BackgroundBiomarkers that predict durable benefit from immune checkpoint inhibitors (ICIs) and antibody-drug conjugates (ADCs) in metastatic urothelial carcinoma (mUC) remain limited. Tertiary lymphoid structures (TLS), particularly mature TLS (mTLS) characterized by follicular dendritic cell networks and germinal centers, may reflect a highly compartmentalized antitumor immune niche.Case presentationA 77-year-old man with bladder cancer and lung metastases experienced disease progression after two cycles of gemcitabine/cisplatin. Pembrolizumab was initiated but discontinued after a single dose due to destructive thyroiditis; nevertheless, his lung metastases subsequently regressed. Because residual urothelial disease persisted in the bladder, enfortumab vedotin (EV) was initiated. A complete response (CR) was achieved after three cycles and maintained through 10 cycles, at which point treatment was stopped due to fatigue. The patient has remained recurrence-free for over 4 years since the initiation of pembrolizumab.Methods and resultsImmunohistochemistry of the pre-treatment transurethral resection of bladder tumor (TUR-Bt) specimen (CD3, CD4, CD8, CD20, CD21, BCL6) revealed numerous TLS adjacent to the tumor, many of which met the criteria for mTLS (CD21+ FDC networks and BCL6+ germinal center B cells). A HALO-based digital pathology spatial analysis demonstrated a highly enriched B- and T-cell microenvironment, with the vast majority of these lymphocytes tightly compartmentalized within the mTLS regions. In an exploratory cohort of six additional sequentially treated mUC cases evaluated using the same methodology, mTLS were not detected, even among cases exhibiting a generalized T-cell infiltration.ConclusionPre-existing mTLS identified in routine TUR specimens reflect a highly compartmentalized immune niche—distinct from a simple “hot tumor” phenotype—that may help predict which patients are likely to achieve a durable benefit from sequential ICI and ADC therapy, potentially informing treatment selection and sequencing in mUC.

Osteosarcoma is an aggressive malignant bone tumor with limited treatment options, particularly in metastatic or treatment-resistant disease. Although T cell-based immunotherapies have shown promise in other solid tumors, their application to osteosarcoma has been hindered by the limited characterization of antigens recognized by tumor-infiltrating lymphocytes (TILs). In this study, we performed single-cell RNA sequencing and paired T-cell receptor (TCR) repertoire analysis of TILs isolated from a human osteosarcoma specimen with the aim of characterizing the intratumoral immune landscape and identifying candidate tumor-reactive T cells. Highly expanded and phenotypically exhausted CD8+ T cell clonotypes were selected for TCR engineering and screened using a cDNA expression cloning approach with an allogeneic osteosarcoma cell line panel. We identified a TIL-derived TCR that specifically recognized calpain-2 in the context of HLA-B*40:02. Epitope mapping revealed a 9-mer peptide, NEEILARVV, as the optimal antigenic epitope. Calpain-2 expression was elevated in osteosarcoma and other malignant tissues compared with healthy tissues. Functional analyses revealed that the generation of this epitope depended on both calpain activity and proteasomal processing. Importantly, the 9-mer peptide was detected as a naturally presented ligand on HLA-B*40:02 by mass spectrometry. Collectively, these findings indicated that calpain-2 is a tumor-associated antigen recognized by osteosarcoma-derived TILs, providing mechanistic insight into calpain-dependent antigen processing in tumor cells, contributing to a deeper understanding of antigen recognition in osteosarcoma.

BACKGROUND:The testis is an immune-privileged organ due to its lack of functional human leukocyte antigen (HLA) class I expression. However, the expression status of HLA class I in testicular seminoma, which is characterized histologically by lymphocytic infiltration, remains unclear. MATERIALS AND METHODS:We performed immunohistochemistry to evaluate HLA class I expression and its spatial relationship with CD8-positive cells in 20 cases of stage I seminoma. Each specimen was scored manually and further analyzed using the HALO image analysis platform. RESULTS:Manual scoring and HALO analysis showed 80 % concordance. HLA class I expression was positive in 13 cases (65 %). Tumors were classified into three categories based on the expression levels. The number of infiltrating CD8-positive cells was significantly correlated with HLA class I expression. Spatial analysis revealed that tumors with higher HLA class I expression had shorter distances between HLA class I-positive tumor cells and CD8-positive cells. CONCLUSION:Seminoma cells expressing HLA class I tended to be located in close proximity to CD8-positive T cells, suggesting a spatial relationship that might influence the tumor immune microenvironment.

Isolation of colon CICs as SP cells and the expression of OR7C1

PDF file - 74K, RT-PCR analysis

PDF file - 67K, Western blot analysis

OR7C1 gene overexpression experiments

PDF file - 91K, SP analysis of DNAJB8- transduced cells

PDF file - 142K, Protein expression of DNAJB8 in SP and MP cells

PDF file - 68K, Isolation of CSCs/CICs from human RCC cells

PDF file - 23K, List of Primers

Establishment of CIC-targeting immunotherapy using antigenic peptide derived from OR7C1.

Supplementary Figure 1 from A Novel Isoform of TUCAN Is Overexpressed in Human Cancer Tissues and Suppresses Both Caspase-8– and Caspase-9–Mediated Apoptosis

Summary of the OR7C1 peptides carrying HLA-A24 binding motief.

Supplementary Figure Legend from A Novel Isoform of TUCAN Is Overexpressed in Human Cancer Tissues and Suppresses Both Caspase-8– and Caspase-9–Mediated Apoptosis

PDF file - 92K, Serine-rich region of DNAJB8 has role for induction of SP cells

Supplemental Materials and Methods and figure legends

Background Although many cervical cytology diagnostic support systems have been developed, it is challenging to classify overlapping cell clusters with a variety of patterns in the same way that humans do. In this study, we developed a fast and accurate system for the detection and classification of atypical cell clusters by using a two-step algorithm based on two different deep learning algorithms. Methods We created 919 cell images from liquid-based cervical cytological samples collected at Sapporo Medical University and annotated them based on the Bethesda system as a dataset for machine learning. Most of the images captured overlapping and crowded cells, and images were oversampled by digital processing. The detection system consists of two steps: (1) detection of atypical cells using You Only Look Once v4 (YOLOv4) and (2) classification of the detected cells using ResNeSt. A label smoothing algorithm was used for the dataset in the second classification step. This method annotates multiple correct classes from a single cell image with a smooth probability distribution. Results The first step, cell detection by YOLOv4, was able to detect all atypical cells above ASC-US without any observed false negatives. The detected cell images were then analyzed in the second step, cell classification by the ResNeSt algorithm, which exhibited average accuracy and F-measure values of 90.5% and 70.5%, respectively. The oversampling of the training image and label smoothing algorithm contributed to the improvement of the system's accuracy. Conclusion This system combines two deep learning algorithms to enable accurate detection and classification of cell clusters based on the Bethesda system, which has been difficult to achieve in the past. We will conduct further research and development of this system as a platform for augmented reality microscopes for cytological diagnosis.