ERα dysfunction caused by ESR1 mutations and therapeutic pressure promotes lineage plasticity in ER breast cancer.

Multiple next-generation molecules targeting estrogen receptor α (ERα) are being investigated in breast cancer clinical trials, encompassing thousands of women globally. Development of these molecules was partly motivated by the discovery of resistance-associated mutations in ESR1 (encodes ERα). Here, we studied the impact of ERα antagonist/degraders against Esr1 mutations expressed in mouse mammary glands.

Transformer-based modeling of Clonal Selection and Expression Dynamics reveals resistance mechanisms in breast cancer.

Understanding transcriptional heterogeneity in cancer cells and its implication for treatment response is critical to identify how resistance occurs and may be targeted. Such heterogeneity can be captured by in vitro studies through clonal barcoding methods. We present TraCSED (Transformer-based modeling of Clonal Selection and Expression Dynamics), a dynamic deep learning approach for modeling clonal selection.

A spheroid whole mount drug testing pipeline with machine-learning based image analysis identifies cell-type specific differences in drug efficacy on a single-cell level.

Background: The growth and drug response of tumors are influenced by their stromal composition, both in vivo and 3D-cell culture models. Cell-type inherent features as well as mutual relationships between the different cell types in a tumor might affect drug susceptibility of the tumor as a whole and/or of its cell populations. However, a lack of single-cell procedures with sufficient detail has hampered the automated observation of cell-type-specific effects in three-dimensional stroma-tumor cell co-cultures.

Exploring Cationic Substitutions in the Solid Electrolyte NaAlCl with Density Functional Theory.

NaAlCl is an established solid electrolyte in high-temperature Na-based battery systems, but its ionic conductivity is not sufficiently high for room-temperature applications. We employ density functional theory and thermodynamic corrections to evaluate the efficacy of various elements for substitution, utilizing on-the-fly machine-learned potentials to accelerate the required phonon calculations by 1 order of magnitude at a minor error of -0.7 ± 1.

High iASPP (PPP1R13L) expression is an independent predictor of adverse clinical outcome in acute myeloid leukemia (AML).

Apoptosis-stimulating proteins of p53 (ASPPs) are a family of proteins that modulate key tumor suppressor pathways via direct interaction with p53. Deregulation of these proteins promotes cancer development and impairs sensitivity to systemic (chemo)therapy and radiation. In this study, we describe that the inhibitor of ASPP (iASPP) is frequently highly expressed in acute myeloid leukemia (AML) and that overexpression correlates with a poor clinical outcome.