A Semiautomated Proteomics and Phosphoproteomics Protocol for the Identification of Novel Therapeutic Targets and Predictive Biomarkers in In Vivo Xenograft Models of Pediatric Cancers
Methods in Molecular Biology, ISSN: 1940-6029, Vol: 2806, Page: 229-242
2024
- 1Citations
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
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- Citations1
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Book Chapter Description
Genomic profiling has identified therapeutic targets for precision treatment of certain cancers, but many patients lack actionable mutations. Additional omics approaches, like proteomics and phosphoproteomics, are essential for comprehensive mapping of cancer-associated molecular phenotypes. In vivo models, such as cell line and patient-derived xenografts (PDX), offer valuable insights into cancer biology and treatment strategies. This chapter presents a semiautomated high-throughput workflow for integrated proteomics and phosphoproteomics analysis on the Kingfish platform coupled with MagReSynZr-IMAC HP. It enhances protein extraction from in vivo xenograft samples and provides better insights into cancers with poor prognosis. The approach successfully identified over 11,000 unique phosphosites and ~6000 proteins in SJSA-1 pediatric osteosarcoma xenografts, demonstrating its efficacy. This workflow is a valuable tool for studying tumor biology and developing precision oncology strategies.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85191622851&origin=inward; http://dx.doi.org/10.1007/978-1-0716-3858-3_17; http://www.ncbi.nlm.nih.gov/pubmed/38676807; https://link.springer.com/10.1007/978-1-0716-3858-3_17; https://dx.doi.org/10.1007/978-1-0716-3858-3_17; https://link.springer.com/protocol/10.1007/978-1-0716-3858-3_17
Springer Science and Business Media LLC
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