Exploring Nucleophagy and Inflammation in the Tumor Microenvironment

Nuclear stress and inflammation are intimately tied in tumorigenesis and cancer cell survival. Here, I explore immune environments across various types of cancers and nucleophagy, a mechanism by which cancer cells respond to nuclear stress. Nucleophagy, a selective form of autophagy, is an intracellular catabolic process involved in the degradation of nuclear material. A mechanistic understanding of nucleophagy remains limited due to a lack of chemical or genetic regulators that can modulate the process. I describe a high content drug screen that identifies a panel of drugs that bi-directionally regulate autophagy marker MAP1LC3B nuclear localization in a renal cancer cell line. I investigate the effects of two hit compounds from the screen on the degradation of nuclear envelope protein Lamin B1 under normal and nuclear stress conditions. Novel chemical tools from the screen allow for deeper exploration of the players involved in nucleophagy. Nuclear envelope degradation in cancer cells is often accompanied by DNA damage and chromatin rearrangement, which leads to inflammation in the tumor microenvironment (TME). Though the contribution of immune infiltration on cancer prognosis is widely recognized, a precise characterization of the immune environment across different tumors and tumor states remains incomplete. The recent advent of immunotherapy for pediatric cancers calls for a better understanding of immune cell interactions in the TME. Here, I analyze controlled-access whole transcriptomic and exome sequencing datasets to map the immune landscape of pediatric kidney cancers, specifically Wilms tumors. This analysis is the first precise characterization of the immune environment for pediatric kidney cancers. Investigating the immune landscape may offer predictive insight into pediatric cancer prognosis and immunotherapy response as well as guide future personalized medicine approaches

SUMO targeting of a stress-tolerant Ulp1 SUMO protease

SUMO proteases of the SENP/Ulp family are master regulators of both sumoylation and desumoylation and regulate SUMO homeostasis in eukaryotic cells. SUMO conjugates rapidly increase in response to cellular stress, including nutrient starvation, hypoxia, osmotic stress, DNA damage, heat shock, and other proteotoxic stressors. Nevertheless, little is known about the regulation and targeting of SUMO proteases during stress. To this end we have undertaken a detailed comparison of the SUMO-binding activity of the budding yeast protein Ulp1 (ScUlp1) and its ortholog in the thermotolerant yeast Kluyveromyces marxianus, KmUlp1. We find that the catalytic UD domains of both ScUlp1 and KmUlp1 show a high degree of sequence conservation, complement a ulp1 Delta mutant in vivo, and process a SUMO precursor in vitro. Next, to compare the SUMO-trapping features of both SUMO proteases we produced catalytically inactive recombinant fragments of the UD domains of ScUlp1 and KmUlp1, termed ScUTAG and KmUTAG respectively. Both ScUTAG and KmUTAG were able to efficiently bind a variety of purified SUMO isoforms and bound immobilized SUMO1 with nanomolar affinity. However, KmUTAG showed a greatly enhanced ability to bind SUMO and SUMO-modified proteins in the presence of oxidative, temperature and other stressors that induce protein misfolding. We also investigated whether a SUMO-interacting motif (SIM) in the UD domain of KmULP1 that is not conserved in ScUlp1 may contribute to the SUMO-binding properties of KmUTAG. In summary, our data reveal important details about how SUMO proteases target and bind their sumoylated substrates, especially under stress conditions. We also show that the robust pan-SUMO binding features of KmUTAG can be exploited to detect and study SUMO-modified proteins in cell culture systems

Towards optimal model evaluation: enhancing active testing with actively improved estimators

Abstract With rapid advancements in machine learning and statistical models, ensuring the reliability of these models through accurate evaluation has become imperative. Traditional evaluation methods often rely on fully labeled test data, a requirement that is becoming increasingly impractical due to the growing size of datasets. In this work, we address this issue by extending existing work on active testing (AT) methods which are designed to sequentially sample and label data for evaluating pre-trained models. We propose two novel estimators: the Actively Improved Levelled Unbiased Risk (AILUR) and the Actively Improved Inverse Probability Weighting (AIIPW) estimators which are derived from nonparametric smoothing estimation. In addition, a model recalibration process is designed for the AIIPW estimator to optimize the sampling probability within the AT framework. We evaluate the proposed estimators on four real-world datasets and demonstrate that they consistently outperform existing AT methods. Our study also shows that the proposed methods are robust to changes in subsample sizes, and effective at reducing labeling costs

Supplemental material - Time of Clinic Appointment and Serious Illness Communication in Oncology

Supplemental material for Supplemental material - Time of Clinic Appointment and Serious Illness Communication in Oncology by Likhitha Kolla, Jinbo Chen and Ravi B. Parikh in Cancer Control Journal</p

Time of Clinic Appointment and Serious Illness Communication in Oncology

Introduction Serious illness communication in oncology increases goal concordant care. Factors associated with the frequency of serious illness conversations are not well understood. Given prior evidence of the association between suboptimal decision-making and clinic time, we aimed to investigate the relationship between appointment time and the likelihood of serious illness conversations in oncology. Methods We conducted a retrospective study of electronic health record data from 55 367 patient encounters between June 2019 to April 2020, using generalized estimating equations to model the likelihood of a serious illness conversation across clinic time. Results Documentation rate decreased from 2.1 to 1.5% in the morning clinic session (8am-12pm) and from 1.2% to .9% in the afternoon clinic session (1pm-4pm). Adjusted odds ratios for Serious illness conversations documentation rates were significantly lower for all hours of each session after the earliest hour (adjusted odds ratios .91 [95% CI, .84-.97], P = .006 for overall linear trend). Conclusions Serious illness conversations between oncologists and patients decrease considerably through the clinic day, and proactive strategies to avoid missed conversations should be investigated

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