Showing papers by "Stuart S. Levine published in 2022"
TL;DR: In this paper , the authors leverage a bioengineered human microliver platform to culture patient-derived Plasmodium vivax parasites for transcriptional profiling and find that infection suppresses the transcription of key hepatocyte function genes and elicits an antiparasite innate immune response.
19 citations
TL;DR: Findings identify a potential enhancer-based biomarker of resistance to anti-PD-1 and suggest enhancer blockade in combination with ICB as a potential strategy to improve responses.
Abstract: Immune checkpoint blockade (ICB) therapy has improved long-term survival for patients with advanced melanoma. However, there is critical need to identify potential biomarkers of response and actionable strategies to improve response rates. Through generation and analysis of 148 chromatin modification maps for 36 melanoma samples from patients treated with anti-PD- 1, we identified significant enrichment of active enhancer states in non-responders at baseline. Analysis of an independent cohort of 20 samples identified a set of 437 enhancers that predicted response to anti-PD-1 therapy (Area Under the Curve of 0.8417). The activated non-responder enhancers marked a group of key regulators of several pathways in melanoma cells (including c- MET, TGFβ, EMT and AKT) that are known to mediate resistance to ICB therapy and several checkpoint receptors in T cells. Epigenetic editing experiments implicated involvement of c-MET enhancers in the modulation of immune response. Finally, inhibition of enhancers and repression of these pathways using bromodomain inhibitors along with anti-PD-1 therapy significantly decreased melanoma tumor burden and increased T-cell infiltration. Together, these findings identify a potential enhancer-based biomarker of resistance to anti-PD-1 and suggest enhancer blockade in combination with ICB as a potential strategy to improve responses.
1 citations
TL;DR: A map of tumor developmental origins, provide a tool for diagnostic pathology, and suggest developmental classification may be a useful approach for otherwise unclassified patient tumors are provided.
Abstract:
Cancer is a disease manifesting in abrogation of developmental programs, and malignancies are namedbased on their cell or tissue of origin. However, a systematic atlas of tumor origins is lacking. Here we map the single cell organogenesis of 56 developmental trajectories to the transcriptomes of over 10,000 tumors across 33 cancer types. We use this map to deconvolute individual tumors into their constituent developmental components. Based on these deconvoluted developmental programs, we construct a Developmental Machine Learning Perceptron (D-MLP) classifier that outputs cancer origin. The D-MLP classifier (ROC-AUC: 0.974 for top prediction) outperforms classification based on expression of either oncogenes or highly variable genes. We analyze tumors from patients with cancer of unknown primary (CUP), selecting the most difficult cases where extensive multimodal workup yielded no definitive tumor type. D-MLP revealed insights into developmental origins and diagnosis for most patient tumors. Our results provide a map of tumor developmental origins, provide a tool for diagnostic pathology, and suggest developmental classification may be a useful approach for otherwise unclassified patient tumors.
Citation Format: Enrico Moiso, Alexander Farahani, Hetal Marble, Austin Hendricks, Samuel Mildrum, Stuart Levine, Jochen Lennerz, Salil Garg. Developmental deconvolution for classification of cancer origin [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 4100.
TL;DR: A modified wrapper around the SEEK platform is created that allows for active data management by establishing more discrete sample types that are mutable to permit the expansion of the types of metadata, allowing researchers to track additional information.
Abstract: Data management is a critical challenge required to improve the rigor and reproducibility of large projects. Adhering to Findable, Accessible, Interoperable, and Reusable (FAIR) standards provides a baseline for meeting these requirements. Although many existing repositories handle data in a FAIR-compliant manner, there are limited tools in the public domain to handle the metadata burden required to connect data from multi-omic projects that span multiple institutions and are deposited in diverse repositories. One promising approach is the SEEK platform, which allows for diverse metadata and provides an established repository. SEEK is challenged by the assumption of single deposition events where a sample is immutable once entered in the database. This is structured for published data but presents a limitation for ongoing studies where multiple sequential events may occur in a single sample at different sites. To address this issue, we have created a modified wrapper around the SEEK platform that allows for active data management by establishing more discrete sample types that are mutable to permit the expansion of the types of metadata, allowing researchers to track additional information. The use of discrete nodes also converts assays from nodes to edges, creating a network model of the study and more accurately representing the experimental process. With these changes to SEEK, users are able to collect and organize the information that researchers need to improve reusability and reproducibility as well as make data and metadata available to the scientific community through public repositories.