Tumor-associated macrophages (TAMs) and tumor-associated neutrophils (TANs) can control cancer growth and exist in almost all solid neoplasms. The cells are known to descend from immature monocytic and granulocytic cells, respectively, which are produced in the bone marrow. However, the spleen is also a recently identified reservoir of monocytes, which can play a significant role in the inflammatory response that follows acute injury. Here, we evaluated the role of the splenic reservoir in a genetic mouse model of lung adenocarcinoma driven by activation of oncogenic Kras and inactivation of p53. We found that high numbers of TAM and TAN precursors physically relocated from the spleen to the tumor stroma, and that recruitment of tumor-promoting spleen-derived TAMs required signaling of the chemokine receptor CCR2. Also, removal of the spleen, either before or after tumor initiation, reduced TAM and TAN responses significantly and delayed tumor growth. The mechanism by which the spleen was able to maintain its reservoir capacity throughout tumor progression involved, in part, local accumulation in the splenic red pulp of typically rare extramedullary hematopoietic stem and progenitor cells, notably granulocyte and macrophage progenitors, which produced CD11b + Ly-6C hi monocytic and CD11b + Ly-6G hi granulocytic cells locally. Splenic granulocyte and macrophage progenitors and their descendants were likewise identified in clinical specimens. The present study sheds light on the origins of TAMs and TANs, and positions the spleen as an important extramedullary site, which can continuously supply growing tumors with these cells.

The quantitative analysis of magnetic resonance imaging (MRI) data has become increasingly important in both research and clinical studies aiming at human brain development, function, and pathology. Inevitably, the role of quantitative image analysis in the evaluation of drug therapy will increase, driven in part by requirements imposed by regulatory agencies. However, the prohibitive length of time involved and the significant intraand inter-rater variability of the measurements obtained from manual analysis of large MRI databases represent major obstacles to the wider application of quantitative MRI analysis. We have developed a fully automatic "pipeline" image analysis framework and have successfully applied it to a number of large-scale, multicenter studies (more than 1,000 MRI scans). This pipeline system is based on robust image processing algorithms, executed in a parallel, distributed fashion. This paper describes the application of this system to the automatic quantification of multiple sclerosis lesion load in MRI, in the context of a phase III clinical trial. The pipeline results were evaluated through an extensive validation study, revealing that the obtained lesion measurements are statistically indistinguishable from those obtained by trained human observers. Given that intra- and inter-rater measurement variability is eliminated by automatic analysis, this system enhances the ability to detect small treatment effects not readily detectable through conventional analysis techniques. While useful for clinical trial analysis in multiple sclerosis, this system holds widespread potential for applications in other neurological disorders, as well as for the study of neurobiology in general.