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Biology subjects

Mazor, T.

Publications and source records attributed to Mazor, T..

3 recordsLinked to original sources

Helical charge distribution at the transmembrane-luminal interface determines subcellular localization

Transmembrane protein localization is typically dictated by short cytosolic tail sequences, whereas luminal domain contribution remains less understood. Here, we identify a short luminal juxtamembrane peptide as a key determinant of subcellular localization for Class I -1,2 mannosidases--ERManI, ManIA, ManIB, and ManIC--members of the glycoside hydrolase 47 family involved in N-glycoprotein processing. We previously showed that ERManI and ManIA localize to specialized quality control vesicles (QCVs); we now find that ManIB also partially localizes to these vesicles, whereas ManIC is predominantly Golgi-localized. ERManI, ManIA, and ManIB share a conserved luminal juxtamembrane distribution of charged residues, which diverges in ManIC. Structural predictions suggest that this region maintains an -helical conformation, with the charge pattern oriented on one face. Site-directed mutagenesis that disrupted this charge pattern or altered its helical register (via alanine insertions) shifted localization between QCVs and the Golgi. Moreover, grafting this peptide onto an unrelated transmembrane protein, {beta}-1,3-galactosyltransferase, redirected it from the Golgi to QCVs. These findings suggest that a specific three-dimensional charge pattern at the transmembrane-luminal interface serves as a localization signal. Unlike canonical linear motifs, this mechanism relies on the structural arrangement, revealing a previously unrecognized mode of organelle targeting within the secretory pathway.

cell biology↗

Clarifying the Transcriptional Profiles of Malignant Clones through Multiscale and Multiomic Analysis of Individual Tumors

Tumors may contain billions of cells including distinct malignant clones and nonmalignant cell types. Clarifying the evolutionary histories, prevalence, and defining molecular features of these cells is essential for improving clinical outcomes, since intratumoral heterogeneity provides fuel for acquired resistance to targeted therapies. Here we present a statistically motivated strategy for deconstructing intratumoral heterogeneity through multiomic and multiscale analysis of serial tumor sections (MOMA). By combining deep sampling of IDH-mutant astrocytomas with integrative analysis of single-nucleotide variants, copy-number variants, and gene expression, we reconstruct and validate the phylogenies, spatial distributions, and transcriptional profiles of distinct malignant clones. By genotyping nuclei analyzed by single-nucleus RNA-seq for truncal mutations, we further show that commonly used algorithms for identifying cancer cells from single-cell transcriptomes may be inaccurate. We also demonstrate that correlating gene expression with tumor purity in bulk samples can reveal optimal markers of malignant cells and use this approach to identify a core set of genes that is consistently expressed by astrocytoma truncal clones, including AKR1C3, whose expression is associated with poor outcomes in several types of cancer. In summary, MOMA provides a robust and flexible strategy for precisely deconstructing intratumoral heterogeneity and clarifying the core molecular properties of distinct cellular populations in solid tumors.

cancer biology↗

MatchMiner: An open source computational platform for real-time matching of cancer patients to precision medicine clinical trials using genomic and clinical criteria

BackgroundMolecular profiling of cancers is now routine at many cancer centers, and the number of precision cancer medicine clinical trials, which are informed by profiling, is steadily rising. Additionally, these trials are becoming increasingly complex, often having multiple arms and many genomic eligibility criteria. Currently, it is a challenging for physicians to match patients to relevant clinical trials using the patients genomic profile, which can lead to missed opportunities. Automated matching against uniformly structured and encoded genomic eligibility criteria is essential to keep pace with the complex landscape of precision medicine clinical trials.\n\nResultsTo meet these needs, we built and deployed an automated clinical trial matching platform called MatchMiner at the Dana-Farber Cancer Institute (DFCI). The platform has been integrated with Profile, DFCIs enterprise genomic profiling project, which contains tumor profile data for >20,000 patients, and has been made available to physicians across the Institute. As no current standard exists for encoding clinical trial eligibility criteria, a new language called Clinical Trial Markup Language (CTML) was developed, and over 178 genomically-driven clinical trials were encoded using this language. The platform is open source and freely available for adoption by other institutions.\n\nConclusionMatchMiner is the first open platform developed to enable computational matching of patient-specific genomic profiles to precision cancer medicine clinical trials. Creating MatchMiner required developing clinical trial eligibility standards to support genome-driven matching and developing intuitive interfaces to support practical use-cases. Given the complexity of tumor profiling and the rapidly changing multi-site nature of genome-driven clinical trials, open source software is the most efficient, scalable, and economical option for matching cancer patients to clinical trials.

bioinformatics↗