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Sun XY, Wang CQ, Mao Y, Zhang ZQ, Cui J, Dong XN, Wang HB. Prognostic value and distribution pattern of tumor infiltrating lymphocytes and their subsets in distant metastases of advanced breast cancer. Clin Breast Cancer 2024; 24:e167-e176. [PMID: 38212189 DOI: 10.1016/j.clbc.2023.12.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2023] [Revised: 11/09/2023] [Accepted: 12/29/2023] [Indexed: 01/13/2024]
Abstract
BACKGROUND There are significant correlations between the levels of tumor infiltrating lymphocytes (TILs) and the prognosis of primary breast cancer. While little is known about immunological mechanisms in the distant metastasis of advanced breast cancer. PATIENTS AND METHODS A total of 106 patients with advanced metastatic breast cancer were enrolled in this study between 2016 and 2022. Hematoxylin and eosin staining and immunohistochemistry were used to assess the densities of stromal TILs (sTILs), intratumoral TILs (iTILs) and invasive marginal TILs (imTILs) and CD4+, CD8+, CD20+, FOXP3+ TILs in the primary tumor and metastasis (bone, lung, liver, and distant lymph node) of advanced breast cancer. RESULTS Higher levels of sTILs at metastatic sites were associated with better progression-free survival (PFS), postmetastasis survival (PMS) and overall survival (OS) (p = .026, .001 and .005, respectively). The levels of iTILs were significantly lower than those of sTILs and imTILs in both primary tumor (p< .001, both) and metastasis (p< .001, both). The level of CD4+ T cells was higher than those of CD8+ T cells and CD20+ B cells in both primary tumor (p < .001) and metastasis (p < .001). The levels of sTILs (p=0. 001) and imTILs (p< .001) in the primary tumor were generally higher than those in the metastasis. CONCLUSION The levels of TILs and their subsets can predict the survival and prognosis of patients with advanced breast cancer. The distributions of TILs and their subsets are similar between the primary tumor and metastasis. The metastases have a lower degree of lymphocytes infiltration than its corresponding primary tumor.
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Affiliation(s)
- Xin-Yi Sun
- Department of Breast Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, PR China
| | - Cheng-Qin Wang
- Department of Pathology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, PR China
| | - Yan Mao
- Department of Breast Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, PR China
| | - Zhen-Qi Zhang
- Department of Pathology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, PR China
| | - Jian Cui
- Department of Breast Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, PR China
| | - Xian-Ning Dong
- Department of Pathology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, PR China
| | - Hai-Bo Wang
- Department of Breast Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, PR China.
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Makhlouf S, Wahab N, Toss M, Ibrahim A, Lashen AG, Atallah NM, Ghannam S, Jahanifar M, Lu W, Graham S, Mongan NP, Bilal M, Bhalerao A, Snead D, Minhas F, Raza SEA, Rajpoot N, Rakha E. Evaluation of tumour infiltrating lymphocytes in luminal breast cancer using artificial intelligence. Br J Cancer 2023; 129:1747-1758. [PMID: 37777578 PMCID: PMC10667537 DOI: 10.1038/s41416-023-02451-3] [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: 02/26/2023] [Revised: 09/08/2023] [Accepted: 09/20/2023] [Indexed: 10/02/2023] Open
Abstract
BACKGROUND Tumour infiltrating lymphocytes (TILs) are a prognostic parameter in triple-negative and human epidermal growth factor receptor 2 (HER2)-positive breast cancer (BC). However, their role in luminal (oestrogen receptor positive and HER2 negative (ER + /HER2-)) BC remains unclear. In this study, we used artificial intelligence (AI) to assess the prognostic significance of TILs in a large well-characterised cohort of luminal BC. METHODS Supervised deep learning model analysis of Haematoxylin and Eosin (H&E)-stained whole slide images (WSI) was applied to a cohort of 2231 luminal early-stage BC patients with long-term follow-up. Stromal TILs (sTILs) and intratumoural TILs (tTILs) were quantified and their spatial distribution within tumour tissue, as well as the proportion of stroma involved by sTILs were assessed. The association of TILs with clinicopathological parameters and patient outcome was determined. RESULTS A strong positive linear correlation was observed between sTILs and tTILs. High sTILs and tTILs counts, as well as their proximity to stromal and tumour cells (co-occurrence) were associated with poor clinical outcomes and unfavourable clinicopathological parameters including high tumour grade, lymph node metastasis, large tumour size, and young age. AI-based assessment of the proportion of stroma composed of sTILs (as assessed visually in routine practice) was not predictive of patient outcome. tTILs was an independent predictor of worse patient outcome in multivariate Cox Regression analysis. CONCLUSION AI-based detection of TILs counts, and their spatial distribution provides prognostic value in luminal early-stage BC patients. The utilisation of AI algorithms could provide a comprehensive assessment of TILs as a morphological variable in WSIs beyond eyeballing assessment.
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Affiliation(s)
- Shorouk Makhlouf
- Academic Unit for Translational Medical Sciences, School of Medicine, University of Nottingham, Nottingham, UK
- Department of Pathology, Faculty of Medicine, Assiut University, Assiut, Egypt
| | - Noorul Wahab
- Tissue Image Analytics Centre, University of Warwick, Coventry, UK
| | - Michael Toss
- Academic Unit for Translational Medical Sciences, School of Medicine, University of Nottingham, Nottingham, UK
- Department of Histopathology, Sheffield Teaching Hospitals NHS Trust, Sheffield, UK
| | - Asmaa Ibrahim
- Academic Unit for Translational Medical Sciences, School of Medicine, University of Nottingham, Nottingham, UK
- Department of Pathology, Faculty of Medicine, Suez Canal University, Ismailia, Egypt
| | - Ayat G Lashen
- Academic Unit for Translational Medical Sciences, School of Medicine, University of Nottingham, Nottingham, UK
- Department of Pathology, Faculty of Medicine, Menoufia University, Menoufia, Egypt
| | - Nehal M Atallah
- Academic Unit for Translational Medical Sciences, School of Medicine, University of Nottingham, Nottingham, UK
- Department of Pathology, Faculty of Medicine, Menoufia University, Menoufia, Egypt
| | - Suzan Ghannam
- Academic Unit for Translational Medical Sciences, School of Medicine, University of Nottingham, Nottingham, UK
- Department of Histology and cell biology, Faculty of Medicine, Suez Canal University, Ismailia, Egypt
| | | | - Wenqi Lu
- Tissue Image Analytics Centre, University of Warwick, Coventry, UK
| | - Simon Graham
- Tissue Image Analytics Centre, University of Warwick, Coventry, UK
| | - Nigel P Mongan
- Biodiscovery Institute, School of Veterinary Medicine and Sciences, University of Nottingham, Nottingham, UK
- Department of Pharmacology, Weill Cornell Medicine, New York, NY, 10065, USA
| | - Mohsin Bilal
- Tissue Image Analytics Centre, University of Warwick, Coventry, UK
| | - Abhir Bhalerao
- Tissue Image Analytics Centre, University of Warwick, Coventry, UK
| | - David Snead
- University Hospital Coventry and Warwickshire, Coventry, UK
| | - Fayyaz Minhas
- Tissue Image Analytics Centre, University of Warwick, Coventry, UK
| | | | - Nasir Rajpoot
- Tissue Image Analytics Centre, University of Warwick, Coventry, UK.
| | - Emad Rakha
- Academic Unit for Translational Medical Sciences, School of Medicine, University of Nottingham, Nottingham, UK.
- Department of Histopathology, Nottingham University Hospitals NHS Trust, Nottingham, UK.
- Department of Pathology, Hamad Medical Corporation, Doha, Qatar.
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Tapak L, Hamidi O, Amini P, Afshar S, Salimy S, Dinu I. Identification of Prognostic Biomarkers for Breast Cancer Metastasis
Using Penalized Additive Hazards Regression Model. Cancer Inform 2023; 22:11769351231157942. [PMID: 36968522 PMCID: PMC10034277 DOI: 10.1177/11769351231157942] [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: 11/02/2022] [Accepted: 01/30/2023] [Indexed: 03/24/2023] Open
Abstract
Background: Breast cancer (BC) has been reported as one of the most common cancers
diagnosed in females throughout the world. Survival rate of BC patients is
affected by metastasis. So, exploring its underlying mechanisms and
identifying related biomarkers to monitor BC relapse/recurrence using new
statistical methods is essential. This study investigated the
high-dimensional gene-expression profiles of BC patients using penalized
additive hazards regression models. Methods: A publicly available dataset related to the time to metastasis in BC patients
(GSE2034) was used. There was information of 22 283 genes expression
profiles related to 286 BC patients. Penalized additive hazards regression
models with different penalties, including LASSO, SCAD, SICA, MCP and
Elastic net were used to identify metastasis related genes. Results: Five regression models with penalties were applied in the additive hazards
model and jointly found 9 genes including SNU13,
CLINT1, MAPK9, ABCC5,
NKX3-1, NCOR2,
COL2A1, and ZNF219. According the median
of the prognostic index calculated using the regression coefficients of the
penalized additive hazards model, the patients were labeled as high/low risk
groups. A significant difference was detected in the survival curves of the
identified groups. The selected genes were examined using validation data
and were significantly associated with the hazard of metastasis. Conclusion: This study showed that MAPK9, NKX3-1,
NCOR1, ABCC5, and
CD44 are the potential recurrence and metastatic
predictors in breast cancer and can be taken into account as candidates for
further research in tumorigenesis, invasion, metastasis, and
epithelial-mesenchymal transition of breast cancer.
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Affiliation(s)
- Leili Tapak
- Department of Biostatistics, School of
Public Health and Modeling of Noncommunicable Diseases Research Center, Hamadan
University of Medical Sciences, Hamadan, Iran
| | - Omid Hamidi
- Department of Science, Hamedan
University of Technology, Hamedan, Iran
- Omid Hamidi, Department of Science, Hamedan
University of Technology, Pajouhesh Square, Hamedan 6516717432, Iran.
| | - Payam Amini
- Department of Biostatistics, School of
Public Health, Iran University of Medical Sciences, Tehran, Iran
| | - Saeid Afshar
- Research Center for Molecular Medicine,
Hamadan University of Medical Sciences, Hamadan, Iran
| | - Siamak Salimy
- Laboratory of System Biology and
Bioinformatics (LBB), Department of Bioinformatics, University of Tehran, Kish,
Iran
| | - Irina Dinu
- School of Public Health, University of
Alberta, Edmonton, AB, Canada
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Huang H, Nie C, Liu XF, Song B, Yue JH, Xu J, He J, Li K, Feng YL, Wan T, Zheng M, Zhang Y, Ye WJ, Li JD, Li YF, Li JY, Cao XP, Liu ZM, Zhang XS, Liu Q, Zhang X, Liu JH, Li J. Phase I study of adjuvant immunotherapy with autologous tumor-infiltrating lymphocytes in locally advanced cervical cancer. J Clin Invest 2022; 132:157726. [PMID: 35727633 PMCID: PMC9337833 DOI: 10.1172/jci157726] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Accepted: 06/14/2022] [Indexed: 11/17/2022] Open
Abstract
BACKGROUND Adoptive cell therapy (ACT) with tumor-infiltrating lymphocytes (TILs) has achieved remarkable clinical efficacy in metastatic cancers such as melanoma and cervical cancer (CC). Here we explored the safety, feasibility and preliminary tumor response and performed translational investigations of adjuvant immunotherapy using infusion of autogenous (auto)-TILs following concurrent chemoradiotherapy (CCRT) in CC patients with locally advanced disease. METHODS Twenty-seven CC patients with stage III to IV disease were recruited in this single-center, phase I study. TILs were isolated from lesions in the uterine cervix and generated under good manufacturing practices (GMP) conditions and then infused after CCRT plus intramuscular interleukin (IL)-2 injections. RESTULTS From 27 patients, TILs were successfully expanded from 20 patients, with a feasibility of 74.1%. Twelve patients received TILs following CCRT. Adverse events (AEs) were primarily attributable to CCRT. Only 1 (8.3%) patient experienced severe toxicity with a grade 3 hypersensitivity reaction after TIL infusion. No autoimmune AEs, such as pneumonitis, hepatitis, or myocarditis, occurred, and there was no treatment-related mortality. Nine of 12 patients (75.0%) attained complete response, with a disease control duration of 9 to 22 months. Translational investigation showed that the transcriptomic characteristics of the infused TIL products and some immune biomarkers in the tumor microenvironment and serum of CC patients at baseline were correlated with the clinical response. CONCULSION TIL-based ACT following CCRT was safe in an academic center setting, with potential effective responses in locally advanced CC patients. 'Hot' inflammatory immune environments are beneficial to the clinical efficacy of TIL-based ACT as adjuvant therapy. TRIAL REGISTRATION CLINICALTRIALS gov NCT04443296. FUNDING Natinoal Key R&D Program: Sci-Tech Key Program of the Guangzhou City Science Foundation; the Guangdong Provinve Sci-Tech International Key Program; the National Natural Science Foundation of China.
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Affiliation(s)
- He Huang
- Department of Gynecological Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Caiping Nie
- Department of Biotherapy, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Xiu-Feng Liu
- Department of Biotherapy, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Bin Song
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China
| | - Jian-Hui Yue
- Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Jingxiao Xu
- Department of Biotherapy, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Jia He
- Department of Biotherapy, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Kui Li
- State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Yan-Ling Feng
- Department of Gynecological Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Ting Wan
- Department of Gynecological Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Min Zheng
- Department of Gynecological Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Yanna Zhang
- Department of Gynecological Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Wei-Jun Ye
- Department of Radiation Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Jun-Dong Li
- Department of Gynecological Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Yan-Fang Li
- Department of Gynecological Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Jun-Yun Li
- Department of Radiation Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Xin-Ping Cao
- Department of Radiation Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Zhi-Min Liu
- Department of Gynecological Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Xiao-Shi Zhang
- State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Qing Liu
- State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Xi Zhang
- BGI-Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Ther, BGI-Shenzhen, Guangzhou, China
| | - Ji-Hong Liu
- Department of Gynecological Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Jiang Li
- Department of Biotherapy, Sun Yat-sen University Cancer Center, Guangzhou, China
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