Jianting Sheng

Epithelial-to-mesenchymal transition is not required for lung metastasis but contributes to chemoresistance.

The role of epithelial-to-mesenchymal transition (EMT) in metastasis is a longstanding source of debate, largely owing to an inability to monitor transient and reversible EMT phenotypes in vivo. Here we establish an EMT lineage-tracing system to monitor this process in mice, using a mesenchymal-specific Cre-mediated fluorescent marker switch system in spontaneous breast-to-lung metastasis models. We show that within a predominantly epithelial primary tumour, a small proportion of tumour cells undergo EMT. Notably, lung metastases mainly consist of non-EMT tumour cells that maintain their epithelial phenotype. Inhibiting EMT by overexpressing the microRNA miR-200 does not affect lung metastasis development. However, EMT cells significantly contribute to recurrent lung metastasis formation after chemotherapy. These cells survived cyclophosphamide treatment owing to reduced proliferation, apoptotic tolerance and increased expression of chemoresistance-related genes. Overexpression of miR-200 abrogated this resistance. This study suggests the potential of an EMT-targeting strategy, in conjunction with conventional chemotherapies, for breast cancer treatment.The role of epithelial to mesenchymal transition (EMT) in metastasis is a longstanding source of controversy, largely due to an inability to monitor transient and reversible EMT phenotypes in vivo. We established an EMT lineage tracing system to monitor this process, using a mesenchymal-specific Cre-mediated fluorescent marker switch system in spontaneous breast-to-lung metastasis models. We confirmed that within a predominantly epithelial primary tumor, a small portion of tumor cells undergo EMT. Strikingly, lung metastases mainly consisted of non-EMT tumor cells maintaining their epithelial phenotype. Inhibiting EMT by overexpressing miR-200 did not impact lung metastasis development. However, EMT cells significantly contribute to recurrent lung metastasis formation after chemotherapy. These cells survived cyclophosphamide treatment due to reduced proliferation, apoptotic tolerance, and elevated expression of chemoresistance-related genes. Overexpression of miR-200 abrogated this resistance. This study suggests the potential of an EMT-targeting strategy, in conjunction with conventional chemotherapies, for breast cancer treatment.

Efficient and Rapid Derivation of Primitive Neural Stem Cells and Generation of Brain Subtype Neurons From Human Pluripotent Stem Cells

Human pluripotent stem cells (hPSCs), including human embryonic stem cells and human induced pluripotent stem cells, are unique cell sources for disease modeling, drug discovery screens, and cell therapy applications. The first step in producing neural lineages from hPSCs is the generation of neural stem cells (NSCs). Current methods of NSC derivation involve the time‐consuming, labor‐intensive steps of an embryoid body generation or coculture with stromal cell lines that result in low‐efficiency derivation of NSCs. In this study, we report a highly efficient serum‐free pluripotent stem cell neural induction medium that can induce hPSCs into primitive NSCs (pNSCs) in 7 days, obviating the need for time‐consuming, laborious embryoid body generation or rosette picking. The pNSCs expressed the neural stem cell markers Pax6, Sox1, Sox2, and Nestin; were negative for Oct4; could be expanded for multiple passages; and could be differentiated into neurons, astrocytes, and oligodendrocytes, in addition to the brain region‐specific neuronal subtypes GABAergic, dopaminergic, and motor neurons. Global gene expression of the transcripts of pNSCs was comparable to that of rosette‐derived and human fetal‐derived NSCs. This work demonstrates an efficient method to generate expandable pNSCs, which can be further differentiated into central nervous system neurons and glia with temporal, spatial, and positional cues of brain regional heterogeneity. This method of pNSC derivation sets the stage for the scalable production of clinically relevant neural cells for cell therapy applications in good manufacturing practice conditions.

DrugComboRanker: drug combination discovery based on target network analysis

Motivation: Currently there are no curative anticancer drugs, and drug resistance is often acquired after drug treatment. One of the reasons is that cancers are complex diseases, regulated by multiple signaling pathways and cross talks among the pathways. It is expected that drug combinations can reduce drug resistance and improve patients’ outcomes. In clinical practice, the ideal and feasible drug combinations are combinations of existing Food and Drug Administration-approved drugs or bioactive compounds that are already used on patients or have entered clinical trials and passed safety tests. These drug combinations could directly be used on patients with less concern of toxic effects. However, there is so far no effective computational approach to search effective drug combinations from the enormous number of possibilities. Results: In this study, we propose a novel systematic computational tool DrugComboRanker to prioritize synergistic drug combinations and uncover their mechanisms of action. We first build a drug functional network based on their genomic profiles, and partition the network into numerous drug network communities by using a Bayesian non-negative matrix factorization approach. As drugs within overlapping community share common mechanisms of action, we next uncover potential targets of drugs by applying a recommendation system on drug communities. We meanwhile build disease-specific signaling networks based on patients’ genomic profiles and interactome data. We then identify drug combinations by searching drugs whose targets are enriched in the complementary signaling modules of the disease signaling network. The novel method was evaluated on lung adenocarcinoma and endocrine receptor positive breast cancer, and compared with other drug combination approaches. These case studies discovered a set of effective drug combinations top ranked in our prediction list, and mapped the drug targets on the disease signaling network to highlight the mechanisms of action of the drug combinations. Availability and implementation: The program is available on request. Contact: stwong@tmhs.org

Platelets reduce anoikis and promote metastasis by activating YAP1 signaling

Thrombocytosis is present in more than 30% of patients with solid malignancies and correlates with worsened patient survival. Tumor cell interaction with various cellular components of the tumor microenvironment including platelets is crucial for tumor growth and metastasis. Although it is known that platelets can infiltrate into tumor tissue, secrete pro-angiogenic and pro-tumorigenic factors and thereby increase tumor growth, the precise molecular interactions between platelets and metastatic cancer cells are not well understood. Here we demonstrate that platelets induce resistance to anoikis in vitro and are critical for metastasis in vivo. We further show that platelets activate RhoA-MYPT1-PP1-mediated YAP1 dephosphorylation and promote its nuclear translocation which induces a pro-survival gene expression signature and inhibits apoptosis. Reduction of YAP1 in cancer cells in vivo protects against thrombocytosis-induced increase in metastasis. Collectively, our results indicate that cancer cells depend on platelets to avoid anoikis and succeed in the metastatic process.Platelets have been associated with increased tumor growth and metastasis but the mechanistic details of this interaction are still unclear. Here the authors show that platelets improve anoikis resistance of cancer cells and increase metastasis by activating Yap through a RhoA/MYPT-PP1 pathway.

Epithelial derived CTGF promotes breast tumor progression via inducing EMT and collagen I fibers deposition

Interactions among tumor cells, stromal cells, and extracellular matrix compositions are mediated through cytokines during tumor progression. Our analysis of 132 known cytokines and growth factors in published clinical breast cohorts and our 84 patient-derived xenograft models revealed that the elevated connective tissue growth factor (CTGF) in tumor epithelial cells significantly correlated with poor clinical prognosis and outcomes. CTGF was able to induce tumor cell epithelial-mesenchymal transition (EMT), and promote stroma deposition of collagen I fibers to stimulate tumor growth and metastasis. This process was mediated through CTGF-tumor necrosis factor receptor I (TNFR1)-IκB autocrine signaling. Drug treatments targeting CTGF, TNFR1, and IκB signaling each prohibited the EMT and tumor progression.

Cancer-associated fibroblasts regulate endothelial adhesion protein LPP to promote ovarian cancer chemoresistance

The molecular mechanism by which cancer-associated fibroblasts (CAFs) confer chemoresistance in ovarian cancer is poorly understood. The purpose of the present study was to evaluate the roles of CAFs in modulating tumor vasculature, chemoresistance, and disease progression. Here, we found that CAFs upregulated the lipoma-preferred partner (LPP) gene in microvascular endothelial cells (MECs) and that LPP expression levels in intratumoral MECs correlated with survival and chemoresistance in patients with ovarian cancer. Mechanistically, LPP increased focal adhesion and stress fiber formation to promote endothelial cell motility and permeability. siRNA-mediated LPP silencing in ovarian tumor–bearing mice improved paclitaxel delivery to cancer cells by decreasing intratumoral microvessel leakiness. Further studies showed that CAFs regulate endothelial LPP via a calcium-dependent signaling pathway involving microfibrillar-associated protein 5 (MFAP5), focal adhesion kinase (FAK), ERK, and LPP. Thus, our findings suggest that targeting endothelial LPP enhances the efficacy of chemotherapy in ovarian cancer. Our data highlight the importance of CAF–endothelial cell crosstalk signaling in cancer chemoresistance and demonstrate the improved efficacy of using LPP-targeting siRNA in combination with cytotoxic drugs.

Optimal Drug Prediction From Personal Genomics Profiles

Cancer patients often show heterogeneous drug responses such that only a small subset of patients is sensitive to a given anticancer drug. With the availability of large-scale genomic profiling via next-generation sequencing, it is now economically feasible to profile the whole transcriptome and genome of individual patients in order to identify their unique genetic mutations and differentially expressed genes, which are believed to be responsible for heterogeneous drug responses. Although subtyping analysis has identified patient subgroups sharing common biomarkers, there is no effective method to predict the drug response of individual patients precisely and reliably. Herein, we propose a novel computational algorithm to predict the drug response of individual patients based on personal genomic profiles, as well as pharmacogenomic and drug sensitivity data. Specifically, more than 600 cancer cell lines (viewed as individual patients) across over 50 types of cancers and their responses to 75 drugs were obtained from the genomics of drug sensitivity in cancer database. The drug-specific sensitivity signatures were determined from the changes in genomic profiles of individual cell lines in response to a specific drug. The optimal drugs for individual cell lines were predicted by integrating the votes from other cell lines. The experimental results show that the proposed drug prediction algorithm can be used to improve greatly the reliability of finding optimal drugs for individual patients and will, thus, form a key component in the precision medicine infrastructure for oncology care.

A computational method for clinically relevant cancer stratification and driver mutation module discovery using personal genomics profiles

BackgroundPersonalized genomics instability, e.g., somatic mutations, is believed to contribute to the heterogeneous drug responses in patient cohorts. However, it is difficult to discover personalized driver mutations that are predictive of drug sensitivity owing to diverse and complex mutations of individual patients. To circumvent this problem, a novel computational method is presented to discover potential drug sensitivity relevant cancer subtypes and identify driver mutation modules of individual subtypes by coupling differentially expressed genes (DEGs) based subtyping analysis with the driver mutation network analysis.ResultsThe proposed method was applied to breast cancer and lung cancer samples available from The Cancer Genome Atlas (TCGA). Cancer subtypes were uncovered with significantly different survival rates, and more interestingly, distinct driver mutation modules were also discovered among different subtypes, indicating the potential mechanism of heterogeneous drug sensitivity.ConclusionsThe research findings can be used to help guide the repurposing of known drugs and their combinations in order to target these dysfunctional modules and their downstream signaling effectively for achieving personalized or precision medicine treatment.

Systematic Identification of Druggable Epithelial-Stromal Crosstalk Signaling Networks in Ovarian Cancer

Abstract Background Bulk tumor tissue samples are used for generating gene expression profiles in most research studies, making it difficult to decipher the stroma–cancer crosstalk networks. In the present study, we describe the use of microdissected transcriptome profiles for the identification of cancer–stroma crosstalk networks with prognostic value, which presents a unique opportunity for developing new treatment strategies for ovarian cancer. Methods Transcriptome profiles from microdissected ovarian cancer–associated fibroblasts (CAFs) and ovarian cancer cells from patients with high-grade serous ovarian cancer (n = 70) were used as input data for the computational systems biology program CCCExplorer to uncover crosstalk networks between various cell types within the tumor microenvironment. The crosstalk analysis results were subsequently used for discovery of new indications for old drugs in ovarian cancer by computational ranking of candidate agents. Survival analysis was performed on ovarian tumor–bearing Dicer/Pten double-knockout mice treated with calcitriol, a US Food and Drug Administration–approved agent that suppresses the Smad signaling cascade, or vehicle control (9–11 mice per group). All statistical tests were two-sided. Results Activation of TGF-β-dependent and TGF-β-independent Smad signaling was identified in a particular subtype of CAFs and was associated with poor patient survival (patients with higher levels of Smad-regulated gene expression by CAFs: median overall survival = 15 months, 95% confidence interval [CI] = 12.7 to 17.3 months; vs patients with lower levels of Smad-regulated gene expression: median overall survival = 26 months, 95% CI = 15.9 to 36.1 months, P = .02). In addition, the activated Smad signaling identified in CAFs was found to be targeted by repositioning calcitriol. Calcitriol suppressed Smad signaling in CAFs, inhibited tumor progression in mice, and prolonged the median survival duration of ovarian cancer–bearing mice from 36 to 48 weeks (P = .04). Conclusions Our findings suggest the feasibility of using novel multicellular systems biology modeling to identify and repurpose known drugs targeting cancer–stroma crosstalk networks, potentially leading to faster and more effective cures for cancers.

DrugMoaMiner: A computational tool for mechanism of action discovery and personalized drug sensitivity prediction

Heterogeneity of genomic instabilities among individual patients is believed to be a major cause of drug response heterogeneity. Cancer patients who are sensitive to anti-cancer drugs are often re-examined to understand the unknown mechanism of action (MoA) of given drugs. For example, a non-small cell lung cancer (NSCLC) patient was reported to be responsive to Dasatinib treatment and remained cancer-free four years later. Though follow-up genomic analysis showed the patient bears an inactivating BRAF [1]mutation in the tumor, the MoA remains unclear. There are two challenges in uncovering the MoA. First, Dasatinib is a kinase inhibitor, which often has many protein targets. Second, the downstream MoA signaling pathways regulated by these targets are too complicated to delineate. Currently, there is no computational tool that can effectively address both challenges. To fill this gap, we developed a computational tool DrugMoaMiner (Drug MoA Miner) that can be used to identify the comprehensive set of kinase inhibitor targets; delineate the underlying drug MoA; and predict personalized sensitivity to a given drug based on an individuals gene expression profiles. We applied the DrugMoaMiner to lung cancer cell lines to uncover the potential MoA signaling network of Dasatinib sensitivity; our result is in agreement with previous protein data analysis. Moreover, we can predict Dasatinib response of an independent set of NSCLC cell lines using the MoA signaling network uncovered.