New Tool K2Taxonomer Outperforms Existing Methods in Single-Cell Cancer Analysis
Scientists have developed a powerful tool called K2Taxonomer, which outperforms existing methods in analyzing single-cell data. Published in Nucleic Acals Research, the study shows that K2Taxonomer can identify key pathways in head and neck cancer, with implications for breast cancer survival. The tool is publicly available and supported by major health foundations.
K2Taxonomer, a new computational methodology, enables automated discovery and annotation of molecular classifications from high-throughput bulk and single-cell 'omics' data. Researchers from leading cancer institutes and universities have applied this method to single-cell transcriptome data of head and neck cancer, identifying key signaling pathways like PI3K/AKT and EGFR, which influence tumor aggressiveness and responsiveness to therapy.
In breast cancer, activation of the identified signature is associated with better patient survival. A case study using K2Taxonomer on breast tumor-infiltrating lymphocytes (TILs) transcriptome characterized a common transcriptional signature across multiple immune T cell subsets. The tool can analyze other tumor components, including malignant cells, stroma, and cancer-associated adipocytes.
K2Taxonomer, publicly available at https://github.com/montilab/K2Taxonomer, has shown high accuracy and superior performance in both simulated and real data. Supported by major health foundations, this tool offers new insights into cancer research, with potential applications in various tumor components and types.
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