Wnt signaling contributes to the reprogramming and maintenance of cancer stem cell (CSC) states that are activated by epithelial-mesenchymal transition (EMT). However, the mechanistic relationship between EMT and the Wnt pathway in CSC is not entirely clear. Chromatin immunoprecipitation with highthroughput sequencing (ChIP-seq) indicated that EMT induces a switch from the b-catenin/E-cadherin/Sox15 complex to the b-catenin/Twist1/TCF4 complex, the latter of which then binds to CSC-related gene promoters. Tandem coimmunoprecipitation and re-ChIP experiments with epithelial-type cells further revealed that Sox15 associates with the b-catenin/E-cadherin complex, which then binds to the proximal promoter region of CASP3. Through this mechanism, Twist1 cleavage is triggered to regulate a b-catenin-elicited promotion of the CSC phenotype. During EMT, we documented that Twist1 binding to b-catenin enhanced the transcriptional activity of the b-catenin/TCF4 complex, including by binding to the proximal promoter region of ABCG2, a CSC marker.

Highlights d Aberrant, nuclear-localized SFRPs bind with b-catenin to modulate TCF4 recruitment d The CRD and NTR domains of SFRPs have opposing regulatory effects on Wnt signaling d SFRPs are biphasic modulators of Wnt signaling beyond extracellular control d Tumor growth can be regulated by CRISPR/Cas9 for modulation of SFRPs expression

Epithelial–mesenchymal transition (EMT)/mesenchymal–epithelial transition (MET) processes are proposed to be a driving force of cancer metastasis. By studying metastasis in bone marrow-derived mesenchymal stem cell (BM-MSC)-driven lung cancer models, microarray time-series data analysis by systems biology approaches revealed BM-MSC-induced signaling triggers early dissemination of CD133+/CD83+ cancer stem cells (CSCs) from primary sites shortly after STAT3 activation but promotes proliferation towards secondary sites. The switch from migration to proliferation was regulated by BM-MSC-secreted LIF and activated LIFR/p-ERK/pS727-STAT3 signaling to promote early disseminated cancer cells MET and premetastatic niche formation. Then, tumor-tropic BM-MSCs circulated to primary sites and triggered CD151+/CD38+ cells acquiring EMT-associated CSC properties through IL6R/pY705-STAT3 signaling to promote tumor initiation and were also attracted by and migrated towards the premetastatic niche. In summary, STAT3 phosphorylation at tyrosine 705 and serine 727 differentially regulates the EMT–MET switch within the distinct molecular subtypes of CSCs to complete the metastatic process.

CD133 is widely used as a surface marker to isolate cancer stem cells (CSCs). Here we show that in CSCs CD133 contributes to β-catenin-mediated transcriptional activation and to the self-renewal capacity of sphere-forming and side-population (SP) cells in cell lines from brain, colon and lung cancers, but not gastric or breast cancers. In chromatin immunoprecipitation assays, β-catenin binding to the proximal promoter regions of ITGA2-4 and ITGA10-11 in brain, colon and lung cancer cell lines could be triggered by CD133, and β-catenin also bound to the proximal promoter regions of ITGB6 and ITGB8 in cell lines from gastric and breast cancers. CD133 thus induces β-catenin binding and transcriptional activation of diverse targets that are cancer type-specific. Cell migration triggered by wounding CD133+ cells cultured on ECM-coated dishes can induce polarity and lipid raft coalescence, enhancing CD133/integrin signaling and asymmetric cell division. In response to directional cues, integrins, Src and the Par complex were enriched in lipid rafts, and the assembly and activation of an integrated CD133-integrin-Par signaling complex was followed by Src/Akt/GSK3β signaling. The subsequent increase and nuclear translocation of β-catenin may be a regulatory switch to increase drug resistance and stemness properties. Collectively, these findings 1) indicate that a polarized cell migration-induced CD133/integrin/Src/Akt/GSK3β/β-catenin axis is required for maintenance of CSC properties, 2) establish a function for CD133 and 3) support the rationale for targeting CD133 in cancer treatment.

Background The Skeletal Oncology Research Group machine-learning algorithms (SORG-MLAs) estimate 90day and 1-year survival in patients with long-bone metastases undergoing surgical treatment and have demonstrated good discriminatory ability on internal validation. However, the performance of a prediction model could potentially vary by race or region, and the SORG-MLA must be externally validated in an Asian cohort. Furthermore, the authors of the original developmental study did not consider the Eastern Cooperative Oncology Group (ECOG) performance status, a survival prognosticator repeatedly validated in other studies, in their algorithms because of missing data. Questions/purposes (1) Is the SORG-MLA generalizable to Taiwanese patients for predicting 90-day and 1-year Each author certifies that there are no funding or commercial associations (consultancies, stock ownership, equity interest, patent/licensing arrangements, etc.) that might pose a conflict of interest in connection with the submitted article related to the author or any immediate family members. All ICMJE Conflict of Interest Forms for authors and Clinical Orthopaedics and Related Research® editors and board members are on file with the publication and can be viewed on request.

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R et al. Increased fracture risk after discontinuation of anti-osteoporosis medications among hip fracture patients: A population-based cohort study.

Background and purpose: Predicted survival may influence the treatment decision for patients with skeletal extremity metastasis, and PATHFx was designed to predict the likelihood of a patient dying in the next 24 months. However, the performance of prediction models could have ethnogeographical variations. We asked if PATHFx generalized well to our Taiwanese cohort consisting of 356 surgically treated patients with extremity metastasis.Patients and methods: We included 356 patients who underwent surgery for skeletal extremity metastasis in a tertiary center in Taiwan between 2014 and 2019 to validate PATHFx’s survival predictions at 6 different time points. Model performance was assessed by concordance index (c-index), calibration analysis, decision curve analysis (DCA), Brier score, and model consistency (MC).Results: The c-indexes for the 1-, 3-, 6-, 12-, 18-, and 24-month survival estimations were 0.71, 0.66, 0.65, 0.69, 0.68, and 0.67, respectively. The calibration analysis demonstrated positive calibration intercepts for survival predictions at all 6 timepoints, indicating PATHFx tended to underestimate the actual survival. The Brier scores for the 6 models were all less than their respective null model’s. DCA demonstrated that only the 6-, 12-, 18-, and 24-month predictions appeared useful for clinical decision-making across a wide range of threshold probabilities. The MC was < 0.9 when the 6- and 12-month models were compared with the 12-month and 18-month models, respectively.Interpretation: In this Asian cohort, PATHFx’s performance was not as encouraging as those of prior validation studies. Clinicians should be cognizant of the potential decline in validity of any tools designed using data outside their particular patient population. Developers of survival prediction tools such as PATHFx might refine their algorithms using data from diverse, contemporary patients that is more reflective of the world’s population.