A novel survival prediction signature outperforms PAM50 and artificial intelligence-based feature-selection methods
The robustness of a breast cancer gene signature, the super-proliferation set (SPS), is initially tested and investigated on breast cancer cell lines from the Cancer Cell Line Encyclopaedia (CCLE). Previously, SPS was derived via a meta-analysis of 47 independent breast cancer gene signatures, bench...
| Main Authors: | , , , |
|---|---|
| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2023
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/165805 |
| _version_ | 1826116559058239488 |
|---|---|
| author | Foo, Reuben Jyong Kiat Tian, Siqi Tan, Ern Yu Goh, Wilson Wen Bin |
| author2 | School of Chemical and Biomedical Engineering |
| author_facet | School of Chemical and Biomedical Engineering Foo, Reuben Jyong Kiat Tian, Siqi Tan, Ern Yu Goh, Wilson Wen Bin |
| author_sort | Foo, Reuben Jyong Kiat |
| collection | NTU |
| description | The robustness of a breast cancer gene signature, the super-proliferation set (SPS), is initially tested and investigated on breast cancer cell lines from the Cancer Cell Line Encyclopaedia (CCLE). Previously, SPS was derived via a meta-analysis of 47 independent breast cancer gene signatures, benchmarked on survival information from clinical data in the NKI dataset. Here, relying on the stability of cell line data and associative prior knowledge, we first demonstrate through Principal Component Analysis (PCA) that SPS prioritizes survival information over secondary subtype information, surpassing both PAM50 and Boruta, an artificial intelligence-based feature-selection algorithm, in this regard. We can also extract higher resolution 'progression' information using SPS, dividing survival outcomes into several clinically relevant stages ('good', 'intermediate', and 'bad) based on different quadrants of the PCA scatterplot. Furthermore, by transferring these 'progression' annotations onto independent clinical datasets, we demonstrate the generalisability of our method on actual patient data. Finally, via the characteristic genetic profiles of each quadrant/stage, we identified efficacious drugs using their gene reversal scores that can shift signatures across quadrants/stages, in a process known as gene signature reversal. This confirms the power of meta-analytical approaches for gene signature inference in breast cancer, as well as the clinical benefit in translating these inferences onto real-world patient data for more targeted therapies. |
| first_indexed | 2024-10-01T04:13:23Z |
| format | Journal Article |
| id | ntu-10356/165805 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T04:13:23Z |
| publishDate | 2023 |
| record_format | dspace |
| spelling | ntu-10356/1658052023-04-16T15:37:46Z A novel survival prediction signature outperforms PAM50 and artificial intelligence-based feature-selection methods Foo, Reuben Jyong Kiat Tian, Siqi Tan, Ern Yu Goh, Wilson Wen Bin School of Chemical and Biomedical Engineering Lee Kong Chian School of Medicine (LKCMedicine) School of Biological Sciences Tan Tock Seng Hospital Centre for Biomedical Informatics Science::Medicine Science::Biological sciences Breast Cancer Data Science The robustness of a breast cancer gene signature, the super-proliferation set (SPS), is initially tested and investigated on breast cancer cell lines from the Cancer Cell Line Encyclopaedia (CCLE). Previously, SPS was derived via a meta-analysis of 47 independent breast cancer gene signatures, benchmarked on survival information from clinical data in the NKI dataset. Here, relying on the stability of cell line data and associative prior knowledge, we first demonstrate through Principal Component Analysis (PCA) that SPS prioritizes survival information over secondary subtype information, surpassing both PAM50 and Boruta, an artificial intelligence-based feature-selection algorithm, in this regard. We can also extract higher resolution 'progression' information using SPS, dividing survival outcomes into several clinically relevant stages ('good', 'intermediate', and 'bad) based on different quadrants of the PCA scatterplot. Furthermore, by transferring these 'progression' annotations onto independent clinical datasets, we demonstrate the generalisability of our method on actual patient data. Finally, via the characteristic genetic profiles of each quadrant/stage, we identified efficacious drugs using their gene reversal scores that can shift signatures across quadrants/stages, in a process known as gene signature reversal. This confirms the power of meta-analytical approaches for gene signature inference in breast cancer, as well as the clinical benefit in translating these inferences onto real-world patient data for more targeted therapies. Ministry of Education (MOE) National Research Foundation (NRF) Submitted/Accepted version This research/project is supported by the National Research Foundation, Singapore under its Industry Alignment Fund – Pre-positioning (IAF-PP) Funding Initiative. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not reflect the views of National Research Foundation, Singapore. WWBG also acknowledges support from a Ministry of Education (MOE), Singapore Tier 1 grant (Grant No. RS08/21). 2023-04-11T00:59:09Z 2023-04-11T00:59:09Z 2023 Journal Article Foo, R. J. K., Tian, S., Tan, E. Y. & Goh, W. W. B. (2023). A novel survival prediction signature outperforms PAM50 and artificial intelligence-based feature-selection methods. Computational Biology and Chemistry, 104, 107845-. https://dx.doi.org/10.1016/j.compbiolchem.2023.107845 1476-9271 https://hdl.handle.net/10356/165805 10.1016/j.compbiolchem.2023.107845 36889140 2-s2.0-85150917383 104 107845 en RS08/21 Computational Biology and Chemistry © 2023 Elsevier Ltd. All rights reserved. This paper was published in Computational Biology and Chemistry and is made available with permission of Elsevier Ltd. application/pdf application/pdf |
| spellingShingle | Science::Medicine Science::Biological sciences Breast Cancer Data Science Foo, Reuben Jyong Kiat Tian, Siqi Tan, Ern Yu Goh, Wilson Wen Bin A novel survival prediction signature outperforms PAM50 and artificial intelligence-based feature-selection methods |
| title | A novel survival prediction signature outperforms PAM50 and artificial intelligence-based feature-selection methods |
| title_full | A novel survival prediction signature outperforms PAM50 and artificial intelligence-based feature-selection methods |
| title_fullStr | A novel survival prediction signature outperforms PAM50 and artificial intelligence-based feature-selection methods |
| title_full_unstemmed | A novel survival prediction signature outperforms PAM50 and artificial intelligence-based feature-selection methods |
| title_short | A novel survival prediction signature outperforms PAM50 and artificial intelligence-based feature-selection methods |
| title_sort | novel survival prediction signature outperforms pam50 and artificial intelligence based feature selection methods |
| topic | Science::Medicine Science::Biological sciences Breast Cancer Data Science |
| url | https://hdl.handle.net/10356/165805 |
| work_keys_str_mv | AT fooreubenjyongkiat anovelsurvivalpredictionsignatureoutperformspam50andartificialintelligencebasedfeatureselectionmethods AT tiansiqi anovelsurvivalpredictionsignatureoutperformspam50andartificialintelligencebasedfeatureselectionmethods AT tanernyu anovelsurvivalpredictionsignatureoutperformspam50andartificialintelligencebasedfeatureselectionmethods AT gohwilsonwenbin anovelsurvivalpredictionsignatureoutperformspam50andartificialintelligencebasedfeatureselectionmethods AT fooreubenjyongkiat novelsurvivalpredictionsignatureoutperformspam50andartificialintelligencebasedfeatureselectionmethods AT tiansiqi novelsurvivalpredictionsignatureoutperformspam50andartificialintelligencebasedfeatureselectionmethods AT tanernyu novelsurvivalpredictionsignatureoutperformspam50andartificialintelligencebasedfeatureselectionmethods AT gohwilsonwenbin novelsurvivalpredictionsignatureoutperformspam50andartificialintelligencebasedfeatureselectionmethods |