetic lethality interactions between multiple genes. Frequently, known oncogenes may be essential for survival of all cells (not only cancer cells). Thus, drugs intended to knock out these oncogenes (and thereby kill cancer cells) may also cause serious negative effects to normal cells: i.e., significant illness may be directly induced by the cancer therapy. To generate therapies that more specifically target cancer cells, scientists are now working to systematically examine the effect of suppressing every gene in the human genome, one at a time, in combination with the presence of the cancer-associated mutation of some other gene which has previously been identified as an oncogene.[17][18] This type of search can thus identify targets for cancer therapy by exploiting the mutations that are present exclusively in cancer cells; if the knockout of an otherwise nonessential gene has little or no effect on healthy cells, but is lethal to cancerous cells containing the mutated form of a given oncogene, then the system-wide suppression of the normally nonessential gene can destroy cancerous cells while leaving healthy ones intact or relatively undamaged. (The term "synthetic lethality," here, describes this sort of synergistic effect.) Success has been observed with this method both in discovering cancer targets and in developing therapies. One example is the case of PARP-1 inhibitors specifically applied to treat BRCA1/BRCA2-associated cancers.[19][20] 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. Phase I clinical trials of this technique suggest that it may show promise in patients with BRCA1 or BRCA2 mutations, and Phase II trials are currently underway

Databases for Cancer Research[edit]

Many databases are available to cancer researchers as resources which have banked oncogenomic research data. The Cancer Genome Project is an initiative to map out all the somatic intragenic mutations in cancer. To do this, they are systematically sequencing the exons and flanking splice junctions of all the genes in the genomes of primary tumors and cancerous cell lines. COSMIC is a resource which displays the data generated from these experiments. As of February 2008, the CGP has identified 4746 genes and 2985 mutations in 1848 tumours analyzed.

The Cancer Genome Anatomy Project from National Cancer Institute also has banked much information of research on cancer genome, transcriptome, and proteome. Progenetix is another oncogenomic reference database, presenting cytogenetic and molecular-cytogenetic tumor data. Oncomine has compiled data from cancer transcriptome profiles.

The Integrative Oncogenomics database IntOGen integrates multidimensional human oncogenomic data classified by tissue type using the ICD-O terms.[21] Data mining for different alteration types, such as Gene Expression and CNV are made available in the IntOGen database.

The International Cancer Genome Consortium is so far the biggest project to collect human cancer genome data. The data is accessible through the ICGC website. The BioExpress® Oncology Suite [1] contains gene expression data from primary, metastatic, and benign tumor samples, and normal samples, including matched adjacent controls. The suite includes valuable hematological malignancy samples for many well-known cancers.

Specific databases for model animals also exist, as for example the Retrovirus Tagged Cancer Gene Database (RTCGD) has compiled research on retroviral and transposon insertional mutagenesis in mouse tumors.

Advances from Oncogenomics[edit]

Mutational analysis of entire gene families has been a powerful approach to oncogenomics which has been informative. Genes of the same family have similar functions, as predicted by similar coding sequences and protein domains, have been systematically sequenced in cancerous genomes to identify particular pathways which may be associated with cancer progression. One such class of families which has been studied is the kinase family genes, involved in adding phosphate groups to proteins, and phosphatase family genes, involved with removing phosphate groups from proteins.[22] 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 were found to carry a mutation in a kinase or phosphatase gene. Phosphatidylinositold 3-kinases (PIK3CA) gene encode for lipid kinases which were identified to commonly contain mutations in colorectal, breast, gastric, lung, and various other types of cancer.[23][24] Drug therapies have already been developed to inhibit PIK3CA. Another example is the BRAF gene was identified in 2004, which was one of the first genes ever to be implicated in melanomas.[25] BRAF encodes a serine/threonine kinase which is involved in the RAS-RAF-MAPK growth signaling pathway, and they found that mutations in BRAF causing constitutive phosphorylation and activity were found in 59% of melanomas. Before BRAF, there was very little understanding of the genetic mechanism of the development of melanomas, and therefore, prognosis for patients was poor. Thus, the CGP set out to discover genes involved with melanomas and identified BRAF, which is now a target of new cancer therapies, with clinical data of BRAF inhibiting targets already generated[26]

Mutations in mitochondrial DNA and cancer[edit]

Recent research has found links between mitochondrial DNA (mtDNA) mutations and the formation of tumors. There have been 4 different types of mtDNA mutations that have been identified:[27]

1. Point mutations[edit]

Point mutations have been observed in the coding and non-coding region of the mtDNA contained in cancerous cells. In individuals with bladder, head and neck, and lung cancers the point mutations within the coding region showed signs of being homoplasmic (resembling each other). This suggested that when a healthy cell transforms into a tumor cell (i.e. goes through a neoplastic transformation) then the mitochondria seem to become homogenous (the same). There was also a high prevalence of point mutations located within the non-coding region, D-loop, of the cancerous mitochondria suggesting that mutations within this region might also be an important characteristic in some cancers.[27]

2. Deletions[edit]

This type of mutation is sporadically detected due to their 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 will increase as the tumor progresses. There is an exception to this though. There is a relatively large deletion that has been found to appear in a multitude of different cancers (known as the "common deletion"), but it has been observed that more mtDNA large scale deletions have been found in normal cells compared to tumor cells. It is believed that this is due to a seemingly adaptive process of tumor cells to eliminate any mitochondria which contain these large scale deletions (the "common deletion" is > 4kb).[27]

3. Insertions[edit]

Two small mtDNA insertions of ~260 and ~520 bp have been found to be present in Breast cancer, Gastric cancer, Hepatocellular carcinoma (HCC), and Colon cancer but such changes have also been found in normal healthy cells. Therefore a correlation between these insertions and cancer has yet to be established.[28]

4. Quantitative mtDNA alterations (copy number mutations)[edit]

The characterization of mtDNA by using real-time PCR assays showed a large presence of quantitative alteration of mtDNA copy number in a wide range of cancers when compared to normal cells. This increase in copy number is expected to occur because of oxidative stress. On the other hand, the 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. Although there is an increase/decrease in copy number it does remain constant within tumor cells. The fact that the amount of mtDNA is maintained as constant in tumor cells when compared to non-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 is estimated to have some degree of specificity for particular tumor types or sites.[27]

Cancer Type Location of Point mutations Nucleotide Position of Deletions Increase of mtDNA copy # Decrease of mtDNA copy # References
D-Loop mRNAs tRNAs rRNAs
Bladder X X X 15,642-15,662 [29]
Breast X X X X 8470-13,447 and 8482-13459 X [30][31][32][33]
Head and Neck X X X X 8470-13,447 and 8482-13459 X [30][34][35]
Oral X X 8470-13,447 and 8482-13459 [36]
Hepatocellular Carcinoma (HCC) X X X X 306-556 and 3894-3960 X [37][38]
Esophageal X X X 8470-13,447 and 8482-13459 X [39]
Gastric X X X 298-348 X [40][41][42]
Prostate X X 8470-13,447 and 8482-13459 X [43][44]

Above Table: Mutations in mitochondrial DNA in various cancers. This table only includes a few of the cancers that were looked at. One of the studies included which looked at the location of point mutations in different cancers, contained 867 patients and 23 different types of cancers.[citation needed] 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.

Potential Diagnostic Applications[edit]

Currently anticancer drugs have been manufactured to target mtDNA and have shown positive results in killing tumor cells. There has also been research done in using mitochondrial mutations as biomarkers for cancer cell therapy. It is easier to target mutation within the mitochondrial DNA as opposed to nuclear DNA because the mitochondrial genome is much smaller and therefore easier to screen for specific mutations. It is also thought that the 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 also not under the control of the cell cycle and it is important for maintaining ATP generation and mitochondrial homeostasis. These characteristics make targeting mtDNA a practical therapeutic strategy.[27]

Cancer Biomarkers[edit]

With advances in this field, scientists are discovering potential biomarkers for use in cancer staging, prognosis, and treatment. There are several different biomarkers that can be used for these purposes. They can range from single-nucleotide polymorphisms (SNPs), chromosomal aberrations, changes in DNA copy number, mirosatellite instability, differential promoter region methylation, or even high or low protein levels. The identification of characteristics associated with various cancers has the possibility for more effective personalized treatment options by developing drugs designed to target the biomarkers which are present in an individual patient. For a table that outlines some cancers and their respective biomarkers see the Cancer Biomakers page.[45]

References[edit]

  1. ^ a b Strausberg R.L. et al.; Simpson, Andrew J.G.; Old, Lloyd J.; Riggins, Gregory J. (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. ^ 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. 
  5. ^ 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. 
  6. ^ 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. 
  7. ^ 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. 
  8. ^ 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. 
  9. ^ 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. 
  10. ^ Korner H. et al.; Epanchintsev, Alexey; Berking, Carola; Schuler-Thurner, Beatrice; Speicher, Michael R.; Menssen, Antje; Hermeking, Heiko (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. 
  11. ^ Volik S. et al.; 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. 
  12. ^ 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. 
  13. ^ 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. 
  14. ^ 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. 
  15. ^ 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. 
  16. ^ 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. 
  17. ^ 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. 
  18. ^ 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. 
  19. ^ Farmer H., McCabe N., Lord C.J. et al.; McCabe; Lord; Tutt; Johnson; Richardson; Santarosa; Dillon; Hickson; Knights; Martin; Jackson; Smith; Ashworth (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. 
  20. ^ Bryant H.E. et al.; Schultz, Niklas; Thomas, Huw D.; Parker, Kayan M.; Flower, Dan; Lopez, Elena; Kyle, Suzanne; Meuth, Mark; Curtin, Nicola J.; Helleday, Thomas (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. 
  21. ^ Gundem G. et al.; Perez-Llamas, Christian; Jene-Sanz, Alba; Kedzierska, Anna; Islam, Abul; Deu-Pons, Jordi; Furney, Simon J; Lopez-Bigas, Nuria (2010). ", "IntOGen: integration and data mining of multidimensional oncogenomic data". Nature Methods 300 (5621): 92–93. doi:10.1038/nmeth0210-92. 
  22. ^ Blume-Jensen P., Hunter T. (2001). "Oncogenic kinase signalling". Nature 411 (6835): 355–365. doi:10.1038/35077225. PMID 11357143. 
  23. ^ 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. 
  24. ^ 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. 
  25. ^ Davies H. et al.; 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. 
  26. ^ 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. 
  27. ^ 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. 
  28. ^ 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. 
  29. ^ Fliss, M.S.; Usadel; Caballero; Wu; Buta; Eleff; Jen; Sidransky; H. Usadel, O.L. Caballero 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. 
  30. ^ 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. 
  31. ^ 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. 
  32. ^ 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. 
  33. ^ 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. 
  34. ^ Zhou, S.; Kachhap; Sun; Wu; Chuang; Poeta; Grumbine; Mithani; Chatterjee; S. Kachhap, W. Sun 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. 
  35. ^ 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. 
  36. ^ 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. 
  37. ^ 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. 
  38. ^ 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. 
  39. ^ 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. 
  40. ^ 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. 
  41. ^ 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. 
  42. ^ 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. 
  43. ^ 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. 
  44. ^ 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. 
  45. ^ 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. 

External links[edit]