MUSTANG: Multi-sample spatial transcriptomics data analysis with cross-sample transcriptional similarity guidance.

Spatially resolved transcriptomics has revolutionized genome-scale transcriptomic profiling by providing high-resolution characterization of transcriptional patterns. Here, we present our spatial transcriptomics analysis framework, MUSTANG (MUlti-sample Spatial Transcriptomics data ANalysis with cross-sample transcriptional similarity Guidance), which is capable of performing multi-sample spatial transcriptomics spot cellular deconvolution by allowing both cross-sample expression-based similarity information sharing as well as spatial correlation in gene expression patterns within samples. Experiments on a semi-synthetic spatial transcriptomics dataset and three real-world spatial transcriptomics datasets demonstrate the effectiveness of MUSTANG in revealing biological insights inherent in the cellular characterization of tissue samples under study.

Label-free optical imaging for brain cancer assessment.

Advances in label-free optical imaging offer a promising avenue for brain cancer assessment, providing high-resolution, real-time insights without the need for radiation or exogeneous agents. These cost-effective and intricately detailed techniques overcome the limitations inherent in magnetic resonance imaging (MRI), computed tomography (CT), and positron emission tomography (PET) scans by offering superior resolution and more readily accessible imaging options. This comprehensive review explores a variety of such methods, including photoacoustic imaging (PAI), optical coherence tomography (OCT), Raman imaging, and IR microscopy. It focuses on their roles in the detection, diagnosis, and management of brain tumors. By highlighting recent advances in these imaging techniques, the review aims to underscore the importance of label-free optical imaging in enhancing early detection and refining therapeutic strategies for brain cancer.

Effect of the ROCK inhibitor fasudil on the brain proteomic profile in the tau transgenic mouse model of Alzheimer's disease.

Introduction: The goal of this study is to explore the pharmacological potential of the amyloid-reducing vasodilator fasudil, a selective Ras homolog (Rho)-associated kinases (ROCK) inhibitor, in the P301S tau transgenic mouse model (Line PS19) of neurodegenerative tauopathy and Alzheimer's disease (AD). Methods: We used LC-MS/MS, ELISA and bioinformatic approaches to investigate the effect of treatment with fasudil on the brain proteomic profile in PS19 tau transgenic mice. We also explored the efficacy of fasudil in reducing tau phosphorylation, and the potential beneficial and/or toxic effects of its administration in mice. Results: Proteomic profiling of mice brains exposed to fasudil revealed the activation of the mitochondrial tricarboxylic acid (TCA) cycle and blood-brain barrier (BBB) gap junction metabolic pathways. We also observed a significant negative correlation between the brain levels of phosphorylated tau (pTau) at residue 396 and both fasudil and its metabolite hydroxyfasudil. Conclusions: Our results provide evidence on the activation of proteins and pathways related to mitochondria and BBB functions by fasudil treatment and support its further development and therapeutic potential for AD.

CD4+ T cell immunity is dependent on an intrinsic stem-like program.

CD4+ T cells are central to various immune responses, but the molecular programs that drive and maintain CD4+ T cell immunity are not entirely clear. Here we identify a stem-like program that governs the CD4+ T cell response in transplantation models. Single-cell-transcriptomic analysis revealed that naive alloantigen-specific CD4+ T cells develop into TCF1hi effector precursor (TEP) cells and TCF1-CXCR6+ effectors in transplant recipients. The TCF1-CXCR6+CD4+ effectors lose proliferation capacity and do not reject allografts upon adoptive transfer into secondary hosts. By contrast, the TCF1hiCD4+ TEP cells have dual features of self-renewal and effector differentiation potential, and allograft rejection depends on continuous replenishment of TCF1-CXCR6+ effectors from TCF1hiCD4+ TEP cells. Mechanistically, TCF1 sustains the CD4+ TEP cell population, whereas the transcription factor IRF4 and the glycolytic enzyme LDHA govern the effector differentiation potential of CD4+ TEP cells. Deletion of IRF4 or LDHA in T cells induces transplant acceptance. These findings unravel a stem-like program that controls the self-renewal capacity and effector differentiation potential of CD4+ TEP cells and have implications for T cell-related immunotherapies.

Charting Single Cell Lineage Dynamics and Mutation Networks via Homing CRISPR.

Single cell lineage tracing, essential for unraveling cellular dynamics in disease evolution is critical for developing targeted therapies. CRISPR-Cas9, known for inducing permanent and cumulative mutations, is a cornerstone in lineage tracing. The novel homing guide RNA (hgRNA) technology enhances this by enabling dynamic retargeting and facilitating ongoing genetic modifications. Charting these mutations, especially through successive hgRNA edits, poses a significant challenge. Our solution, LINEMAP, is a computational framework designed to trace and map these mutations with precision. LINEMAP meticulously discerns mutation alleles at single-cell resolution and maps their complex interrelationships through a mutation evolution network. By utilizing a Markov Process model, we can predict mutation transition probabilities, revealing potential mutational routes and pathways. Our reconstruction algorithm, anchored in the Markov model's attributes, reconstructs cellular lineage pathways, shedding light on the cell's evolutionary journey to the minutiae of single-cell division. Our findings reveal an intricate network of mutation evolution paired with a predictive Markov model, advancing our capability to reconstruct single-cell lineage via hgRNA. This has substantial implications for advancing our understanding of biological mechanisms and propelling medical research forward.

Prevalence of Delirium After Abdominal Surgery and Association With Ketamine: A Retrospective, Propensity-Matched Cohort Study.

IMPORTANCE: Delirium is a common postoperative complication for older patients in the ICU. Ketamine, used primarily as an analgesic, has been thought to prevent delirium. OBJECTIVE: Determine the prevalence and association of delirium with low-dose ketamine use in ICU patients after abdominal surgery. DESIGN: Single-center, retrospective, propensity-matched cohort study. SETTING: Eight hospital academic medical center. PATIENTS: Cohort comprising 1836 patients admitted to the ICU after abdominal surgery between June 23, 2018 and September 1, 2022. MAIN OUTCOMES AND MEASURES: Propensity score matching (PSM) with a 3:1 ratio between no-ketamine use and ketamine use was performed through a greedy algorithm (caliper of 0.005). Outcomes of interest included: delirium (assessed by Confusion Assessment Method-ICU), mean pain score (Numeric Pain Scale or Critical Care Pain Observation Tool score as available), mean opioid consumption (morphine milligram equivalents), length of stay (d), and mortality. RESULTS: Prevalence of delirium was 47.71% (95% CI, 45.41-50.03%) in the cohort. Of 1836 patients, 120 (6.54%) used low-dose ketamine infusion. After PSM, the prevalence of delirium was 56.02% (95% CI, 51.05-60.91%) in all abdominal surgery patients. The ketamine group had 41% less odds of delirium (odds ratio [OR] = 0.59; 95% CI, 0.37-0.94; p = 0.026) than patients with no-ketamine use. Patients with ketamine use had higher mean pain scores (3.57 ± 2.86 vs. 2.21 ± 2.09, p < 0.001). In the subgroup analysis, patients in the ketamine-use group 60 years old or younger had 64% less odds of delirium (OR = 0.36; 95% CI, 0.13-0.95; p = 0.039). The mean pain scores were higher in the ketamine group for patients 60 years old or older. There was no significant difference in mortality and opioid consumption. CONCLUSIONS AND RELEVANCE: Low-dose ketamine infusion was associated with lower prevalence of delirium in ICU patients following abdominal surgery. Prospective studies should further evaluate ketamine use and delirium.

Automated catheter segmentation and tip detection in cerebral angiography with topology-aware geometric deep learning.

BACKGROUND: Visual perception of catheters and guidewires on x-ray fluoroscopy is essential for neurointervention. Endovascular robots with teleoperation capabilities are being developed, but they cannot 'see' intravascular devices, which precludes artificial intelligence (AI) augmentation that could improve precision and autonomy. Deep learning has not been explored for neurointervention and prior works in cardiovascular scenarios are inadequate as they only segment device tips, while neurointervention requires segmentation of the entire structure due to coaxial devices. Therefore, this study develops an automatic and accurate image-based catheter segmentation method in cerebral angiography using deep learning. METHODS: Catheters and guidewires were manually annotated on 3831 fluoroscopy frames collected prospectively from 40 patients undergoing cerebral angiography. We proposed a topology-aware geometric deep learning method (TAG-DL) and compared it with the state-of-the-art deep learning segmentation models, UNet, nnUNet and TransUNet. All models were trained on frontal view sequences and tested on both frontal and lateral view sequences from unseen patients. Results were assessed with centerline Dice score and tip-distance error. RESULTS: The TAG-DL and nnUNet models outperformed TransUNet and UNet. The best performing model was nnUNet, achieving a mean centerline-Dice score of 0.98 ±0.01 and a median tip-distance error of 0.43 (IQR 0.88) mm. Incorporating digital subtraction masks, with or without contrast, significantly improved performance on unseen patients, further enabling exceptional performance on lateral view fluoroscopy despite not being trained on this view. CONCLUSIONS: These results are the first step towards AI augmentation for robotic neurointervention that could amplify the reach, productivity, and safety of a limited neurointerventional workforce.

TP-0184 inhibits FLT3/ACVR1 to overcome FLT3 inhibitor resistance and hinder AML growth synergistically with venetoclax.

We identified activin A receptor type I (ACVR1), a member of the TGF-ß superfamily, as a factor favoring acute myeloid leukemia (AML) growth and a new potential therapeutic target. ACVR1 is overexpressed in FLT3-mutated AML and inhibition of ACVR1 expression sensitized AML cells to FLT3 inhibitors. We developed a novel ACVR1 inhibitor, TP-0184, which selectively caused growth arrest in FLT3-mutated AML cell lines. Molecular docking and in vitro kinase assays revealed that TP-0184 binds to both ACVR1 and FLT3 with high affinity and inhibits FLT3/ACVR1 downstream signaling. Treatment with TP-0184 or in combination with BCL2 inhibitor, venetoclax dramatically inhibited leukemia growth in FLT3-mutated AML cell lines and patient-derived xenograft models in a dose-dependent manner. These findings suggest that ACVR1 is a novel biomarker and plays a role in AML resistance to FLT3 inhibitors and that FLT3/ACVR1 dual inhibitor TP-0184 is a novel potential therapeutic tool for AML with FLT3 mutations.

A Deep Learning Decision Support Tool to Improve Risk Stratification and Reduce Unnecessary Biopsies in BI-RADS 4 Mammograms.

Purpose: To evaluate the performance of a biopsy decision support algorithmic model, the intelligent-augmented breast cancer risk calculator (iBRISK), on a multicenter patient dataset. Materials and Methods: iBRISK was previously developed by applying deep learning to clinical risk factors and mammographic descriptors from 9700 patient records at the primary institution and validated using another 1078 patients. All patients were seen from March 2006 to December 2016. In this multicenter study, iBRISK was further assessed on an independent, retrospective dataset (January 2015-June 2019) from three major health care institutions in Texas, with Breast Imaging Reporting and Data System (BI-RADS) category 4 lesions. Data were dichotomized and trichotomized to measure precision in risk stratification and probability of malignancy (POM) estimation. iBRISK score was also evaluated as a continuous predictor of malignancy, and cost savings analysis was performed. Results: The iBRISK model's accuracy was 89.5%, area under the receiver operating characteristic curve (AUC) was 0.93 (95% CI: 0.92, 0.95), sensitivity was 100%, and specificity was 81%. A total of 4209 women (median age, 56 years [IQR, 45-65 years]) were included in the multicenter dataset. Only two of 1228 patients (0.16%) in the "low" POM group had malignant lesions, while in the "high" POM group, the malignancy rate was 85.9%. iBRISK score as a continuous predictor of malignancy yielded an AUC of 0.97 (95% CI: 0.97, 0.98). Estimated potential cost savings were more than $420 million. Conclusion: iBRISK demonstrated high sensitivity in the malignancy prediction of BI-RADS 4 lesions. iBRISK may safely obviate biopsies in up to 50% of patients in low or moderate POM groups and reduce biopsy-associated costs.Keywords: Mammography, Breast, Oncology, Biopsy/Needle Aspiration, Radiomics, Precision Mammography, AI-augmented Biopsy Decision Support Tool, Breast Cancer Risk Calculator, BI-RADS 4 Mammography Risk Stratification, Overbiopsy Reduction, Probability of Malignancy (POM) Assessment, Biopsy-based Positive Predictive Value (PPV3) Supplemental material is available for this article. Published under a CC BY 4.0 license.See also the commentary by McDonald and Conant in this issue.

Life-Course Brain Health as a Determinant of Late-Life Mental Health: American Association for Geriatric Psychiatry Expert Panel Recommendations.

This position statement of the Expert Panel on Brain Health of the American Association for Geriatric Psychiatry (AAGP) emphasizes the critical role of life course brain health in shaping mental well-being during the later stages of life. Evidence posits that maintaining optimal brain health earlier in life is crucial for preventing and managing brain aging-related disorders such as dementia/cognitive decline, depression, stroke, and anxiety. We advocate for a holistic approach that integrates medical, psychological, and social frameworks with culturally tailored interventions across the lifespan to promote brain health and overall mental well-being in aging adults across all communities. Furthermore, our statement underscores the significance of prevention, early detection, and intervention in identifying cognitive decline, mood changes, and related mental illness. Action should also be taken to understand and address the needs of communities that traditionally have unequal access to preventive health information and services. By implementing culturally relevant and tailored evidence-based practices and advancing research in geriatric psychiatry, behavioral neurology, and geroscience, we can enhance the quality of life for older adults facing the unique challenges of aging. This position statement emphasizes the intrinsic link between brain health and mental health in aging, urging healthcare professionals, policymakers, and a broader society to prioritize comprehensive strategies that safeguard and promote brain health from birth through later years across all communities. The AAGP Expert Panel has the goal of launching further activities in the coming months and years.

The promise of digital healthcare technologies.

Digital health technologies have been in use for many years in a wide spectrum of healthcare scenarios. This narrative review outlines the current use and the future strategies and significance of digital health technologies in modern healthcare applications. It covers the current state of the scientific field (delineating major strengths, limitations, and applications) and envisions the future impact of relevant emerging key technologies. Furthermore, we attempt to provide recommendations for innovative approaches that would accelerate and benefit the research, translation and utilization of digital health technologies.

Targeting Ribosome Biogenesis as a Novel Therapeutic Approach to Overcome EMT-related Chemoresistance in Breast Cancer.

Epithelial-to-mesenchymal transition (EMT) contributes significantly to chemotherapy resistance and remains a critical challenge in treating advanced breast cancer. The complexity of EMT, involving redundant pro-EMT signaling pathways and its paradox reversal process, mesenchymal-to-epithelial transition (MET), has hindered the development of effective treatments. In this study, we utilized a Tri-PyMT EMT lineage-tracing model and single-cell RNA sequencing (scRNA-seq) to comprehensively analyze the EMT status of tumor cells. Our findings revealed elevated ribosome biogenesis (RiBi) during the transitioning phases of both EMT and MET processes. RiBi and its subsequent nascent protein synthesis mediated by ERK and mTOR signalings are essential for EMT/MET completion. Importantly, inhibiting excessive RiBi genetically or pharmacologically impaired the EMT/MET capability of tumor cells. Combining RiBi inhibition with chemotherapy drugs synergistically reduced metastatic outgrowth of epithelial and mesenchymal tumor cells under chemotherapies. Our study suggests that targeting the RiBi pathway presents a promising strategy for treating patients with advanced breast cancer. Significance: This study uncovers the crucial involvement of ribosome biogenesis (RiBi) in the regulation of epithelial and mesenchymal state oscillations in breast cancer cells, which plays a major role in the development of chemoresistant metastasis. By proposing a novel therapeutic strategy targeting the RiBi pathway, the study offers significant potential to enhance treatment efficacy and outcomes for patients with advanced breast cancer. This approach could help overcome the limitations of current chemotherapy options and address the complex challenges posed by EMT-mediated chemoresistance.

Reliability of the National Institutes of Health (NIH) Stroke Scale Between Emergency Room and Neurology Physicians for Initial Stroke Severity Scoring.

INTRODUCTION: In patients with acute ischemic stroke (AIS), the National Institutes of Health Stroke Scale (NIHSS) is essential to establishing a patient's initial stroke severity. While previous research has validated NIHSS scoring reliability between neurologists and other clinicians, it has not specifically evaluated NIHSS scoring reliability between emergency room (ER) and neurology physicians within the same clinical scenario and timeframe in a large cohort of patients. This study specifically addresses the key question: does an ER physician's NIHSS score agree with the neurologist's NIHSS score in the same patient at the same time in a real-world context? METHODS: Data was retrospectively collected from 1,946 patients being evaluated for AIS at Houston Methodist Hospital from 05/2016 - 04/2018. Triage NIHSS scores assessed by both the ER and neurology providers within one hour of each other under the same clinical context were evaluated for comparison. Ultimately, 129 patients were included in the analysis. All providers in this study were NIHSS rater-certified. RESULTS: The distribution of the NIHSS score differences (ER score - neurology score) had a mean of -0.46 and a standard deviation of 2.11. The score difference between provider teams ranged ±5 points. The intraclass correlation coefficient (ICC) for the NIHSS scores between the ER and neurology teams was 0.95 (95% CI, 0.93 - 0.97) with an F-test of 42.41 and a p-value of 4.43E-69. Overall reliability was excellent between the ER and neurology teams. CONCLUSION: We evaluated triage NIHSS scores performed by ER and neurology providers under matching time and treatment conditions and found excellent interrater reliability. The excellent score agreement has important implications for treatment decision-making during patient handoff and further in stroke modeling, prediction, and clinical trial registries where missing NIHSS scores may be equivalently substituted from either provider team.

Interferon-gamma is quintessential for NOS2 and COX2 expression in ER- breast tumors that lead to poor outcome.

A strong correlation between NOS2 and COX2 tumor expression and poor clinical outcomes in ER breast cancer has been established. However, the mechanisms of tumor induction of these enzymes are unclear. Analysis of The Cancer Genome Atlas (TCGA) revealed correlations between NOS2 and COX2 expression and Th1 cytokines. Herein, single-cell RNAseq analysis of TNBC cells shows potent NOS2 and COX2 induction by IFNγ combined with IL1ß or TNFα. Given that IFNγ is secreted by cytolytic lymphocytes, which improve clinical outcomes, this role of IFNγ presents a dichotomy. To explore this conundrum, tumor NOS2, COX2, and CD8+ T cells were spatially analyzed in aggressive ER-, TNBC, and HER2 + breast tumors. High expression and clustering of NOS2-expressing tumor cells occurred at the tumor/stroma interface in the presence of stroma-restricted CD8+ T cells. High expression and clustering of COX2-expressing tumor cells extended into immune desert regions in the tumor core where CD8+ T cell penetration was limited or absent. Moreover, high NOS2-expressing tumor cells were proximal to areas with increased satellitosis, suggestive of cell clusters with a higher metastatic potential. Further in vitro experiments revealed that IFNγ + IL1ß/TNFα increased the elongation and migration of treated tumor cells. This spatial analysis of the tumor microenvironment provides important insight into distinct neighborhoods where stroma-restricted CD8+ T cells exist proximal to NOS2-expressing tumor niches that could have increased metastatic potential.

BBox-Guided Segmentor: Leveraging expert knowledge for accurate stroke lesion segmentation using weakly supervised bounding box prior.

Stroke is one of the leading causes of death and disability in the world. Despite intensive research on automatic stroke lesion segmentation from non-invasive imaging modalities including diffusion-weighted imaging (DWI), challenges remain such as a lack of sufficient labeled data for training deep learning models and failure in detecting small lesions. In this paper, we propose BBox-Guided Segmentor, a method that significantly improves the accuracy of stroke lesion segmentation by leveraging expert knowledge. Specifically, our model uses a very coarse bounding box label provided by the expert and then performs accurate segmentation automatically. The small overhead of having the expert provide a rough bounding box leads to large performance improvement in segmentation, which is paramount to accurate stroke diagnosis. To train our model, we employ a weakly-supervised approach that uses a large number of weakly-labeled images with only bounding boxes and a small number of fully labeled images. The scarce fully labeled images are used to train a generator segmentation network, while adversarial training is used to leverage the large number of weakly-labeled images to provide additional learning signals. We evaluate our method extensively using a unique clinical dataset of 99 fully labeled cases (i.e., with full segmentation map labels) and 831 weakly labeled cases (i.e., with only bounding box labels), and the results demonstrate the superior performance of our approach over state-of-the-art stroke lesion segmentation models. We also achieve competitive performance as a SOTA fully supervised method using less than one-tenth of the complete labels. Our proposed approach has the potential to improve stroke diagnosis and treatment planning, which may lead to better patient outcomes.

Interferon-gamma is Quintessential for NOS2 and COX2 Expression in ER - Breast Tumors that Lead to Poor Outcome.

A strong correlation between NOS2 and COX2 tumor expression and poor clinical outcomes in ER-breast cancer has been established. However, mechanisms of tumor induction of these enzymes are unclear. Analysis of The Cancer Genome Atlas (TCGA) revealed correlations between NOS2 and COX2 expression and Th1 cytokines. Herein, single cell RNAseq analysis of TNBC cells shows potent NOS2 and COX2 induction by IFNγ combined with IL1ß or TNFα. Given that IFNγ is secreted by cytolytic lymphocytes, which improve clinical outcomes, this role of IFNγpresents a dichotomy. To explore this conundrum, tumor NOS2, COX2, and CD8 + T cells were spatially analyzed in aggressive ER-, TNBC, and HER2+ breast tumors. High expression and clustering of NOS2-expressing tumor cells occurred at the tumor/stroma interface in the presence of stroma-restricted CD8 + T cells. High expression and clustering of COX2-expressing tumor cells extended into immune desert regions in the tumor core where CD8 + T cell penetration was limited or absent. Moreover, high NOS2-expressing tumor cells were proximal to areas with increased satellitosis suggestive of cell clusters with a higher metastatic potential. Further in vitro experiments revealed that IFNγ+IL1ß/TNFα increased elongation and migration of treated tumor cells. This spatial analysis of the tumor microenvironment provides important insight of distinct neighborhoods where stroma-restricted CD8 + T cells exist proximal to NOS2-expressing tumor niches that could have increased metastatic potential.

Spatial Transcriptomics Depict Ligand-Receptor Cross-talk Heterogeneity at the Tumor-Stroma Interface in Long-Term Ovarian Cancer Survivors.

Advanced high-grade serous ovarian cancer (HGSC) is an aggressive disease that accounts for 70% of all ovarian cancer deaths. Nevertheless, 15% of patients diagnosed with advanced HGSC survive more than 10 years. The elucidation of predictive markers of these long-term survivors (LTS) could help identify therapeutic targets for the disease, and thus improve patient survival rates. To investigate the stromal heterogeneity of the tumor microenvironment (TME) in ovarian cancer, we used spatial transcriptomics to generate spatially resolved transcript profiles in treatment-naïve advanced HGSC from LTS and short-term survivors (STS) and determined the association between cancer-associated fibroblasts (CAF) heterogeneity and survival in patients with advanced HGSC. Spatial transcriptomics and single-cell RNA-sequencing data were integrated to distinguish tumor and stroma regions, and a computational method was developed to investigate spatially resolved ligand-receptor interactions between various tumor and CAF subtypes in the TME. A specific subtype of CAFs and its spatial location relative to a particular ovarian cancer cell subtype in the TME correlated with long-term survival in patients with advanced HGSC. Also, increased APOE-LRP5 cross-talk occurred at the stroma-tumor interface in tumor tissues from STS compared with LTS. These findings were validated using multiplex IHC. Overall, this spatial transcriptomics analysis revealed spatially resolved CAF-tumor cross-talk signaling networks in the ovarian TME that are associated with long-term survival of patients with HGSC. Further studies to confirm whether such cross-talk plays a role in modulating the malignant phenotype of HGSC and could serve as a predictive biomarker of patient survival are warranted. SIGNIFICANCE: Generation of spatially resolved gene expression patterns in tumors from patients with ovarian cancer surviving more than 10 years allows the identification of novel predictive biomarkers and therapeutic targets for better patient management. See related commentary by Kelliher and Lengyel, p. 1383.

Addressing the genetic/nongenetic duality in cancer with systems biology.

The dogma that cancer is a genetic disease is being questioned. Recent findings suggest that genetic/nongenetic duality is necessary for cancer progression. A think tank organized by the Shraman Foundation's Institute for Theoretical Biology compiled key challenges and opportunities that theoreticians, experimentalists, and clinicians can explore from a systems biology perspective to provide a better understanding of the disease as well as help discover new treatment options and therapeutic strategies.

Bone Metastasis Initiation Is Coupled with Bone Remodeling through Osteogenic Differentiation of NG2+ Cells.

The bone microenvironment is dynamic and undergoes remodeling in normal and pathologic conditions. Whether such remodeling affects disseminated tumor cells (DTC) and bone metastasis remains poorly understood. Here, we demonstrated that pathologic fractures increase metastatic colonization around the injury. NG2+ cells are a common participant in bone metastasis initiation and bone remodeling in both homeostatic and fractured conditions. NG2+ bone mesenchymal stem/stromal cells (BMSC) often colocalize with DTCs in the perivascular niche. Both DTCs and NG2+ BMSCs are recruited to remodeling sites. Ablation of NG2+ lineage impaired bone remodeling and concurrently diminished metastatic colonization. In cocultures, NG2+ BMSCs, especially when undergoing osteodifferentiation, enhanced cancer cell proliferation and migration. Knockout of N-cadherin in NG2+ cells abolished these effects in vitro and phenocopied NG2+ lineage depletion in vivo. These findings uncover dual roles of NG2+ cells in metastasis and remodeling and indicate that osteodifferentiation of BMSCs promotes metastasis initiation via N-cadherin-mediated cell-cell interaction. SIGNIFICANCE: The bone colonization of cancer cells occurs in an environment that undergoes constant remodeling. Our study provides mechanistic insights into how bone homeostasis and pathologic repair lead to the outgrowth of disseminated cancer cells, thereby opening new directions for further etiologic and epidemiologic studies of tumor recurrences. This article is highlighted in the In This Issue feature, p. 247.