Development and validation of a simple prostate cancer risk prediction model based on age, family history, and polygenic risk.

BACKGROUND: Accurate prostate cancer risk assessment will enable identification of men who are at increased risk of the disease. Using the UK Biobank population-based cohort, we developed and validated a simple model comprising age, family history, and a polygenic risk score (PRS) to predict 5-year risk of prostate cancer. METHODS: Eligible participants were unaffected Caucasian men aged 40-69 years at their baseline assessment who had genotyping data available and had completed 6 or more weeks of follow-up. Family history was the number of affected first-degree relatives: 0, 1, or 2+. We used 264 single-nucleotide polymorphisms (SNPs) of a previously developed 269-SNP PRS and population standardized the PRS to have a mean of 1. Age was categorized into 10-year groups: 40-49, 50-59, and 60-69. In a 70% training data set, we used Cox regression with age as the time axis to model family history, PRS, and age group. The model estimates were used with prostate cancer incidences to derive 5-year risks of prostate cancer. Using 5 years of follow-up in a 30% testing data set, the model was tested in terms of its association per quintile of risk, discrimination, and calibration. RESULTS: Of the 198 334 eligible participants, 8996 (4.5%) were diagnosed with incident prostate cancer during follow-up and had a mean age of 67.9 (SD = 5.8) years at diagnosis. The best-fitting model included the PRS, family history, 10-year age group, interactions between age and PRS, and age and family history. In the 30% testing data set with follow-up limited to 5 years, the hazard ratio per SD of 5-year risk was 3.058 (95% confidence interval [CI], 2.720-3.438) and the Harrell's C-index was 0.811 (95% CI, 0.800-0.821). Overall, there were 1088 observed and 1159.1 expected prostate cancers, a standardized incidence ratio of 0.939 (95% CI, 0.885-0.996). CONCLUSIONS: Men at increased risk of prostate cancer could benefit from informed discussions around the risks and benefits of available options for screening for prostate cancer. Although the model was developed in Caucasian men, it can be used with ethnicity-specific polygenic risk and incidence rates for other populations.

Melanoma risk prediction based on a polygenic risk score and clinical risk factors.

Melanoma is one of the most commonly diagnosed cancers in the Western world: third in Australia, fifth in the USA and sixth in the European Union. Predicting an individual's personal risk of developing melanoma may aid them in undertaking effective risk reduction measures. The objective of this study was to use the UK Biobank to predict the 10-year risk of melanoma using a newly developed polygenic risk score (PRS) and an existing clinical risk model. We developed the PRS using a matched case-control training dataset ( N  = 16 434) in which age and sex were controlled by design. The combined risk score was developed using a cohort development dataset ( N  = 54 799) and its performance was tested using a cohort testing dataset ( N  = 54 798). Our PRS comprises 68 single-nucleotide polymorphisms and had an area under the receiver operating characteristic curve of 0.639 [95% confidence interval (CI) = 0.618-0.661]. In the cohort testing data, the hazard ratio per SD of the combined risk score was 1.332 (95% CI = 1.263-1.406). Harrell's C-index was 0.685 (95% CI = 0.654-0.715). Overall, the standardized incidence ratio was 1.193 (95% CI = 1.067-1.335). By combining a PRS and a clinical risk score, we have developed a risk prediction model that performs well in terms of discrimination and calibration. At an individual level, information on the 10-year risk of melanoma can motivate people to take risk-reduction action. At the population level, risk stratification can allow more effective population-level screening strategies to be implemented.

Validation of an Abridged Breast Cancer Risk Prediction Model for the General Population.

PREVENTION RELEVANCE: In this prospective population-based cohort study, we show the improved performance of a new risk assessment model compared with a gold-standard model (BCRAT). The classification of at-risk women using this new model highlights the opportunity to improve risk stratification and implement existing clinical risk-reduction interventions.

Validation of a breast cancer risk prediction model based on the key risk factors: family history, mammographic density and polygenic risk.

PURPOSE: We compared a simple breast cancer risk prediction model, BRISK (which includes mammographic density, polygenic risk and clinical factors), against a similar model with more risk factors (simplified Rosner) and against two commonly used clinical models (Gail and IBIS). METHODS: Using nested case-control data from the Nurses' Health Study, we compared the models' association, discrimination and calibration. Classification performance was compared between Gail and BRISK for 5-year risks and between IBIS and BRISK for remaining lifetime risk. RESULTS: The odds ratio per standard deviation was 1.43 (95% CI 1.32, 1.55) for BRISK 5-year risk, 1.07 (95% CI 0.99, 1.14) for Gail 5-year risk, 1.72 (95% CI 1.59, 1.87) for simplified Rosner 10-year risk, 1.51 (95% CI 1.41, 1.62) for BRISK remaining lifetime risk and 1.26 (95% CI 1.16, 1.36) for IBIS remaining lifetime risk. The area under the receiver operating characteristic curve (AUC) was improved for BRISK over Gail for 5-year risk (AUC = 0.636 versus 0.511, P < 0.0001) and for BRISK over IBIS for remaining lifetime risk (AUC = 0.647 versus 0.571, P < 0.0001). BRISK was well calibrated for the estimation of both 5-year risk (expected/observed [E/O] = 1.03; 95% CI 0.73, 1.46) and remaining lifetime risk (E/O = 1.01; 95% CI 0.86, 1.17). The Gail 5-year risk (E/O = 0.85; 95% CI 0.58, 1.24) and IBIS remaining lifetime risk (E/O = 0.73; 95% CI 0.60, 0.87) were not well calibrated, with both under-estimating risk. BRISK improves classification of risk compared to Gail 5-year risk (NRI = 0.31; standard error [SE] = 0.031) and IBIS remaining lifetime risk (NRI = 0.287; SE = 0.035). CONCLUSION: BRISK performs better than two commonly used clinical risk models and no worse compared to a similar model with more risk factors.

A combined clinical and genetic model for predicting risk of ovarian cancer.

OBJECTIVE: Women with a family history of ovarian cancer or a pathogenic or likely pathogenic gene variant are at high risk of the disease, but very few women have these risk factors. We assessed whether a combined polygenic and clinical risk score could predict risk of ovarian cancer in population-based women who would otherwise be considered as being at average risk. METHODS: We used the UK Biobank to conduct a prospective cohort study assessing the performance of 10-year ovarian cancer risks based on a polygenic risk score, a clinical risk score and a combined risk score. We used Cox regression to assess association, Harrell's C-index to assess discrimination and Poisson regression to assess calibration. RESULTS: The combined risk model performed best and problems with calibration were overcome by recalibrating the model, which then had a hazard ratio per quintile of risk of 1.338 [95% confidence interval (CI), 1.152-1.553], a Harrell's C-index of 0.663 (95% CI, 0.629-0.698) and overall calibration of 1.000 (95% CI, 0.874-1.145). In the refined model with estimates based on the entire dataset, women in the top quintile of 10-year risk were at 1.387 (95% CI, 1.086-1.688) times increased risk, while women in the top quintile of full-lifetime risk were at 1.527 (95% CI, 1.187-1.866) times increased risk compared with the population. CONCLUSION: Identification of women who are at high risk of ovarian cancer can allow healthcare providers and patients to engage in joint decision-making discussions around the risks and benefits of screening options or risk-reducing surgery.

Predicting 10-Year Risk of Pancreatic Cancer Using a Combined Genetic and Clinical Model.

Background and Aims: Pancreatic cancer has the poorest 5-year survival rate of any major solid tumor, but when diagnosed at an early stage, survival rates improve. Population screening is impractical because pancreatic cancer is rare with a lifetime risk of 1.7%, but accurate risk stratification in the general population could enable health care providers to focus early detection strategies to at-risk individuals. Here, we validate a combined risk prediction model that integrates a polygenic risk score and a clinical risk model. Methods: Using the UK Biobank, we conducted a prospective cohort study assessing 10-year pancreatic cancer risks based on a polygenic risk score, a clinical risk score, and a combined risk score. We assessed the association, discrimination, calibration, cumulative hazards, and standardized incidence ratios compared to population incidence rates for the risk scores. We also conducted net reclassification analyses. Results: While all of the risk scores discriminated well between affected and unaffected participants, the combined risk score - with a Harrell's C-index of 0.714 (95% confidence interval [CI] = 0.698, 0.730) - discriminated better than both the polygenic risk score (P = .001) and the clinical risk score (P = .02). In terms of calibration, there was no problem with dispersion for the combined risk score (ß = 0.952, 95% CI = 0.865-1.039, P = .3) and overall there was a small overestimation of risk (α = -0.089, 95% CI = -0.156 to -0.021, P = .009). Participants in the top decile of 10-year risk were at 1.413 (95% CI = 1.242-1.607) times population risk. Conclusion: The combined risk score was able to identify individuals at substantially increased risk of pancreatic cancer and to whom targeted screening could be useful.

A streamlined model for use in clinical breast cancer risk assessment maintains predictive power and is further improved with inclusion of a polygenic risk score.

Five-year absolute breast cancer risk prediction models are required to comply with national guidelines regarding risk reduction regimens. Models including the Gail model are under-utilized in the general population for various reasons, including difficulty in accurately completing some clinical fields. The purpose of this study was to determine if a streamlined risk model could be designed without substantial loss in performance. Only the clinical risk factors that were easily answered by women will be retained and combined with an objective validated polygenic risk score (PRS) to ultimately improve overall compliance with professional recommendations. We first undertook a review of a series of 2,339 Caucasian, African American and Hispanic women from the USA who underwent clinical testing. We first used deidentified test request forms to identify the clinical risk factors that were best answered by women in a clinical setting and then compared the 5-year risks for the full model and the streamlined model in this clinical series. We used OPERA analysis on previously published case-control data from 11,924 Gail model samples to determine clinical risk factors to include in a streamlined model: first degree family history and age that could then be combined with the PRS. Next, to ensure that the addition of PRS to the streamlined model was indeed beneficial, we compared risk stratification using the Streamlined model with and without PRS for the existing case-control datasets comprising 1,313 cases and 10,611 controls of African-American (n = 7421), Caucasian (n = 1155) and Hispanic (n = 3348) women, using the area under the curve to determine model performance. The improvement in risk discrimination from adding the PRS risk score to the Streamlined model was 52%, 46% and 62% for African-American, Caucasian and Hispanic women, respectively, based on changes in log OPERA. There was no statistically significant difference in mean risk scores between the Gail model plus risk PRS compared to the Streamlined model plus PRS. This study demonstrates that validated PRS can be used to streamline a clinical test for primary care practice without diminishing test performance. Importantly, by eliminating risk factors that women find hard to recall or that require obtaining medical records, this model may facilitate increased clinical adoption of 5-year risk breast cancer risk prediction test in keeping with national standards and guidelines for breast cancer risk reduction.

Sustained Adrenergic Signaling Promotes Intratumoral Innervation through BDNF Induction.

Mounting clinical and preclinical evidence supports a key role for sustained adrenergic signaling in the tumor microenvironment as a driver of tumor growth and progression. However, the mechanisms by which adrenergic neurotransmitters are delivered to the tumor microenvironment are not well understood. Here we present evidence for a feed-forward loop whereby adrenergic signaling leads to increased tumoral innervation. In response to catecholamines, tumor cells produced brain-derived neurotrophic factor (BDNF) in an ADRB3/cAMP/Epac/JNK-dependent manner. Elevated BDNF levels in the tumor microenvironment increased innervation by signaling through host neurotrophic receptor tyrosine kinase 2 receptors. In patients with cancer, high tumor nerve counts were significantly associated with increased BDNF and norepinephrine levels and decreased overall survival. Collectively, these data describe a novel pathway for tumor innervation, with resultant biological and clinical implications.Significance: Sustained adrenergic signaling promotes tumor growth and metastasis through BDNF-mediated tumoral innervation. Cancer Res; 78(12); 3233-42. ©2018 AACR.

Bridging the Data Gap in Breast Cancer Risk Assessment to Enable Widespread Clinical Implementation across the Multiethnic Landscape of the US.

Breast cancer remains the second leading cause of cancer death among women and is the most commonly diagnosed cancer in women. Breast cancer risk assessment has been clinically available for nearly 30 years yet is under-utilized in practice for multiple reasons. Incorporation of polygenic risk as well as breast density measurements, promise to increase the accuracy of risk assessment. With that comes the hope that both prevention and screening become more personalized and thus more effective. Incidence rates have been static over the past 15 years and have even increased slightly in African American and Asian/Pacific Islander populations despite the robust data on breast cancer risk reduction measures that exist. Current challenges in reducing breast cancer incidence begin with robust data curation that allows for appropriate risk stratification across our multiethnic population and conclude with the implementation of prevention strategies within our fractured healthcare system.

Role of neoplastic monocyte-derived fibrocytes in primary myelofibrosis.

Primary myelofibrosis (PMF) is a fatal neoplastic disease characterized by clonal myeloproliferation and progressive bone marrow (BM) fibrosis thought to be induced by mesenchymal stromal cells stimulated by overproduced growth factors. However, tissue fibrosis in other diseases is associated with monocyte-derived fibrocytes. Therefore, we sought to determine whether fibrocytes play a role in the induction of BM fibrosis in PMF. In this study, we show that BM from patients with PMF harbors an abundance of clonal, neoplastic collagen- and fibronectin-producing fibrocytes. Immunodeficient mice transplanted with myelofibrosis patients' BM cells developed a lethal myelofibrosis-like phenotype. Treatment of the xenograft mice with the fibrocyte inhibitor serum amyloid P (SAP; pentraxin-2) significantly prolonged survival and slowed the development of BM fibrosis. Collectively, our data suggest that neoplastic fibrocytes contribute to the induction of BM fibrosis in PMF, and inhibiting fibrocyte differentiation with SAP may interfere with this process.

Reciprocal leukemia-stroma VCAM-1/VLA-4-dependent activation of NF-κB mediates chemoresistance.

Leukemia cells are protected from chemotherapy-induced apoptosis by their interactions with bone marrow mesenchymal stromal cells (BM-MSCs). Yet the underlying mechanisms associated with this protective effect remain unclear. Genome-wide gene expression profiling of BM-MSCs revealed that coculture with leukemia cells upregulated the transcription of genes associated with nuclear factor (NF)-κB signaling. Moreover, primary BM-MSCs from leukemia patients expressed NF-κB target genes at higher levels than their normal BM-MSC counterparts. The blockade of NF-κB activation via chemical agents or the overexpression of the mutant form of inhibitor κB-α (IκBα) in BM-MSCs markedly reduced the stromal-mediated drug resistance in leukemia cells in vitro and in vivo. In particular, our unique in vivo model of human leukemia BM microenvironment illustrated a direct link between NF-κB activation and stromal-associated chemoprotection. Mechanistic in vitro studies revealed that the interaction between vascular cell adhesion molecule 1 (VCAM-1) and very late antigen-4 (VLA-4) played an integral role in the activation of NF-κB in the stromal and tumor cell compartments. Together, these results suggest that reciprocal NF-κB activation in BM-MSCs and leukemia cells is essential for promoting chemoresistance in the transformed cells, and targeting NF-κB or VLA-4/VCAM-1 signaling could be a clinically relevant mechanism to overcome stroma-mediated chemoresistance in BM-resident leukemia cells.

Quantitative multispectral analysis following fluorescent tissue transplant for visualization of cell origins, types, and interactions.

With the desire to understand the contributions of multiple cellular elements to the development of a complex tissue; such as the numerous cell types that participate in regenerating tissue, tumor formation, or vasculogenesis, we devised a multi-colored cellular transplant model of tumor development in which cell populations originate from different fluorescently colored reporter gene mice and are transplanted, engrafted or injected in and around a developing tumor. These colored cells are then recruited and incorporated into the tumor stroma. In order to quantitatively assess bone marrow derived tumor stromal cells, we transplanted GFP expressing transgenic whole bone marrow into lethally irradiated RFP expressing mice as approved by IACUC. 0ovarian tumors that were orthotopically injected into the transplanted mice were excised 6-8 weeks post engraftment and analyzed for bone marrow marker of origin (GFP) as well as antibody markers to detect tumor associated stroma using multispectral imaging techniques. We then adapted a methodology we call MIMicc- Multispectral Interrogation of Multiplexed cellular compositions, using multispectral unmixing of fluoroprobes to quantitatively assess which labeled cell came from which starting populations (based on original reporter gene labels), and as our ability to unmix 4, 5, 6 or more spectra per slide increases, we've added additional immunohistochemistry associated with cell lineages or differentiation to increase precision. Utilizing software to detect co-localized multiplexed-fluorescent signals, tumor stromal populations can be traced, enumerated and characterized based on marker staining.

Mesenchymal CD44 expression contributes to the acquisition of an activated fibroblast phenotype via TWIST activation in the tumor microenvironment.

Tumor-stroma interactions play a crucial role in cancer progression by eliciting factors that promote proliferative, angiogenic, and invasive supports to the tumor microenvironment. Mesenchymal stromal/stem cells (MSC) contribute to stroma in part as cancer-associated fibroblasts (CAF), but a complete understanding of how MSC contribute to the tumor stroma is lacking. In this study, we show how CAF phenotypes rely upon MSC expression of the multifunctional cell surface glycoprotein CD44, a putative stem cell marker. Through bone marrow transplantation experiments in a transgenic mouse model of cancer, we determined that CD44 deficiency leads to a relative reduction in the contribution of bone marrow-derived cells to tumor stroma. CD44 attenuation in MSC limited their expression of CAF markers induced by tumor conditioning, and these MSC migrated poorly and provided weak angiogenic support compared with wild-type MSC. These defects were linked to deficiencies in the ability of CD44-attenuated MSC to transcriptionally upregulate Twist expression. Together, our results establish that CD44 expression contributes to critical functions in the tumor stroma.

The CXCR4 antagonist AMD3465 regulates oncogenic signaling and invasiveness in vitro and prevents breast cancer growth and metastasis in vivo.

CXCR4, the receptor for stromal-derived factor-1, is reportedly involved in breast carcinogenesis. However, the mechanisms through which CXCR4 contributes to breast cancer cell growth and metastases are poorly understood. In this study, we examined the putative in vitro and in vivo anti-cancer effects of the specific CXCR4 inhibitor AMD3465. Here, we report that AMD3465 triggers a reduction in breast cancer cell invasiveness in vitro, and promotes marked changes in oncogenic signaling proteins including a reduction in STAT3, JAK2, AKT, and CXCR4 phosphorylation and the reduced expression of GSK3 and cMYC. Using three breast cancer cell lines as murine syngeneic immunocompetent breast cancer models, we found that AMD3465 inhibited breast tumor formation and reduced tumor cell metastases to the lung and liver. Furthermore, treatment with AMD3465 significantly reduced the infiltration of myeloid CD11b positive cells at the aforementioned metastatic sites as well as the spleen implying this agent could regulate the formation of the tumor microenvironment and conceivably the premetastatic niche. In conclusion, our studies suggest that AMD3465 inhibits breast cancer growth and metastases by acting on tumor cells as well as immune cells that constitute the tumor microenvironment. This process appears to be regulated, at least in part, through the modulation of oncogenic signaling that includes the STAT3 pathway. Thus, CXCR4 could be a novel target for breast cancer therapy.

Tumor stroma engraftment of gene-modified mesenchymal stem cells as anti-tumor therapy against ovarian cancer.

BACKGROUND AIMS: Many ovarian cancers originate from ovarian surface epithelium, where they develop from cysts intermixed with stroma. The stromal layer is critical to the progression and survival of the neoplasm and consequently is recruited into the tumor microenvironment. METHODS: Using both syngeneic mouse tumors (ID8-R) and human xenograft (OVCAR3, SKOV3) tumor models, we first confirmed that intraperitoneally injected circulating mesenchymal stem cells (MSCs) could target, preferentially engraft and differentiate into α-smooth muscle actin-positive myofibroblasts, suggesting their role as "reactive stroma" in ovarian carcinoma development and confirming their potential as a targeted delivery vehicle for the intratumoral production of interferon-ß (IFN-ß). Mice with ovarian carcinomas then received weekly intraperitoneal injections of IFN-ß expressing MSCs. RESULTS: Intraperitoneal injections of IFN-ß expressing MSCs resulted in complete eradication of tumors in 70% of treated OVCAR3 mice (P = 0.004) and an increased survival of treated SKOV3 mice compared with controls (P = 0.01). Similar tumor growth control was observed using murine IFN-ß delivered by murine MSCs in ID8-R ovarian carcinoma. As a potential mechanism of tumor killing, MSCs produced IFN-ß-induced caspase-dependent tumor cell apoptosis. CONCLUSIONS: Our results demonstrate that ovarian carcinoma engrafts MSCs to participate in myofibrovascular networks and that IFN-ß produced by MSCs intratumorally modulates tumor kinetics, resulting in prolonged survival.

Tracking inflammation-induced mobilization of mesenchymal stem cells.

The act of migration is similar for many cell types. The migratory mechanism of mesenchymal stem cells (MSC) is not completely elucidated, yet many of the initial studies have been based on current understanding of the leukocyte migration. A normal function of MSC is the ability of the cell to migrate to and repair wounded tissue. This wound healing property of MSC originates with migration towards inflammatory signals produced by the wounded environment [1]. A tumor and its microenvironment are capable of eliciting a similar inflammatory response from the MSC, thus resulting in migration of the MSC towards the tumor microenvironment. We have shown MSC migration both in vitro and in vivo. In this chapter, we elucidate several in vivo methods to study MSC migration and mobilization to the tumor microenvironment. The first model is an exogenous model of MSC migration that can be performed in both xenograft and syngenic systems with in vitro expanded MSC. The second model utilizes transgenic-fluorescent--colored mice to follow endogenous bone marrow components including MSC. The third technique enables us to analyze data from the transgenic model through multispectral imaging. Furthermore, the migratory phenotype of MSC can be exploited for use in tumor-targeted gene delivery therapy has been efficacious in animal model studies and is an anticipated therapeutic device in clinical trials.

Ganglioside GD2 identifies breast cancer stem cells and promotes tumorigenesis.

Cancer stem cells (CSCs) are a small subpopulation of cancer cells that have increased resistance to conventional therapies and are capable of establishing metastasis. However, only a few biomarkers of CSCs have been identified. Here, we report that ganglioside GD2 (a glycosphingolipid) identifies a small fraction of cells in human breast cancer cell lines and patient samples that are capable of forming mammospheres and initiating tumors with as few as 10 GD2+ cells. In addition, the majority of GD2+ cells are also CD44hiCD24lo, the previously established CSC-associated cell surface phenotype. Gene expression analysis revealed that GD3 synthase (GD3S) is highly expressed in GD2+ as well as in CD44hiCD24lo cells and that interference with GD3S expression, either by shRNA or using a pharmacological inhibitor, reduced the CSC population and CSC-associated properties. GD3S knockdown completely abrogated tumor formation in vivo. Also, induction of epithelial-mesenchymal transition (EMT) in transformed human mammary epithelial cells (HMLER cells) dramatically increased GD2 as well as GD3S expression in these cells, suggesting a role of EMT in the origin of GD2+ breast CSCs. In summary, we identified GD2 as a new CSC-specific cell surface marker and GD3S as a potential therapeutic target for CSCs, with the possibility of improving survival and cure rates in patients with breast cancer.

Origins of the tumor microenvironment: quantitative assessment of adipose-derived and bone marrow-derived stroma.

To meet the requirements for rapid tumor growth, a complex array of non-neoplastic cells are recruited to the tumor microenvironment. These cells facilitate tumor development by providing matrices, cytokines, growth factors, as well as vascular networks for nutrient and waste exchange, however their precise origins remain unclear. Through multicolored tissue transplant procedures; we have quantitatively determined the contribution of bone marrow-derived and adipose-derived cells to stromal populations within syngeneic ovarian and breast murine tumors. Our results indicate that subpopulations of tumor-associated fibroblasts (TAFs) are recruited from two distinct sources. The majority of fibroblast specific protein (FSP) positive and fibroblast activation protein (FAP) positive TAFs originate from mesenchymal stem/stromal cells (MSC) located in bone marrow sources, whereas most vascular and fibrovascular stroma (pericytes, α-SMA(+) myofibroblasts, and endothelial cells) originates from neighboring adipose tissue. These results highlight the capacity for tumors to utilize multiple sources of structural cells in a systematic and discriminative manner.

Dissecting mesenchymal stem cell movement: migration assays for tracing and deducing cell migration.

Targeted migration is a necessary attribute for any gene delivery vehicle. Mesenchymal stem cells (MSC) have been used as effective delivery vehicles for treatments against cancer, graft versus host disease, -arthritis, multiple sclerosis, and many other diseases. MSC migrate toward sites of inflammation, however, the true migratory mechanism has yet to be elucidated. There are several receptors and respective chemokines known to be involved in the migration of the MSC. Further insight to MSC migration will be revealed both in vivo and in vitro through the application of migration assays from the most simple, to the more technologically demanding.

Concise review: Dissecting a discrepancy in the literature: do mesenchymal stem cells support or suppress tumor growth?

The discovery that mesenchymal stem cells (MSCs) are recruited into tumors has led to a great deal of interest over the past decade in the function of MSCs in tumors. To address this, investigators have used a variety of tumor models in which MSCs are added exogenously to determine their impact on tumor development. Interestingly, many studies have reported contradicting results, with some investigators finding that MSCs promote tumor growth and others reporting that MSCs inhibit tumor growth. Many mechanisms have been reported to account for these observations, such as chemokine signaling, modulation of apoptosis, vascular support, and immune modulation. In this review, we analyzed the differences in the methodology of the studies reported and found that the timing of MSC introduction into tumors may be a critical element. Understanding the conditions in which MSCs enhance tumor growth and metastasis is crucial, both to safely develop MSCs as a therapeutic tool and to advance our understanding of the role of tumor stroma in carcinogenesis.