Data from Proteogenomic Approaches for the Identification of NF1/Neurofibromin-depleted Estrogen Receptor–positive Breast Cancers for Targeted Treatment
Year: 2023
Type: other
Abstract: NF1 is a key tumor suppressor that represses both RAS and estrogen receptor-α (ER) signaling in breast cancer. Blocking both pathways by fulvestrant (F), a selective ER degrader, together with binimetinib (B), a MEK inhibitor, promotes tumor regression in NF1-depleted ER+ models. We aimed to establish approaches to determine how NF1 protein levels impact B+F treatment response to improve our ability to identify B+F sensitive tumors. We examined a panel of ER+ patient-derived xenograft (PDX) models by DNA and mRNA sequencing and found that more than half of these models carried an NF1 shallow deletion and generally have low mRNA levels. Consistent with RAS and ER activation, RET and MEK levels in NF1-depleted tumors were elevated when profiled by mass spectrometry (MS) after kinase inhibitor bead pulldown. MS showed that NF1 can also directly and selectively bind to palbociclib-conjugated beads, aiding quantification. An IHC assay was also established to measure NF1, but the MS-based approach was more quantitative. Combined IHC and MS analysis defined a threshold of NF1 protein loss in ER+ breast PDX, below which tumors regressed upon treatment with B+F. These results suggest that we now have a MS-verified NF1 IHC assay that can be used for patient selection as a complement to somatic genomic analysis. Significance: A major challenge for targeting the consequence of tumor suppressor disruption is the accurate assessment of protein functional inactivation. NF1 can repress both RAS and ER signaling, and a ComboMATCH trial is underway to treat the patients with binimetinib and fulvestrant. Herein we report a MS-verified NF1 IHC assay that can determine a threshold for NF1 loss to predict treatment response. These approaches may be used to identify and expand the eligible patient population.
Authors: Beom-Jun Kim, Ze-Yi Zheng, Jonathan T. Lei, Matthew V. Holt, Anran Chen, Jianheng Peng, Diana Fandino, Purba Singh, Hilda Kennedy, Yongchao Dou, María del Rosario Chica-Parrado, Emmanuel Bikorimana, Dan Ye, Yunguan Wang, Ariella B. Hanker, Nada Mohamed, Susan G. Hilsenbeck, Bora Lim, Jaya Ruth Asirvatham, Arun Sreekumar, Bing Zhang, George Miles, Meenakshi Anurag, Matthew J. Ellis, Eric C. Chang
Language: English
Subfield: Neurology
Field: Medicine
Domain: Health Sciences
Open Access status: gold
OpenAlex ID: W4383373721