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  1. Article ; Online: The Promise of Multicancer Early Detection. Comment on Pons-Belda et al. Can Circulating Tumor DNA Support a Successful Screening Test for Early Cancer Detection? The Grail Paradigm. Diagnostics 2021, 11 , 2171

    Eric A. Klein / Tomasz M. Beer / Michael Seiden

    Diagnostics, Vol 12, Iss 1243, p

    2022  Volume 1243

    Abstract: Multicancer Early Detection (MCED) represents a new and exciting paradigm for the early detection of cancer, which is the leading cause of death worldwide. Current screening tests, recommended for only five cancer types (breast, lung, colon, cervical, ... ...

    Abstract Multicancer Early Detection (MCED) represents a new and exciting paradigm for the early detection of cancer, which is the leading cause of death worldwide. Current screening tests, recommended for only five cancer types (breast, lung, colon, cervical, and prostate), are limited by a lack of complete adherence to guideline-based use and by the fact that they have cumulative high false positive rates. MCED tests agnostically detect cancer signals in the blood with good sensitivity and low false positive rates, can predict the cancer site of origin with high accuracy, can detect highly lethal cancers that have no current screening tests, and promise to improve cancer screening by improving efficiency and reducing the overall number needed to screen. Herein we outline this promise and clarify several published misconceptions about this field.
    Keywords multicancer early detection ; methylation ; circulating cell-free genome atlas ; Medicine (General) ; R5-920
    Subject code 610
    Language English
    Publishing date 2022-05-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Article ; Online: Integrated analysis of multiple microarray datasets identifies a reproducible survival predictor in ovarian cancer.

    Panagiotis A Konstantinopoulos / Stephen A Cannistra / Helen Fountzilas / Aedin Culhane / Kamana Pillay / Bo Rueda / Daniel Cramer / Michael Seiden / Michael Birrer / George Coukos / Lin Zhang / John Quackenbush / Dimitrios Spentzos

    PLoS ONE, Vol 6, Iss 3, p e

    2011  Volume 18202

    Abstract: Public data integration may help overcome challenges in clinical implementation of microarray profiles. We integrated several ovarian cancer datasets to identify a reproducible predictor of survival.Four microarray datasets from different institutions ... ...

    Abstract Public data integration may help overcome challenges in clinical implementation of microarray profiles. We integrated several ovarian cancer datasets to identify a reproducible predictor of survival.Four microarray datasets from different institutions comprising 265 advanced stage tumors were uniformly reprocessed into a single training dataset, also adjusting for inter-laboratory variation ("batch-effect"). Supervised principal component survival analysis was employed to identify prognostic models. Models were independently validated in a 61-patient cohort using a custom array genechip and a publicly available 229-array dataset. Molecular correspondence of high- and low-risk outcome groups between training and validation datasets was demonstrated using Subclass Mapping. Previously established molecular phenotypes in the 2(nd) validation set were correlated with high and low-risk outcome groups. Functional representational and pathway analysis was used to explore gene networks associated with high and low risk phenotypes. A 19-gene model showed optimal performance in the training set (median OS 31 and 78 months, p < 0.01), 1(st) validation set (median OS 32 months versus not-yet-reached, p = 0.026) and 2(nd) validation set (median OS 43 versus 61 months, p = 0.013) maintaining independent prognostic power in multivariate analysis. There was strong molecular correspondence of the respective high- and low-risk tumors between training and 1(st) validation set. Low and high-risk tumors were enriched for favorable and unfavorable molecular subtypes and pathways, previously defined in the public 2(nd) validation set.Integration of previously generated cancer microarray datasets may lead to robust and widely applicable survival predictors. These predictors are not simply a compilation of prognostic genes but appear to track true molecular phenotypes of good- and poor-outcome.
    Keywords Medicine ; R ; Science ; Q
    Subject code 616
    Language English
    Publishing date 2011-03-01T00:00:00Z
    Publisher Public Library of Science (PLoS)
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

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    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

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