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He Y, Amer HM, Xu Z, Liu L, Wu S, He B, Liu J, Kai G. Exploration of the underlying mechanism of Astragaloside III in attenuating immunosuppression via network pharmacology and vitro/vivo pharmacological validation. JOURNAL OF ETHNOPHARMACOLOGY 2024; 330:118235. [PMID: 38648891 DOI: 10.1016/j.jep.2024.118235] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Revised: 04/17/2024] [Accepted: 04/19/2024] [Indexed: 04/25/2024]
Abstract
ETHNOPHARMACOLOGICAL RELEVANCE Astragalus mongholicus Bunge (AM, recorded in http://www.worldfloraonline.org, 2023-08-03) is a kind of medicine food homology plant with a long medicinal history in China. Astragaloside III (AS-III) has immunomodulatory effects and is one of the most active components in AM. However, its underlying mechanism of action is still not fully explained. AIM OF THE STUDY The research was designed to discuss the protective effects of AS-III on immunosuppression and to elucidate its prospective mechanism. MATERIALS AND METHODS Molecular docking methods and network pharmacology analysis were used to comprehensively investigate potential targets and relative pathways for AS-III and immunosuppression. In order to study and verify the pharmacological activity and mechanism of AS-III in alleviating immunosuppression, immunosuppression mouse model induced by cyclophosphamide (CTX) in vivo and macrophage RAW264.7 cell model induced by hypoxia/lipopolysaccharide (LPS) in vitro were used. RESULTS A total of 105 common targets were obtained from the AS-III-related and immunosuppression-related target networks. The results of network pharmacology and molecular docking demonstrate that AS-III may treat immunosuppression through by regulating glucose metabolism-related pathways such as regulation of lipolysis in adipocytes, carbohydrate digestion and absorption, cGMP-PKG signaling pathway, central carbon metabolism in cancer together with HIF-1 pathway. The results of molecular docking showed that AS-III has good binding relationship with LDHA, AKT1 and HIF1A. In CTX-induced immunosuppressive mouse model, AS-III had a significant protective effect on the reduction of body weight, immune organ index and hematological indices. It can also protect immune organs from damage. In addition, AS-III could significantly improve the expression of key proteins involved in energy metabolism and serum inflammatory factors. To further validate the animal results, an initial inflammatory/immune response model of macrophage RAW264.7 cells was constructed through hypoxia and LPS. AS-III improved the immune function of macrophages, reduced the release of NO, TNF-α, IL-1β, PDHK-1, LDH, lactate, HK, PK and GLUT-1, and restored the decrease of ATP caused by hypoxia. Besides, AS-III was also demonstrated that it could inhibit the increase of HIF-1α, PDHK-1 and LDH by adding inhibitors and agonists. CONCLUSIONS In this study, the main targets of AS-III for immunosuppressive therapy were initially analyzed. AS-III was systematically confirmed to attenuates immunosuppressive state through the HIF-1α/PDHK-1 pathway. These findings offer an experimental foundation for the use of AS-III as a potential candidate for the treatment of immunosuppression.
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Affiliation(s)
- Yining He
- Zhejiang Key TCM Laboratory for Chinese Resource Innovation and Transformation, School of Pharmaceutical Sciences, Jinhua Academy, Zhejiang Chinese Medical University, Hangzhou, 311402, China
| | - Heba M Amer
- Medicinal and Aromatic Plants Research Dept, National Research Centre, 12622, Dokki, Cairo, Egypt
| | - Zonghui Xu
- Zhejiang Key TCM Laboratory for Chinese Resource Innovation and Transformation, School of Pharmaceutical Sciences, Jinhua Academy, Zhejiang Chinese Medical University, Hangzhou, 311402, China
| | - Lin Liu
- Zhejiang Key TCM Laboratory for Chinese Resource Innovation and Transformation, School of Pharmaceutical Sciences, Jinhua Academy, Zhejiang Chinese Medical University, Hangzhou, 311402, China
| | - Shujing Wu
- Zhejiang Key TCM Laboratory for Chinese Resource Innovation and Transformation, School of Pharmaceutical Sciences, Jinhua Academy, Zhejiang Chinese Medical University, Hangzhou, 311402, China
| | - Beihui He
- Zhejiang Key TCM Laboratory for Chinese Resource Innovation and Transformation, School of Pharmaceutical Sciences, Jinhua Academy, Zhejiang Chinese Medical University, Hangzhou, 311402, China
| | - Junqiu Liu
- Zhejiang Key TCM Laboratory for Chinese Resource Innovation and Transformation, School of Pharmaceutical Sciences, Jinhua Academy, Zhejiang Chinese Medical University, Hangzhou, 311402, China.
| | - Guoyin Kai
- Zhejiang Key TCM Laboratory for Chinese Resource Innovation and Transformation, School of Pharmaceutical Sciences, Jinhua Academy, Zhejiang Chinese Medical University, Hangzhou, 311402, China; The Third Affiliated Hospital, Zhejiang Chinese Medical University, Hangzhou, 310053, China.
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Zhuo X, Deng H, Qiu M, Qiu X. Pathomic model based on histopathological features and machine learning to predict IDO1 status and its association with breast cancer prognosis. Breast Cancer Res Treat 2024:10.1007/s10549-024-07350-6. [PMID: 38780888 DOI: 10.1007/s10549-024-07350-6] [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: 02/07/2024] [Accepted: 04/18/2024] [Indexed: 05/25/2024]
Abstract
PURPOSE To establish a pathomic model using histopathological image features for predicting indoleamine 2,3-dioxygenase 1 (IDO1) status and its relationship with overall survival (OS) in breast cancer. METHODS A pathomic model was constructed using machine learning and histopathological images obtained from The Cancer Genome Atlas database to predict IDO1 expression. The model performance was evaluated based on the area under the curve, calibration curve, and decision curve analysis (DCA). Prediction scores (PSes) were generated from the model and applied to divide the patients into two groups. Survival outcomes, gene set enrichment, immune microenvironment, and tumor mutations were assessed between the two groups. RESULTS Survival analysis followed by multivariate correction revealed that high IDO1 is a protective factor for OS. Further, the model was calibrated, and it exhibited good discrimination. Additionally, the DCA showed that the proposed model provided a good clinical net benefit. The Kaplan-Meier analysis revealed a positive correlation between high PS and improved OS. Univariate and multivariate Cox regression analyses demonstrated that PS is an independent protective factor for OS. Moreover, differentially expressed genes were enriched in various essential biological processes, including extracellular matrix receptor interaction, angiogenesis, transforming growth factor β signaling, epithelial mesenchymal transition, cell junction, tryptophan metabolism, and heme metabolic processes. PS was positively correlated with M1 macrophages, CD8 + T cells, T follicular helper cells, and tumor mutational burden. CONCLUSION These results indicate the potential ability of the proposed pathomic model to predict IDO1 status and the OS of breast cancer patients.
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Affiliation(s)
- Xiaohua Zhuo
- Department of Pathology, Longyan First Affiliated Hospital of Fujian Medical University, Longyan, Fujian, China
| | - Hailong Deng
- Department of Pathology, Longyan First Affiliated Hospital of Fujian Medical University, Longyan, Fujian, China
| | - Mingzhu Qiu
- Department of Pathology, Longyan First Affiliated Hospital of Fujian Medical University, Longyan, Fujian, China
| | - Xiaoming Qiu
- Department of Pathology, Longyan First Affiliated Hospital of Fujian Medical University, Longyan, Fujian, China.
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Mir M, Khan AU, Khan A. Pharmacological investigation of taxifolin for its therapeutic potential in depression. Heliyon 2024; 10:e30467. [PMID: 38694040 PMCID: PMC11061746 DOI: 10.1016/j.heliyon.2024.e30467] [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: 12/23/2023] [Revised: 04/26/2024] [Accepted: 04/26/2024] [Indexed: 05/03/2024] Open
Abstract
The current study aimed to investigate the influence of taxifolin on depression symptoms alleviation in Male Sprague-Dawley rats by targeting underlying pathways of depression. Molecular docking analyses were conducted to validate taxifolin's binding affinities against various targets. In silico analysis of taxifolin revealed various aspects of post docking interactions with different protein targets. Depression was induced in rats via intraperitoneal injection of Lipopolysaccharide (LPS; 500 μ g/Kg) for 14 alternative days. Rats (n = 6/group) were randomly assigned to four groups: (i) Saline/Control, (ii) Disease (LPS 500 μg/kg), (iii) Standard (fluoxetine 20 mg/kg), and (iv) Treatment (taxifolin 20 mg/kg). At the end of the in vivo study, brain samples were used for biochemical and morphological analysis. Taxifolin exhibited neuroprotective effects, as evidenced by behavioral studies, antioxidant analysis, histopathological examination, immunohistochemistry, ELISA and RT PCR, indicating an increase number of surviving neurons, normalization of cell size and shape, and reduction in vacuolization. Taxifolin also decreased inflammatory markers such as TNF-α, NF-κb, IL-6 and COX-2, while significantly upregulating and activating the protective PPAR-γ pathway, through which it reduces the oxidative stress, neuroinflammation, neurodegeneration, thereby ameliorating depression symptoms in experimental rat model of depression. Our finding suggests that taxifolin act as neuroprotective agent partially mediated through PPAR-γ pathway.
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Affiliation(s)
- Maha Mir
- Riphah Institute of Pharmaceutical Sciences, Riphah International University, Islamabad, Pakistan
| | - Arif-ullah Khan
- Riphah Institute of Pharmaceutical Sciences, Riphah International University, Islamabad, Pakistan
| | - Aslam Khan
- Riphah Institute of Pharmaceutical Sciences, Riphah International University, Islamabad, Pakistan
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4
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Kazemi A, Rasouli-Saravani A, Gharib M, Albuquerque T, Eslami S, Schüffler PJ. A systematic review of machine learning-based tumor-infiltrating lymphocytes analysis in colorectal cancer: Overview of techniques, performance metrics, and clinical outcomes. Comput Biol Med 2024; 173:108306. [PMID: 38554659 DOI: 10.1016/j.compbiomed.2024.108306] [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: 10/14/2023] [Revised: 03/04/2024] [Accepted: 03/12/2024] [Indexed: 04/02/2024]
Abstract
The incidence of colorectal cancer (CRC), one of the deadliest cancers around the world, is increasing. Tissue microenvironment (TME) features such as tumor-infiltrating lymphocytes (TILs) can have a crucial impact on diagnosis or decision-making for treating patients with CRC. While clinical studies showed that TILs improve the host immune response, leading to a better prognosis, inter-observer agreement for quantifying TILs is not perfect. Incorporating machine learning (ML) based applications in clinical routine may promote diagnosis reliability. Recently, ML has shown potential for making progress in routine clinical procedures. We aim to systematically review the TILs analysis based on ML in CRC histological images. Deep learning (DL) and non-DL techniques can aid pathologists in identifying TILs, and automated TILs are associated with patient outcomes. However, a large multi-institutional CRC dataset with a diverse and multi-ethnic population is necessary to generalize ML methods.
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Affiliation(s)
- Azar Kazemi
- Department of Medical Informatics, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran; Institute of General and Surgical Pathology, Technical University of Munich, Munich, Germany.
| | - Ashkan Rasouli-Saravani
- Student Research Committee, Department of Immunology, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| | - Masoumeh Gharib
- Department of Pathology, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
| | | | - Saeid Eslami
- Department of Medical Informatics, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran; Pharmaceutical Sciences Research Center, Institute of Pharmaceutical Technology, Mashhad University of Medical Sciences, Mashhad, Iran; Department of Medical Informatics, University of Amsterdam, Amsterdam, the Netherlands.
| | - Peter J Schüffler
- Institute of General and Surgical Pathology, Technical University of Munich, Munich, Germany; TUM School of Computation, Information and Technology, Technical University of Munich, Munich, Germany; Munich Center for Machine Learning, Munich, Germany; Munich Data Science Institute, Munich, Germany.
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He Q, Ge S, Zeng S, Wang Y, Ye J, He Y, Li J, Wang Z, Guan T. Global attention based GNN with Bayesian collaborative learning for glomerular lesion recognition. Comput Biol Med 2024; 173:108369. [PMID: 38552283 DOI: 10.1016/j.compbiomed.2024.108369] [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: 08/27/2023] [Revised: 03/18/2024] [Accepted: 03/24/2024] [Indexed: 04/17/2024]
Abstract
BACKGROUND Glomerular lesions reflect the onset and progression of renal disease. Pathological diagnoses are widely regarded as the definitive method for recognizing these lesions, as the deviations in histopathological structures closely correlate with impairments in renal function. METHODS Deep learning plays a crucial role in streamlining the laborious, challenging, and subjective task of recognizing glomerular lesions by pathologists. However, the current methods treat pathology images as data in regular Euclidean space, limiting their ability to efficiently represent the complex local features and global connections. In response to this challenge, this paper proposes a graph neural network (GNN) that utilizes global attention pooling (GAP) to more effectively extract high-level semantic features from glomerular images. The model incorporates Bayesian collaborative learning (BCL), enhancing node feature fine-tuning and fusion during training. In addition, this paper adds a soft classification head to mitigate the semantic ambiguity associated with a purely hard classification. RESULTS This paper conducted extensive experiments on four glomerular datasets, comprising a total of 491 whole slide images (WSIs) and 9030 images. The results demonstrate that the proposed model achieves impressive F1 scores of 81.37%, 90.12%, 87.72%, and 98.68% on four private datasets for glomerular lesion recognition. These scores surpass the performance of the other models used for comparison. Furthermore, this paper employed a publicly available BReAst Carcinoma Subtyping (BRACS) dataset with an 85.61% F1 score to further prove the superiority of the proposed model. CONCLUSION The proposed model not only facilitates precise recognition of glomerular lesions but also serves as a potent tool for diagnosing kidney diseases effectively. Furthermore, the framework and training methodology of the GNN can be adeptly applied to address various pathology image classification challenges.
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Affiliation(s)
- Qiming He
- Department of Life and Health, Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, Guangdong, China
| | - Shuang Ge
- Department of Life and Health, Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, Guangdong, China; Peng Cheng Laboratory, Shenzhen, China
| | - Siqi Zeng
- Department of Life and Health, Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, Guangdong, China; Greater Bay Area National Center of Technology Innovation, Guangzhou, China
| | - Yanxia Wang
- Department of Pathology, State Key Laboratory of Cancer Biology, Xijing Hospital, Fourth Military Medical University, Xi'an, China; School of Basic Medicine, Fourth Military Medical University, Xi'an, China
| | - Jing Ye
- Department of Pathology, State Key Laboratory of Cancer Biology, Xijing Hospital, Fourth Military Medical University, Xi'an, China; School of Basic Medicine, Fourth Military Medical University, Xi'an, China
| | - Yonghong He
- Department of Life and Health, Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, Guangdong, China
| | - Jing Li
- Department of Pathology, State Key Laboratory of Cancer Biology, Xijing Hospital, Fourth Military Medical University, Xi'an, China; School of Basic Medicine, Fourth Military Medical University, Xi'an, China.
| | - Zhe Wang
- Department of Pathology, State Key Laboratory of Cancer Biology, Xijing Hospital, Fourth Military Medical University, Xi'an, China; School of Basic Medicine, Fourth Military Medical University, Xi'an, China
| | - Tian Guan
- Department of Life and Health, Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, Guangdong, China
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Lotter W, Hassett MJ, Schultz N, Kehl KL, Van Allen EM, Cerami E. Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions. Cancer Discov 2024; 14:711-726. [PMID: 38597966 PMCID: PMC11131133 DOI: 10.1158/2159-8290.cd-23-1199] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Revised: 01/29/2024] [Accepted: 02/28/2024] [Indexed: 04/11/2024]
Abstract
Artificial intelligence (AI) in oncology is advancing beyond algorithm development to integration into clinical practice. This review describes the current state of the field, with a specific focus on clinical integration. AI applications are structured according to cancer type and clinical domain, focusing on the four most common cancers and tasks of detection, diagnosis, and treatment. These applications encompass various data modalities, including imaging, genomics, and medical records. We conclude with a summary of existing challenges, evolving solutions, and potential future directions for the field. SIGNIFICANCE AI is increasingly being applied to all aspects of oncology, where several applications are maturing beyond research and development to direct clinical integration. This review summarizes the current state of the field through the lens of clinical translation along the clinical care continuum. Emerging areas are also highlighted, along with common challenges, evolving solutions, and potential future directions for the field.
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Affiliation(s)
- William Lotter
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Pathology, Brigham and Women’s Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Michael J. Hassett
- Harvard Medical School, Boston, MA, USA
- Division of Population Sciences, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Nikolaus Schultz
- Marie-Josée and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center; New York, NY, USA
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Kenneth L. Kehl
- Harvard Medical School, Boston, MA, USA
- Division of Population Sciences, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Eliezer M. Van Allen
- Harvard Medical School, Boston, MA, USA
- Division of Population Sciences, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
- Cancer Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Ethan Cerami
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
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Katayama A, Aoki Y, Watanabe Y, Horiguchi J, Rakha EA, Oyama T. Current status and prospects of artificial intelligence in breast cancer pathology: convolutional neural networks to prospective Vision Transformers. Int J Clin Oncol 2024:10.1007/s10147-024-02513-3. [PMID: 38619651 DOI: 10.1007/s10147-024-02513-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Accepted: 03/12/2024] [Indexed: 04/16/2024]
Abstract
Breast cancer is the most prevalent cancer among women, and its diagnosis requires the accurate identification and classification of histological features for effective patient management. Artificial intelligence, particularly through deep learning, represents the next frontier in cancer diagnosis and management. Notably, the use of convolutional neural networks and emerging Vision Transformers (ViT) has been reported to automate pathologists' tasks, including tumor detection and classification, in addition to improving the efficiency of pathology services. Deep learning applications have also been extended to the prediction of protein expression, molecular subtype, mutation status, therapeutic efficacy, and outcome prediction directly from hematoxylin and eosin-stained slides, bypassing the need for immunohistochemistry or genetic testing. This review explores the current status and prospects of deep learning in breast cancer diagnosis with a focus on whole-slide image analysis. Artificial intelligence applications are increasingly applied to many tasks in breast pathology ranging from disease diagnosis to outcome prediction, thus serving as valuable tools for assisting pathologists and supporting breast cancer management.
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Affiliation(s)
- Ayaka Katayama
- Diagnostic Pathology, Gunma University Graduate School of Medicine, 3-39-22 Showamachi, Maebashi, Gunma, 371-8511, Japan.
| | - Yuki Aoki
- Center for Mathematics and Data Science, Gunma University, Maebashi, Japan
| | - Yukako Watanabe
- Clinical Training Center, Gunma University Hospital, Maebashi, Japan
| | - Jun Horiguchi
- Department of Breast Surgery, International University of Health and Welfare, Narita, Japan
| | - Emad A Rakha
- Department of Histopathology School of Medicine, University of Nottingham, University Park, Nottingham, UK
- Department of Pathology, Hamad Medical Corporation, Doha, Qatar
| | - Tetsunari Oyama
- Diagnostic Pathology, Gunma University Graduate School of Medicine, 3-39-22 Showamachi, Maebashi, Gunma, 371-8511, Japan
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Jin D, Liang S, Shmatko A, Arnold A, Horst D, Grünewald TGP, Gerstung M, Bai X. Teacher-student collaborated multiple instance learning for pan-cancer PDL1 expression prediction from histopathology slides. Nat Commun 2024; 15:3063. [PMID: 38594278 PMCID: PMC11004138 DOI: 10.1038/s41467-024-46764-0] [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: 07/30/2023] [Accepted: 03/08/2024] [Indexed: 04/11/2024] Open
Abstract
Programmed cell death ligand 1 (PDL1), as an important biomarker, is quantified by immunohistochemistry (IHC) with few established histopathological patterns. Deep learning aids in histopathological assessment, yet heterogeneity and lacking spatially resolved annotations challenge precise analysis. Here, we present a weakly supervised learning approach using bulk RNA sequencing for PDL1 expression prediction from hematoxylin and eosin (H&E) slides. Our method extends the multiple instance learning paradigm with the teacher-student framework, which assigns dynamic pseudo-labels for intra-slide heterogeneity and retrieves unlabeled instances using temporal ensemble model distillation. The approach, evaluated on 12,299 slides across 20 solid tumor types, achieves a weighted average area under the curve of 0.83 on fresh-frozen and 0.74 on formalin-fixed specimens for 9 tumors with PDL1 as an established biomarker. Our method predicts PDL1 expression patterns, validated by IHC on 20 slides, offering insights into histologies relevant to PDL1. This demonstrates the potential of deep learning in identifying diverse histological patterns for molecular changes from H&E images.
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Affiliation(s)
- Darui Jin
- Image Processing Center, Beihang University, Beijing, 102206, China
- Division of AI in Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Shen Yuan Honors College, Beihang University, Beijing, 100191, China
| | - Shangying Liang
- Image Processing Center, Beihang University, Beijing, 102206, China
| | - Artem Shmatko
- Division of AI in Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Alexander Arnold
- Charité - Universitätsmedizin Berlin, Institute of Pathology, 10117, Berlin, Germany
| | - David Horst
- Charité - Universitätsmedizin Berlin, Institute of Pathology, 10117, Berlin, Germany
- German Cancer Consortium (DKTK), partner site Berlin, a partnership between DKFZ and Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Thomas G P Grünewald
- Institute of Pathology, Heidelberg University Hospital, Heidelberg, Germany.
- Division of Translational Pediatric Sarcoma Research, German Cancer Research Center (DKFZ), German Cancer Consortium (DKTK), Heidelberg, Germany.
- Hopp Children's Cancer Center (KiTZ) Heidelberg, Heidelberg, Germany.
- National Center for Tumor Diseases (NCT), NCT Heidelberg, a partnership between DKFZ and Heidelberg University Hospital, Heidelberg, Germany.
| | - Moritz Gerstung
- Division of AI in Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany.
| | - Xiangzhi Bai
- Image Processing Center, Beihang University, Beijing, 102206, China.
- State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, 100191, China.
- Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, 100083, China.
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Chen Z, Wang X, Jin Z, Li B, Jiang D, Wang Y, Jiang M, Zhang D, Yuan P, Zhao Y, Feng F, Lin Y, Jiang L, Wang C, Meng W, Ye W, Wang J, Qiu W, Liu H, Huang D, Hou Y, Wang X, Jiao Y, Ying J, Liu Z, Liu Y. Deep learning on tertiary lymphoid structures in hematoxylin-eosin predicts cancer prognosis and immunotherapy response. NPJ Precis Oncol 2024; 8:73. [PMID: 38519580 PMCID: PMC10959936 DOI: 10.1038/s41698-024-00579-w] [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: 12/04/2023] [Accepted: 03/14/2024] [Indexed: 03/25/2024] Open
Abstract
Tertiary lymphoid structures (TLSs) have been associated with favorable immunotherapy responses and prognosis in various cancers. Despite their significance, their quantification using multiplex immunohistochemistry (mIHC) staining of T and B lymphocytes remains labor-intensive, limiting its clinical utility. To address this challenge, we curated a dataset from matched mIHC and H&E whole-slide images (WSIs) and developed a deep learning model for automated segmentation of TLSs. The model achieved Dice coefficients of 0.91 on the internal test set and 0.866 on the external validation set, along with intersection over union (IoU) scores of 0.819 and 0.787, respectively. The TLS ratio, defined as the segmented TLS area over the total tissue area, correlated with B lymphocyte levels and the expression of CXCL13, a chemokine associated with TLS formation, in 6140 patients spanning 16 tumor types from The Cancer Genome Atlas (TCGA). The prognostic models for overall survival indicated that the inclusion of the TLS ratio with TNM staging significantly enhanced the models' discriminative ability, outperforming the traditional models that solely incorporated TNM staging, in 10 out of 15 TCGA tumor types. Furthermore, when applied to biopsied treatment-naïve tumor samples, higher TLS ratios predicted a positive immunotherapy response across multiple cohorts, including specific therapies for esophageal squamous cell carcinoma, non-small cell lung cancer, and stomach adenocarcinoma. In conclusion, our deep learning-based approach offers an automated and reproducible method for TLS segmentation and quantification, highlighting its potential in predicting immunotherapy response and informing cancer prognosis.
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Affiliation(s)
- Ziqiang Chen
- MOE Key Laboratory of Metabolism and Molecular Medicine, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences and Shanghai Xuhui Central Hospital, Fudan University, Shanghai, China
- State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Institutes of Brain Science, Fudan University, Shanghai, China
| | - Xiaobing Wang
- State Key Laboratory of Molecular Oncology, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Zelin Jin
- MOE Key Laboratory of Metabolism and Molecular Medicine, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences and Shanghai Xuhui Central Hospital, Fudan University, Shanghai, China
- State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Institutes of Brain Science, Fudan University, Shanghai, China
| | - Bosen Li
- Department of General Surgery/Gastric Cancer Center, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Dongxian Jiang
- Department of Pathology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Yanqiu Wang
- Departments of Pathology, International Peace Maternity and Child Health Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Mengping Jiang
- MOE Key Laboratory of Metabolism and Molecular Medicine, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences and Shanghai Xuhui Central Hospital, Fudan University, Shanghai, China
- State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Institutes of Brain Science, Fudan University, Shanghai, China
| | - Dandan Zhang
- MOE Key Laboratory of Metabolism and Molecular Medicine, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences and Shanghai Xuhui Central Hospital, Fudan University, Shanghai, China
- State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Institutes of Brain Science, Fudan University, Shanghai, China
| | - Pei Yuan
- Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yahui Zhao
- State Key Laboratory of Molecular Oncology, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Feiyue Feng
- Thoracic Surgery Department, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yicheng Lin
- MOE Key Laboratory of Metabolism and Molecular Medicine, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences and Shanghai Xuhui Central Hospital, Fudan University, Shanghai, China
- State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Institutes of Brain Science, Fudan University, Shanghai, China
| | - Liping Jiang
- State Key Laboratory of Molecular Oncology, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Chenxi Wang
- State Key Laboratory of Molecular Oncology, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Weida Meng
- MOE Key Laboratory of Metabolism and Molecular Medicine, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences and Shanghai Xuhui Central Hospital, Fudan University, Shanghai, China
- State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Institutes of Brain Science, Fudan University, Shanghai, China
| | - Wenjing Ye
- Division of Rheumatology and Immunology, Huashan Hospital, Fudan University, Shanghai, China
| | - Jie Wang
- Departments of Thoracic Surgery, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Wenqing Qiu
- Shanghai Xuhui Central Hospital, Shanghai, China
| | - Houbao Liu
- Shanghai Xuhui Central Hospital, Shanghai, China
- Department of General Surgery/Biliary Tract Disease Center, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Dan Huang
- Department of Pathology, Fudan University Shanghai Cancer Center, Shanghai, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China; Institute of Pathology, Fudan University, Shanghai, China
| | - Yingyong Hou
- Department of Pathology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Xuefei Wang
- Department of General Surgery/Gastric Cancer Center, Zhongshan Hospital, Fudan University, Shanghai, China
- Department of General Surgery, Zhongshan Hospital (Xiamen), Fudan University, Shanghai, China
| | - Yuchen Jiao
- State Key Laboratory of Molecular Oncology, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
| | - Jianming Ying
- Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
| | - Zhihua Liu
- State Key Laboratory of Molecular Oncology, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
| | - Yun Liu
- MOE Key Laboratory of Metabolism and Molecular Medicine, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences and Shanghai Xuhui Central Hospital, Fudan University, Shanghai, China.
- State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Institutes of Brain Science, Fudan University, Shanghai, China.
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10
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Huang ZH, Chen L, Sun Y, Liu Q, Hu P. Conditional generative adversarial network driven radiomic prediction of mutation status based on magnetic resonance imaging of breast cancer. J Transl Med 2024; 22:226. [PMID: 38429796 PMCID: PMC10908206 DOI: 10.1186/s12967-024-05018-9] [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: 07/23/2023] [Accepted: 02/22/2024] [Indexed: 03/03/2024] Open
Abstract
BACKGROUND Breast Cancer (BC) is a highly heterogeneous and complex disease. Personalized treatment options require the integration of multi-omic data and consideration of phenotypic variability. Radiogenomics aims to merge medical images with genomic measurements but encounter challenges due to unpaired data consisting of imaging, genomic, or clinical outcome data. In this study, we propose the utilization of a well-trained conditional generative adversarial network (cGAN) to address the unpaired data issue in radiogenomic analysis of BC. The generated images will then be used to predict the mutations status of key driver genes and BC subtypes. METHODS We integrated the paired MRI and multi-omic (mRNA gene expression, DNA methylation, and copy number variation) profiles of 61 BC patients from The Cancer Imaging Archive (TCIA) and The Cancer Genome Atlas (TCGA). To facilitate this integration, we employed a Bayesian Tensor Factorization approach to factorize the multi-omic data into 17 latent features. Subsequently, a cGAN model was trained based on the matched side-view patient MRIs and their corresponding latent features to predict MRIs for BC patients who lack MRIs. Model performance was evaluated by calculating the distance between real and generated images using the Fréchet Inception Distance (FID) metric. BC subtype and mutation status of driver genes were obtained from the cBioPortal platform, where 3 genes were selected based on the number of mutated patients. A convolutional neural network (CNN) was constructed and trained using the generated MRIs for mutation status prediction. Receiver operating characteristic area under curve (ROC-AUC) and precision-recall area under curve (PR-AUC) were used to evaluate the performance of the CNN models for mutation status prediction. Precision, recall and F1 score were used to evaluate the performance of the CNN model in subtype classification. RESULTS The FID of the images from the well-trained cGAN model based on the test set is 1.31. The CNN for TP53, PIK3CA, and CDH1 mutation prediction yielded ROC-AUC values 0.9508, 0.7515, and 0.8136 and PR-AUC are 0.9009, 0.7184, and 0.5007, respectively for the three genes. Multi-class subtype prediction achieved precision, recall and F1 scores of 0.8444, 0.8435 and 0.8336 respectively. The source code and related data implemented the algorithms can be found in the project GitHub at https://github.com/mattthuang/BC_RadiogenomicGAN . CONCLUSION Our study establishes cGAN as a viable tool for generating synthetic BC MRIs for mutation status prediction and subtype classification to better characterize the heterogeneity of BC in patients. The synthetic images also have the potential to significantly augment existing MRI data and circumvent issues surrounding data sharing and patient privacy for future BC machine learning studies.
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Affiliation(s)
- Zi Huai Huang
- Department of Biochemistry, Schulich School of Medicine & Dentistry, Western University, London, ON, Canada
| | - Lianghong Chen
- Department of Computer Science, Western University, London, ON, Canada
| | - Yan Sun
- Department of Biochemistry, Schulich School of Medicine & Dentistry, Western University, London, ON, Canada
- Department of Computer Science, Western University, London, ON, Canada
| | - Qian Liu
- Department of Applied Computer Science, University of Winnipeg, CH Room 3C08B, 515 Portage Avenue, Winnipeg, MB, R3B 2E9, Canada.
| | - Pingzhao Hu
- Department of Biochemistry, Schulich School of Medicine & Dentistry, Western University, London, ON, Canada.
- Department of Computer Science, Western University, London, ON, Canada.
- Department of Epidemiology and Biostatistics, Schulich School of Medicine & Dentistry, Western University, London, ON, Canada.
- Department of Oncology, Schulich School of Medicine & Dentistry, Western University, London, ON, Canada.
- The Children's Health Research Institute, Lawson Health Research Institute, London, ON, Canada.
- Department of Biochemistry, Western University, Siebens Drake Research Institute, SDRI Room 201-203B, 1400 Western Road, London, ON, N6G 2V4, Canada.
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11
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Liu H, Xie X, Wang B. Deep learning infers clinically relevant protein levels and drug response in breast cancer from unannotated pathology images. NPJ Breast Cancer 2024; 10:18. [PMID: 38413598 PMCID: PMC10899601 DOI: 10.1038/s41523-024-00620-y] [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: 06/06/2023] [Accepted: 02/02/2024] [Indexed: 02/29/2024] Open
Abstract
The computational pathology has been demonstrated to effectively uncover tumor-related genomic alterations and transcriptomic patterns. Although proteomics has indeed shown great potential in the field of precision medicine, few studies have focused on the computational prediction of protein levels from pathology images. In this paper, we assume that deep learning-based pathological features imply the protein levels of tumor biomarkers that are indicative of prognosis and drug response. For this purpose, we propose wsi2rppa, a weakly supervised contrastive learning framework to infer the protein levels of tumor biomarkers from whole slide images (WSIs) in breast cancer. We first conducted contrastive learning-based pre-training on tessellated tiles to extract pathological features, which are then aggregated by attention pooling and adapted to downstream tasks. We conducted extensive evaluation experiments on the TCGA-BRCA cohort (1978 WSIs of 1093 patients with protein levels of 223 biomarkers) and the CPTAC-BRCA cohort (642 WSIs of 134 patients). The results showed that our method achieved state-of-the-art performance in tumor diagnostic tasks, and also performed well in predicting clinically relevant protein levels and drug response. To show the model interpretability, we spatially visualized the WSIs colored the tiles by their attention scores, and found that the regions with high scores were highly consistent with the tumor and necrotic regions annotated by a 10-year experienced pathologist. Moreover, spatial transcriptomic data further verified that the heatmap generated by attention scores agrees greatly with the spatial expression landscape of two typical tumor biomarker genes. In predicting the response to drug trastuzumab treatment, our method achieved a 0.79 AUC value which is much higher than the previous study reported 0.68. These findings showed the remarkable potential of computational pathology in the prediction of clinically relevant protein levels, drug response, and clinical outcomes.
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Affiliation(s)
- Hui Liu
- College of Computer and Information Engineering, Nanjing Tech University, 211816, Nanjing, Jiangsu, China
| | - Xiaodong Xie
- College of Computer and Information Engineering, Nanjing Tech University, 211816, Nanjing, Jiangsu, China
| | - Bin Wang
- Department of Cardiothoracic Surgery, The Third Affiliated Hospital of Soochow University, 213110, Changzhou, Jiangsu, China.
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12
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Fang Z, Xue Y, Leng Y, Zhang L, Ren X, Yang N, Chen J, Chen L, Wang H. Erzhi pills reverse PD-L1-mediated immunosuppression in melanoma microenvironment. Heliyon 2024; 10:e24988. [PMID: 38317912 PMCID: PMC10839997 DOI: 10.1016/j.heliyon.2024.e24988] [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: 11/23/2023] [Revised: 01/17/2024] [Accepted: 01/17/2024] [Indexed: 02/07/2024] Open
Abstract
Background Cancer immunotherapies aimed at activating immune system, especially by blocking immune checkpoints, have become a successful modality for treating patients with advanced cancers. However, its clinical practice is frequently conceded by high outcomes, low initial response rates and severe side effects. New strategies are necessary to complement and advance this biological therapy. Erzhi Pills (EZP) have diverse pharmaceutical effects including immune regulation, anti-tumor and anti-senescence. We hypothesized that EZP could exert its antitumor effect through immunomodulation. Purpose The aim of this study was to investigate the effects of EZP on anti-tumor activities, and define its molecular mechanisms. Methods By applying melanoma model with high immune infiltrates, we determined the anti-melanoma effect of EZP. To identify whether this effect was mediated by direct targeting tumor cells, cell viability and apoptosis were examined in vitro. Network pharmacology analysis was used to predict the potential mechanisms of EZP for melanoma via immune response. Flow cytometry, immunohistochemistry (IHC), enzyme-linked immunosorbent assay (ELISA) and crystal violet (CV) experiments were performed to detect T cell infiltrations and functions mediated by EZP. The mechanism of EZP was further investigated by western blotting both in vivo and in vitro. Results The administration of EZP significantly inhibited tumor weight and volume. EZP extract could only slightly reduce cell viability and induce melanoma apoptosis. Network pharmacology analysis predicted that JAK-STAT signaling pathway and T cell receptor signaling pathway might be involved during EZP treatment. Flow cytometry and IHC analyses showed that EZP increased the number of CD4+ T cells and enhanced the function of CD8+ T cells. In co-culture experiments, EZP elevated killing ability of T cells. Western blotting showed that EZP treatment reduced PD-L1 signaling pathway. Conclusion These findings indicated that EZP exerted anti-melanoma effects by inducing apoptosis and blocking PD-L1 to activate T cells. EZP might represent a promising candidate drug for cancer immunotherapies.
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Affiliation(s)
- Zhirui Fang
- State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 301617, Tianjin, China
- School of Medical Technology, Tianjin University of Traditional Chinese Medicine, 301617, Tianjin, China
| | - Yuejin Xue
- State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 301617, Tianjin, China
- School of Medical Technology, Tianjin University of Traditional Chinese Medicine, 301617, Tianjin, China
| | - Yuze Leng
- School of Medical Technology, Tianjin University of Traditional Chinese Medicine, 301617, Tianjin, China
| | - Lusha Zhang
- State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 301617, Tianjin, China
- School of Medical Technology, Tianjin University of Traditional Chinese Medicine, 301617, Tianjin, China
| | - Xiuyun Ren
- State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 301617, Tianjin, China
- School of Medical Technology, Tianjin University of Traditional Chinese Medicine, 301617, Tianjin, China
| | - Ning Yang
- Second Affiliated Hospital of Tianjin University of Traditional Chinese Medicine, Department of Dermatology, 300250, Tianjin, China
| | - Jing Chen
- Second Affiliated Hospital of Tianjin University of Traditional Chinese Medicine, Department of Dermatology, 300250, Tianjin, China
| | - Lu Chen
- State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 301617, Tianjin, China
- Instrumental Analysis and Research Center, Tianjin University of Traditional Chinese Medicine, 301617, Tianjin, China
| | - Hong Wang
- State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 301617, Tianjin, China
- School of Medical Technology, Tianjin University of Traditional Chinese Medicine, 301617, Tianjin, China
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13
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Ligero M, Serna G, El Nahhas OS, Sansano I, Mauchanski S, Viaplana C, Calderaro J, Toledo RA, Dienstmann R, Vanguri RS, Sauter JL, Sanchez-Vega F, Shah SP, Ramón y Cajal S, Garralda E, Nuciforo P, Perez-Lopez R, Kather JN. Weakly Supervised Deep Learning Predicts Immunotherapy Response in Solid Tumors Based on PD-L1 Expression. CANCER RESEARCH COMMUNICATIONS 2024; 4:92-102. [PMID: 38126740 PMCID: PMC10782919 DOI: 10.1158/2767-9764.crc-23-0287] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Revised: 10/11/2023] [Accepted: 12/12/2023] [Indexed: 12/23/2023]
Abstract
Programmed death-ligand 1 (PD-L1) IHC is the most commonly used biomarker for immunotherapy response. However, quantification of PD-L1 status in pathology slides is challenging. Neither manual quantification nor a computer-based mimicking of manual readouts is perfectly reproducible, and the predictive performance of both approaches regarding immunotherapy response is limited. In this study, we developed a deep learning (DL) method to predict PD-L1 status directly from raw IHC image data, without explicit intermediary steps such as cell detection or pigment quantification. We trained the weakly supervised model on PD-L1-stained slides from the non-small cell lung cancer (NSCLC)-Memorial Sloan Kettering (MSK) cohort (N = 233) and validated it on the pan-cancer-Vall d'Hebron Institute of Oncology (VHIO) cohort (N = 108). We also investigated the performance of the model to predict response to immune checkpoint inhibitors (ICI) in terms of progression-free survival. In the pan-cancer-VHIO cohort, the performance was compared with tumor proportion score (TPS) and combined positive score (CPS). The DL model showed good performance in predicting PD-L1 expression (TPS ≥ 1%) in both NSCLC-MSK and pan-cancer-VHIO cohort (AUC 0.88 ± 0.06 and 0.80 ± 0.03, respectively). The predicted PD-L1 status showed an improved association with response to ICIs [HR: 1.5 (95% confidence interval: 1-2.3), P = 0.049] compared with TPS [HR: 1.4 (0.96-2.2), P = 0.082] and CPS [HR: 1.2 (0.79-1.9), P = 0.386]. Notably, our explainability analysis showed that the model does not just look at the amount of brown pigment in the IHC slides, but also considers morphologic factors such as lymphocyte conglomerates. Overall, end-to-end weakly supervised DL shows potential for improving patient stratification for cancer immunotherapy by analyzing PD-L1 IHC, holistically integrating morphology and PD-L1 staining intensity. SIGNIFICANCE The weakly supervised DL model to predict PD-L1 status from raw IHC data, integrating tumor staining intensity and morphology, enables enhanced patient stratification in cancer immunotherapy compared with traditional pathologist assessment.
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Affiliation(s)
- Marta Ligero
- Radiomics Group, Vall d'Hebron Institute of Oncology (VHIO), Barcelona, Spain
| | - Garazi Serna
- Molecular Oncology Group, Vall d'Hebron Institute of Oncology (VHIO), Barcelona, Spain
| | - Omar S.M. El Nahhas
- Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany
| | - Irene Sansano
- Pathology Department, Vall d'Hebron University Hospital (VHUH), Barcelona, Spain
| | - Siarhei Mauchanski
- Molecular Oncology Group, Vall d'Hebron Institute of Oncology (VHIO), Barcelona, Spain
| | - Cristina Viaplana
- Oncology Data Science (ODysSey) Group, Vall d'Hebron Institute of Oncology (VHIO), Barcelona, Spain
| | - Julien Calderaro
- Assistance Publique-Hôpitaux de Paris, Département de Pathologie, CHU Henri Mondor, Créteil, France
- Université Paris-Est Créteil, Faculté de Médecine, Créteil, France
| | - Rodrigo A. Toledo
- Biomakers and Clonal Dynamics Group, Vall d'Hebron Institute of Oncology (VHIO), Barcelona, Spain
| | - Rodrigo Dienstmann
- Oncology Data Science (ODysSey) Group, Vall d'Hebron Institute of Oncology (VHIO), Barcelona, Spain
| | - Rami S. Vanguri
- Department of Pathology and Laboratory Medicine, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - Jennifer L. Sauter
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York
| | | | - Sohrab P. Shah
- Computational Oncology, Memorial Sloan Kettering Cancer Center, New York, New York
| | | | - Elena Garralda
- Department of Medical Oncology, Vall d'Hebron University Hospital and Institute of Oncology (VHIO), Barcelona, Spain
| | - Paolo Nuciforo
- Molecular Oncology Group, Vall d'Hebron Institute of Oncology (VHIO), Barcelona, Spain
| | - Raquel Perez-Lopez
- Radiomics Group, Vall d'Hebron Institute of Oncology (VHIO), Barcelona, Spain
| | - Jakob Nikolas Kather
- Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany
- Pathology and Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, United Kingdom
- Department of Medicine I, University Hospital Dresden, Dresden, Germany
- Medical Oncology, National Center for Tumor Diseases (NCT), University Hospital Heidelberg, Heidelberg, Germany
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14
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Li J, Wang D, Zhang C. Establishment of a pathomic-based machine learning model to predict CD276 (B7-H3) expression in colon cancer. Front Oncol 2024; 13:1232192. [PMID: 38260829 PMCID: PMC10802857 DOI: 10.3389/fonc.2023.1232192] [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: 05/31/2023] [Accepted: 11/29/2023] [Indexed: 01/24/2024] Open
Abstract
CD276 is a promising prognostic indicator and an attractive therapeutic target in various malignancies. However, current methods for CD276 detection are time-consuming and expensive, limiting extensive studies and applications of CD276. We aimed to develop a pathomic model for CD276 prediction from H&E-stained pathological images, and explore the underlying mechanism of the pathomic features by associating the pathomic model with transcription profiles. A dataset of colon adenocarcinoma (COAD) patients was retrieved from the Cancer Genome Atlas (TCGA) database. The dataset was divided into the training and validation sets according to the ratio of 8:2 by a stratified sampling method. Using the gradient boosting machine (GBM) algorithm, we established a pathomic model to predict CD276 expression in COAD. Univariate and multivariate Cox regression analyses were conducted to assess the predictive performance of the pathomic model for overall survival in COAD. Gene Set Enrichment Analysis (GESA) was performed to explore the underlying biological mechanisms of the pathomic model. The pathomic model formed by three pathomic features for CD276 prediction showed an area under the curve (AUC) of 0.833 (95%CI: 0.784-0.882) in the training set and 0.758 (95%CI: 0.637-0.878) in the validation set, respectively. The calibration curves and Hosmer-Lemeshow goodness of fit test showed that the prediction probability of high/low expression of CD276 was in favorable agreement with the real situation in both the training and validation sets (P=0.176 and 0.255, respectively). The DCA curves suggested that the pathomic model acquired high clinical benefit. All the subjects were categorized into high pathomic score (PS) (PS-H) and low PS (PS-L) groups according to the cutoff value of PS. Univariate and multivariate Cox regression analysis indicated that PS was a risk factor for overall survival in COAD. Furthermore, through GESA analysis, we found several immune and inflammatory-related pathways and genes were associated with the pathomic model. We constructed a pathomics-based machine learning model for CD276 prediction directly from H&E-stained images in COAD. Through integrated analysis of the pathomic model and transcriptomics, the interpretability of the pathomic model provide a theoretical basis for further hypothesis and experimental research.
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Affiliation(s)
- Jia Li
- Department of Gastroenterology, The 983rd Hospital of Joint Logistic Support Force of PLA, Tianjin, China
| | - Dongxu Wang
- Department of Gastroenterology, The 983rd Hospital of Joint Logistic Support Force of PLA, Tianjin, China
| | - Chenxin Zhang
- Department of General Surgery, The 983rd Hospital of Joint Logistic Support Force of PLA, Tianjin, China
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15
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Ricker CA, Meli K, Van Allen EM. Historical perspective and future directions: computational science in immuno-oncology. J Immunother Cancer 2024; 12:e008306. [PMID: 38191244 PMCID: PMC10826578 DOI: 10.1136/jitc-2023-008306] [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] [Accepted: 12/07/2023] [Indexed: 01/10/2024] Open
Abstract
Immuno-oncology holds promise for transforming patient care having achieved durable clinical response rates across a variety of advanced and metastatic cancers. Despite these achievements, only a minority of patients respond to immunotherapy, underscoring the importance of elucidating molecular mechanisms responsible for response and resistance to inform the development and selection of treatments. Breakthroughs in molecular sequencing technologies have led to the generation of an immense amount of genomic and transcriptomic sequencing data that can be mined to uncover complex tumor-immune interactions using computational tools. In this review, we discuss existing and emerging computational methods that contextualize the composition and functional state of the tumor microenvironment, infer the reactivity and clonal dynamics from reconstructed immune cell receptor repertoires, and predict the antigenic landscape for immune cell recognition. We further describe the advantage of multi-omics analyses for capturing multidimensional relationships and artificial intelligence techniques for integrating omics data with histopathological and radiological images to encapsulate patterns of treatment response and tumor-immune biology. Finally, we discuss key challenges impeding their widespread use and clinical application and conclude with future perspectives. We are hopeful that this review will both serve as a guide for prospective researchers seeking to use existing tools for scientific discoveries and inspire the optimization or development of novel tools to enhance precision, ultimately expediting advancements in immunotherapy that improve patient survival and quality of life.
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Affiliation(s)
- Cora A Ricker
- Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
| | - Kevin Meli
- Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
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16
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Zdrenka M, Kowalewski A, Ahmadi N, Sadiqi RU, Chmura Ł, Borowczak J, Maniewski M, Szylberg Ł. Refining PD-1/PD-L1 assessment for biomarker-guided immunotherapy: A review. BIOMOLECULES & BIOMEDICINE 2024; 24:14-29. [PMID: 37877810 PMCID: PMC10787614 DOI: 10.17305/bb.2023.9265] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 06/23/2023] [Accepted: 06/23/2023] [Indexed: 10/26/2023]
Abstract
Anti-programmed cell death ligand 1 (anti-PD-L1) immunotherapy is an increasingly crucial in cancer treatment. To date, the Federal Drug Administration (FDA) has approved four PD-L1 immunohistochemistry (IHC) staining protocols, commercially available in the form of "kits", facilitating testing for PD-L1 expression. These kits comprise four PD-L1 antibodies on two separate IHC platforms, each utilizing distinct, non-interchangeable scoring systems. Several factors, including tumor heterogeneity and the size of the tissue specimens assessed, can lead to PD-L1 status misclassification, potentially hindering the initiation of therapy. Therefore, the development of more accurate predictive biomarkers to distinguish between responders and non-responders prior to anti-PD-1/PD-L1 therapy warrants further research. Achieving this goal necessitates refining sampling criteria, enhancing current methods of PD-L1 detection, and deepening our understanding of the impact of additional biomarkers. In this article, we review potential solutions to improve the predictive accuracy of PD-L1 assessment in order to more precisely anticipate patients' responses to anti-PD-1/PD-L1 therapy, monitor disease progression and predict clinical outcomes.
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Affiliation(s)
- Marek Zdrenka
- Department of Tumor Pathology and Pathomorphology, Oncology Centre-Prof. Franciszek Łukaszczyk Memorial Hospital, Bydgoszcz, Poland
| | - Adam Kowalewski
- Department of Tumor Pathology and Pathomorphology, Oncology Centre-Prof. Franciszek Łukaszczyk Memorial Hospital, Bydgoszcz, Poland
| | - Navid Ahmadi
- Department of Cardiothoracic Surgery, Royal Papworth Hospital, Cambridge, UK
| | | | - Łukasz Chmura
- Department of Pathomorphology, Jagiellonian University Medical College, Kraków, Poland
| | - Jędrzej Borowczak
- Department of Obstetrics, Gynaecology and Oncology, Chair of Pathomorphology and Clinical Placentology, Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Torun, Bydgoszcz, Poland
| | - Mateusz Maniewski
- Department of Obstetrics, Gynaecology and Oncology, Chair of Pathomorphology and Clinical Placentology, Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Torun, Bydgoszcz, Poland
| | - Łukasz Szylberg
- Department of Tumor Pathology and Pathomorphology, Oncology Centre-Prof. Franciszek Łukaszczyk Memorial Hospital, Bydgoszcz, Poland
- Department of Obstetrics, Gynaecology and Oncology, Chair of Pathomorphology and Clinical Placentology, Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Torun, Bydgoszcz, Poland
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17
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Prelaj A, Miskovic V, Zanitti M, Trovo F, Genova C, Viscardi G, Rebuzzi SE, Mazzeo L, Provenzano L, Kosta S, Favali M, Spagnoletti A, Castelo-Branco L, Dolezal J, Pearson AT, Lo Russo G, Proto C, Ganzinelli M, Giani C, Ambrosini E, Turajlic S, Au L, Koopman M, Delaloge S, Kather JN, de Braud F, Garassino MC, Pentheroudakis G, Spencer C, Pedrocchi ALG. Artificial intelligence for predictive biomarker discovery in immuno-oncology: a systematic review. Ann Oncol 2024; 35:29-65. [PMID: 37879443 DOI: 10.1016/j.annonc.2023.10.125] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 08/31/2023] [Accepted: 10/08/2023] [Indexed: 10/27/2023] Open
Abstract
BACKGROUND The widespread use of immune checkpoint inhibitors (ICIs) has revolutionised treatment of multiple cancer types. However, selecting patients who may benefit from ICI remains challenging. Artificial intelligence (AI) approaches allow exploitation of high-dimension oncological data in research and development of precision immuno-oncology. MATERIALS AND METHODS We conducted a systematic literature review of peer-reviewed original articles studying the ICI efficacy prediction in cancer patients across five data modalities: genomics (including genomics, transcriptomics, and epigenomics), radiomics, digital pathology (pathomics), and real-world and multimodality data. RESULTS A total of 90 studies were included in this systematic review, with 80% published in 2021-2022. Among them, 37 studies included genomic, 20 radiomic, 8 pathomic, 20 real-world, and 5 multimodal data. Standard machine learning (ML) methods were used in 72% of studies, deep learning (DL) methods in 22%, and both in 6%. The most frequently studied cancer type was non-small-cell lung cancer (36%), followed by melanoma (16%), while 25% included pan-cancer studies. No prospective study design incorporated AI-based methodologies from the outset; rather, all implemented AI as a post hoc analysis. Novel biomarkers for ICI in radiomics and pathomics were identified using AI approaches, and molecular biomarkers have expanded past genomics into transcriptomics and epigenomics. Finally, complex algorithms and new types of AI-based markers, such as meta-biomarkers, are emerging by integrating multimodal/multi-omics data. CONCLUSION AI-based methods have expanded the horizon for biomarker discovery, demonstrating the power of integrating multimodal data from existing datasets to discover new meta-biomarkers. While most of the included studies showed promise for AI-based prediction of benefit from immunotherapy, none provided high-level evidence for immediate practice change. A priori planned prospective trial designs are needed to cover all lifecycle steps of these software biomarkers, from development and validation to integration into clinical practice.
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Affiliation(s)
- A Prelaj
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan; Nearlab, Department of Electronics, Information, and Bioengineering, Politecnico di Milano, Milano, Italy; ESMO Real World Data and Digital Health Working Group, ESMO, Lugano, Switzerland.
| | - V Miskovic
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan; Nearlab, Department of Electronics, Information, and Bioengineering, Politecnico di Milano, Milano, Italy
| | - M Zanitti
- Department of Electronic Systems, Aalborg University Copenhagen, Denmark
| | - F Trovo
- Nearlab, Department of Electronics, Information, and Bioengineering, Politecnico di Milano, Milano, Italy
| | - C Genova
- UO Clinica di Oncologia Medica, IRCCS Ospedale Policlinico San Martino, Genoa; Department of Internal Medicine and Medical Specialties (Di.M.I.), University of Genoa, Genoa
| | - G Viscardi
- Precision Medicine Department, Università degli Studi della Campania Luigi Vanvitelli, Naples
| | - S E Rebuzzi
- Department of Internal Medicine and Medical Specialties (Di.M.I.), University of Genoa, Genoa; Medical Oncology Unit, Ospedale San Paolo, Savona, Italy
| | - L Mazzeo
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan; Nearlab, Department of Electronics, Information, and Bioengineering, Politecnico di Milano, Milano, Italy
| | - L Provenzano
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan
| | - S Kosta
- Department of Electronic Systems, Aalborg University Copenhagen, Denmark
| | - M Favali
- Nearlab, Department of Electronics, Information, and Bioengineering, Politecnico di Milano, Milano, Italy
| | - A Spagnoletti
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan
| | - L Castelo-Branco
- ESMO European Society for Medical Oncology, Lugano, Switzerland; NOVA National School of Public Health, Lisboa, Portugal
| | - J Dolezal
- Section of Hematology/Oncology, Department of Medicine, University of Chicago, Chicago, USA
| | - A T Pearson
- Section of Hematology/Oncology, Department of Medicine, University of Chicago, Chicago, USA
| | - G Lo Russo
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan
| | - C Proto
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan
| | - M Ganzinelli
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan
| | - C Giani
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan
| | - E Ambrosini
- Nearlab, Department of Electronics, Information, and Bioengineering, Politecnico di Milano, Milano, Italy
| | - S Turajlic
- Cancer Dynamics Laboratory, The Francis Crick Institute, London
| | - L Au
- Renal and Skin Unit, The Royal Marsden NHS Foundation Trust, London, UK; Department of Medical Oncology, Peter MacCallum Cancer Centre, Melbourne; Sir Peter MacCallum Department of Medical Oncology, The University of Melbourne, Melbourne, Australia
| | - M Koopman
- Department of Research and Development, Netherlands Comprehensive Cancer Organisation, Utrecht, The Netherlands; ESMO Real World Data and Digital Health Working Group, ESMO, Lugano, Switzerland
| | - S Delaloge
- Department of Cancer Medicine, Gustave Roussy, Villejuif, France; ESMO Real World Data and Digital Health Working Group, ESMO, Lugano, Switzerland
| | - J N Kather
- Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, Technical University Dresden, Dresden, Germany
| | - F de Braud
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan
| | - M C Garassino
- Section of Hematology/Oncology, Department of Medicine, University of Chicago, Chicago, USA
| | | | - C Spencer
- Cancer Dynamics Laboratory, The Francis Crick Institute, London.
| | - A L G Pedrocchi
- Nearlab, Department of Electronics, Information, and Bioengineering, Politecnico di Milano, Milano, Italy
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18
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Shen H, Wu J, Shen X, Hu J, Liu J, Zhang Q, Sun Y, Chen K, Li X. An efficient context-aware approach for whole-slide image classification. iScience 2023; 26:108175. [PMID: 38047071 PMCID: PMC10690557 DOI: 10.1016/j.isci.2023.108175] [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: 03/20/2023] [Revised: 08/29/2023] [Accepted: 10/08/2023] [Indexed: 12/05/2023] Open
Abstract
Computational pathology for gigapixel whole-slide images (WSIs) at slide level is helpful in disease diagnosis and remains challenging. We propose a context-aware approach termed WSI inspection via transformer (WIT) for slide-level classification via holistically modeling dependencies among patches on WSI. WIT automatically learns feature representation of WSI by aggregating features of all image patches. We evaluate classification performance of WIT and state-of-the-art baseline method. WIT achieved an accuracy of 82.1% (95% CI, 80.7%-83.3%) in the detection of 32 cancer types on the TCGA dataset, 0.918 (0.910-0.925) in diagnosis of cancer on the CPTAC dataset, and 0.882 (0.87-0.890) in the diagnosis of prostate cancer from needle biopsy slide, outperforming the baseline by 31.6%, 5.4%, and 9.3%, respectively. WIT can pinpoint the WSI regions that are most influential for its decision. WIT represents a new paradigm for computational pathology, facilitating the development of digital pathology tools.
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Affiliation(s)
- Hongru Shen
- Tianjin Cancer Institute, Tianjin’s Clinical Research Center for Cancer, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin, China
| | - Jianghua Wu
- Department of Pathology, Peking University Cancer Hospital & Institute, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Beijing, China
| | - Xilin Shen
- Tianjin Cancer Institute, Tianjin’s Clinical Research Center for Cancer, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin, China
| | - Jiani Hu
- Tianjin Cancer Institute, Tianjin’s Clinical Research Center for Cancer, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin, China
| | - Jilei Liu
- Tianjin Cancer Institute, Tianjin’s Clinical Research Center for Cancer, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin, China
| | - Qiang Zhang
- Department of Maxillofacial and Otorhinolaryngology Oncology, Tianjin’s Clinical Research Center for Cancer, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin, China
| | - Yan Sun
- Department of Pathology, Tianjin’s Clinical Research Center for Cancer, Key Laboratory of Cancer Immunology and Biotherapy, National Clinical Research Center for Cancer, Tianjin Cancer Institute and Hospital, Tianjin Medical University, Tianjin, China
| | - Kexin Chen
- Department of Epidemiology and Biostatistics, Tianjin’s Clinical Research Center for Cancer, Key Laboratory of Molecular Cancer Epidemiology of Tianjin, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin, China
| | - Xiangchun Li
- Tianjin Cancer Institute, Tianjin’s Clinical Research Center for Cancer, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin, China
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19
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Ng CW, Wong KK. Deep learning-enabled breast cancer endocrine response determination from H&E staining based on ESR1 signaling activity. Sci Rep 2023; 13:21454. [PMID: 38052873 PMCID: PMC10698147 DOI: 10.1038/s41598-023-48830-x] [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: 06/01/2023] [Accepted: 11/30/2023] [Indexed: 12/07/2023] Open
Abstract
Estrogen receptor (ER) positivity by immunohistochemistry has long been a main selection criterium for breast cancer patients to be treated with endocrine therapy. However, ER positivity might not directly correlate with activated ER signaling activity, which is a better predictor for endocrine therapy responsiveness. In this study, we investigated if a deep learning method using whole-slide H&E-stained images could predict ER signaling activity. First, ER signaling activity score was determined using RNAseq data available from each of the 1082 breast cancer samples in the TCGA Pan-Cancer dataset based on the Hallmark Estrogen Response Early gene set from the Molecular Signature Database (MSigDB). Then the processed H&E-stained images and ER signaling activity scores from a training cohort were fed into ResNet101 with three additional fully connected layers to generate a predicted ER activity score. The trained models were subsequently applied to an independent testing cohort. The result demonstrated that ER + /HER2- breast cancer patients with a higher predicted ER activity score had longer progression-free survival (p = 0.0368) than those with lower predicted ER activity score. In conclusion, a convolutional deep neural network can predict prognosis and endocrine therapy response in breast cancer patients based on whole-slide H&E-stained images. The trained models were found to robustly predict the prognosis of ER + /HER2- patients. This information is valuable for patient management, as it does not require RNA-seq or microarray data analyses. Thus, these models can reduce the cost of the diagnosis workflow if such information is required.
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Affiliation(s)
- Chun Wai Ng
- Department of Gynecologic Oncology and Reproductive Medicine, Unit 1362, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX, 77030, USA
| | - Kwong-Kwok Wong
- Department of Gynecologic Oncology and Reproductive Medicine, Unit 1362, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX, 77030, USA.
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20
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Chen P, Rojas FR, Hu X, Serrano A, Zhu B, Chen H, Hong L, Bandyoyadhyay R, Aminu M, Kalhor N, Lee JJ, El Hussein S, Khoury JD, Pass HI, Moreira AL, Velcheti V, Sterman DH, Fukuoka J, Tabata K, Su D, Ying L, Gibbons DL, Heymach JV, Wistuba II, Fujimoto J, Solis Soto LM, Zhang J, Wu J. Pathomic Features Reveal Immune and Molecular Evolution From Lung Preneoplasia to Invasive Adenocarcinoma. Mod Pathol 2023; 36:100326. [PMID: 37678674 PMCID: PMC10841057 DOI: 10.1016/j.modpat.2023.100326] [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: 03/26/2023] [Revised: 08/12/2023] [Accepted: 08/29/2023] [Indexed: 09/09/2023]
Abstract
Recent statistics on lung cancer, including the steady decline of advanced diseases and the dramatically increasing detection of early-stage diseases and indeterminate pulmonary nodules, mark the significance of a comprehensive understanding of early lung carcinogenesis. Lung adenocarcinoma (ADC) is the most common histologic subtype of lung cancer, and atypical adenomatous hyperplasia is the only recognized preneoplasia to ADC, which may progress to adenocarcinoma in situ (AIS) and minimally invasive adenocarcinoma (MIA) and eventually to invasive ADC. Although molecular evolution during early lung carcinogenesis has been explored in recent years, the progress has been significantly hindered, largely due to insufficient materials from ADC precursors. Here, we employed state-of-the-art deep learning and artificial intelligence techniques to robustly segment and recognize cells on routinely used hematoxylin and eosin histopathology images and extracted 9 biology-relevant pathomic features to decode lung preneoplasia evolution. We analyzed 3 distinct cohorts (Japan, China, and United States) covering 98 patients, 162 slides, and 669 regions of interest, including 143 normal, 129 atypical adenomatous hyperplasia, 94 AIS, 98 MIA, and 205 ADC. Extracted pathomic features revealed progressive increase of atypical epithelial cells and progressive decrease of lymphocytic cells from normal to AAH, AIS, MIA, and ADC, consistent with the results from tissue-consuming and expensive molecular/immune profiling. Furthermore, pathomics analysis manifested progressively increasing cellular intratumor heterogeneity along with the evolution from normal lung to invasive ADC. These findings demonstrated the feasibility and substantial potential of pathomics in studying lung cancer carcinogenesis directly from the low-cost routine hematoxylin and eosin staining.
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Affiliation(s)
- Pingjun Chen
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas.
| | - Frank R Rojas
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Xin Hu
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Alejandra Serrano
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Bo Zhu
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Hong Chen
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Lingzhi Hong
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas; Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Rukhmini Bandyoyadhyay
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Muhammad Aminu
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Neda Kalhor
- Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - J Jack Lee
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Siba El Hussein
- Department of Pathology and Laboratory Medicine, University of Rochester Medical Center, Rochester, New York
| | - Joseph D Khoury
- Department of Pathology and Microbiology, University of Nebraska Medical Center, Omaha, Nebraska
| | - Harvey I Pass
- Department of Surgery, NYU Langone Health, New York, New York
| | - Andre L Moreira
- Department of Pathology, NYU Langone Health, New York, New York
| | - Vamsidhar Velcheti
- Department of Medicine, NYU Grossman School of Medicine, New York, New York
| | - Daniel H Sterman
- Department of Medicine, NYU Grossman School of Medicine, New York, New York; Department of Cardiothoracic Surgery, NYU Grossman School of Medicine, New York, New York
| | - Junya Fukuoka
- Department of Pathology, Graduate School of Biomedical Sciences, Nagasaki University, Nagasaki, Japan
| | - Kazuhiro Tabata
- Department of Pathology, Kagoshima University Graduate School of Medical and Dental Sciences, Kagoshima, Japan
| | - Dan Su
- Cancer Research Institute, Zhejiang Cancer Hospital, Hangzhou, Zhejiang, China
| | - Lisha Ying
- Cancer Research Institute, Zhejiang Cancer Hospital, Hangzhou, Zhejiang, China
| | - Don L Gibbons
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - John V Heymach
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Ignacio I Wistuba
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Junya Fujimoto
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Luisa M Solis Soto
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Jianjun Zhang
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas; Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas.
| | - Jia Wu
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas; Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas.
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21
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Cortacero K, McKenzie B, Müller S, Khazen R, Lafouresse F, Corsaut G, Van Acker N, Frenois FX, Lamant L, Meyer N, Vergier B, Wilson DG, Luga H, Staufer O, Dustin ML, Valitutti S, Cussat-Blanc S. Evolutionary design of explainable algorithms for biomedical image segmentation. Nat Commun 2023; 14:7112. [PMID: 37932311 PMCID: PMC10628266 DOI: 10.1038/s41467-023-42664-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Accepted: 10/18/2023] [Indexed: 11/08/2023] Open
Abstract
An unresolved issue in contemporary biomedicine is the overwhelming number and diversity of complex images that require annotation, analysis and interpretation. Recent advances in Deep Learning have revolutionized the field of computer vision, creating algorithms that compete with human experts in image segmentation tasks. However, these frameworks require large human-annotated datasets for training and the resulting "black box" models are difficult to interpret. In this study, we introduce Kartezio, a modular Cartesian Genetic Programming-based computational strategy that generates fully transparent and easily interpretable image processing pipelines by iteratively assembling and parameterizing computer vision functions. The pipelines thus generated exhibit comparable precision to state-of-the-art Deep Learning approaches on instance segmentation tasks, while requiring drastically smaller training datasets. This Few-Shot Learning method confers tremendous flexibility, speed, and functionality to this approach. We then deploy Kartezio to solve a series of semantic and instance segmentation problems, and demonstrate its utility across diverse images ranging from multiplexed tissue histopathology images to high resolution microscopy images. While the flexibility, robustness and practical utility of Kartezio make this fully explicable evolutionary designer a potential game-changer in the field of biomedical image processing, Kartezio remains complementary and potentially auxiliary to mainstream Deep Learning approaches.
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Affiliation(s)
- Kévin Cortacero
- Institut National de la Santé et de la Recherche Médicale (INSERM) UMR1037, Centre de Recherche en Cancérologie de Toulouse (CRCT), Toulouse, France
- Centre National de la Recherche Scientifique (CNRS) UMR5071, Toulouse, France
- University of Toulouse III - Paul Sabatier, Toulouse, France
| | - Brienne McKenzie
- Institut National de la Santé et de la Recherche Médicale (INSERM) UMR1037, Centre de Recherche en Cancérologie de Toulouse (CRCT), Toulouse, France
- Centre National de la Recherche Scientifique (CNRS) UMR5071, Toulouse, France
- University of Toulouse III - Paul Sabatier, Toulouse, France
| | - Sabina Müller
- Institut National de la Santé et de la Recherche Médicale (INSERM) UMR1037, Centre de Recherche en Cancérologie de Toulouse (CRCT), Toulouse, France
- Centre National de la Recherche Scientifique (CNRS) UMR5071, Toulouse, France
- University of Toulouse III - Paul Sabatier, Toulouse, France
| | - Roxana Khazen
- Institut National de la Santé et de la Recherche Médicale (INSERM) UMR1037, Centre de Recherche en Cancérologie de Toulouse (CRCT), Toulouse, France
- Centre National de la Recherche Scientifique (CNRS) UMR5071, Toulouse, France
- University of Toulouse III - Paul Sabatier, Toulouse, France
| | - Fanny Lafouresse
- Institut National de la Santé et de la Recherche Médicale (INSERM) UMR1037, Centre de Recherche en Cancérologie de Toulouse (CRCT), Toulouse, France
- Centre National de la Recherche Scientifique (CNRS) UMR5071, Toulouse, France
- University of Toulouse III - Paul Sabatier, Toulouse, France
| | - Gaëlle Corsaut
- Institut National de la Santé et de la Recherche Médicale (INSERM) UMR1037, Centre de Recherche en Cancérologie de Toulouse (CRCT), Toulouse, France
- Centre National de la Recherche Scientifique (CNRS) UMR5071, Toulouse, France
- University of Toulouse III - Paul Sabatier, Toulouse, France
| | - Nathalie Van Acker
- Department of Pathology, Institut Universitaire du Cancer-Oncopole de Toulouse (IUCT), Toulouse, France
| | - François-Xavier Frenois
- Department of Pathology, Institut Universitaire du Cancer-Oncopole de Toulouse (IUCT), Toulouse, France
| | - Laurence Lamant
- Department of Pathology, Institut Universitaire du Cancer-Oncopole de Toulouse (IUCT), Toulouse, France
| | | | - Béatrice Vergier
- Service de Pathologie, Centre Hospitalier Universitaire de Bordeaux, Bordeaux, France
- INSERM UMR1053 -UMR BaRITOn, Université de Bordeaux, Bordeaux, France
| | - Dennis G Wilson
- University of Toulouse - Institut de Recherche en Informatique de Toulouse (IRIT) - UMR5505, Artificial and Natural Intelligence Toulouse Institute, Toulouse, France
| | - Hervé Luga
- University of Toulouse - Institut de Recherche en Informatique de Toulouse (IRIT) - UMR5505, Artificial and Natural Intelligence Toulouse Institute, Toulouse, France
| | - Oskar Staufer
- Kennedy Institute of Rheumatology (KIR), Nuffield Department of Orthopedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
- Leibniz Institute for New Materials, 66123, Saarbrücken, Germany
| | - Michael L Dustin
- Kennedy Institute of Rheumatology (KIR), Nuffield Department of Orthopedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Salvatore Valitutti
- Institut National de la Santé et de la Recherche Médicale (INSERM) UMR1037, Centre de Recherche en Cancérologie de Toulouse (CRCT), Toulouse, France.
- Centre National de la Recherche Scientifique (CNRS) UMR5071, Toulouse, France.
- University of Toulouse III - Paul Sabatier, Toulouse, France.
- Department of Pathology, Institut Universitaire du Cancer-Oncopole de Toulouse (IUCT), Toulouse, France.
| | - Sylvain Cussat-Blanc
- University of Toulouse - Institut de Recherche en Informatique de Toulouse (IRIT) - UMR5505, Artificial and Natural Intelligence Toulouse Institute, Toulouse, France.
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22
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Ahn JS, Shin S, Yang SA, Park EK, Kim KH, Cho SI, Ock CY, Kim S. Artificial Intelligence in Breast Cancer Diagnosis and Personalized Medicine. J Breast Cancer 2023; 26:405-435. [PMID: 37926067 PMCID: PMC10625863 DOI: 10.4048/jbc.2023.26.e45] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 09/25/2023] [Accepted: 10/06/2023] [Indexed: 11/07/2023] Open
Abstract
Breast cancer is a significant cause of cancer-related mortality in women worldwide. Early and precise diagnosis is crucial, and clinical outcomes can be markedly enhanced. The rise of artificial intelligence (AI) has ushered in a new era, notably in image analysis, paving the way for major advancements in breast cancer diagnosis and individualized treatment regimens. In the diagnostic workflow for patients with breast cancer, the role of AI encompasses screening, diagnosis, staging, biomarker evaluation, prognostication, and therapeutic response prediction. Although its potential is immense, its complete integration into clinical practice is challenging. Particularly, these challenges include the imperatives for extensive clinical validation, model generalizability, navigating the "black-box" conundrum, and pragmatic considerations of embedding AI into everyday clinical environments. In this review, we comprehensively explored the diverse applications of AI in breast cancer care, underlining its transformative promise and existing impediments. In radiology, we specifically address AI in mammography, tomosynthesis, risk prediction models, and supplementary imaging methods, including magnetic resonance imaging and ultrasound. In pathology, our focus is on AI applications for pathologic diagnosis, evaluation of biomarkers, and predictions related to genetic alterations, treatment response, and prognosis in the context of breast cancer diagnosis and treatment. Our discussion underscores the transformative potential of AI in breast cancer management and emphasizes the importance of focused research to realize the full spectrum of benefits of AI in patient care.
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Affiliation(s)
| | | | | | | | | | | | | | - Seokhwi Kim
- Department of Pathology, Ajou University School of Medicine, Suwon, Korea
- Department of Biomedical Sciences, Ajou University Graduate School of Medicine, Suwon, Korea.
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23
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Sajjadi E, Frascarelli C, Venetis K, Bonizzi G, Ivanova M, Vago G, Guerini-Rocco E, Fusco N. Computational pathology to improve biomarker testing in breast cancer: how close are we? Eur J Cancer Prev 2023; 32:460-467. [PMID: 37038997 DOI: 10.1097/cej.0000000000000804] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/12/2023]
Abstract
The recent advancements in breast cancer precision medicine have highlighted the urgency for the precise and reproducible characterization of clinically actionable biomarkers. Despite numerous standardization efforts, biomarker testing by conventional methodologies is challenged by several issues such as high inter-observer variabilities, the spatial heterogeneity of biomarkers expression, and technological heterogeneity. In this respect, artificial intelligence-based digital pathology approaches are being increasingly recognized as promising methods for biomarker testing and subsequently improved clinical management. Here, we provide an overview on the most recent advances for artificial intelligence-assisted biomarkers testing in breast cancer, with a particular focus on tumor-infiltrating lymphocytes, programmed death-ligand 1, phosphatidylinositol-3 kinase catalytic alpha, and estrogen receptor 1. Challenges and solutions for this integrative analysis in pathology laboratories are also provided.
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Affiliation(s)
- Elham Sajjadi
- Department of Oncology and Hemato-Oncology, University of Milan
- Division of Pathology, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Chiara Frascarelli
- Department of Oncology and Hemato-Oncology, University of Milan
- Division of Pathology, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | | | - Giuseppina Bonizzi
- Division of Pathology, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Mariia Ivanova
- Division of Pathology, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Gianluca Vago
- Department of Oncology and Hemato-Oncology, University of Milan
| | - Elena Guerini-Rocco
- Department of Oncology and Hemato-Oncology, University of Milan
- Division of Pathology, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Nicola Fusco
- Department of Oncology and Hemato-Oncology, University of Milan
- Division of Pathology, IEO, European Institute of Oncology IRCCS, Milan, Italy
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24
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Verghese G, Lennerz JK, Ruta D, Ng W, Thavaraj S, Siziopikou KP, Naidoo T, Rane S, Salgado R, Pinder SE, Grigoriadis A. Computational pathology in cancer diagnosis, prognosis, and prediction - present day and prospects. J Pathol 2023; 260:551-563. [PMID: 37580849 PMCID: PMC10785705 DOI: 10.1002/path.6163] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 06/12/2023] [Accepted: 06/17/2023] [Indexed: 08/16/2023]
Abstract
Computational pathology refers to applying deep learning techniques and algorithms to analyse and interpret histopathology images. Advances in artificial intelligence (AI) have led to an explosion in innovation in computational pathology, ranging from the prospect of automation of routine diagnostic tasks to the discovery of new prognostic and predictive biomarkers from tissue morphology. Despite the promising potential of computational pathology, its integration in clinical settings has been limited by a range of obstacles including operational, technical, regulatory, ethical, financial, and cultural challenges. Here, we focus on the pathologists' perspective of computational pathology: we map its current translational research landscape, evaluate its clinical utility, and address the more common challenges slowing clinical adoption and implementation. We conclude by describing contemporary approaches to drive forward these techniques. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.
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Affiliation(s)
- Gregory Verghese
- School of Cancer & Pharmaceutical Sciences, Faculty of Life Sciences and MedicineKing's College LondonLondonUK
- The Breast Cancer Now Research Unit, School of Cancer and Pharmaceutical Sciences, Faculty of Life Sciences and MedicineKing's College LondonLondonUK
| | - Jochen K Lennerz
- Center for Integrated Diagnostics, Department of PathologyMassachusetts General Hospital/Harvard Medical SchoolBostonMAUSA
| | - Danny Ruta
- Guy's CancerGuy's and St Thomas’ NHS Foundation TrustLondonUK
| | - Wen Ng
- Department of Cellular PathologyGuy's and St Thomas NHS Foundation TrustLondonUK
| | - Selvam Thavaraj
- Head & Neck PathologyGuy's and St Thomas NHS Foundation TrustLondonUK
- Centre for Clinical, Oral & Translational Science, Faculty of Dentistry, Oral & Craniofacial SciencesKing's College LondonLondonUK
| | - Kalliopi P Siziopikou
- Department of Pathology, Section of Breast PathologyNorthwestern University Feinberg School of MedicineChicagoILUSA
| | - Threnesan Naidoo
- Department of Laboratory Medicine and Pathology, Walter Sisulu University, Mthatha, Eastern CapeSouth Africa and Africa Health Research InstituteDurbanSouth Africa
| | - Swapnil Rane
- Department of PathologyTata Memorial Centre – ACTRECHBNINavi MumbaiIndia
- Computational Pathology, AI & Imaging LaboratoryTata Memorial Centre – ACTREC, HBNINavi MumbaiIndia
| | - Roberto Salgado
- Department of PathologyGZA–ZNA ZiekenhuizenAntwerpBelgium
- Division of ResearchPeter MacCallum Cancer CentreMelbourneVictoriaAustralia
| | - Sarah E Pinder
- School of Cancer & Pharmaceutical Sciences, Faculty of Life Sciences and MedicineKing's College LondonLondonUK
- Department of Cellular PathologyGuy's and St Thomas NHS Foundation TrustLondonUK
| | - Anita Grigoriadis
- School of Cancer & Pharmaceutical Sciences, Faculty of Life Sciences and MedicineKing's College LondonLondonUK
- The Breast Cancer Now Research Unit, School of Cancer and Pharmaceutical Sciences, Faculty of Life Sciences and MedicineKing's College LondonLondonUK
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Wang R, Xiong K, Wang Z, Wu D, Hu B, Ruan J, Sun C, Ma D, Li L, Liao S. Immunodiagnosis - the promise of personalized immunotherapy. Front Immunol 2023; 14:1216901. [PMID: 37520576 PMCID: PMC10372420 DOI: 10.3389/fimmu.2023.1216901] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Accepted: 06/21/2023] [Indexed: 08/01/2023] Open
Abstract
Immunotherapy showed remarkable efficacy in several cancer types. However, the majority of patients do not benefit from immunotherapy. Evaluating tumor heterogeneity and immune status before treatment is key to identifying patients that are more likely to respond to immunotherapy. Demographic characteristics (such as sex, age, and race), immune status, and specific biomarkers all contribute to response to immunotherapy. A comprehensive immunodiagnostic model integrating all these three dimensions by artificial intelligence would provide valuable information for predicting treatment response. Here, we coined the term "immunodiagnosis" to describe the blueprint of the immunodiagnostic model. We illustrated the features that should be included in immunodiagnostic model and the strategy of constructing the immunodiagnostic model. Lastly, we discussed the incorporation of this immunodiagnosis model in clinical practice in hopes of improving the prognosis of tumor immunotherapy.
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Affiliation(s)
- Renjie Wang
- Department of Obstetrics and Gynecology, Cancer Biology Research Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Kairong Xiong
- Department of Obstetrics and Gynecology, Cancer Biology Research Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Zhimin Wang
- Division of Endocrinology and Metabolic Diseases, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Di Wu
- Department of Obstetrics and Gynecology, Cancer Biology Research Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Bai Hu
- Department of Obstetrics and Gynecology, Cancer Biology Research Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jinghan Ruan
- Department of Obstetrics and Gynecology, Cancer Biology Research Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Chaoyang Sun
- Department of Obstetrics and Gynecology, Cancer Biology Research Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Ding Ma
- Department of Obstetrics and Gynecology, Cancer Biology Research Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Li Li
- Department of Obstetrics and Gynecology, Cancer Biology Research Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Shujie Liao
- Department of Obstetrics and Gynecology, Cancer Biology Research Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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Exarchos KP, Gkrepi G, Kostikas K, Gogali A. Recent Advances of Artificial Intelligence Applications in Interstitial Lung Diseases. Diagnostics (Basel) 2023; 13:2303. [PMID: 37443696 DOI: 10.3390/diagnostics13132303] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 07/02/2023] [Accepted: 07/05/2023] [Indexed: 07/15/2023] Open
Abstract
Interstitial lung diseases (ILDs) comprise a rather heterogeneous group of diseases varying in pathophysiology, presentation, epidemiology, diagnosis, treatment and prognosis. Even though they have been recognized for several years, there are still areas of research debate. In the majority of ILDs, imaging modalities and especially high-resolution Computed Tomography (CT) scans have been the cornerstone in patient diagnostic approach and follow-up. The intricate nature of ILDs and the accompanying data have led to an increasing adoption of artificial intelligence (AI) techniques, primarily on imaging data but also in genetic data, spirometry and lung diffusion, among others. In this literature review, we describe the most prominent applications of AI in ILDs presented approximately within the last five years. We roughly stratify these studies in three categories, namely: (i) screening, (ii) diagnosis and classification, (iii) prognosis.
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Affiliation(s)
- Konstantinos P Exarchos
- Respiratory Medicine Department, University of Ioannina School of Medicine, 45110 Ioannina, Greece
| | - Georgia Gkrepi
- Respiratory Medicine Department, University of Ioannina School of Medicine, 45110 Ioannina, Greece
| | - Konstantinos Kostikas
- Respiratory Medicine Department, University of Ioannina School of Medicine, 45110 Ioannina, Greece
| | - Athena Gogali
- Respiratory Medicine Department, University of Ioannina School of Medicine, 45110 Ioannina, Greece
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Computational Pathology for Breast Cancer and Gynecologic Cancer. Cancers (Basel) 2023; 15:cancers15030942. [PMID: 36765900 PMCID: PMC9913809 DOI: 10.3390/cancers15030942] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Revised: 01/31/2023] [Accepted: 02/01/2023] [Indexed: 02/05/2023] Open
Abstract
Advances in computation pathology have continued at an impressive pace in recent years [...].
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Juhas M. Artificial Intelligence in Microbiology. BRIEF LESSONS IN MICROBIOLOGY 2023:93-109. [DOI: 10.1007/978-3-031-29544-7_8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
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Zheng Q, Yang R, Ni X, Yang S, Xiong L, Yan D, Xia L, Yuan J, Wang J, Jiao P, Wu J, Hao Y, Wang J, Guo L, Jiang Z, Wang L, Chen Z, Liu X. Accurate Diagnosis and Survival Prediction of Bladder Cancer Using Deep Learning on Histological Slides. Cancers (Basel) 2022; 14:cancers14235807. [PMID: 36497289 PMCID: PMC9737237 DOI: 10.3390/cancers14235807] [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: 10/26/2022] [Revised: 11/19/2022] [Accepted: 11/22/2022] [Indexed: 11/29/2022] Open
Abstract
(1) Background: Early diagnosis and treatment are essential to reduce the mortality rate of bladder cancer (BLCA). We aimed to develop deep learning (DL)-based weakly supervised models for the diagnosis of BLCA and prediction of overall survival (OS) in muscle-invasive bladder cancer (MIBC) patients using whole slide digitized histological images (WSIs). (2) Methods: Diagnostic and prognostic models were developed using 926 WSIs of 412 BLCA patients from The Cancer Genome Atlas cohort. We collected 250 WSIs of 150 BLCA patients from the Renmin Hospital of Wuhan University cohort for external validation of the models. Two DL models were developed: a BLCA diagnostic model (named BlcaMIL) and an MIBC prognostic model (named MibcMLP). (3) Results: The BlcaMIL model identified BLCA with accuracy 0.987 in the external validation set, comparable to that of expert uropathologists and outperforming a junior pathologist. The C-index values for the MibcMLP model on the internal and external validation sets were 0.631 and 0.622, respectively. The risk score predicted by MibcMLP was a strong predictor independent of existing clinical or histopathologic indicators, as demonstrated by univariate Cox (HR = 2.390, p < 0.0001) and multivariate Cox (HR = 2.414, p < 0.0001) analyses. The interpretability of DL models can help in the analysis of critical regions associated with tumors to enrich the information obtained from WSIs. Furthermore, the expression of six genes (ANAPC7, MAPKAPK5, COX19, LINC01106, AL161431.1 and MYO16-AS1) was significantly associated with MibcMLP-predicted risk scores, revealing possible potential biological correlations. (4) Conclusions: Our study developed DL models for accurately diagnosing BLCA and predicting OS in MIBC patients, which will help promote the precise pathological diagnosis of BLCA and risk stratification of MIBC to improve clinical treatment decisions.
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Affiliation(s)
- Qingyuan Zheng
- Department of Urology, Renmin Hospital of Wuhan University, Wuhan 430060, China
- Institute of Urologic Disease, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Rui Yang
- Department of Urology, Renmin Hospital of Wuhan University, Wuhan 430060, China
- Institute of Urologic Disease, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Xinmiao Ni
- Department of Urology, Renmin Hospital of Wuhan University, Wuhan 430060, China
- Institute of Urologic Disease, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Song Yang
- Department of Urology, Renmin Hospital of Wuhan University, Wuhan 430060, China
- Institute of Urologic Disease, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Lin Xiong
- Department of Pathology, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Dandan Yan
- Department of Pathology, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Lingli Xia
- Department of Pathology, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Jingping Yuan
- Department of Pathology, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Jingsong Wang
- Department of Urology, Renmin Hospital of Wuhan University, Wuhan 430060, China
- Institute of Urologic Disease, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Panpan Jiao
- Department of Urology, Renmin Hospital of Wuhan University, Wuhan 430060, China
- Institute of Urologic Disease, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Jiejun Wu
- Department of Urology, Renmin Hospital of Wuhan University, Wuhan 430060, China
- Institute of Urologic Disease, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Yiqun Hao
- Division of Nephrology, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Jianguo Wang
- Department of Hepatic-Biliary-Pancreatic Surgery, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Liantao Guo
- Department of Breast and Thyroid Surgery, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Zhengyu Jiang
- Department of Urology, Renmin Hospital of Wuhan University, Wuhan 430060, China
- Institute of Urologic Disease, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Lei Wang
- Department of Urology, Renmin Hospital of Wuhan University, Wuhan 430060, China
- Institute of Urologic Disease, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Zhiyuan Chen
- Department of Urology, Renmin Hospital of Wuhan University, Wuhan 430060, China
- Institute of Urologic Disease, Renmin Hospital of Wuhan University, Wuhan 430060, China
- Correspondence: (Z.C.); (X.L.)
| | - Xiuheng Liu
- Department of Urology, Renmin Hospital of Wuhan University, Wuhan 430060, China
- Institute of Urologic Disease, Renmin Hospital of Wuhan University, Wuhan 430060, China
- Correspondence: (Z.C.); (X.L.)
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