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Abstract Targeting the estrogen receptor alpha (ERα) pathway is validated in the clinic as an effective means to treat ER+ breast cancers. Here we present the development of a VHL-targeting and orally bioavailable proteolysis-targeting chimera (PROTAC) degrader of ERα. In vitro studies with this PROTAC demonstrate excellent ERα degradation and ER antagonism in ER+ breast cancer cell lines. However, upon dosing the compound in vivo we observe an in vitro-in vivo disconnect. ERα degradation is lower in vivo than expected based on the in vitro data. Investigation into potential causes for the reduced maximal degradation reveals that metabolic instability of the PROTAC linker generates metabolites that compete for binding to ERα with the full PROTAC, limiting degradation. This observation highlights the requirement for metabolically stable PROTACs to ensure maximal efficacy and thus optimisation of the linker should be a key consideration when designing PROTACs.

The presence of dark melanin (eumelanin) within human epidermis represents one of the strongest predictors of low skin cancer risk. Topical rescue of eumelanin synthesis, previously achieved in “redhaired” Mc1r-deficient mice, demonstrated significant protection against UV damage. However, application of a topical strategy for human skin pigmentation has not been achieved, largely due to the greater barrier function of human epidermis. Salt-inducible kinase (SIK) has been demonstrated to regulate MITF, the master regulator of pigment gene expression, through its effects on CRTC and CREB activity. Here, we describe the development of small-molecule SIK inhibitors that were optimized for human skin penetration, resulting in MITF upregulation and induction of melanogenesis. When topically applied, pigment production was induced in Mc1r-deficient mice and normal human skin. These findings demonstrate a realistic pathway toward UV-independent topical modulation of human skin pigmentation, potentially impacting UV protection and skin cancer risk.

Summary: Immune checkpoint inhibitors have improved patient survival in melanoma, but the innate resistance of many patients necessitates the investigation of alternative immune targets. Many immune checkpoint proteins lack proper characterization, including V-domain Ig suppressor of T cell activation (VISTA). VISTA expression on immune cells can suppress T cell activity; however, few studies have investigated its expression and regulation in cancer cells. In this study, we observe that VISTA is expressed in melanoma patient samples and cell lines. Tumor cell-specific expression of VISTA promotes tumor onset in vivo, associated with increased intratumoral T regulatory cells, and enhanced PDL-1 expression on tumor-infiltrating macrophages. VISTA transcript levels are regulated by the stemness factor Forkhead box D3 (FOXD3). BRAF inhibition upregulates FOXD3 and reduces VISTA expression. Overall, this study demonstrates melanoma cell expression of VISTA and its regulation by FOXD3, contributing to the rationale for therapeutic strategies that combine targeted inhibitors with immune checkpoint blockade. : VISTA is an understudied immune checkpoint protein. Through the analysis of patient samples and studies in mouse models, Rosenbaum et al. investigate the functional consequences of VISTA expression on melanoma cells. Furthermore, they demonstrate that the BRAF-regulated transcription factor FOXD3 negatively regulates VISTA expression. Keywords: VISTA, PD-1H, Dies1, immune checkpoint, FOXD3, DD1α, VSIR

The use of linear mixed effects models (LMMs) is increasingly common in the analysis of biological data. Whilst LMMs offer a flexible approach to modelling a broad range of data types, ecological data are often complex and require complex model structures, and the fitting and interpretation of such models is not always straightforward. The ability to achieve robust biological inference requires that practitioners know how and when to apply these tools. Here, we provide a general overview of current methods for the application of LMMs to biological data, and highlight the typical pitfalls that can be encountered in the statistical modelling process. We tackle several issues regarding methods of model selection, with particular reference to the use of information theory and multi-model inference in ecology. We offer practical solutions and direct the reader to key references that provide further technical detail for those seeking a deeper understanding. This overview should serve as a widely accessible code of best practice for applying LMMs to complex biological problems and model structures, and in doing so improve the robustness of conclusions drawn from studies investigating ecological and evolutionary questions.

Melanoma is an invasive malignancy with a high frequency of blood-borne metastases, but circulating tumor cells (CTCs) have not been readily isolated. We adapted microfluidic CTC capture to a tamoxifen-driven B-RAF/PTEN mouse melanoma model. CTCs were detected in all tumor-bearing mice and rapidly declined after B-RAF inhibitor treatment. CTCs were shed early from localized tumors, and a short course of B-RAF inhibition following surgical resection was sufficient to dramatically suppress distant metastases. The large number of CTCs in melanoma-bearing mice enabled a comparison of RNA-sequencing profiles with matched primary tumors. A mouse melanoma CTC-derived signature correlated with invasiveness and cellular motility in human melanoma. CTCs were detected in smaller numbers in patients with metastatic melanoma and declined with successful B-RAF-targeted therapy. Together, the capture and molecular characterization of CTCs provide insight into the hematogenous spread of melanoma.