The ubiquitin-proteasome system is the major degradation pathway for short-lived proteins in eukaryotic cells. Targets of the ubiquitin-proteasome-system are proteins regulating a broad range of cellular processes including cell cycle progression, gene expression, the quality control of proteostasis and the response to geno- and proteotoxic stress. Prior to degradation, the proteasomal substrate is marked with a poly-ubiquitin chain. The key protease of the ubiquitin system is the proteasome. In dividing cells, proteasomes exist as holo-enzymes composed of regulatory and core particles. The regulatory complex confers ubiquitin-recognition and ATP dependence on proteasomal protein degradation. The catalytic sites are located in the proteasome core particle. Proteasome holo-enzymes are predominantly nuclear suggesting a major requirement for proteasomal proteolysis in the nucleus. In cell cycle arrested mammalian or quiescent yeast cells, proteasomes deplete from the nucleus and accumulate in granules at the nuclear envelope (NE) / endoplasmic reticulum (ER) membranes. In prolonged quiescence, proteasome granules drop off the NE / ER membranes and migrate as stable organelles throughout the cytoplasm, as thoroughly investigated in yeast. When quiescence yeast cells are allowed to resume growth, proteasome granules clear and proteasomes are rapidly imported into the nucleus.Here, we summarize our knowledge about the enigmatic structure of proteasome storage granules and the trafficking of proteasomes and their substrates between the cyto- and nucleoplasm.Most of our current knowledge is based on studies in yeast. Their translation to mammalian cells promises to provide keen insight into protein degradation in non-dividing cells which comprise the majority of our body’s cells.

The ubiquitin-proteasome system is the major degradation pathway for short-lived proteins in eukaryotic cells. Targets of the ubiquitin-proteasome-system are proteins regulating a broad range of cellular processes including cell cycle progression, gene expression, the quality control of proteostasis and the response to geno- and proteotoxic stress. Prior to degradation, the proteasomal substrate is marked with a poly-ubiquitin chain. The key protease of the ubiquitin system is the proteasome. In dividing cells, proteasomes exist as holo-enzymes composed of regulatory and core particles. The regulatory complex confers ubiquitin-recognition and ATP dependence on proteasomal protein degradation. The catalytic sites are located in the proteasome core particle. Proteasome holo-enzymes are predominantly nuclear suggesting a major requirement for proteasomal proteolysis in the nucleus. In cell cycle arrested mammalian or quiescent yeast cells, proteasomes deplete from the nucleus and accumulate in granules at the nuclear envelope (NE) / endoplasmic reticulum (ER) membranes. In prolonged quiescence, proteasome granules drop off the NE / ER membranes and migrate as stable organelles throughout the cytoplasm, as thoroughly investigated in yeast. When quiescence yeast cells are allowed to resume growth, proteasome granules clear and proteasomes are rapidly imported into the nucleus. Here, we summarize our knowledge about the enigmatic structure of proteasome storage granules and the trafficking of proteasomes and their substrates between the cyto- and nucleoplasm. Most of our current knowledge is based on studies in yeast. Their translation to mammalian cells promises to provide keen insight into protein degradation in non-dividing cells which comprise the majority of our body's cells.

No abstract

Background: The widespread uptake of electronic health records (EHRs) has made the creation of custom, real-world datasets for research more feasible. As a result, multiple research datasets with overlapping populations are often generated, using different methodologies, and frequently siloed within and between research groups, limiting the scope of the data’s use. Currently, there is no standard for collating and evaluating such data. Using existing lung oncology datasets, we developed an approach to determine optimal methods of combining and curating clinical data from different sources. Methods: Two separate study datasets containing data for lung cancer patients diagnosed and/or treated within Princess Margaret Cancer Centre (PM, Toronto) were investigated. Study 1 manually abstracted clinical data for 1,990 patients, first seen at PM between 2014-2016; Study 2 leveraged the artificial intelligence engine, DARWEN™, to extract clinical data directly from EHRs for 4,466 patients, diagnosed between 2014-2018. Each dataset was individually assessed for internal consistency before comparing the overlapping population (Test Group, n=1892) to identify, investigate, and resolve differences. Patterns of data extraction performance were evaluated to define optimal methods for combining datasets and informing future data collection. Herein, epidermal growth factor receptor (EGFR) mutation status is used as an illustrative example. Results: Study 1 and 2 had similar distributions of clinicodemographic data and frequency of EGFR mutations. The Test Group had 100% agreement for date of birth, and >99% agreement for sex, with all discrepancies resulting from human error in Study 1. The Test Group had a 98% agreement for EGFR positivity and 98-99% agreement for specific exon mutations. Of the 106 disagreements for specific mutations, 50% (n=53) were due to Study 1 human error. Study 2 prioritized specificity over sensitivity for biomarker extraction, resulting in more false negatives (25% of errors, n=26). As DARWEN™ only extracted EGFR data from pathology reports, 18% (n=19) of discrepancies were due to lack of access to relevant information captured elsewhere in patients’ EHRs. Adjudicators could not resolve the remaining 7% of disagreements (n=8). Conclusions: By comparing overlapping datasets, the strengths and weaknesses of each study design and extraction methodology were identified. This process demonstrated the effectiveness of artificial intelligence for extracting accurate patient-level clinicodemographic and mutation status data from EHRs, and the value of targeted manual chart review. Our approach provides a roadmap for leveraging existing clinical datasets to their fullest potential, which is relevant across diverse data extraction methods and study designs. Citation Format: Benjamin M. Grant, Aein Zarrin, Luna Zhan, Rami Ajaj, Lina Darwish, Khaleeq Khan, Devalben Patel, Kaitlyn Chiasson, Karmugi Balaratnam, Maisha T. Chowdhury, Amir-Arsalan Sabouhanian, Joshua Herman, Preet Walia, Evan Strom, Catherine Brown, Miguel Garcia-Pardo, Sabine Schmid, Christopher Pettengell, Erin L. Stewart, Geoffrey Liu. Developing a standardized framework for curating oncology datasets generated by manual abstraction and artificial intelligence [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 4087.

No abstract

No abstract

Despite the introduction of a number of new therapeutic classes including targeted inhibitors, immunotherapy, and antibody-drug conjugates, the ability to achieve long-term disease control in the majority of patients with advanced cancer remains elusive due to the emergence of resistance. While our understanding of resistance mechanisms continues to expand through preclinical and clinical sequencing efforts, comprehensive approaches to map the distribution of potential resistance mechanisms in a given cell line and drug context require the development of novel approaches. Here, we describe the design of a platform for profiling cancer resistance at scale. Beginning with a single sensitive parental cancer cell line, the EGFR-mutant lung cancer line PC9, we optimize pre-existing diversity using a combination of single-cell subcloning and alkylator mutagenesis, and generate 50 independently evolved clones resistant to the EGFR inhibitor osimertinib. Profiling of this library via multiplexed whole genome sequencing (WGS) demonstrates convergent mutations in the MAPK pathway. Taking a similar approach, we profile the NTRK1-fusion-driven KM12 colon cancer cell line, yielding convergent, clinically observed, on-target resistance mutations to the NTRK inhibitor larotrectinib. Taken together, these results provide an initial proof-of-concept for the high-throughput generation of viable drug-resistant cancer clones. Future efforts to increase mutational diversity as well as assess additional drug and disease contexts will broaden the utility of this approach. Citation Format: Arvind Ravi, Sainikhil Sontha, Maisha Chowdhury, Jacob Smigiel, Matthew Rees, Brian Danysh, Jennifer Roth, Laxmi Parida, Eric Lander, Gad Getz. A platform for deep evolutionary profiling of cancer resistance [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 143.

Methods: Patients with CT-detected suspected lung cancer were enrolled. The sample collected by bronchial brushing was used for RNA sequencing. The e1071 and pROC packages in R software was applied to build the model.Results: Eventually, a total of 283 patients, including 183 with lung cancer and 100 with benign lesions, were included into final analysis. When incorporating transcriptomic data with radiological characteristics, the advanced model yielded 0.903 AUC with 81.1% NPV. Moreover, the classifier performed well regardless of lesion size, location, stage, histologic type, or smoking status. Pathway analysis showed enhanced epithelial differentiation, tumor metastasis, and impaired immunity were predominant in smokers with cancer, whereas tumorigenesis played a central role in non-smokers with cancer. Apoptosis and oxidative stress contributed critically in metastatic lung cancer; by contrast, immune dysfunction was pivotal in locally advanced lung cancer.Conclusions: Collectively, we devised a minimal-to-noninvasive, efficient diagnostic classifier for smokers and non-smokers with lung cancer, which provides evidence for different mechanisms of cancer development and metastasis associated with smoking. A negative classifier result will help the physician make conservative diagnostic decisions.