Cell morphology best predicts tumorigenicity and metastasis in vivo across multiple TNBC cell lines of different metastatic potential.

BACKGROUND: Metastasis is the leading cause of death in breast cancer patients. For metastasis to occur, tumor cells must invade locally, intravasate, and colonize distant tissues and organs, all steps that require tumor cell migration. The majority of studies on invasion and metastasis rely on human breast cancer cell lines. While it is known that these cells have different properties and abilities for growth and metastasis, the in vitro morphological, proliferative, migratory, and invasive behavior of these cell lines and their correlation to in vivo behavior is poorly understood. Thus, we sought to classify each cell line as poorly or highly metastatic by characterizing tumor growth and metastasis in a murine model of six commonly used human triple-negative breast cancer xenografts, as well as determine which in vitro assays commonly used to study cell motility best predict in vivo metastasis. METHODS: We evaluated the liver and lung metastasis of human TNBC cell lines MDA-MB-231, MDA-MB-468, BT549, Hs578T, BT20, and SUM159 in immunocompromised mice. We characterized each cell line's cell morphology, proliferation, and motility in 2D and 3D to determine the variation in these parameters between cell lines. RESULTS: We identified MDA-MB-231, MDA-MB-468, and BT549 cells as highly tumorigenic and metastatic, Hs578T as poorly tumorigenic and metastatic, BT20 as intermediate tumorigenic with poor metastasis to the lungs but highly metastatic to the livers, and SUM159 as intermediate tumorigenic but poorly metastatic to the lungs and livers. We showed that metrics that characterize cell morphology are the most predictive of tumor growth and metastatic potential to the lungs and liver. Further, we found that no single in vitro motility assay in 2D or 3D significantly correlated with metastasis in vivo. CONCLUSIONS: Our results provide an important resource for the TNBC research community, identifying the metastatic potential of 6 commonly used cell lines. Our findings also support the use of cell morphological analysis to investigate the metastatic potential and emphasize the need for multiple in vitro motility metrics using multiple cell lines to represent the heterogeneity of metastasis in vivo.

Cell morphology best predicts tumorigenicity and metastasis in vivo across multiple TNBC cell lines of different metastatic potential.

Background: Metastasis is the leading cause of death in breast cancer patients. For metastasis to occur, tumor cells must invade locally, intravasate, and colonize distant tissues and organs, all steps that require tumor cell migration. The majority of studies on invasion and metastasis rely on human breast cancer cell lines. While it is known that these cells have different properties and abilities for growth and metastasis, the in vitro morphological, proliferative, migratory, and invasive behavior of these cell lines and their correlation to in vivo behavior is poorly understood. Thus, we sought to classify each cell line as poorly or highly metastatic by characterizing tumor growth and metastasis in a murine model of six commonly used human triple-negative breast cancer xenografts, as well as determine which in vitro assays commonly used to study cell motility best predict in vivo metastasis. Methods: We evaluated the liver and lung metastasis of human TNBC cell lines MDA-MB-231, MDA-MB-468, BT549, Hs578T, BT20, and SUM159 in immunocompromised mice. We characterized each cell line's cell morphology, proliferation, and motility in 2D and 3D to determine the variation in these parameters between cell lines. Results: We identified MDA-MB-231, MDA-MB-468, and BT549 cells as highly tumorigenic and metastatic, Hs578T as poorly tumorigenic and metastatic, BT20 as intermediate tumorigenic with poor metastasis to the lungs but highly metastatic to the livers, and SUM159 as intermediate tumorigenic but poorly metastatic to the lungs and livers. We showed that metrics that characterize cell morphology are the most predictive of tumor growth and metastatic potential to the lungs and liver. Further, we found that no single in vitro motility assay in 2D or 3D significantly correlated with metastasis in vivo. Conclusions: Our results provide an important resource for the TNBC research community, identifying the metastatic potential of 6 commonly used cell lines. Our findings also support the use of cell morphological analysis to investigate the metastatic potential and emphasize the need for multiple in vitro motility metrics using multiple cell lines to represent the heterogeneity of metastasis in vivo.

Chemotherapy-Induced Collagen IV Drives Cancer Cell Motility through Activation of Src and Focal Adhesion Kinase.

Triple-negative breast cancer (TNBC) is the most aggressive and deadly subtype of breast cancer, accounting for 30,000 cases annually in the United States. While there are several clinical trials ongoing to identify new agents to treat TNBC, the majority of patients with TNBC are treated with anthracycline- or taxane-based chemotherapies in the neoadjuvant setting, followed by surgical resection and adjuvant chemotherapy. While many patients respond well to this approach, as many as 25% will suffer local or metastatic recurrence within 5 years. Understanding the mechanisms that drive recurrence after chemotherapy treatment is critical to improving survival for patients with TNBC. It is well established that the extracellular matrix (ECM), which provides structure and support to tissues, is a major driver of tumor growth, local invasion, and dissemination of cancer cells to distant metastatic sites. In the present study, we show that decellularized ECM (dECM) obtained from chemotherapy-treated mice increases motility of treatment-naïve breast cancer cells compared with vehicle-treated dECM. Tandem-mass-tag proteomics revealed that anthracycline- and taxane-based chemotherapies induce drug-specific changes in tumor ECM composition. The basement membrane protein collagen IV was significantly upregulated in the ECM of chemotherapy-treated mice and patients treated with neoadjuvant chemotherapy. Collagen IV drove invasion via activation of Src and focal adhesion kinase signaling downstream of integrin α1 and α2, and inhibition of collagen IV-driven signaling decreased motility in chemotherapy-treated dECM. These studies provide a novel mechanism by which chemotherapy may induce metastasis via its effects on ECM composition. SIGNIFICANCE: Cytotoxic chemotherapy induces significant changes in the composition of tumor ECM, inducing a more invasive and aggressive phenotype in residual tumor cells following chemotherapy.

Decellularized extracellular matrix scaffolds identify full-length collagen VI as a driver of breast cancer cell invasion in obesity and metastasis.

The extracellular matrix (ECM), a major component of the tumor microenvironment, promotes local invasion to drive metastasis. Here, we describe a method to study whole-tissue ECM effects from disease states associated with metastasis on tumor cell phenotypes and identify the individual ECM proteins and signaling pathways that are driving these effects. We show that decellularized ECM from tumor-bearing and obese mammary glands drives TNBC cell invasion. Proteomics of the ECM from the obese mammary gland led us to identify full-length collagen VI as a novel driver of TNBC cell invasion whose abundance in tumor stroma increases with body mass index in human TNBC patients. Last, we describe the mechanism by which collagen VI contributes to TNBC cell invasion via NG2-EGFR cross-talk and MAPK signaling. Overall, these studies demonstrate the value of decellularized ECM scaffolds obtained from tissues to identify novel functions of the ECM.

A machine learning approach predicts future risk to suicidal ideation from social media data.

Machine learning analysis of social media data represents a promising way to capture longitudinal environmental influences contributing to individual risk for suicidal thoughts and behaviors. Our objective was to generate an algorithm termed "Suicide Artificial Intelligence Prediction Heuristic (SAIPH)" capable of predicting future risk to suicidal thought by analyzing publicly available Twitter data. We trained a series of neural networks on Twitter data queried against suicide associated psychological constructs including burden, stress, loneliness, hopelessness, insomnia, depression, and anxiety. Using 512,526 tweets from N = 283 suicidal ideation (SI) cases and 3,518,494 tweets from 2655 controls, we then trained a random forest model using neural network outputs to predict binary SI status. The model predicted N = 830 SI events derived from an independent set of 277 suicidal ideators relative to N = 3159 control events in all non-SI individuals with an AUC of 0.88 (95% CI 0.86-0.90). Using an alternative approach, our model generates temporal prediction of risk such that peak occurrences above an individual specific threshold denote a ~7 fold increased risk for SI within the following 10 days (OR = 6.7 ± 1.1, P = 9 × 10-71). We validated our model using regionally obtained Twitter data and observed significant associations of algorithm SI scores with county-wide suicide death rates across 16 days in August and in October, 2019, most significantly in younger individuals. Algorithmic approaches like SAIPH have the potential to identify individual future SI risk and could be easily adapted as clinical decision tools aiding suicide screening and risk monitoring using available technologies.

DNA methylation biomarkers prospectively predict both antenatal and postpartum depression.

We sought to replicate and expand upon previous work demonstrating antenatal TTC9B and HP1BP3 gene DNA methylation is prospectively predictive of postpartum depression (PPD) with ~80% accuracy. In a preterm birth study from Emory, Illumina MethylEPIC microarray derived 1st but not 3rd trimester biomarker models predicted 3rd trimester Edinburgh Postnatal Depression Scale (EPDS) scores ≥ 13 with an AUC=0.8 (95% CI: 0.63-0.8). Bisulfite pyrosequencing derived biomarker methylation was generated using bisulfite pyrosequencing across all trimesters in a pregnancy cohort at UC Irvine and in 3rd trimester from an independent Johns Hopkins pregnancy cohort. A support vector machine model incorporating 3rd trimester EPDS scores, TTC9B, and HP1BP3 methylation status predicted 4 week to 6 week postpartum EPDS ≥ 13 from 3rd trimester blood in the UC Irvine cohort (AUC=0.78, 95% CI: 0.64-0.78) and from the Johns Hopkins cohort (AUC=0.84, 95% CI: 0.72-0.97), both independent of previous psychiatric diagnosis. Technical replicate predictions in a subset of the Johns Hopkins cohort exhibited strong cross experiment correlation. This study confirms the PPD prediction model has the potential to be developed into a clinical tool enabling the identification of pregnant women at future risk of PPD who may benefit from clinical intervention.