Seventy percent of breast cancers express estrogen receptor (ER) and most of these are sensitive to ER inhibition. However, many such tumors become refractory to inhibition of estrogen action in the metastatic setting for unknown reasons. We conducted a comprehensive genetic analysis of two independent cohorts of metastatic ER+ breast tumors and identified mutations in the ligand binding domain (LBD) of ESR1 in 14/80 cases. These included highly recurrent mutations p.Tyr537Ser/Asn and p.Asp538Gly. Molecular dynamics simulations suggest the Tyr537Ser and Asp538Gly structures lead to hydrogen bonding of the mutant amino acid with Asp351, thus favoring the receptor’s agonist conformation. Consistent with this model, mutant receptors drive ER-dependent transcription and proliferation in the absence of hormone and reduce the efficacy of ER antagonists. These data implicate LBD mutant forms of ER in mediating clinical resistance to hormonal therapy and suggest that more potent ER antagonists may have significant therapeutic benefit.

Circulating tumor cells (CTCs) seed cancer metastases; however, the underlying cellular and molecular mechanisms remain unclear. CTC clusters were less frequently detected but more metastatic than single CTCs of triple negative breast cancer patients and representative patient-derived-xenograft (PDX) models. Using intravital multiphoton microscopic imaging, we found that clustered tumor cells in migration and circulation resulted from aggregation of individual tumor cells rather than collective migration and cohesive shedding. Aggregated tumor cells exhibited enriched expression of the breast cancer stem cell marker CD44 and promoted tumorigenesis and polyclonal metastasis. Depletion of CD44 effectively prevented tumor cell aggregation and decreased PAK2 levels. The intercellular CD44-CD44 homophilic interactions directed multicellular aggregation, requiring its N-terminal domain, and initiated CD44-PAK2 interactions for further activation of FAK signaling. Our studies highlight that CD44+ CTC clusters, whose presence is correlated with a poor prognosis of breast cancer patients, can serve as novel therapeutic targets of polyclonal metastasis.

Motivation Drug discovery demands rapid quantification of compound–protein interaction (CPI). However, there is a lack of methods that can predict compound–protein affinity from sequences alone with high applicability, accuracy and interpretability. Results We present a seamless integration of domain knowledges and learning-based approaches. Under novel representations of structurally annotated protein sequences, a semi-supervised deep learning model that unifies recurrent and convolutional neural networks has been proposed to exploit both unlabeled and labeled data, for jointly encoding molecular representations and predicting affinities. Our representations and models outperform conventional options in achieving relative error in IC50 within 5-fold for test cases and 20-fold for protein classes not included for training. Performances for new protein classes with few labeled data are further improved by transfer learning. Furthermore, separate and joint attention mechanisms are developed and embedded to our model to add to its interpretability, as illustrated in case studies for predicting and explaining selective drug–target interactions. Lastly, alternative representations using protein sequences or compound graphs and a unified RNN/GCNN-CNN model using graph CNN (GCNN) are also explored to reveal algorithmic challenges ahead. Availability and implementation Data and source codes are available at https://github.com/Shen-Lab/DeepAffinity. Supplementary information Supplementary data are available at Bioinformatics online.

The second-generation antiandrogen enzalutamide was recently approved for patients with castration-resistant prostate cancer. Despite its success, the duration of response is often limited. For previous antiandrogens, one mechanism of resistance is mutation of the androgen receptor (AR). To prospectively identify AR mutations that might confer resistance to enzalutamide, we performed a reporter-based mutagenesis screen and identified a novel mutation, F876L, which converted enzalutamide into an AR agonist. Ectopic expression of AR F876L rescued the growth inhibition of enzalutamide treatment. Molecular dynamics simulations performed on antiandrogen–AR complexes suggested a mechanism by which the F876L substitution alleviates antagonism through repositioning of the coactivator recruiting helix 12. This model then provided the rationale for a focused chemical screen which, based on existing antiandrogen scaffolds, identified three novel compounds that effectively antagonized AR F876L (and AR WT) to suppress the growth of prostate cancer cells resistant to enzalutamide.DOI: http://dx.doi.org/10.7554/eLife.00499.001

Somatic mutations in the estrogen receptor alpha (ERα) gene (ESR1), especially Y537S and D538G, have been linked to acquired resistance to endocrine therapies. Cell-based studies demonstrated that these mutants confer ERα constitutive activity and antiestrogen resistance and suggest that ligand-binding domain dysfunction leads to endocrine therapy resistance. Here, we integrate biophysical and structural biology data to reveal how these mutations lead to a constitutively active and antiestrogen-resistant ERα. We show that these mutant ERs recruit coactivator in the absence of hormone while their affinities for estrogen agonist (estradiol) and antagonist (4-hydroxytamoxifen) are reduced. Further, they confer antiestrogen resistance by altering the conformational dynamics of the loop connecting Helix 11 and Helix 12 in the ligand-binding domain of ERα, which leads to a stabilized agonist state and an altered antagonist state that resists inhibition.