The tumour microenvironment is the primary location in which tumour cells and the host immune system interact. Different immune cell subsets are recruited into the tumour microenvironment via interactions between chemokines and chemokine receptors, and these populations have distinct effects on tumour progression and therapeutic outcomes. In this Review, we focus on the main chemokines that are found in the human tumour microenvironment; we elaborate on their patterns of expression, their regulation and their roles in immune cell recruitment and in cancer and stromal cell biology, and we consider how they affect cancer immunity and tumorigenesis. We also discuss the potential of targeting chemokine networks, in combination with other immunotherapies, for the treatment of cancer.

Chemokines in the cancer microenvironment and their relevance in cancer immunotherapy

This is probably the most popular design of experiments (DOE) book. It is unquestionably the leading DOE book in industry. So I am excepting it from the usual disdain I show for the  fth edition (5E) of any book, even though it follows the fourth edition (4E) by only four years. See Grice (2000) for a report on the 4E. Very likely this book bears little resemblance to the  rst edition way back in the 1970s. The author has taken particular care with this latest edition to reorganize the book to be consistent with modern DOE practice rather than classical DOE presentation. The primary change has been to move the chapters on regression modeling and response surface methods forward in the book. In the 4E these were the last two chapters in the book. In the 5E they follow the chapters on factorial and fractional factorial designs. Three chapters on “other designs” now close out the book. This certainly gives the book a better  ow for industry short courses. Perhaps these will simply be referenced as arcane methods in the sixth edition! Other production values in the book also continue to improve. There are many high-quality graphical displays. There is much use of printouts from Minitab and Design-Expert. A student version of Design-Export comes with the book. There is also an instructor’s CD-ROM with supplemental material to make the book suitable for more advanced courses. There is the now-essential Web site that has more information to help students and instructors. Perhaps if I were teaching DOE for master’s-level statisticians, the recent book by Dean and Voss (1999), reviewed by Amidan (2000), would be my choice. However, for most other audiences this is the complete DOE book. Industrial statisticians should get a copy of the new version and ensure its exposure to the relevant people in their organization.

Introduction to the Practice of Statistics

Chemokines are crucial autocrine and paracrine players in tumor development. In particular, CXCL12, through its receptors CXCR4 and CXCR7, affects tumor progression by controlling cancer cell survival, proliferation and migration, and, indirectly, via angiogenesis or recruiting immune cells.Glioblastoma (GBM) is the most prevalent primary malignant brain tumor in adults and despite current multimodal therapies it remains almost incurable. The aggressive and recurrent phenotype of GBM is ascribed to high growth rate, invasiveness to normal brain, marked angiogenesis, ability to escape the immune system and resistance to standard of care therapies. Tumor molecular and cellular heterogeneity severely hinders GBM therapeutic improvement. In particular, a subpopulation of chemo- and radio-therapy resistant tumorigenic cancer stem–like cells (CSCs) is believed to be the main responsible for tumor cell dissemination to the brain.GBM cells display heterogeneous expression levels of CXCR4 and CXCR7 that are overexpressed in CSCs, representing a molecular correlate for the invasive potential of GBM. The microenvironment contribution in GBM development is increasingly emphasized. An interplay exists between CSCs, differentiated GBM cells, and the microenvironment, mainly through secreted chemokines (e.g. CXCL12) causing recruitment of fibroblasts, endothelial, mesenchymal and inflammatory cells to the tumor, via specific receptors such as CXCR4.This review covers recent developments on the role of CXCL12/CXCR4-CXCR7 networks in GBM progression and the potential translational impact of their targeting. The biological and molecular understanding of the heterogeneous GBM cell behavior, phenotype and signaling is still limited. Progress in the identification of chemokine-dependent mechanisms that affect GBM cell survival, trafficking and chemo-attractive functions, opens new perspectives for development of more specific therapeutic approaches that include chemokine-based drugs.

/pdf/cxcl12-modulation-of-cxcr4-and-cxcr7-activity-in-human-qrgclgmt8q.pdf

CXCL12 modulation of CXCR4 and CXCR7 activity in human glioblastoma stem-like cells and regulation of the tumor microenvironment.

The cancer stem cell (CSC) concept stands for undifferentiated tumor cells with the ability to initiate heterogeneous tumors. It is also relevant in metastasis and can explain how metastatic tumors mirror the heterogeneity of primary tumors. Cellular plasticity, including the epithelial-to-mesenchymal transition (EMT), enables the generation of CSCs at different steps of the metastatic process including metastatic colonization. In this review, we update the concept of CSCs and provide evidence of the existence of metastatic stem cells (MetSCs). In addition, we highlight the nuanced understanding of EMT that has been gained recently and the association of mesenchymal-to-epithelial transition (MET) with the acquisition of CSCs properties during metastasis. We also comment on the computational approaches that have profoundly influenced our understanding of CSCs and EMT; and how these studies and new experimental technologies can yield a deeper understanding of the biological aspects of metastasis.

http://perspectivesinmedicine.cshlp.org/content/10/7/a036905.full.pdf

Cancer Stem Cells and Epithelial-to-Mesenchymal Transition in Cancer Metastasis.

Preface to the Fourth Edition. 1 Initial Steps . 1.1 Reasons for Studying Biostatistics. 1.2 Initial Steps in Designing a Biomedical Study. 1.3 Common Types of Biomedical Studies. Problems. References. 2 Populations and Samples. 2.1 Basic Concepts. 2.2 Definitions of Types of Samples. 2.3 Methods of Selecting Simple Random Samples. 2.4 Application of Sampling Methods in Biomedical Studies. Problems. References. 3 Collecting and Entering Data. 3.1 Initial Steps. 3.2 Data Entry. 3.3 Screening the Data. 3.4 Code Book. Problems. References. 4 Frequency Tables and Their Graphs. 4.1 Numerical Methods of Organizing Data. 4.2 Graphs. Problems. References. 5 Measures of Location and Variability. 5.1 Measures of Location. 5.2 Measures of Variability. 5.3 Sampling Properties of the Mean and Variance. 5.4 Considerations in Selecting Appropriate Statistics. 5.5 A Common Graphical Method for Displaying Statistics. Problems. References. 6 The Normal Distribution. 6.1 Properties of the Normal Distribution. 6.2 Areas Under the Normal Curve. 6.3 Importance of the Normal Distribution. 6.4 Examining Data for Normality. 6.5 Transformations. Problems. References. 7 Estimation of Population Means: Confidence Intervals. 7.1 Confidence Intervals. 7.2 Sample Size Needed for a Desired Confidence Interval. 7.3 The t Distribution. 7.4 Confidence Interval for the Mean, Using the t Distribution. 7.5 Estimating the Difference Between Two Means: Unpaired Data. 7.6 Estimating the Difference Between Two Means: Paired Comparison. Problems. References. 8 Tests of Hypotheses on Population Means. 8.1 Tests of Hypotheses for a Single Mean. 8.2 Tests for Equality of two Means: Unpaired Data. 8.3 Testing for Equality of Means: Paired Data. 8.4 Concepts Used in Statistical Testing. 8.5 Sample Size. 8.6 Confidence Intervals Versus Tests. 8.7 Correcting for Multiple Testing. 8.8 Reporting the Results. Problems. References. 9 Variances: Estimation and Tests. 9.1 Point Estimates for Variances and Standard Deviations. 9.2 Testing Whether Two Variances Are Equal: F Test. 9.3 Approximate t Test. 9.4 Other Tests. Problems. References. 10 Categorical Data: Proportions. 10.1 Single Population Proportion. 10.2 Samples from Categorical Data. 10.3 The Normal Approximation to the Binomial. 10.4 Confidence Intervals for a Single Population Proportion. 10.5 Confidence Intervals for the Difference in Two Proportions. 10.6 Tests of Hypothesis for Population Proportions. 10.7 Sample Size for Testing Two Proportions. 10.8 Data Entry and Analysis Using Statistical Programs. Problems. References. 11 Categorical Data: Analysis of Two-Way Frequency Tables. 11.1 Different Types of Tables. 11.2 Relative Risk and Odds Ratio. 11.3 Chi-Square Tests for Frequency Tables: two-by-two Tables. 11.4 Chi-Square Tests for Larger Tables. 11.5 Remarks. Problems. References. 12 Regression and Correlation. 12.1 The Scatter Diagram: Single Sample. 12.2 Linear Regression: Single Sample. 12.3 The Correlation Coefficient for two Variables from a Single Sample. 12.4 Linear Regression Assuming the Fixed-X Model. 12.5 Other Topics in Linear Regression. Problems. References. 13 Nonparametric Statistics. 13.1 The Sign Test. 13.2 The Wilcoxon Signed Rank Test. 13.3 The Wilcoxon-Mann-Whitney Test. 13.4 Spearman's Rank Correlation. Problems. References. 14 Introduction to Survival Analysis. 14.1 Survival Analysis Data. 14.2 Survival Functions. 14.3 Computing Estimates of f(t), S(t), and h(t). 14.4 Comparison of Clinical Life Tables and the Kaplan-Meier Method. 14.5 Additional Analyses Using Survival Data. Problems. References. Appendix A: Statistical Tables. Appendix B: Answers to Selected Problems. Appendix C: Computer Statistical Program Resources. C.1 Computer Systems for Biomedical Education and Research. C.2 A Brief Indication of Statistics Computer Program Advances and Some Relevant Publications Since 2000. C.3 Choices of Computer Statistical Software. Bibliography. Index.

http://library.um.ac.id/free-contents/downloadpdf.php/buku/basic-statistics-a-primer-for-the-biomedical-sciences-by-olive-jean-dunn-13621.pdf

Basic statistics : a primer for the biomedical sciences / by Olive Jean Dunn

Colorectal cancer (CRC), which frequently metastasizes to the liver, is one of the three leading causes of cancer-related deaths worldwide. Growing evidence suggests that a subset of cells exists among cancer stem cells. This distinct subpopulation is thought to contribute to liver metastasis; however, it has not been fully explored in CRC yet. Flow cytometry analysis was performed to detect distinct subsets with CD133 and CXCR4 markers in human primary and metastatic CRC tissues. The 'stemness' and metastatic capacities of different subpopulations derived from the colon cancer cell line HCT116 were compared in vitro and in vivo. The roles of epithelial-mesenchymal transition (EMT) and stromal-cell derived factor-1 (SDF-1) in the metastatic process were also investigated. A survival curve was used to explore the correlation between the content of CD133+CXCR4+ cancer cells and patient survival. In human specimens, the content of CD133+CXCR4+ cells was higher in liver metastases than in primary colorectal tumors. Clonogenic and tumorigenic cells were restricted to CD133+ cells in the HCT116 cell line, with CXCR4 expression having no impact on the 'stemness' properties. We found that CD133+CXCR4+ cancer cells had a high metastatic capacity in vitro and in vivo. Compared with CD133+CXCR4- cells, CD133+CXCR4+ cancer cells experienced EMT, which contributed partly to their metastatic phenotype. We then determined that SDF-1/CXCL12 treatment could further induce EMT in CD133+CXCR4+ cancer cells and enhance their invasive behavior, while this could not be observed in CD133+CXCR4- cancer cells. Blocking SDF-1/CXCR4 interaction with a CXCR4 antagonist, AMD3100 (1,10-[1,4-phenylenebis(methylene)]bis-1,4,8,11 -tetraazacyclotetradecane octahydrochloride), inhibited metastatic tumor growth in a mouse hepatic metastasis model. Finally, a high percentage of CD133+CXCR4+ cells in human primary CRC was associated with a reduced two-year survival rate. Strategies targeting the SDF-1/CXCR4 interaction may have important clinical applications in the suppression of colon cancer metastasis. Further investigations on how high expression of CXCR4 and EMT occur in this identified cancer stem cell subset are warranted to provide insights into our understanding of tumor biology.

https://link.springer.com/content/pdf/10.1186%2F1741-7015-10-85.pdf

CD133 + CXCR4 + colon cancer cells exhibit metastatic potential and predict poor prognosis of patients

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Hao-Hao Wang

Author Tools

Chat about Author

Papers

CD133 + CXCR4 + colon cancer cells exhibit metastatic potential and predict poor prognosis of patients