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Biomedical subjects

L Loï

Publications and source records attributed to L Loï.

2 recordsLinked to original sources

Gene expression profiling identifies molecular subgroups among nodal peripheral T-cell lymphomas.

The classification of peripheral T-cell lymphomas (PTCL) is still a matter of debate. To establish a molecular classification of PTCL, we analysed 59 primary nodal T-cell lymphomas using cDNA microarrays, including 56 PTCL and three T-lymphoblastic lymphoma (T-LBL). The expression profiles could discriminate angioimmunoblastic lymphoma, anaplastic large-cell lymphoma and T-LBL. In contrast, cases belonging to the broad category of 'PTCL, unspecified' (PTCL-U) did not share a single molecular profile. Using a multiclass predictor, we could separate PTCL-U into three molecular subgroups called U1, U2 and U3. The U1 gene expression signature included genes known to be associated with poor outcome in other tumors, such as CCND2. The U2 subgroup was associated with overexpression of genes involved in T-cell activation and apoptosis, including NFKB1 and BCL-2. The U3 subgroup was mainly defined by overexpression of genes involved in the IFN/JAK/STAT pathway. It comprised a majority of histiocyte-rich PTCL samples. Gene Ontology annotations revealed different functional profile for each subgroup. These results suggest the existence of distinct subtypes of PTCL-U with specific molecular profiles, and thus provide a basis to improve their classification and to develop new therapeutic targets.

Gene Expression Profiling↗

Feature extraction and signal processing for nylon DNA microarrays.

BACKGROUND: High-density DNA microarrays require automatic feature extraction methodologies and softwares. These can be a potential source of non-reproducibility of gene expression measurements. Variation in feature location or in signal integration methodology may be a significant contribution to the observed variance in gene expression levels. RESULTS: We explore sources of variability in feature extraction from DNA microarrays on Nylon membrane with radioactive detection. We introduce a mathematical model of the signal emission and derive methods for correcting biases such as overshining, saturation or variation in probe amount. We also provide a quality metric which can be used qualitatively to flag weak or untrusted signals or quantitatively to modulate the weight of each experiment or gene in higher level analyses (clustering or discriminant analysis). CONCLUSIONS: Our novel feature extraction methodology, based on a mathematical model of the radioactive emission, reduces variability due to saturation, neighbourhood effects and variable probe amount. Furthermore, we provide a fully automatic feature extraction software, BZScan, which implements the algorithms described in this paper.

Algorithms↗