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Franzén B, Auer G, Lewensohn R. Minimally invasive biopsy-based diagnostics in support of precision cancer medicine. Mol Oncol 2024. [PMID: 38519839 DOI: 10.1002/1878-0261.13640] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Revised: 01/31/2024] [Accepted: 03/14/2024] [Indexed: 03/25/2024] Open
Abstract
Precision cancer medicine (PCM) to support the treatment of solid tumors requires minimally invasive diagnostics. Here, we describe the development of fine-needle aspiration biopsy-based (FNA) molecular cytology which will be increasingly important in diagnostics and adaptive treatment. We provide support for FNA-based molecular cytology having a significant potential to replace core needle biopsy (CNB) as a patient-friendly potent technique for tumor sampling for various tumor types. This is not only because CNB is a more traumatic procedure and may be associated with more complications compared to FNA-based sampling, but also due to the recently developed molecular methods used with FNA. Recent studies show that image-guided FNA in combination with ultrasensitive molecular methods also offers opportunities for characterization of the tumor microenvironment which can aid therapeutic decisions. Here we provide arguments for an increased implementation of molecular FNA-based sampling as a patient-friendly diagnostic method, which may, due to its repeatability, facilitate regular sampling that is needed during different treatment lines, to provide tumor information, supporting treatment decisions, shortening lead times in healthcare, and benefit healthcare economics.
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Affiliation(s)
- Bo Franzén
- Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden
- Cancer Centre Karolinska (CCK) Foundation, Karolinska University Hospital, Stockholm, Sweden
| | - Gert Auer
- Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden
| | - Rolf Lewensohn
- Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden
- Theme Cancer, Medical Unit Head and Neck, Lung, and Skin Tumors, Thoracic Oncology Center, Karolinska University Hospital, Stockholm, Sweden
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Molière S, Lodi M, Leblanc S, Gressel A, Mathelin C, Alpy F, Chenard MP, Tomasetto C. MMP-11 expression in early luminal breast cancer: associations with clinical, MRI, pathological characteristics, and disease-free survival. BMC Cancer 2024; 24:295. [PMID: 38438841 PMCID: PMC10913243 DOI: 10.1186/s12885-024-11998-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2023] [Accepted: 02/13/2024] [Indexed: 03/06/2024] Open
Abstract
BACKGROUND Early hormone-positive breast cancers typically have favorable outcomes, yet long-term surveillance is crucial due to the risk of late recurrences. While many studies associate MMP-11 expression with poor prognosis in breast cancer, few focus on early-stage cases. This study explores MMP-11 as an early prognostic marker in hormone-positive breast cancers. METHODS In this retrospective study, 228 women with early hormone-positive invasive ductal carcinoma, treated surgically between 2011 and 2016, were included. MMP-11 expression was measured by immunohistochemistry, and its association with clinical and MRI data was analyzed. RESULTS Among the patients (aged 31-89, median 60, with average tumor size of 15.7 mm), MMP-11 staining was observed in half of the cases. This positivity correlated with higher uPA levels and tumor grade but not with nodal status or size. Furthermore, MMP-11 positivity showed specific associations with MRI features. Over a follow-up period of 6.5 years, only 12 oncological events occurred. Disease-free survival was linked to Ki67 and MMP-11. CONCLUSION MMP-11, primarily present in tumor-surrounding stromal cells, correlates with tumor grade and uPA levels. MMP-11 immunohistochemical score demonstrates a suggestive trend in association with disease-free survival, independent of Ki67 and other traditional prognostic factors. This highlights the potential of MMP-11 as a valuable marker in managing early hormone-positive breast cancer.
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Affiliation(s)
- Sébastien Molière
- Institute of Genetics and Molecular and Cellular Biology, Illkirch, France.
- Centre National de la Recherche Scientifique, UMR 7104, Illkirch, France.
- Institut National de la Santé et de la Recherche Médicale, U1258, Illkirch, France.
- University of Strasbourg, Illkirch, France.
- Department of Radiology, Strasbourg University Hospital, Hôpital de Hautepierre, Strasbourg, France.
- Breast and Thyroid Imaging Unit, ICANS, Strasbourg, France.
| | - Massimo Lodi
- Institute of Genetics and Molecular and Cellular Biology, Illkirch, France
- Centre National de la Recherche Scientifique, UMR 7104, Illkirch, France
- Institut National de la Santé et de la Recherche Médicale, U1258, Illkirch, France
| | | | - Anne Gressel
- Department of Pathology, Strasbourg University Hospital, Hôpital de Hautepierre, Avenue Molière, Strasbourg, France
| | - Carole Mathelin
- University of Strasbourg, Illkirch, France
- Department of Senology, ICANS, Strasbourg, France
- Department of Gynecology and Obstetrics, Strasbourg University Hospital, Hôpital de Hautepierre, Avenue Molière, Strasbourg, France
| | - Fabien Alpy
- Institute of Genetics and Molecular and Cellular Biology, Illkirch, France
- Centre National de la Recherche Scientifique, UMR 7104, Illkirch, France
- Institut National de la Santé et de la Recherche Médicale, U1258, Illkirch, France
- University of Strasbourg, Illkirch, France
| | - Marie-Pierre Chenard
- University of Strasbourg, Illkirch, France
- Department of Pathology, Strasbourg University Hospital, Hôpital de Hautepierre, Avenue Molière, Strasbourg, France
| | - Catherine Tomasetto
- Institute of Genetics and Molecular and Cellular Biology, Illkirch, France
- Centre National de la Recherche Scientifique, UMR 7104, Illkirch, France
- Institut National de la Santé et de la Recherche Médicale, U1258, Illkirch, France
- University of Strasbourg, Illkirch, France
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Tong L, Shi W, Isgut M, Zhong Y, Lais P, Gloster L, Sun J, Swain A, Giuste F, Wang MD. Integrating Multi-Omics Data With EHR for Precision Medicine Using Advanced Artificial Intelligence. IEEE Rev Biomed Eng 2024; 17:80-97. [PMID: 37824325 DOI: 10.1109/rbme.2023.3324264] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2023]
Abstract
With the recent advancement of novel biomedical technologies such as high-throughput sequencing and wearable devices, multi-modal biomedical data ranging from multi-omics molecular data to real-time continuous bio-signals are generated at an unprecedented speed and scale every day. For the first time, these multi-modal biomedical data are able to make precision medicine close to a reality. However, due to data volume and the complexity, making good use of these multi-modal biomedical data requires major effort. Researchers and clinicians are actively developing artificial intelligence (AI) approaches for data-driven knowledge discovery and causal inference using a variety of biomedical data modalities. These AI-based approaches have demonstrated promising results in various biomedical and healthcare applications. In this review paper, we summarize the state-of-the-art AI models for integrating multi-omics data and electronic health records (EHRs) for precision medicine. We discuss the challenges and opportunities in integrating multi-omics data with EHRs and future directions. We hope this review can inspire future research and developing in integrating multi-omics data with EHRs for precision medicine.
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Bhattacharyya S, Ehsan SF, Karacosta LG. Phenotypic maps for precision medicine: a promising systems biology tool for assessing therapy response and resistance at a personalized level. FRONTIERS IN NETWORK PHYSIOLOGY 2023; 3:1256104. [PMID: 37964768 PMCID: PMC10642209 DOI: 10.3389/fnetp.2023.1256104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Accepted: 09/28/2023] [Indexed: 11/16/2023]
Abstract
In this perspective we discuss how tumor heterogeneity and therapy resistance necessitate a focus on more personalized approaches, prompting a shift toward precision medicine. At the heart of the shift towards personalized medicine, omics-driven systems biology becomes a driving force as it leverages high-throughput technologies and novel bioinformatics tools. These enable the creation of systems-based maps, providing a comprehensive view of individual tumor's functional plasticity. We highlight the innovative PHENOSTAMP program, which leverages high-dimensional data to construct a visually intuitive and user-friendly map. This map was created to encapsulate complex transitional states in cancer cells, such as Epithelial-Mesenchymal Transition (EMT) and Mesenchymal-Epithelial Transition (MET), offering a visually intuitive way to understand disease progression and therapeutic responses at single-cell resolution in relation to EMT-related single-cell phenotypes. Most importantly, PHENOSTAMP functions as a reference map, which allows researchers and clinicians to assess one clinical specimen at a time in relation to their phenotypic heterogeneity, setting the foundation on constructing phenotypic maps for personalized medicine. This perspective argues that such dynamic predictive maps could also catalyze the development of personalized cancer treatment. They hold the potential to transform our understanding of cancer biology, providing a foundation for a future where therapy is tailored to each patient's unique molecular and cellular tumor profile. As our knowledge of cancer expands, these maps can be continually refined, ensuring they remain a valuable tool in precision oncology.
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Affiliation(s)
- Sayantan Bhattacharyya
- Department of Cancer Systems Imaging, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Shafqat F. Ehsan
- Department of Cancer Systems Imaging, University of Texas MD Anderson Cancer Center, Houston, TX, United States
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Loukia G. Karacosta
- Department of Cancer Systems Imaging, University of Texas MD Anderson Cancer Center, Houston, TX, United States
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Lee DJ, Tsai PH, Chen CC, Dai YH. Incorporating knowledge of disease-defining hub genes and regulatory network into a machine learning-based model for predicting treatment response in lupus nephritis after the first renal flare. J Transl Med 2023; 21:76. [PMID: 36737814 PMCID: PMC9898995 DOI: 10.1186/s12967-023-03931-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Accepted: 01/25/2023] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND Identifying candidates responsive to treatment is important in lupus nephritis (LN) at the renal flare (RF) because an effective treatment can lower the risk of progression to end-stage kidney disease. However, machine learning (ML)-based models that address this issue are lacking. METHODS Transcriptomic profiles based on DNA microarray data were extracted from the GSE32591 and GSE112943 datasets. Comprehensive bioinformatics analyses were performed to identify disease-defining genes (DDGs). Peripheral blood samples (GSE81622, GSE99967, and GSE72326) were used to evaluate the effect of DDGs. Single-sample gene set enrichment analysis (ssGSEA) scores of the DDGs were calculated and correlated with specific immunology genes listed in the nCounter panel. GSE60681 and GSE69438 were used to examine the ability of the DDGs to discriminate LN from other renal diseases. K-means clustering was used to obtain the separate gene sets. The clustering results were extended to data derived using the nCounter technique. The least absolute shrinkage and selection operator (LASSO) algorithm was used to identify genes with high predictive value for treatment response after the first RF in each cluster. LASSO models with tenfold validation were built in GSE200306 and assessed by receiver operating characteristic (ROC) analysis with area under curve (AUC). The models were validated by using an independent dataset (GSE113342). RESULTS Forty-five hub genes specific to LN were identified. Eight optimal disease-defining clusters (DDCs) were identified in this study. Th1 and Th2 cell differentiation pathway was significantly enriched in DDC-6. LCK in DDC-6, whose expression positively correlated with various subsets of T cell infiltrations, was found to be differentially expressed between responders and non-responders and was ranked high in regulatory network analysis. Based on DDC-6, the prediction model had the best performance (AUC: 0.75; 95% confidence interval: 0.44-1 in the testing set) and high precision (0.83), recall (0.71), and F1 score (0.77) in the validation dataset. CONCLUSIONS Our study demonstrates that incorporating knowledge of biological phenotypes into the ML model is feasible for evaluating treatment response after the first RF in LN. This knowledge-based incorporation improves the model's transparency and performance. In addition, LCK may serve as a biomarker for T-cell infiltration and a therapeutic target in LN.
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Affiliation(s)
- Ding-Jie Lee
- grid.260565.20000 0004 0634 0356Division of Nephrology, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
| | - Ping-Huang Tsai
- grid.260565.20000 0004 0634 0356Division of Nephrology, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
| | - Chien-Chou Chen
- grid.260565.20000 0004 0634 0356Division of Nephrology, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan ,grid.260565.20000 0004 0634 0356Department of Internal Medicine, Tri-Service General Hospital Songshan Branch, National Defense Medical Center, Taipei, Taiwan
| | - Yang-Hong Dai
- Department of Radiation Oncology, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan.
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Wang S, Wang S, Wang Z. A survey on multi-omics-based cancer diagnosis using machine learning with the potential application in gastrointestinal cancer. Front Med (Lausanne) 2023; 9:1109365. [PMID: 36703893 PMCID: PMC9871466 DOI: 10.3389/fmed.2022.1109365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2022] [Accepted: 12/28/2022] [Indexed: 01/12/2023] Open
Abstract
Gastrointestinal cancer is becoming increasingly common, which leads to over 3 million deaths every year. No typical symptoms appear in the early stage of gastrointestinal cancer, posing a significant challenge in the diagnosis and treatment of patients with gastrointestinal cancer. Many patients are in the middle and late stages of gastrointestinal cancer when they feel uncomfortable, unfortunately, most of them will die of gastrointestinal cancer. Recently, various artificial intelligence techniques like machine learning based on multi-omics have been presented for cancer diagnosis and treatment in the era of precision medicine. This paper provides a survey on multi-omics-based cancer diagnosis using machine learning with potential application in gastrointestinal cancer. Particularly, we make a comprehensive summary and analysis from the perspective of multi-omics datasets, task types, and multi-omics-based integration methods. Furthermore, this paper points out the remaining challenges of multi-omics-based cancer diagnosis using machine learning and discusses future topics.
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Affiliation(s)
- Suixue Wang
- School of Information and Communication Engineering, Hainan University, Haikou, China
| | - Shuling Wang
- Department of Neurology, Affiliated Haikou Hospital of Xiangya School of Medicine, Central South University, Haikou, China
| | - Zhengxia Wang
- School of Computer Science and Technology, Hainan University, Haikou, China
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Fabre ML, Canzoneri R, Gurruchaga A, Lee J, Tatineni P, Kil H, Lacunza E, Aldaz CM, Abba MC. MALINC1 an Immune-Related Long Non-Coding RNA Associated with Early-Stage Breast Cancer Progression. Cancers (Basel) 2022; 14:cancers14122819. [PMID: 35740485 PMCID: PMC9221538 DOI: 10.3390/cancers14122819] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2022] [Revised: 05/19/2022] [Accepted: 06/02/2022] [Indexed: 11/29/2022] Open
Abstract
Simple Summary Here we characterize the phenotypic and molecular effects of MALINC1, a long non-coding RNA (lncRNA) that we found significantly upregulated in premalignant ductal carcinoma in-situ lesions. We provide evidence that MALINC1 behaves as an oncogenic and immune-related lncRNA involved with early-stage breast cancer progression, showing prognostic and predictive value to immunotherapy in invasive breast carcinomas. Abstract Long non-coding RNAs are increasingly being recognized as cancer biomarkers in various malignancies, acting as either tumor suppressors or oncogenes. The long non-coding MALINC1 intergenic RNA was identified as significantly upregulated in breast ductal carcinoma in situ. The aim of this study was to characterize MALINC1 expression, localization, and phenotypic and molecular effects in non-invasive and invasive breast cancer cells. We determined that MALINC1 is an estrogen–estrogen receptor-modulated lncRNA enriched in the cytoplasmic fraction of luminal A/B breast cancer cells that is associated with worse overall survival in patients with primary invasive breast carcinomas. Transcriptomic studies in normal and DCIS cells identified the main signaling pathways modulated by MALINC1, which mainly involve bioprocesses related to innate and adaptive immune responses, extracellular matrix remodeling, cell adhesion, and activation of AP-1 signaling pathway. We determined that MALINC1 induces premalignant phenotypic changes by increasing cell migration in normal breast cells. Moreover, high MALINC1 expression in invasive carcinomas was associated with a pro-tumorigenic immune environment and a favorable predicted response to immunotherapy both in luminal and basal-like subtypes compared with low-MALINC1-expression tumors. We conclude that MALINC1 behaves as an oncogenic and immune-related lncRNA involved with early-stage breast cancer progression.
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Affiliation(s)
- María Laura Fabre
- Centro de Investigaciones Inmunológicas Básicas y Aplicadas (CINIBA), Facultad de Ciencias Médicas, Universidad Nacional de La Plata, La Plata 1900, Argentina; (M.L.F.); (R.C.); (A.G.); (E.L.)
| | - Romina Canzoneri
- Centro de Investigaciones Inmunológicas Básicas y Aplicadas (CINIBA), Facultad de Ciencias Médicas, Universidad Nacional de La Plata, La Plata 1900, Argentina; (M.L.F.); (R.C.); (A.G.); (E.L.)
| | - Agustina Gurruchaga
- Centro de Investigaciones Inmunológicas Básicas y Aplicadas (CINIBA), Facultad de Ciencias Médicas, Universidad Nacional de La Plata, La Plata 1900, Argentina; (M.L.F.); (R.C.); (A.G.); (E.L.)
| | - Jaeho Lee
- Department of Epigenetics and Molecular Carcinogenesis, The University of Texas MD Anderson Cancer Center, Houston, TX 77054, USA; (J.L.); (P.T.); (H.K.)
| | - Pradeep Tatineni
- Department of Epigenetics and Molecular Carcinogenesis, The University of Texas MD Anderson Cancer Center, Houston, TX 77054, USA; (J.L.); (P.T.); (H.K.)
| | - Hyunsuk Kil
- Department of Epigenetics and Molecular Carcinogenesis, The University of Texas MD Anderson Cancer Center, Houston, TX 77054, USA; (J.L.); (P.T.); (H.K.)
| | - Ezequiel Lacunza
- Centro de Investigaciones Inmunológicas Básicas y Aplicadas (CINIBA), Facultad de Ciencias Médicas, Universidad Nacional de La Plata, La Plata 1900, Argentina; (M.L.F.); (R.C.); (A.G.); (E.L.)
| | - C. Marcelo Aldaz
- Department of Epigenetics and Molecular Carcinogenesis, The University of Texas MD Anderson Cancer Center, Houston, TX 77054, USA; (J.L.); (P.T.); (H.K.)
- Correspondence: (C.M.A.); (M.C.A.)
| | - Martín Carlos Abba
- Centro de Investigaciones Inmunológicas Básicas y Aplicadas (CINIBA), Facultad de Ciencias Médicas, Universidad Nacional de La Plata, La Plata 1900, Argentina; (M.L.F.); (R.C.); (A.G.); (E.L.)
- Correspondence: (C.M.A.); (M.C.A.)
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Chen L, Kong X, Huang S, Su Z, Zhu M, Fang Y, Zhang L, Li X, Wang J. Preoperative Breast Immune Prognostic Index as Prognostic Factor Predicts the Clinical Outcomes of Breast Cancer Patients Receiving Neoadjuvant Chemotherapy. Front Immunol 2022; 13:831848. [PMID: 35320931 PMCID: PMC8937039 DOI: 10.3389/fimmu.2022.831848] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Accepted: 02/10/2022] [Indexed: 11/13/2022] Open
Abstract
Objective This study aims at investigating the potential prognostic significance of the breast immune prognostic index (BIPI) in breast cancer patients who received neoadjuvant chemotherapy (NACT). Methods The optimal cutoff value was calculated through the receiver operating characteristic curve (ROC). The correlations between BIPI and clinicopathologic characteristics were determined by the chi-square test or Fisher’s exact test. The Kaplan–Meier method was used to estimate the survival probability, and the log-rank test was used to analyze the differences in the survival probability among patients. The univariate and multivariate Cox proportional hazard regression model was used to screen the independent prognostic factors. A prognostic nomogram for disease-free survival (DFS) and overall survival (OS) was built on the basis of the multivariate analyses. Furthermore, the calibration curve and decision curve analysis (DCA) were used to assess the predictive performance of the nomogram. Results All enrolled patients were split into three subgroups based on the BIPI score. The mean DFS and OS of the BIPI score 0 group and BIPI score 1 group were significantly longer than those of the BIPI score 2 group (42.02 vs. 38.61 vs. 26.01 months, 77.61 vs. 71.83 vs. 53.15 months; p < 0.05). Univariate and multivariate analyses indicated that BIPI was an independent prognostic factor for patients’ DFS and OS (DFS, hazard ratio (HR): 6.720, 95% confidence interval (CI): 1.629–27.717; OS, HR: 8.006, 95% CI: 1.638–39.119). A nomogram with a C-index of 0.873 (95% CI: 0.779–0.966) and 0.801 (95% CI: 0.702–0.901) had a favorable performance for predicting DFS and OS survival rates for clinical use by combining immune scores with other clinical features. The calibration curves at 1-, 3-, and 5-year survival suggested a good consistency between the predicted and actual DFS and OS probability. The DCA demonstrated that the constructed nomogram had better clinical predictive usefulness than only BIPI in predictive clinical applications of 5-year DFS and OS prognostic assessments. Conclusions The patients with low BIPI score have better prognoses and longer DFS and OS. Furthermore, the BIPI-based nomogram may serve as a convenient prognostic tool for breast cancer and help in clinical decision-making.
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Affiliation(s)
- Li Chen
- Department of Thyroid and Breast Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Department of Breast Surgical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xiangyi Kong
- Department of Breast Surgical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Shaolong Huang
- Department of Thyroid & Breast, Burn and Plastic Surgery, Tongren City People’s Hospital, Tongren, China
| | - Zhaohui Su
- Center on Smart and Connected Health Technologies, Mays Cancer Center, School of Nursing, University of Texas (UT) Health San Antonio, San Antonio, TX, United States
| | - Mengliu Zhu
- Department of Breast Surgical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yi Fang
- Department of Breast Surgical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Lin Zhang
- Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC, Australia
- Centre of Cancer Research, Victorian Comprehensive Cancer Centre, Melbourne VIC, Australia
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- *Correspondence: Lin Zhang, ; Xingrui Li, ; Jing Wang,
| | - Xingrui Li
- Department of Thyroid and Breast Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- *Correspondence: Lin Zhang, ; Xingrui Li, ; Jing Wang,
| | - Jing Wang
- Department of Breast Surgical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- *Correspondence: Lin Zhang, ; Xingrui Li, ; Jing Wang,
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