Huntington's disease (HD) is a genetic progressive neurodegenerative disorder, caused by a mutation in the gene, for which there is currently no cure. The identification of sensitive indicators of disease progression and therapeutic outcome could help the development of effective strategies for treating HD. We assessed mutant huntingtin (mHTT) and neurofilament light (NfL) protein concentrations in cerebrospinal fluid (CSF) and blood in parallel with clinical evaluation and magnetic resonance imaging in premanifest and manifest HD mutation carriers. Among HD mutation carriers, NfL concentrations in plasma and CSF correlated with all nonbiofluid measures more closely than did CSF mHTT concentration. Longitudinal analysis over 4 to 8 weeks showed that CSF mHTT, CSF NfL, and plasma NfL concentrations were highly stable within individuals. In our cohort, concentration of CSF mHTT accurately distinguished between controls and HD mutation carriers, whereas NfL concentration, in both CSF and plasma, was able to segregate premanifest from manifest HD. In silico modeling indicated that mHTT and NfL concentrations in biofluids might be among the earliest detectable alterations in HD, and sample size prediction suggested that low participant numbers would be needed to incorporate these measures into clinical trials. These findings provide evidence that biofluid concentrations of mHTT and NfL have potential for early and sensitive detection of alterations in HD and could be integrated into both clinical trials and the clinic.

Use of a composite motor, cognitive, and global functional clinical outcome measure in HD provides an improved measure of clinical progression more related to measures of progressive brain atrophy and provides an opportunity for enhanced clinical trial efficiency relative to currently used individual motor, cognitive, and functional outcome measures.

IGHV3-21-using chronic lymphocytic leukemia (CLL) is a distinct entity

Metastases have been widely thought to arise from rare, selected, mutation-bearing cells in the primary tumor. Recently, however, it has been proposed that breast tumors are imprinted ab initio with metastatic ability. Thus, there is a debate over whether 'phenotypic' disease progression is really associated with 'molecular' progression. We profiled 26 matched primary breast tumors and lymph node metastases and identified 270 probesets that could discriminate between the two categories. We then used an independent cohort of breast tumors (81 samples) and unmatched distant metastases (32 samples) to validate and refine this list down to a 126-probeset list. A representative subset of these genes was subjected to analysis by in situ hybridization, on a third independent cohort (57 primary breast tumors and matched lymph node metastases). This not only confirmed the expression profile data, but also allowed us to establish the cellular origin of the signals. One-third of the analysed representative genes (4 of 11) were expressed by the epithelial component. The four epithelial genes alone were able to discriminate primary breast tumors from their metastases. Finally, engineered alterations in the expression of two of the epithelial genes (SERPINB5 and LTF) modified cell motility in vitro, in accordance with a possible causal role in metastasis. Our results show that breast cancer metastases are molecularly distinct from their primary tumors.

Multivariate partial least square (PLS) regression allows the modeling of complex biological events, by considering different factors at the same time. It is unaffected by data collinearity, representing a valuable method for modeling high-dimensional biological data (as derived from genomics, proteomics and peptidomics). In presence of multiple responses, it is of particular interest how to appropriately “dissect” the model, to reveal the importance of single attributes with regard to individual responses (for example, variable selection). In this paper, performances of multivariate PLS regression coefficients, in selecting relevant predictors for different responses in omics-type of data, were investigated by means of a receiver operating characteristic (ROC) analysis. For this purpose, simulated data, mimicking the covariance structures of microarray and liquid chromatography mass spectrometric data, were used to generate matrices of predictors and responses. The relevant predictors were set a priori. The influences of noise, the source of data with different covariance structure and the size of relevant predictors were investigated. Results demonstrate the applicability of PLS regression coefficients in selecting variables for each response of a multivariate PLS, in omics-type of data. Comparisons with other feature selection methods, such as variable importance in the projection scores, principal component regression, and least absolute shrinkage and selection operator regression were also provided.