High frequency of putative ovarian cancer stem cells with CD44/CK19 coexpression is associated with decreased progression-free intervals in patients with recurrent epithelial ovarian cancer

Reproductive Sciences

Volumn 20, Issue 5, 2013, Pages 605-615

  Liu, Ming ^a,b   Mor, Gil ^b   Cheng, Huan ^b   Xiang, Xue ^a   Hui, Pei ^b   Rutherford, Thomas ^b   Yin, Gang ^b   Rimm, David L ^b   Holmberg, Jennie ^b   Alvero, Ayesha ^b   Silasi, Dan Arin ^b  

^a XI'AN JIAOTONG UNIVERSITY   (China)

^b YALE UNIVERSITY SCHOOL OF MEDICINE   (United States)

Author keywords

AQUA; CD44; CK19; ovarian cancer stem cells

Indexed keywords

CYTOKERATIN 19; HERMES ANTIGEN;

ADULT; AGED; ARTICLE; CANCER GROWTH; CANCER PROGNOSIS; CANCER RESISTANCE; CANCER STAGING; CLINICAL ARTICLE; CONTROLLED STUDY; DISEASE FREE INTERVAL; FEMALE; FOLLOW UP; HUMAN; IMMUNOFLUORESCENCE; MALE; OVARY CARCINOMA; PRIORITY JOURNAL; PROGRESSION FREE SURVIVAL; PROTEIN ANALYSIS; RETROSPECTIVE STUDY; STEM CELL; TISSUE MICROARRAY; TREATMENT RESPONSE; TUMOR VOLUME;

ADULT; AGED; AGED, 80 AND OVER; ANALYSIS OF VARIANCE; ANTIGENS, CD44; DISEASE PROGRESSION; DISEASE-FREE SURVIVAL; DRUG RESISTANCE, NEOPLASM; FEMALE; HUMANS; KAPLAN-MEIER ESTIMATE; KERATIN-19; MIDDLE AGED; MULTIVARIATE ANALYSIS; NEOPLASM RECURRENCE, LOCAL; NEOPLASM STAGING; NEOPLASMS, GLANDULAR AND EPITHELIAL; NEOPLASTIC STEM CELLS; OVARIAN NEOPLASMS; PROPORTIONAL HAZARDS MODELS; RETROSPECTIVE STUDIES; RISK FACTORS; TIME FACTORS; TREATMENT OUTCOME; TUMOR MARKERS, BIOLOGICAL;

EID: 84876713098     PISSN: 19337191     EISSN: 19337205     Source Type: Journal    
DOI: 10.1177/1933719112461183     Document Type: Article

References (44)

1. Holschneider CH, Berek JS. Ovarian cancer: epidemiology, biology, and prognostic factors. Semin Surg Oncol. 2000 ; 19 (1). 3-10
2. Nossov V, Amneus M, Su F, et al. The early detection of ovarian cancer: from traditional methods to proteomics. Can we really do better than serum CA-125?. Am J Obstet Gynecol. 2008 ; 199 (3). 215-223
3. Jemal A, Siegel R, Xu J, Ward E. Cancer statistics, 2010. CA Cancer J Clin. 2010 ; 60 (5). 277-300
4. Garvalov BK, Acker T. Cancer stem cells: a new framework for the design of tumor therapies. J Mol Med (Berl). 2011 ; 89 (2). 95-107
5. O'Brien CA, Kreso A, Dick JE. Cancer stem cells in solid tumors: an overview. Semin Radiat Oncol. 2009 ; 19 (2). 71-77
6. Bao S, Wu Q, McLendon RE, et al. Glioma stem cells promote radioresistance by preferential activation of the DNA damage response. Nature. 2006 ; 444 (7120). 756-760
7. Hermann PC, Huber SL, Herrler T, et al. Distinct populations of cancer stem cells determine tumor growth and metastatic activity in human pancreatic cancer. Cell Stem Cell. 2007 ; 1 (3). 313-323
8. Alvero AB, Chen R, Fu HH, et al. Molecular phenotyping of human ovarian cancer stem cells unravels the mechanisms for repair and chemoresistance. Cell Cycle. 2009 ; 8 (1). 158-166
9. Mor G, Yin G, Chefetz I, Yang Y, Alvero A.. Ovarian cancer stem cells and inflammation. Cancer Biol Ther. 2011 ; 11 (8). 708-713
10. Alvero AB, Fu HH, Holmberg J, et al. Stem-like ovarian cancer cells can serve as tumor vascular progenitors. Stem Cells. 2009 ; 27 (10). 2405-2413
11. Curley MD, Therrien VA, Cummings CL, et al. CD133 expression defines a tumor initiating cell population in primary human ovarian cancer. Stem Cells. 2009 ; 27 (12). 2875-2883
12. Kryczek I, Liu S, Roh M, et al. Expression of aldehyde dehydrogenase and CD133 defines ovarian cancer stem cells. Int J Cancer. 2012 ; 130 (1). 29-39
13. Gao MQ, Choi YP, Kang S, Youn JH, Cho NH. CD24+ cells from hierarchically organized ovarian cancer are enriched in cancer stem cells. Oncogene. 2010 ; 29 (18). 2672-2680
14. Luo L, Zeng J, Liang B, et al. Ovarian cancer cells with the CD117 phenotype are highly tumorigenic and are related to chemotherapy outcome. Exp Mol Pathol. 2011 ; 91 (2). 596-602
15. Naor D, Sionov RV, Ish-Shalom D. CD44: structure, function, and association with the malignant process. Adv Cancer Res. 1997 ; 71: 241-319
16. Shipitsin M, Campbell LL, Argani P, et al. Molecular definition of breast tumor heterogeneity. Cancer Cell. 2007 ; 11 (3). 259-273
17. Yin G, Chen R, Alvero AB, et al. TWISTing stemness, inflammation and proliferation of epithelial ovarian cancer cells through MIR199A2/214. Oncogene. 2010 ; 29 (24). 3545-3553
18. Zoller M. CD44: can a cancer-initiating cell profit from an abundantly expressed molecule?. Nat Rev Cancer. 2011 ; 11 (4). 254-267
19. Steffensen KD, Alvero AB, Yang Y, et al. Prevalence of epithelial ovarian cancer stem cells correlates with recurrence in early-stage ovarian cancer. J Oncol. 2011 ; 620523: 2011
20. Lim R, Lappas M, Ahmed N, Permezel M, Quinn MA. Rice GE. 2D-PAGE of ovarian cancer: analysis of soluble and insoluble fractions using medium-range immobilized pH gradients. Biochem Biophys Res Commun. 2011 ; 406 (3). 408-413
21. McCabe A, Dolled-Filhart M, Camp RL, Rimm DL. Automated quantitative analysis (AQUA) of in situ protein expression, antibody concentration, and prognosis. J Natl Cancer Inst. 2005 ; 97 (24). 1808-1815
22. Kluger HM, Dolled-Filhart M, Rodov S, Kacinski BM, Camp RL, Rimm DL. Macrophage colony-stimulating factor-1 receptor expression is associated with poor outcome in breast cancer by large cohort tissue microarray analysis. Clin Cancer Res. 2004 ; 10 (1 pt 1). 173-177
23. Psyrri A, Kountourakis P, Yu Z, et al. Analysis of p53 protein expression levels on ovarian cancer tissue microarray using automated quantitative analysis elucidates prognostic patient subsets. Ann Oncol. 2007 ; 18 (4). 709-715
24. Camp RL, Chung GG, Rimm DL. Automated subcellular localization and quantification of protein expression in tissue microarrays. Nat Med. 2002 ; 8 (11). 1323-1327
25. Gustavson MD, Bourke-Martin B, Reilly D, et al. Standardization of HER2 immunohistochemistry in breast cancer by automated quantitative analysis. Arch Pathol Lab Med. 2009 ; 133 (9). 1413-1419
26. Gustavson MD, Bourke-Martin B, Reilly DM, et al. Development of an unsupervised pixel-based clustering algorithm for compartmentalization of immunohistochemical expression using Automated QUantitative Analysis. Appl Immunohistochem Mol Morphol. 2009 ; 17 (4). 329-337
27. Camp RL, Chung GG, Rimm DL. Automated subcellular localization and quantification of protein expression in tissue microarrays. Nat Med. 2002 ; 8 (11). 1323-1327
28. Gustavson MD, Molinaro AM, Tedeschi G, Camp RL, Rimm DL. AQUA analysis of thymidylate synthase reveals localization to be a key prognostic biomarker in 2 large cohorts of colorectal carcinoma. Arch Pathol Lab Med. 2008 ; 132 (11). 1746-1752
29. Camp RL, Dolled-Filhart M, Rimm DL. X-tile: a new bio-informatics tool for biomarker assessment and outcome-based cut-point optimization. Clin Cancer Res. 2004 ; 10 (21). 7252-7259
30. Rustin GJ, Quinn M, Thigpen T, et al. Re: New guidelines to evaluate the response to treatment in solid tumors (ovarian cancer). J Natl Cancer Inst. 2004 ; 96 (6). 487-488
31. Rustin GJ. Can we now agree to use the same definition to measure response according to CA-125?. J Clin Oncol. 2004 ; 22 (20). 4035-4036
32. Neumeister V, Agarwal S, Bordeaux J, Camp RL, Rimm DL.. In situ identification of putative cancer stem cells by multiplexing ALDH1, CD44, and cytokeratin identifies breast cancer patients with poor prognosis. Am J Pathol. 2010 ; 176 (5). 2131-2138
33. Jain R, Fischer S, Serra S, Chetty R. The use of Cytokeratin 19 (CK19) immunohistochemistry in lesions of the pancreas, gastrointestinal tract, and liver. Appl Immunohistochem Mol Morphol. 2010 ; 18 (1). 9-15
34. Lacroix M. Significance, detection and markers of disseminated breast cancer cells. Endocr Relat Cancer. 2006 ; 13 (4). 1033-1067
35. Mertz KD, Demichelis F, Sboner A, et al. Association of cytokeratin 7 and 19 expression with genomic stability and favorable prognosis in clear cell renal cell cancer. Int J Cancer. 2008 ; 123 (3). 569-576
36. Ponta H, Sherman L, Herrlich PA. CD44: from adhesion molecules to signalling regulators. Nat Rev Mol Cell Biol. 2003 ; 4 (1). 33-45
37. Marhaba R, Klingbeil P, Nuebel T, Nazarenko I, Buechler MW, Zoeller M. CD44 and EpCAM: cancer-initiating cell markers. Curr Mol Med. 2008 ; 8 (8). 784-804
38. Yin G, Alvero AB, Craveiro V, et al Oncogene. 2012 :
39. Casagrande F, Cocco E, Bellone S, et al. Eradication of chemotherapy-resistant CD44+ human ovarian cancer stem cells in mice by intraperitoneal administration of clostridium perfringens enterotoxin. Cancer. 2011 ; 117 (24). 5519-5528
40. Kayastha S, Freedman AN, Piver MS, Mukkamalla J, Romero-Guittierez M, Werness BA.. Expression of the hyaluronan receptor, CD44S, in epithelial ovarian cancer is an independent predictor of survival. Clin Cancer Res. 1999 ; 5 (5). 1073-1076
41. Rodriguez-Rodriguez L, Sancho-Torres I, Mesonero C, Gibbon DG, Shih WJ, Zotalis G. The CD44 receptor is a molecular predictor of survival in ovarian cancer. Med Oncol. 2003 ; 20 (3). 255-263
42. Berner HS, Davidson B, Berner A, et al. Expression of CD44 in effusions of patients diagnosed with serous ovarian carcinoma - diagnostic and prognostic implications. Clin Exp Metastasis. 2000 ; 18 (2). 197-202
43. Sillanpaa S, Anttila MA, Voutilainen K, et al. CD44 expression indicates favorable prognosis in epithelial ovarian cancer. Clin Cancer Res. 2003 ; 9 (14). 5318-5324
44. Dolled-Filhart M, Gustavson M, Camp RL, Rimm DL, Tonkinson JL, Christiansen J. Automated analysis of tissue microarrays. Methods Mol Biol. 2010 ; 664: 151-162