Journal of Pathology and Translational Medicine

Articles in E-pub version are posted online ahead of regular printed publication.

Case Study

Uncommon granulomatous manifestation in Epstein-Barr virus–positive follicular dendritic cell sarcoma: a case report: Henry Goh Di Shen, Yue Zhang, Wei Qiang Leow; Received July 1, 2024 Accepted September 26, 2024 Published online October 31, 2024; DOI: https://doi.org/10.4132/jptm.2024.09.27 [Epub ahead of print]

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Abstract PDF: Hepatic Epstein-Barr virus–positive inflammatory follicular dendritic cell sarcoma (EBV+ IFDCS) represents a rare form of liver malignancy. The absence of distinct clinical and radiological characteristics, compounded by its rare occurrence, contributes to a challenging diagnosis. Here, we report a case of a 54-year-old Chinese female with a background of chronic hepatitis B virus treated with entecavir and complicated by advanced fibrosis presenting with a liver mass found on her annual surveillance ultrasound. Hepatectomy was performed under clinical suspicion of hepatocellular carcinoma. Immunomorphologic characteristics of the tumor were consistent with EBV+ IFDCS with distinct non-caseating granulomatous inflammation. Our case illustrates the importance of considering EBV+ IFDCS in the differential diagnosis of hepatic inflammatory lesions. Awareness of this entity and its characteristic features is essential for accurately diagnosing and managing this rare neoplasm.

Original Articles

The combination of CDX2 expression status and tumor-infiltrating lymphocyte density as a prognostic factor in adjuvant FOLFOX-treated patients with stage III colorectal cancers: Ji-Ae Lee, Hye Eun Park, Hye-Yeong Jin, Lingyan Jin, Seung Yeon Yoo, Nam-Yun Cho, Jeong Mo Bae, Jung Ho Kim, Gyeong Hoon Kang; Received June 3, 2024 Accepted September 26, 2024 Published online October 24, 2024; DOI: https://doi.org/10.4132/jptm.2024.09.26 [Epub ahead of print]

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Abstract PDF Supplementary Material: Background
Colorectal carcinomas (CRCs) with caudal-type homeobox 2 (CDX2) loss are recognized to pursue an aggressive behavior but tend to be accompanied by a high density of tumor-infiltrating lymphocytes (TILs). However, little is known about whether there is an interplay between CDX2 loss and TIL density in the survival of patients with CRC.
Methods
Stage III CRC tissues were assessed for CDX2 loss using immunohistochemistry and analyzed for their densities of CD8 TILs in both intraepithelial (iTILs) and stromal areas using a machine learning-based analytic method.
Results
CDX2 loss was significantly associated with a higher density of CD8 TILs in both intraepithelial and stromal areas. Both CDX2 loss and a high CD8 iTIL density were found to be prognostic parameters and showed hazard ratios of 2.314 (1.050–5.100) and 0.378 (0.175–0.817), respectively, for cancer-specific survival. A subset of CRCs with retained CDX2 expression and a high density of CD8 iTILs showed the best clinical outcome (hazard ratio of 0.138 [0.023–0.826]), whereas a subset with CDX2 loss and a high density of CD8 iTILs exhibited the worst clinical outcome (15.781 [3.939–63.230]).
Conclusions
Altogether, a high density of CD8 iTILs did not make a difference in the survival of patients with CRC with CDX2 loss. The combination of CDX2 expression and intraepithelial CD8 TIL density was an independent prognostic marker in adjuvant chemotherapy-treated patients with stage III CRC.

Diagnosis of invasive encapsulated follicular variant papillary thyroid carcinoma by protein-based machine learning: Truong Phan-Xuan Nguyen, Minh-Khang Le, Sittiruk Roytrakul, Shanop Shuangshoti, Nakarin Kitkumthorn, Somboon Keelawat; Received July 23, 2024 Accepted September 14, 2024 Published online October 24, 2024; DOI: https://doi.org/10.4132/jptm.2024.09.14 [Epub ahead of print]

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Abstract PDF Supplementary Material: Background
Although the criteria for follicular-pattern thyroid tumors are well-established, diagnosing these lesions remains challenging in some cases. In the recent World Health Organization Classification of Endocrine and Neuroendocrine Tumors (5th edition), the invasive encapsulated follicular variant of papillary thyroid carcinoma was reclassified as its own entity. It is crucial to differentiate this variant of papillary thyroid carcinoma from low-risk follicular pattern tumors due to their shared morphological characteristics. Proteomics holds significant promise for detecting and quantifying protein biomarkers. We investigated the potential value of a protein biomarker panel defined by machine learning for identifying the invasive encapsulated follicular variant of papillary thyroid carcinoma, initially using formalin- fixed paraffin-embedded samples.
Methods
We developed a supervised machine-learning model and tested its performance using proteomics data from 46 thyroid tissue samples.
Results
We applied a random forest classifier utilizing five protein biomarkers (ZEB1, NUP98, C2C2L, NPAP1, and KCNJ3). This classifier achieved areas under the curve (AUCs) of 1.00 and accuracy rates of 1.00 in training samples for distinguishing the invasive encapsulated follicular variant of papillary thyroid carcinoma from non-malignant samples. Additionally, we analyzed the performance of single-protein/gene receiver operating characteristic in differentiating the invasive encapsulated follicular variant of papillary thyroid carcinoma from others within The Cancer Genome Atlas projects, which yielded an AUC > 0.5.
Conclusions
We demonstrated that integration of high-throughput proteomics with machine learning can effectively differentiate the invasive encapsulated follicular variant of papillary thyroid carcinoma from other follicular pattern thyroid tumors.

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