Reinventing Diagnostics for Personalized Therapy in Oncology
Abstract
:1. Introduction
2. The Financial Imperative for Personalized Therapy
Rank | OECD Country | Public $ per capita | Total $ per capita | Percentage public |
---|---|---|---|---|
1 | United States | 307 | 1,015 | 30.2 |
2 | Canada | 302 | 770 | 39.2 |
3 | Belgium | 353 | 703 | 50.1 |
4 | France | 472 | 679 | 69.4 |
5 | Spain | 464 | 642 | 72.3 |
6 | Japan | 436 | 609 | 71.7 |
7 | Germany | 447 | 602 | 74.3 |
Cancer | Total | Per case cost | Annual Canadian Costs a | Crude 5 year Survival rates (%) | Mortality at 5 years (%) | Cost of treatment failure | |
---|---|---|---|---|---|---|---|
1 | Lung | 23,400 | $27,295 | $638,703,000 | 14 | 86 | $549,284,580 |
2 | Colorectal | 22,000 | $26,742 | $588,324,000 | 66 | 34 | $200,030,160 |
3 | Pancreas | 3,900 | $29,395 | $114,640,500 | 7 | 93 | $106,615,665 |
4 | Lymphoma | 7,200 | $23,759 | $171,064,800 | 57 | 43 | $73,557,864 |
5 | Ovary | 2,500 | $40,666 | $101,665,000 | 41 | 59 | $59,982,350 |
6 | Leukemia | 4,700 | $19,891 | $93,487,700 | 57 | 43 | $40,199,711 |
7 | Kidney | 4,600 | $27,958 | $128,606,800 | 60 | 40 | $51,442,720 |
8 | Head/Neck* | 9,250 | $19,891 | $183,991,750 | 74 | 26 | $47,837,855 |
9 | Breast | 22,900 | $12,156 | $278,372,400 | 89 | 11 | $30,620,964 |
10 | Bladder | 6,900 | $13,592 | $93,784,800 | 80 | 20 | $18,756,960 |
11 | Prostate | 25,500 | $12,156 | $309,978,000 | 96 | 4 | $12,399,120 |
12 | Endometrial | 4,400 | $17,902 | $78,768,800 | 87 | 13 | $10,239,944 |
13 | Cervix | 1,300 | $22,212 | $28,875,600 | 78 | 22 | $6,352,632 |
14 | Melanoma | 5,000 | $5,304 | $26,520,000 | 94 | 6 | $1,591,200 |
Total Cost of treatment failure | $1,208,911,725 | ||||||
Top 5 cancers | $989,470,619 | ||||||
Top 10 cancers | $1,178,328,829 |
3. Lung Cancer
3.1. Immunohistochemistry in NSCLC
3.2. Gene Expression Profiling in NSCLC
3.3. SAGE Transcriptome Profiles in Carcinoma-in-Situ and Invasive NSCLC
3.4. Gene Copy Number Variation
3.5. Mutation of Tyrosine Kinase Domains in the Epidermal Growth Factor Receptor (EGFR) Gene
3.6. MicroRNAs in Typing of NSCLC
4. Breast Cancer
4.1. Gene Expression Profiling
4.2. Gene Expression Profiling and Response to Neoadjuvant Therapies
4.3. Protein Expression and Subcellular Location in Breast Cancer Cells
4.4. Gene Copy Number and Response to Neoadjuvant Chemotherapy
4.5. Detection of Chromosomal Aneuploidies and Gene Copy Number Changes in Fine Needle Aspirates Is Diagnostic of Breast Cancer
4.6. Translocations
4.7. TP53 Mutations
4.8. miRNA
4.9. Fusion Genes
4.10. Cancer Stem Cells in NSCLC and Breast Cancer
4.11. Plasticity of Cancer Stem Cells
4.12. Next Generation Sequencing Technology and Cancer Genomes
Patients to be tested per year | Test cost per year | ROI per year (Cost avoidance) | Net savings | |
---|---|---|---|---|
Top 5 cancers | 59,000 | $59,000,000 | $989,470,619 | $930,470,619 |
Top 10 cancers | 107,350 | $107,350,000 | $1,178,328,829 | $1,070,978,829 |
Top 14 cancers | 120,150 | $120,150,000 | $1,208,911,725 | $1,088,761,725 |
5. Conclusions
References
- Uluc, K.; Kujoth, G.C.; Baskaya, M.K. Operating microscopes: past, present, and future. Neurosurg. Focus 2009, 27, E4. [Google Scholar]
- Titford, M. The long history of hematoxylin. Biotech. Histochem. 2005, 80, 73–78. [Google Scholar] [CrossRef]
- Miyamoto, H.; Miller, J.S.; Fajardo, D.A.; Lee, T.K.; Netto, G.J.; Epstein, J.I. Non-invasive papillary urothelial neoplasms: the 2004 WHO/ISUP classification system. Pathol. Int. 2010, 60, 1–8. [Google Scholar] [CrossRef]
- Hodges, K.B.; Lopez-Beltran, A.; Davidson, D.D.; Montironi, R.; Cheng, L. Urothelial dysplasia and other flat lesions of the urinary bladder: clinicopathologic and molecular featuRes. Hum. Pathol. 2010, 41, 155–162. [Google Scholar] [CrossRef]
- D'Angelo, E.; Prat, J. Uterine sarcomas: a review. Gynecol. Oncol. 2010, 116, 131–139. [Google Scholar] [CrossRef]
- Weis, E.; Rootman, J.; Joly, T.J.; Berean, K.W.; Al-Katan, H.M.; Pasternak, S.; Bonavolonta, G.; Strianese, D.; Saeed, P.; Feldman, K.A.; Vangveeravong, S.; Lapointe, J.S.; White, V.A. Epithelial lacrimal gland tumors: pathologic classification and current understanding. Arch. Ophthalmol. 2009, 127, 1016–1028. [Google Scholar] [CrossRef]
- Wallace, W.A. The challenge of classifying poorly differentiated tumors in the lung. Histopathology 2009, 54, 28–42. [Google Scholar] [CrossRef]
- Tefferi, A.; Thiele, J.; Vardiman, J.W. The 2008 World Health Organization classification system for myeloproliferative neoplasms: order out of chaos. Cancer 2009, 115, 3842–3847. [Google Scholar]
- Scheithauer, B.W. Development of the WHO classification of tumors of the central nervous system: a historical perspective. Brain Pathol. 2009, 19, 551–564. [Google Scholar] [CrossRef]
- Grignon, D.J. The current classification of urothelial neoplasms. Mod. Pathol. 2009, 22 (Suppl. 2), S60–S69. [Google Scholar] [CrossRef]
- Verghese, E.T.; den Bakker, M.A.; Campbell, A.; Hussein, A.; Nicholson, A.G.; Rice, A.; Corrin, B.; Rassl, D.; Langman, G.; Monaghan, H.; Gosney, J.; Seet, J.; Kerr, K.; Suvarna, S.K.; Burke, M.; Bishop, P.; Pomplun, S.; Willemsen, S.; Addis, B. Interobserver variation in the classification of thymic tumors––a multicenter study using the WHO classification system. Histopathology 2008, 53, 218–223. [Google Scholar] [CrossRef]
- Trembath, D.; Miller, C.R.; Perry, A. Gray zones in brain tumor classification: evolving concepts. Adv. Anat. Pathol. 2008, 15, 287–297. [Google Scholar] [CrossRef]
- Schiffer, C.A. World Health Organization and international prognostic scoring system: the limitations of current classification systems in assessing prognosis and determining appropriate therapy in myelodysplastic syndromes. Semin. Hematol. 2008, 45, 3–7. [Google Scholar] [CrossRef]
- Scheithauer, B.W.; Fuller, G.N.; VandenBerg, S.R. The 2007 WHO classification of tumors of the nervous system: controversies in surgical neuropathology. Brain Pathol. 2008, 18, 307–316. [Google Scholar]
- Okumura, M.; Shiono, H.; Minami, M.; Inoue, M.; Utsumi, T.; Kadota, Y.; Sawa, Y. Clinical and pathological aspects of thymic epithelial tumors. Gen. Thorac. Cardiovasc. Surg. 2008, 56, 10–16. [Google Scholar]
- Marchevsky, A.M.; McKenna, R.J., Jr.; Gupta, R. Thymic epithelial neoplasms: a review of current concepts using an evidence-based pathology approach. Hematol. Oncol. Clin. North Am. 2008, 22, 543–562. [Google Scholar]
- Ito, Y.; Hirokawa, M.; Fukushima, M.; Inoue, H.; Yabuta, T.; Uruno, T.; Kihara, M.; Higashiyama, T.; Takamura, Y.; Miya, A.; Kobayashi, K.; Matsuzuka, F.; Miyauchi, A. Prevalence and prognostic significance of poor differentiation and tall cell variant in papillary carcinoma in Japan. World J. Surg. 2008, 32, 1535–1543. [Google Scholar] [CrossRef]
- Fuller, G.N. The WHO Classification of Tumors of the Central Nervous System, 4th edition. Arch. Pathol. Lab. Med. 2008, 132, 906. [Google Scholar]
- Egevad, L. Recent trends in Gleason grading of prostate cancer: I. Pattern interpretation. Anal. Quant. Cytol. Histol. 2008, 30, 190–198. [Google Scholar]
- Burger, M.; Denzinger, S.; Wieland, W.F.; Stief, C.G.; Hartmann, A.; Zaak, D. Does the current World Health Organization classification predict the outcome better in patients with noninvasive bladder cancer of early or regular onset? BJU Int 2008, 102, 194–197. [Google Scholar] [CrossRef]
- Ferrone, C.R.; Tang, L.H.; Tomlinson, J.; Gonen, M.; Hochwald, S.N.; Brennan, M.F.; Klimstra, D.S.; Allen, P.J. Determining prognosis in patients with pancreatic endocrine neoplasms: can the WHO classification system be simplified? J. Clin. Oncol. 2007, 25, 5609–5615. [Google Scholar] [CrossRef]
- Riquet, M.; Foucault, C.; Berna, P.; Assouad, J.; Dujon, A.; Danel, C. Prognostic value of histology in resected lung cancer with emphasis on the relevance of the adenocarcinoma subtyping. Ann. Thorac. Surg. 2006, 81, 1988–1995. [Google Scholar] [CrossRef]
- Pajtler, M.; Audy-Jurkovic, S.; Milicic-Juhas, V.; Staklenac, B.; Pauzar, B. Interobserver variability in cytologic subclassification of squamous intraepithelial lesions--the Bethesda System vs. World Health Organization classification. Coll. Antropol. 2006, 30, 137–142. [Google Scholar]
- Epstein, J.I.; Allsbrook, W.C., Jr.; Amin, M.B.; Egevad, L.L. Update on the Gleason grading system for prostate cancer: results of an international consensus conference of urologic pathologists. Adv. Anat. Pathol. 2006, 13, 57–59. [Google Scholar] [CrossRef]
- Willis, J.; Smith, C.; Ironside, J.W.; Erridge, S.; Whittle, I.R.; Everington, D. The accuracy of meningioma grading: a 10-year retrospective audit. Neuropathol. Appl. Neurobiol. 2005, 31, 141–149. [Google Scholar] [CrossRef]
- Oyama, T.; Allsbrook, W.C., Jr.; Kurokawa, K.; Matsuda, H.; Segawa, A.; Sano, T.; Suzuki, K.; Epstein, J.I. A comparison of interobserver reproducibility of Gleason grading of prostatic carcinoma in Japan and the United States. Arch. Pathol. Lab. Med. 2005, 129, 1004–1010. [Google Scholar]
- Wolfson, W.L. Interobserver variability among expert uropathologists. Am. J. Surg.Pathol. 2009, 33, 801–802. [Google Scholar] [CrossRef]
- Izadi-Mood, N.; Yarmohammadi, M.; Ahmadi, S.A.; Irvanloo, G.; Haeri, H.; Meysamie, A.P.; Khaniki, M. Reproducibility determination of WHO classification of endometrial hyperplasia/well differentiated adenocarcinoma and comparison with computerized morphometric data in curettage specimens in Iran. Diagn. Pathol. 2009, 4, 10. [Google Scholar] [CrossRef]
- Eefting, D.; Schrage, Y.M.; Geirnaerdt, M.J.; Le Cessie, S.; Taminiau, A.H.; Bovee, J.V.; Hogendoorn, P.C. Assessment of interobserver variability and histologic parameters to improve reliability in classification and grading of central cartilaginous tumors. Am. J. Surg. Pathol. 2009, 33, 50–57. [Google Scholar] [CrossRef]
- Darvishian, F.; Singh, B.; Simsir, A.; Ye, W.; Cangiarella, J.F. Atypia on breast core needle biopsies: reproducibility and significance. Ann. Clin. Lab. Sci. 2009, 39, 270–276. [Google Scholar]
- Adams, A.L.; Chhieng, D.C.; Bell, W.C.; Winokur, T.; Hameed, O. Histologic grading of invasive lobular carcinoma: does use of a 2-tiered nuclear grading system improve interobserver variability? Ann. Diagn. Pathol. 2009, 13, 223–225. [Google Scholar] [CrossRef]
- Mhawech-Fauceglia, P.; Herrmann, F.; Bshara, W.; Zhang, S.; Penetrante, R.; Lele, S.; Odunsi, K.; Rodabaugh, K. Intraobserver and interobserver variability in distinguishing between endocervical and endometrial adenocarcinoma on problematic cases of cervical curettings. Int. J. Gynecol. Pathol. 2008, 27, 431–436. [Google Scholar] [CrossRef]
- Kummerlin, I.; ten Kate, F.; Smedts, F.; Horn, T.; Algaba, F.; Trias, I.; de la Rosette, J.; Laguna, M.P. Core biopsies of renal tumors: a study on diagnostic accuracy, interobserver, and intraobserver variability. Eur. Urol. 2008, 53, 1219–1225. [Google Scholar] [CrossRef]
- Gilles, F.H.; Tavare, C.J.; Becker, L.E.; Burger, P.C.; Yates, A.J.; Pollack, I.F.; Finlay, J.L. Pathologist interobserver variability of histologic featuRes. in childhood brain tumors: Results from the CCG-945 study. Pediatr. Dev. Pathol. 2008, 11, 108–117. [Google Scholar] [CrossRef]
- Evans, A.J.; Henry, P.C.; Van der Kwast, T.H.; Tkachuk, D.C.; Watson, K.; Lockwood, G.A.; Fleshner, N.E.; Cheung, C.; Belanger, E.C.; Amin, M.B.; Boccon-Gibod, L.; Bostwick, D.G.; Egevad, L.; Epstein, J.I.; Grignon, D.J.; Jones, E.C.; Montironi, R.; Moussa, M.; Sweet, J.M.; Trpkov, K.; Wheeler, T.M.; Srigley, J.R. Interobserver variability between expert urologic pathologists for extraprostatic extension and surgical margin status in radical prostatectomy specimens. Am. J. Surg. Pathol. 2008, 32, 1503–1512. [Google Scholar] [CrossRef]
- Elsheikh, T.M.; Asa, S.L.; Chan, J.K.; DeLellis, R.A.; Heffess, C.S.; LiVolsi, V.A.; Wenig, B.M. Interobserver and intraobserver variation among experts in the diagnosis of thyroid follicular lesions with borderline nuclear featuRes. of papillary carcinoma. Am. J. Clin. Pathol. 2008, 130, 736–744. [Google Scholar] [CrossRef]
- Veloso, S.G.; Lima, M.F.; Salles, P.G.; Berenstein, C.K.; Scalon, J.D.; Bambirra, E.A. Interobserver agreement of Gleason score and modified Gleason score in needle biopsy and in surgical specimen of prostate cancer. Int. Braz. J. Urol. 2007, 33, 639–646; discussion 647–651. [Google Scholar] [CrossRef]
- Gonul, I.I.; Poyraz, A.; Unsal, C.; Acar, C.; Alkibay, T. Comparison of 1998 WHO/ISUP and 1973 WHO classifications for interobserver variability in grading of papillary urothelial neoplasms of the bladder. Pathological evaluation of 258 cases. Urol. Int. 2007, 78, 338–344. [Google Scholar] [CrossRef]
- Engers, R. Reproducibility and reliability of tumor grading in urological neoplasms. World J. Urol. 2007, 25, 595–605. [Google Scholar] [CrossRef]
- Vainer, B. Interobserver variability in gastrointestinal pathology. Scand. J. Gastroenterol. 2006, 41, 765–766. [Google Scholar] [CrossRef]
- Raab, S.S.; Meier, F.A.; Zarbo, R.J.; Jensen, D.C.; Geisinger, K.R.; Booth, C.N.; Krishnamurti, U.; Stone, C.H.; Janosky, J.E.; Grzybicki, D.M. The "Big Dog" effect: variability assessing the causes of error in diagnoses of patients with lung cancer. J. Clin. Oncol. 2006, 24, 2808–2814. [Google Scholar] [CrossRef]
- Glaessgen, A.; Hamberg, H.; Pihl, C.G.; Sundelin, B.; Nilsson, B.; Egevad, L. Interobserver reproducibility of percent Gleason grade 4/5 in prostate biopsies. J. Urol. 2004, 171, 664–667. [Google Scholar] [CrossRef]
- Costantini, M.; Sciallero, S.; Giannini, A.; Gatteschi, B.; Rinaldi, P.; Lanzanova, G.; Bonelli, L.; Casetti, T.; Bertinelli, E.; Giuliani, O.; Castiglione, G.; Mantellini, P.; Naldoni, C.; Bruzzi, P. Interobserver agreement in the histologic diagnosis of colorectal polyps. the experience of the multicenter adenoma colorectal study (SMAC). J. Clin. Epidemiol. 2003, 56, 209–214. [Google Scholar] [CrossRef]
- Nicholson, A.G.; Perry, L.J.; Cury, P.M.; Jackson, P.; McCormick, C.M.; Corrin, B.; Wells, A.U. Reproducibility of the WHO/IASLC grading system for pre-invasive squamous lesions of the bronchus: a study of inter-observer and intra-observer variation. Histopathology 2001, 38, 202–208. [Google Scholar] [CrossRef]
- Granados, R.; Aramburu, J.A.; Murillo, N.; Camarmo, E.; de la Cal, M.A.; Fernandez-Segoviano, P. Fine-needle aspiration biopsy of liver masses: diagnostic value and reproducibility of cytological criteria. Diagn. Cytopathol. 2001, 25, 365–375. [Google Scholar] [CrossRef]
- Jaffe, E.S.; Harris, N.L.; Stein, H.; Isaacson, P.G. Classification of lymphoid neoplasms: the microscope as a tool for disease discovery. Blood 2008, 112, 4384–4399. [Google Scholar] [CrossRef]
- He, Y.D.; Friend, S.H. Microarrays--the 21st century divining rod? Nat. Med. 2001, 7, 658–659. [Google Scholar] [CrossRef]
- Fujita, H.; So, Y.; Asada, Y.; Tatibana, K.; Hayakawa, M.; Matuo, T.; Minami, T.; Imamura, S.; Fujisawa, S.; Ito, K. Studies on lymphoma, reticulosis and its related diseases, especially about classification, histology and cytology with electron microscopy. Hifuka Kiyo 1962, 57, 3–61. [Google Scholar]
- Kohler, G.; Milstein, C. Continuous cultuRes. of fused cells secreting antibody of predefined specificity. Nature 1975, 256, 495–497. [Google Scholar] [CrossRef]
- Mullis, K.; Faloona, F.; Scharf, S.; Saiki, R.; Horn, G.; Erlich, H. Specific enzymatic amplification of DNA in vitro: the polymerase chain reaction. Cold Spring Harb. Symp. Quant. Biol. 1986, 51, 263–273. [Google Scholar] [CrossRef]
- Herzenberg, L.A.; Parks, D.; Sahaf, B.; Perez, O.; Roederer, M. The history and future of the fluorescence activated cell sorter and flow cytometry: a view from Stanford. Clin. Chem. 2002, 48, 1819–1827. [Google Scholar]
- Pennisi, E. Human genome: Finally, the book of life and instructions for navigating it. Science 2000, 288, 2304–2307. [Google Scholar] [CrossRef]
- Canadian Institute for Health Information, Drug Expenditure in Canada, 1985 to 2008; Canadian Institute for Health Information: Ottawa, Ontario, Canada, 2009.
- Spear, B.B.; Heath-Chiozzi, M.; Huff, J. Clinical application of pharmacogenetics. Trends Mol. Med. 2001, 7, 201–204. [Google Scholar] [CrossRef]
- Parkin, D.M.; Bray, F.; Ferlay, J.; Pisani, P. Global Cancer Statistics, 2002. CA Cancer J. Clin. 2005, 55, 74–108. [Google Scholar] [CrossRef]
- World Health Organization, Cancer. Fact sheet No. 297; WHO: Geneva, Swizerland, 2009.
- US National Cancer Insitute. Surveillance, Epidemiology and End Results (SEER) Program; NCI: Rockville, MD, USA, 2009. Available online: http://seer.cancer.gov/ (accessed online on 24 May 2010).
- Rossi, G.; Pelosi, G.; Graziano, P.; Barbareschi, M.; Papotti, M. A reevaluation of the clinical significance of histological subtyping of non--small-cell lung carcinoma: diagnostic algorithms in the era of personalized treatments. Int. J. Surg. Pathol. 2009, 17, 206–218. [Google Scholar] [CrossRef]
- Travis, W.D.; Brambilla, E.; Muller-Hermelink, H.K.; Harris, C.C. Pathology and Genetics of Tumors of the Lung, Pleura, Thymus and Heart, 4th ed.; IARC Press: Lyon, France, 2004. [Google Scholar]
- Thomas, J.S.; Lamb, D.; Ashcroft, T.; Corrin, B.; Edwards, C.W.; Gibbs, A.R.; Kenyon, W.E.; Stephens, R.J.; Whimster, W.F. How reliable is the diagnosis of lung cancer using small biopsy specimens? Report of a UKCCCR Lung Cancer Working Party. Thorax 1993, 48, 1135–1139. [Google Scholar] [CrossRef]
- Edwards, S.L.; Roberts, C.; McKean, M.E.; Cockburn, J.S.; Jeffrey, R.R.; Kerr, K.M. Preoperative histological classification of primary lung cancer: accuracy of diagnosis and use of the non-small cell category. J. Clin. Pathol. 2000, 53, 537–540. [Google Scholar] [CrossRef]
- Trani, L.; Myerson, J.; Ashley, S.; Young, K.; Sheri, A.; Hubner, R.; Puglisi, M.; Popat, S.; O'Brien, M.E. Histology classification is not a predictor of clinical outcomes in advanced non-small cell lung cancer (NSCLC) treated with vinorelbine or gemcitabine combinations. Lung Cancer 2010. [Google Scholar] [CrossRef]
- Hirsch, F.R.; Spreafico, A.; Novello, S.; Wood, M.D.; Simms, L.; Papotti, M. The prognostic and predictive role of histology in advanced non-small cell lung cancer: a literature review. J. Thorac. Oncol. 2008, 3, 1468–1481. [Google Scholar] [CrossRef]
- Pelosi, G.; Fraggetta, F.; Pasini, F.; Maisonneuve, P.; Sonzogni, A.; Iannucci, A.; Terzi, A.; Bresaola, E.; Valduga, F.; Lupo, C.; Viale, G. Immunoreactivity for thyroid transcription factor-1 in stage I non-small cell carcinomas of the lung. Am. J. Surg. Pathol. 2001, 25, 363–372. [Google Scholar] [CrossRef]
- Tan, D.; Li, Q.; Deeb, G.; Ramnath, N.; Slocum, H.K.; Brooks, J.; Cheney, R.; Wiseman, S.; Anderson, T.; Loewen, G. Thyroid transcription factor-1 expression prevalence and its clinical implications in non-small cell lung cancer: a high-throughput tissue microarray and immunohistochemistry study. Hum. Pathol. 2003, 34, 597–604. [Google Scholar] [CrossRef]
- Au, N.H.C.M.; Gown, A.M.M.; Cheang, M.M.; Huntsman, D.M.; Yorida, E.B.; Elliott, W.M.P.; Flint, J.M.; English, J.M.; Gilks, C.B.M.; Grimes, H.L.P. p63 Expression in Lung Carcinoma: A Tissue Microarray Study of 408 Cases. Appl. Immunohistochem. Mol. Morphol. 2004, 12, 240–247. [Google Scholar] [CrossRef]
- Monica, V.; Ceppi, P.; Righi, L.; Tavaglione, V.; Volante, M.; Pelosi, G.; Scagliotti, G.V.; Papotti, M. Desmocollin-3: a new marker of squamous differentiation in undifferentiated large-cell carcinoma of the lung. Mod. Pathol. 2009, 22, 709–717. [Google Scholar] [CrossRef]
- Wigle, D.A.; Jurisica, I.; Radulovich, N.; Pintilie, M.; Rossant, J.; Liu, N.; Lu, C.; Woodgett, J.; Seiden, I.; Johnston, M.; Keshavjee, S.; Darling, G.; Winton, T.; Breitkreutz, B.J.; Jorgenson, P.; Tyers, M.; Shepherd, F.A.; Tsao, M.S. Molecular profiling of non-small cell lung cancer and correlation with disease-free survival. Cancer Res. 2002, 62, 3005–3008. [Google Scholar]
- Blackhall, F.H.; Wigle, D.A.; Jurisica, I.; Pintilie, M.; Liu, N.; Darling, G.; Johnston, M.R.; Keshavjee, S.; Waddell, T.; Winton, T.; Shepherd, F.A.; Tsao, M.S. Validating the prognostic value of marker genes derived from a non-small cell lung cancer microarray study. Lung Cancer 2004, 46, 197–204. [Google Scholar] [CrossRef]
- Choi, N.; Son, D.S.; Lee, J.; Song, I.S.; Kim, K.A.; Park, S.H.; Lim, Y.S.; Seo, G.J.; Han, J.; Kim, H.; Lee, H.W.; Kang, J.J.; Seo, J.S.; Kim, J.H.; Kim, J. The signature from messenger RNA expression profiling can predict lymph node metastasis with high accuracy for non-small cell lung cancer. J. Thorac. Oncol. 2006, 1, 622–628. [Google Scholar] [CrossRef]
- Potti, A.; Mukherjee, S.; Petersen, R.; Dressman, H.K.; Bild, A.; Koontz, J.; Kratzke, R.; Watson, M.A.; Kelley, M.; Ginsburg, G.S.; West, M.; Harpole, D.H., Jr.; Nevins, J.R. A genomic strategy to refine prognosis in early-stage non-small-cell lung cancer. N. Engl. J. Med. 2006, 355, 570–580. [Google Scholar] [CrossRef]
- Corson, T.W.; Zhu, C.Q.; Lau, S.K.; Shepherd, F.A.; Tsao, M.S.; Gallie, B.L. KIF14 messenger RNA expression is independently prognostic for outcome in lung cancer. Clin. Cancer Res. 2007, 13, 3229–3234. [Google Scholar] [CrossRef]
- Guo, N.L.; Wan, Y.W.; Tosun, K.; Lin, H.; Msiska, Z.; Flynn, D.C.; Remick, S.C.; Vallyathan, V.; Dowlati, A.; Shi, X.; Castranova, V.; Beer, D.G.; Qian, Y. Confirmation of gene expression-based prediction of survival in non-small cell lung cancer. Clin. Cancer Res. 2008, 14, 8213–8220. [Google Scholar] [CrossRef]
- Lonergan, K.M.; Chari, R.; Coe, B.P.; Wilson, I.M.; Tsao, M.S.; Ng, R.T.; Macaulay, C.; Lam, S.; Lam, W.L. Transcriptome profiles of carcinoma-in-situ and invasive non-small cell lung cancer as revealed by SAGE. PLoS One 2010, 5, e9162. [Google Scholar] [CrossRef]
- Zhu, C.Q.; Blackhall, F.H.; Pintilie, M.; Iyengar, P.; Liu, N.; Ho, J.; Chomiak, T.; Lau, D.; Winton, T.; Shepherd, F.A.; Tsao, M.S. Skp2 gene copy number aberrations are common in non-small cell lung carcinoma, and its overexpression in tumors with ras mutation is a poor prognostic marker. Clin. Cancer Res. 2004, 10, 1984–1991. [Google Scholar] [CrossRef]
- Zhu, C.Q.; Cutz, J.C.; Liu, N.; Lau, D.; Shepherd, F.A.; Squire, J.A.; Tsao, M.S. Amplification of telomerase (hTERT) gene is a poor prognostic marker in non-small-cell lung cancer. Br. J. Cancer 2006, 94, 1452–1459. [Google Scholar] [CrossRef]
- Go, H.; Jeon, Y.K.; Park, H.J.; Sung, S.W.; Seo, J.W.; Chung, D.H. High MET gene copy number leads to shorter survival in patients with non-small cell lung cancer. J. Thorac. Oncol. 2010, 5, 305–313. [Google Scholar] [CrossRef]
- Tsao, M.S.; Sakurada, A.; Cutz, J.C.; Zhu, C.Q.; Kamel-Reid, S.; Squire, J.; Lorimer, I.; Zhang, T.; Liu, N.; Daneshmand, M.; Marrano, P.; da Cunha Santos, G.; Lagarde, A.; Richardson, F.; Seymour, L.; Whitehead, M.; Ding, K.; Pater, J.; Shepherd, F.A. Erlotinib in lung cancer - molecular and clinical predictors of outcome. N. Engl. J. Med. 2005, 353, 133–144. [Google Scholar] [CrossRef]
- Zhu, C.; da Cunha Santos, G.; Ding, K.; Sakurada, A.; Cutz, J.; Liu, N.; Zhang, T.; Marrano, P.; Whitehead, M.; Squire, J.; Kamel-Reid, S.; Seymour, L.; Shepherd, F.; Tsao, M. Role of KRAS and EGFR as biomarkers of response to erlotinib in National Cancer Institute of Canada Clinical Trials Group Study BR.21. J. Clin. Oncol. 2008, 26, 4268–4275. [Google Scholar] [CrossRef]
- Dahabreh, I.; Linardou, H.; Siannis, F.; Kosmidis, P.; Bafaloukos, D.; Murray, S. Somatic EGFR mutation and gene copy gain as predictive biomarkers for response to tyrosine kinase inhibitors in non-small cell lung cancer. Clin. Cancer Res. 2010, 16, 291–303. [Google Scholar] [CrossRef]
- Lynch, T.; Bell, D.; Sordella, R.; Gurubhagavatula, S.; Okimoto, R.; Brannigan, B.; Harris, P.; Haserlat, S.; Supko, J.; Haluska, F.; Louis, D.; Christiani, D.; Settleman, J.; Haber, D. Activating mutations in the epidermal growth factor receptor underlying responsiveness of non-small-cell lung cancer to gefitinib. N. Engl. J. Med. 2004, 350, 2129–2139. [Google Scholar] [CrossRef]
- Paez, J.; Jänne, P.; Lee, J.; Tracy, S.; Greulich, H.; Gabriel, S.; Herman, P.; Kaye, F.; Lindeman, N.; Boggon, T.; Naoki, K.; Sasaki, H.; Fujii, Y.; Eck, M.; Sellers, W.; Johnson, B.; Meyerson, M. EGFR mutations in lung cancer: correlation with clinical response to gefitinib therapy. Science 2004, 304, 1497–1500. [Google Scholar] [CrossRef]
- Price, D.; Figg, W. Mutations in the EGFR: the importance of genotyping. Cancer Biol. Ther. 2004, 3, 434–435. [Google Scholar] [CrossRef]
- Bell, D.; Lynch, T.; Haserlat, S.; Harris, P.; Okimoto, R.; Brannigan, B.; Sgroi, D.; Muir, B.; Riemenschneider, M.; Iacona, R.; Krebs, A.; Johnson, D.; Giaccone, G.; Herbst, R.; Manegold, C.; Fukuoka, M.; Kris, M.; Baselga, J.; Ochs, J.; Haber, D. Epidermal growth factor receptor mutations and gene amplification in non-small-cell lung cancer: molecular analysis of the IDEAL/INTACT gefitinib trials. J. Clin. Oncol. 2005, 23, 8081–8092. [Google Scholar] [CrossRef]
- Kelly, K.; Chansky, K.; Gaspar, L.; Albain, K.; Jett, J.; Ung, Y.; Lau, D.; Crowley, J.; Gandara, D. Phase III trial of maintenance gefitinib or placebo after concurrent chemoradiotherapy and docetaxel consolidation in inoperable stage III non-small-cell lung cancer: SWOG S0023. J. Clin. Oncol. 2008, 26, 2450–2456. [Google Scholar] [CrossRef]
- Valencia-Sanchez, M.A.; Liu, J.; Hannon, G.J.; Parker, R. Control of translation and mRNA degradation by miRNAs and siRNAs. Genes Dev. 2006, 20, 515–524. [Google Scholar] [CrossRef]
- Bishop, J.A.; Benjamin, H.; Cholakh, H.; Chajut, A.; Clark, D.P.; Westra, W.H. Accurate classification of non-small cell lung carcinoma using a novel microRNA-based approach. Clin. Cancer Res. 2010, 16, 610–619. [Google Scholar] [CrossRef]
- Hu, Z.; Chen, X.; Zhao, Y.; Tian, T.; Jin, G.; Shu, Y.; Chen, Y.; Xu, L.; Zen, K.; Zhang, C.; Shen, H. Serum MicroRNA signatures identified in a genome-wide serum microrna expression profiling predict survival of non-small-cell lung cancer. J. Clin. Oncol. 2010, 28, 1721–1726. [Google Scholar] [CrossRef]
- Yu, L.; Todd, N.W.; Xing, L.; Xie, Y.; Zhang, H.; Liu, Z.; Fang, H.; Zhang, J.; Katz, R.L.; Jiang, F. Early detection of lung adenocarcinoma in sputum by a panel of microRNA markers. Int. J. Cancer 2010. [Google Scholar] [CrossRef]
- Perou, C.M.; Sorlie, T.; Eisen, M.B.; van de Rijn, M.; Jeffrey, S.S.; Rees, C.A.; Pollack, J.R.; Ross, D.T.; Johnsen, H.; Akslen, L.A.; Fluge, O.; Pergamenschikov, A.; Williams, C.; Zhu, S.X.; Lonning, P.E.; Borresen-Dale, A.L.; Brown, P.O.; Botstein, D. Molecular portraits of human breast tumors. Nature 2000, 406, 747–752. [Google Scholar] [CrossRef]
- Lonning, P.E.; Sorlie, T.; Perou, C.M.; Brown, P.O.; Botstein, D.; Borresen-Dale, A.L. Microarrays in primary breast cancer--lessons from chemotherapy studies. Endocr. Relat. Cancer 2001, 8, 259–263. [Google Scholar] [CrossRef]
- Sorlie, T.; Perou, C.M.; Tibshirani, R.; Aas, T.; Geisler, S.; Johnsen, H.; Hastie, T.; Eisen, M.B.; van de Rijn, M.; Jeffrey, S.S.; Thorsen, T.; Quist, H.; Matese, J.C.; Brown, P.O.; Botstein, D.; Eystein Lonning, P.; Borresen-Dale, A.L. Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc. Natl. Acad. Sci. USA 2001, 98, 10869–10874. [Google Scholar] [CrossRef]
- Sorlie, T.; Tibshirani, R.; Parker, J.; Hastie, T.; Marron, J.S.; Nobel, A.; Deng, S.; Johnsen, H.; Pesich, R.; Geisler, S.; Demeter, J.; Perou, C.M.; Lonning, P.E.; Brown, P.O.; Borresen-Dale, A.L.; Botstein, D. Repeated observation of breast tumor subtypes in independent gene expression data sets. Proc. Natl. Acad. Sci. USA 2003, 100, 8418–8423. [Google Scholar] [CrossRef]
- Rouzier, R.; Perou, C.M.; Symmans, W.F.; Ibrahim, N.; Cristofanilli, M.; Anderson, K.; Hess, K.R.; Stec, J.; Ayers, M.; Wagner, P.; Morandi, P.; Fan, C.; Rabiul, I.; Ross, J.S.; Hortobagyi, G.N.; Pusztai, L. Breast cancer molecular subtypes respond differently to preoperative chemotherapy. Clin. Cancer Res. 2005, 11, 5678–5685. [Google Scholar] [CrossRef]
- Sotiriou, C.; Powles, T.J.; Dowsett, M.; Jazaeri, A.A.; Feldman, A.L.; Assersohn, L.; Gadisetti, C.; Libutti, S.K.; Liu, E.T. Gene expression profiles derived from fine needle aspiration correlate with response to systemic chemotherapy in breast cancer. Breast Cancer Res. 2002, 4, R3. [Google Scholar] [CrossRef]
- van de Vijver, M.; He, Y.; van't Veer, L.; Dai, H.; Hart, A.; Voskuil, D.; Schreiber, G.; Peterse, J.; Roberts, C.; Marton, M.; Parrish, M.; Atsma, D.; Witteveen, A.; Glas, A.; Delahaye, L.; van der Velde, T.; Bartelink, H.; Rodenhuis, S.; Rutgers, E.; Friend, S.; Bernards, R. A gene-expression signature as a predictor of survival in breast cancer. N. Engl. J. Med. 2002, 347, 1999–2009. [Google Scholar] [CrossRef]
- Ma, X.J.; Salunga, R.; Tuggle, J.T.; Gaudet, J.; Enright, E.; McQuary, P.; Payette, T.; Pistone, M.; Stecker, K.; Zhang, B.M.; Zhou, Y.X.; Varnholt, H.; Smith, B.; Gadd, M.; Chatfield, E.; Kessler, J.; Baer, T.M.; Erlander, M.G.; Sgroi, D.C. Gene expression profiles of human breast cancer progression. Proc. Natl. Acad. Sci. USA 2003, 100, 5974–5979. [Google Scholar] [CrossRef]
- Weigelt, B.; Glas, A.; Wessels, L.; Witteveen, A.; Peterse, J.; van't Veer, L. Gene expression profiles of primary breast tumors maintained in distant metastases. Proc. Natl. Acad. Sci. USA 2003, 100, 15901–15905. [Google Scholar]
- Fischer, D.C.; Noack, K.; Runnebaum, I.B.; Watermann, D.O.; Kieback, D.G.; Stamm, S.; Stickeler, E. Expression of splicing factors in human ovarian cancer. Oncol. Rep. 2004, 11, 1085–1090. [Google Scholar]
- Weigelt, B.; van't Veer, L. Hard-wired genotype in metastatic breast cancer. Cell Cycle 2004, 3, 756–757. [Google Scholar] [CrossRef]
- Nakatsu, N.; Yoshida, Y.; Yamazaki, K.; Nakamura, T.; Dan, S.; Fukui, Y.; Yamori, T. Chemosensitivity profile of cancer cell lines and identification of genes determining chemosensitivity by an integrated bioinformatical approach using cDNA arrays. Mol. Cancer Ther. 2005, 4, 399–412. [Google Scholar]
- Weigelt, B.; Hu, Z.; He, X.; Livasy, C.; Carey, L.; Ewend, M.; Glas, A.; Perou, C.; Van't Veer, L. Molecular portraits and 70-gene prognosis signature are preserved throughout the metastatic process of breast cancer. Cancer Res. 2005, 65, 9155–9158. [Google Scholar] [CrossRef]
- Ioannidis, J.P.; Allison, D.B.; Ball, C.A.; Coulibaly, I.; Cui, X.; Culhane, A.C.; Falchi, M.; Furlanello, C.; Game, L.; Jurman, G.; Mangion, J.; Mehta, T.; Nitzberg, M.; Page, G.P.; Petretto, E.; van Noort, V. Repeatability of published microarray gene expression analyses. Nat. Genet. 2009, 41, 149–155. [Google Scholar] [CrossRef]
- Chen, X.; Wang, L. Integrating biological knowledge with gene expression profiles for survival prediction of cancer. J. Comput. Biol. 2009, 16, 265–278. [Google Scholar] [CrossRef]
- Smith, D.D.; Saetrom, P.; Snove, O., Jr.; Lundberg, C.; Rivas, G.E.; Glackin, C.; Larson, G.P. Meta-analysis of breast cancer microarray studies in conjunction with conserved cis-elements suggest patterns for coordinate regulation. BMC Bioinformatics 2008, 9, 63. [Google Scholar] [CrossRef] [Green Version]
- Lu, X.; Wang, Z.C.; Iglehart, J.D.; Zhang, X.; Richardson, A.L. Predicting featuRes. of breast cancer with gene expression patterns. Breast Cancer Res. Treat. 2008, 108, 191–201. [Google Scholar] [CrossRef]
- Naderi, A.; Teschendorff, A.E.; Barbosa-Morais, N.L.; Pinder, S.E.; Green, A.R.; Powe, D.G.; Robertson, J.F.; Aparicio, S.; Ellis, I.O.; Brenton, J.D.; Caldas, C. A gene-expression signature to predict survival in breast cancer across independent data sets. Oncogene 2007, 26, 1507–1516. [Google Scholar] [CrossRef]
- Frkovic-Grazio, S.; Bracko, M. Long term prognostic value of NottinghAm. histological grade and its components in early (pT1N0M0) breast carcinoma. J. Clin. Pathol. 2002, 55, 88–92. [Google Scholar] [CrossRef]
- Tang, P.; Skinner, K.A.; Hicks, D.G. Molecular classification of breast carcinomas by immunohistochemical analysis: are we ready? Diagn. Mol. Pathol. 2009, 18, 125–132. [Google Scholar]
- Nielsen, T.O.; Hsu, F.D.; Jensen, K.; Cheang, M.; Karaca, G.; Hu, Z.; Hernandez-Boussard, T.; Livasy, C.; Cowan, D.; Dressler, L.; Akslen, L.A.; Ragaz, J.; Gown, A.M.; Gilks, C.B.; van de Rijn, M.; Perou, C.M. Immunohistochemical and clinical characterization of the basal-like subtype of invasive breast carcinoma. Clin. Cancer Res. 2004, 10, 5367–5374. [Google Scholar] [CrossRef]
- Moyano, J.V.; Evans, J.R.; Chen, F.; Lu, M.; Werner, M.E.; Yehiely, F.; Diaz, L.K.; Turbin, D.; Karaca, G.; Wiley, E.; Nielsen, T.O.; Perou, C.M.; Cryns, V.L. AlphaB-crystallin is a novel oncoprotein that predicts poor clinical outcome in breast cancer. J. Clin. Invest 2006, 116, 261–270. [Google Scholar]
- Cheang, M.C.; Chia, S.K.; Voduc, D.; Gao, D.; Leung, S.; Snider, J.; Watson, M.; Davies, S.; Bernard, P.S.; Parker, J.S.; Perou, C.M.; Ellis, M.J.; Nielsen, T.O. Ki67 index, HER2 status, and prognosis of patients with luminal B breast cancer. J. Natl. Cancer Inst. 2009, 101, 736–750. [Google Scholar] [CrossRef]
- Weigelt, B.; Baehner, F.L.; Reis-Filho, J.S. The contribution of gene expression profiling to breast cancer classification, prognostication and prediction: a retrospective of the last decade. J. Pathol. 2010, 220, 263–280. [Google Scholar]
- Dressman, H.K.; Hans, C.; Bild, A.; Olson, J.A.; Rosen, E.; Marcom, P.K.; Liotcheva, V.B.; Jones, E.L.; Vujaskovic, Z.; Marks, J.; Dewhirst, M.W.; West, M.; Nevins, J.R.; Blackwell, K. Gene expression profiles of multiple breast cancer phenotypes and response to neoadjuvant chemotherapy. Clin. Cancer Res. 2006, 12, 819–826. [Google Scholar] [CrossRef]
- Sorlie, T.; Perou, C.M.; Fan, C.; Geisler, S.; Aas, T.; Nobel, A.; Anker, G.; Akslen, L.A.; Botstein, D.; Borresen-Dale, A.L.; Lonning, P.E. Gene expression profiles do not consistently predict the clinical treatment response in locally advanced breast cancer. Mol. Cancer Ther. 2006, 5, 2914–2918. [Google Scholar] [CrossRef]
- Lee, J.K.; Coutant, C.; Kim, Y.C.; Qi, Y.; Theodorescu, D.; Symmans, W.F.; Baggerly, K.; Rouzier, R.; Pusztai, L. Prospective comparison of clinical and genomic multivariate predictors of response to neoadjuvant chemotherapy in breast cancer. Clin. Cancer Res. 2010, 16, 711–718. [Google Scholar] [CrossRef]
- Bauer, J.A.; Chakravarthy, A.B.; Rosenbluth, J.M.; Mi, D.; Seeley, E.H.; De Matos Granja-Ingram, N.; Olivares, M.G.; Kelley, M.C.; Mayer, I.A.; Meszoely, I.M.; Means-Powell, J.A.; Johnson, K.N.; Tsai, C.J.; Ayers, G.D.; Sanders, M.E.; Schneider, R.J.; Formenti, S.C.; Caprioli, R.M.; Pietenpol, J.A. Identification of markers of taxane sensitivity using proteomic and genomic analyses of breast tumors from patients receiving neoadjuvant paclitaxel and radiation. Clin. Cancer Res. 2010, 16, 681–690. [Google Scholar] [CrossRef]
- Osako, T.; Horii, R.; Matsuura, M.; Domoto, K.; Ide, Y.; Miyagi, Y.; Takahashi, S.; Ito, Y.; Iwase, T.; Akiyama, F. High-grade breast cancers include both highly sensitive and highly resistant subsets to cytotoxic chemotherapy. J. Cancer Res. Clin. Oncol. 2010. [Google Scholar] [CrossRef]
- Fu, G.; Song, X.C.; Yang, X.; Peng, T.; Wang, Y.; Zhou, G.W. Protein Subcellular Localization Profiling of Breast Cancer Cells by Dissociable Antibody MicroArray (DAMA) Staining. Proteomics 2010, 10, 1536–1544. [Google Scholar] [CrossRef]
- Isakoff, S.J. Triple-Negative Breast Cancer: Role of Specific Chemotherapy Agents. Cancer J. 2010, 16, 53–61. [Google Scholar] [CrossRef]
- Seal, M.D.; Chia, S.K. What Is the Difference Between Triple-Negative and Basal Breast Cancers? Cancer J. 2010, 16, 12–16. [Google Scholar] [CrossRef]
- Venkitaraman, R. Triple-negative/basal-like breast cancer: clinical, pathologic and molecular featuRes. Expert Rev. Anticancer Ther. 2010, 10, 199–207. [Google Scholar] [CrossRef]
- Perez, E.A.; Moreno-Aspitia, A.; Aubrey Thompson, E.; Andorfer, C.A. Adjuvant therapy of triple negative breast cancer. Breast Cancer Res. Treat. 2010, 120, 285–291. [Google Scholar] [CrossRef]
- Schulz, D.M.; Bollner, C.; Thomas, G.; Atkinson, M.; Esposito, I.; Hofler, H.; Aubele, M. Identification of differentially expressed proteins in triple-negative breast carcinomas using DIGE and mass spectrometry. J. Proteome Res. 2009, 8, 3430–3438. [Google Scholar] [CrossRef]
- Agarwal, R.; Gonzalez-Angulo, A.M.; Myhre, S.; Carey, M.; Lee, J.S.; Overgaard, J.; Alsner, J.; Stemke-Hale, K.; Lluch, A.; Neve, R.M.; Kuo, W.L.; Sorlie, T.; Sahin, A.; Valero, V.; Keyomarsi, K.; Gray, J.W.; Borresen-Dale, A.L.; Mills, G.B.; Hennessy, B.T. Integrative analysis of cyClin. protein levels identifies cyClin. b1 as a classifier and predictor of outcomes in breast cancer. Clin. Cancer Res. 2009, 15, 3654–3662. [Google Scholar] [CrossRef]
- Rha, S.Y.; Jeung, H.C.; Seo, M.Y.; Kim, S.C.; Yang, W.I.; Moon, Y.W.; Chung, H.C. Prediction of high-risk patients by genome-wide copy number alterations from remaining cancer after neoadjuvant chemotherapy and surgery. Int. J. Oncol. 2009, 34, 837–846. [Google Scholar]
- Shadeo, A.; Lam, W.L. Comprehensive copy number profiles of breast cancer cell model genomes. Breast Cancer Res. 2006, 8, R9. [Google Scholar] [CrossRef]
- Heselmeyer-Haddad, K.; Chaudhri, N.; Stoltzfus, P.; Cheng, J.C.; Wilber, K.; Morrison, L.; Auer, G.; Ried, T. Detection of chromosomal aneuploidies and gene copy number changes in fine needle aspirates is a specific, sensitive, and objective genetic test for the diagnosis of breast cancer. Cancer Res. 2002, 62, 2365–2369. [Google Scholar]
- Raphael, B.J.; Volik, S.; Yu, P.; Wu, C.; Huang, G.; Linardopoulou, E.V.; Trask, B.J.; Waldman, F.; Costello, J.; Pienta, K.J.; Mills, G.B.; Bajsarowicz, K.; Kobayashi, Y.; Sridharan, S.; Paris, P.L.; Tao, Q.; Aerni, S.J.; Brown, R.P.; Bashir, A.; Gray, J.W.; Cheng, J.F.; de Jong, P.; Nefedov, M.; Ried, T.; Padilla-Nash, H.M.; Collins, C.C. A sequence-based survey of the complex structural organization of tumor genomes. Genome Biol. 2008, 9, R59. [Google Scholar] [CrossRef]
- Letessier, A.; Mozziconacci, M.J.; Murati, A.; Juriens, J.; Adelaide, J.; Birnbaum, D.; Chaffanet, M. Multicolour-banding fluorescence in situ hybridization (mbanding-FISH) to identify recurrent chromosomal alterations in breast tumor cell lines. Br. J. Cancer 2005, 92, 382–388. [Google Scholar]
- Sigurdsson, S.; Bodvarsdottir, S.K.; Anamthawat-Jonsson, K.; Steinarsdottir, M.; Jonasson, J.G.; Ogmundsdottir, H.M.; Eyfjord, J.E. p53 abnormality and chromosomal instability in the same breast tumor cells. Cancer Genet. Cytogenet. 2000, 121, 150–155. [Google Scholar] [CrossRef]
- Bozhanov, S.S.; Angelova, S.G.; Krasteva, M.E.; Markov, T.L.; Christova, S.L.; Gavrilov, I.G.; Georgieva, E.I. Alterations in p53, BRCA1, ATM, PIK3CA, and HER2 genes and their effect in modifying clinicopathological characteristics and overall survival of Bulgarian patients with breast cancer. J. Cancer Res. Clin. Oncol. 2010. [Google Scholar] [CrossRef]
- Takahashi, S.; Moriya, T.; Ishida, T.; Shibata, H.; Sasano, H.; Ohuchi, N.; Ishioka, C. Prediction of breast cancer prognosis by gene expression profile of TP53 status. Cancer Sci. 2008, 99, 324–332. [Google Scholar] [CrossRef]
- Ozcelik, H.; Pinnaduwage, D.; Bull, S.B.; Andrulis, I.L. Type of TP53 mutation and ERBB2 amplification affects survival in node-negative breast cancer. Breast Cancer Res. Treat. 2007, 105, 255–265. [Google Scholar] [CrossRef]
- Langerod, A.; Zhao, H.; Borgan, O.; Nesland, J.M.; Bukholm, I.R.; Ikdahl, T.; Karesen, R.; Borresen-Dale, A.L.; Jeffrey, S.S. TP53 mutation status and gene expression profiles are powerful prognostic markers of breast cancer. Breast Cancer Res. 2007, 9, R30. [Google Scholar] [CrossRef]
- Di Leo, A.; Tanner, M.; Desmedt, C.; Paesmans, M.; Cardoso, F.; Durbecq, V.; Chan, S.; Perren, T.; Aapro, M.; Sotiriou, C.; Piccart, M.J.; Larsimont, D.; Isola, J. p-53 gene mutations as a predictive marker in a population of advanced breast cancer patients randomly treated with doxorubicin or docetaxel in the context of a phase III clinical trial. Ann. Oncol. 2007, 18, 997–1003. [Google Scholar] [CrossRef]
- Werner, G.; Bartel, M.; Wellinghausen, N.; Essig, A.; Klare, I.; Witte, W.; Poppert, S. Detection of mutations conferring resistance to linezolid in Enterococcus spp. by fluorescence in situ hybridization. J. Clin. Microbiol. 2007, 45, 3421–3423. [Google Scholar] [CrossRef]
- O'Day, E.; Lal, A. MicroRNAs and their target gene networks in breast cancer. Breast Cancer Res. 2010, 12, 201. [Google Scholar] [CrossRef]
- Cascio, S.; D'Andrea, A.; Ferla, R.; Surmacz, E.; Gulotta, E.; Amodeo, V.; Bazan, V.; Gebbia, N.; Russo, A. miR-20b modulates VEGF expression by targeting HIF-1alpha and STAT3 in MCF-7 breast cancer cells. J. Cell Physiol. 2010, 224, 242–249. [Google Scholar]
- Heneghan, H.M.; Miller, N.; Lowery, A.J.; Sweeney, K.J.; Newell, J.; Kerin, M.J. Circulating microRNAs as novel minimally invasive biomarkers for breast cancer. Ann. Surg. 2010, 251, 499–505. [Google Scholar] [CrossRef]
- Sempere, L.F.; Christensen, M.; Silahtaroglu, A.; Bak, M.; Heath, C.V.; Schwartz, G.; Wells, W.; Kauppinen, S.; Cole, C.N. Altered MicroRNA expression confined to specific epithelial cell subpopulations in breast cancer. Cancer Res. 2007, 67, 11612–11620. [Google Scholar] [CrossRef]
- Edwards, P.A. Fusion genes and chromosome translocations in the common epithelial cancers. J. Pathol. 2010, 220, 244–254. [Google Scholar]
- Scopelliti, A.; Cammareri, P.; Catalano, V.; Saladino, V.; Todaro, M.; Stassi, G. Therapeutic implications of Cancer Initiating Cells. Expert Opin. Biol. Ther. 2009, 9, 1005–1016. [Google Scholar] [CrossRef]
- Sakariassen, P.; Immervoll, H.; Chekenya, M. Cancer stem cells as mediators of treatment resistance in brain tumors: status and controversies. Neoplasia 2007, 9, 882–892. [Google Scholar] [CrossRef]
- Bidlingmaier, S.; Zhu, X.; Liu, B. The utility and limitations of glycosylated human CD133 epitopes in defining cancer stem cells. J. Mol. Med. 2008, 86, 1025–1032. [Google Scholar] [CrossRef]
- Bertolini, G.; Roz, L.; Perego, P.; Tortoreto, M.; Fontanella, E.; Gatti, L.; Pratesi, G.; Fabbri, A.; Andriani, F.; Tinelli, S.; Roz, E.; Caserini, R.; Lo Vullo, S.; Camerini, T.; Mariani, L.; Delia, D.; Calabro, E.; Pastorino, U.; Sozzi, G. Highly tumorigenic lung cancer CD133+ cells display stem-like featuRes. and are spared by cisplatin treatment. Proc. Natl. Acad. Sci. USA 2009, 106, 16281–16286. [Google Scholar] [CrossRef]
- Levina, V.; Marrangoni, A.M.; DeMarco, R.; Gorelik, E.; Lokshin, A.E. Drug-selected human lung cancer stem cells: cytokine network, tumorigenic and metastatic properties. PLoS One 2008, 3, e3077. [Google Scholar]
- Levina, V.; Marrangoni, A.; Wang, T.; Parikh, S.; Su, Y.; Herberman, R.; Lokshin, A.; Gorelik, E. Elimination of human lung cancer stem cells through targeting of the stem cell factor-c-kit autocrine signaling loop. Cancer Res. 2010, 70, 338–346. [Google Scholar]
- Salnikov, A.V.; Gladkich, J.; Moldenhauer, G.; Volm, M.; Mattern, J.; Herr, I. CD133 is indicative for a resistance phenotype but does not represent a prognostic marker for survival of non-small cell lung cancer patients. Int. J. Cancer 2010, 126, 950–958. [Google Scholar]
- Al-Hajj, M.; Wicha, M.; Benito-Hernandez, A.; Morrison, S.; Clarke, M. Prospective identification of tumorigenic breast cancer cells. Proc. Natl. Acad. Sci. USA 2003, 100, 3983–3988. [Google Scholar] [CrossRef]
- Pece, S.; Tosoni, D.; Confalonieri, S.; Mazzarol, G.; Vecchi, M.; Ronzoni, S.; Bernard, L.; Viale, G.; Pelicci, P.G.; Di Fiore, P.P. Biological and molecular heterogeneity of breast cancers correlates with their cancer stem cell content. Cell 2010, 140, 62–73. [Google Scholar] [CrossRef]
- Hennessy, B.T.; Gonzalez-Angulo, A.M.; Stemke-Hale, K.; Gilcrease, M.Z.; Krishnamurthy, S.; Lee, J.S.; Fridlyand, J.; Sahin, A.; Agarwal, R.; Joy, C.; Liu, W.; Stivers, D.; Baggerly, K.; Carey, M.; Lluch, A.; Monteagudo, C.; He, X.; Weigman, V.; Fan, C.; Palazzo, J.; Hortobagyi, G.N.; Nolden, L.K.; Wang, N.J.; Valero, V.; Gray, J.W.; Perou, C.M.; Mills, G.B. Characterization of a naturally occurring breast cancer subset enriched in epithelial-to-mesenchymal transition and stem cell characteristics. Cancer Res. 2009, 69, 4116–4124. [Google Scholar] [CrossRef]
- Silva, F.P.; Swagemakers, S.M.; Erpelinck-Verschueren, C.; Wouters, B.J.; Delwel, R.; Vrieling, H.; van der Spek, P.; Valk, P.J.; Giphart-Gassler, M. Gene expression profiling of minimally differentiated acute myeloid leukemia: M0 is a distinct entity subdivided by RUNX1 mutation status. Blood 2009, 114, 3001–3007. [Google Scholar] [CrossRef]
- Hicks, M.J.; Mackay, B. Comparison of ultrastructural featuRes. among neuroblastic tumors: maturation from neuroblastoma to ganglioneuroma. Ultrastruct. Pathol. 1995, 19, 311–322. [Google Scholar] [CrossRef]
- Estrov, Z. Stem cells and somatic cells: reprogramming and plasticity. Clin. Lymphoma Myeloma 2009, 9 (Suppl. 3), S319–S328. [Google Scholar] [CrossRef]
- Kasemeier-Kulesa, J.; Teddy, J.; Postovit, L.; Seftor, E.; Seftor, R.; Hendrix, M.; Kulesa, P. Reprogramming multipotent tumor cells with the embryonic neural crest microenvironment. Dev. Dyn. 2008, 237, 2657–2666. [Google Scholar] [CrossRef]
- Postovit, L.; Margaryan, N.; Seftor, E.; Hendrix, M. Role of nodal signaling and the microenvironment underlying melanoma plasticity. Pigment Cell Melanoma Res. 2008, 21, 348–357. [Google Scholar] [CrossRef]
- Hendrix, M.; Seftor, E.; Seftor, R.; Kasemeier-Kulesa, J.; Kulesa, P.; Postovit, L. Reprogramming metastatic tumour cells with embryonic microenvironments. Nat. Rev. Cancer 2007, 7, 246–255. [Google Scholar] [CrossRef]
- Postovit, L.; Costa, F.; Bischof, J.; Seftor, E.; Wen, B.; Seftor, R.; Feinberg, A.; Soares, M.; Hendrix, M. The commonality of plasticity underlying multipotent tumor cells and embryonic stem cells. J. Cell. Biochem. 2007, 101, 908–917. [Google Scholar] [CrossRef]
- Wang, J.; Rao, S.; Chu, J.; Shen, X.; Levasseur, D.N.; Theunissen, T.W.; Orkin, S.H. A protein interaction network for pluripotency of embryonic stem cells. Nature 2006, 444, 364–368. [Google Scholar] [CrossRef]
- Taranger, C.; Noer, A.; Sørensen, A.; Håkelien, A.; Boquest, A.; Collas, P. Induction of dedifferentiation, genomewide transcriptional programming, and epigenetic reprogramming by extracts of carcinoma and embryonic stem cells. Mol. Biol. Cell 2005, 16, 5719–5735. [Google Scholar] [CrossRef]
- Summerer, D.; Schracke, N.; Wu, H.; Cheng, Y.; Bau, S.; Stahler, C.F.; Stahler, P.F.; Beier, M. Targeted high throughput sequencing of a cancer-related exome subset by specific sequence capture with a fully automated microarray platform. Genomics 2010, 95, 241–246. [Google Scholar] [CrossRef]
- Roukos, D.H. Novel clinico-genome network modeling for revolutionizing genotype-phenotype-based personalized cancer care. Expert Rev. Mol. Diagn. 2010, 10, 33–48. [Google Scholar] [CrossRef]
- Huang, Y.W.; Huang, T.H.; Wang, L.S. Profiling DNA methylomes from microarray to genome-scale sequencing. Technol. Cancer Res. Treat. 2010, 9, 139–147. [Google Scholar]
- Bell, D.W. Our changing view of the genomic landscape of cancer. J. Pathol. 2010, 220, 231–243. [Google Scholar]
- Aparicio, S.A.; Huntsman, D.G. Does massively parallel DNA resequencing signify the end of histopathology as we know it? J. Pathol. 2010, 220, 307–315. [Google Scholar]
- Shah, S.P.; Morin, R.D.; Khattra, J.; Prentice, L.; Pugh, T.; Burleigh, A.; Delaney, A.; Gelmon, K.; Guliany, R.; Senz, J.; Steidl, C.; Holt, R.A.; Jones, S.; Sun, M.; Leung, G.; Moore, R.; Severson, T.; Taylor, G.A.; Teschendorff, A.E.; Tse, K.; Turashvili, G.; Varhol, R.; Warren, R.L.; Watson, P.; Zhao, Y.; Caldas, C.; Huntsman, D.; Hirst, M.; Marra, M.A.; Aparicio, S. Mutational evolution in a lobular breast tumor profiled at single nucleotide resolution. Nature 2009, 461, 809–813. [Google Scholar] [CrossRef]
- Reis-Filho, J.S. Next-generation sequencing. Breast Cancer Res. 2009, 11 (Suppl. 3), S12. [Google Scholar] [CrossRef]
- Morrissy, A.S.; Morin, R.D.; Delaney, A.; Zeng, T.; McDonald, H.; Jones, S.; Zhao, Y.; Hirst, M.; Marra, M.A. Next-generation tag sequencing for cancer gene expression profiling. Genome Res. 2009, 19, 1825–1835. [Google Scholar] [CrossRef]
- Mardis, E.R.; Wilson, R.K. Cancer genome sequencing: a review. Hum Mol. Genet 2009, 18, R163–R168. [Google Scholar] [CrossRef]
- Mardis, E.R. New strategies and emerging technologies for massively parallel sequencing: applications in medical research. Genome Med. 2009, 1, 40. [Google Scholar] [CrossRef]
- Levin, J.Z.; Berger, M.F.; Adiconis, X.; Rogov, P.; Melnikov, A.; Fennell, T.; Nusbaum, C.; Garraway, L.A.; Gnirke, A. Targeted next-generation sequencing of a cancer transcriptome enhances detection of sequence variants and novel fusion transcripts. Genome Biol. 2009, 10, R115. [Google Scholar] [CrossRef]
- Kato, K. Impact of the next generation DNA sequencers. Int. J. Clin. Exp Med. 2009, 2, 193–202. [Google Scholar]
- Salehi-Ashtiani, K.; Yang, X.; Derti, A.; Tian, W.; Hao, T.; Lin, C.; Makowski, K.; Shen, L.; Murray, R.R.; Szeto, D.; Tusneem, N.; Smith, D.R.; Cusick, M.E.; Hill, D.E.; Roth, F.P.; Vidal, M. Isoform discovery by targeted cloning, 'deep-well' pooling and parallel sequencing. Nat. Methods 2008, 5, 597–600. [Google Scholar] [CrossRef]
- Morozova, O.; Marra, M.A. From cytogenetics to next-generation sequencing technologies: advances in the detection of genome rearrangements in tumors. Biochem. Cell Biol. 2008, 86, 81–91. [Google Scholar] [CrossRef]
- Morozova, O.; Marra, M.A. Applications of next-generation sequencing technologies in functional genomics. Genomics 2008, 92, 255–264. [Google Scholar] [CrossRef]
- Marguerat, S.; Wilhelm, B.T.; Bahler, J. Next-generation sequencing: applications beyond genomes. Biochem. Soc. Trans. 2008, 36, 1091–1096. [Google Scholar] [CrossRef]
- Kobel, M.; Gilks, C.B.; Huntsman, D.G. Adult-type granulosa cell tumors and FOXL2 mutation. Cancer Res. 2009, 69, 9160–9162. [Google Scholar] [CrossRef]
- Schrader, K.A.; Gorbatcheva, B.; Senz, J.; Heravi-Moussavi, A.; Melnyk, N.; Salamanca, C.; Maines-Bandiera, S.; Cooke, S.L.; Leung, P.; Brenton, J.D.; Gilks, C.B.; Monahan, J.; Huntsman, D.G. The specificity of the FOXL2 c.402C>G Somatic mutation: a survey of solid tumors. PLoS One 2009, 4, e7988. [Google Scholar] [CrossRef]
- Shah, S.P.; Kobel, M.; Senz, J.; Morin, R.D.; Clarke, B.A.; Wiegand, K.C.; Leung, G.; Zayed, A.; Mehl, E.; Kalloger, S.E.; Sun, M.; Giuliany, R.; Yorida, E.; Jones, S.; Varhol, R.; Swenerton, K.D.; Miller, D.; Clement, P.B.; Crane, C.; Madore, J.; Provencher, D.; Leung, P.; DeFazio, A.; Khattra, J.; Turashvili, G.; Zhao, Y.; Zeng, T.; Glover, J.N.; Vanderhyden, B.; Zhao, C.; Parkinson, C.A.; Jimenez-Linan, M.; Bowtell, D.D.; Mes-Masson, A.M.; Brenton, J.D.; Aparicio, S.A.; Boyd, N.; Hirst, M.; Gilks, C.B.; Marra, M.; Huntsman, D.G. Mutation of FOXL2 in granulosa-cell tumors of the ovary. N. Engl. J. Med. 2009, 360, 2719–2729. [Google Scholar] [CrossRef]
© 2010 by the authors; licensee MDPI, Basel, Switzerland. This article is an Open Access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/).
Share and Cite
Banerjee, D. Reinventing Diagnostics for Personalized Therapy in Oncology. Cancers 2010, 2, 1066-1091. https://doi.org/10.3390/cancers2021066
Banerjee D. Reinventing Diagnostics for Personalized Therapy in Oncology. Cancers. 2010; 2(2):1066-1091. https://doi.org/10.3390/cancers2021066
Chicago/Turabian StyleBanerjee, Diponkar. 2010. "Reinventing Diagnostics for Personalized Therapy in Oncology" Cancers 2, no. 2: 1066-1091. https://doi.org/10.3390/cancers2021066