Special Issue "Tumor Microenvironment and Immune Response"
Deadline for manuscript submissions: 31 December 2021.
2. Department of Medicine/Hematologic Malignancies & Cell Therapy, Duke University, Durham, NC, Italy
Interests: inflammation; macrophage; dendritic cells; signal transduction; hematopoiesis; hematopoietic stem cell; tumor immunology
More than 150 years ago, Rudolf Ludwig Carl Virchow noted that tumors were often infiltrated by leukocytes, proposing for the first time a link between inflammation and cancer. A few years later, Stephen Paget incorporated these pioneering observations into a more structured theory, elaborating the concept of “seed and soil” to explain the mechanisms supporting the spreading of the primary cancer cells to the metastatic niche. In 1889, the scientific community was largely unprepared to explore the biological significance of the “seed and soil” concept, spending the following century on “seed”-oriented studies and leaving the “soil” component largely unexplored.
In the last 30 years, a large body of evidence has demonstrated that the landscape of immune cells infiltrating cancer tissue has a key role in the mechanism of tumor initiation, growth, invasion, and metastasis. After more than a century from the seminal observations of Virchow and Paget, the “soil” has become an important therapeutic target, such as immunotherapy, and is a valuable therapeutic option for an increasing number of advanced cancers.
This collection hosts review articles from scientists that have been studying the tumor microenvironment using animal models, microfluidic on-chip models, and translational immunotherapeutic studies, all alongside the most advanced approaches which exploit big data analysis and machine learning to dissect the complexity of the tumor microenvironment in human cancer.
Prof. Dr. Luigi Racioppi
Manuscript Submission Information
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- Tumor microenvironment
- Tumor-associated macrophages
- Myeloid-derived suppressive cells
- Tumor-infiltrating lymphocytes
- Immune suppression
- Precision oncology
- Machine learning