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Reconstruction of cancer marker analysis with holistic anatomical precision implicates heterogeneity development during breast tumor progression

Authors :
Zhicheng Ge
Jing Wang
Libing He
Meng Zhao
Yang Si
Siyuan Chang
Guoyan Zhang
Shan Cheng
Wei Ding
Source :
Discover Oncology, Vol 15, Iss 1, Pp 1-13 (2024)
Publication Year :
2024
Publisher :
Springer, 2024.

Abstract

Abstract Biomarkers are not only of significant importance for cancer diagnosis and selection of treatment plans but also recently increasingly used for the evaluation of malignancy development and tumor heterogeneity. Large-size tumors from clinical patients can be unique and valuable sources for the study of cancer progression, particularly to the extent of intratumoral heterogeneity. In the present study, we obtained a series of post-surgery puncture samples from a breast cancer patient with a 4 × 3.5 × 2 cm tumor in its original size. Immunohistochemistry for Ki-67, COX-2, and CA IX was performed and the expression levels within the breast cancer tumor mass were evaluated in the reconstructed 3D models. To further evaluate the intratumoral heterogeneity, we performed high throughput whole transcriptome sequencing of 12 samples from different spatial positions within the tumor tissue. Comparing the reconstructed 3D distribution of biomarkers with projected tumor growth models, asymmetric and heterogeneous expansion of tumor mass was found to be possibly influenced by factors such as blood supply, inflammation and/or hypoxia stimulations, as suggested from the correlation between the results of Ki-67 and CA IX or COX-2 staining. Furthermore, high-throughput RNA sequencing data provided additional information for profiling the intratumoral heterogeneity and expanded the understanding of cancer progression. Digital technology for medical imaging once properly integrated with molecular pathology examinations will become particularly helpful in dissecting out in-depth information for precision medicine. We prospect that this approach, facilitated by rapidly advancing artificial intelligence, could provide new insights for clinical decision-making in the future. Strategies for the continuous development from the present study for better performance and application were discussed.

Details

Language :
English
ISSN :
27306011
Volume :
15
Issue :
1
Database :
Directory of Open Access Journals
Journal :
Discover Oncology
Publication Type :
Academic Journal
Accession number :
edsdoj.b7b4bd0283a4af2adf2e627dd94c4b8
Document Type :
article
Full Text :
https://doi.org/10.1007/s12672-024-01442-x