Moanna: Multi-Omics Autoencoder-Based Neural Network Algorithm for Predicting Breast Cancer Subtypes
Cancer subtyping delivers valuable insights into the study of cancer heterogeneity and fulfills an essential step toward personalized medicine. For example, studies in breast cancer have shown that cancer subtypes based on molecular differences are associated with different patient survival and trea...
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| Format: | Article |
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IEEE
2023-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10029336/ |
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| author | Richard Lupat Rashindrie Perera Sherene Loi Jason Li |
| author_facet | Richard Lupat Rashindrie Perera Sherene Loi Jason Li |
| author_sort | Richard Lupat |
| collection | DOAJ |
| description | Cancer subtyping delivers valuable insights into the study of cancer heterogeneity and fulfills an essential step toward personalized medicine. For example, studies in breast cancer have shown that cancer subtypes based on molecular differences are associated with different patient survival and treatment responses. However, recent studies have suggested inconsistent breast cancer subtype classifications using alternative approaches, suggesting that current methods are yet to be optimized. Existing computation-based methods have also been limited by their dependency on incomplete prior knowledge and ineffectiveness in handling high-dimensional data beyond gene expression. Here, we propose a novel deep-learning-based algorithm, Moanna, that is trained to integrate multi-omics data for predicting breast cancer subtypes. Moanna’s architecture consists of a semi-supervised Autoencoder attached to a multi-task learning network for generalizing the combination of gene expression, copy number and somatic mutation data. We trained Moanna on a subset of the METABRIC breast cancer dataset and evaluated the performance on the remaining hold-out METABRIC samples and a fully independent cohort of TCGA samples. We evaluated our use of Autoencoder against other dimensionality reduction techniques and demonstrated its superiority in learning patterns associated with breast cancer subtypes. The overall Moanna model also achieved high accuracy in predicting samples’ ER status (96%), differentiating basal-like samples (98%), and classifying samples into PAM50 subtypes (85%). Moreover, Moanna’s predicted subtypes show a stronger correlation with patient survival when compared to the original PAM50 subtypes. |
| format | Article |
| id | doaj-art-1a35ec6ee62d4fd793cadaba0110e28f |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-1a35ec6ee62d4fd793cadaba0110e28f2025-08-20T04:03:21ZengIEEEIEEE Access2169-35362023-01-0111109121092410.1109/ACCESS.2023.324051510029336Moanna: Multi-Omics Autoencoder-Based Neural Network Algorithm for Predicting Breast Cancer SubtypesRichard Lupat0https://orcid.org/0000-0002-6435-7100Rashindrie Perera1https://orcid.org/0000-0001-6822-2900Sherene Loi2https://orcid.org/0000-0001-6137-9171Jason Li3https://orcid.org/0000-0002-1150-3549Division of Cancer Research, Peter MacCallum Cancer Centre, Melbourne, VIC, AustraliaDivision of Cancer Research, Peter MacCallum Cancer Centre, Melbourne, VIC, AustraliaDivision of Cancer Research, Peter MacCallum Cancer Centre, Melbourne, VIC, AustraliaDivision of Cancer Research, Peter MacCallum Cancer Centre, Melbourne, VIC, AustraliaCancer subtyping delivers valuable insights into the study of cancer heterogeneity and fulfills an essential step toward personalized medicine. For example, studies in breast cancer have shown that cancer subtypes based on molecular differences are associated with different patient survival and treatment responses. However, recent studies have suggested inconsistent breast cancer subtype classifications using alternative approaches, suggesting that current methods are yet to be optimized. Existing computation-based methods have also been limited by their dependency on incomplete prior knowledge and ineffectiveness in handling high-dimensional data beyond gene expression. Here, we propose a novel deep-learning-based algorithm, Moanna, that is trained to integrate multi-omics data for predicting breast cancer subtypes. Moanna’s architecture consists of a semi-supervised Autoencoder attached to a multi-task learning network for generalizing the combination of gene expression, copy number and somatic mutation data. We trained Moanna on a subset of the METABRIC breast cancer dataset and evaluated the performance on the remaining hold-out METABRIC samples and a fully independent cohort of TCGA samples. We evaluated our use of Autoencoder against other dimensionality reduction techniques and demonstrated its superiority in learning patterns associated with breast cancer subtypes. The overall Moanna model also achieved high accuracy in predicting samples’ ER status (96%), differentiating basal-like samples (98%), and classifying samples into PAM50 subtypes (85%). Moreover, Moanna’s predicted subtypes show a stronger correlation with patient survival when compared to the original PAM50 subtypes.https://ieeexplore.ieee.org/document/10029336/Feature extractioncancer subtypingartificial neural networksmachine learningclassification algorithmscancer genomics |
| spellingShingle | Richard Lupat Rashindrie Perera Sherene Loi Jason Li Moanna: Multi-Omics Autoencoder-Based Neural Network Algorithm for Predicting Breast Cancer Subtypes IEEE Access Feature extraction cancer subtyping artificial neural networks machine learning classification algorithms cancer genomics |
| title | Moanna: Multi-Omics Autoencoder-Based Neural Network Algorithm for Predicting Breast Cancer Subtypes |
| title_full | Moanna: Multi-Omics Autoencoder-Based Neural Network Algorithm for Predicting Breast Cancer Subtypes |
| title_fullStr | Moanna: Multi-Omics Autoencoder-Based Neural Network Algorithm for Predicting Breast Cancer Subtypes |
| title_full_unstemmed | Moanna: Multi-Omics Autoencoder-Based Neural Network Algorithm for Predicting Breast Cancer Subtypes |
| title_short | Moanna: Multi-Omics Autoencoder-Based Neural Network Algorithm for Predicting Breast Cancer Subtypes |
| title_sort | moanna multi omics autoencoder based neural network algorithm for predicting breast cancer subtypes |
| topic | Feature extraction cancer subtyping artificial neural networks machine learning classification algorithms cancer genomics |
| url | https://ieeexplore.ieee.org/document/10029336/ |
| work_keys_str_mv | AT richardlupat moannamultiomicsautoencoderbasedneuralnetworkalgorithmforpredictingbreastcancersubtypes AT rashindrieperera moannamultiomicsautoencoderbasedneuralnetworkalgorithmforpredictingbreastcancersubtypes AT shereneloi moannamultiomicsautoencoderbasedneuralnetworkalgorithmforpredictingbreastcancersubtypes AT jasonli moannamultiomicsautoencoderbasedneuralnetworkalgorithmforpredictingbreastcancersubtypes |