Cancer Research and Treatment

Original Articles

Machine Learning–Based Prognostic Gene Signature for Early Triple-Negative Breast Cancer: Ju Won Kim, Jonghyun Lee, Sung Hak Lee, Sangjeong Ahn, Kyong Hwa Park; Received September 26, 2024 Accepted November 18, 2024 Published online November 19, 2024; DOI: https://doi.org/10.4143/crt.2024.937 [Epub ahead of print]

Abstract PDF Supplementary Material: Purpose
This study aimed to develop a machine learning–based approach to identify prognostic gene signatures for early-stage triple-negative breast cancer (TNBC) using next-generation sequencing data from Asian populations.
Materials and Methods
We utilized next-generation sequencing data to analyze gene expression profiles and identify potential biomarkers. Our methodology involved integrating various machine learning techniques, including feature selection and model optimization. We employed logistic regression, Kaplan-Meier survival analysis, and receiver operating characteristic (ROC) curves to validate the identified gene signatures.
Results
We identified a gene signature significantly associated with relapse in TNBC patients. The predictive model demonstrated robustness and accuracy, with an area under the ROC curve of 0.9087, sensitivity of 0.8750, and specificity of 0.9231. The Kaplan-Meier survival analysis revealed a strong association between the gene signature and patient relapse, further validated by logistic regression analysis.
Conclusion
This study presents a novel machine learning-based prognostic tool for TNBC, offering significant implications for early detection and personalized treatment. The identified gene signature provides a promising approach for improving the management of TNBC, contributing to the advancement of precision oncology.

520 View
44 Download

Breast cancer

Molecular Classification of Breast Cancer Using Weakly Supervised Learning: Wooyoung Jang, Jonghyun Lee, Kyong Hwa Park, Aeree Kim, Sung Hak Lee, Sangjeong Ahn; Cancer Res Treat. 2025;57(1):116-125. Published online June 25, 2024; DOI: https://doi.org/10.4143/crt.2024.113

Abstract PDF Supplementary Material PubReader ePub: Purpose
The molecular classification of breast cancer is crucial for effective treatment. The emergence of digital pathology has ushered in a new era in which weakly supervised learning leveraging whole-slide images has gained prominence in developing deep learning models because this approach alleviates the need for extensive manual annotation. Weakly supervised learning was employed to classify the molecular subtypes of breast cancer.
Materials and Methods
Our approach capitalizes on two whole-slide image datasets: one consisting of breast cancer cases from the Korea University Guro Hospital (KG) and the other originating from The Cancer Genomic Atlas dataset (TCGA). Furthermore, we visualized the inferred results using an attention-based heat map and reviewed the histomorphological features of the most attentive patches.
Results
The KG+TCGA-trained model achieved an area under the receiver operating characteristics value of 0.749. An inherent challenge lies in the imbalance among subtypes. Additionally, discrepancies between the two datasets resulted in different molecular subtype proportions. To mitigate this imbalance, we merged the two datasets, and the resulting model exhibited improved performance. The attentive patches correlated well with widely recognized histomorphologic features. The triple-negative subtype has a high incidence of high-grade nuclei, tumor necrosis, and intratumoral tumor-infiltrating lymphocytes. The luminal A subtype showed a high incidence of collagen fibers.
Conclusion
The artificial intelligence (AI) model based on weakly supervised learning showed promising performance. A review of the most attentive patches provided insights into the predictions of the AI model. AI models can become invaluable screening tools that reduce costs and workloads in practice.

2,038 View
173 Download

Review Article

Applicability of Spatial Technology in Cancer Research: Sangjeong Ahn, Hye Seung Lee; Cancer Res Treat. 2024;56(2):343-356. Published online January 30, 2024; DOI: https://doi.org/10.4143/crt.2023.1302

Abstract PDF PubReader ePub

This review explores spatial mapping technologies in cancer research, highlighting their crucial role in understanding the complexities of the tumor microenvironment (TME). The TME, which is an intricate ecosystem of diverse cell types, has a significant impact on tumor dynamics and treatment outcomes. This review closely examines cutting-edge spatial mapping technologies, categorizing them into capture-, imaging-, and antibody-based approaches. Each technology was scrutinized for its advantages and disadvantages, factoring in aspects such as spatial profiling area, multiplexing capabilities, and resolution. Additionally, we draw attention to the nuanced choices researchers face, with capture-based methods lending themselves to hypothesis generation, and imaging/antibody-based methods that fit neatly into hypothesis testing. Looking ahead, we anticipate a scenario in which multi-omics data are seamlessly integrated, artificial intelligence enhances data analysis, and spatiotemporal profiling opens up new dimensions.

Citations

Citations to this article as recorded by

Navigating the landscape of plant proteomics
Tian Sang, Zhen Zhang, Guting Liu, Pengcheng Wang
Journal of Integrative Plant Biology.2025;[Epub] CrossRef
Distinctive Phenotypic and Microenvironmental Characteristics of Neuroendocrine Carcinoma and Adenocarcinoma Components in Gastric Mixed Adenoneuroendocrine Carcinoma
Yoonjin Kwak, Soo Kyung Nam, Yujun Park, Yun-Suhk Suh, Sang-Hoon Ahn, Seong-Ho Kong, Do Joong Park, Hyuk-Joon Lee, Hyung-Ho Kim, Han-Kwang Yang, Hye Seung Lee
Modern Pathology.2024; 37(10): 100568. CrossRef
Effector Function Characteristics of Exhausted CD8+ T-Cell in Microsatellite Stable and Unstable Gastric Cancer
Dong-Seok Han, Yoonjin Kwak, Seungho Lee, Soo Kyung Nam, Seong-Ho Kong, Do Joong Park, Hyuk-Joon Lee, Nak-Jung Kwon, Hye Seung Lee, Han-Kwang Yang
Cancer Research and Treatment.2024; 56(4): 1146. CrossRef
Prognostic significance of CD8 and TCF1 double positive T cell subset in microsatellite unstable gastric cancer
Juhyeong Park, Soo Kyung Nam, Yoonjin Kwak, Hyeon Jeong Oh, Seong-Ho Kong, Do Joong Park, Hyuk-Joon Lee, Han-Kwang Yang, Hye Seung Lee
Scientific Reports.2024;[Epub] CrossRef

4,841 View
250 Download
4 Web of Science
4 Crossref

Original Article

Pancreatic High-Grade Neuroendocrine Neoplasms in the Korean Population: A Multicenter Study: Haeryoung Kim, Soyeon An, Kyoungbun Lee, Sangjeong Ahn, Do Youn Park, Jo-Heon Kim, Dong-Wook Kang, Min-Ju Kim, Mee Soo Chang, Eun Sun Jung, Joon Mee Kim, Yoon Jung Choi, So-Young Jin, Hee Kyung Chang, Mee-Yon Cho, Yun Kyung Kang, Myunghee Kang, Soomin Ahn, Youn Wha Kim, Seung-Mo Hong, on behalf of the Gastrointestinal Pathology Study Group of the Korean Society of Pathologists; Cancer Res Treat. 2020;52(1):263-276. Published online July 12, 2019; DOI: https://doi.org/10.4143/crt.2019.192

Abstract PDF Supplementary Material PubReader ePub

Purpose
The most recent 2017 World Health Organization (WHO) classification of pancreatic neuroendocrine neoplasms (PanNENs) has refined the three-tiered 2010 scheme by separating grade 3 pancreatic neuroendocrine tumors (G3 PanNETs) from poorly differentiated pancreatic neuroendocrine carcinomas (PanNECs). However, differentiating between G3 Pan- NETs and PanNECs is difficult in clinical practice.
Materials and Methods
Eighty-two surgically resected PanNENs were collected from 16 institutions and reclassified according to the 2017 WHO classification based on the histological features and proliferation index (mitosis and Ki-67). Immunohistochemical stains for ATRX, DAXX, retinoblastoma, p53, Smad4, p16, and MUC1 were performed for 15 high-grade PanNENs.
Results
Re-classification resulted in 20 G1 PanNETs (24%), 47 G2 PanNETs (57%), eight G3 well-differentiated PanNETs (10%), and seven poorly differentiated PanNECs (9%). PanNECs showed more frequent diffuse nuclear atypia, solid growth patterns and apoptosis, less frequent organoid growth and regular vascular patterns, and absence of low-grade PanNET components than PanNETs. The Ki-67 index was significantly higher in PanNEC (58.2%± 15.1%) compared to G3 PanNET (22.6%±6.1%, p < 0.001). Abnormal expression of any two of p53, p16, MUC1, and Smad4 could discriminate PanNECs from G3 PanNETs with 100% specificity and 87.5% sensitivity.
Conclusion
Histological features supporting the diagnosis of PanNECs over G3 PanNETs were the absence of a low-grade PanNET component in the tumor, the presence of diffuse marked nuclear atypia, solid growth pattern, frequent apoptosis and markedly increased proliferative activity with homogeneous Ki-67 labeling. Immunohistochemical stains for p53, p16, MUC1, and Smad4 may be helpful in distinguishing PanNECs from G3 PanNETs in histologically ambiguous cases, especially in diagnostic practice when only small biopsied tissues are available.

Citations

Citations to this article as recorded by

Malignant potential of neuroendocrine microtumor of the pancreas harboring high-grade transformation: lesson learned from a patient with von Hippel-Lindau syndrome
Jongwon Lee, Kyung Jin Lee, Dae Wook Hwang, Seung-Mo Hong
Journal of Pathology and Translational Medicine.2024; 58(2): 91. CrossRef
Rapid Evolution of Metastases in Patients with Treated G3 Neuroendocrine Tumors Associated with NEC-Like Transformation and TP53 Mutation
Atsuko Kasajima, Nicole Pfarr, Eva-Maria Mayr, Ayako Ura, Elisa Moser, Alexander von Werder, Abbas Agaimy, Marianne Pavel, Günter Klöppel
Endocrine Pathology.2024; 35(4): 313. CrossRef
The Complex Histopathological and Immunohistochemical Spectrum of Neuroendocrine Tumors—An Overview of the Latest Classifications
Ancuța-Augustina Gheorghișan-Gălățeanu, Andreea Ilieșiu, Ioana Maria Lambrescu, Dana Antonia Țăpoi
International Journal of Molecular Sciences.2023; 24(2): 1418. CrossRef
All Together Now
Pari Jafari, Aliya N. Husain, Namrata Setia
Surgical Pathology Clinics.2023; 16(1): 131. CrossRef
A systematic review of therapeutic strategies in gastroenteropancreatic grade 3 neuroendocrine tumors
Mauro D. Donadio, Ângelo B. Brito, Rachel P. Riechelmann
Therapeutic Advances in Medical Oncology.2023;[Epub] CrossRef
MicroRNAs associated with postoperative outcomes in patients with limited stage neuroendocrine carcinoma of the esophagus
Tomoyuki Okumura, Tsutomu Fujii, Kenji Terabayashi, Takashi Kojima, Shigeru Takeda, Tomomi Kashiwada, Kazuhiro Toriyama, Susumu Hijioka, Tatsuya Miyazaki, Miho Yamamoto, Shunsuke Tanabe, Yasuhiro Shirakawa, Masayuki Furukawa, Yoshitaka Honma, Isamu Hoshin
Oncology Letters.2023;[Epub] CrossRef
The association between jaundice and poorly differentiated pancreatic neuroendocrine neoplasms (Ki67 index > 55.0%)
Yongkang Liu, Jiangchuan Wang, Hao Zhou, Zicheng Wei, Jianhua Wang, Zhongqiu Wang, Xiao Chen
BMC Gastroenterology.2023;[Epub] CrossRef
An analysis of 130 neuroendocrine tumors G3 regarding prevalence, origin, metastasis, and diagnostic features
Atsuko Kasajima, Björn Konukiewitz, Anna Melissa Schlitter, Wilko Weichert, Günter Klöppel
Virchows Archiv.2022; 480(2): 359. CrossRef
An update on genetically engineered mouse models of pancreatic neuroendocrine neoplasms
Tiago Bordeira Gaspar, José Manuel Lopes, Paula Soares, João Vinagre
Endocrine-Related Cancer.2022; 29(12): R191. CrossRef
Solid pancreatic masses in children: A review of current evidence and clinical challenges
Kelli N. Patterson, Andrew T. Trout, Archana Shenoy, Maisam Abu-El-Haija, Jaimie D. Nathan
Frontiers in Pediatrics.2022;[Epub] CrossRef
Neuroendocrine Carcinomas with Atypical Proliferation Index and Clinical Behavior: A Systematic Review
Tiziana Feola, Roberta Centello, Franz Sesti, Giulia Puliani, Monica Verrico, Valentina Di Vito, Cira Di Gioia, Oreste Bagni, Andrea Lenzi, Andrea M. Isidori, Elisa Giannetta, Antongiulio Faggiano
Cancers.2021; 13(6): 1247. CrossRef
Risk of cancer in patients with recurrent aphthous stomatitis in Korea
Ki Jin Kwon, Su Jin Jeong, Young-Gyu Eun, In Hwan Oh, Young Chan Lee
Medicine.2021; 100(16): e25628. CrossRef
Digestive Well-Differentiated Grade 3 Neuroendocrine Tumors: Current Management and Future Directions
Anna Pellat, Anne Ségolène Cottereau, Lola-Jade Palmieri, Philippe Soyer, Ugo Marchese, Catherine Brezault, Romain Coriat
Cancers.2021; 13(10): 2448. CrossRef
Neuroendocrine Carcinomas of the Digestive Tract: What Is New?
Anna Pellat, Anne Ségolène Cottereau, Benoit Terris, Romain Coriat
Cancers.2021; 13(15): 3766. CrossRef
Pancreatic Masses in Children and Young Adults: Multimodality Review with Pathologic Correlation
Lisa Qiu, Andrew T. Trout, Rama S. Ayyala, Sara Szabo, Jaimie D. Nathan, James I. Geller, Jonathan R. Dillman
RadioGraphics.2021; 41(6): 1766. CrossRef
CD56 Expression Is Associated with Biological Behavior of Pancreatic Neuroendocrine Neoplasms

Xin Chen, Chuangen Guo, Wenjing Cui, Ke Sun, Zhongqiu Wang, Xiao Chen
Cancer Management and Research.2020; Volume 12: 4625. CrossRef
Prognostic and predictive factors on overall survival and surgical outcomes in pancreatic neuroendocrine tumors: recent advances and controversies
Lingaku Lee, Tetsuhide Ito, Robert T Jensen
Expert Review of Anticancer Therapy.2019; 19(12): 1029. CrossRef

10,561 View
320 Download
15 Web of Science
17 Crossref

First
Prev
Page of 1
Next
Last

Search