Establishing Optimized Staining Protocol and Cutoff Values of Pan-Trk Immunohistochemistry for Detecting NTRK Fusions in Solid Tumors: A Chinese Multicenter Cross-Sectional Study.
Journal of Clinical Oncology(2024)
Peking Union Medical College Hospital
Abstract
e15160 Background: Using IHC to screen NTRK+ patients, followed by NGS confirmation, is a strategy recommended by ESMO guidelines. However, the current IHC staining protocol (Protocol) and cutoff value (CV) are suboptimal, resulting in limited screening performance. This study aims to optimize the Protocol and redefine the CV to improve the efficacy of pan-TRK IHC in screening NTRK fusions. Methods: 126 NTRK+ and 162 NTRK- solid tumor samples (spls), previously confirmed by NGS and/or FISH, were collected retrospectively from 12 hospitals across China in 2023. All spls were stained with VENTANA pan-TRK IHC assay by using two different Protocols: 1) the conventional one recommended by the manufacturer's instruction and 2) an amplification one that included additional amplification steps to enhance the staining. The IHC stained spls were interpreted based on the manufacturer's instruction, and the results were analyzed to calculate the CVs. The sensitivity, specificity, accuracy, PPV, and NPV based on these CVs and the CV reported in the literature were calculated. Results: 126 NTRK+ spls involve 25 fusion-partner types and 23 tumor types. IHC showed 35 NTRK- spls without staining, and 253 spls (126NTRK+/127NTRK-) stained. The staining predominantly localized to the cytoplasm and nucleus (132 cytoplasmic positive, 105 cytoplasmic and nuclear positive). Due to the limited samples with membrane staining, only CVs for cytoplasmic and nuclear staining were defined. In order to enhance the sensitivity and feasibility of the pan-TRK IHC assay in NTRK+ screening, the statistically calculated CVs were adjusted, and we recommended using the following CVs in clinical practice: 1) ≥1 intensity of cytoplasmic staining in ≥50% of tumor cells and/or 2) ≥1 intensity of nuclear staining in any percentage of tumor cells. Compared with the statistically calculated CVs, although the accuracy of the recommended CVs was slightly reduced (87.5% vs 93.75%), the sensitivity was improved (94.44% vs 89.68%). The sensitivity and specificity of the recommended CVs were higher than that of the previously reported CV (≥1%): sensitivity (94.44% vs. 87.9%) and specificity (82.1% vs. 81.1%). Conclusions: The pan-TRK IHC assay demonstrated high sensitivity while maintaining good specificity and accuracy, improving screening and enrichment efficacy for identifying NTRK fusions in solid tumors by utilizing the amplification protocol and the recommended CVs. Clinical trial information: ChiCTR2200066850 . [Table: see text]
MoreTranslated text
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined