HyunSoo Kim

Combination therapy of renal cell carcinoma or breast cancer patients with dendritic cell vaccine and IL-2: results from a phase I/II trial

BackgroundTen cancer patients (Six renal cell carcinoma and four breast cancer patients) were treated in a phase I/II study with a vaccine composed of autologous dendritic cells (DCs) and IL-2 to evaluate the DC vaccine-related toxicity and antigen-specific immune alteration.MethodsCancer patients were treated twice with autologous CD34+ hematopoietic stem cell-derived, GM-CSF/IFN-γ-differentiated DCs pulsed with autologous tumor lysate and KLH, by 4-week interval. Following each subcutaneous injection of therapeutic DCs, low-dose (200 MIU) IL-2 was introduced for 14 consecutive days as an immune adjuvant. To determine the DC vaccine-induced immunological alterations, the KLH-specific lymphocyte proliferation, number of IFN-γ secreting T cells (ELISPOT assay), NK activity and the cytokine modulation were measured.ResultsCultured-DCs expressing HLA-DR, CD11c, CD83, and B7.1/B7.2 produced IL-12p70. After vaccination, the patients tolerated it. Clinical response was observed in one RCC patient as stable disease. However DC-vaccine related antigen-specific immune responses including peripheral blood lymphocyte proliferation and the number of IFN-r secreting cells were induced in six patients without clear correlation with clinical responses. Also NK activity was induced significantly in six patients after vaccination. DC vaccine-related decrease of TGF-β level or increase of IL-12p70 level and decline of CD4+CD25+ T cells were observed in three patients. However only in the RCC patient whose disease stabilized, combination of stimulatory as well as inhibitory immune alterations including induction of IFN-γ secreting T cell with reduction of CD4+ CD25+ T cell were correlated with clinical responses.ConclusionData indicated that DC vaccine combined with IL-2 is well tolerated without major side effects. DC vaccine induced the specific immunity against introduced antigen. Combinatorial alterations of immunological parameters indicating antigen-specific immune induction along with reduction of inhibitory immunity were correlated with clinical responses in DC vaccine treated patients.

Therapeutic DC vaccination with IL-2 as a consolidation therapy for ovarian cancer patients: a phase I/II trial

While ovarian cancer (OvCa) responds well to surgery and conventional chemotherapy, a high recurrence rate of advanced OvCa is observed. In this phase I/II study, 10 OvCa patients with minimal residual disease were treated with autologous dendritic cells (DCs) and IL-2 to evaluate the safety and feasibility of this therapeutic strategy and to characterize the antigen-specific immune alterations induced through this treatment. Approximately 4 months after initial debulking and chemotherapy, patients received two subcutaneous doses of autologous monocyte-derived DCs pulsed with autologous tumor lysate and keyhole limpet hemocyanin (KLH) at 4-week intervals. After each DC inoculation, low-dose (200 mIU) IL-2 was introduced for 14 consecutive days as an immune adjuvant. The vaccination was well tolerated. In three out of 10 patients, the inclusion status after the initial therapy showed the maintenance of complete remission (CR) after DC vaccination for 83, 80.9 and 38.2 months without disease relapse. One patient with stable disease (SD) experienced the complete disappearance of tumor after DC vaccination, and this status was maintained for 50.8 months until tumor recurrence. In two patients with partial response (PR) was not responding to DC vaccination and their disease recurred. In the three patients with disease free long-term survival, significant immune alterations were observed, including increased natural killer (NK) activity, IFN-γ-secreting T cells, immune-stimulatory cytokine secretion and reduced immune-suppressive factor secretion after DC vaccination. Thus, in patients with NED status and increased overall survival, DC vaccination induced tumor-related immunity, potentially associated with long-term clinical responses against OvCa.

Audio Recorder Identification Using Reduced Noise Features

In this paper, we propose an audio recorder identification method as one of digital forensic technologies, where Wiener filter is used to extract noise sounds of recorders and their features are extracted by MIRtoolbox. A recorder identification model is generated by training SVM with the extracted noise features. To improve the identification performance, a new feature reduction method which uses inter-classes standard deviations of features is adopted. The experimental results for 11 audio recorders show 1% improvement over the method with no feature reduction. The improvement is not too noticeable as expected, but the number of features can be reduced up to one third of the method with no feature reduction. The results also show that the proposed feature reduction method is competitive over the other well-known methods such as PCA, LDA and R-squared.

A Fast Way for Alignment Marker Detection and Position Calibration

The core of the machine vision that is frequently used at the pre/post-production stages is a marker alignment technology. In this paper, a method to detect the angle and position of a product at high speed by use of a unique pattern present in the marker stamped on the product, and calibrate them is proposed. In the proposed method, to determine the angle and position of a marker, the candidates of the marker are extracted by using a variation of the integral histogram, and then clustering is applied to reduce the candidates. The experimental results revealed about 5s 719ms improvement in processing time and better precision in detecting the rotation angle of a product.

Performance Evaluation of a Generalized Music Mood Classification Model

This paper focuses on building a generalized mood classification model with many mood classes instead of a personalized one with few mood classes. Two methods are adopted to improve the performance of mood classification. The one of them is a new feature reduction based on standard deviation of feature values, which is designed to solve the problem of lowered performance when all 391 features provided by MIR toolbox used to extract features of music. Our experiments show that the feature reduction method suggested in this paper has better performance than that of the conventional dimension reduction methods, R-Squared and PCA. As performance improvement by feature reduction only is limited, the modular neural network approach is used additionally. The experiments also show that the method improves the performance effectively.

POS Data Analysis System based on Association Rule Analysis

Merchandise recommendations service based on electronic commerce has been actively studied and on service these days. By virtue of progress in IT industry, POS has been widely used even in small shops, but the merchandise recommendations service using POS has not been much facilitated compared with that of using electronic commerce. This paper proposes a merchandise recommendations service system using association analysis by applying data mining algorithm to POS sales data. This paper, also, suggests novel services such as annihilation rule and new rule, and ascending and descending rules. The analysis results are applied to the customers enabling to offer merchandise recommendations service. In addition, prompt responses against the changes in demands from customers are possible by identifying the annihilation rule and new rule, and ascending and descending rules, and providing the management with the rules as managerial decision making information.

Detection of Potential Memory Access Errors based on Assembly Codes

Memory errors can cause not only program malfunctions but also even unexpected system halt. Though a programmer checks memory errors, some memory errors with low occurrence frequency are missed to detect. In this paper, we propose a method for effectively detecting such memory errors using instruction transition diagrams through analyzing assembly codes obtained by disassembling an executable file. Out of various memory errors, local memory return errors, null pointer access errors and uninitialized pointer access errors are targeted for detection. When applying the proposed method to various programs including well-known open source programs such as Apache web server and PHP script interpreter, some potential memory errors are detected.