Identification of metabolic pathways contributing to ER+ breast cancer disparities using a machine-learning pipeline
Abstract African American (AA) women in the United States have a 40% higher breast cancer mortality rate than Non-Hispanic White (NHW) women. The survival disparity is particularly striking among (estrogen receptor positive) ER+ breast cancer cases. The purpose of this study is to examine whether th...
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2023-07-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-39215-1 |
| _version_ | 1797769523033538560 |
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| author | Ashlie Santaliz-Casiano Dhruv Mehta Oana C. Danciu Hariyali Patel Landan Banks Ayesha Zaidi Jermya Buckley Garth H. Rauscher Lauren Schulte Lauren Ro Weller Deanna Taiym Elona Liko-Hazizi Natalie Pulliam Sarah M. Friedewald Seema Khan J. Julie Kim William Gradishar Scott Hegerty Jonna Frasor Kent F. Hoskins Zeynep Madak-Erdogan |
| author_facet | Ashlie Santaliz-Casiano Dhruv Mehta Oana C. Danciu Hariyali Patel Landan Banks Ayesha Zaidi Jermya Buckley Garth H. Rauscher Lauren Schulte Lauren Ro Weller Deanna Taiym Elona Liko-Hazizi Natalie Pulliam Sarah M. Friedewald Seema Khan J. Julie Kim William Gradishar Scott Hegerty Jonna Frasor Kent F. Hoskins Zeynep Madak-Erdogan |
| author_sort | Ashlie Santaliz-Casiano |
| collection | DOAJ |
| description | Abstract African American (AA) women in the United States have a 40% higher breast cancer mortality rate than Non-Hispanic White (NHW) women. The survival disparity is particularly striking among (estrogen receptor positive) ER+ breast cancer cases. The purpose of this study is to examine whether there are racial differences in metabolic pathways typically activated in patients with ER+ breast cancer. We collected pretreatment plasma from AA and NHW ER+ breast cancer cases (AA n = 48, NHW n = 54) and cancer-free controls (AA n = 100, NHW n = 48) to conduct an untargeted metabolomics analysis using gas chromatography mass spectrometry (GC–MS) to identify metabolites that may be altered in the different racial groups. Unpaired t-test combined with multiple feature selection and prediction models were employed to identify race-specific altered metabolic signatures. This was followed by the identification of altered metabolic pathways with a focus in AA patients with breast cancer. The clinical relevance of the identified pathways was further examined in PanCancer Atlas breast cancer data set from The Cancer Genome Atlas Program (TCGA). We identified differential metabolic signatures between NHW and AA patients. In AA patients, we observed decreased circulating levels of amino acids compared to healthy controls, while fatty acids were significantly higher in NHW patients. By mapping these metabolites to potential epigenetic regulatory mechanisms, this study identified significant associations with regulators of metabolism such as methionine adenosyltransferase 1A (MAT1A), DNA Methyltransferases and Histone methyltransferases for AA individuals, and Fatty acid Synthase (FASN) and Monoacylglycerol lipase (MGL) for NHW individuals. Specific gene Negative Elongation Factor Complex E (NELFE) with histone methyltransferase activity, was associated with poor survival exclusively for AA individuals. We employed a comprehensive and novel approach that integrates multiple machine learning and statistical methods, coupled with human functional pathway analyses. The metabolic profile of plasma samples identified may help elucidate underlying molecular drivers of disproportionately aggressive ER+ tumor biology in AA women. It may ultimately lead to the identification of novel therapeutic targets. To our knowledge, this is a novel finding that describes a link between metabolic alterations and epigenetic regulation in AA breast cancer and underscores the need for detailed investigations into the biological underpinnings of breast cancer health disparities. |
| first_indexed | 2024-03-12T21:10:19Z |
| format | Article |
| id | doaj.art-beae8bb87c8d4735bd2357ef0fcaedbc |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-12T21:10:19Z |
| publishDate | 2023-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-beae8bb87c8d4735bd2357ef0fcaedbc2023-07-30T11:14:27ZengNature PortfolioScientific Reports2045-23222023-07-0113111410.1038/s41598-023-39215-1Identification of metabolic pathways contributing to ER+ breast cancer disparities using a machine-learning pipelineAshlie Santaliz-Casiano0Dhruv Mehta1Oana C. Danciu2Hariyali Patel3Landan Banks4Ayesha Zaidi5Jermya Buckley6Garth H. Rauscher7Lauren Schulte8Lauren Ro Weller9Deanna Taiym10Elona Liko-Hazizi11Natalie Pulliam12Sarah M. Friedewald13Seema Khan14J. Julie Kim15William Gradishar16Scott Hegerty17Jonna Frasor18Kent F. Hoskins19Zeynep Madak-Erdogan20Division of Nutritional Sciences, University of Illinois, Urbana-ChampaignFood Science and Human Nutrition Department, University of Illinois, Urbana-ChampaignDivision of Hematology/Oncology, University of Illinois at ChicagoDivision of Hematology/Oncology, University of Illinois at ChicagoDivision of Hematology/Oncology, University of Illinois at ChicagoDivision of Hematology/Oncology, University of Illinois at ChicagoDivision of Hematology/Oncology, University of Illinois at ChicagoSchool of Public Health, University of Illinois at ChicagoRobert H. Lurie Cancer Center of Northwestern UniversityRobert H. Lurie Cancer Center of Northwestern UniversityRobert H. Lurie Cancer Center of Northwestern UniversityNorthwestern Memorial HospitalNorthwestern University Feinberg School of MedicineNorthwestern University Feinberg School of MedicineNorthwestern University Feinberg School of MedicineNorthwestern University Feinberg School of MedicineNorthwestern University Feinberg School of MedicineNortheastern Illinois UniversityDepartment Physiology and Biophysics, University of Illinois at ChicagoDivision of Hematology/Oncology, University of Illinois at ChicagoDivision of Nutritional Sciences, University of Illinois, Urbana-ChampaignAbstract African American (AA) women in the United States have a 40% higher breast cancer mortality rate than Non-Hispanic White (NHW) women. The survival disparity is particularly striking among (estrogen receptor positive) ER+ breast cancer cases. The purpose of this study is to examine whether there are racial differences in metabolic pathways typically activated in patients with ER+ breast cancer. We collected pretreatment plasma from AA and NHW ER+ breast cancer cases (AA n = 48, NHW n = 54) and cancer-free controls (AA n = 100, NHW n = 48) to conduct an untargeted metabolomics analysis using gas chromatography mass spectrometry (GC–MS) to identify metabolites that may be altered in the different racial groups. Unpaired t-test combined with multiple feature selection and prediction models were employed to identify race-specific altered metabolic signatures. This was followed by the identification of altered metabolic pathways with a focus in AA patients with breast cancer. The clinical relevance of the identified pathways was further examined in PanCancer Atlas breast cancer data set from The Cancer Genome Atlas Program (TCGA). We identified differential metabolic signatures between NHW and AA patients. In AA patients, we observed decreased circulating levels of amino acids compared to healthy controls, while fatty acids were significantly higher in NHW patients. By mapping these metabolites to potential epigenetic regulatory mechanisms, this study identified significant associations with regulators of metabolism such as methionine adenosyltransferase 1A (MAT1A), DNA Methyltransferases and Histone methyltransferases for AA individuals, and Fatty acid Synthase (FASN) and Monoacylglycerol lipase (MGL) for NHW individuals. Specific gene Negative Elongation Factor Complex E (NELFE) with histone methyltransferase activity, was associated with poor survival exclusively for AA individuals. We employed a comprehensive and novel approach that integrates multiple machine learning and statistical methods, coupled with human functional pathway analyses. The metabolic profile of plasma samples identified may help elucidate underlying molecular drivers of disproportionately aggressive ER+ tumor biology in AA women. It may ultimately lead to the identification of novel therapeutic targets. To our knowledge, this is a novel finding that describes a link between metabolic alterations and epigenetic regulation in AA breast cancer and underscores the need for detailed investigations into the biological underpinnings of breast cancer health disparities.https://doi.org/10.1038/s41598-023-39215-1 |
| spellingShingle | Ashlie Santaliz-Casiano Dhruv Mehta Oana C. Danciu Hariyali Patel Landan Banks Ayesha Zaidi Jermya Buckley Garth H. Rauscher Lauren Schulte Lauren Ro Weller Deanna Taiym Elona Liko-Hazizi Natalie Pulliam Sarah M. Friedewald Seema Khan J. Julie Kim William Gradishar Scott Hegerty Jonna Frasor Kent F. Hoskins Zeynep Madak-Erdogan Identification of metabolic pathways contributing to ER+ breast cancer disparities using a machine-learning pipeline Scientific Reports |
| title | Identification of metabolic pathways contributing to ER+ breast cancer disparities using a machine-learning pipeline |
| title_full | Identification of metabolic pathways contributing to ER+ breast cancer disparities using a machine-learning pipeline |
| title_fullStr | Identification of metabolic pathways contributing to ER+ breast cancer disparities using a machine-learning pipeline |
| title_full_unstemmed | Identification of metabolic pathways contributing to ER+ breast cancer disparities using a machine-learning pipeline |
| title_short | Identification of metabolic pathways contributing to ER+ breast cancer disparities using a machine-learning pipeline |
| title_sort | identification of metabolic pathways contributing to er breast cancer disparities using a machine learning pipeline |
| url | https://doi.org/10.1038/s41598-023-39215-1 |
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