Personalised Drug Identifier for Cancer Treatment with Transformers using Auxiliary Information
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arXiv
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| Format: | Preprint |
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2024
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| author | Jayagopal, Aishwarya Xue, Hansheng He, Ziyang Walsh, Robert J. Hariprasannan, Krishna Kumar Tan, David Shao Peng Tan, Tuan Zea Pitt, Jason J. Jeyasekharan, Anand D. Rajan, Vaibhav |
| author_facet | Jayagopal, Aishwarya Xue, Hansheng He, Ziyang Walsh, Robert J. Hariprasannan, Krishna Kumar Tan, David Shao Peng Tan, Tuan Zea Pitt, Jason J. Jeyasekharan, Anand D. Rajan, Vaibhav |
| contents | Cancer remains a global challenge due to its growing clinical and economic burden. Its uniquely personal manifestation, which makes treatment difficult, has fuelled the quest for personalized treatment strategies. Thus, genomic profiling is increasingly becoming part of clinical diagnostic panels. Effective use of such panels requires accurate drug response prediction (DRP) models, which are challenging to build due to limited labelled patient data. Previous methods to address this problem have used various forms of transfer learning. However, they do not explicitly model the variable length sequential structure of the list of mutations in such diagnostic panels. Further, they do not utilize auxiliary information (like patient survival) for model training. We address these limitations through a novel transformer based method, which surpasses the performance of state-of-the-art DRP models on benchmark data. We also present the design of a treatment recommendation system (TRS), which is currently deployed at the National University Hospital, Singapore and is being evaluated in a clinical trial. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_10551 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Personalised Drug Identifier for Cancer Treatment with Transformers using Auxiliary Information Jayagopal, Aishwarya Xue, Hansheng He, Ziyang Walsh, Robert J. Hariprasannan, Krishna Kumar Tan, David Shao Peng Tan, Tuan Zea Pitt, Jason J. Jeyasekharan, Anand D. Rajan, Vaibhav Machine Learning Quantitative Methods Cancer remains a global challenge due to its growing clinical and economic burden. Its uniquely personal manifestation, which makes treatment difficult, has fuelled the quest for personalized treatment strategies. Thus, genomic profiling is increasingly becoming part of clinical diagnostic panels. Effective use of such panels requires accurate drug response prediction (DRP) models, which are challenging to build due to limited labelled patient data. Previous methods to address this problem have used various forms of transfer learning. However, they do not explicitly model the variable length sequential structure of the list of mutations in such diagnostic panels. Further, they do not utilize auxiliary information (like patient survival) for model training. We address these limitations through a novel transformer based method, which surpasses the performance of state-of-the-art DRP models on benchmark data. We also present the design of a treatment recommendation system (TRS), which is currently deployed at the National University Hospital, Singapore and is being evaluated in a clinical trial. |
| title | Personalised Drug Identifier for Cancer Treatment with Transformers using Auxiliary Information |
| topic | Machine Learning Quantitative Methods |
| url | https://arxiv.org/abs/2402.10551 |