Proteomic Profiling of Ovarian Cancer Models Using TMT-LC-MS/MS

Author(s):  
John Sinclair ◽  
John F. Timms
2012 ◽  
Vol 12 (4) ◽  
pp. 336-346 ◽  
Author(s):  
Ellie S. M. Chu ◽  
Stephen C. W. Sze ◽  
Ho P. Cheung ◽  
Qing Liu ◽  
Tzi B. Ng ◽  
...  

2002 ◽  
Vol 126 (12) ◽  
pp. 1518-1526 ◽  
Author(s):  
Alex J. Rai ◽  
Zhen Zhang ◽  
Jason Rosenzweig ◽  
Ie-ming Shih ◽  
Thang Pham ◽  
...  

Abstract Context.—Current tumor markers for ovarian cancer still lack adequate sensitivity and specificity to be applicable in large populations. High-throughput proteomic profiling and bioinformatics tools allow for the rapid screening of a large number of potential biomarkers in serum, plasma, or other body fluids. Objective.—To determine whether protein profiles of plasma can be used to identify potential biomarkers that improve the detection of ovarian cancer. Design.—We analyzed plasma samples that had been collected between 1998 and 2001 from patients with sporadic ovarian serous neoplasms before tumor resection at various International Federation of Gynecology and Obstetrics stages (stage I [n = 11], stage II [n = 3], and stage III [n = 29]) and from women without known neoplastic disease (n = 38) using proteomic profiling and bioinformatics. We compared results between the patients with and without cancer and evaluated their discriminatory performance against that of the cancer antigen 125 (CA125) tumor marker. Results.—We selected 7 biomarkers based on their collective contribution to the separation of the 2 patient groups. Among them, we further purified and subsequently identified 3 biomarkers. Individually, the biomarkers did not perform better than CA125. However, a combination of 4 of the biomarkers significantly improved performance (P ≤ .001). The new biomarkers were complementary to CA125. At a fixed specificity of 94%, an index combining 2 of the biomarkers and CA125 achieves a sensitivity of 94% (95% confidence interval, 85%–100.0%) in contrast to a sensitivity of 81% (95% confidence interval, 68%–95%) for CA125 alone. Conclusions.—The combined use of bioinformatics tools and proteomic profiling provides an effective approach to screen for potential tumor markers. Comparison of plasma profiles from patients with and without known ovarian cancer uncovered a panel of potential biomarkers for detection of ovarian cancer with discriminatory power complementary to that of CA125. Additional studies are required to further validate these biomarkers.


2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Hyoung Kim ◽  
Haineng Xu ◽  
Erin George ◽  
Dorothy Hallberg ◽  
Sushil Kumar ◽  
...  

Cells ◽  
2019 ◽  
Vol 8 (5) ◽  
pp. 505 ◽  
Author(s):  
Yoshiaki Maru ◽  
Yoshitaka Hippo

Ovarian cancer (OC) is one of the leading causes of female cancer death. Recent studies have documented its extensive variations as a disease entity, in terms of cell or tissue of origin, pre-cancerous lesions, common mutations, and therapeutic responses, leading to the notion that OC is a generic term referring to a whole range of different cancer subtypes. Despite such heterogeneity, OC treatment is stereotypic; aggressive surgery followed by conventional chemotherapy could result in chemo-resistant diseases. Whereas molecular-targeted therapies will become shortly available for a subset of OC, there still remain many patients without effective drugs, requiring development of groundbreaking therapeutic agents. In preclinical studies for drug discovery, cancer cell lines used to be the gold standard, but now this has declined due to frequent failure in predicting therapeutic responses in patients. In this regard, patient-derived cells and tumors are gaining more attention in precise and physiological modeling of in situ tumors, which could also pave the way to implementation of precision medicine. In this article, we comprehensively overviewed the current status of various platforms for patient-derived OC models. We highly appreciate the potentials of organoid culture in achieving high success rate and retaining tumor heterogeneity.


Author(s):  
Panagiotis A. Konstantinopoulos ◽  
Graeme Hodgson ◽  
Nisha Rajagopal ◽  
Liv Johannessen ◽  
Joyce F. Liu ◽  
...  

2019 ◽  
Vol 25 (20) ◽  
pp. 6127-6140 ◽  
Author(s):  
Kalindi Parmar ◽  
Bose S. Kochupurakkal ◽  
Jean-Bernard Lazaro ◽  
Zhigang C. Wang ◽  
Sangeetha Palakurthi ◽  
...  

2008 ◽  
Vol 18 (Suppl 1) ◽  
pp. 1-6 ◽  
Author(s):  
C. M. Annunziata ◽  
N. Azad ◽  
A. S. Dhamoon ◽  
G. Whiteley ◽  
E. C. Kohn

Ovarian cancer presents a diagnostic challenge because of its subtle clinical presentation and elusive cell of origin. Two new technologies of proteomics have advanced the dissection of the underlying molecular signaling events and the proteomic characterization of ovarian cancer: mass spectrometry and protein array analysis. Mass spectrometry can provide a snapshot of a proteome in time and space, with sensitivity and resolution that may allow identification of the elusive “needle in the haystack” heralding ovarian cancer. Proteomic profiling of tumor tissue samples can survey molecular targets during treatment and quantify changes using reverse phase protein arrays generated from tumor samples captured by microdissection, lysed and spotted in serial dilutions for high-throughput analysis. This approach can be applied to identify the optimal biological dose of a targeted agent and to validate target to outcome link. The evolution of proteomic technologies has the capacity to advance rapidly our understanding of ovarian cancer at a molecular level and thus elucidate new directions for the treatment of this disease


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