Multiple mechanisms have been described that confer BRAF inhibitor resistance to melanomas, yet the basis of this resistance remains undefined in a sizable portion of patient samples. Here, we characterized samples from a set of patients with melanoma that included individuals at baseline diagnosis, on BRAF inhibitor treatment, and with resistant tumors at both the protein and RNA levels. Using RNA and DNA sequencing, we identified known resistance-conferring mutations in 50% (6 of 12) of the resistant samples. In parallel, targeted proteomic analysis by protein array categorized the resistant samples into 3 stable groups, 2 of which were characterized by reactivation of MAPK signaling to different levels and 1 that was MAPK independent. The molecular relevance of these classifications identified in patients was supported by both mutation data and the similarity of resistance patterns that emerged during a co-clinical trial in a genetically engineered mouse (GEM) model of melanoma that recapitulates the development of BRAF inhibitor resistance. Additionally, we defined candidate biomarkers in pre- and early-treatment patient samples that have potential for predicting clinical responses. On the basis of these observations, we suggest that BRAF inhibitor–resistant melanomas can be actionably classified using protein expression patterns, even without identification of the underlying genetic alteration.
Lawrence N. Kwong, Genevieve M. Boland, Dennie T. Frederick, Timothy L. Helms, Ahmad T. Akid, John P. Miller, Shan Jiang, Zachary A. Cooper, Xingzhi Song, Sahil Seth, Jennifer Kamara, Alexei Protopopov, Gordon B. Mills, Keith T. Flaherty, Jennifer A. Wargo, Lynda Chin
Usage data is cumulative from December 2018 through December 2019.
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.