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Galadima H, Anson-Dwamena R, Johnson A, Bello G, Adunlin G, Blando J. Machine Learning as a Tool for Early Detection: A Focus on Late-Stage Colorectal Cancer across Socioeconomic Spectrums. Cancers (Basel) 2024; 16:540. [PMID: 38339293 PMCID: PMC10854986 DOI: 10.3390/cancers16030540] [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: 12/30/2023] [Revised: 01/19/2024] [Accepted: 01/23/2024] [Indexed: 02/12/2024] Open
Abstract
PURPOSE To assess the efficacy of various machine learning (ML) algorithms in predicting late-stage colorectal cancer (CRC) diagnoses against the backdrop of socio-economic and regional healthcare disparities. METHODS An innovative theoretical framework was developed to integrate individual- and census tract-level social determinants of health (SDOH) with sociodemographic factors. A comparative analysis of the ML models was conducted using key performance metrics such as AUC-ROC to evaluate their predictive accuracy. Spatio-temporal analysis was used to identify disparities in late-stage CRC diagnosis probabilities. RESULTS Gradient boosting emerged as the superior model, with the top predictors for late-stage CRC diagnosis being anatomic site, year of diagnosis, age, proximity to superfund sites, and primary payer. Spatio-temporal clusters highlighted geographic areas with a statistically significant high probability of late-stage diagnoses, emphasizing the need for targeted healthcare interventions. CONCLUSIONS This research underlines the potential of ML in enhancing the prognostic predictions in oncology, particularly in CRC. The gradient boosting model, with its robust performance, holds promise for deployment in healthcare systems to aid early detection and formulate localized cancer prevention strategies. The study's methodology demonstrates a significant step toward utilizing AI in public health to mitigate disparities and improve cancer care outcomes.
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Affiliation(s)
- Hadiza Galadima
- School of Community and Environmental Health, Old Dominion University, Norfolk, VA 23529, USA; (R.A.-D.); (A.J.); (J.B.)
| | - Rexford Anson-Dwamena
- School of Community and Environmental Health, Old Dominion University, Norfolk, VA 23529, USA; (R.A.-D.); (A.J.); (J.B.)
| | - Ashley Johnson
- School of Community and Environmental Health, Old Dominion University, Norfolk, VA 23529, USA; (R.A.-D.); (A.J.); (J.B.)
| | - Ghalib Bello
- Department of Environmental Medicine & Public Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA;
| | - Georges Adunlin
- Department of Pharmaceutical, Social and Administrative Sciences, Samford University, Birmingham, AL 35229, USA;
| | - James Blando
- School of Community and Environmental Health, Old Dominion University, Norfolk, VA 23529, USA; (R.A.-D.); (A.J.); (J.B.)
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Zhang S, Wang X, Yang Z, Zhu Y, Zhao N, Li Y, He J, Sun H, Xie Z. Intra- and Peritumoral Radiomics Model Based on Early DCE-MRI for Preoperative Prediction of Molecular Subtypes in Invasive Ductal Breast Carcinoma: A Multitask Machine Learning Study. Front Oncol 2022; 12:905551. [PMID: 35814460 PMCID: PMC9263840 DOI: 10.3389/fonc.2022.905551] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2022] [Accepted: 05/23/2022] [Indexed: 12/29/2022] Open
Abstract
Purpose The aim of this study is to investigate radiomics features extracted from the optimal peritumoral region and the intratumoral area on the early phase of dynamic contrast-enhanced MRI (DCE-MRI) for predicting molecular subtypes of invasive ductal breast carcinoma (IDBC). Methods A total of 422 IDBC patients with immunohistochemical and fluorescence in situ hybridization results from two hospitals (Center 1: 327 cases, Center 2: 95 cases) who underwent preoperative DCE-MRI were retrospectively enrolled. After image preprocessing, radiomic features were extracted from the intratumoral area and four peritumoral regions on DCE-MRI from two centers, and selected the optimal peritumoral region. Based on the intratumoral, peritumoral radiomics features, and clinical–radiological characteristics, five radiomics models were constructed through support vector machine (SVM) in multiple classification tasks related to molecular subtypes and visualized by nomogram. The performance of radiomics models was evaluated by receiver operating characteristic curves, confusion matrix, calibration curves, and decision curve analysis. Results A 6-mm peritumoral size was defined the optimal peritumoral region in classification tasks of hormone receptor (HR)-positive vs others, triple-negative breast cancer (TNBC) vs others, and HR-positive vs human epidermal growth factor receptor 2 (HER2)-enriched vs TNBC, and 8 mm was applied in HER2-enriched vs others. The combined clinical–radiological and radiomics models in three binary classification tasks (HR-positive vs others, HER2-enriched vs others, TNBC vs others) obtained optimal performance with AUCs of 0.838, 0.848, and 0.930 in the training cohort, respectively; 0.827, 0.813, and 0.879 in the internal test cohort, respectively; and 0.791, 0.707, and 0.852 in the external test cohort, respectively. Conclusion Radiomics features in the intratumoral and peritumoral regions of IDBC on DCE-MRI had a potential to predict the HR-positive, HER2-enriched, and TNBC molecular subtypes preoperatively.
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Affiliation(s)
- Shuhai Zhang
- Department of Radiology, The First Affiliated Hospital of Bengbu Medical College, Bengbu, China
| | - Xiaolei Wang
- Department of Radiology, The First Affiliated Hospital of Bengbu Medical College, Bengbu, China
| | - Zhao Yang
- Department of Radiology, The First Affiliated Hospital of Bengbu Medical College, Bengbu, China
| | - Yun Zhu
- Department of Radiology, The First Affiliated Hospital of Bengbu Medical College, Bengbu, China
| | - Nannan Zhao
- Department of Radiology, The First Affiliated Hospital of Bengbu Medical College, Bengbu, China
| | - Yang Li
- Department of Radiology, The First Affiliated Hospital of Bengbu Medical College, Bengbu, China
| | - Jie He
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Haitao Sun
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China
- Shanghai Institute of Medical Imaging, Department of Cancer Center, Zhongshan Hospital, Fudan University, Shanghai, China
- *Correspondence: Haitao Sun, ; Zongyu Xie,
| | - Zongyu Xie
- Department of Radiology, The First Affiliated Hospital of Bengbu Medical College, Bengbu, China
- *Correspondence: Haitao Sun, ; Zongyu Xie,
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Investigating How Reproducibility and Geometrical Representation in UMAP Dimensionality Reduction Impact the Stratification of Breast Cancer Tumors. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12094247] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Advances in next-generation sequencing have provided high-dimensional RNA-seq datasets, allowing the stratification of some tumor patients based on their transcriptomic profiles. Machine learning methods have been used to reduce and cluster high-dimensional data. Recently, uniform manifold approximation and projection (UMAP) was applied to project genomic datasets in low-dimensional Euclidean latent space. Here, we evaluated how different representations of the UMAP embedding can impact the analysis of breast cancer (BC) stratification. We projected BC RNA-seq data on Euclidean, spherical, and hyperbolic spaces, and stratified BC patients via clustering algorithms. We also proposed a pipeline to yield more reproducible clustering outputs. The results show how the selection of the latent space can affect downstream stratification results and suggest that the exploration of different geometrical representations is recommended to explore data structure and samples’ relationships.
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Núñez-Iglesias MJ, Novio S, García C, Pérez-Muñuzuri ME, Martínez MC, Santiago JL, Boso S, Gago P, Freire-Garabal M. Co-Adjuvant Therapy Efficacy of Catechin and Procyanidin B2 with Docetaxel on Hormone-Related Cancers In Vitro. Int J Mol Sci 2021; 22:7178. [PMID: 34281228 PMCID: PMC8268784 DOI: 10.3390/ijms22137178] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Revised: 06/25/2021] [Accepted: 06/28/2021] [Indexed: 01/16/2023] Open
Abstract
Prostate (PC) and breast cancer (BC) are heterogeneous hormonal cancers. Treatment resistance and adverse effects are the main limitations of conventional chemotherapy treatment. The use of sensitizing agents could improve the effectiveness of chemotherapeutic drugs as well as obviate these limitations. This study analyzes the effect of single catechin (CAT), procyanidin B2 (ProB2) treatment as well as the co-adjuvant treatment of each of these compounds with docetaxel (DOCE). We used PC- and BC-derived cell lines (PC3, DU-145, T47D, MCF-7 and MDA-MB-231). The short and long-term pro-apoptotic, anti-proliferative and anti-migratory effects were analyzed. RT-qPCR was used to discover molecular bases of the therapeutic efficacy of these compounds. ProB2 treatment induced a two- to five-fold increase in anti-proliferative and pro-apoptotic effects compared to single DOCE treatment, and also had a more sensitizing effect than DOCE on DU145 cells. Regarding BC cells, ProB2- and CAT-mediated sensitization to DOCE anti-proliferative and pro-apoptotic effects was cell-independent and cell-dependent, respectively. Combined treatment led to high-efficacy effects on MCF-7 cells, which were associated to the up-regulation of CDKN1A, BAX, caspase 9 and E-cadherin mRNA under combined treatment compared to single DOCE treatment. CAT and ProB2 can enhance the efficacy of DOCE therapy on PC and BC cells by the sensitizing mechanism.
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Affiliation(s)
- Mª Jesús Núñez-Iglesias
- SNL Laboratory, School of Medicine and Dentistry, University of Santiago de Compostela, c/San Francisco, s/n, Santiago de Compostela, 15782 A Coruña, Spain; (M.J.N.-I.); (C.G.); (M.E.P.-M.); (M.F.-G.)
| | - Silvia Novio
- SNL Laboratory, School of Medicine and Dentistry, University of Santiago de Compostela, c/San Francisco, s/n, Santiago de Compostela, 15782 A Coruña, Spain; (M.J.N.-I.); (C.G.); (M.E.P.-M.); (M.F.-G.)
| | - Carlota García
- SNL Laboratory, School of Medicine and Dentistry, University of Santiago de Compostela, c/San Francisco, s/n, Santiago de Compostela, 15782 A Coruña, Spain; (M.J.N.-I.); (C.G.); (M.E.P.-M.); (M.F.-G.)
| | - Mª Elena Pérez-Muñuzuri
- SNL Laboratory, School of Medicine and Dentistry, University of Santiago de Compostela, c/San Francisco, s/n, Santiago de Compostela, 15782 A Coruña, Spain; (M.J.N.-I.); (C.G.); (M.E.P.-M.); (M.F.-G.)
| | - María-Carmen Martínez
- Group of Viticulture, Olive and Rose (VIOR), Misión Biológica de Galicia, Consejo Superior de Investigaciones Científicas (CSIC), Carballeira 8, 36143 Salcedo, Spain; (M.-C.M.); (J.-L.S.); (S.B.); (P.G.)
| | - José-Luis Santiago
- Group of Viticulture, Olive and Rose (VIOR), Misión Biológica de Galicia, Consejo Superior de Investigaciones Científicas (CSIC), Carballeira 8, 36143 Salcedo, Spain; (M.-C.M.); (J.-L.S.); (S.B.); (P.G.)
| | - Susana Boso
- Group of Viticulture, Olive and Rose (VIOR), Misión Biológica de Galicia, Consejo Superior de Investigaciones Científicas (CSIC), Carballeira 8, 36143 Salcedo, Spain; (M.-C.M.); (J.-L.S.); (S.B.); (P.G.)
| | - Pilar Gago
- Group of Viticulture, Olive and Rose (VIOR), Misión Biológica de Galicia, Consejo Superior de Investigaciones Científicas (CSIC), Carballeira 8, 36143 Salcedo, Spain; (M.-C.M.); (J.-L.S.); (S.B.); (P.G.)
| | - Manuel Freire-Garabal
- SNL Laboratory, School of Medicine and Dentistry, University of Santiago de Compostela, c/San Francisco, s/n, Santiago de Compostela, 15782 A Coruña, Spain; (M.J.N.-I.); (C.G.); (M.E.P.-M.); (M.F.-G.)
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Yang L, Qin Y, Jian C. Screening for Core Genes Related to Pathogenesis of Alzheimer's Disease. Front Cell Dev Biol 2021; 9:668738. [PMID: 33968940 PMCID: PMC8101499 DOI: 10.3389/fcell.2021.668738] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Accepted: 04/01/2021] [Indexed: 12/18/2022] Open
Abstract
Alzheimer’s disease (AD), a nervous system disease, lacks effective therapies at present. RNA expression is the basic way to regulate life activities, and identifying related characteristics in AD patients may aid the exploration of AD pathogenesis and treatment. This study developed a classifier that could accurately classify AD patients and healthy people, and then obtained 3 core genes that may be related to the pathogenesis of AD. To this end, RNA expression data of the middle temporal gyrus of AD patients were firstly downloaded from GEO database, and the data were then normalized using limma package following a supplementation of missing data by k-Nearest Neighbor (KNN) algorithm. Afterwards, the top 500 genes of the most feature importance were obtained through Max-Relevance and Min-Redundancy (mRMR) analysis, and based on these genes, a series of AD classifiers were constructed through Support Vector Machine (SVM), Random Forest (RF), and KNN algorithms. Then, the KNN classifier with the highest Matthews correlation coefficient (MCC) value composed of 14 genes in incremental feature selection (IFS) analysis was identified as the best AD classifier. As analyzed, the 14 genes played a pivotal role in determination of AD and may be core genes associated with the pathogenesis of AD. Finally, protein-protein interaction (PPI) network and Random Walk with Restart (RWR) analysis were applied to obtain core gene-associated genes, and key pathways related to AD were further analyzed. Overall, this study contributed to a deeper understanding of AD pathogenesis and provided theoretical guidance for related research and experiments.
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Affiliation(s)
- Longxiu Yang
- Department of Neurology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Yuan Qin
- Department of Neurology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Chongdong Jian
- Department of Neurology, The Affiliated Hospital of Youjiang Medical University for Nationalities, Baise, China
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He Z, Zhang J, Yuan X, Zhang Y. Integrating Somatic Mutations for Breast Cancer Survival Prediction Using Machine Learning Methods. Front Genet 2021; 11:632901. [PMID: 33537063 PMCID: PMC7848170 DOI: 10.3389/fgene.2020.632901] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Accepted: 12/30/2020] [Indexed: 12/13/2022] Open
Abstract
Breast cancer is the most common malignancy in women, and because it has a high mortality rate, it is urgent to develop computational methods to increase the accuracy of breast cancer survival predictive models. Although multi-omics data such as gene expression have been extensively used in recent studies, the accurate prognosis of breast cancer remains a challenge. Somatic mutations are another important and promising data source for studying cancer development, and its effect on the prognosis of breast cancer remains to be further explored. Meanwhile, these omics datasets are high-dimensional and redundant. Therefore, we adopted multiple kernel learning (MKL) to efficiently integrate somatic mutation to currently molecular data including gene expression, copy number variation (CNV), methylation, and protein expression data for the prediction of breast cancer survival. Before integration, the maximum relevance minimum redundancy (mRMR) feature selection method was utilized to select features that present high relevance to survival and low redundancy among themselves for each type of data. The experimental results demonstrated that the proposed method achieved the most optimal performance and there was a remarkable improvement in the prediction performance when somatic mutations were included, indicating that somatic mutations are critical for improving breast cancer survival predictions. Moreover, mRMR was superior to other feature selection methods used in previous studies. Furthermore, MKL outperformed the other traditional classifiers in multi-omics data integration. Our analysis indicated that through employing promising omics data such as somatic mutations and harnessing the power of proper feature selection methods and effective integration frameworks, the breast cancer survival predictive accuracy can be further increased, thereby providing a more optimal clinical diagnosis and more effective treatment for breast cancer patients.
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Affiliation(s)
- Zongzhen He
- School of Computer Science and Technology, Xidian University, Xi'an, China
| | - Junying Zhang
- School of Computer Science and Technology, Xidian University, Xi'an, China
| | - Xiguo Yuan
- School of Computer Science and Technology, Xidian University, Xi'an, China
| | - Yuanyuan Zhang
- School of Information and Control Engineering, Qingdao University of Technology, Qingdao, China
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Conforte AJ, Alves L, Coelho FC, Carels N, da Silva FAB. Modeling Basins of Attraction for Breast Cancer Using Hopfield Networks. Front Genet 2020; 11:314. [PMID: 32318098 PMCID: PMC7154169 DOI: 10.3389/fgene.2020.00314] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2019] [Accepted: 03/16/2020] [Indexed: 12/26/2022] Open
Abstract
Cancer is a genetic disease for which traditional treatments cause harmful side effects. After two decades of genomics technological breakthroughs, personalized medicine is being used to improve treatment outcomes and mitigate side effects. In mathematical modeling, it has been proposed that cancer matches an attractor in Waddington's epigenetic landscape. The use of Hopfield networks is an attractive modeling approach because it requires neither previous biological knowledge about protein-protein interactions nor kinetic parameters. In this report, Hopfield network modeling was used to analyze bulk RNA-Seq data of paired breast tumor and control samples from 70 patients. We characterized the control and tumor attractors with respect to their size and potential energy and correlated the Euclidean distances between the tumor samples and the control attractor with their corresponding clinical data. In addition, we developed a protocol that outlines the key genes involved in tumor state stability. We found that the tumor basin of attraction is larger than that of the control and that tumor samples are associated with a more substantial negative energy than control samples, which is in agreement with previous reports. Moreover, we found a negative correlation between the Euclidean distances from tumor samples to the control attractor and patient overall survival. The ascending order of each node's density in the weight matrix and the descending order of the number of patients that have the target active only in the tumor sample were the parameters that withdrew more tumor samples from the tumor basin of attraction with fewer gene inhibitions. The combinations of therapeutic targets were specific to each patient. We performed an initial validation through simulation of trastuzumab treatment effects in HER2+ breast cancer samples. For that, we built an energy landscape composed of single-cell and bulk RNA-Seq data from trastuzumab-treated and non-treated HER2+ samples. The trajectory from the non-treated bulk sample toward the treated bulk sample was inferred through the perturbation of differentially expressed genes between these samples. Among them, we characterized key genes involved in the trastuzumab response according to the literature.
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Affiliation(s)
- Alessandra Jordano Conforte
- Laboratory of Biological Systems Modeling, Center for Technological Development in Health, Oswaldo Cruz Foundation, Rio de Janeiro, Brazil.,Laboratory of Computational Modeling of Biological Systems, Scientific Computing Program, Oswaldo Cruz Foundation, Rio de Janeiro, Brazil
| | - Leon Alves
- Applied Math School, Getúlio Vargas Foundation, Oswaldo Cruz Foundation, Rio de Janeiro, Brazil
| | | | - Nicolas Carels
- Laboratory of Biological Systems Modeling, Center for Technological Development in Health, Oswaldo Cruz Foundation, Rio de Janeiro, Brazil
| | - Fabrício Alves Barbosa da Silva
- Laboratory of Computational Modeling of Biological Systems, Scientific Computing Program, Oswaldo Cruz Foundation, Rio de Janeiro, Brazil
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