Transcriptional profiling of patient tumors is a much-heralded advancement in cancer therapy, as it provides the opportunity to identify patients who would benefit from more or less aggressive therapy and thus allows the development of individualized treatment. However, translation of this promise into patient benefit has proven challenging. In this issue of the JCI, Glinsky and colleagues used human and murine models to identify a potential stem cell mRNA signature, based on the hypothesis that tumors with stem cell–like characteristics are likely to have a poor prognosis. Remarkably, an 11-gene “expression signature” associated with “stem cell–ness” separated patients with different cancers into good- and poor-prognosis groups. Such a “magic marker” would, if validated, have a major impact on patient care. However, there remain challenges incumbent with creating and validating such signatures.
John P. Lahad, Gordon B. Mills, Kevin R. Coombes
Usage data is cumulative from January 2024 through January 2025.
Usage | JCI | PMC |
---|---|---|
Text version | 455 | 53 |
121 | 25 | |
Figure | 63 | 3 |
Table | 78 | 0 |
Citation downloads | 58 | 0 |
Totals | 775 | 81 |
Total Views | 856 |
Usage information is collected from two different sources: this site (JCI) and Pubmed Central (PMC). JCI information (compiled daily) shows human readership based on methods we employ to screen out robotic usage. PMC information (aggregated monthly) is also similarly screened of robotic usage.
Various methods are used to distinguish robotic usage. For example, Google automatically scans articles to add to its search index and identifies itself as robotic; other services might not clearly identify themselves as robotic, or they are new or unknown as robotic. Because this activity can be misinterpreted as human readership, data may be re-processed periodically to reflect an improved understanding of robotic activity. Because of these factors, readers should consider usage information illustrative but subject to change.