Cancer Research and Treatment

Original Articles

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; Received June 2, 2024 Accepted December 13, 2024 Published online December 16, 2024; DOI: https://doi.org/10.4143/crt.2024.521 [Accepted]

Abstract PDF: 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.

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Gastrointestinal cancer

Integrating Deep Learning–Based Dose Distribution Prediction with Bayesian Networks for Decision Support in Radiotherapy for Upper Gastrointestinal Cancer: Dong-Yun Kim, Bum-Sup Jang, Eunji Kim, Eui Kyu Chie; Cancer Res Treat. 2025;57(1):186-197. Published online August 2, 2024; DOI: https://doi.org/10.4143/crt.2024.333

Abstract PDF Supplementary Material PubReader ePub: Purpose
Selecting the better techniques to harbor optimal motion management, either a stereotactic linear accelerator delivery using TrueBeam (TBX) or magnetic resonance–guided gated delivery using MRIdian (MRG), is time-consuming and costly. To address this challenge, we aimed to develop a decision-supporting algorithm based on a combination of deep learning-generated dose distributions and clinical data.
Materials and Methods
We retrospectively analyzed 65 patients with liver or pancreatic cancer who underwent both TBX and MRG simulations and planning process. We trained three-dimensional U-Net deep learning models to predict dose distributions and generated dose volume histograms (DVHs) for each system. We integrated predicted DVH metrics into a Bayesian network (BN) model incorporating clinical data.
Results
The MRG prediction model outperformed the TBX model, demonstrating statistically significant superiorities in predicting normalized dose to the planning target volume (PTV) and liver. We developed a final BN prediction model integrating the predictive DVH metrics with patient factors like age, PTV size, and tumor location. This BN model an area under the receiver operating characteristic curve index of 83.56%. The decision tree derived from the BN model showed that the tumor location (abutting vs. apart of PTV to hollow viscus organs) was the most important factor to determine TBX or MRG. It provided a potential framework for selecting the optimal radiation therapy (RT) system based on individual patient characteristics.
Conclusion
We demonstrated a decision-supporting algorithm for selecting optimal RT plans in upper gastrointestinal cancers, incorporating both deep learning-based dose prediction and BN-based treatment selection. This approach might streamline the decision-making process, saving resources and improving treatment outcomes for patients undergoing RT.

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Breast cancer

Locoregional Recurrence in Adenoid Cystic Carcinoma of the Breast: A Retrospective, Multicenter Study (KROG 22-14): Sang Min Lee, Bum-Sup Jang, Won Park, Yong Bae Kim, Jin Ho Song, Jin Hee Kim, Tae Hyun Kim, In Ah Kim, Jong Hoon Lee, Sung-Ja Ahn, Kyubo Kim, Ah Ram Chang, Jeanny Kwon, Hae Jin Park, Kyung Hwan Shin; Cancer Res Treat. 2025;57(1):150-158. Published online July 12, 2024; DOI: https://doi.org/10.4143/crt.2024.201

Abstract PDF PubReader ePub

Purpose
This study aims to evaluate the treatment approaches and locoregional patterns for adenoid cystic carcinoma (ACC) in the breast, which is an uncommon malignant tumor with limited clinical data.
Materials and Methods
A total of 93 patients diagnosed with primary ACC in the breast between 1992 and 2022 were collected from multi-institutions. All patients underwent surgical resection, including breast-conserving surgery (BCS) or total mastectomy (TM). Recurrence patterns and locoregional recurrence-free survival (LRFS) were assessed.
Results
Seventy-five patients (80.7%) underwent BCS, and 71 of them (94.7%) received post-operative radiation therapy (PORT). Eighteen patients (19.3%) underwent TM, with five of them (27.8%) also receiving PORT. With a median follow-up of 50 months, the LRFS rate was 84.2% at 5 years. Local recurrence (LR) was observed in five patients (5.4%) and four cases (80%) of the LR occurred in the tumor bed. Three of LR (3/75, 4.0%) had a history of BCS and PORT, meanwhile, two of LR (2/18, 11.1%) had a history of mastectomy. Regional recurrence occurred in two patients (2.2%), and both cases had a history of PORT with (n=1) and without (n=1) irradiation of the regional lymph nodes. Partial breast irradiation (p=0.35), BCS (p=0.96) and PORT in BCS group (p=0.33) had no significant association with LRFS.
Conclusion
BCS followed by PORT was the predominant treatment approach for ACC of the breast and LR mostly occurred in the tumor bed. The findings of this study suggest that partial breast irradiation might be considered for PORT in primary breast ACC.

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Adenoid Cystic Carcinoma of the Breast: A Narrative Review and Update on Management
Taylor Neilson, Zaibo Li, Christina Minami, Sara P. Myers
Cancers.2025; 17(7): 1079. CrossRef

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General

Radiation Response Prediction Model Based on Integrated Clinical and Genomic Data Analysis: Bum-Sup Jang, Ji-Hyun Chang, Seung Hyuck Jeon, Myung Geun Song, Kyung-Hun Lee, Seock-Ah Im, Jong-Il Kim, Tae-You Kim, Eui Kyu Chie; Cancer Res Treat. 2022;54(2):383-395. Published online August 24, 2021; DOI: https://doi.org/10.4143/crt.2021.759

Abstract PDF Supplementary Material PubReader ePub

Purpose
The value of the genomic profiling by targeted gene-sequencing on radiation therapy response prediction was evaluated through integrated analysis including clinical information. Radiation response prediction model was constructed based on the analyzed findings.
Materials and Methods
Patients who had the tumor sequenced using institutional cancer panel after informed consent and received radiotherapy for the measurable disease served as the target cohort. Patients with irradiated tumor locally controlled for more than 6 months after radiotherapy were defined as the durable local control (DLC) group, otherwise, non-durable local control (NDLC) group. Significant genomic factors and domain knowledge were used to develop the Bayesian Network model to predict radiotherapy response.
Results
Altogether, 88 patients were collected for analysis. Of those, 41 (43.6%) and 47 (54.4%) patients were classified as the NDLC and DLC group, respectively. Somatic mutations of NOTCH2 and BCL were enriched in the NDLC group, whereas, mutations of CHEK2, MSH2, and NOTCH1 were more frequently found in the DLC group. Altered DNA repair pathway was associated with better local failure–free survival (hazard ratio, 0.40; 95% confidence interval, 0.19 to 0.86; p=0.014). Smoking somatic signature was found more frequently in the DLC group. Area under the receiver operating characteristic curve of the Bayesian network model predicting probability of 6-month local control was 0.83.
Conclusion
Durable radiation response was associated with alterations of DNA repair pathway and smoking somatic signature. Bayesian network model could provide helpful insights for high precision radiotherapy. However, these findings should be verified in prospective cohort for further individualization.

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Estimating the risk and benefit of radiation therapy in (y)pN1 stage breast cancer patients: A Bayesian network model incorporating expert knowledge (KROG 22–13)
Bum-Sup Jang, Seok-Joo Chun, Hyeon Seok Choi, Ji Hyun Chang, Kyung Hwan Shin
Computer Methods and Programs in Biomedicine.2024; 245: 108049. CrossRef
Prediction of Overall Disease Burden in (y)pN1 Breast Cancer Using Knowledge-Based Machine Learning Model
Seok-Joo Chun, Bum-Sup Jang, Hyeon Seok Choi, Ji Hyun Chang, Kyung Hwan Shin
Cancers.2024; 16(8): 1494. CrossRef
Selection of patients with pancreatic adenocarcinoma who may benefit from radiotherapy
I-Shiow Jan, Hui Ju Ch’ang
Radiation Oncology.2023;[Epub] CrossRef
Characterization of the gene signature correlated with favorable response to chemoradiotherapy in rectal cancer: A hypothesis‐generating study
Seung Hyuck Jeon, Eui Kyu Chie
Cancer Medicine.2023; 12(7): 8981. CrossRef
Krüppel-like Factor 10 as a Prognostic and Predictive Biomarker of Radiotherapy in Pancreatic Adenocarcinoma
Yi-Chih Tsai, Min-Chieh Hsin, Rui-Jun Liu, Ting-Wei Li, Hui-Ju Ch’ang
Cancers.2023; 15(21): 5212. CrossRef

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Genitourinary cancer

Prediction of Pathologic Findings with MRI-Based Clinical Staging Using the Bayesian Network Modeling in Prostate Cancer: A Radiation Oncologist Perspective: Chan Woo Wee, Bum-Sup Jang, Jin Ho Kim, Chang Wook Jeong, Cheol Kwak, Hyun Hoe Kim, Ja Hyeon Ku, Seung Hyup Kim, Jeong Yeon Cho, Sang Youn Kim; Cancer Res Treat. 2022;54(1):234-244. Published online May 17, 2021; DOI: https://doi.org/10.4143/crt.2020.1221

Abstract PDF Supplementary Material PubReader ePub

Purpose
This study aimed to develop a model for predicting pathologic extracapsular extension (ECE) and seminal vesicle invasion (SVI) while integrating magnetic resonance imaging-based T-staging (cT_MRI, cT1c-cT3b).
Materials and Methods
A total of 1,915 who underwent radical prostatectomy between 2006-2016 met the inclusion/exclusion criteria. We performed a multivariate logistic regression analysis as well as Bayesian network (BN) modeling based on possible confounding factors. The BN model was internally validated using 5-fold validation.
Results
According to the multivariate logistic regression analysis, initial prostate-specific antigen (iPSA) (β=0.050, p < 0.001), percentage of positive biopsy cores (PPC) (β=0.033, p < 0.001), both lobe involvement on biopsy (β=0.359, p=0.009), Gleason score (β=0.358, p < 0.001), and cT_MRI (β=0.259, p < 0.001) were significant factors for ECE. For SVI, iPSA (β=0.037, p < 0.001), PPC (β=0.024, p < 0.001), Gleason score (β=0.753, p < 0.001), and cT_MRI (β=0.507, p < 0.001) showed statistical significance. BN models to predict ECE and SVI were also successfully established. The overall area under the receiver operating characteristic curve (AUC)/accuracy of the BN models were 0.76/73.0% and 0.88/89.6% for ECE and SVI, respectively. According to internal comparison between the BN model and Roach formula, BN model had improved AUC values for predicting ECE (0.76 vs. 0.74, p=0.060) and SVI (0.88 vs. 0.84, p < 0.001).
Conclusion
Two models to predict pathologic ECE and SVI integrating cT_MRI were established and installed on a separate website for public access to guide radiation oncologists.

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Measurements of target volumes and organs at risk using DW‑MRI in patients with central lung cancer accompanied with atelectasis
Xinli Zhang, Tong Liu, Hong Zhang, Mingbin Zhang
Molecular and Clinical Oncology.2023;[Epub] CrossRef

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A Radiosensitivity Gene Signature and PD-L1 Status Predict Clinical Outcome of Patients with Glioblastoma Multiforme in The Cancer Genome Atlas Dataset: Bum-Sup Jang, In Ah Kim; Cancer Res Treat. 2020;52(2):530-542. Published online November 20, 2019; DOI: https://doi.org/10.4143/crt.2019.440

Abstract PDF Supplementary Material PubReader ePub

Purpose
Combination of radiotherapy and immune checkpoint blockade such as programmed death- 1 (PD-1) or programmed death-ligand 1 (PD-L1) blockade is being actively tested in clinical trial. We aimed to identify a subset of patients that could potentially benefit from this strategy using The Cancer Genome Atlas (TCGA) dataset for glioblastoma (GBM).
Materials and Methods
A total of 399 cases were clustered into radiosensitive versus radioresistant (RR) groups based on a radiosensitivity gene signature and were also stratified as PD-L1 high versus PD-L1 low groups by expression of CD274 mRNA. Differential and integrated analyses with expression and methylation data were performed. CIBERSORT was used to enumerate the immune repertoire that resulted from transcriptome profiles.
Results
We identified a subset of GBM, PD-L1-high-RR group which showed worse survival compared to others. In PD-L1-high-RR, differentially expressed genes (DEG) were highly enriched for immune response and mapped into activation of phosphoinositide 3-kinase–AKT and mitogen-activated protein kinase (MAPK) signaling pathways. Integration of DEG and differentially methylated region identified that the kinase MAP3K8-involved in T-cell receptor signaling was upregulated and BAI1, a factor which inhibits angiogenesis, was silenced. CIBERSORT showed that a higher infiltration of the immune repertoire, which included M2 macrophages and regulatory T cells.
Conclusion
Taken together, PD-L1-high-RR group could potentially benefit from radiotherapy combined with PD-1/PD-L1 blockade and angiogenesis inhibition.

Citations

Citations to this article as recorded by

Clinical Biomarkers of Tumour Radiosensitivity and Predicting Benefit from Radiotherapy: A Systematic Review
Christopher W. Bleaney, Hebatalla Abdelaal, Mark Reardon, Carmel Anandadas, Peter Hoskin, Ananya Choudhury, Laura Forker
Cancers.2024; 16(10): 1942. CrossRef
A retrospective cohort study of neoadjuvant chemoradiotherapy combined with immune checkpoint inhibitors in locally advanced rectal cancer
Zhuo Chen, Zhuoling Zou, Min Qian, Qin Xu, Guojuan Xue, Juan Yang, Tinglan Luo, Lianjie Hu, Bin Wang
Translational Oncology.2024; 44: 101955. CrossRef
Understanding the immunosuppressive microenvironment of glioma: mechanistic insights and clinical perspectives
Hao Lin, Chaxian Liu, Ankang Hu, Duanwu Zhang, Hui Yang, Ying Mao
Journal of Hematology & Oncology.2024;[Epub] CrossRef
PSF-lncRNA interaction as a target for novel targeted anticancer therapies
Ren Liu, Xiaojing Wang, Min Zhou, Jingfang Zhai, Jie Sun
Biomedicine & Pharmacotherapy.2024; 180: 117491. CrossRef
Bladder Cancer Treatments in the Age of Personalized Medicine: A Comprehensive Review of Potential Radiosensitivity Biomarkers
Charbel Feghaly, Rafka Challita, Hanine Bou Hadir, Tala Mobayed, Tarek Al Bitar, Mohammad Harbi, Hala Ghorayeb, Rana El-Hassan, Larry Bodgi
Biomarker Insights.2024;[Epub] CrossRef
Improving the efficacy of combined radiotherapy and immunotherapy: focusing on the effects of radiosensitivity
Zhiru Gao, Qian Zhao, Yiyue Xu, Linlin Wang
Radiation Oncology.2023;[Epub] CrossRef
In Vivo Evaluation of Near-Infrared Fluorescent Probe for TIM3 Targeting in Mouse Glioma
Michael Zhang, Quan Zhou, Chinghsin Huang, Carmel T. Chan, Wei Wu, Gordon Li, Michael Lim, Sanjiv S. Gambhir, Heike E. Daldrup-Link
Molecular Imaging and Biology.2022; 24(2): 280. CrossRef
Relationship between Macrophage and Radiosensitivity in Human Primary and Recurrent Glioblastoma: In Silico Analysis with Publicly Available Datasets
Bum-Sup Jang, In Ah Kim
Biomedicines.2022; 10(2): 292. CrossRef
Optimal management of recurrent and metastatic upper tract urothelial carcinoma: Implications of intensity modulated radiation therapy
Mi Sun Kim, Woong Sub Koom, Jae Ho Cho, Se-Young Kim, Ik Jae Lee
Radiation Oncology.2022;[Epub] CrossRef
Translational landscape of glioblastoma immunotherapy for physicians: guiding clinical practice with basic scientific evidence
Daniel Kreatsoulas, Chelsea Bolyard, Bill X. Wu, Hakan Cam, Pierre Giglio, Zihai Li
Journal of Hematology & Oncology.2022;[Epub] CrossRef
The Next Frontier in Health Disparities—A Closer Look at Exploring Sex Differences in Glioma Data and Omics Analysis, from Bench to Bedside and Back
Maria Diaz Rosario, Harpreet Kaur, Erdal Tasci, Uma Shankavaram, Mary Sproull, Ying Zhuge, Kevin Camphausen, Andra Krauze
Biomolecules.2022; 12(9): 1203. CrossRef
Immunosuppression in Gliomas via PD-1/PD-L1 Axis and Adenosine Pathway
Thamiris Becker Scheffel, Nathália Grave, Pedro Vargas, Fernando Mendonça Diz, Liliana Rockenbach, Fernanda Bueno Morrone
Frontiers in Oncology.2021;[Epub] CrossRef
The Combination of Radiotherapy With Immunotherapy and Potential Predictive Biomarkers for Treatment of Non-Small Cell Lung Cancer Patients
Lu Meng, Jianfang Xu, Ying Ye, Yingying Wang, Shilan Luo, Xiaomei Gong
Frontiers in Immunology.2021;[Epub] CrossRef
Explore association of genes in PDL1/PD1 pathway to radiotherapy survival benefit based on interaction model strategy
Junjie Shen, Jingfang Liu, Huijun Li, Lu Bai, Zixuan Du, Ruirui Geng, Jianping Cao, Peng Sun, Zaixiang Tang
Radiation Oncology.2021;[Epub] CrossRef
Combination of Radiosensitivity Gene Signature and PD-L1 Status Predicts Clinical Outcome of Patients With Locally Advanced Head and Neck Squamous Cell Carcinoma: A Study Based on The Cancer Genome Atlas Dataset
Dongjun Dai, Yinglu Guo, Yongjie Shui, Jinfan Li, Biao Jiang, Qichun Wei
Frontiers in Molecular Biosciences.2021;[Epub] CrossRef
Prognostic Values of Radiosensitivity Genes and CD19 Status in Gastric Cancer: A Retrospective Study Using TCGA Database

Li-Bo Liang, Xin-Yan Huang, He He, Ji-Yan Liu
Pharmacogenomics and Personalized Medicine.2020; Volume 13: 365. CrossRef
Gene signature based on B cell predicts clinical outcome of radiotherapy and immunotherapy for patients with lung adenocarcinoma
Linzhi Han, Hongjie Shi, Yuan Luo, Wenjie Sun, Shuying Li, Nannan Zhang, Xueping Jiang, Yan Gong, Conghua Xie
Cancer Medicine.2020; 9(24): 9581. CrossRef

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