Previous research has found that cognitive maps are not consistently oriented towards north as people tend to bias their north-pointing estimates towards nearby roads (Brunyé et al., 2015). While pointing studies are typically conducted within familiar environments, it is not clear whether north-pointing estimates will show a similar bias towards nearby roads when individuals are not physically located in the environment. In essence, a north-pointing task when not located within the environment is a perspective-taking task. In a series of experiments, participants rated their familiarity with the Texas A&M campus and two nearby cities, completed a self-assessment of sense-of-direction, and then pointed towards north. The pointing task used photographs of the A&M campus to provide a location and initial orientation. These experiments provide new insights into individual differences in north pointing and perspective-taking skills when an individual is not physically present within the environment.

When considering the location of objects and places, we often take perspectives in reference to ourselves or someone/something else. Using ourselves as a reference is considered using an egocentric reference frame, while using something external as a reference is considered using an allocentric reference frame. Of interest is the similarity of how these reference frames inform our understanding of both spatial and social cognitive processes. Similar to how we understand objects in relation to ourselves or an external reference, mentalizing and theory of mind processes have also been described using reference frames. Whether there is a common mechanism for using reference frames for processing both spatial and social information is unclear. The present study explored this idea with an online study where participants performed both a spatial and social (i.e., mentalizing) perspective taking task, along with questionnaire gauging personality, visualization ability, and anxiety. Participants who were better at taking someone else’s spatial perspective tended to be better at mentalizing. This relationship was not present when taking one’s own spatial perspective or when mentalizing was not necessary. We provide preliminary evidence that reference frames contribute to both spatial and social cognitive processes.

UNLABELLED: Rapid proliferation is a hallmark of cancer associated with sensitivity to therapeutics that cause DNA replication stress (RS). Many tumors exhibit drug resistance, however, via molecular pathways that are incompletely understood. Here, we develop an ensemble of predictive models that elucidate how cancer mutations impact the response to common RS-inducing (RSi) agents. The models implement recent advances in deep learning to facilitate multidrug prediction and mechanistic interpretation. Initial studies in tumor cells identify 41 molecular assemblies that integrate alterations in hundreds of genes for accurate drug response prediction. These cover roles in transcription, repair, cell-cycle checkpoints, and growth signaling, of which 30 are shown by loss-of-function genetic screens to regulate drug sensitivity or replication restart. The model translates to cisplatin-treated cervical cancer patients, highlighting an RTK-JAK-STAT assembly governing resistance. This study defines a compendium of mechanisms by which mutations affect therapeutic responses, with implications for precision medicine. SIGNIFICANCE: Zhao and colleagues use recent advances in machine learning to study the effects of tumor mutations on the response to common therapeutics that cause RS. The resulting predictive models integrate numerous genetic alterations distributed across a constellation of molecular assemblies, facilitating a quantitative and interpretable assessment of drug response. This article is featured in Selected Articles from This Issue, p. 384.

Cyclin-dependent kinase 4 and 6 inhibitors (CDK4/6is) have revolutionized breast cancer therapy. However, <50% of patients have an objective response, and nearly all patients develop resistance during therapy. To elucidate the underlying mechanisms, we constructed an interpretable deep learning model of the response to palbociclib, a CDK4/6i, based on a reference map of multiprotein assemblies in cancer. The model identifies eight core assemblies that integrate rare and common alterations across 90 genes to stratify palbociclib-sensitive versus palbociclib-resistant cell lines. Predictions translate to patients and patient-derived xenografts, whereas single-gene biomarkers do not. Most predictive assemblies can be shown by CRISPR-Cas9 genetic disruption to regulate the CDK4/6i response. Validated assemblies relate to cell-cycle control, growth factor signaling and a histone regulatory complex that we show promotes S-phase entry through the activation of the histone modifiers KAT6A and TBL1XR1 and the transcription factor RUNX1. This study enables an integrated assessment of how a tumors genetic profile modulates CDK4/6i resistance.