Project description:41 lung adenocarcinoma from never-smokers hybridized on Illumina SNP arrays on 13 HumanCNV370-Quadv3 chips. High-resolution array comparative genomic hybridization analysis of lung adenocarcinoma in 41 never smokers for identification of new minimal common regions (MCR) of gain or loss. The SNP array analysis validated copy-number aberrations and revealed that RB1 and WRN were altered by recurrent copy-neutral loss of heterozygosity.The present study has uncovered new aberrations containing cancer genes. The oncogene FUS is a candidate gene in the 16p region that is frequently gained in never smokers. Multiple genetic pathways defined by gains of MYC, deletions of RB1 and WRN or gains on 7p and 7q are involved in lung adenocarcinoma in never smokers. A 'Cartes d'Identite des Tumeurs' (CIT) project from the French National League Against Cancer (http://cit.ligue-cancer.net) 41 samples hybridized on Illumina SNP arrays. Submitter : Fabien PETEL petelf@ligue-cancer.net . Project leader : Pr Pierre FOURET pierre.fouret@psl.aphp.fr
Project description:Gleason grading is an important prognostic indicator for prostate adenocarcinoma and is crucial for patient treatment decisions. However, intermediate-risk patients diagnosed in Gleason Grade Groups (GG) 2 and GG3 can harbour either aggressive or non-aggressive disease, resulting in under- or over-treatment of a significant number of patients. Here, we performed proteomic, differential expression, machine learning, and survival analyses for 1,348 matched tumour and benign samples from 278 patients. Three proteins (F5, TMEM126B and EARS2) were identified as candidate biomarkers in patients with biochemical recurrence. Multivariate Cox regression yielded 18 proteins, from which a risk score was constructed to dichotomise prostate cancer patients into low- and high-risk groups. This 18-protein signature is prognostic for the risk of biochemical recurrence and completely independent of the intermediate GG. Our results suggest that markers generated by computational proteomic profiling have the potential for clinical applications including integration into prostate cancer management.
Project description:Prostate cancer is the most common form of non-skin cancer in American men. Although the 5-year survival rate is 100%, an important proportion of patients will recur after seven years following treatment. Despite attempts to delineate clinical and molecular prognostic signatures of recurrence, accurate prediction remains an elusive task. The purpose of this project was, therefore, to identify candidate genomic signatures of biochemical failure present at the moment of diagnosis. To do this, we carried out array comparative genomic hybridization on surgical specimens from a training cohort of patients with PRCA who were followed on average for ten years. Due to the unstable nature of malignant genomes, we selected non-random genomic aberrations through Significance Testing of Aberrant Copy Number (STAC), a statistical permutation algorithm. Further analysis of STAC regions between recurrent and non-recurrent groups showed that recurring cases had a greater proportion of gain in 4p14-12, 11p14.3-14.1 or 15q21.1-22.2 in comparison to non-recurrent cases. All three genomic signatures were independently, better predictors of recurrence than previously known clinical covariates. The importance of these regions in PRCA progression was further validated in publicly available expression data sets, where over-expression of ADAM10, BDNF and CYP19A1 (all coded in the aforementioned regions) was found to increase with progression of disease. We hypothesize that amplification of ADAM10, BDNF, and CYP19A1 may serve as candidate biomarkers of recurrence to be validated in larger datasets. Keywords: BAC Array CGH, Biomarker study
Project description:Purpose: Clinicopathologic features and biochemical recurrence are sensitive, but not specific, predictors of metastatic disease and lethal prostate cancer. We hypothesize that a genomic expression signature detected in the primary tumor represents true biological potential of aggressive disease and provides improved prediction of early prostate cancer metastasis. Methods: A nested case-control design was used to select 639 patients from the Mayo Clinic tumor registry that underwent radical prostatectomy between 1987 and 2001. A genomic classifier (GC) was developed by modeling differential RNA expression using 1.4 million feature high-density expression arrays of men enriched for rising PSA after prostatectomy, including 213 that experienced early clinical metastasis after biochemical recurrence. A training set was used to develop a random forest classifier of 22 markers to predict for cases - men with early clinical metastasis after rising PSA. Performance of GC was compared to prognostic factors such as Gleason score and previous gene expression signatures in a withheld validation set. Results: Expression profiles were generated from 545 unique patient samples, with median follow-up of 16.9 years. GC achieved an area under the receiver operating characteristic curve of 0.75 (0.67 - 0.83) in validation, outperforming clinical variables and gene signatures. GC was the only significant prognostic factor in multivariable analyses. Within Gleason score groups, cases with high GC scores experienced earlier death from prostate cancer and reduced overall survival. The markers in the classifier were found to be associated with a number of key biological processes in prostate cancer metastatic disease progression. Conclusion: A genomic classifier was developed and validated in a large patient cohort enriched with prostate cancer metastasis patients and a rising PSA that went on to experience metastatic disease. This early metastasis prediction model based on genomic expression in the primary tumor may be useful for identification of aggressive prostate cancer. 545 formalin-fixed paraffin-embedded (FFPE) tissue samples from primary prostate cancer obtained from Radical Prostatectomy.
Project description:By combining extensive biochemical fractionation with quantitative mass spectrometry, we directly examined the composition of soluble multiprotein complexes among diverse animal models. The project has been jointly supervised by Andrew Emili and Edward M. Marcotte. Project website: http://metazoa.med.utoronto.ca
Project description:Kynureninase is a member of a large family of catalytically diverse but structurally homologous pyridoxal 5'-phosphate (PLP) dependent enzymes known as the aspartate aminotransferase superfamily or alpha-family. The Homo sapiens and other eukaryotic constitutive kynureninases preferentially catalyze the hydrolytic cleavage of 3-hydroxy-l-kynurenine to produce 3-hydroxyanthranilate and l-alanine, while l-kynurenine is the substrate of many prokaryotic inducible kynureninases. The human enzyme was cloned with an N-terminal hexahistidine tag, expressed, and purified from a bacterial expression system using Ni metal ion affinity chromatography. Kinetic characterization of the recombinant enzyme reveals classic Michaelis-Menten behavior, with a Km of 28.3 +/- 1.9 microM and a specific activity of 1.75 micromol min-1 mg-1 for 3-hydroxy-dl-kynurenine. Crystals of recombinant kynureninase that diffracted to 2.0 A were obtained, and the atomic structure of the PLP-bound holoenzyme was determined by molecular replacement using the Pseudomonas fluorescens kynureninase structure (PDB entry 1qz9) as the phasing model. A structural superposition with the P. fluorescens kynureninase revealed that these two structures resemble the "open" and "closed" conformations of aspartate aminotransferase. The comparison illustrates the dynamic nature of these proteins' small domains and reveals a role for Arg-434 similar to its role in other AAT alpha-family members. Docking of 3-hydroxy-l-kynurenine into the human kynureninase active site suggests that Asn-333 and His-102 are involved in substrate binding and molecular discrimination between inducible and constitutive kynureninase substrates.
Project description:Radical prostatectomy remains one of the more widely-used treatment options for men with prostate cancer. However, few molecular biomarkers have been established to predict patients who are at high risk of biochemical failure. To our knowledge, this is the first such report on miRNA profiling in radical prostatectomy tissue using Nanostring. In addition, this is the first report to look at the prognostic value of miRNAs in the setting of biochemical failure and salvage radiation therapy post-radical prostatectomy.