The heterogeneity of common variable immunodeficiency (CVID) calls for a classification addressing pathogenic mechanisms as well as clinical relevance. This European multicenter trial was initiated to develop a consensus of 2 existing classification schemes based on flowcytometric B-cell phenotyping and the clinical course. The clinical evaluation of 303 patients with the established diagnosis of CVID demonstrated a significant coincidence of granulomatous disease, autoimmune cytopenia, and splenomegaly. Phenotyping of B-cell subpopulations confirmed a severe reduction of switched memory B cells in most of the patients that was associated with a higher risk for splenomegaly and granulomatous disease. An expansion of CD21^low B cells marked patients with splenomegaly. Lymphadenopathy was significantly linked with transitional B-cell expansion. Based on these findings and pathogenic consideration of B-cell differentiation, we suggest an improved classification for CVID (EUROclass), separating patients with nearly absent B cells (less than 1%), severely reduced switched memory B cells (less than 2%), and expansion of transitional (more than 9%) or CD21^low B cells (more than 10%). Whereas the first group contains all patients with severe defects of early B-cell differentiation, severely reduced switched memory B cells indicate a defective germinal center development as found in inducible constimulator (ICOS) or CD40L deficiency. The underlying defects of expanded transitional or CD21^low B cells remain to be elucidated. This trial is re-gistered at http://www.uniklinik-freiburg.de/zks/live/uklregister/Oeffentlich.html as UKF000308.

Primary prostate cancer is genomically highly heterogeneous and is thought to derive from multiple independent clones of cancer cells. Using high-resolution genomic analyses, Bova et al. now show that, in contrast to primary tumors, metastases are monoclonal, originating from a single cancer cell. These findings call into question current views of the origins of primary prostate cancer and suggest that the genomic profile of prostate cancer metastases should inform therapeutic decisions. Many studies have shown that primary prostate cancers are multifocal1,2,3 and are composed of multiple genetically distinct cancer cell clones4,5,6. Whether or not multiclonal primary prostate cancers typically give rise to multiclonal or monoclonal prostate cancer metastases is largely unknown, although studies at single chromosomal loci are consistent with the latter case. Here we show through a high-resolution genome-wide single nucleotide polymorphism and copy number survey that most, if not all, metastatic prostate cancers have monoclonal origins and maintain a unique signature copy number pattern of the parent cancer cell while also accumulating a variable number of separate subclonally sustained changes. We find no relationship between anatomic site of metastasis and genomic copy number change pattern. Taken together with past animal and cytogenetic studies of metastasis7 and recent single-locus genetic data in prostate and other metastatic cancers8,9,10, these data indicate that despite common genomic heterogeneity in primary cancers, most metastatic cancers arise from a single precursor cancer cell. This study establishes that genomic archeology of multiple anatomically separate metastatic cancers in individuals can be used to define the salient genomic features of a parent cancer clone of proven lethal metastatic phenotype.

Single nucleotide polymorphisms (SNPs) are the most common form of genetic variation in humans. The number of SNPs identified in the human genome is growing rapidly, but attaining experimental knowledge about the possible disease association of variants is laborious and time-consuming. Several computational methods have been developed for the classification of SNPs according to their predicted pathogenicity. In this study, we have evaluated the performance of nine widely used pathogenicity prediction methods available on the Internet. The evaluated methods were MutPred, nsSNPAnalyzer, Panther, PhD-SNP, PolyPhen, PolyPhen2, SIFT, SNAP, and SNPs&GO. The methods were tested with a set of over 40,000 pathogenic and neutral variants. We also assessed whether the type of original or substituting amino acid residue, the structural class of the protein, or the structural environment of the amino acid substitution, had an effect on the prediction performance. The performances of the programs ranged from poor (MCC 0.19) to reasonably good (MCC 0.65), and the results from the programs correlated poorly. The overall best performing methods in this study were SNPs&GO and MutPred, with accuracies reaching 0.82 and 0.81, respectively. Hum Mutat 32:1–11, 2011.

(1989). Microbial Amylolytic Enzyme. Critical Reviews in Biochemistry and Molecular Biology: Vol. 24, No. 4, pp. 329-418. doi: 10.3109/10409238909082556

Stability is a fundamental property affecting function, activity, and regulation of biomolecules. Stability changes are often found for mutated proteins involved in diseases. Stability predictors computationally predict protein–stability changes caused by mutations. We performed a systematic analysis of 11 online stability predictors' performances. These predictors are CUPSAT, Dmutant, FoldX, I-Mutant2.0, two versions of I-Mutant3.0 (sequence and structure versions), MultiMutate, MUpro, SCide, Scpred, and SRide. As input, 1,784 single mutations found in 80 proteins were used, and these mutations did not include those used for training. The programs' performances were also assessed according to where the mutations were found in the proteins, that is, in secondary structures and on the surface or in the core of a protein, and according to protein structure type. The extents to which the mutations altered the occupied volumes at the residue sites and the charge interactions were also characterized. The predictions of all programs were in line with the experimental data. I-Mutant3.0 (utilizing structural information), Dmutant, and FoldX were the most reliable predictors. The stability-center predictors performed with similar accuracy. However, at best, the predictions were only moderately accurate (∼60%) and significantly better tools would be needed for routine analysis of mutation effects. Hum Mutat 31:1–10, 2010.