Improved estimation of cancer dependencies from large-scale RNAi screens using model-based normalization and data integration.

The availability of multiple datasets comprising genome-scale RNAi viability screens in hundreds of diverse cancer cell lines presents new opportunities for understanding cancer vulnerabilities. Integrated analyses of these data to assess differential dependency across genes and cell lines are challenging due to confounding factors such as batch effects and variable screen quality, as well as difficulty assessing gene dependency on an absolute scale. To address these issues, we incorporated cell line screen-quality parameters and hierarchical Bayesian inference into DEMETER2, an analytical framework for analyzing RNAi screens ( https://depmap.org/R2-D2 ). This model substantially improves estimates of gene dependency across a range of performance measures, including identification of gold-standard essential genes and agreement with CRISPR/Cas9-based viability screens. It also allows us to integrate information across three large RNAi screening datasets, providing a unified resource representing the most extensive compilation of cancer cell line genetic dependencies to date.

Parallel genome-scale loss of function screens in 216 cancer cell lines for the identification of context-specific genetic dependencies.

Using a genome-scale, lentivirally delivered shRNA library, we performed massively parallel pooled shRNA screens in 216 cancer cell lines to identify genes that are required for cell proliferation and/or viability. Cell line dependencies on 11,000 genes were interrogated by 5 shRNAs per gene. The proliferation effect of each shRNA in each cell line was assessed by transducing a population of 11M cells with one shRNA-virus per cell and determining the relative enrichment or depletion of each of the 54,000 shRNAs after 16 population doublings using Next Generation Sequencing. All the cell lines were screened using standardized conditions to best assess differential genetic dependencies across cell lines. When combined with genomic characterization of these cell lines, this dataset facilitates the linkage of genetic dependencies with specific cellular contexts (e.g., gene mutations or cell lineage). To enable such comparisons, we developed and provided a bioinformatics tool to identify linear and nonlinear correlations between these features.

Eleven-year implant survival rates of the all-polyethylene and metal-backed modular Optetrak posterior stabilized knee in bilateral simultaneous cases.

A prospective randomized study was conducted to determine if a design change in the articular surface geometry introduced in the Optetrak total knee to increase prosthetic joint conformity and further reduce polyethylene stress had any impact on implant survival, particularly when the all-polyethylene version of the implant was used. Forty-seven patients undergoing bilateral simultaneous total knee arthroplasties were randomized for the side, receiving an all-polyethylene tibial component and followed up for a mean 11.6 years. Survival rates for the all-polyethylene and metal-backed modular versions of the implant were both 98%, excluding a single case of deep infection. Survival rates with revision for aseptic loosening as an end point were 100%. The increase in tibial and femoral radii in the coronal plane of the Optetrak posterior stabilized knee did not result in a reduced implant survival rate in either the metal-backed modular or all-polyethylene versions of the implant.