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Kenneth P. Nephew

Bio: Kenneth P. Nephew is an academic researcher from Indiana University. The author has contributed to research in topics: Ovarian cancer & DNA methylation. The author has an hindex of 66, co-authored 267 publications receiving 15106 citations. Previous affiliations of Kenneth P. Nephew include University of Tennessee Health Science Center & Indiana University – Purdue University Indianapolis.


Papers
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Journal ArticleDOI
TL;DR: It is asserted that epithelial ovarian cancers derive from a subpopulation of CD44(+)CD117(+) cells, thus representing a possible therapeutic target for this devastating disease.
Abstract: The objective of this study was to identify and characterize a self-renewing subpopulation of human ovarian tumor cells (ovarian cancer-initiating cells, OCICs) fully capable of serial propagation of their original tumor phenotype in animals. Ovarian serous adenocarcinomas were disaggregated and subjected to growth conditions selective for self-renewing, nonadherent spheroids previously shown to derive from tissue stem cells.To affirm the existence of OCICs, xenoengraftment of as few as 100 dissociated spheroid cells allowed full recapitulation of the original tumor (grade 2/grade 3 serous adenocarcinoma), whereas >10 5 unselected cells remained nontumorigenic.Stemness properties of OCICs (under stem cell–selective conditions) were further established by cell proliferation assays and reverse transcription–PCR, demonstrating enhanced chemoresistance to the ovarian cancer chemotherapeutics cisplatin or paclitaxel and up-regulation of stem cell markers (Bmi-1, stem cell factor, Notch-1, Nanog, nestin, ABCG2, and Oct-4) compared with parental tumor cells or OCICs under differentiating conditions.To identify an OCIC cell surface phenotype, spheroid immunostaining showed significant up-regulation of the hyaluronate receptor CD44 and stem cell factor receptor CD117 (c-kit), a tyrosine kinase oncoprotein.Similar to sphere-forming OCICs, injection of only 100 CD44 + CD117 + cells could also serially propagate their original tumors, whereas 10 5 CD44CD117 cells remained nontumorigenic.Based on these findings, we assert that epithelial ovarian cancers derive from a subpopulation of CD44 + CD117 + cells, thus representing a possible therapeutic target for this devastating disease. [Cancer Res 2008;68(11):4311–20]

1,264 citations

Journal ArticleDOI
TL;DR: Nine major recommendations that should be taken to improve the outcome for women with ovarian cancer are outlined in this Opinion article.
Abstract: There have been major advances in our understanding of the cellular and molecular biology of the human malignancies that are collectively referred to as ovarian cancer. At a recent Helene Harris Memorial Trust meeting, an international group of researchers considered actions that should be taken to improve the outcome for women with ovarian cancer. Nine major recommendations are outlined in this Opinion article.

1,130 citations

Journal ArticleDOI
TL;DR: This 'roadmap' for HGSOC was determined after extensive discussions at an Ovarian Cancer Action meeting in January 2015 and aims to reduce incidence and improve outcomes for women with this disease.
Abstract: High-grade serous ovarian cancer (HGSOC) accounts for 70-80% of ovarian cancer deaths, and overall survival has not changed significantly for several decades. In this Opinion article, we outline a set of research priorities that we believe will reduce incidence and improve outcomes for women with this disease. This 'roadmap' for HGSOC was determined after extensive discussions at an Ovarian Cancer Action meeting in January 2015.

801 citations

Journal ArticleDOI
TL;DR: Modulation of specific microRNAs may provide a therapeutic approach for future treatment of NSCLC by inhibiting the expression of Bim, APAF-1, PKC-ε and SRC genes.
Abstract: The involvement of the MET oncogene in de novo and acquired resistance of non-small cell lung cancers (NSCLCs) to tyrosine kinase inhibitors (TKIs) has previously been reported, but the precise mechanism by which MET overexpression contributes to TKI-resistant NSCLC remains unclear. MicroRNAs (miRNAs) negatively regulate gene expression, and their dysregulation has been implicated in tumorigenesis. To understand their role in TKI-resistant NSCLCs, we examined changes in miRNA that are mediated by tyrosine kinase receptors. Here we report that miR-30b, miR-30c, miR-221 and miR-222 are modulated by both epidermal growth factor (EGF) and MET receptors, whereas miR-103 and miR-203 are controlled only by MET. We showed that these miRNAs have important roles in gefitinib-induced apoptosis and epithelial-mesenchymal transition of NSCLC cells in vitro and in vivo by inhibiting the expression of the genes encoding BCL2-like 11 (BIM), apoptotic peptidase activating factor 1 (APAF-1), protein kinase C ɛ (PKC-ɛ) and sarcoma viral oncogene homolog (SRC). These findings suggest that modulation of specific miRNAs may provide a therapeutic approach for the treatment of NSCLCs.

347 citations

Journal ArticleDOI
TL;DR: The results of this study suggest that low-dose decitabine altered DNA methylation of genes and cancer pathways, restoring sensitivity to carboplatin in patients with heavily pretreated ovarian cancer and resulting in a high RR and prolonged PFS.
Abstract: Preclinical studies have shown that hypomethylating agents reverse platinum resistance in ovarian cancer. In this phase II clinical trial, based upon the results of our phase I dose defining study, we tested the clinical and biologic activity of low-dose decitabine administered before carboplatin in platinum-resistant ovarian cancer patients. Among 17 patients with heavily pretreated and platinum-resistant ovarian cancer, the regimen induced a 35% objective response rate (RR) and progression-free survival (PFS) of 10.2 months, with nine patients (53%) free of progression at 6 months. Global and gene-specific DNA demethylation was achieved in peripheral blood mononuclear cells and tumors. The number of demethylated genes was greater (P < 0.05) in tumor biopsies from patients with PFS more than 6 versus less than 6 months (311 vs. 244 genes). Pathways enriched at baseline in tumors from patients with PFS more than 6 months included cytokine-cytokine receptor interactions, drug transporters, and mitogen-activated protein kinase, toll-like receptor and Jak-STAT signaling pathways, whereas those enriched in demethylated genes after decitabine treatment included pathways involved in cancer, Wnt signaling, and apoptosis (P < 0.01). Demethylation of MLH1, RASSF1A, HOXA10, and HOXA11 in tumors positively correlated with PFS (P < 0.05). Together, the results of this study suggest that low-dose decitabine altered DNA methylation of genes and cancer pathways, restoring sensitivity to carboplatin in patients with heavily pretreated ovarian cancer and resulting in a high RR and prolonged PFS.

326 citations


Cited by
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28 Jul 2005
TL;DR: PfPMP1)与感染红细胞、树突状组胞以及胎盘的单个或多个受体作用,在黏附及免疫逃避中起关键的作�ly.
Abstract: 抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1(PfPMP1)与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用,在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员,通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

18,940 citations

Journal ArticleDOI
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

13,246 citations

01 Aug 2000
TL;DR: Assessment of medical technology in the context of commercialization with Bioentrepreneur course, which addresses many issues unique to biomedical products.
Abstract: BIOE 402. Medical Technology Assessment. 2 or 3 hours. Bioentrepreneur course. Assessment of medical technology in the context of commercialization. Objectives, competition, market share, funding, pricing, manufacturing, growth, and intellectual property; many issues unique to biomedical products. Course Information: 2 undergraduate hours. 3 graduate hours. Prerequisite(s): Junior standing or above and consent of the instructor.

4,833 citations