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Tschodu D, Lippoldt J, Gottheil P, Wegscheider AS, Käs JA, Niendorf A. Re-evaluation of publicly available gene-expression databases using machine-learning yields a maximum prognostic power in breast cancer. Sci Rep 2023; 13:16402. [PMID: 37798300 PMCID: PMC10556090 DOI: 10.1038/s41598-023-41090-9] [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: 03/17/2023] [Accepted: 08/22/2023] [Indexed: 10/07/2023] Open
Abstract
Gene expression signatures refer to patterns of gene activities and are used to classify different types of cancer, determine prognosis, and guide treatment decisions. Advancements in high-throughput technology and machine learning have led to improvements to predict a patient's prognosis for different cancer phenotypes. However, computational methods for analyzing signatures have not been used to evaluate their prognostic power. Contention remains on the utility of gene expression signatures for prognosis. The prevalent approaches include random signatures, expert knowledge, and machine learning to construct an improved signature. We unify these approaches to evaluate their prognostic power. Re-evaluation of publicly available gene-expression data from 8 databases with 9 machine-learning models revealed previously unreported results. Gene-expression signatures are confirmed to be useful in predicting a patient's prognosis. Convergent evidence from [Formula: see text] 10,000 signatures implicates a maximum prognostic power. By calculating the concordance index, which measures how well patients with different prognoses can be discriminated, we show that a signature can correctly discriminate patients' prognoses no more than 80% of the time. Additionally, we show that more than 50% of the potentially available information is still missing at this value. We surmise that an accurate prognosis must incorporate molecular, clinical, histological, and other complementary factors.
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Affiliation(s)
- Dimitrij Tschodu
- Peter Debye Institute for Soft Matter Physics, Leipzig University, 04103, Leipzig, Germany.
| | - Jürgen Lippoldt
- Peter Debye Institute for Soft Matter Physics, Leipzig University, 04103, Leipzig, Germany
| | - Pablo Gottheil
- Peter Debye Institute for Soft Matter Physics, Leipzig University, 04103, Leipzig, Germany
| | - Anne-Sophie Wegscheider
- Institute for Histology, Cytology and Molecular Diagnostics, MVZ Prof. Dr. med. A. Niendorf Pathologie Hamburg-West GmbH, 22767, Hamburg, Germany
| | - Josef A Käs
- Peter Debye Institute for Soft Matter Physics, Leipzig University, 04103, Leipzig, Germany.
| | - Axel Niendorf
- Institute for Histology, Cytology and Molecular Diagnostics, MVZ Prof. Dr. med. A. Niendorf Pathologie Hamburg-West GmbH, 22767, Hamburg, Germany.
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Chen Y, Qi Y, Wang K. Neoadjuvant chemotherapy for breast cancer: an evaluation of its efficacy and research progress. Front Oncol 2023; 13:1169010. [PMID: 37854685 PMCID: PMC10579937 DOI: 10.3389/fonc.2023.1169010] [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: 02/18/2023] [Accepted: 09/14/2023] [Indexed: 10/20/2023] Open
Abstract
Neoadjuvant chemotherapy (NAC) for breast cancer is widely used in the clinical setting to improve the chance of surgery, breast conservation and quality of life for patients with advanced breast cancer. A more accurate efficacy evaluation system is important for the decision of surgery timing and chemotherapy regimen implementation. However, current methods, encompassing imaging techniques such as ultrasound and MRI, along with non-imaging approaches like pathological evaluations, often fall short in accurately depicting the therapeutic effects of NAC. Imaging techniques are subjective and only reflect macroscopic morphological changes, while pathological evaluation is the gold standard for efficacy assessment but has the disadvantage of delayed results. In an effort to identify assessment methods that align more closely with real-world clinical demands, this paper provides an in-depth exploration of the principles and clinical applications of various assessment approaches in the neoadjuvant chemotherapy process.
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Affiliation(s)
- Yushi Chen
- Department of Pathology, Xiangya Hospital, Central South University, Changsha, Hunan, China
- Department of Pathology, Basic Medical School, Central South University, Changsha, Hunan, China
| | - Yu Qi
- Department of Pathology, Xiangya Hospital, Central South University, Changsha, Hunan, China
- Department of Pathology, Basic Medical School, Central South University, Changsha, Hunan, China
| | - Kuansong Wang
- Department of Pathology, Xiangya Hospital, Central South University, Changsha, Hunan, China
- Department of Pathology, Basic Medical School, Central South University, Changsha, Hunan, China
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García-Pérez R, Ramirez JM, Ripoll-Cladellas A, Chazarra-Gil R, Oliveros W, Soldatkina O, Bosio M, Rognon PJ, Capella-Gutierrez S, Calvo M, Reverter F, Guigó R, Aguet F, Ferreira PG, Ardlie KG, Melé M. The landscape of expression and alternative splicing variation across human traits. CELL GENOMICS 2022; 3:100244. [PMID: 36777183 PMCID: PMC9903719 DOI: 10.1016/j.xgen.2022.100244] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 11/08/2022] [Accepted: 12/07/2022] [Indexed: 12/31/2022]
Abstract
Understanding the consequences of individual transcriptome variation is fundamental to deciphering human biology and disease. We implement a statistical framework to quantify the contributions of 21 individual traits as drivers of gene expression and alternative splicing variation across 46 human tissues and 781 individuals from the Genotype-Tissue Expression project. We demonstrate that ancestry, sex, age, and BMI make additive and tissue-specific contributions to expression variability, whereas interactions are rare. Variation in splicing is dominated by ancestry and is under genetic control in most tissues, with ribosomal proteins showing a strong enrichment of tissue-shared splicing events. Our analyses reveal a systemic contribution of types 1 and 2 diabetes to tissue transcriptome variation with the strongest signal in the nerve, where histopathology image analysis identifies novel genes related to diabetic neuropathy. Our multi-tissue and multi-trait approach provides an extensive characterization of the main drivers of human transcriptome variation in health and disease.
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Affiliation(s)
- Raquel García-Pérez
- Department of Life Sciences, Barcelona Supercomputing Center (BCN-CNS), Barcelona, Catalonia 08034, Spain
| | - Jose Miguel Ramirez
- Department of Life Sciences, Barcelona Supercomputing Center (BCN-CNS), Barcelona, Catalonia 08034, Spain
| | - Aida Ripoll-Cladellas
- Department of Life Sciences, Barcelona Supercomputing Center (BCN-CNS), Barcelona, Catalonia 08034, Spain
| | - Ruben Chazarra-Gil
- Department of Life Sciences, Barcelona Supercomputing Center (BCN-CNS), Barcelona, Catalonia 08034, Spain
| | - Winona Oliveros
- Department of Life Sciences, Barcelona Supercomputing Center (BCN-CNS), Barcelona, Catalonia 08034, Spain
| | - Oleksandra Soldatkina
- Department of Life Sciences, Barcelona Supercomputing Center (BCN-CNS), Barcelona, Catalonia 08034, Spain
| | - Mattia Bosio
- Department of Life Sciences, Barcelona Supercomputing Center (BCN-CNS), Barcelona, Catalonia 08034, Spain
| | - Paul Joris Rognon
- Department of Life Sciences, Barcelona Supercomputing Center (BCN-CNS), Barcelona, Catalonia 08034, Spain,Department of Economics and Business, Universitat Pompeu Fabra, Barcelona, Catalonia 08005, Spain,Department of Statistics and Operations Research, Universitat Politècnica de Catalunya, Barcelona, Catalonia 08034, Spain
| | - Salvador Capella-Gutierrez
- Department of Life Sciences, Barcelona Supercomputing Center (BCN-CNS), Barcelona, Catalonia 08034, Spain
| | - Miquel Calvo
- Statistics Section, Faculty of Biology, Universitat de Barcelona (UB), Barcelona, Catalonia 08028, Spain
| | - Ferran Reverter
- Statistics Section, Faculty of Biology, Universitat de Barcelona (UB), Barcelona, Catalonia 08028, Spain
| | - Roderic Guigó
- Bioinformatics and Genomics, Center for Genomic Regulation, Barcelona, Catalonia 08003, Spain
| | | | - Pedro G. Ferreira
- Department of Computer Science, Faculty of Sciences, University of Porto, Rua do Campo Alegre, 4169-007 Porto, Portugal,Laboratory of Artificial Intelligence and Decision Support, INESC TEC, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal,Institute of Molecular Pathology and Immunology of the University of Porto, Institute for Research and Innovation in Health (i3s), R. Alfredo Allen 208, 4200-135 Porto, Portugal
| | | | - Marta Melé
- Department of Life Sciences, Barcelona Supercomputing Center (BCN-CNS), Barcelona, Catalonia 08034, Spain,Corresponding author
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