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Murali VP, Karunakaran V, Murali M, Lekshmi A, Kottarathil S, Deepika S, Saritha VN, Ramya AN, Raghu KG, Sujathan K, Maiti KK. A clinically feasible diagnostic spectro-histology built on SERS-nanotags for multiplex detection and grading of breast cancer biomarkers. Biosens Bioelectron 2023; 227:115177. [PMID: 36871528 DOI: 10.1016/j.bios.2023.115177] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Revised: 02/14/2023] [Accepted: 02/22/2023] [Indexed: 02/27/2023]
Abstract
Simultaneous detection of multiple biomarkers is always an obstacle in immunohistochemical (IHC) analysis. Herein, a straightforward spectroscopy-driven histopathologic approach has emerged as a paradigm of Raman-label (RL) nanoparticle probes for multiplex recognition of pertinent biomarkers in heterogeneous breast cancer. The nanoprobes are constructed by sequential incorporation of signature RL and target specific antibodies on gold nanoparticles, which are coined as Raman-Label surface enhanced Raman scattering (RL-SERS)-nanotags to evaluate simultaneous recognition of clinically relevant breast cancer biomarkers i.e., estrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor receptor2 (HER2). As a foot-step assessment, breast cancer cell lines having varied expression levels of the triple biomarkers are investigated. Subsequently, the optimized detection strategy using RL-SERS-nanotags is subjected to clinically confirmed, retrospective formalin-fixed paraffin embedded (FFPE) breast cancer tissue samples to fish out the quick response of singleplex, duplex as well as triplex biomarkers in a single tissue specimen by adopting a ratiometric signature RL-SERS analysis which enabled to minimize the false negative and positive results. Significantly, sensitivity and specificity of 95% and 92% for singleplex, 88% and 85% for duplex, and 75% and 67% for triplex biomarker has been achieved by assessing specific Raman fingerprints of the respective SERS-tags. Furthermore, a semi-quantitative evaluation of HER2 grading between 4+/2+/1+ tissue samples was also achieved by the Raman intensity profiling of the SERS-tag, which is fully in agreement with the expensive fluorescent in situ hybridization analysis. Additionally, the practical diagnostic applicability of RL-SERS-tags has been achieved by large area SERS imaging of areas covering 0.5-5 mm2 within 45 min. These findings unveil an accurate, inexpensive and multiplex diagnostic modality envisaging large-scale multi-centric clinical validation.
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Affiliation(s)
- Vishnu Priya Murali
- CSIR-National Institute for Interdisciplinary Science & Technology (NIIST), Chemical Sciences & Technology Division (CSTD), Organic Chemistry Section, Industrial Estate, Thiruvananthapuram, 695019, Kerala, India
| | - Varsha Karunakaran
- CSIR-National Institute for Interdisciplinary Science & Technology (NIIST), Chemical Sciences & Technology Division (CSTD), Organic Chemistry Section, Industrial Estate, Thiruvananthapuram, 695019, Kerala, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India
| | - Madhukrishnan Murali
- CSIR-National Institute for Interdisciplinary Science & Technology (NIIST), Chemical Sciences & Technology Division (CSTD), Organic Chemistry Section, Industrial Estate, Thiruvananthapuram, 695019, Kerala, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India
| | - Asha Lekshmi
- Regional Cancer Centre (RCC), Division of Cancer Research, Thiruvananthapuram, 695011, Kerala, India
| | - Shamna Kottarathil
- CSIR-National Institute for Interdisciplinary Science & Technology (NIIST), Chemical Sciences & Technology Division (CSTD), Organic Chemistry Section, Industrial Estate, Thiruvananthapuram, 695019, Kerala, India
| | - Selvakumar Deepika
- CSIR-National Institute for Interdisciplinary Science & Technology (NIIST), Chemical Sciences & Technology Division (CSTD), Organic Chemistry Section, Industrial Estate, Thiruvananthapuram, 695019, Kerala, India
| | - Valliamma N Saritha
- Regional Cancer Centre (RCC), Division of Cancer Research, Thiruvananthapuram, 695011, Kerala, India
| | - Adukkadan N Ramya
- CSIR-National Institute for Interdisciplinary Science & Technology (NIIST), Chemical Sciences & Technology Division (CSTD), Organic Chemistry Section, Industrial Estate, Thiruvananthapuram, 695019, Kerala, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India
| | - Kozhiparambil G Raghu
- CSIR-National Institute for Interdisciplinary Science & Technology (NIIST), Agro-Processing and Technology Division (APTD), Industrial Estate, Thiruvananthapuram, 695019, Kerala, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India
| | - Kunjuraman Sujathan
- Regional Cancer Centre (RCC), Division of Cancer Research, Thiruvananthapuram, 695011, Kerala, India.
| | - Kaustabh Kumar Maiti
- CSIR-National Institute for Interdisciplinary Science & Technology (NIIST), Chemical Sciences & Technology Division (CSTD), Organic Chemistry Section, Industrial Estate, Thiruvananthapuram, 695019, Kerala, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India.
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Hossain MS, Syeed MMM, Fatema K, Hossain MS, Uddin MF. Singular Nuclei Segmentation for Automatic HER2 Quantification Using CISH Whole Slide Images. Sensors (Basel) 2022; 22:7361. [PMID: 36236459 PMCID: PMC9571354 DOI: 10.3390/s22197361] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/03/2022] [Revised: 09/20/2022] [Accepted: 09/22/2022] [Indexed: 06/16/2023]
Abstract
Human epidermal growth factor receptor 2 (HER2) quantification is performed routinely for all breast cancer patients to determine their suitability for HER2-targeted therapy. Fluorescence in situ hybridization (FISH) and chromogenic in situ hybridization (CISH) are the US Food and Drug Administration (FDA) approved tests for HER2 quantification in which at least 20 cancer-affected singular nuclei are quantified for HER2 grading. CISH is more advantageous than FISH for cost, time and practical usability. In clinical practice, nuclei suitable for HER2 quantification are selected manually by pathologists which is time-consuming and laborious. Previously, a method was proposed for automatic HER2 quantification using a support vector machine (SVM) to detect suitable singular nuclei from CISH slides. However, the SVM-based method occasionally failed to detect singular nuclei resulting in inaccurate results. Therefore, it is necessary to develop a robust nuclei detection method for reliable automatic HER2 quantification. In this paper, we propose a robust U-net-based singular nuclei detection method with complementary color correction and deconvolution adapted for accurate HER2 grading using CISH whole slide images (WSIs). The efficacy of the proposed method was demonstrated for automatic HER2 quantification during a comparison with the SVM-based approach.
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Affiliation(s)
- Md Shakhawat Hossain
- Department of CS, American International University-Bangladesh, Dhaka 1229, Bangladesh
- RIoT Research Center, Independent University, Bangladesh, Dhaka 1229, Bangladesh
| | - M. M. Mahbubul Syeed
- RIoT Research Center, Independent University, Bangladesh, Dhaka 1229, Bangladesh
- Department of CSE, Independent University, Bangladesh, Dhaka 1229, Bangladesh
| | - Kaniz Fatema
- RIoT Research Center, Independent University, Bangladesh, Dhaka 1229, Bangladesh
- Department of CSE, Independent University, Bangladesh, Dhaka 1229, Bangladesh
| | - Md Sakir Hossain
- Department of CS, American International University-Bangladesh, Dhaka 1229, Bangladesh
| | - Mohammad Faisal Uddin
- RIoT Research Center, Independent University, Bangladesh, Dhaka 1229, Bangladesh
- Department of CSE, Independent University, Bangladesh, Dhaka 1229, Bangladesh
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