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Supplementary materials and methods

IL-34 expression in cancers

Correlation between IL-34 and clinic-pathological characteristics, and Cox's proportional hazards model analysis of prognostic factors for OS in NSCLC patients

The ability of tumor cells to escape immune destruction and their acquired resistance to chemotherapy are major obstacles to effective cancer therapy. Although immune checkpoint therapies such as anti-PD-1 address these issues in part, clinical responses remain limited to a subpopulation of patients. In this report, we identified IL34 produced by cancer cells as a driver of chemoresistance. In particular, we found that IL34 modulated the functions of tumor-associated macrophages to enhance local immunosuppression and to promote the survival of chemoresistant cancer cells by activating AKT signaling. Targeting IL34 in chemoresistant tumors resulted in a remarkable inhibition of tumor growth when accompanied with chemotherapy. Our results define a pathogenic role for IL34 in mediating immunosuppression and chemoresistance and identify it as a tractable target for anticancer therapy. Cancer Res; 76(20); 6030–42. ©2016 AACR.

Gemcitabine amplifies the expression of multiple MDSCs-inducing cytokines including GM-CSF in PANC-1 cell line.

Supplementary Table A. Association between GM-CSF and clinicopathological variables Supplementary Table B. Clinicopathological features of enrolled PDAC patients with or without chemotherapy Supplementary Table C. Clinicopathological features including drug regimen or chemotherapeutic response of enrolled 6 PDAC patients with pre-operative chemotherapy.

No significant difference in the frequencies of CD68+ macrophages in cancer patients after treatment with pre-operative chemotherapy.

Supplemental Figure Legends

Blockade of GM-CSF contributes to the recovery of CD8+ T cells proliferation when cultured with monocytes stimulated with chemotherapy-treated PDAC CM.

5-FU-treated Capan-1 supernatants induce the differentiation of monocytes into MDSCs.

Gemcitabine amplifies the expression of IL-6 or IL8 in Capan-1 cells.

Pancreatic ductal adenocarcinoma (PDAC) is the most common type of pancreatic malignancies. PDAC builds a tumor microenvironment that plays critical roles in tumor progression and metastasis. However, the relationship between chemotherapy and modulation of PDAC-induced tumor microenvironment remains poorly understood. In this study, we report a role of chemotherapy-derived inflammatory response in the enrichment of PDAC microenvironment with immunosuppressive myeloid cells. Granulocyte macrophage colony-stimulating factor (GM-CSF) is a major cytokine associated with oncogenic KRAS in PDAC cells. GM-CSF production was significantly enhanced in various PDAC cell lines or PDAC tumor tissues from patients after treatment with chemotherapy, which induced the differentiation of monocytes into myeloid-derived suppressor cells (MDSC). Furthermore, blockade of GM-CSF with monoclonal antibodies helped to restore T-cell proliferation when cocultured with monocytes stimulated with tumor supernatants. GM-CSF expression was also observed in primary tumors and correlated with poor prognosis in PDAC patients. Together, these results describe a role of GM-CSF in the modification of chemotherapy-treated PDAC microenvironment and suggest that the targeting of GM-CSF may benefit PDAC patients' refractory to current anticancer regimens by defeating MDSC-mediated immune escape. Cancer Res; 75(13); 2629–40. ©2015 AACR.

Popular data concentration is a widely accepted storage energy-saving technique which places frequently-accessed data on a small subset of hard disks and spins-down other infrequently-accessed disks. Many previous studies use intuitive heuristic algorithms for data placement that promote the imbalance in the access frequencies across hard disks. However, the relevance and the optimality of such file placements have not been rigorously investigated. In this paper, we formally define the energy-saving file placement problem under the capacity and performance constraints as a combinatorial optimization problem and show the theory of the optimal file placement where the file access rates in the next period are given. Our analysis based on a stochastic process of disk state transitions gives the theoretical support for the common heuristic placement method. To examine the effectiveness of the optimal file placement, we experimentally evaluate the energy-efficiency of a test storage system using the file access rates generated from the real access traces from Flickr. The experimental results show that the energy consumption can be reduced by 31.8% with the optimal file placement compared to the evenly distributed file placement. We also conduct simulation experiments to confirm the energy-saving impacts in larger-scale storage systems.

Big-data processing systems such as Hadoop, which usually utilize distributed file systems (DFSs), require data reduction schemes to maximize storage space efficiency. These schemes have different tradeoffs, and there are no all-purpose schemes applicable to all data. Users must select a suitable scheme in accordance with their data. To accommodate this requirement, application software or file system (FS) have a fixed selection of these schemes. However, these provided schemes are insufficient for all data types, and when novel schemes emerge, extending the selection can be problematic. If the source code of the application or FS is available, the source code could potentially be extended with extensive labor, but could be virtually impossible without the code maintainers’ assistance. If the source code is unavailable, there is no way to tackle the problem. This paper proposes an unexplored solution through a modular DFS design that eases data reduction scheme usage through existing programming techniques. The advantages of this presented approach are threefold. First, adding new schemes is easy and they are transparent to the application code requiring no extensions to it. Second, the modular structure requires minimal modification to the existing DFSs and performance overhead. Third, users can compile schemes separately from the DFS without the FS or DFS source code. To demonstrate the design’s effectiveness, we implemented it by minimally extending the Hadoop DFS (HDFS) and named it the Hadoop Data Reduction Framework (HDRF). We designed HDRF to work with minimal overhead and tested it extensively. Experimental results indicate that it has negligible overhead over existing approaches. In a number of cases, it can offer up to 48.96% higher throughput while achieving the best result in storage reduction within our tested setups because of the incorporated data reduction schemes.

This paper reports the 5-year operational status of the third phase of the"All Japan E-Learning Cloud of the Training Program for Oncology Professionals"by tabulating the viewing trends of available lecture contents. In this phase, the goal was to train cancer genome medical professionals in this new, advanced medical technology field as well as train personnel to treat rarely encountered pediatric, adolescent/young adult, and other life stage cancers. Additionally, new lecture items have been added to the e-learning cloud in collaboration with 7 oncology specialist centers, contributing to the development of human capital in cancer care(including graduate student education)and faculty development for local medical professionals.