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. The mother was heterozygous for the HbA2' δ-globin mutation (δ16 (A13) Gly → Arg), thus β-thalassemia trait was unrecognized due to coinheritance of HbA2'. The infant developed anemia, splenomegaly, and began transfusion therapy by the age 6 of months. This is the first report of β-thalassemia major with homozygous IVS-II-849 (A → G) mutations. This case highlights the importance of δ-globin gene mutations in prenatal testing.

An asymptomatic infant of Ghanaian descent had hemoglobin F only detected on newborn screening. beta-globin gene sequencing identified the intervening sequence (IVS)-II-849 (A -> G) mutation with no normal beta-globin gene. beta-globin/delta-globin gene sequencing showed that both parents were heterozygous for the IVS-II-849 (A -> G) mutation. The mother was heterozygous for the HbA(2)' delta-globin mutation (delta 16 (A13) Gly -> Arg), thus beta-thalassemia trait was unrecognized due to coinheritance of HbA(2)'. The infant developed anemia, splenomegaly, and began transfusion therapy by the age 6 of months. This is the first report of beta-thalassemia major with homozygous IVS-II-849 (A -> G) mutations. This case highlights the importance of delta-globin gene mutations in prenatal testing.

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. Although, much is known about the tumorigenic roles of the canonical NF‐kB pathway, the precise role of the noncanonical NF‐kB pathway remains unknown. Here we show that activation of the noncanonical NF‐κB pathway regulates chromosome stability, DNA damage response and centrosome duplication in DLBCL. Analysis of 92 DLBCL samples revealed that activation of the noncanonical NF‐κB pathway is associated with low levels of DNA damage and centrosome amplification. Inhibiting the noncanonical pathway in lymphoma cells uncovered baseline DNA damage and prevented doxorubicin‐induced DNA damage repair. In addition, it triggered centrosome amplification and chromosome instability, indicated by anaphase bridges, multipolar spindles and chromosome missegregation. We determined that the noncanonical NF‐κB pathway execute these functions through the regulation of GADD45α and REDD1 in a p53‐independent manner, while it collaborates with p53 to regulate cyclin G2 expression. Furthermore, this pathway regulates GADD45α, REDD1 and cyclin G2 through direct binding of NF‐κB sites to their promoter region. Overall, these results indicate that the noncanonical NF‐κB pathway plays a central role in maintaining genome integrity in DLBCL. Our data suggests that inhibition of the noncanonical NF‐kB pathway should be considered as an important component in DLBCL therapeutic approach.

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. Batch effects can change quantitative morphological image features and decrease the prediction performance. Using four batches of renal tumor images, we compare one image-level and five feature-level batch effect removal methods. Principal component variation analysis shows that batch is a large source of variance in image features. Results show that feature-level normalization methods reduce batch-contributed variance to almost zero. Moreover, feature-level normalization, especially ComBatN, improves cross-batch and combined-batch prediction performance. Compared to no normalization, ComBatN improves performance in 83% and 90% of cross-batch and combined-batch prediction models, respectively.

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 alpha-globin gene [alpha 20(B1) His! Pro; HBA1: c.62A>C]. This a chain variant was identified in an adult African-American female with Hb SC disease who was also heterozygous for the alpha-thalassemia-2 (alpha-thal-2) (3.7 kb deletion or alpha alpha/-alpha(3.7)). The Hb Fulton-Georgia mutation was located on the intact alpha 1-globin gene not involved by alpha-thal-2. Molecular models indicated that the alpha 20 residue of Hb Fulton-Georgia was the first amino acid of the B helix, and was not involved in alpha 1/beta 1 or alpha 1/beta 2 contacts in Hb S [beta 6(A3) Glu -> Val; HBB: c.20A>T] or Hb C [beta 6(A3) Glu -> Lys; HBB: c.19G>A] tetramers. Furthermore, the histidine -> proline substitution at alpha 20 did not disrupt the helical structure. High performance liquid chromatography (HPLC) detected Hb Fulton-Georgia in 16.0% of total Hb, consistent with inheritance on the alpha 1 gene. Coinheritance of Hb Fulton-Georgia, heterozygous alpha-thal-2 and Hb SC disease was associated with a mild phenotype, consisting of microcytosis and anisocytosis, but no anemia or other hematological abnormality.

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. We selected 17 clear cell papillary renal cell carcinoma, 15 clear cell renal cell carcinoma, and 13 papillary renal cell carcinoma cases for molecular analysis following histological review. cDNA from formalin-fixed paraffin-embedded tissue was prepared. Quantitative real-time PCR targeting alpha-methylacyl coenzyme-A racemase (AMACR), BMP and activin membrane-bound inhibitor homolog (BAMBI), carbonic anhydrase IX (CA9), ceruloplasmin (CP), nicotinamide N-methyltransferase (NNMT), schwannomin-interacting protein 1 (SCHIP1), solute carrier family 34 (sodium phosphate) member 2 (SLC34A2), and vimentin (VIM) was performed. Gene expression data were normalized relative to 28S ribosomal RNA. Clear cell papillary renal cell carcinoma expressed all eight genes at variable levels. Compared with papillary renal cell carcinoma, clear cell papillary renal cell carcinoma expressed more CA9, CP, NNMT, and VIM, less AMACR, BAMBI, and SLC34A2, and similar levels of SCHIP1. Compared with clear cell renal cell carcinoma, clear cell papillary renal cell carcinoma expressed slightly less NNMT, but similar levels of the other seven genes. Although clear cell papillary renal cell carcinoma exhibits a unique molecular signature, it expresses several genes at comparable levels to clear cell renal cell carcinoma relative to papillary renal cell carcinoma. Understanding the molecular pathogenesis of clear cell papillary renal cell carcinoma will have a key role in future sub-classifications of this unique tumor.

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. These properties are well suited for dynamic imaging at the single-molecule level and for multiplexed biomedical diagnostics at ultrahigh sensitivity. Here, we discuss the fundamental properties of QDs; the development of next-generation QDs; and their applications in bioanalytical chemistry, dynamic cellular imaging, and medical diagnostics. For in vivo and clinical imaging, the potential toxicity of QDs remains a major concern. However, the toxic nature of cadmium-containing QDs is no longer a factor for in vitro diagnostics, so the use of multicolor QDs for molecular diagnostics and pathology is probably the most important and clinically relevant application for semiconductor QDs in the immediate future.

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). Five of each of these samples were tested by ion-exchange high-performance liquid chromatography, immunoturbidimetric assay, second-generation immunoturbidimetric assay, and affinity chromatography.RESULTS:Eleven (2.2%) homozygous Hb SS, 6 (1.2%) Hb SC, and 5 (1.0%) Hb S-β-thalassemia samples were identified out of the 500 samples tested. Three of 4 instruments investigated in this study are known to not be plagued by analytic interference from these Hb variants but disturbingly reported Hb A1c values in the absence of Hb A.CONCLUSIONS:The improved analytic specificity of Hb A1c platforms has by and large eliminated interferences from the most common heterozygous Hb variants. A consequence, however, is the potential for unintended reporting of Hb A1c results in the presence of homozygous and compound heterozygous Hb variants that lack Hb A and the inability to distinguish those samples not recommended to be used for patient care. The ability to identify samples harboring Hb variants that preclude the utility of Hb A1c may be beneficial in high prevalence populations.

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. However, because many of these features lack a clear biological interpretation, pathologists may be reluctant to adopt these features for clinical diagnosis.METHODS:We examine the utility of biologically interpretable shape-based features for classification of histological renal tumor images. Using Fourier shape descriptors, we extract shape-based features that capture the distribution of stain-enhanced cellular and tissue structures in each image and evaluate these features using a multi-class prediction model. We compare the predictive performance of the shape-based diagnostic model to that of traditional models, i.e., using textural, morphological and topological features.RESULTS:The shape-based model, with an average accuracy of 77%, outperforms or complements traditional models. We identify the most informative shapes for each renal tumor subtype from the top-selected features. Results suggest that these shapes are not only accurate diagnostic features, but also correlate with known biological characteristics of renal tumors.CONCLUSIONS:Shape-based analysis of histological renal tumor images accurately classifies disease subtypes and reveals biologically insightful discriminatory features. This method for shape-based analysis can be extended to other histological datasets to aid pathologists in diagnostic and therapeutic decisions.

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. We develop a simple meta-analysis-based feature selection method that captures the knowledge in each individual dataset and combines the results using a simple rank average. In a comprehensive study that measures robustness in terms of clinical application (i.e., breast, renal, and pancreatic cancer), microarray platform heterogeneity, and classifier (i.e., logistic regression, diagonal LDA, and linear SVM), we compare the rank average meta-analysis method to five other meta-analysis methods. Results indicate that rank average meta-analysis consistently performs well compared to five other meta-analysis methods.

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., small-sample size compared to large-feature size) as well as the large number of available feature selection algorithms. Thus, it is unclear which algorithm should be used for a particular dataset. These factors lead to instability in biomarker identification and affect the reproducibility of results. We introduce a method for computing the biological relevance of feature selection algorithms using an externally validated knowledge base of manually curated cancer biomarkers. Results suggest that knowledge-driven biomarker identification can improve microarray-based clinical prediction performance. omniBiomarker can be accessed at http://omnibiomarker.bme.gatech.edu/.

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.

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. Herein, we report that, for assessment of intestinal inflammation, fecal lipocalin 2 (Lcn-2), measured by ELISA, serves this purpose. Specifically, using a well-characterized mouse model of DSS colitis, we observed that fecal Lcn-2 and intestinal expression of pro-inflammatory cytokines (IL-1β, CXCL1, TNFα) are modestly but significantly induced by very low concentrations of DSS (0.25 and 0.5%), and become markedly elevated at higher concentrations of DSS (1.0 and 4.0%). As expected, careful histopathologic analysis noted only modest immune infiltrates at low DSS concentration and robust colitis at higher DSS concentrations. In accordance, increased levels of the neutrophil product myeloperoxidase (MPO) was only detected in mice given 1.0 and 4.0% DSS. In addition, fecal Lcn-2 marks the severity of spontaneous colitis development in IL-10 deficient mice. Unlike histopathology, MPO, and q-RT-PCR, the assay of fecal Lcn-2 requires only a stool sample, permits measurement over time, and can detect inflammation as early as 1 day following DSS administration. Thus, assay of fecal Lcn-2 by ELISA can function as a non-invasive, sensitive, dynamic, stable and cost-effective means to monitor intestinal inflammation in mice.

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. caCORRECT represents an advance over state-of-art quality control methods such as Harshlighting, and acts to improve gene expression calculation techniques such as PLIER, RMA and MAS5.0, because it incorporates spatial information into outlier detection as well as outlier information into probe normalization. The ability of caCORRECT to recover accurate gene expressions from low quality probe intensity data is assessed using a combination of real and synthetic artifacts with PCR follow-up confirmation and the affycomp spike in data. The caCORRECT tool can be accessed at the website: http://cacorrect.bme.gatech.edu.RESULTS:We demonstrate that (1) caCORRECT's artifact-aware normalization avoids the undesirable global data warping that happens when any damaged chips are processed without caCORRECT; (2) When used upstream of RMA, PLIER, or MAS5.0, the data imputation of caCORRECT generally improves the accuracy of microarray gene expression in the presence of artifacts more than using Harshlighting or not using any quality control; (3) Biomarkers selected from artifactual microarray data which have undergone the quality control procedures of caCORRECT are more likely to be reliable, as shown by both spike in and PCR validation experiments. Finally, we present a case study of the use of caCORRECT to reliably identify biomarkers for renal cell carcinoma, yielding two diagnostic biomarkers with potential clinical utility, PRKAB1 and NNMT.CONCLUSIONS:caCORRECT is shown to improve the accuracy of gene expression, and the reproducibility of experimental results in clinical application. This study suggests that caCORRECT will be useful to clean up possible artifacts in new as well as archived microarray data.

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. However, CRP levels remained at baseline. Subsequent resection revealed a benign mass. CRP values have been shown to carry prognostic significance for overall and disease free survival. This case suggests a novel use for CRP: a sensitive biomarker of disease recurrence and metastasis.

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. In this paper, we define a comprehensive set of common image features (consisting of 12 distinct feature subsets) that quantify a variety of image properties. We use a data-mining approach to determine which feature subsets and image properties emerge as part of an "optimal" diagnostic model when applied to specific cancer endpoints. Our goal is to assess the performance of such comprehensive image feature sets for application to a wide variety of diagnostic problems. We perform this study on 12 endpoints including 6 renal tumor subtype endpoints and 6 renal cancer grade endpoints.