BRCA1 mediates protein homeostasis through the ubiquitination of PERK and IRE1.

Tumors with BRCA1 mutations have poor prognoses due to genomic instability. Yet this genomic instability has risks and BRCA1-deficient (def) cancer cells must develop pathways to mitigate these risks. One such risk is the accumulation of unfolded proteins in BRCA1-def cancers from increased mutations due to their loss of genomic integrity. Little is known about how BRCA1-def cancers survive their genomic instability. Here we show that BRCA1 is an E3 ligase in the endoplasmic reticulum (ER) that targets the unfolded protein response (UPR) stress sensors, Eukaryotic Translation Initiation Factor 2-alpha Kinase 3 (PERK) and Serine/Threonine-Protein Kinase/Endoribonuclease Inositol-Requiring Enzyme 1 (IRE1) for ubiquitination and subsequent proteasome-mediated degradation. When BRCA1 is mutated or depleted, both PERK and IRE1 protein levels are increased, resulting in a constitutively activated UPR. Furthermore, the inhibition of protein folding or UPR signaling markedly decreases the overall survival of BRCA1-def cancer cells. Our findings define a mechanism used by the BRCA1-def cancer cells to survive their increased unfolded protein burden which can be used to develop new therapeutic strategies to treat these cancers.

An Efficient Simulation Budget Allocation Method Incorporating Regression for Partitioned Domains.

Simulation can be a very powerful tool to help decision making in many applications but exploring multiple courses of actions can be time consuming. Numerous ranking & selection (R&S) procedures have been developed to enhance the simulation efficiency of finding the best design. To further improve efficiency, one approach is to incorporate information from across the domain into a regression equation. However, the use of a regression metamodel also inherits some typical assumptions from most regression approaches, such as the assumption of an underlying quadratic function and the simulation noise is homogeneous across the domain of interest. To extend the limitation while retaining the efficiency benefit, we propose to partition the domain of interest such that in each partition the mean of the underlying function is approximately quadratic. Our new method provides approximately optimal rules for between and within partitions that determine the number of samples allocated to each design location. The goal is to maximize the probability of correctly selecting the best design. Numerical experiments demonstrate that our new approach can dramatically enhance efficiency over existing efficient R&S methods.