Hemoglobin F Only Syndrome at Birth: A Case of Maternal HbA2' Complicating the Diagnosis of β-Thalassemia.

An asymptomatic infant of Ghanaian descent had hemoglobin F only detected on newborn screening. β-globin gene sequencing identified the intervening sequence (IVS)-II-849 (A → G) mutation with no normal β-globin gene. β-globin/δ-globin gene sequencing showed that both parents were heterozygous for the IVS-II-849 (A → G) mutation.

Chromosome instability in diffuse large B cell lymphomas is suppressed by activation of the noncanonical NF-κB pathway.

Diffuse large B cell lymphoma (DLBCL) is the most common form of lymphoma in the United States. DLBCL comprises biologically distinct subtypes including germinal center-like (GCB) and activated-B-cell-like DLBCL (ABC). The most aggressive type, ABC-DLBCL, displays dysregulation of both canonical and noncanonical NF-κB pathway as well as genomic instability.

Removing batch effects from histopathological images for enhanced cancer diagnosis.

Researchers have developed computer-aided decision support systems for translational medicine that aim to objectively and efficiently diagnose cancer using histopathological images. However, the performance of such systems is confounded by nonbiological experimental variations or "batch effects" that can commonly occur in histopathological data, especially when images are acquired using different imaging devices and patient samples. This is even more problematic in large-scale studies in which cross-laboratory sharing of large volumes of data is necessary.

Unintended reporting of misleading Hb A(1c) values when using assays incapable of detecting hemoglobin variants.

Context: It is recommended that hemoglobin (Hb) A1c (Hb A1c) not be used to assess average glycemia in patients who have altered red blood cell life span.

Objective: To investigate the frequency of reporting an Hb A1c value for Hb variant samples that do not include Hb A.

Design: Hb A1c samples (n = 500) were procured and screened for Hb variants that may affect Hb A1c interpretation (Hb SS, Hb SC, and Hb S-β-thalassemia).

Hb Fulton-Georgia [α20(B1)His→Pro; HBA1: c.62A>C]: a new α-globin variant coinherited with α-thalassemia-2 (3.7 kb deletion) and Hb SC disease.

We report a novel hemoglobin (Hb) variant that we named Hb Fulton-Georgia, caused by a point mutation in exon 1/codon 20 of the α-globin gene [α20(B1)His→Pro; HBA1: c.62A>C]. This α chain variant was identified in an adult African-American female with Hb SC disease who was also heterozygous for the α-thalassemia-2 (α-thal-2) (3.

Gene expression profiling of clear cell papillary renal cell carcinoma: comparison with clear cell renal cell carcinoma and papillary renal cell carcinoma.

Clear cell papillary renal cell carcinoma is a distinct variant of renal cell carcinoma that shares some overlapping histological and immunohistochemical features of clear cell renal cell carcinoma and papillary renal cell carcinoma. Although the clear cell papillary renal cell carcinoma immunohistochemical profile is well described, clear cell papillary renal cell carcinoma mRNA expression has not been well characterized. We investigated the clear cell papillary renal cell carcinoma gene expression profile using previously identified candidate genes.

Semiconductor quantum dots for bioimaging and biodiagnostic applications.

Semiconductor quantum dots (QDs) are light-emitting particles on the nanometer scale that have emerged as a new class of fluorescent labels for chemical analysis, molecular imaging, and biomedical diagnostics. Compared with traditional fluorescent probes, QDs have unique optical and electronic properties such as size-tunable light emission, narrow and symmetric emission spectra, and broad absorption spectra that enable the simultaneous excitation of multiple fluorescence colors. QDs are also considerably brighter and more resistant to photobleaching than are organic dyes and fluorescent proteins.

Histological image classification using biologically interpretable shape-based features.

Background: Automatic cancer diagnostic systems based on histological image classification are important for improving therapeutic decisions. Previous studies propose textural and morphological features for such systems. These features capture patterns in histological images that are useful for both cancer grading and subtyping.

Robust microarray meta-analysis identifies differentially expressed genes for clinical prediction.

Combining multiple microarray datasets increases sample size and leads to improved reproducibility in identification of informative genes and subsequent clinical prediction. Although microarrays have increased the rate of genomic data collection, sample size is still a major issue when identifying informative genetic biomarkers. Because of this, feature selection methods often suffer from false discoveries, resulting in poorly performing predictive models.

Fecal lipocalin 2, a sensitive and broadly dynamic non-invasive biomarker for intestinal inflammation.

Inflammation has classically been defined histopathologically, especially by the presence of immune cell infiltrates. However, more recent studies suggest a role for "low-grade" inflammation in a variety of disorders ranging from metabolic syndrome to cancer, which is defined by modest elevations in pro-inflammatory gene expression. Consequently, there is a need for cost-effective, non-invasive biomarkers that, ideally, would have the sensitivity to detect low-grade inflammation and have a dynamic range broad enough to reflect classic robust intestinal inflammation.

omniBiomarker: A Web-Based Application for Knowledge-Driven Biomarker Identification.

We have developed omniBiomarker, a web-based application that uses knowledge from the NCI Cancer Gene Index to guide the selection of biologically relevant algorithms for identifying biomarkers. Biomarker identification from high-throughput genomic expression data is difficult because of data properties (i.e.

Frequency and determinants of disagreement and error in gleason scores: a population-based study of prostate cancer.

Background: To examine factors that affect accuracy and reliability of prostate cancer grade we compared Gleason scores documented in pathology reports and those assigned by urologic pathologists in a population-based study.

Methods: A stratified random sample of 318 prostate cancer cases was selected to ensure representation of whites and African-Americans and to include facilities of various types. The slides borrowed from reporting facilities were scanned and the resulting digital images were re-reviewed by two urologic pathologists.

Histological Image Feature Mining Reveals Emergent Diagnostic Properties for Renal Cancer.

Computer-aided histological image classification systems are important for making objective and timely cancer diagnostic decisions. These systems use combinations of image features that quantify a variety of image properties. Because researchers tend to validate their diagnostic systems on specific cancer endpoints, it is difficult to predict which image features will perform well given a new cancer endpoint.

An analysis of scale and rotation invariance in the bag-of-features method for histopathological image classification.

The bag-of-features method has emerged as a useful and flexible tool that can capture medically relevant image characteristics. In this paper, we study the effect of scale and rotation invariance in the bag-of-features framework for Renal Cell Carcinoma subtype classification. We estimated the performance of different features by linear support vector machine over 10 iterations of 3-fold cross validation.

caCORRECT2: Improving the accuracy and reliability of microarray data in the presence of artifacts.

Background: In previous work, we reported the development of caCORRECT, a novel microarray quality control system built to identify and correct spatial artifacts commonly found on Affymetrix arrays. We have made recent improvements to caCORRECT, including the development of a model-based data-replacement strategy and integration with typical microarray workflows via caCORRECT's web portal and caBIG grid services. In this report, we demonstrate that caCORRECT improves the reproducibility and reliability of experimental results across several common Affymetrix microarray platforms.

C-reactive protein may represent sensitive measure of renal cell carcinoma metastasis.

A patient with localized kidney cancer underwent potentially curative nephrectomy. Routinely post-operative serum C-Reactive Protein (CRP) values were measured. At 25 months post-operatively, lung imaging suggested possible metastasis.

Expression of C-reactive protein and cyclooxygenase enzyme-2 in clear cell renal cell carcinoma: correlation with pathological parameters in 110 patients.

C-reactive protein is produced in response to cytokines such as interleukin (IL)-6. It is known that increased plasma IL-6 levels induce increased hepatic and intratumoral production of C-reactive protein. Cyclooxygenase enzyme-2 is induced by various stimuli, including inflammation and various growth factors.

A novel tumor grading scheme for chromophobe renal cell carcinoma: prognostic utility and comparison with Fuhrman nuclear grade.

Chromophobe renal cell carcinoma (RCC) is a histologic subtype of RCC that portends a favorable prognosis. It is controversial whether the Fuhrman nuclear grade of chromophobe RCC has prognostic utility. Irregular nuclei, prominent nucleoli, and nuclear pleomorphism are inherently present in chromophobe RCC.

C-reactive protein as a clinically useful biomarker of metastasis of renal cell carcinoma.

C-reactive protein (CRP) is an acute-phase reactant that can increase dramatically in response to a variety of pathologic states. Elevated pre- and postoperative CRP levels have been associated with an increased tumor burden and metastasis in kidney cancer. We report on a case that serves to highlight a potentially novel use for CRP monitoring in the postoperative management of renal cell carcinoma.

Molecular mapping of tumor heterogeneity on clinical tissue specimens with multiplexed quantum dots.

Tumor heterogeneity is one of the most important and challenging problems not only in studying the mechanisms of cancer development but also in developing therapeutics to eradicate cancer cells. Here we report the use of multiplexed quantum dots (QDs) and wavelength-resolved spectral imaging for molecular mapping of tumor heterogeneity on human prostate cancer tissue specimens. By using a panel of just four protein biomarkers (E-cadherin, high-molecular-weight cytokeratin, p63, and alpha-methylacyl CoA racemase), we show that structurally distinct prostate glands and single cancer cells can be detected and characterized within the complex microenvironments of radical prostatectomy and needle biopsy tissue specimens.

Consolidation of the cancer genome into domains of repressive chromatin by long-range epigenetic silencing (LRES) reduces transcriptional plasticity.

Silencing of individual genes can occur by genetic and epigenetic processes during carcinogenesis, but the underlying mechanisms remain unclear. By creating an integrated prostate cancer epigenome map using tiling arrays, we show that contiguous regions of gene suppression commonly occur through long-range epigenetic silencing (LRES). We identified 47 LRES regions in prostate cancer, typically spanning about 2 Mb and harbouring approximately 12 genes, with a prevalence of tumour suppressor and miRNA genes.

Automated classification of renal cell carcinoma subtypes using scale invariant feature transform.

The task of analyzing tissue biopsies performed by a pathologist is challenging and time consuming. It suffers from intra- and inter-user variability. Computer assisted diagnosis (CAD) helps to reduce such variations and speed up the diagnostic process.

Emerging translational bioinformatics: knowledge-guided biomarker identification for cancer diagnostics.

Advances in high-throughput genomic and proteomic technology have led to a growing interest in cancer biomarkers. These biomarkers can potentially improve the accuracy of cancer subtype prediction and subsequently, the success of therapy. In this paper, we describe emerging technology for enabling translational bioinformatics by improving biomarker identification.