Cancer Research and Treatment

Original Articles

Gastrointestinal cancer

Relationships between the Microbiome and Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer: Hye In Lee, Bum-Sup Jang, Ji Hyun Chang, Eunji Kim, Tae Hoon Lee, Jeong Hwan Park, Eui Kyu Chie; Cancer Res Treat. 2025;57(3):840-851. Published online December 16, 2024; DOI: https://doi.org/10.4143/crt.2024.521

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Purpose
This study aimed to investigate the dynamic changes in the microbiome of patients with locally advanced rectal cancer (LARC) undergoing neoadjuvant chemoradiotherapy (nCRT), focusing on the relationship between the microbiome and response to nCRT.
Materials and Methods
We conducted a longitudinal study involving 103 samples from 26 patients with LARC. Samples were collected from both the tumor and normal rectal tissues before and after nCRT. Diversity, taxonomic, and network analyses were performed to compare the microbiome profiles across different tissue types, pre- and post-nCRT time-points, and nCRT responses.
Results
Between the tumor and normal tissue samples, no differences in microbial diversity and composition were observed. However, when pre- and post-nCRT samples were compared, there was a significant decrease in diversity, along with notable changes in composition. Non-responders exhibited more extensive changes in their microbiome composition during nCRT, characterized by an increase in pathogenic microbes. Meanwhile, responders had relatively stable microbiome communities with more enriched butyrate-producing bacteria. Network analysis revealed distinct patterns of microbial interactions between responders and non-responders, where butyrate-producing bacteria formed strong networks in responders, while opportunistic pathogens formed strong networks in non-responders. A Bayesian network model for predicting the nCRT response was established, with butyrate-producing bacteria playing a major predictive role.
Conclusion
Our study demonstrated a significant association between the microbiome and nCRT response in LARC patients, leading to the development of a microbiome-based response-prediction model. These findings suggest potential applications of microbiome signatures for predicting and optimizing nCRT treatment in LARC patients.

Citations

Citations to this article as recorded by

The Mediating Role of the Gut Microbiome in the Nutritional Prevention of Cancer
Priyanka Chambial, Neelam Thakur, Umesh Kumar, Saurabh Gupta
The Journal of Nutrition.2026; 156(2): 101301. CrossRef
Gut microbiota-associated predictors as biomarkers of neoadjuvant treatment response in rectal cancer-a systematic review
Astghik Stepanyan, Andromachi Kotsafti, Antonio Rosato, Ignazio Castagliuolo, Marco Scarpa, Melania Scarpa, Marco Agostini, Imerio Angriman, Michele Antoniutti, Quoc Riccardo Bao, Andrea Baldo, Mattia Barbareschi, Romeo Bardini, Giulia Becherucci, Frances
British Journal of Cancer.2026; 135(1): 139. CrossRef
The role of the microbiome in uterine cancer: insights into tumorigenesis, therapeutic implications, and clinical prospects
Yating Zhang, Xiaochuan Yu, Li Shi, Chen Qi, Huali Wang
Journal of the Egyptian National Cancer Institute.2026;[Epub] CrossRef
Gut-Microbiome Signatures Predicting Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer: A Systematic Review
Ielmina Domilescu, Bogdan Miutescu, Florin George Horhat, Alina Popescu, Camelia Nica, Ana Maria Ghiuchici, Eyad Gadour, Ioan Sîrbu, Delia Hutanu
Metabolites.2025; 15(6): 412. CrossRef

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Genitourinary cancer

Clear Cell Adenocarcinoma of Urethra: Clinical and Pathologic Implications and Characterization of Molecular Aberrations: Boram Song, Seok Hyun Lee, Jeong Hwan Park, Kyung Chul Moon; Cancer Res Treat. 2024;56(1):280-293. Published online September 11, 2023; DOI: https://doi.org/10.4143/crt.2023.577

Abstract PDF Supplementary Material PubReader ePub

Purpose
This study aimed to evaluate the molecular features of clear cell adenocarcinoma (CCA) of the urinary tract and investigate its pathogenic pathways and possible actionable targets.
Materials and Methods
We retrospectively collected the data of patients with CCA between January 1999 and December 2016; the data were independently reviewed by two pathologists. We selected five cases of urinary CCA, based on the clinicopathological features. We analyzed these five cases by whole exome sequencing (WES) and subsequent bioinformatics analyses to determine the mutational spectrum and possible pathogenic pathways.
Results
All patients were female with a median age of 62 years. All tumors were located in the urethra and showed aggressive behavior with disease progression. WES revealed several genetic alterations, including driver gene mutations (AMER1, ARID1A, CHD4, KMT2D, KRAS, PBRM1, and PIK3R1) and mutations in other important genes with tumor-suppressive and oncogenic roles (CSMD3, KEAP1, SMARCA4, and CACNA1D). We suggest putative pathogenic pathways (chromatin remodeling pathway, mitogen-activated protein kinase signaling pathway, phosphoinositide 3-kinase/AKT/mammalian target of rapamycin pathway, and Wnt/β-catenin pathway) as candidates for targeted therapies.
Conclusion
Our findings shed light on the molecular background of this extremely rare tumor with poor prognosis and can help improve treatment options.

Citations

Citations to this article as recorded by

Molecular characterization of clear cell adenocarcinoma of the urinary bladder
Shilpy Jha, Anandi Lobo, Ankur R Sangoi, Shivani R Kandukuri, Sourav K Mishra, Samriti Arora, Aditi Aggarwal, Shivani Sharma, Ekta Jain, Mahmut Akgul, Andres M Acosta, Niharika Pattnaik, Seema Kaushal, Manas Baisakh, Sudhasmita Routa, Nada Shaker, Jasrema
Histopathology.2026; 88(6): 1167. CrossRef
Clear Cell Adenocarcinoma of the Urinary Tract Arising in a Young Man With the Congenital Malformation Disorder Opitz Syndrome: A Case Report
Jessica F. Williams, Juan F. Santoscoy Gutierrez, José I. Nolazco, Graeme S. Steele, Kristine S. Burk, Michelle S. Hirsch, Natalie M. Rizzo
International Journal of Surgical Pathology.2026; 34(4): 1074. CrossRef
Histologic mimics of urothelial carcinoma
Douglas Jian-Xian Wu, Emily Chan
Seminars in Diagnostic Pathology.2026; 43(2): 150993. CrossRef
Clinical and Molecular Characterization of Clear Cell Adenocarcinoma of the Urinary Tract
Manju Aron, Darshan S. Chandrashekar, Eman Adulfatah, Lakshmi P. Kunju, Bassel Zein-Sabatto, João Lobo, Sofia Canete-Portillo, Roni M. Cox, Christopher G. Przybycin, Khaleel Al-Obaidy, Muhammad T. Idrees, Sara M. Falzarano, Angela J. Wu, Shuko Harada, Geo
American Journal of Surgical Pathology.2026; 50(6): 589. CrossRef
Primary urethral clear cell carcinoma: a rare case report
Jiaxiang Yi, Weigang Ren, Weijie Song, Yixing Duan
Frontiers in Oncology.2026;[Epub] CrossRef
Management and sequelae of a local skene's gland adenocarcinoma recurrence
Naziba Nuha, Katherine Trymbulak, Martin King, Benjamin Margolis
Gynecologic Oncology Reports.2026; 66: 102150. CrossRef
Durable disease control after multimodal therapy and platinum-based chemotherapy rechallenge in advanced clear cell adenocarcinoma of the urethra with lymph node metastases: a case report
Akihiro Maeda, Shohei Tobu, Shuhei Kusano, Maki Kawasaki, Hiroaki Kakinoki, Mitsuru Noguchi
Frontiers in Urology.2026;[Epub] CrossRef
Urethral clear cell adenocarcinoma in an adult female: A rare case report
Yacob Sheiferawe Seman, Michael Teklehaimanot Abera, Fadil Nuredin Abrar, Tesfaye Kebede Legesse, Mesfin Asefa Tola, Tsiyon Nigusie Alemu
Urology Case Reports.2025; 58: 102882. CrossRef
Two rare cases of primary clear cell adenocarcinoma of the urethra: clinical experience, case report and literature review
Bohao Jiang, Jiyuan Hu, Benqiao Wang, Xujia Liu, Ling Tong, Yitong Xu, Hao Zhang
Frontiers in Oncology.2025;[Epub] CrossRef
Comprehensive Molecular, Pathological, and Clinical Characterization of Clear Cell Adenocarcinoma of the Urinary Tract
Rayan Rammal, Florestan J. Koll, Ziyu Chen, Jacob E. Tallman, Syed Muneeb Alam, Jordan E. Eichholz, Walid Chatila, Tejiri Agbamu, Andrew T. Lenis, Merve Basar, Cansu Yol, Jie-Fu Chen, Judy Sarungbam, Ying-Bei Chen, Anuradha Gopalan, Samson W. Fine, Satish
Modern Pathology.2025; 38(10): 100821. CrossRef
Association between CACNA1D polymorphisms and hypospadias in a southern Chinese population
Ye He, Binyao Li, Xinying Zhao, Lingling Pan, Yanqing Liu, Chaoting Lan, Fuming Deng, Wen Fu, Yan Zhang, Xiaoyu Zuo
Journal of Pediatric Urology.2024; 20(3): 438.e1. CrossRef
The L‐type calcium channel CaV1.3: A potential target for cancer therapy
Xuerun Liu, Boqiang Shen, Jingyi Zhou, Juan Hao, Jianliu Wang
Journal of Cellular and Molecular Medicine.2024;[Epub] CrossRef

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Breast cancer

Challenge for Diagnostic Assessment of Deep Learning Algorithm for Metastases Classification in Sentinel Lymph Nodes on Frozen Tissue Section Digital Slides in Women with Breast Cancer: Young-Gon Kim, In Hye Song, Hyunna Lee, Sungchul Kim, Dong Hyun Yang, Namkug Kim, Dongho Shin, Yeonsoo Yoo, Kyowoon Lee, Dahye Kim, Hwejin Jung, Hyunbin Cho, Hyungyu Lee, Taeu Kim, Jong Hyun Choi, Changwon Seo, Seong il Han, Young Je Lee, Young Seo Lee, Hyung-Ryun Yoo, Yongju Lee, Jeong Hwan Park, Sohee Oh, Gyungyub Gong; Cancer Res Treat. 2020;52(4):1103-1111. Published online June 30, 2020; DOI: https://doi.org/10.4143/crt.2020.337

Abstract PDF PubReader ePub

Purpose
Assessing the status of metastasis in sentinel lymph nodes (SLNs) by pathologists is an essential task for the accurate staging of breast cancer. However, histopathological evaluation of sentinel lymph nodes by a pathologist is not easy and is a tedious and time-consuming task. The purpose of this study is to review a challenge competition (HeLP 2018) to develop automated solutions for the classification of metastases in hematoxylin and eosin–stained frozen tissue sections of SLNs in breast cancer patients.
Materials and Methods
A total of 297 digital slides were obtained from frozen SLN sections, which include post–neoadjuvant cases (n = 144, 48.5%) in Asan Medical Center, South Korea. The slides were divided into training, development, and validation sets. All of the imaging datasets have been manually segmented by expert pathologists. A total of 10 participants were allowed to use the Kakao challenge platform for six weeks with two P40 GPUs. The algorithms were assessed in terms of the AUC (area under receiver operating characteristic curve).
Results
The top three teams showed 0.986, 0.985, and 0.945 AUCs for the development set and 0.805, 0.776, and 0.765 AUCs for the validation set. Micrometastatic tumors, neoadjuvant systemic therapy, invasive lobular carcinoma, and histologic grade 3 were associated with lower diagnostic accuracy.
Conclusion
In a challenge competition, accurate deep learning algorithms have been developed, which can be helpful in making frozen diagnosis of intraoperative sentinel lymph node biopsy. Whether this approach has clinical utility will require evaluation in a clinical setting

Citations

Citations to this article as recorded by

Detection of metastatic breast carcinoma in sentinel lymph node frozen sections using an artificial intelligence-assisted system
Chia-Ping Chang, Chih-Yi Hsu, Hsiang Sheng Wang, Peng-Chuna Feng, Wen-Yih Liang
Pathology - Research and Practice.2025; 267: 155836. CrossRef
Fusing Domains for Tumor Detection in Frozen Section Histopathology Using Joint Feature Alignment and Self-Knowledge Distillation
Eunsu Kim, Yoo Jin Lee, Jonghyun Lee, Hyeseong Lee, D M Bappy, Sangjeong Ahn, Sung Hak Lee
IEEE Access.2025; 13: 190745. CrossRef
Diagnostic Assessment of Deep Learning Algorithms for Frozen Tissue Section Analysis in Women with Breast Cancer
Young-Gon Kim, In Hye Song, Seung Yeon Cho, Sungchul Kim, Milim Kim, Soomin Ahn, Hyunna Lee, Dong Hyun Yang, Namkug Kim, Sungwan Kim, Taewoo Kim, Daeyoung Kim, Jonghyeon Choi, Ki-Sun Lee, Minuk Ma, Minki Jo, So Yeon Park, Gyungyub Gong
Cancer Research and Treatment.2023; 55(2): 513. CrossRef
Artificial intelligence and frozen section histopathology: A systematic review
Benjamin G. Gorman, Mark A. Lifson, Nahid Y. Vidal
Journal of Cutaneous Pathology.2023; 50(9): 852. CrossRef
Effectiveness of transfer learning for enhancing tumor classification with a convolutional neural network on frozen sections
Young-Gon Kim, Sungchul Kim, Cristina Eunbee Cho, In Hye Song, Hee Jin Lee, Soomin Ahn, So Yeon Park, Gyungyub Gong, Namkug Kim
Scientific Reports.2020;[Epub] CrossRef

12,023 View
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