Human synthetic lethal inference as potential anti-cancer target gene detection

Background: Two genes are called synthetic lethal (SL) if mutation of either alone is not lethal, but mutation of both leads to death or a significant decrease in organism's fitness. The detection of SL gene pairs constitutes a promising alternative for anti-cancer therapy. As cancer cells exhibit a large number of mutations, the identification of these mutated genes' SL partners may provide specific anti-cancer drug candidates, with minor perturbations to the healthy cells. Since existent SL data is mainly restricted to yeast screenings, the road towards human SL candidates is limited to inference methods. Results: In the present work, we use phylogenetic analysis and database manipulation (BioGRID for interactions, Ensembl and NCBI for homology, Gene Ontology for GO attributes) in order to reconstruct the phylogenetically-inferred SL gene network for human. In addition, available data on cancer mutated genes (COSMIC and Cancer Gene Census databases) as well as on existent approved drugs (DrugBank database) supports our selection of cancer-therapy candidates.Conclusions: Our work provides a complementary alternative to the current methods for drug discovering and gene target identification in anti-cancer research. Novel SL screening analysis and the use of highly curated databases would contribute to improve the results of this methodology.

O-chromosome lethal frequencies in Serbian and Montenegrin Drosophila subobscura populations

Lethal chromosomal frequencies were obtained from three Drosophila subobscura samples from the Mt. Avala (Serbia) population in September 2003 (0.218), June 2004 (0.204) and September 2004 (0.250). These values and those from other Balkan populations studied previously (Petnica, Kamariste, Zanjic and Djerdap) were used to analyze the possible effect of population, year, month and altitude above sea level on lethal chromosomal frequencies. According to ANOVAS no effect were observed. Furthermore, the lethal frequencies of the Balkan populations did not vary according to latitude. This is probably due to the relative proximity and high gene flow between these populations. From a joint study of all the Palearctic D. subobscura populations so far analyzed, it can be deduced that the Balkan populations are located in the central area of the species distribution. Finally, it seems that lethal chromosomal frequencies are a consequence of the genetic structure of the populations.

O-chromosome lethal frequencies in Serbian and Montenegrin Drosophila subobscura populations

Lethal chromosomal frequencies were obtained from three Drosophila subobscura samples from the Mt. Avala (Serbia) population in September 2003 (0.218), June 2004 (0.204) and September 2004 (0.250). These values and those from other Balkan populations studied previously (Petnica, Kamariste, Zanjic and Djerdap) were used to analyze the possible effect of population, year, month and altitude above sea level on lethal chromosomal frequencies. According to ANOVAS no effect were observed. Furthermore, the lethal frequencies of the Balkan populations did not vary according to latitude. This is probably due to the relative proximity and high gene flow between these populations. From a joint study of all the Palearctic D. subobscura populations so far analyzed, it can be deduced that the Balkan populations are located in the central area of the species distribution. Finally, it seems that lethal chromosomal frequencies are a consequence of the genetic structure of the populations.

“Unmixing” Tissue Gene Expression Signatures from Tumor Biopsies

Emergent molecular measurement methods, such as DNA microarray, qRTPCR, andmany others, offer tremendous promise for the personalized treatment of cancer. Thesetechnologies measure the amount of specific proteins, RNA, DNA or other moleculartargets from tumor specimens with the goal of “fingerprinting” individual cancers. Tumorspecimens are heterogeneous; an individual specimen typically contains unknownamounts of multiple tissues types. Thus, the measured molecular concentrations resultfrom an unknown mixture of tissue types, and must be normalized to account for thecomposition of the mixture.For example, a breast tumor biopsy may contain normal, dysplastic and cancerousepithelial cells, as well as stromal components (fatty and connective tissue) and bloodand lymphatic vessels. Our diagnostic interest focuses solely on the dysplastic andcancerous epithelial cells. The remaining tissue components serve to “contaminate”the signal of interest. The proportion of each of the tissue components changes asa function of patient characteristics (e.g., age), and varies spatially across the tumorregion. Because each of the tissue components produces a different molecular signature,and the amount of each tissue type is specimen dependent, we must estimate the tissuecomposition of the specimen, and adjust the molecular signal for this composition.Using the idea of a chemical mass balance, we consider the total measured concentrationsto be a weighted sum of the individual tissue signatures, where weightsare determined by the relative amounts of the different tissue types. We develop acompositional source apportionment model to estimate the relative amounts of tissuecomponents in a tumor specimen. We then use these estimates to infer the tissuespecificconcentrations of key molecular targets for sub-typing individual tumors. Weanticipate these specific measurements will greatly improve our ability to discriminatebetween different classes of tumors, and allow more precise matching of each patient tothe appropriate treatment