1
|
Tong Y, Hu Z, Wang H, Huang J, Zhan Y, Chai W, Deng Y, Yuan Y, Shen K, Wang Y, Chen X, Yu J. Anti-HER2 therapy response assessment for guiding treatment (de-)escalation in early HER2-positive breast cancer using a novel deep learning radiomics model. Eur Radiol 2024; 34:5477-5486. [PMID: 38329503 PMCID: PMC11255056 DOI: 10.1007/s00330-024-10609-7] [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] [Revised: 11/24/2023] [Accepted: 01/01/2024] [Indexed: 02/09/2024]
Abstract
OBJECTIVES Anti-HER2 targeted therapy significantly reduces risk of relapse in HER2 + breast cancer. New measures are needed for a precise risk stratification to guide (de-)escalation of anti-HER2 strategy. METHODS A total of 726 HER2 + cases who received no/single/dual anti-HER2 targeted therapies were split into three respective cohorts. A deep learning model (DeepTEPP) based on preoperative breast magnetic resonance (MR) was developed. Patients were scored and categorized into low-, moderate-, and high-risk groups. Recurrence-free survival (RFS) was compared in patients with different risk groups according to the anti-HER2 treatment they received, to validate the value of DeepTEPP in predicting treatment efficacy and guiding anti-HER2 strategy. RESULTS DeepTEPP was capable of risk stratification and guiding anti-HER2 treatment strategy: DeepTEPP-Low patients (60.5%) did not derive significant RFS benefit from trastuzumab (p = 0.144), proposing an anti-HER2 de-escalation. DeepTEPP-Moderate patients (19.8%) significantly benefited from trastuzumab (p = 0.048), but did not obtain additional improvements from pertuzumab (p = 0.125). DeepTEPP-High patients (19.7%) significantly benefited from dual HER2 blockade (p = 0.045), suggesting an anti-HER2 escalation. CONCLUSIONS DeepTEPP represents a pioneering MR-based deep learning model that enables the non-invasive prediction of adjuvant anti-HER2 effectiveness, thereby providing valuable guidance for anti-HER2 (de-)escalation strategies. DeepTEPP provides an important reference for choosing the appropriate individualized treatment in HER2 + breast cancer patients, warranting prospective validation. CLINICAL RELEVANCE STATEMENT We built an MR-based deep learning model DeepTEPP, which enables the non-invasive prediction of adjuvant anti-HER2 effectiveness, thus guiding anti-HER2 (de-)escalation strategies in early HER2-positive breast cancer patients. KEY POINTS • DeepTEPP is able to predict anti-HER2 effectiveness and to guide treatment (de-)escalation. • DeepTEPP demonstrated an impressive prognostic efficacy for recurrence-free survival and overall survival. • To our knowledge, this is one of the very few, also the largest study to test the efficacy of a deep learning model extracted from breast MR images on HER2-positive breast cancer survival and anti-HER2 therapy effectiveness prediction.
Collapse
Affiliation(s)
- Yiwei Tong
- Department of General Surgery, Comprehensive Breast Health Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, 197 Ruijin Er Road, Shanghai, 200025, China
| | - Zhaoyu Hu
- School of Information Science and Technology, Fudan University, No. 220, Handan Road, Yangpu District, Shanghai, 200433, China
| | - Haoyu Wang
- Department of General Surgery, Comprehensive Breast Health Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, 197 Ruijin Er Road, Shanghai, 200025, China
| | - Jiahui Huang
- Department of General Surgery, Comprehensive Breast Health Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, 197 Ruijin Er Road, Shanghai, 200025, China
| | - Ying Zhan
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Weimin Chai
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Yinhui Deng
- School of Information Science and Technology, Fudan University, No. 220, Handan Road, Yangpu District, Shanghai, 200433, China
| | - Ying Yuan
- Department of Radiology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Kunwei Shen
- Department of General Surgery, Comprehensive Breast Health Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, 197 Ruijin Er Road, Shanghai, 200025, China
| | - Yuanyuan Wang
- School of Information Science and Technology, Fudan University, No. 220, Handan Road, Yangpu District, Shanghai, 200433, China
| | - Xiaosong Chen
- Department of General Surgery, Comprehensive Breast Health Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, 197 Ruijin Er Road, Shanghai, 200025, China.
| | - Jinhua Yu
- School of Information Science and Technology, Fudan University, No. 220, Handan Road, Yangpu District, Shanghai, 200433, China.
| |
Collapse
|
2
|
Sigauke RF, Sanford L, Maas ZL, Jones T, Stanley JT, Townsend HA, Allen MA, Dowell RD. Atlas of nascent RNA transcripts reveals enhancer to gene linkages. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.07.570626. [PMID: 38105978 PMCID: PMC10723487 DOI: 10.1101/2023.12.07.570626] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Gene transcription is controlled and modulated by regulatory regions, including enhancers and promoters. These regions are abundant in unstable, non-coding bidirectional transcription. Using nascent RNA transcription data across hundreds of human samples, we identified over 800,000 regions containing bidirectional transcription. We then identify highly correlated transcription between bidirectional and gene regions. The identified correlated pairs, a bidirectional region and a gene, are enriched for disease associated SNPs and often supported by independent 3D data. We present these resources as an SQL database which serves as a resource for future studies into gene regulation, enhancer associated RNAs, and transcription factors.
Collapse
Affiliation(s)
- Rutendo F. Sigauke
- BioFrontiers Institute, University of Colorado Boulder, 3415 Colorado Ave., UCB 596, Boulder, 80309, CO, USA
| | - Lynn Sanford
- BioFrontiers Institute, University of Colorado Boulder, 3415 Colorado Ave., UCB 596, Boulder, 80309, CO, USA
| | - Zachary L. Maas
- BioFrontiers Institute, University of Colorado Boulder, 3415 Colorado Ave., UCB 596, Boulder, 80309, CO, USA
- Computer Science, University of Colorado Boulder, 1111 Engineering Drive, UCB 430, Boulder, 80309, CO, USA
| | - Taylor Jones
- BioFrontiers Institute, University of Colorado Boulder, 3415 Colorado Ave., UCB 596, Boulder, 80309, CO, USA
| | - Jacob T. Stanley
- BioFrontiers Institute, University of Colorado Boulder, 3415 Colorado Ave., UCB 596, Boulder, 80309, CO, USA
| | - Hope A. Townsend
- BioFrontiers Institute, University of Colorado Boulder, 3415 Colorado Ave., UCB 596, Boulder, 80309, CO, USA
- Molecular, Cellular and Developmental Biology, University of Colorado Boulder, 1945 Colorado Ave, UCB 347, Boulder, 80309, CO, USA
| | - Mary A. Allen
- BioFrontiers Institute, University of Colorado Boulder, 3415 Colorado Ave., UCB 596, Boulder, 80309, CO, USA
| | - Robin D. Dowell
- BioFrontiers Institute, University of Colorado Boulder, 3415 Colorado Ave., UCB 596, Boulder, 80309, CO, USA
- Computer Science, University of Colorado Boulder, 1111 Engineering Drive, UCB 430, Boulder, 80309, CO, USA
- Molecular, Cellular and Developmental Biology, University of Colorado Boulder, 1945 Colorado Ave, UCB 347, Boulder, 80309, CO, USA
| |
Collapse
|
3
|
Zhang L, Piipponen M, Liu Z, Li D, Bian X, Niu G, Geara J, Toma MA, Sommar P, Xu Landén N. Human skin specific long noncoding RNA HOXC13-AS regulates epidermal differentiation by interfering with Golgi-ER retrograde transport. Cell Death Differ 2023; 30:1334-1348. [PMID: 36869179 PMCID: PMC10154349 DOI: 10.1038/s41418-023-01142-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Revised: 02/15/2023] [Accepted: 02/21/2023] [Indexed: 03/05/2023] Open
Abstract
After a skin injury, keratinocytes switch from a state of homeostasis to one of regeneration leading to the reconstruction of the epidermal barrier. The regulatory mechanism of gene expression underpinning this key switch during human skin wound healing is enigmatic. Long noncoding RNAs (lncRNAs) constitute a new horizon in the understanding of the regulatory programs encoded in the mammalian genome. By comparing the transcriptome of an acute human wound and skin from the same donor as well as keratinocytes isolated from these paired tissue samples, we generated a list of lncRNAs showing changed expression in keratinocytes during wound repair. Our study focused on HOXC13-AS, a recently evolved human lncRNA specifically expressed in epidermal keratinocytes, and we found that its expression was temporally downregulated during wound healing. In line with its enrichment in suprabasal keratinocytes, HOXC13-AS was found to be increasingly expressed during keratinocyte differentiation, but its expression was reduced by EGFR signaling. After HOXC13-AS knockdown or overexpression in human primary keratinocytes undergoing differentiation induced by cell suspension or calcium treatment and in organotypic epidermis, we found that HOXC13-AS promoted keratinocyte differentiation. Moreover, RNA pull-down assays followed by mass spectrometry and RNA immunoprecipitation analysis revealed that mechanistically HOXC13-AS sequestered the coat complex subunit alpha (COPA) protein and interfered with Golgi-to-endoplasmic reticulum (ER) molecular transport, resulting in ER stress and enhanced keratinocyte differentiation. In summary, we identified HOXC13-AS as a crucial regulator of human epidermal differentiation.
Collapse
Affiliation(s)
- Letian Zhang
- Dermatology and Venereology Division, Department of Medicine Solna, Center for Molecular Medicine, Karolinska Institutet, 17176, Stockholm, Sweden
| | - Minna Piipponen
- Dermatology and Venereology Division, Department of Medicine Solna, Center for Molecular Medicine, Karolinska Institutet, 17176, Stockholm, Sweden
| | - Zhuang Liu
- Dermatology and Venereology Division, Department of Medicine Solna, Center for Molecular Medicine, Karolinska Institutet, 17176, Stockholm, Sweden
| | - Dongqing Li
- Dermatology and Venereology Division, Department of Medicine Solna, Center for Molecular Medicine, Karolinska Institutet, 17176, Stockholm, Sweden.,Key Laboratory of Basic and Translational Research on Immune-Mediated Skin Diseases, Chinese Academy of Medical Sciences, Jiangsu Key Laboratory of Molecular Biology for Skin Diseases and STIs, Institute of Dermatology, Chinese Academy of Medical Sciences and Peking Union Medical College, Nanjing, China
| | - Xiaowei Bian
- Dermatology and Venereology Division, Department of Medicine Solna, Center for Molecular Medicine, Karolinska Institutet, 17176, Stockholm, Sweden
| | - Guanglin Niu
- Dermatology and Venereology Division, Department of Medicine Solna, Center for Molecular Medicine, Karolinska Institutet, 17176, Stockholm, Sweden
| | - Jennifer Geara
- Dermatology and Venereology Division, Department of Medicine Solna, Center for Molecular Medicine, Karolinska Institutet, 17176, Stockholm, Sweden
| | - Maria A Toma
- Dermatology and Venereology Division, Department of Medicine Solna, Center for Molecular Medicine, Karolinska Institutet, 17176, Stockholm, Sweden
| | - Pehr Sommar
- Department of Plastic and Reconstructive Surgery, Karolinska University Hospital, Stockholm, Sweden
| | - Ning Xu Landén
- Dermatology and Venereology Division, Department of Medicine Solna, Center for Molecular Medicine, Karolinska Institutet, 17176, Stockholm, Sweden. .,Ming Wai Lau Centre for Reparative Medicine, Stockholm Node, Karolinska Institutet, 17176, Stockholm, Sweden.
| |
Collapse
|
4
|
Dixon PH, Levine AP, Cebola I, Chan MMY, Amin AS, Aich A, Mozere M, Maude H, Mitchell AL, Zhang J, Chambers J, Syngelaki A, Donnelly J, Cooley S, Geary M, Nicolaides K, Thorsell M, Hague WM, Estiu MC, Marschall HU, Gale DP, Williamson C. GWAS meta-analysis of intrahepatic cholestasis of pregnancy implicates multiple hepatic genes and regulatory elements. Nat Commun 2022; 13:4840. [PMID: 35977952 PMCID: PMC9385867 DOI: 10.1038/s41467-022-29931-z] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Accepted: 04/08/2022] [Indexed: 12/15/2022] Open
Abstract
Intrahepatic cholestasis of pregnancy (ICP) is a pregnancy-specific liver disorder affecting 0.5-2% of pregnancies. The majority of cases present in the third trimester with pruritus, elevated serum bile acids and abnormal serum liver tests. ICP is associated with an increased risk of adverse outcomes, including spontaneous preterm birth and stillbirth. Whilst rare mutations affecting hepatobiliary transporters contribute to the aetiology of ICP, the role of common genetic variation in ICP has not been systematically characterised to date. Here, we perform genome-wide association studies (GWAS) and meta-analyses for ICP across three studies including 1138 cases and 153,642 controls. Eleven loci achieve genome-wide significance and have been further investigated and fine-mapped using functional genomics approaches. Our results pinpoint common sequence variation in liver-enriched genes and liver-specific cis-regulatory elements as contributing mechanisms to ICP susceptibility.
Collapse
Affiliation(s)
- Peter H Dixon
- Department of Women and Children's Health, School of Life Course Sciences, King's College London, London, UK
| | - Adam P Levine
- Department of Renal Medicine, University College London, London, UK
- Research Department of Pathology, University College London, London, UK
| | - Inês Cebola
- Section of Genetics and Genomics, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - Melanie M Y Chan
- Department of Renal Medicine, University College London, London, UK
| | - Aliya S Amin
- Department of Women and Children's Health, School of Life Course Sciences, King's College London, London, UK
| | - Anshul Aich
- Department of Renal Medicine, University College London, London, UK
| | - Monika Mozere
- Department of Renal Medicine, University College London, London, UK
| | - Hannah Maude
- Section of Genetics and Genomics, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - Alice L Mitchell
- Department of Women and Children's Health, School of Life Course Sciences, King's College London, London, UK
| | - Jun Zhang
- Department of Renal Medicine, University College London, London, UK
- Division of Nephrology, Department of Medicine, Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Jenny Chambers
- ICP Support, 69 Mere Green Road, Sutton Coldfield, UK
- Women's Health Research Centre, Imperial College London, London, UK
| | - Argyro Syngelaki
- Harris Birthright Research Centre for Fetal Medicine, King's College Hospital, London, UK
| | | | | | | | - Kypros Nicolaides
- Harris Birthright Research Centre for Fetal Medicine, King's College Hospital, London, UK
| | | | - William M Hague
- Robinson Research Institute, The University of Adelaide, Adelaide, SA, Australia
| | | | - Hanns-Ulrich Marschall
- Department of Molecular and Clinical Medicine/Wallenberg Laboratory, University of Gothenburg, Gothenburg, Sweden
| | - Daniel P Gale
- Department of Renal Medicine, University College London, London, UK
| | - Catherine Williamson
- Department of Women and Children's Health, School of Life Course Sciences, King's College London, London, UK.
| |
Collapse
|
5
|
Kyritsis KA, Ouzounis CA, Angelis L, Vizirianakis I. Sequence variation, common tissue expression patterns and learning models: a genome-wide survey of vertebrate ribosomal proteins. NAR Genom Bioinform 2020; 2:lqaa088. [PMID: 33575632 PMCID: PMC7671327 DOI: 10.1093/nargab/lqaa088] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Revised: 10/07/2020] [Accepted: 10/14/2020] [Indexed: 12/14/2022] Open
Abstract
Ribosomal genes produce the constituents of the ribosome, one of the most conserved subcellular structures of all cells, from bacteria to eukaryotes, including animals. There are notions that some protein-coding ribosomal genes vary in their roles across species, particularly vertebrates, through the involvement of some in a number of genetic diseases. Based on extensive sequence comparisons and systematic curation, we establish a reference set for ribosomal proteins (RPs) in eleven vertebrate species and quantify their sequence conservation levels. Moreover, we correlate their coordinated gene expression patterns within up to 33 tissues and assess the exceptional role of paralogs in tissue specificity. Importantly, our analysis supported by the development and use of machine learning models strongly proposes that the variation in the observed tissue-specific gene expression of RPs is rather species-related and not due to tissue-based evolutionary processes. The data obtained suggest that RPs exhibit a complex relationship between their structure and function that broadly maintains a consistent expression landscape across tissues, while most of the variation arises from species idiosyncrasies. The latter may be due to evolutionary change and adaptation, rather than functional constraints at the tissue level throughout the vertebrate lineage.
Collapse
Affiliation(s)
- Konstantinos A Kyritsis
- Laboratory of Pharmacology, School of Pharmacy, Aristotle University of Thessaloniki, GR-54124 Thessalonica, Greece
- Biological Computation & Process Laboratory, Chemical Process & Energy Resources Institute, Centre for Research & Technology Hellas, GR-57001 Thessalonica, Greece
| | - Christos A Ouzounis
- Biological Computation & Process Laboratory, Chemical Process & Energy Resources Institute, Centre for Research & Technology Hellas, GR-57001 Thessalonica, Greece
- Department of Informatics, Aristotle University of Thessaloniki, GR-54124 Thessalonica, Greece
| | - Lefteris Angelis
- Department of Informatics, Aristotle University of Thessaloniki, GR-54124 Thessalonica, Greece
| | - Ioannis S Vizirianakis
- Laboratory of Pharmacology, School of Pharmacy, Aristotle University of Thessaloniki, GR-54124 Thessalonica, Greece
- FunPATH (Functional Proteomics and Systems Biology Research Group at AUTH) Research Group, KEDEK—Aristotle University of Thessaloniki, Balkan Center, GR-57001 Thessalonica, Greece
- Department of Life and Health Sciences, University of Nicosia, CY-1700 Nicosia, Cyprus
| |
Collapse
|