A deep profile of gene expression across 18 human cancers.

Clinically and biologically valuable information may reside untapped in large cancer gene expression data sets. Deep unsupervised learning has the potential to extract this information with unprecedented efficacy but has thus far been hampered by a lack of biological interpretability and robustness. Here, we present DeepProfile, a comprehensive framework that addresses current challenges in applying unsupervised deep learning to gene expression profiles. We use DeepProfile to learn low-dimensional latent spaces for 18 human cancers from 50,211 transcriptomes. DeepProfile outperforms existing dimensionality reduction methods with respect to biological interpretability. Using DeepProfile interpretability methods, we show that genes that are universally important in defining the latent spaces across all cancer types control immune cell activation, while cancer type-specific genes and pathways define molecular disease subtypes. By linking DeepProfile latent variables to secondary tumor characteristics, we discover that tumor mutation burden is closely associated with the expression of cell cycle-related genes. DNA mismatch repair and MHC class II antigen presentation pathway expression, on the other hand, are consistently associated with patient survival. We validate these results through Kaplan-Meier analyses and nominate tumor-associated macrophages as an important source of survival-correlated MHC class II transcripts. Our results illustrate the power of unsupervised deep learning for discovery of novel cancer biology from existing gene expression data.

Engagement of CD300c by a novel monoclonal antibody induces the differentiation of monocytes to M1 macrophages.

Human CD300c is expressed on various immune or cancer cells and is a novel B7 family member, functioning as an activity modulator on immune cells. To elucidate the function of CD300c, we developed CL7, a human CD300c-specific monoclonal antibody, and assessed its biological activity. The specific binding of CL7 monoclonal antibody against recombinant CD300c antigen was confirmed using enzyme-linked immunosorbent assay and surface plasmon resonance analysis. The binding affinity of CL7 was strong at the sub-nanomolar level. Furthermore, CL7 effectively bound to exogenously expressed CD300c on 293T cells. CL7 antibody differentiated monocytes to M1 macrophages, as evidenced by the upregulated expression of M1-specific cell surface markers and increased secretion of M1-specific cytokines in vitro in THP-1 cells and primary macrophages, as well as the increased population size of M1 macrophages in tumors grafted into mice. Additionally, CL7 treatment upregulated PD-L1 expression on THP-1 cells. We confirmed that the mechanism of M1 macrophage differentiation was through the mitogen-activated protein kinase and NF-κB signaling pathways. CD300c expression on various immune and cancer cells was similar to that of the well-known immune checkpoint PD-L1, suggesting the possibility of CD300c as a novel tumor biomarker. We also confirmed that the tumor size was substantially reduced by CL7 antibody treatment in the CT26 mouse model. Our study supports that CD300c is a potential therapeutic target in immuno-oncology. Overall, the CD300c-specific monoclonal antibody, CL7, is a promising immunotherapeutic agent, and it induces enhanced differentiation of M1 macrophages and/or their infiltration into the tumor microenvironment.

Auditing the inference processes of medical-image classifiers by leveraging generative AI and the expertise of physicians.

The inferences of most machine-learning models powering medical artificial intelligence are difficult to interpret. Here we report a general framework for model auditing that combines insights from medical experts with a highly expressive form of explainable artificial intelligence. Specifically, we leveraged the expertise of dermatologists for the clinical task of differentiating melanomas from melanoma 'lookalikes' on the basis of dermoscopic and clinical images of the skin, and the power of generative models to render 'counterfactual' images to understand the 'reasoning' processes of five medical-image classifiers. By altering image attributes to produce analogous images that elicit a different prediction by the classifiers, and by asking physicians to identify medically meaningful features in the images, the counterfactual images revealed that the classifiers rely both on features used by human dermatologists, such as lesional pigmentation patterns, and on undesirable features, such as background skin texture and colour balance. The framework can be applied to any specialized medical domain to make the powerful inference processes of machine-learning models medically understandable.

Uncovering expression signatures of synergistic drug responses via ensembles of explainable machine-learning models.

Machine learning may aid the choice of optimal combinations of anticancer drugs by explaining the molecular basis of their synergy. By combining accurate models with interpretable insights, explainable machine learning promises to accelerate data-driven cancer pharmacology. However, owing to the highly correlated and high-dimensional nature of transcriptomic data, naively applying current explainable machine-learning strategies to large transcriptomic datasets leads to suboptimal outcomes. Here by using feature attribution methods, we show that the quality of the explanations can be increased by leveraging ensembles of explainable machine-learning models. We applied the approach to a dataset of 133 combinations of 46 anticancer drugs tested in ex vivo tumour samples from 285 patients with acute myeloid leukaemia and uncovered a haematopoietic-differentiation signature underlying drug combinations with therapeutic synergy. Ensembles of machine-learning models trained to predict drug combination synergies on the basis of gene-expression data may improve the feature attribution quality of complex machine-learning models.

GNAQ-Regulated ZO-1 and ZO-2 Act as Tumor Suppressors by Modulating EMT Potential and Tumor-Repressive Microenvironment in Lung Cancer.

Epithelial-to-mesenchymal transition (EMT) plays a critical role in the development and progression of lung cancer by promoting its invasiveness and metastasis. Using integrative analyses of the public lung cancer database, we found that the expression levels of the tight junction proteins, zonula occluden (ZO)-1 and ZO-2, were lower in lung cancer tissues, including both lung adenocarcinoma and lung squamous cell carcinoma than in normal lung tissues analyzed using The Cancer Genome Atlas (TCGA). Although the ectopic expression or knockdown of ZO-1 and ZO-2 did not affect the growth of lung cancer cells, they significantly regulated cell migration and invasion. When M0 macrophages were co-cultured with ZO-1 or ZO-2 knockdown Calu-1 cells, M2-like polarization was efficiently induced. Conversely, co-culture of M0 THP-1 cells with A549 cells stably expressing ZO-1 or ZO-2 significantly reduced M2 differentiation. We also identified G protein subunit alpha q (GNAQ) as a potential ZO-1- and ZO-2-specific activator through analysis of correlated genes with the TCGA lung cancer database. Our results suggest that the GNAQ-ZO-1/2 axis may play a tumor-suppressive role in lung cancer development and progression and highlight ZO-1 and ZO-2 as key EMT- and tumor microenvironment-suppressive proteins. These findings provide new insights for the development of targeted therapies for lung cancer.

Epigenome-wide analysis of long-term air pollution exposure and DNA methylation in monocytes: results from the Multi-Ethnic Study of Atherosclerosis.

Air pollution might affect atherosclerosis through DNA methylation changes in cells crucial to atherosclerosis, such as monocytes. We conducted an epigenome-wide study of DNA methylation in CD14+ monocytes and long-term ambient air pollution exposure in adults participating in the Multi-Ethnic Study of Atherosclerosis (MESA). We also assessed the association between differentially methylated signals and cis-gene expression. Using spatiotemporal models, one-year average concentrations of outdoor fine particulate matter (PM2.5) and oxides of nitrogen (NOX) were estimated at participants' homes. We assessed DNA methylation and gene expression using Illumina 450k and HumanHT-12 v4 Expression BeadChips, respectively (n = 1,207). We used bump hunting and site-specific approaches to identify differentially methylated signals (false discovery rate of 0.05) and used linear models to assess associations between differentially methylated signals and cis-gene expression. Four differentially methylated regions (DMRs) located on chromosomes 5, 6, 7, and 16 (within or near SDHAP3, ZFP57, HOXA5, and PRM1, respectively) were associated with PM2.5. The DMRs on chromosomes 5 and 6 also associated with NOX. The DMR on chromosome 5 had the smallest p-value for both PM2.5 (p = 1.4×10-6) and NOX (p = 7.7×10-6). Three differentially methylated CpGs were identified for PM2.5, and cg05926640 (near TOMM20) had the smallest p-value (p = 5.6×10-8). NOX significantly associated with cg11756214 within ZNF347 (p = 5.6×10-8). Several differentially methylated signals were also associated with cis-gene expression. The DMR located on chromosome 7 was associated with the expression of HOXA5, HOXA9, and HOXA10. The DMRs located on chromosomes 5 and 16 were associated with expression of MRPL36 and DEXI, respectively. The CpG cg05926640 was associated with expression of ARID4B, IRF2BP2, and TOMM20. We identified differential DNA methylation in monocytes associated with long-term air pollution exposure. Methylation signals associated with gene expression might help explain how air pollution contributes to cardiovascular disease.

Dopamine receptor antagonists induce differentiation of PC-3 human prostate cancer cell-derived cancer stem cell-like cells.

BACKGROUND: The objective of this study was to determine whether PC-3 human prostate cancer cell-derived cancer stem cells (CSC)-like cells grown in a regular cell culture plate not coated with a matrix molecule might be useful for finding differentiation-inducing agents that could alter properties of prostate CSC. METHODS: Monolayer cells prepared from sphere culture of PC-3 cells were characterized for the presence of pluripotency and tumorigenicity. They were then applied to screen a compound library to find compounds that could induce morphology changes of cells. Mechanisms of action of compounds selected from the chemical library that induced the loss of pluripotency of cells were also investigated. RESULTS: C5A cells prepared from PC-3 cell-derived sphere culture expressed pluripotency markers such as Oct4, Sox2, and Klf4. C5A cells were highly proliferative. They were invasive in vitro and tumorigenic in vivo. Some dopamine receptor antagonists such as thioridazine caused reduction of pluripotency markers and tumorigenicity. Thioridazine, unlike promazine, inhibited phosphorylation of AMPK in a dose dependent manner. BML-275, an AMPK inhibitor, also induced differentiation of C5A cells as seen with thioridazine whereas A769663, an AMPK activator, blocked its differentiation-inducing ability. Transfection of C5A cells with siRNAs of dopamine receptor subtypes revealed that knockdown of DRD2 or DRD4 induced morphology changes of C5A cells. CONCLUSIONS: Some dopamine receptor antagonists such as thioridazine can induce differentiation of CSC-like cells by inhibiting phosphorylation of AMPK. Binding to DRD2 or DRD4 might have mediated the action of thioridazine involved in the differentiation of CSC-like cells.

Gomisin G Suppresses the Growth of Colon Cancer Cells by Attenuation of AKT Phosphorylation and Arrest of Cell Cycle Progression.

Colorectal cancer is one of the leading causes of cancer related death due to a poor prognosis. In this study, we investigated the effect of Gomisin G on colon cancer growth and examined the underlying mechanism of action. We found that Gomisin G significantly suppressed the viability and colony formation of LoVo cells. Gomisin G reduced the phosphorylation level of AKT implying that Gomisin G suppressed the PI3K-AKT signaling pathway. Gomisin G also induced apoptosis shown by Annexin V staining and an increased level of cleaved poly-ADP ribose polymerase (PARP) and Caspase-3 proteins. Furthermore, Gomisin G remarkably triggered the accumulation of cells at the sub-G1 phase which represents apoptotic cells. In addition, the level of cyclin D1 and phosphorylated retinoblastoma tumor suppressor protein (Rb) was also reduced by the treatment with Gomisin G thus curtailing cell cycle progression. These findings show the suppressive effect of Gomisin G by inhibiting proliferation and inducing apoptosis in LoVo cells. Taken together, these results suggest Gomisin G could be developed as a potential therapeutic compound against colon cancer.

Generation and characterization of a monoclonal antibody against MERS-CoV targeting the spike protein using a synthetic peptide epitope-CpG-DNA-liposome complex.

Middle East respiratory syndrome coronavirus (MERS-CoV) uses the spike (S) glycoprotein to recognize and enter target cells. In this study, we selected two epitope peptide sequences within the receptor binding domain (RBD) of the MERS-CoV S protein. We used a complex consisting of the epitope peptide of the MERS-CoV S protein and CpG-DNA encapsulated in liposome complex to immunize mice, and produced the monoclonal antibodies 506-2G10G5 and 492-1G10E4E2. The western blotting data showed that both monoclonal antibodies detected the S protein and immunoprecipitated the native form of the S protein. Indirect immunofluorescence and confocal analysis suggested strong reactivity of the antibodies towards the S protein of MERS-CoV virus infected Vero cells. Furthermore, the 506-2G10G5 monoclonal antibody significantly reduced plaque formation in MERS-CoV infected Vero cells compared to normal mouse IgG and 492-1G10E4E2. Thus, we successfully produced a monoclonal antibody directed against the RBD domain of the S protein which could be used in the development of diagnostics and therapeutic applications in the future. [BMB Reports 2019; 52(6): 397-402].

Erratum to "Gomisin G Inhibits the Growth of Triple-Negative Breast Cancer Cells by Suppressing AKT Phosphorylation and Decreasing Cyclin D1" [Biomol.Ther. 26 (2018) 322-327].

The authors request to correct the author name from Yoonho Lim to Yoongho Lim page 322.

TRRAP stimulates the tumorigenic potential of ovarian cancer stem cells.

Ovarian cancer is the most fatal gynecological malignancy in women and identification of new therapeutic targets is essential for the continued development of therapy for ovarian cancer. TRRAP (transformation/transcription domain-associated protein) is an adaptor protein and a component of histone acetyltransferase complex. The present study was undertaken to investigate the roles played by TRRAP in the proliferation and tumorigenicity of ovarian cancer stem cells. TRRAP expression was found to be up-regulated in the sphere cultures of A2780 ovarian cancer cells. Knockdown of TRRAP significantly decreased cell proliferation and the number of A2780 spheroids. In addition, TRRAP knockdown induced cell cycle arrest and increased apoptotic percentages of A2780 sphere cells. Notably, the mRNA levels of stemness-associated markers, that is, OCT4, SOX2, and NANOG, were suppressed in TRRAP-silenced A2780 sphere cells. In addition, TRRAP overexpression increased the mRNA level of NANOG and the transcriptional activity of NANOG promoter in these cells. Furthermore, TRRAP knockdown significantly reduced tumor growth in a murine xenograft transplantation model. Taken together, the findings of the present study suggest that TRRAP plays an important role in the regulation of the proliferation and stemness of ovarian cancer stem cells. [BMB Reports 2018; 51(10): 515-520].

Production of an anti-TM4SF5 monoclonal antibody and its application in the detection of TM4SF5 as a possible marker of a poor prognosis in colorectal cancer.

The cell surface transmembrane 4 superfamily member 5 protein (TM4SF5) has been implicated in various human cancers. Immunization with a peptide vaccine targeting human TM4SF5 has been shown to exert prophylactic and therapeutic effects against the development of hepatocellular carcinoma and colon cancer in mouse models. In this study, we developed a novel monoclonal antibody (mEC2­CF) targeting a cyclic epitope of TM4SF5 and evaluated its reactivity to TM4SF5 in colorectal cancer (CRC) cells and cancer tissues. The isotype of mEC2­CF was IgG2a and the antibody specifically recognized the cyclic peptide, based on ELISA. The antibody recognized recombinant TM4SF5 overexpressed in 293F cells, irrespective of N­glycosidase F treatment. The antibody was internalized into the cytosol after binding to the surface of TM4SF5­expressing CRC cells, suggesting that this antibody may be useful in therapeutics. In addition, we evaluated TM4SF5 expression in the tissues of patients with CRC patients to determine its prognostic significance. TM4SF5 expression was assessed by immunohistochemistry using mEC2­CF and tissue microarray blocks of 204 primary CRC samples. The overall rate of TM4SF5 overexpression in the samples (immunohistochemical score >4) was 27.0% (55 of 204). The increased expression of TM4SF5 was significantly associated with a shorter survival rate (P=0.0014) and a worse disease­free survival (P=0.0483) of patients with CRC. No association was observed between TM4SF5 expression and clinicopathological characteristics, apart from tumor depth of invasion (P=0.027). These results suggest that our novel antibody can be used to detect endogenous and recombinant TM4SF5, and that TM4SF5 may be a possible marker for the poor prognosis of patients with CRC.

Gomisin G Inhibits the Growth of Triple-Negative Breast Cancer Cells by Suppressing AKT Phosphorylation and Decreasing Cyclin D1.

A type of breast cancer with a defect in three molecular markers such as the estrogen receptor, progesterone receptor, and human epidermal growth factor receptor is called triple-negative breast cancer (TNBC). Many patients with TNBC have a lower survival rate than patients with other types due to a poor prognosis. In this study, we confirmed the anti-cancer effect of a natural compound, Gomisin G, in TNBC cancer cells. Treatment with Gomisin G suppressed the viability of two TNBC cell lines, MDA-MB-231 and MDA-MB-468 but not non-TNBC cell lines such as MCF-7, T47D, and ZR75-1. To investigate the molecular mechanism of this activity, we examined the signal transduction pathways after treatment with Gomisin G in MDA-MB-231 cells. Gomisin G did not induce apoptosis but drastically inhibited AKT phosphorylation and reduced the amount of retinoblastoma tumor suppressor protein (Rb) and phosphorylated Rb. Gomisin G induced in a proteasome-dependent manner a decrease in Cyclin D1. Consequently, Gomisin G causes cell cycle arrest in the G1 phase. In contrast, there was no significant change in T47D cells except for a mild decrease in AKT phosphorylation. These results show that Gomisin G has an anti-cancer activity by suppressing proliferation rather than inducing apoptosis in TNBC cells. Our study suggests that Gomisin G could be used as a therapeutic agent in the treatment of TNBC patients.

A machine learning approach to integrate big data for precision medicine in acute myeloid leukemia.

Cancers that appear pathologically similar often respond differently to the same drug regimens. Methods to better match patients to drugs are in high demand. We demonstrate a promising approach to identify robust molecular markers for targeted treatment of acute myeloid leukemia (AML) by introducing: data from 30 AML patients including genome-wide gene expression profiles and in vitro sensitivity to 160 chemotherapy drugs, a computational method to identify reliable gene expression markers for drug sensitivity by incorporating multi-omic prior information relevant to each gene's potential to drive cancer. We show that our method outperforms several state-of-the-art approaches in identifying molecular markers replicated in validation data and predicting drug sensitivity accurately. Finally, we identify SMARCA4 as a marker and driver of sensitivity to topoisomerase II inhibitors, mitoxantrone, and etoposide, in AML by showing that cell lines transduced to have high SMARCA4 expression reveal dramatically increased sensitivity to these agents.

Explainable machine-learning predictions for the prevention of hypoxaemia during surgery.

Although anaesthesiologists strive to avoid hypoxemia during surgery, reliably predicting future intraoperative hypoxemia is not currently possible. Here, we report the development and testing of a machine-learning-based system that, in real time during general anaesthesia, predicts the risk of hypoxemia and provides explanations of the risk factors. The system, which was trained on minute-by-minute data from the electronic medical records of over fifty thousand surgeries, improved the performance of anaesthesiologists when providing interpretable hypoxemia risks and contributing factors. The explanations for the predictions are broadly consistent with the literature and with prior knowledge from anaesthesiologists. Our results suggest that if anaesthesiologists currently anticipate 15% of hypoxemia events, with this system's assistance they would anticipate 30% of them, a large portion of which may benefit from early intervention because they are associated with modifiable factors. The system can help improve the clinical understanding of hypoxemia risk during anaesthesia care by providing general insights into the exact changes in risk induced by certain patient or procedure characteristics.

Role of Krüppel-Like Factor 4 in the Maintenance of Chemoresistance of Anaplastic Thyroid Cancer.

BACKGROUND: Anaplastic thyroid cancer (ATC) has a very poor prognosis due to its aggressive nature and resistance to conventional treatment. Radiotherapy and chemotherapy are not fully effective because of the undifferentiated phenotype and enhanced drug resistance of ATC. The objective of this study was to evaluate the involvement of Krüppel-like factor 4 (KLF4), a stemness-associated transcription factor, in the undifferentiated phenotype and drug resistance of ATC. METHODS: ATC cells were compared to papillary thyroid cancer cells in drug resistance and gene expression. The effects of KLF4 knockdown in ATC cells on in vitro and in vivo drug resistance were measured. The effects of KLF4 overexpression and knockdown on ABC transporter activity were determined. RESULTS: ATC cells, such as HTH83, 8505C, and SW1736, exhibited higher resistance to the anticancer drug paclitaxel and higher expression of KLF4 than TPC-1 papillary thyroid cancer cells. Knockdown of KLF4 expression in ATC cells increased the expression of the thyroid-specific differentiation genes, such as thyrotropin receptor, thyroid peroxidase, thyroglobulin, and sodium-iodide symporter. Knockdown of KLF4 expression in ATC cells decreased the resistance to doxorubicin and paclitaxel, and reduced ABC transporter expression. Luciferase reporter assay results showed that KLF4 overexpression increased ABCG2 promoter activity, which was abolished by KLF4 knockdown. A tumorigenicity assay showed that the combination of paclitaxel treatment and KLF4 knockdown significantly decreased tumor mass originated from HTH83 cells in mice. CONCLUSIONS: ATC cells show high expression of KLF4, and KLF4 expression is necessary for maintaining the undifferentiated phenotype and drug resistance in vitro and in vivo. The present study identifies KLF4 as a potential therapeutic target for eliminating ATC cells.

Monoclonal antibodies against autocrine motility factor suppress gastric cancer.

Autocrine motility factor (AMF), which is a secreted form of phosphoglucose isomerase, is mainly secreted by various tumors and has cytokine-like activity. AMF is known to stimulate proliferation, survival and metastasis of cancer cells, and angiogenesis within a tumor. The present study investigated whether inhibition of AMF using targeted-antibodies was able to suppress the growth of cancer. A migration assay using a Boyden chamber was utilized to measure the activity of AMF on the motility of cancer cells. A recombinant human AMF (rhAMF) prepared from E. coli transformed with the pET22b-AMF vector increased the motility of MDA-MB-231 and A549 cells, but it did not affect that of NCI-N87 or HepG2 cells, which exhibited the ability to secrete high amounts of their own endogenous AMF into the culture medium. The extent to which the AMF receptor was expressed on cancer cells did not correlate clearly with the cell motility stimulated by rhAMF. In A549-xenografted nude mice treated with sunitinib or cetuximab, a decrease in the plasma AMF concentration was accompanied by a reduction in tumor weight, suggesting an association between the plasma AMF concentration and anticancer activity. A monoclonal antibody (9A-4H), which revealed a high binding affinity for E. coli-derived rhAMF, significantly suppressed the growth of tumors in Balb/c nude mice transplanted with the human gastric cancer cell line NCI-N87, to the similar extent as trastuzumab, an anticancer antibody. The present study suggests, for the first time, that an antibody specific to AMF may be a therapeutic agent for gastric cancer.

Anti-metastatic effect of the TM4SF5-specific peptide vaccine and humanized monoclonal antibody on colon cancer in a mouse lung metastasis model.

Transmembrane 4 superfamily member 5 protein (TM4SF5) is a potential therapeutic target for hepatocellular carcinoma (HCC) and colon cancer. In a previous study, we demonstrated the prophylactic and therapeutic effects of a TM4SF5-specific peptide vaccine and monoclonal antibody in HCC and colon cancer in a mouse model. Here, we designed a cyclic peptide targeting TM4SF5. Cyclic peptide-specific antibodies were produced in mice after immunization with a complex of the peptide, CpG-DNA, and liposomes. Intravenous injection of the CT-26 mouse colon cancer cell line into mice induced tumors in the lung. Immunization with the peptide vaccine improved the survival rate and reduced the growth of lung tumors. We established a monoclonal antibody specific to the cyclic TM4SF5-based peptide and humanized the antibody sequence by complementarity determining region-grafting. The humanized antibody was reactive to the cyclic peptide and TM4SF5 protein. Treatment of CT-26 cells with the humanized antibody reduced cell motility in vitro. Furthermore, direct injection of the humanized anti-TM4SF5 antibody in vivo reduced growth of lung tumors in mouse metastasis model. Therefore, we conclude that the immunization with the cyclic peptide vaccine and injection of the TM4SF5-specifc humanized antibody have an anti-metastatic effect against colon cancer in mice. Importantly, the humanized antibody may serve as a starting platf.

Breast cancer cells evade paclitaxel-induced cell death by developing resistance to dasatinib.

Triple negative breast cancer (TNBC), which does not express the progesterone, estrogen, or HER2/neu receptor, is aggressive and difficult to treat. Paclitaxel, a tubulin stabilizing agent, is one of the most frequently prescribed anticancer agents for breast cancers, including TNBC. Residual disease that occurs due to resistance or partial resistance of cancer cells in a tumor against anticancer agents is the most important issue in oncology. In the present study, when MDA-MB-231 cells, a TNBC cell line, were treated with 30 µM paclitaxel, a slightly higher concentration than its GI50 value, for 6 days, a small number of cells with different morphologies survived. Among the surviving cells, small round cells were isolated, cloned, and named MDA-MB-231-JYJ cells. MDA-MB-231-JYJ cells were observed to be highly proliferative and tumorigenic. In addition, signal transduction molecules involved in proliferation, survival, malignancy, or stemness of cancer cells, such as c-Src, c-Met, Notch 1, c-Myc, Sox2, Oct3/4, Nanog, and E-cadherin were highly expressed or activated. While further study is required, MDA-MB-231-JYJ cells appear to have some of the characteristics of cancer precursor cells. Although MDA-MB-231-JYJ cells were isolated from the cells that survived in the continuous presence of paclitaxel, they were not resistant to paclitaxel but developed resistance to dasatinib, a Bcr-Abl and Src kinase family inhibitor. The activated state of Src and Notch 1, and the expression levels of c-Myc and cyclins in MDA-MB-231-JYJ cells were less affected than MDA-MB-231 cells by the treatment of dasatinib, which may explain the resistance of MDA-MB-231-JYJ cells to dasatinib. These results suggest that cancer cells that become resistant to dasatinib during the process of paclitaxel therapy in patients may appear, and caution is required in the design of clinical trials using these two agents.

Crucial role of HMGA1 in the self-renewal and drug resistance of ovarian cancer stem cells.

Cancer stem cells are a subpopulation of cancer cells characterized by self-renewal ability, tumorigenesis and drug resistance. The aim of this study was to investigate the role of HMGA1, a chromatin remodeling factor abundantly expressed in many different cancers, in the regulation of cancer stem cells in ovarian cancer. Spheroid-forming cancer stem cells were isolated from A2780, SKOV3 and PA1 ovarian cancer cells by three-dimensional spheroid culture. Elevated expression of HMGA1 was observed in spheroid cells along with increased expression of stemness-related genes, such as SOX2, KLF4, ALDH, ABCB1 and ABCG2. Furthermore, spheroid A2780 cells, compared with adherent cells, showed higher resistance to chemotherapeutic agents such as paclitaxel and doxorubicin. HMGA1 knockdown in spheroid cells reduced the proliferative advantage and spheroid-forming efficiency of the cells and the expression of stemness-related genes. HMGA1 overexpression in adherent A2780 cells increased cancer stem cell properties, including proliferation, spheroid-forming efficiency and the expression of stemness-related genes. In addition, HMGA1 regulated ABCG2 promoter activity through HMGA1-binding sites. Knockdown of HMGA1 in spheroid cells reduced resistance to chemotherapeutic agents, whereas the overexpression of HMGA1 in adherent ovarian cancer cells increased resistance to chemotherapeutic agents in vitro. Furthermore, HMGA1-overexpressing A2780 cells showed a significant survival advantage after chemotherapeutic agent treatment in a xenograft tumorigenicity assay. Together, our results provide novel insights regarding the critical role of HMGA1 in the regulation of the cancer stem cell characteristics of ovarian cancer cells, thus suggesting that HMGA1 may be an important target in the development of therapeutics for ovarian cancer patients.