Hideo Matsuda

Alopecia Areata (AA) is an autoimmune disease resulting from a breach in the immune privilege of the hair follicles. To gain insights into the mechanism, we focused on the interactions between cells in the hair follicles. Dermal Papilla (DP) cells are especially essential for developing hair follicles and maintaining the growth phase of the hair cycle. We used a single-cell RNA sequencing dataset of hair follicles to analyze the interaction of immune cells, primarily with DP cells. This dataset was obtained from a patient with AA. To identify cell types that interact with DP cells and contribute to AA, we performed clustering of fibroblasts and T cells to identify DP and T cell subtype populations. Through cell-cell communication analysis, we also identified DP as the most statistically frequent interacting cell type. In addition, we identified T cell subtypes that interact with papilla cells and ligand-receptor pairs. By integrating the results of these analyses, we inferred specific T cell subtypes that interact with DP cells and predicted novel ligand-receptor pairs that shorten the growth (anagen) phase of the hair cycle by causing inflammation in both follicular and DP cells.

Background: Fusion genes are important biomarkers in cancer research because their expression can produce abnormal proteins with oncogenic properties. Long-read RNA sequencing (long-read RNA-seq), which can sequence full-length mRNA transcripts, facilitates the detection of such fusion genes. Several tools have been proposed for detecting fusion genes in long-read RNA-seq datasets derived from cancer cells. However, the high sequencing error rate in long-read RNA-seq makes fusion gene detection challenging. Methods: To address this issue, additional steps were incorporated into the fusion detection tool to improve detection accuracy. These steps include anchoring breakpoints to exon boundaries, realigning unaligned regions, and clustering breakpoints. To evaluate the accuracy of our tool in detecting fusion genes, we compared its detection accuracy with two representative existing tools, JAFFAL and FusionSeeker. Results: Our tool outperformed the two existing tools in detecting fusion genes, as demonstrated in long-read RNA-seq datasets. We also identified potentially novel fusion genes consistently detected across multiple tools or datasets. Conclusions: The application of our tool to the detection of fusion genes in long-read RNA-seq datasets from two different cancer cell lines demonstrated the detection effectiveness of this tool.

Purpose: Breast cancer tumors frequently have intratumoral heterogeneity (ITH). Tumors with high ITH cause therapeutic resistance and have human epidermal growth factor receptor 2 (HER2) heterogeneity in response to HER2-targeted therapies. This study aimed to investigate whether high HER2 heterogeneity levels were clinically related to a poor prognosis for HER2-targeted adjuvant therapy resistance in primary breast cancers. Methods: This study included patients with primary breast cancer (n = 251) treated with adjuvant HER2-targeted therapies. HER2 heterogeneity was manifested by the shape of HER2 fluorescence in situ hybridization amplification (FISH) distributed histograms with the HER2 gene copy number within a tumor sample. Each tumor was classified into a biphasic grade graph (high heterogeneity [HH]) group or a monophasic grade graph (low heterogeneity [LH]) group based on heterogeneity. Both groups were evaluated for disease-free survival (DFS) and overall survival (OS) for a median of ten years of annual follow-up. Results: Of 251 patients with HER2-positive breast cancer, 46 (18.3%) and 205 (81.7%) were classified into the HH and LH groups, respectively. The HH group had more distant metastases and a poorer prognosis than the LH group (DFS: p < 0.001 (HH:63% vs. LH:91% at 10 years) and for the OS: p = 0.012 (HH:78% vs. LH:95% at 10 years). Conclusions: High HER2 heterogeneity is a poor prognostic factor in patients with HER2-positive breast cancer. A novel approach to heterogeneity, which is manifested by the shape of HER2 FISH distributions, might be clinically useful in the prognosis prediction of patients after HER2 adjuvant therapy.

PURPOSE: Breast cancer tumors frequently have intratumor heterogeneity. Tumors with high intratumor heterogeneity have caused therapeutic resistance and have human epidermal growth factor receptor 2 (HER2) heterogeneity in response to HER2-targeted therapies. This study aimed to investigate whether high HER2 heterogeneity levels were clinically related to poor prognosis for HER2-targeted adjuvant therapies resistance in primary breast cancers. METHODS: This study included patients with primary breast cancer (n = 251) treated with adjuvant HER2-targeted therapies. HER2 heterogeneity was manifested by the shape of HER2 fluorescence in situ hybridization amplification (FISH) distributed histograms with HER2 gene copy number within a tumor sample. Those heterogeneity classified each tumor into the biphasic grade graph (high heterogeneity [HH]) group and the monophasic grade graph (low heterogeneity [LH]) group. Both groups were evaluated for disease-free survival (DFS) and overall survival (OS) for a median of ten years of annual follow-up. RESULTS: Of 251 patients with HER2-positive breast cancer, 46 (18.3%) and 205 (81.7%) were classified into the HH and LH groups, respectively. The HH group had more distant metastases and a poorer prognosis than the LH group (DFS: P < 0.001 (HH:63% vs LH:91% at 10 years), and OS: P = 0.012 (HH:78% vs LH:95% at 10 years). CONCLUSION: High HER2 heterogeneity is a poor prognostic factor in patients with HER2-positive breast cancer. A novel approach of heterogeneity, which is manifested by the shape of HER2 FISH distributions, might be clinically useful in the prognosis prediction of patients after HER2 adjuvant therapy.

This study explores the comparative accuracy of two state-of-the-art algorithms, YOLOv3 and faster region-based convolutional neural network (R-CNN), in detecting endoscopy artifacts using the EAD2019 dataset. YOLOv3, primarily used for real-time tasks, and Faster R-CNN, which employs a two-step object detection process, exhibit variable performance based on the object characteristics. The analysis performed in this study focuses on identifying the objects or classes where each algorithm performs better. We conduct experiments to support our findings. We introduce a novel metric that quantifies the difference in average pixel intensities inside and outside the bounding boxes of detected objects. This metric forms the basis of a proposed ensemble method, allowing the method to effectively utilize either YOLOv3 or Faster R-CNN, depending on the characteristics of each class. The proposed method demonstrates an improved average precision score compared to using either algorithm separately. This research provides valuable insights into object detection in endoscopy, potentially enhancing artifact detection accuracy in medical imaging.

Fusion genes are important targets and biomarkers for cancer therapy. Methods of accurately detecting fusion genes are needed in clinical practice. RNA-seq is widely used to detect active fusion genes. Long-read RNA-seq can sequence the full length of mRNA, and long-read RNA-seq is expected to detect fusion genes that cannot be detected by short-read RNA-seq. However, long-read RNA-seq has high basecalling error rates, and gap sequences may occur near the breakpoints of long reads that are not aligned to the genome. When gap sequences occur, it is impossible to identify the correct fusion gene or breakpoint using existing methods. To address these challenges in fusion gene detection, we introduce a novel algorithm, FUGAREC (fusion detection with gap re-alignment and breakpoint clustering). FUGAREC uniquely combines gap sequence re-alignment with breakpoint clustering. This approach not only enhances the detection of previously undetectable fusion genes but also significantly reduces false positives. We demonstrate that FUGAREC has high fusion gene detection performance on both simulated data and sequenced data of a breast cancer cell line.