We show that elevated levels of Ret receptor are found in different sub-types of human breast cancers and that high Ret correlates with decreased metastasis-free survival. The role of Ret in ER+ breast cancer models was explored combining in vitro and in vivo approaches. Our analyses revealed that ligand-induced Ret activation: (i) stimulates migration of breast cancer cells; (ii) rescues cells from anti-proliferative effects of endocrine treatment and (iii) stimulates expression of cytokines in the presence of endocrine agents. Indeed, we uncovered a positive feed-forward loop between the inflammatory cytokine IL6 and Ret that links them at the expression and the functional level. In vivo inhibition of Ret in a metastatic breast cancer model inhibits tumour outgrowth and metastatic potential. Ret inhibition blocks the feed-forward loop by down-regulating Ret levels, as well as decreasing activity of Fak, an integrator of IL6-Ret signalling. Our results suggest that Ret kinase should be considered as a novel therapeutic target in subsets of breast cancer.
SummaryBRAF and MEK inhibitors are effective in BRAF mutant melanoma, but most patients eventually relapse with acquired resistance, and others present intrinsic resistance to these drugs. Resistance is often mediated by pathway reactivation through receptor tyrosine kinase (RTK)/SRC-family kinase (SFK) signaling or mutant NRAS, which drive paradoxical reactivation of the pathway. We describe pan-RAF inhibitors (CCT196969, CCT241161) that also inhibit SFKs. These compounds do not drive paradoxical pathway activation and inhibit MEK/ERK in BRAF and NRAS mutant melanoma. They inhibit melanoma cells and patient-derived xenografts that are resistant to BRAF and BRAF/MEK inhibitors. Thus, paradox-breaking pan-RAF inhibitors that also inhibit SFKs could provide first-line treatment for BRAF and NRAS mutant melanomas and second-line treatment for patients who develop resistance.
Purpose: This study sought to explore the predictive value of the insulin-like growth factor (IGF) binding proteins (IGFBP) as markers of response in ovarian cancer patients treated with the aromatase inhibitor letrozole. Experimental Design: IGFBP mRNA expression in cell lines was measured by quantitative reverse transcription-PCR and IGFBP protein expression measured in sections from primary tumors of patients treated with letrozole by semiquantitative immunohistochemistry. Results: Quantitative reverse transcription-PCR analysis showed that IGFBP3 and IGFBP5 were down-regulated and IGFBP4 was up-regulated by 17h-estradiol (E 2 ) in an estrogen receptor (ER)^positive ovarian cancer cell line. Expressions of IGFBP1, IGFBP2, and IGFBP6 were unaffected by E 2 . The E 2 modulation of these genes was reversed by tamoxifen. Using ERa-specific (propyl pyrazole triol) and ERh-specific (diarylpropionitrile) agonists, the gene expression modulations produced by E 2 could be replicated by propyl pyrazole triol but not by diarylpropionitrile. For ovarian cancer patients being treated with letrozole, we tested the predictive value of the IGFBPs in paraffin-fixed sections from their primary tumors by semiquantitative immunohistochemistry. Using serum CA125 as an indicator of progression/response, significant differences in expression levels of IGFBPs were observed between tumors from CA125 responding/stable patients compared with tumors from progressing patients. Mean immunoscores for IGFBP3 and IGFBP5 were significantly lower, and mean expression of IGFBP4 was significantly higher in tumors from patients demonstrating CA125 response or stabilization compared with CA125 progression. Conclusion: These results indicate that expression levels of certain IGFBP family members in ovarian cancers are estrogen regulated and can, thus, help identify patients who could benefit from endocrine therapy.
Understanding cell state transitions and purposefully controlling them is a longstanding challenge in biology. Here, we present cell State Transition Assessment and Regulation (cSTAR), an approach to map cell states, model transitions between them, and predict targeted interventions to convert cell fate decisions. cSTAR uses omics data as input, classifies cell states, and develops a workflow that transforms the input data into mechanistic models that identify a core signaling network, which controls cell fate transitions by influencing whole-cell networks. By integrating signaling and phenotypic data, cSTAR models how cells maneuver in Waddington's landscape 1 and make decisions about which cell fate to adopt. Importantly, cSTAR devises interventions to control the movement of cells in Waddington's landscape. Testing cSTAR in a cellular model of differentiation and proliferation shows a high correlation between quantitative predictions and experimental data. Applying cSTAR to different types of perturbation and omics datasets including single cell data demonstrates its flexibility and scalability and provides new biological insights. The ability of cSTAR to identify targeted perturbations that interconvert cell fates will allow designer approaches for manipulating cellular development pathways and mechanistically underpinned therapeutic interventions.The concept of cell states is a useful lens to view and understand the organization of tissues and organisms, their development, and responses to exogenous and endogenous changes. While initially based on phenotypical descriptions, global analysis methods now can connect phenotypes with underlying molecular processes. These methods characterize cell states with fine molecular resolution and open the door to understand how cell states can evolve and transition into each other. In 1940, Waddington suggested that cells move through a landscape of mountains and valleys as rolling marbles from one (meta)stable state to another 1 . This now famous model appeals through its intuitive nature but leaves open why the marbles roll into certain valleys and whether they can revert to an initial state. Recent efforts have applied computational models to understand cell state transitions, generated by
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