Strong association of Torque teno virus/Torque teno-like minivirus to Kikuchi-Fujimoto lymphadenitis (histiocytic necrotizing lymphadenitis) on quantitative analysis

INTRODUCTION

Kikuchi-Fujimoto disease (KFD) is a rare benign lymphadenitis that mainly involves cervical lymph nodes of young Asian women with unknown etiology. Recently, we experienced a case of KFD found with Torque teno virus/Torque teno-like mini virus (TTV/TTMV) from a 26-year-old woman. TTV/TTMV is a genus of Circoviridae that causes necrotizing lymphadenitis in pigs, which shares the key histologic finding of KFD. The purpose of this study is to investigate the pathogenic role of TTV/TTMV in KFD by quantitative polymerase chain reaction (qPCR) analysis.

METHOD

We performed two-step qPCR specific to TTV/TTMV with formalin-fixed paraffin-embedded tissue of sequentially selected 100 KFD patients and 50 randomly selected, matched normal controls. Consequent direct sequencing was done for confirmation with PCR products.

RESULTS

PCR amplification of TTV and TTMV was found in a significantly higher proportion in KFDs than normal controls (TTV, 85% vs. 18%, p < 0.000; TTMV, 91% vs. 24%, p < 0.000). After the sequencing, KFD samples showed more sequence matching than control samples for TTMV (94% vs. 30%, p < 0.000).

CONCLUSION

This finding strongly suggests the possible implication of TTV/TTMV in the pathogenesis of KFD. Animal or in vivo experimental design should be followed in the future.Key Points• Kikuchi-Fujimoto disease (KFD) is rare and its etiology is still unclear.• Torque teno/Torque teno-like minivirus (TTV/TTMV) is a recently introduced virus in the Circoviridae family that causes necrotizing lymphadenitis in pigs, histologically similar to KFD.• We discovered the significantly increased TTV/TTMV viral loads in the KFD patients than normal controls, which implicates TTV/TTMV in the pathogenesis of KFD.

Prognostic significance of stem cell/ epithelial-mesenchymal transition markers in periampullary/pancreatic cancers: FGFR1 is a promising prognostic marker

BACKGROUND

Periampullary cancers (PAC) including pancreatic, ampulla of Vater (AOV), and common bile duct (CBD) cancers are highly aggressive with a lack of useful prognostic markers beyond T stage. However, T staging can be biased due to the anatomic complexity of this region. Recently, several markers related to cancer stem cells and epithelial-mesenchymal transition (EMT) such as octamer transcription factor-4 (Oct4) and fibroblast growth factor receptor 1 (FGFR1) respectively, have been proposed as new promising markers in other solid cancers. The aim of this study was to assess the expression and prognostic significance of stem cell/EMT markers in PACs.

METHODS

Formalin-fixed, paraffin-embedded tissues of surgically excised PACs from the laboratory archives from 1998 to 2014 were evaluated by immunohistochemical staining for stem cell/EMT markers using tissue microarray. The clinicopathologic parameters were documented and statistically analyzed with the immunohistochemical findings. Survival and recurrence data were collected and analyzed.

RESULTS

A total of 126 PAC cases were evaluated. The average age was 63 years, with 76 male and 50 female patient samples. Age less than 74 years, AOV cancers, lower T & N stage, lower tumor size, no lymphatic, vascular, perineural invasion and histologic well differentiation, intestinal type, no fibrosis, severe inflammation were significantly associated with the better overall survival High expression levels of FGFR1 as well as CK20, CDX2, and VEGF were significantly related to better overall survival, while other stem cell markers were not related. Similar findings were observed for tumor recurrence using disease-free survival.

CONCLUSIONS

In addition to other clinicopathologic parameters, severe fibrosis was related to frequent tumor recurrence, and high FGFR1 expression was associated with better overall survival. Histologic changes such as extensive fibrosis need to be investigated further in relation to EMT of PACs.

Identification of Torque Teno Virus/Torque Teno-Like Minivirus in the Cervical Lymph Nodes of Kikuchi-Fujimoto Lymphadenitis Patients (Histiocytic Necrotizing Lymphadenitis): A Possible Key to Idiopathic Disease

Kikuchi-Fujimoto disease (KFD) is rare, and many infectious agents have been suspected for its etiology. This report presents an interesting case of KFD found with torque teno virus/torque teno minivirus (TTV/TTMV), which closely resembles the circovirus that causes necrotizing lymphadenitis in pigs. Three Korean patients showed several enlarged lymph nodes in their neck. Quantitative polymerase chain reaction (qPCR) and subsequent DNA sequencing for TTV/TTMV using formalin-fixed paraffin-embedded tissue were performed. Histologic examination demonstrated typical features of KFD. qPCR showed successful amplification of TTV/TTMV, and DNA sequencing confirmed the results. It is the first report of TTV/TTMV presence in three patients with KFD.

A machine-learning expert-supporting system for diagnosis prediction of lymphoid neoplasms using a probabilistic decision-tree algorithm and immunohistochemistry profile database

BACKGROUND

Immunohistochemistry (IHC) has played an essential role in the diagnosis of hematolymphoid neoplasms. However, IHC interpretations can be challenging in daily practice, and exponentially expanding volumes of IHC data are making the task increasingly difficult. We therefore developed a machine-learning expert-supporting system for diagnosing lymphoid neoplasms.

METHODS

A probabilistic decision-tree algorithm based on the Bayesian theorem was used to develop mobile application software for iOS and Android platforms. We tested the software with real data from 602 training and 392 validation cases of lymphoid neoplasms and compared the precision hit rates between the training and validation datasets.

RESULTS

IHC expression data for 150 lymphoid neoplasms and 584 antibodies was gathered. The precision hit rates of 94.7% in the training data and 95.7% in the validation data for lymphomas were not statistically significant. Results in most B-cell lymphomas were excellent, and generally equivalent performance was seen in T-cell lymphomas. The primary reasons for lack of precision were atypical IHC profiles for certain cases (e.g., CD15-negative Hodgkin lymphoma), a lack of disease-specific markers, and overlapping IHC profiles of similar diseases.

CONCLUSIONS

Application of the machine-learning algorithm to diagnosis precision produced acceptable hit rates in training and validation datasets. Because of the lack of origin- or disease-specific markers in differential diagnosis, contextual information such as clinical and histological features should be taken into account to make proper use of this system in the pathologic decision-making process.

Diagnosis prediction of tumours of unknown origin using ImmunoGenius, a machine learning-based expert system for immunohistochemistry profile interpretation

BACKGROUND

Immunohistochemistry (IHC) remains the gold standard for the diagnosis of pathological diseases. This technique has been supporting pathologists in making precise decisions regarding differential diagnosis and subtyping, and in creating personalized treatment plans. However, the interpretation of IHC results presents challenges in complicated cases. Furthermore, rapidly increasing amounts of IHC data are making it even harder for pathologists to reach to definitive conclusions.

METHODS

We developed ImmunoGenius, a machine-learning-based expert system for the pathologist, to support the diagnosis of tumors of unknown origin. Based on Bayesian theorem, the most probable diagnoses can be drawn by calculating the probabilities of the IHC results in each disease. We prepared IHC profile data of 584 antibodies in 2009 neoplasms based on the relevant textbooks. We developed the reactive native mobile application for iOS and Android platform that can provide 10 most possible differential diagnoses based on the IHC input.

RESULTS

We trained the software using 562 real case data, validated it with 382 case data, tested it with 164 case data and compared the precision hit rate. Precision hit rate was 78.5, 78.0 and 89.0% in training, validation and test dataset respectively. Which showed no significant difference. The main reason for discordant precision was lack of disease-specific IHC markers and overlapping IHC profiles observed in similar diseases.

CONCLUSION

The results of this study showed a potential that the machine-learning algorithm based expert system can support the pathologic diagnosis by providing second opinion on IHC interpretation based on IHC database. Incorporation with contextual data including the clinical and histological findings might be required to elaborate the system in the future.