ABSTRACT
The genetic alterations contributing to migration proficiency, a phenotypic hallmark of metastatic cells required for colonizing distant organs, remain poorly defined. Here, we used single-cell magneto-optical capture (scMOCa) to isolate fast cells from heterogeneous human breast cancer cell populations, based on their migratory ability alone. We show that captured fast cell subpopulations retain higher migration speed and focal adhesion dynamics over many generations as a result of a motility-related transcriptomic profile. Upregulated genes in isolated fast cells encoded integrin subunits, proto-cadherins and numerous other genes associated with cell migration. Dysregulation of several of these genes correlates with poor survival outcomes in people with breast cancer, and primary tumors established from fast cells generated a higher number of circulating tumor cells and soft tissue metastases in pre-clinical mouse models. Subpopulations of cells selected for a highly migratory phenotype demonstrated an increased fitness for metastasis.
Subject(s)
Breast Neoplasms , Neoplastic Cells, Circulating , Animals , Mice , Humans , Female , Breast Neoplasms/pathology , Cell Line, Tumor , Neoplastic Cells, Circulating/pathology , Cell Movement/genetics , Cadherins , Neoplasm MetastasisABSTRACT
BACKGROUND: Compared to minimally invasive brain metastases (MI BrM), highly invasive (HI) lesions form abundant contacts with cells in the peritumoral brain parenchyma and are associated with poor prognosis. Reactive astrocytes (RAs) labeled by phosphorylated STAT3 (pSTAT3) have recently emerged as a promising therapeutic target for BrM. Here, we explore whether the BrM invasion pattern is influenced by pSTAT3+â RAs and may serve as a predictive biomarker for STAT3 inhibition. METHODS: We used immunohistochemistry to identify pSTAT3+ RAs in HI and MI human and patient-derived xenograft (PDX) BrM. Using PDX, syngeneic, and transgenic mouse models of HI and MI BrM, we assessed how pharmacological STAT3 inhibition or RA-specific STAT3 genetic ablation affected BrM growth in vivo. Cancer cell invasion was modeled in vitro using a brain slice-tumor co-culture assay. We performed single-cell RNA sequencing of human BrM and adjacent brain tissue. RESULTS: RAs expressing pSTAT3 are situated at the brain-tumor interface and drive BrM invasive growth. HI BrM invasion pattern was associated with delayed growth in the context of STAT3 inhibition or genetic ablation. We demonstrate that pSTAT3+ RAs secrete Chitinase 3-like-1 (CHI3L1), which is a known STAT3 transcriptional target. Furthermore, single-cell RNA sequencing identified CHI3L1-expressing RAs in human HI BrM. STAT3 activation, or recombinant CHI3L1 alone, induced cancer cell invasion into the brain parenchyma using a brain slice-tumor plug co-culture assay. CONCLUSIONS: Together, these data reveal that pSTAT3+ RA-derived CHI3L1 is associated with BrM invasion, implicating STAT3 and CHI3L1 as clinically relevant therapeutic targets for the treatment of HI BrM.
Subject(s)
Astrocytes , Brain Neoplasms , Chitinase-3-Like Protein 1 , Neoplasm Invasiveness , STAT3 Transcription Factor , STAT3 Transcription Factor/metabolism , STAT3 Transcription Factor/genetics , Humans , Chitinase-3-Like Protein 1/metabolism , Chitinase-3-Like Protein 1/genetics , Animals , Brain Neoplasms/metabolism , Brain Neoplasms/pathology , Brain Neoplasms/secondary , Brain Neoplasms/genetics , Astrocytes/metabolism , Astrocytes/pathology , Mice , Mice, Transgenic , Cell Proliferation , Xenograft Model Antitumor Assays , Tumor Cells, CulturedABSTRACT
Brain metastases (BM) are associated with significant morbidity and mortality in patients with advanced cancer. Despite significant advances in surgical, radiation, and systemic therapy in recent years, the median overall survival of patients with BM is less than 1 year. The acquisition of medical images, such as computed tomography (CT) and magnetic resonance imaging (MRI), is critical for the diagnosis and stratification of patients to appropriate treatments. Radiomic analyses have the potential to improve the standard of care for patients with BM by applying artificial intelligence (AI) with already acquired medical images to predict clinical outcomes and direct the personalized care of BM patients. Herein, we outline the existing literature applying radiomics for the clinical management of BM. This includes predicting patient response to radiotherapy and identifying radiation necrosis, performing virtual biopsies to predict tumor mutation status, and determining the cancer of origin in brain tumors identified via imaging. With further development, radiomics has the potential to aid in BM patient stratification while circumventing the need for invasive tissue sampling, particularly for patients not eligible for surgical resection.