Mott, K. R. | Search bioRxiv

bioRxiv2026

Vulnerability of triple negative breast cancer to daraxonrasib pan-RAS(on) inhibition

Triple-Negative Breast Cancer (TNBC) is a clinical subtype where aberrant expression of receptor tyrosine kinases (RTKs) leads to the activation of the RAS-RAF-MEK-ERK pathway contributing to tumor progression. Therapies targeting components of the RAF-MEK-ERK pathway in TNBC have been disappointing due to rapid onset of adaptive reprogramming of the RTK landscape leading to pathway reactivation. To overcome the dynamic heterogeneity of RTK expression and their convergence in activating RAS, treatment with the pan-RAS(on) inhibitor daraxonrasib (RMC-6236), which inhibits RAS-GTP regulation of RAF kinases, inhibited proliferation independent of TNBC kinotype or oncogenic RAS mutation in three of four TNBC cell lines in vitro and two patient-derived TNBC xenografts in mice. Daraxonrasib in combination with the MEK inhibitor trametinib prevented reactivation of the RAF-MEK-ERK pathway observed with each inhibitor alone and further enhanced inhibition of tumor growth in TNBC cell lines and xenografts. Inhibiting pan-RAS(on) RAS-GTP, with daraxonrasib, alone or in combination with a MEK inhibitor, provides a new strategy for the treatment of TNBC that inhibits RTK activation of the RAS-RAF-MEK-ERK pathway and tumor proliferation.

cancer biology↗

bioRxiv2026

Transcriptomic profiling of mouse mammary tumors enables prognostic and predictive biomarker discovery for human breast cancers

The development and validation of prognostic and predictive biomarkers in breast cancer is limited by the availability of well-annotated datasets linking tumor molecular features to treatment response and survival outcomes. To address this need, we generated an extensive mouse models dataset comprised of 26 immunocompetent mammary tumor models spanning diverse genetic backgrounds, epithelial-mesenchymal states, the basal-luminal axis, and distinct immune microenvironments. For each model, we measured survival under no treatment, immune checkpoint inhibition (ICI), and carboplatin/paclitaxel chemotherapy. We performed RNA-seq on baseline tumors and on 7-day on-treatment samples for both regimens. Using baseline murine tumor gene expression features, we trained a machine learning Elastic Net model that predicted survival outcomes on multiple human breast cancer datasets with performance comparable to that of existing prognostic assays. We next trained models for ICI benefit, using either the untreated or 7-day ICI treated samples; both models predicted ICI benefit on human ICI treated datasets, with the 7-day treated tumor model showing better performance. We also developed a predictor of carboplatin/paclitaxel response that performed well in mice but did not generalize to human chemotherapy cohorts. Finally, we compared multiple computational approaches, including XGBoost, random forests, and support vector regression; all methods successfully predicted survival outcomes, with Elastic Net offering the best performance and interpretability. These results indicate conserved cancer biology between mouse and human tumors for prognosis and ICI response and establish this large preclinical dataset with linked phenotypic and genomic data, as a resource for benchmarking computational methods for survival prediction. SignificanceThe development of a genomically and phenotypically diverse murine tumor dataset with linked treatment outcomes establishes a robust translational resource to develop, test, and benchmark clinically relevant prognostic and therapeutic response biomarkers.

cancer biology↗