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Amanzholova A, Coşkun A. Enhancing cancer stage prediction through hybrid deep neural networks: a comparative study. Front Big Data 2024; 7:1359703. [PMID: 38586474 PMCID: PMC10995364 DOI: 10.3389/fdata.2024.1359703] [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: 12/22/2023] [Accepted: 02/20/2024] [Indexed: 04/09/2024] Open
Abstract
Efficiently detecting and treating cancer at an early stage is crucial to improve the overall treatment process and mitigate the risk of disease progression. In the realm of research, the utilization of artificial intelligence technologies holds significant promise for enhancing advanced cancer diagnosis. Nonetheless, a notable hurdle arises when striving for precise cancer-stage diagnoses through the analysis of gene sets. Issues such as limited sample volumes, data dispersion, overfitting, and the use of linear classifiers with simple parameters hinder prediction performance. This study introduces an innovative approach for predicting early and late-stage cancers by integrating hybrid deep neural networks. A deep neural network classifier, developed using the open-source TensorFlow library and Keras network, incorporates a novel method that combines genetic algorithms, Extreme Learning Machines (ELM), and Deep Belief Networks (DBN). Specifically, two evolutionary techniques, DBN-ELM-BP and DBN-ELM-ELM, are proposed and evaluated using data from The Cancer Genome Atlas (TCGA), encompassing mRNA expression, miRNA levels, DNA methylation, and clinical information. The models demonstrate outstanding prediction accuracy (89.35%-98.75%) in distinguishing between early- and late-stage cancers. Comparative analysis against existing methods in the literature using the same cancer dataset reveals the superiority of the proposed hybrid method, highlighting its enhanced accuracy in cancer stage prediction.
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Affiliation(s)
- Alina Amanzholova
- Graduate School of Natural and Applied Sciences, Department of Computer Engineering, Gazi University, Ankara, Türkiye
- Khoja Akhmet Yassawi International Kazakh-Turkish University, Faculty of Engineering, Department of Computer Engineering, Turkistan, Kazakhstan
| | - Aysun Coşkun
- Department of Computer Engineering, Faculty of Technology, Gazi University, Ankara, Türkiye
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Gonçalves-Reis M, Proença D, Frazão LP, Neto JL, Silva S, Pinto-Marques H, Pereira-Leal JB, Cardoso J. Analytical validation and algorithm improvement of HepatoPredict kit to assess hepatocellular carcinoma prognosis before a liver transplantation. Pract Lab Med 2024; 39:e00365. [PMID: 38371895 PMCID: PMC10869278 DOI: 10.1016/j.plabm.2024.e00365] [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: 08/01/2023] [Revised: 01/22/2024] [Accepted: 01/23/2024] [Indexed: 02/20/2024] Open
Abstract
Objectives To verify the analytical performance of the HepatoPredict kit, a novel tool developed to stratify Hepatocellular Carcinoma (HCC) patients according to their risk of relapse after a Liver Transplantation (LT). Methods The HepatoPredict tool combines clinical variables and a gene expression signature in an ensemble of machine-learning algorithms to forecast the benefit of a LT in HCC patients. To ensure the accuracy and reliability of this method, extensive analytical validation was conducted to verify its specificity and robustness. The experiments were designed following the guidelines for multi-target genomic assays such as ISO201395-2019, MIQE, CLSI-MM16, CLSI-MM17, and CLSI-EP17-A. The validation process included reproducibility between operators and between RNA extractions and RT-qPCR runs, and interference of input RNA levels or varying reagent levels. A recently retrained version of the HepatoPredict algorithms was also tested. Results The validation process demonstrated that the HepatoPredict kit met the required standards for robustness (p > 0.05), analytical specificity (inclusivity of 95 %), and sensitivity (LoB, LoD, linear range, and amplification efficiency between 90 and 110 %). The operator, equipment, input RNA, and reagents used had no significant effect on the HepatoPredict results. Additionally, the testing of a recently retrained version of the HepatoPredict algorithm, showed that this new version further improved the accuracy of the kit and performed better than existing clinical criteria in accurately identifying HCC patients who are more likely to benefit LT. Conclusions Even with the introduced variations in molecular and clinical variables, the HepatoPredict kit's prognostic information remains consistent. It can accurately identify HCC patients who are more likely to benefit from a LT. Its robust performance also confirms that it can be easily integrated into standard diagnostic laboratories.
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Affiliation(s)
| | | | | | - João L. Neto
- Ophiomics – Precision Medicine, Lisbon, Portugal
| | - Sílvia Silva
- Hepato-Biliary-Pancreatic and Transplantation Centre, Curry Cabral Hospital, Centro Hospitalar Universitário de Lisboa Central, Lisbon, Portugal
| | - Hugo Pinto-Marques
- Hepato-Biliary-Pancreatic and Transplantation Centre, Curry Cabral Hospital, Centro Hospitalar Universitário de Lisboa Central, Lisbon, Portugal
- Chronic Diseases Research Center (CEDOC), NOVA Medical School, Universidade NOVA de Lisboa (NMS/UNL), Lisbon, Portugal
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Neves Rebello Alves L, Dummer Meira D, Poppe Merigueti L, Correia Casotti M, do Prado Ventorim D, Ferreira Figueiredo Almeida J, Pereira de Sousa V, Cindra Sant'Ana M, Gonçalves Coutinho da Cruz R, Santos Louro L, Mendonça Santana G, Erik Santos Louro T, Evangelista Salazar R, Ribeiro Campos da Silva D, Stefani Siqueira Zetum A, Silva Dos Reis Trabach R, Imbroisi Valle Errera F, de Paula F, de Vargas Wolfgramm Dos Santos E, Fagundes de Carvalho E, Drumond Louro I. Biomarkers in Breast Cancer: An Old Story with a New End. Genes (Basel) 2023; 14:1364. [PMID: 37510269 PMCID: PMC10378988 DOI: 10.3390/genes14071364] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Revised: 06/22/2023] [Accepted: 06/26/2023] [Indexed: 07/30/2023] Open
Abstract
Breast cancer is the second most frequent cancer in the world. It is a heterogeneous disease and the leading cause of cancer mortality in women. Advances in molecular technologies allowed for the identification of new and more specifics biomarkers for breast cancer diagnosis, prognosis, and risk prediction, enabling personalized treatments, improving therapy, and preventing overtreatment, undertreatment, and incorrect treatment. Several breast cancer biomarkers have been identified and, along with traditional biomarkers, they can assist physicians throughout treatment plan and increase therapy success. Despite the need of more data to improve specificity and determine the real clinical utility of some biomarkers, others are already established and can be used as a guide to make treatment decisions. In this review, we summarize the available traditional, novel, and potential biomarkers while also including gene expression profiles, breast cancer single-cell and polyploid giant cancer cells. We hope to help physicians understand tumor specific characteristics and support decision-making in patient-personalized clinical management, consequently improving treatment outcome.
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Affiliation(s)
- Lyvia Neves Rebello Alves
- Núcleo de Genética Humana e Molecular, Departamento de Ciências Biológicas, Universidade Federal do Espírito Santo (UFES), Vitória 29075-910, ES, Brazil
- Programa de Pós-Graduação em Biotecnologia, Universidade Federal do Espírito Santo, Vitória 29047-105, ES, Brazil
| | - Débora Dummer Meira
- Núcleo de Genética Humana e Molecular, Departamento de Ciências Biológicas, Universidade Federal do Espírito Santo (UFES), Vitória 29075-910, ES, Brazil
- Programa de Pós-Graduação em Biotecnologia, Universidade Federal do Espírito Santo, Vitória 29047-105, ES, Brazil
| | - Luiza Poppe Merigueti
- Núcleo de Genética Humana e Molecular, Departamento de Ciências Biológicas, Universidade Federal do Espírito Santo (UFES), Vitória 29075-910, ES, Brazil
| | - Matheus Correia Casotti
- Núcleo de Genética Humana e Molecular, Departamento de Ciências Biológicas, Universidade Federal do Espírito Santo (UFES), Vitória 29075-910, ES, Brazil
- Programa de Pós-Graduação em Biotecnologia, Universidade Federal do Espírito Santo, Vitória 29047-105, ES, Brazil
| | - Diego do Prado Ventorim
- Instituto Federal de Educação, Ciência e Tecnologia do Espírito Santo (Ifes), Cariacica 29150-410, ES, Brazil
| | - Jucimara Ferreira Figueiredo Almeida
- Núcleo de Genética Humana e Molecular, Departamento de Ciências Biológicas, Universidade Federal do Espírito Santo (UFES), Vitória 29075-910, ES, Brazil
| | - Valdemir Pereira de Sousa
- Núcleo de Genética Humana e Molecular, Departamento de Ciências Biológicas, Universidade Federal do Espírito Santo (UFES), Vitória 29075-910, ES, Brazil
- Programa de Pós-Graduação em Biotecnologia, Universidade Federal do Espírito Santo, Vitória 29047-105, ES, Brazil
| | - Marllon Cindra Sant'Ana
- Núcleo de Genética Humana e Molecular, Departamento de Ciências Biológicas, Universidade Federal do Espírito Santo (UFES), Vitória 29075-910, ES, Brazil
| | - Rahna Gonçalves Coutinho da Cruz
- Núcleo de Genética Humana e Molecular, Departamento de Ciências Biológicas, Universidade Federal do Espírito Santo (UFES), Vitória 29075-910, ES, Brazil
| | - Luana Santos Louro
- Centro de Ciências da Saúde, Curso de Medicina, Universidade Federal do Espírito Santo (UFES), Vitória 29090-040, ES, Brazil
| | - Gabriel Mendonça Santana
- Centro de Ciências da Saúde, Curso de Medicina, Universidade Federal do Espírito Santo (UFES), Vitória 29090-040, ES, Brazil
| | - Thomas Erik Santos Louro
- Escola Superior de Ciências da Santa Casa de Misericórdia de Vitória (EMESCAM), Vitória 29027-502, ES, Brazil
| | - Rhana Evangelista Salazar
- Núcleo de Genética Humana e Molecular, Departamento de Ciências Biológicas, Universidade Federal do Espírito Santo (UFES), Vitória 29075-910, ES, Brazil
- Programa de Pós-Graduação em Biotecnologia, Universidade Federal do Espírito Santo, Vitória 29047-105, ES, Brazil
| | - Danielle Ribeiro Campos da Silva
- Núcleo de Genética Humana e Molecular, Departamento de Ciências Biológicas, Universidade Federal do Espírito Santo (UFES), Vitória 29075-910, ES, Brazil
- Programa de Pós-Graduação em Biotecnologia, Universidade Federal do Espírito Santo, Vitória 29047-105, ES, Brazil
| | - Aléxia Stefani Siqueira Zetum
- Núcleo de Genética Humana e Molecular, Departamento de Ciências Biológicas, Universidade Federal do Espírito Santo (UFES), Vitória 29075-910, ES, Brazil
- Programa de Pós-Graduação em Biotecnologia, Universidade Federal do Espírito Santo, Vitória 29047-105, ES, Brazil
| | - Raquel Silva Dos Reis Trabach
- Núcleo de Genética Humana e Molecular, Departamento de Ciências Biológicas, Universidade Federal do Espírito Santo (UFES), Vitória 29075-910, ES, Brazil
| | - Flávia Imbroisi Valle Errera
- Núcleo de Genética Humana e Molecular, Departamento de Ciências Biológicas, Universidade Federal do Espírito Santo (UFES), Vitória 29075-910, ES, Brazil
- Programa de Pós-Graduação em Biotecnologia, Universidade Federal do Espírito Santo, Vitória 29047-105, ES, Brazil
| | - Flávia de Paula
- Núcleo de Genética Humana e Molecular, Departamento de Ciências Biológicas, Universidade Federal do Espírito Santo (UFES), Vitória 29075-910, ES, Brazil
- Programa de Pós-Graduação em Biotecnologia, Universidade Federal do Espírito Santo, Vitória 29047-105, ES, Brazil
| | - Eldamária de Vargas Wolfgramm Dos Santos
- Núcleo de Genética Humana e Molecular, Departamento de Ciências Biológicas, Universidade Federal do Espírito Santo (UFES), Vitória 29075-910, ES, Brazil
- Programa de Pós-Graduação em Biotecnologia, Universidade Federal do Espírito Santo, Vitória 29047-105, ES, Brazil
| | - Elizeu Fagundes de Carvalho
- Instituto de Biologia Roberto Alcântara Gomes (IBRAG), Universidade do Estado do Rio de Janeiro (UERJ), Rio de Janeiro 20551-030, RJ, Brazil
| | - Iúri Drumond Louro
- Núcleo de Genética Humana e Molecular, Departamento de Ciências Biológicas, Universidade Federal do Espírito Santo (UFES), Vitória 29075-910, ES, Brazil
- Programa de Pós-Graduação em Biotecnologia, Universidade Federal do Espírito Santo, Vitória 29047-105, ES, Brazil
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Mirza Z, Ansari MS, Iqbal MS, Ahmad N, Alganmi N, Banjar H, Al-Qahtani MH, Karim S. Identification of Novel Diagnostic and Prognostic Gene Signature Biomarkers for Breast Cancer Using Artificial Intelligence and Machine Learning Assisted Transcriptomics Analysis. Cancers (Basel) 2023; 15:3237. [PMID: 37370847 DOI: 10.3390/cancers15123237] [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: 05/15/2023] [Revised: 06/10/2023] [Accepted: 06/13/2023] [Indexed: 06/29/2023] Open
Abstract
BACKGROUND Breast cancer (BC) is one of the most common female cancers. Clinical and histopathological information is collectively used for diagnosis, but is often not precise. We applied machine learning (ML) methods to identify the valuable gene signature model based on differentially expressed genes (DEGs) for BC diagnosis and prognosis. METHODS A cohort of 701 samples from 11 GEO BC microarray datasets was used for the identification of significant DEGs. Seven ML methods, including RFECV-LR, RFECV-SVM, LR-L1, SVC-L1, RF, and Extra-Trees were applied for gene reduction and the construction of a diagnostic model for cancer classification. Kaplan-Meier survival analysis was performed for prognostic signature construction. The potential biomarkers were confirmed via qRT-PCR and validated by another set of ML methods including GBDT, XGBoost, AdaBoost, KNN, and MLP. RESULTS We identified 355 DEGs and predicted BC-associated pathways, including kinetochore metaphase signaling, PTEN, senescence, and phagosome-formation pathways. A hub of 28 DEGs and a novel diagnostic nine-gene signature (COL10A, S100P, ADAMTS5, WISP1, COMP, CXCL10, LYVE1, COL11A1, and INHBA) were identified using stringent filter conditions. Similarly, a novel prognostic model consisting of eight-gene signatures (CCNE2, NUSAP1, TPX2, S100P, ITM2A, LIFR, TNXA, and ZBTB16) was also identified using disease-free survival and overall survival analysis. Gene signatures were validated by another set of ML methods. Finally, qRT-PCR results confirmed the expression of the identified gene signatures in BC. CONCLUSION The ML approach helped construct novel diagnostic and prognostic models based on the expression profiling of BC. The identified nine-gene signature and eight-gene signatures showed excellent potential in BC diagnosis and prognosis, respectively.
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Affiliation(s)
- Zeenat Mirza
- King Fahd Medical Research Center, King Abdulaziz University, Jeddah 21589, Saudi Arabia
- Department of Medical Laboratory Science, Faculty of Applied Medical Sciences, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Md Shahid Ansari
- Department of Clinical Data Analytics, Max Super Speciality Hospital, Saket, New Delhi 110017, India
| | - Md Shahid Iqbal
- Department of Statistics and Computer Applications, Tilka Manjhi Bhagalpur University, Bhagalpur 812007, India
| | - Nesar Ahmad
- Department of Statistics and Computer Applications, Tilka Manjhi Bhagalpur University, Bhagalpur 812007, India
| | - Nofe Alganmi
- Computer Science Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
- Center of Excellence in Genomic Medicine Research, King Abdulaziz University, Jeddah 21589, Saudi Arabia
- Centre of Artificial Intelligence in Precision Medicines, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Haneen Banjar
- Computer Science Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
- Center of Excellence in Genomic Medicine Research, King Abdulaziz University, Jeddah 21589, Saudi Arabia
- Centre of Artificial Intelligence in Precision Medicines, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Mohammed H Al-Qahtani
- Department of Medical Laboratory Science, Faculty of Applied Medical Sciences, King Abdulaziz University, Jeddah 21589, Saudi Arabia
- Center of Excellence in Genomic Medicine Research, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Sajjad Karim
- Department of Medical Laboratory Science, Faculty of Applied Medical Sciences, King Abdulaziz University, Jeddah 21589, Saudi Arabia
- Center of Excellence in Genomic Medicine Research, King Abdulaziz University, Jeddah 21589, Saudi Arabia
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5
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Guttà C, Morhard C, Rehm M. Applying a GAN-based classifier to improve transcriptome-based prognostication in breast cancer. PLoS Comput Biol 2023; 19:e1011035. [PMID: 37011102 PMCID: PMC10101642 DOI: 10.1371/journal.pcbi.1011035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 04/13/2023] [Accepted: 03/17/2023] [Indexed: 04/05/2023] Open
Abstract
Established prognostic tests based on limited numbers of transcripts can identify high-risk breast cancer patients yet are approved only for individuals presenting with specific clinical features or disease characteristics. Deep learning algorithms could hold potential for stratifying patient cohorts based on full transcriptome data, yet the development of robust classifiers is hampered by the number of variables in omics datasets typically far exceeding the number of patients. To overcome this hurdle, we propose a classifier based on a data augmentation pipeline consisting of a Wasserstein generative adversarial network (GAN) with gradient penalty and an embedded auxiliary classifier to obtain a trained GAN discriminator (T-GAN-D). Applied to 1244 patients of the METABRIC breast cancer cohort, this classifier outperformed established breast cancer biomarkers in separating low- from high-risk patients (disease specific death, progression or relapse within 10 years from initial diagnosis). Importantly, the T-GAN-D also performed across independent, merged transcriptome datasets (METABRIC and TCGA-BRCA cohorts), and merging data improved overall patient stratification. In conclusion, the reiterative GAN-based training process allowed generating a robust classifier capable of stratifying low- vs high-risk patients based on full transcriptome data and across independent and heterogeneous breast cancer cohorts.
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Affiliation(s)
- Cristiano Guttà
- Institute of Cell Biology and Immunology, University of Stuttgart, Stuttgart, Germany
| | | | - Markus Rehm
- Institute of Cell Biology and Immunology, University of Stuttgart, Stuttgart, Germany
- Stuttgart Research Center Systems Biology, University of Stuttgart, Stuttgart, Germany
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Curigliano G, Cardoso F, Gnant M, Harbeck N, King J, Laenkholm AV, Penault-Llorca F, Prat A. PROCURE European consensus on breast cancer multigene signatures in early breast cancer management. NPJ Breast Cancer 2023; 9:8. [PMID: 36828834 PMCID: PMC9951144 DOI: 10.1038/s41523-023-00510-9] [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: 05/30/2022] [Accepted: 01/26/2023] [Indexed: 02/26/2023] Open
Abstract
Breast cancer multigene signatures (BCMS) have changed how patients with early-stage breast cancer (eBC) are managed, as they provide prognostic information and can be used to select patients who may avoid adjuvant chemotherapy. Clinical guidelines make recommendations on the use of BCMS; however, little is known on the current use of BCMS in clinical practice. We conduct a two-round Delphi survey to enquire about current use and perceived utility for specific patient profiles, and unmet needs of BCMS. Overall, 133 panellists experienced in breast cancer across 11 European countries have participated, most using BCMS either routinely (66.2%) or in selected cases (27.1%). Our results show that BCMS are mainly used to assess the risk of recurrence and to select patients for adjuvant chemotherapy; notably, no consensus has been reached on the lack of utility of BCMS for selecting the type of chemotherapy to administer. Also, there are discrepancies between the recommended and current use of BCMS in clinical practice, with use in certain patient profiles for which there is no supporting evidence. Our study suggests that physician education initiatives are needed to ensure the correct use and interpretation of BCMS to, ultimately, improve management of patients with eBC.
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Affiliation(s)
- Giuseppe Curigliano
- European Institute of Oncology, IRCCS, Milan, Italy. .,Department of Oncology and Hemato-Oncology, University of Milano, Milan, Italy.
| | - Fatima Cardoso
- grid.421010.60000 0004 0453 9636Breast Unit, Champalimaud Clinical Centre/Champalimaud Foundation, Lisbon, Portugal
| | - Michael Gnant
- grid.22937.3d0000 0000 9259 8492Comprehensive Cancer Center, Medical University of Vienna, Vienna, Austria
| | - Nadia Harbeck
- grid.411095.80000 0004 0477 2585Breast Center, LMU University Hospital, Munich, Germany
| | - Judy King
- grid.437485.90000 0001 0439 3380Royal Free Hospital NHS Foundation Trust, London, UK
| | | | - Frédérique Penault-Llorca
- grid.494717.80000000115480420Centre de Lutte Contre le Cancer Jean Perrin, Imagerie Moléculaire et Stratégies Théranostiques, Université Clermont Auvergne, UMR 1240 INSERM-UCA, Clermont Ferrand, France
| | - Aleix Prat
- grid.10403.360000000091771775Hospital Clínic de Barcelona, August Pi i Sunyer Biomedical Research Institute (IDIBAPS), University of Barcelona, Barcelona, Spain
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Martínez-Pérez C, Turnbull AK, Kay C, Dixon JM. Neoadjuvant endocrine therapy in postmenopausal women with HR+/HER2- breast cancer. Expert Rev Anticancer Ther 2023; 23:67-86. [PMID: 36633402 DOI: 10.1080/14737140.2023.2162043] [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: 10/14/2022] [Accepted: 12/20/2022] [Indexed: 01/13/2023]
Abstract
INTRODUCTION While endocrine therapy is the standard-of-care adjuvant treatment for hormone receptor-positive (HR+) breast cancers, there is also extensive evidence for the role of pre-operative (or neoadjuvant) endocrine therapy (NET) in HR+ postmenopausal women. AREAS COVERED We conducted a thorough review of the published literature, to summarize the evidence to date, including studies of how NET compares to neoadjuvant chemotherapy, which NET agents are preferable, and the optimal duration of NET. We describe the importance of on-treatment assessment of response, the different predictors available (including Ki67, PEPI score, and molecular signatures) and the research opportunities the pre-operative setting offers. We also summarize recent combination trials and discuss how the COVID-19 pandemic led to increases in NET use for safe management of cases with deferred surgery and adjuvant treatments. EXPERT OPINION NET represents a safe and effective tool for the management of postmenopausal women with HR+/HER2- breast cancer, enabling disease downstaging and a wider range of surgical options. Aromatase inhibitors are the preferred NET, with evidence suggesting that longer regimens might yield optimal results. However, NET remains currently underutilised in many territories and institutions. Further validation of predictors for treatment response and benefit is needed to help standardise and fully exploit the potential of NET in the clinic.
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Affiliation(s)
- Carlos Martínez-Pérez
- Translational Oncology Research Group, MRC Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, Scotland
- Edinburgh Breast Cancer Now Research Team, MRC Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, Scotland
| | - Arran K Turnbull
- Translational Oncology Research Group, MRC Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, Scotland
- Edinburgh Breast Cancer Now Research Team, MRC Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, Scotland
| | - Charlene Kay
- Translational Oncology Research Group, MRC Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, Scotland
- Edinburgh Breast Cancer Now Research Team, MRC Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, Scotland
| | - J Michael Dixon
- Translational Oncology Research Group, MRC Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, Scotland
- Edinburgh Breast Cancer Now Research Team, MRC Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, Scotland
- Edinburgh Breast Unit, Western General Hospital, Edinburgh, Scotland
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Lucas MW, Kelly CM. Optimal Choice of Neoadjuvant Chemotherapy for HER2-Negative Breast Cancer: Clinical Insights. Cancer Manag Res 2022; 14:2493-2506. [PMID: 35999966 PMCID: PMC9393016 DOI: 10.2147/cmar.s341466] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2022] [Accepted: 08/05/2022] [Indexed: 11/25/2022] Open
Abstract
The neoadjuvant setting provides immense opportunities for translational research and drug development. The acceptance of pathological complete response (pCR) as a surrogate endpoint for clinical benefit has led to the widespread use of neoadjuvant treatment. Optimal neoadjuvant therapies are determined based on their ability to achieve the highest rates of pCR. Predicted rates of pCR for triple negative breast cancer (TNBC) treated with sequential taxane/anthracycline regimens range from 35% to 48%. With the addition of a platinum agent pCR rates of 55% are predicted. Further increases have been observed with the addition of immune checkpoint inhibitors to this standard chemotherapy backbone. In the pivotal KEYNOTE-522 clinical trial pCR rates of 65% and 69% were reported for chemotherapy plus pembrolizumab in the overall and PD-L1-positive subgroup respectively. The role of the neoadjuvant chemotherapy is less clear in hormone receptor (HR)-positive, human epidermal growth factor receptor-2 (HER2)-negative breast cancer. In general, HR–positive cancers have the least chance of achieving a pCR after neoadjuvant chemotherapy, especially if they are low-grade. If neoadjuvant chemotherapy is given for high-risk HR-positive, HER2-negative breast cancer, standard adjuvant anthracycline/taxane regimens are appropriate. Optimum endocrine therapy is the standard-of-care in the adjuvant setting regardless of pCR. There are several genomic signatures available to guide decisions regarding adjuvant chemotherapy use however these assays are not routinely used in the neoadjuvant setting. For high-risk patients meeting the criteria for the monarchE trial adjuvant abemaciclib in addition to endocrine therapy is associated with an improvement in disease free survival (DFS) at 3 years. Based on the OlympiA trial patients with germline BRCA mutations should be considered for adjuvant olaparib therapy. In this article we review neoadjuvant clinical trials that guide optimum treatment options for TNBC and HR-positive, HER2-negative breast cancer.
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Affiliation(s)
- Mairi W Lucas
- Royal College of Surgeons in Ireland, St Stephen's Green, Dublin, Ireland
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9
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Gindin T, Hsiao SJ. Analytical Principles of Cancer Next Generation Sequencing. Clin Lab Med 2022; 42:395-408. [DOI: 10.1016/j.cll.2022.04.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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10
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Lashen A, Toss MS, Alsaleem M, Green AR, Mongan NP, Rakha E. The characteristics and clinical significance of atypical mitosis in breast cancer. Mod Pathol 2022; 35:1341-1348. [PMID: 35501336 PMCID: PMC9514994 DOI: 10.1038/s41379-022-01080-0] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Revised: 03/23/2022] [Accepted: 03/25/2022] [Indexed: 11/09/2022]
Abstract
Atypical mitosis is considered a feature of malignancy, however, its significance in breast cancer (BC) remains elusive. Here, we aimed to assess the clinical value of atypical mitoses in BC and to explore their underlying molecular features. Atypical and typical mitotic figures were quantified and correlated with clinicopathological variables in a large cohort of primary BC tissue sections (n = 846) using digitalized hematoxylin and eosin whole-slide images (WSIs). In addition, atypical mitoses were assessed in The Cancer Genome Atlas (TCGA) BC dataset (n = 1032) and were linked to the genetic alterations and pathways. In this study, the median of typical mitoses was 17 per 3 mm2 (range 0-120 mitoses), while the median of atypical mitoses was 4 (range 0-103 mitoses). High atypical mitoses were significantly associated with parameters characteristic of aggressive tumor behavior. The total number of mitoses, and a high atypical-to-typical mitoses ratio (>0.27) were associated with poor BC specific survival (BCSS), (p = 0.04 and 0.01, respectively). The atypical-to-typical mitoses ratio dichotomized triple negative-BC (TNBC) patients into two distinct groups in terms of the association with the outcome, while the overall number of mitoses was not. Moreover, TNBC patients with high atypical-to-typical mitoses ratio treated with adjuvant chemotherapy were associated with shorter survival (p = 0.003). Transcriptomic analysis of the TCGA-BRCA cohort dichotomized based on atypical mitoses identified 2494 differentially expressed genes. These included genes linked to pathways involved in chromosomal localization and segregation, centrosome assembly, spindle and microtubule formation, regulation of cell cycle and DNA repair. To conclude, the atypical-to-typical mitoses ratio has prognostic value independent of the overall mitotic count in BC patients and could predict the response to chemotherapy in TNBC.
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Affiliation(s)
- Ayat Lashen
- grid.4563.40000 0004 1936 8868Academic Unit for Translational Medical Sciences, School of Medicine, University of Nottingham, Nottingham, UK ,grid.411775.10000 0004 0621 4712Department of Pathology, Faculty of Medicine, Menoufia University, Shebin El Kom, Egypt
| | - Michael S. Toss
- grid.4563.40000 0004 1936 8868Academic Unit for Translational Medical Sciences, School of Medicine, University of Nottingham, Nottingham, UK
| | - Mansour Alsaleem
- grid.4563.40000 0004 1936 8868Academic Unit for Translational Medical Sciences, School of Medicine, University of Nottingham, Nottingham, UK ,grid.412602.30000 0000 9421 8094Department of Applied Medical Science, Applied College, Qassim University, Qassim, Saudi Arabia
| | - Andrew R Green
- grid.4563.40000 0004 1936 8868Academic Unit for Translational Medical Sciences, School of Medicine, University of Nottingham, Nottingham, UK ,grid.4563.40000 0004 1936 8868Nottingham Breast Cancer Research Centre, University of Nottingham, Nottingham, UK
| | - Nigel P. Mongan
- grid.4563.40000 0004 1936 8868School of Veterinary Medicine and Sciences, University of Nottingham, Nottingham, UK ,grid.5386.8000000041936877XDepartment of Pharmacology, Weill Cornell Medicine, New York, NY USA
| | - Emad Rakha
- Academic Unit for Translational Medical Sciences, School of Medicine, University of Nottingham, Nottingham, UK. .,Department of Pathology, Faculty of Medicine, Menoufia University, Shebin El Kom, Egypt.
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