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Biomedical subjects

C Lottaz

Publications and source records attributed to C Lottaz.

2 recordsLinked to original sources

Activation of the HIF pathway in childhood ALL, prognostic implications of VEGF.

Hypoxia-inducible factor 1 (HIF-1) controls angiogenesis and glycolysis, two leading characteristics of solid tumor invasion, metastasis, and lethality. Increased angiogenesis is also found in the bone marrow (BM) of leukemias. Less is known in leukemia about the role of HIF-1 and vascular endothelial growth factor (VEGF), the most important proangiogenic target gene of HIF-1. We show by immunohistochemistry that the oxygen-regulated component of HIF-1 (HIF-1alpha) is overexpressed in clusters of leukemic cells in BM specimens of childhood acute lymphoblastic leukemia (ALL) and absent in biopsies of normal BM. Half the HIF-1alpha-positive ALL biopsies exhibited VEGF coexpression. Among 96 children with relapsed ALL, diagnostic BM aspirates with high VEGF mRNA levels were associated with a significantly lower probability of event-free survival at 3 years (0.31+/-0.08 vs 0.65+/-0.07, P=0.003). Those with poor molecular response to therapy (evaluated by MRD assessment) had 2.2-fold higher VEGF levels than those responding well to chemotherapy (P=0.005). In conclusion, the data demonstrate activation of the HIF pathway in the BM of ALL patients and indicate that the expression of HIF target genes, such as VEGF, play an important role in leukemia progression, therapy response, and outcome.

Adolescent↗

Modeling sequencing errors by combining Hidden Markov models.

Among the largest resources for biological sequence data is the large amount of expressed sequence tags (ESTs) available in public and proprietary databases. ESTs provide information on transcripts but for technical reasons they often contain sequencing errors. Therefore, when analyzing EST sequences computationally, such errors must be taken into account. Earlier attempts to model error prone coding regions have shown good performance in detecting and predicting these while correcting sequencing errors using codon usage frequencies. In the research presented here, we improve the detection of translation start and stop sites by integrating a more complex mRNA model with codon usage bias based error correction into one hidden Markov model (HMM), thus generalizing this error correction approach to more complex HMMs. We show that our method maintains the performance in detecting coding sequences.

Algorithms↗