Cancer Datasets (cancer + dataset)

Distribution by Scientific Domains
Medical Sciences75%

Selected Abstracts

The new World Health Organization classification of haematopoietic and lymphoid tumours: a dermatopathological perspective

BRITISH JOURNAL OF DERMATOLOGY, Issue 4 2002
D.N. Slater
Summary The World Health Organization (WHO) has published a new consensus classification of tumours of haematopoietic and lymphoid tissue, based on recognizable disease entities defined by clinical and scientific criteria. The WHO does not support the use of stand-alone organ-related classifications, such as for skin. The Royal College of Pathologists (London) has adopted the WHO classification in its minimum dataset for the histopathological reporting of lymphoma and this will be used in the National Health Service Skin Cancer Dataset. The purpose of this review is to highlight the principal primary and secondary cutaneous haematopoietic and lymphoid tumours that are defined in the WHO classification. The review also discusses selected problematical areas in the WHO classification relevant to the skin and contains suggestions to encourage a unified approach in the use of the WHO coded summary. These represent an attempt to facilitate future progress and research in the field of cutaneous lymphoma. They are perceived as possible building-blocks for wider discussion and not as alterations to the classification. The WHO classification has been compared with a road map that indicates directions for future clinical and scientific research. [source]

Regularized Estimation for the Accelerated Failure Time Model

BIOMETRICS, Issue 2 2009
T. Cai
Summary In the presence of high-dimensional predictors, it is challenging to develop reliable regression models that can be used to accurately predict future outcomes. Further complications arise when the outcome of interest is an event time, which is often not fully observed due to censoring. In this article, we develop robust prediction models for event time outcomes by regularizing the Gehan's estimator for the accelerated failure time (AFT) model (Tsiatis, 1996, Annals of Statistics18, 305,328) with least absolute shrinkage and selection operator (LASSO) penalty. Unlike existing methods based on the inverse probability weighting and the Buckley and James estimator (Buckley and James, 1979, Biometrika66, 429,436), the proposed approach does not require additional assumptions about the censoring and always yields a solution that is convergent. Furthermore, the proposed estimator leads to a stable regression model for prediction even if the AFT model fails to hold. To facilitate the adaptive selection of the tuning parameter, we detail an efficient numerical algorithm for obtaining the entire regularization path. The proposed procedures are applied to a breast cancer dataset to derive a reliable regression model for predicting patient survival based on a set of clinical prognostic factors and gene signatures. Finite sample performances of the procedures are evaluated through a simulation study. [source]

Multiple pathways in the FGF signaling network are frequently deregulated by gene amplification in oral dysplasias

INTERNATIONAL JOURNAL OF CANCER, Issue 9 2009
Ivy F.L. Tsui
Abstract Genetic alteration in oral premalignant lesions (OPLs), the precursors of oral squamous cell carcinomas (OSCCs), may represent key changes in disease initiation and development. We ask if DNA amplification occurs at this early stage of cancer development and which oncogenic pathways are disrupted in OPLs. Here, we evaluated 50 high-grade dysplasias and low-grade dysplasias that later progressed to cancer for gene dosage aberrations using tiling-path DNA microarrays. Early occurrences of DNA amplification and homozygous deletion were frequently detected, with 40% (20/50) of these early lesions exhibiting such features. Expression for 88 genes in 7 recurrent amplicons were evaluated in 5 independent head and neck cancer datasets, with 40 candidates found to be overexpressed relative to normal tissues. These genes were significantly enriched in the canonical ERK/MAPK, FGF, p53, PTEN and PI3K/AKT signaling pathways (p = 8.95 × 10,3 to 3.18 × 10,2). These identified pathways share interactions in one signaling network, and amplification-mediated deregulation of this network was found in 30.0% of these preinvasive lesions. No such alterations were found in 14 low-grade dysplasias that did not progress, whereas 43.5% (10/23) of OSCCs were found to have altered genes within the pathways with DNA amplification. Multitarget FISH showed that amplification of EGFR and CCND1 can coexist in single cells of an oral dysplasia, suggesting the dependence on multiple oncogenes for OPL progression. Taken together, these findings identify a critical biological network that is frequently disrupted in high-risk OPLs, with different specific genes disrupted in different individuals. © 2009 UICC [source]

The gene expression signature of genomic instability in breast cancer is an independent predictor of clinical outcome,

INTERNATIONAL JOURNAL OF CANCER, Issue 7 2009
Jens K. Habermann
Abstract Recently, expression profiling of breast carcinomas has revealed gene signatures that predict clinical outcome, and discerned prognostically relevant breast cancer subtypes. Measurement of the degree of genomic instability provides a very similar stratification of prognostic groups. We therefore hypothesized that these features are linked. We used gene expression profiling of 48 breast cancer specimens that profoundly differed in their degree of genomic instability and identified a set of 12 genes that defines the 2 groups. The biological and prognostic significance of this gene set was established through survival prediction in published datasets from patients with breast cancer. Of note, the gene expression signatures that define specific prognostic subtypes in other breast cancer datasets, such as luminal A and B, basal, normal-like, and ERBB2+, and prognostic signatures including MammaPrint® and Oncotype DX, predicted genomic instability in our samples. This remarkable congruence suggests a biological interdependence of poor-prognosis gene signatures, breast cancer subtypes, genomic instability, and clinical outcome. © 2008 Wiley-Liss, Inc. [source]