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At least 415 records · Page 23Linked to original sources
PubMed1992

Determination of T cell monoclonality in non-Hodgkin's lymphoma by frozen section immunohistology. The SWOG Central Repository Members.

Monoclonal antibodies (mAb) reactive with seven distinct T cell receptor (TcR) alpha/beta variable region (V) families have become available. We investigated the potential utility of these mAb to establish T cell clonality (restrictive expression of one single V region family type) by frozen section immunohistology. We studied 40 non-Hodgkin's lymphomas (NHL) previously classified, immunophenotypically and genotypically by the South Western Oncology Group (SWOG) as 20 B and 20 T cell NHL. Frozen sections of each neoplasm were immunostained with the following mAb: beta-V5a, beta-V5b, beta-V6a, beta-V8a, beta-V12a, alpha/beta-Va and alpha-V2a. The large atypical lymphocytes of 18 of 20 T cell NHL showed no reactivity with the seven V region family mAb and only two showed exclusive immunoreactivity (one with anti-alpha V2a and the other with anti-beta V6a). All large atypical B cells in the 20 B cell NHL were non-reactive with the V region family mAb and each of the 40 neoplasms disclosed no or a trace reactivity in small host T cells. The results show that clonality can be determined in only a small percentage of T cell NHL (Sensitivity 10%, specificity 100%). Therefore, until new mAb become available, genotypic analysis remains the most sensitive and reliable method to establish T cell clonality.

Antibodies, Monoclonal↗
PubMed1993

Cytolytic (TIA-1+) tumor infiltrating lymphocytes in B cell non-Hodgkin's lymphomas. SWOG Central Repository Members.

TIA-1 is a monoclonal antibody (mAb) that identifies cytolytic cells. We studied eleven B cell non-Hodgkin's lymphomas (NHL) of low grade, eleven B cell NHL of intermediate-high grade, and 10 benign lymphoid hyperplasias (BLH) to investigate potential differences in the number of host cytolytic tumor infiltrating lymphocytes (TILs). Frozen sections were immunostained with TIA-1 mAb and the number of immunoreactive cells (TIA-1+) per mm2 of tissue was quantitated within reactive or neoplastic lymphoid follicles or random areas of diffuse NHL. The number of TIA-1+ cells/mm2 was significantly higher in intermediate and high grade B cell NHL than in low grade NHL or BLH with means +/- se of 1377.8 +/- 173, 866.2 +/- 92.3 and 774.1 +/- 76.2, respectively (p < 0.0183 and p < 0.0125). There was no significant difference between BLH and low grade NHL. The increased number of TIA-1+ TILs in B cell NHL of intermediate and high grade suggests the possibility of a host cytolytic immune response versus the tumor. Paradoxically, B cell tumors of worst biological outcome contained more cytolytic TILs. Functional defects of host cytolytic TILs in NHL patients should be investigated in future studies.

Antibodies, Monoclonal↗
PubMed2026

GICPIdb: an archival repository of multimodal data focusing on pathological images for gastrointestinal cancers.

INTRODUCTION: Deep learning (DL) shows great potential for predicting biomarkers from routine histopathological slides of gastrointestinal (GI) cancers. Yet most existing models are validated on limited patient cohorts, while pathological image annotation and molecular marker standardization demand substantial professional expertise. To address these gaps, we constructed the Gastrointestinal Cancer Pathological Image Archive (GICPIdb, gicpidb.shubuzuo.top), a dedicated database and web platform covering seven major GI cancer types. METHODS: High-quality hematoxylin and eosin (H&E)-stained whole-slide images were collected from multiple sources and uniformly processed. Image annotations were performed by board-certified pathologists following standardized protocols. GICPIdb offers five interactive web modules for data uploading, quality control, feature extraction, online annotation and AI-based prediction. Its intuitive interface supports data browsing, retrieval, visualization and downloading. RESULTS: The database houses 2,863 pathologist-annotated, uniformly processed, high-quality H&E stained images collected from 2,655 patients. Of these, 1,699 patients were sourced from The Cancer Genome Atlas (TCGA), 182 from the Clinical Proteomic Tumor Analysis Consortium (CPTAC), and 424 from China-Japan Friendship Hospital and 350 from Chifeng Municipal Hospital in Inner Mongolia, China. It also integrates data on over 50 key molecular markers (e.g., MSI, TMB) and prognostic labels related to survival, recurrence and metastasis. DISCUSSION: GICPIdb aims to promote the development of DL-driven AI tools for cancer research and clinical translation. The multi-institutional data collection and standardized annotation pipeline are expected to enhance the generalizability and reproducibility of AI-based prediction models across diverse patient populations.

deep learning↗