Using machine learning to translate tumor dependencies
Author:
Publisher
Springer Science and Business Media LLC
Link
https://www.nature.com/articles/s43018-024-00790-5.pdf
Reference5 articles.
1. Gao, J. et al. Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci. Signal. 6, pl1 (2013). This paper provides an overview and practical guide to clinical cancer genomics datasets available through the cBioPortal.
2. Ostroverkhova, D., Przytycka, T. M. & Panchenko, A. R. Cancer driver mutations: predictions and reality. Trends Mol. Med. 7, 554–566 (2023). This review article describes the state of the art and current limitations of identifying and characterizing driver mutations in patient tumors.
3. Boehm, J. S. et al. Cancer research needs a better map. Nature 589, 524–526 (2021). This commentary describes the cell-based cancer dependency map (DEPMAP).
4. Han, K. et al. CRISPR screens in cancer spheroids identify 3D growth-specific vulnerabilities. Nature 580, 136–141 (2020). This study demonstrates that some cancer vulnerabilities are more prominent in three-dimensional spheroids than in two-dimensional monolayers.
5. Martinez-Ruiz, C. et al. Genomic-transcriptomic evolution in lung cancer and metastasis. Nature 616, 543–552 (2023). This landmark work by the TRACERx Consortium deeply characterized the genomic and transcriptomic evolution of human tumors, highlighting the complexity of intratumor heterogeneity that should be accounted for in future translational dependency maps.
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