Motion for a Resolution tabled by Mr Richard Balfe for entry in the register pursuant to Rule 49 of the Rules of Procedure on supply of military equipment to states where basic human rights are not respected. Working Documents 1982-83, Document 1-265/82, 19 May 1982

Abstract Background The clinical sequencing of cancer genomes to personalize therapy is becoming routine across the world. However, concerns over patient re-identification from these data lead to questions about how tightly access should be controlled. It is not thought to be possible to re-identify patients from somatic variant data. However, somatic variant detection pipelines can mistakenly identify germline variants as somatic ones, a process called “germline leakage”. The rate of germline leakage across different somatic variant detection pipelines is not well-understood, and it is uncertain whether or not somatic variant calls should be considered re-identifiable. To fill this gap, we quantified germline leakage across 259 sets of whole-genome somatic single nucleotide variant (SNVs) predictions made by 21 teams as part of the ICGC-TCGA DREAM Somatic Mutation Calling Challenge. Results The median somatic SNV prediction set contained 4325 somatic SNVs and leaked one germline polymorphism. The level of germline leakage was inversely correlated with somatic SNV prediction accuracy and positively correlated with the amount of infiltrating normal cells. The specific germline variants leaked differed by tumour and algorithm. To aid in quantitation and correction of leakage, we created a tool, called GermlineFilter, for use in public-facing somatic SNV databases. Conclusions The potential for patient re-identification from leaked germline variants in somatic SNV predictions has led to divergent open data access policies, based on different assessments of the risks. Indeed, a single, well-publicized re-identification event could reshape public perceptions of the values of genomic data sharing. We find that modern somatic SNV prediction pipelines have low germline-leakage rates, which can be further reduced, especially for cloud-sharing, using pre-filtering software

Identifying molecular features that distinguish fluvastatin-sensitive breast tumor cells

Statins, routinely used to treat hypercholesterolemia, selectively induce apoptosis in some tumor cells by inhibiting the mevalonate pathway. Recent clinical studies suggest that a subset of breast tumors is particularly susceptible to lipophilic statins, such as fluvastatin. To quickly advance statins as effective anticancer agents for breast cancer treatment, it is critical to identify the molecular features defining this sensitive subset. We have therefore characterized fluvastatin sensitivity by MTT assay in a panel of 19 breast cell lines that reflect the molecular diversity of breast cancer, and have evaluated the association of sensitivity with several clinicopathological and molecular features. A wide range of fluvastatin sensitivity was observed across breast tumor cell lines, with fluvastatin triggering cell death in a subset of sensitive cell lines. Fluvastatin sensitivity was associated with an estrogen receptor alpha (ERa)-negative, basal-like tumor subtype, features that can be scored with routine and/or strong preclinical diagnostics. To ascertain additional candidate sensitivity-associated molecular features, we mined publicly available gene expression datasets, identifying genes encoding regulators of mevalonate production, nonsterol lipid homeostasis, and global cellular metabolism, including the oncogene MYC. Further exploration of this data allowed us to generate a 10-gene mRNA abundance signature predictive of fluvastatin sensitivity, which showed preliminary validation in an independent set of breast tumor cell lines. Here, we have therefore identified several candidate predictors of sensitivity to fluvastatin treatment in breast cancer, which warrant further preclinical and clinical evaluation.Fil: Goard, Carolyn A.. University Health Network. Princess Margaret Cancer Centre. Ontario Cancer Institute and Campbell Family Institute for Breast Cancer Research; Canadá. University Of Toronto; CanadáFil: Chan Seng Yue, Michelle . University Health Network. Princess Margaret Cancer Centre. Ontario Cancer Institute and Campbell Family Institute for Breast Cancer Research; Canadá. Ontario Institute of Cancer Research. Informatics and Biocomputing Platform; CanadáFil: Mullen, Peter J.. University Health Network. Princess Margaret Cancer Centre. Ontario Cancer Institute and Campbell Family Institute for Breast Cancer Research; CanadáFil: Quiroga, Ariel Dario. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Tandil. Centro de Investigaciones en Física e Ingeniería del Centro de la Provincia de Buenos Aires; Argentina. University of Alberta; CanadáFil: Wasylishen, Amanda R.. University Health Network. Princess Margaret Cancer Centre. Ontario Cancer Institute and Campbell Family Institute for Breast Cancer Research; Canadá. University Of Toronto; CanadáFil: Clendening, James W.. University Health Network. Princess Margaret Cancer Centre. Ontario Cancer Institute and Campbell Family Institute for Breast Cancer Research; Canadá. University Of Toronto; CanadáFil: Sendorek, Dorota H. S.. Ontario Institute of Cancer Research. Informatics and Biocomputing Platform; CanadáFil: Haider, Syed. Ontario Institute of Cancer Research. Informatics and Biocomputing Platform; CanadáFil: Lehner, Richard. University of Alberta; CanadáFil: Boutros, Paul C.. University Of Toronto; Canadá. Ontario Institute of Cancer Research. Informatics and Biocomputing Platform; CanadáFil: Penn, Linda Z.. University Health Network. Princess Margaret Cancer Centre. Ontario Cancer Institute and Campbell Family Institute for Breast Cancer Research; Canadá. University Of Toronto; Canad

Tumour genomic and microenvironmental heterogeneity as integrated predictors for prostate cancer recurrence: a retrospective study

Clinical prognostic groupings for localised prostate cancers are imprecise, with 30–50% of patients recurring after image-guided radiotherapy or radical prostatectomy. We aimed to test combined genomic and microenvironmental indices in prostate cancer to improve risk stratification and complement clinical prognostic factors

Environmentally assisted cracking behavior of peak-aged 7010 aluminum alloy containing scandium

The 7010 Al alloy with and without addition of 0.25 wt pct Sc in peak-aged condition was examined for its environmentally assisted cracking (EAC) behavior. Slow strain rate testing (SSRT) per ASTM standard G129-00 was employed to investigate EAC. The base 7010 Al alloy showed 10 pct elongation, 9.9 pct reduction in area, and 561 MPa ultimate tensile strength (UTS), when tested in air. The ductility of the base alloy dropped to 3 and 3.3 pct in terms of elongation and reduction in area, respectively, when tested in 3.5 pct NaCl solution, showing its high susceptibility to EAC. On the other hand, the 0.25 wt pct Sc containing alloy showed a significant improvement in ductility not only in air but also in 3.5 pct NaCl solution, without any loss in the UTS. Thus, the 0.25 wt pct Sc containing alloy exhibited 13.4 pct elongation, 15.8 pct reduction in area, and 560 MPa UTS in air and 12.5 pct elongation, 16.4 pct reduction in area and 560 MPa UTS in 3.5 pct NaCl solution. The study for the first time shows that the high resistance to EAC of 7010 alloy can be imparted even in peak-aged condition by the addition of 0.25 wt pct Sc

Valection: design optimization for validation and verification studies.

BACKGROUND:Platform-specific error profiles necessitate confirmatory studies where predictions made on data generated using one technology are additionally verified by processing the same samples on an orthogonal technology. However, verifying all predictions can be costly and redundant, and testing a subset of findings is often used to estimate the true error profile. RESULTS:To determine how to create subsets of predictions for validation that maximize accuracy of global error profile inference, we developed Valection, a software program that implements multiple strategies for the selection of verification candidates. We evaluated these selection strategies on one simulated and two experimental datasets. CONCLUSIONS:Valection is implemented in multiple programming languages, available at: http://labs.oicr.on.ca/boutros-lab/software/valection

Valection: design optimization for validation and verification studies.

BackgroundPlatform-specific error profiles necessitate confirmatory studies where predictions made on data generated using one technology are additionally verified by processing the same samples on an orthogonal technology. However, verifying all predictions can be costly and redundant, and testing a subset of findings is often used to estimate the true error profile.ResultsTo determine how to create subsets of predictions for validation that maximize accuracy of global error profile inference, we developed Valection, a software program that implements multiple strategies for the selection of verification candidates. We evaluated these selection strategies on one simulated and two experimental datasets.ConclusionsValection is implemented in multiple programming languages, available at: http://labs.oicr.on.ca/boutros-lab/software/valection

Valection:Design optimization for validation and verification studies

Abstract Background Platform-specific error profiles necessitate confirmatory studies where predictions made on data generated using one technology are additionally verified by processing the same samples on an orthogonal technology. However, verifying all predictions can be costly and redundant, and testing a subset of findings is often used to estimate the true error profile. Results To determine how to create subsets of predictions for validation that maximize accuracy of global error profile inference, we developed Valection, a software program that implements multiple strategies for the selection of verification candidates. We evaluated these selection strategies on one simulated and two experimental datasets. Conclusions Valection is implemented in multiple programming languages, available at: http://labs.oicr.on.ca/boutros-lab/software/valectio