Multivariate curve resolution of time course microarray data

BACKGROUND: Modeling of gene expression data from time course experiments often involves the use of linear models such as those obtained from principal component analysis (PCA), independent component analysis (ICA), or other methods. Such methods do not generally yield factors with a clear biological interpretation. Moreover, implicit assumptions about the measurement errors often limit the application of these methods to log-transformed data, destroying linear structure in the untransformed expression data. RESULTS: In this work, a method for the linear decomposition of gene expression data by multivariate curve resolution (MCR) is introduced. The MCR method is based on an alternating least-squares (ALS) algorithm implemented with a weighted least squares approach. The new method, MCR-WALS, extracts a small number of basis functions from untransformed microarray data using only non-negativity constraints. Measurement error information can be incorporated into the modeling process and missing data can be imputed. The utility of the method is demonstrated through its application to yeast cell cycle data. CONCLUSION: Profiles extracted by MCR-WALS exhibit a strong correlation with cell cycle-associated genes, but also suggest new insights into the regulation of those genes. The unique features of the MCR-WALS algorithm are its freedom from assumptions about the underlying linear model other than the non-negativity of gene expression, its ability to analyze non-log-transformed data, and its use of measurement error information to obtain a weighted model and accommodate missing measurements

Clinical significance of disseminated tumour cells in non-small cell lung cancer

BACKGROUND: Early-stage non-small cell lung cancer (NSCLC) patients have a high risk of disease relapse despite curatively intended surgical resection, and the detection of tumour cells in the bone marrow could be one method of determining the presence of the disseminated disease in its early stages. METHODS: Bone marrow aspirates were collected from 296 patients at the time of surgery, and the presence of disseminated tumour cells was determined with the help of immunomagnetic selection (IMS) using the MOC31-antibody recognising EpCAM and with the help of standard immunocytochemistry (ICC) using the anti-cytokeratin (CK) antibodies AE1/AE3. RESULTS: Disseminated tumour cells were found in 152 of 252 (59%) bone marrow samples using IMS and in 25 of 234 (11%) samples using ICC. No association between the two detection methods was observed. The presence of EpCAM(+) cells was not associated with any clinicopathological parameters, whereas a higher frequency of CK(+) cells was found in patients with an advanced pT status. Disseminated tumour cells, as detected using IMS, had no prognostic impact. Patients with CK(+) cells in the bone marrow had a reduced relapse-free survival, but the difference was not statistically significant. CONCLUSION: Our findings do not support the further development of DTC detection for clinical use in early-stage NSCLC. Future studies should include the molecular characterisation of DTCs, along with an attempt to identify subpopulations of cells with biological and clinical significance