npj Precision Oncol
Multiplex bioimaging of single-cell profiles in patient biopsies and artificial intelligence analysis of spatially distinct patient cohorts inform personalized cancer treatments.
In our research, PBRM1 mutation was found to be a negative predictive biomarker of immunotherapy efficacy in NSCLC. Patients with PBRM1 mutation got less survival benefit from immunotherapy for NSCLC.
The author tells the real story behind the paper - “The PGC-1/ERR Network and Its Role in Precision Oncology”, which was published in npj Precision Oncology
The review introduces the idea of the PGC-1/ERR transcriptional network such as a novel category of metabolic target that could be useful for exploitation in future research in precision oncology. It addresses the concept of therapeutically explore biological capabilities acquired during the development of cancer to improve patient care against therapeutic resistance. What is the potential therapeutic target for cancer metabolism? If it exists, we do believe that targeting the PGC-1/ERR network as a mitochondrial vulnerability of metabolic-addictive cancers is a great opportunity to improve patient care. How to study it? The authors think that studying the PGC-1/ERR protein complex in its native state could shed new light on the mechanism of cancer resistance. As a potential tool, single-particle cryo-electron microscopy (cryo-EM) is urgently needed to develop a high-resolution biological structure to fully elucidate the function and molecular biology of the PGC-1/ERR network. In parallel, it would provide the identification of novel binding partners in signal transduction cascades that might have clinical relevance to improve patient care against therapeutic resistance. Here, the author tells the real story behind the paper, explaining what did motivate the authors to write this review? What is the biggest challenge to consider in the field of cryo-electron microscopy? And what next for further exacerbate our hope to improve patient care.
The paper introduces AI and its applications in precision oncology. It discusses major advances and challenges beyond pattern recognition and classification tasks. It argues in favor of wider innovative uses of AI for bringing benefits to patients. In this post, the author talks about the motivation behind the paper, important topics not covered in it and the excitement of doing research in this field.
In oncology research, the CRISPR technology provides a very powerful tool to support the investigation of the relationships between genes and phenotypes.