DEcancer: Machine learning framework tailored to liquid biopsy based cancer detection and biomarker signature selection
Cancer is a leading cause of mortality worldwide. Over 50% of cancers are diagnosed late, rendering many treatments ineffective. Existing liquid biopsy studies demonstrate a minimally invasive and inexpensive approach for disease detection but lack parsimonious biomarker selection, exhibit poor canc...
| Main Authors: | , , , , , , , , , |
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| Format: | Journal article |
| Language: | English |
| Published: |
Cell Press
2023
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| _version_ | 1826310530377187328 |
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| author | Halner, A Hankey, L Liang, Z Pozzetti, F Szulc, D Mi, E Liu, G Kessler, BM Syed, J Liu, PJ |
| author_facet | Halner, A Hankey, L Liang, Z Pozzetti, F Szulc, D Mi, E Liu, G Kessler, BM Syed, J Liu, PJ |
| author_sort | Halner, A |
| collection | OXFORD |
| description | Cancer is a leading cause of mortality worldwide. Over 50% of cancers are diagnosed late, rendering many treatments ineffective. Existing liquid biopsy studies demonstrate a minimally invasive and inexpensive approach for disease detection but lack parsimonious biomarker selection, exhibit poor cancer detection performance and lack appropriate validation and testing. We established a tailored machine learning pipeline, DEcancer, for liquid biopsy analysis that addresses these limitations and improved performance. In a test set from a published cohort of 1,005 patients including 8 cancer types and 812 cancer-free individuals, DEcancer increased stage 1 cancer detection sensitivity across cancer types from 48 to 90%. In addition, with a test set cohort of patients from a high dimensional proteomics dataset of 61 lung cancer patients and 80 cancer-free individuals, DEcancer’s performance using a 14-43 protein panel was comparable to 1,000 original proteins. DEcancer is a promising tool which may facilitate improved cancer detection and management. |
| first_indexed | 2024-03-07T07:53:21Z |
| format | Journal article |
| id | oxford-uuid:ac2e3f1d-1101-4496-800f-4e97728a6cae |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T07:53:21Z |
| publishDate | 2023 |
| publisher | Cell Press |
| record_format | dspace |
| spelling | oxford-uuid:ac2e3f1d-1101-4496-800f-4e97728a6cae2023-08-02T13:39:53ZDEcancer: Machine learning framework tailored to liquid biopsy based cancer detection and biomarker signature selectionJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:ac2e3f1d-1101-4496-800f-4e97728a6caeEnglishSymplectic ElementsCell Press2023Halner, AHankey, LLiang, ZPozzetti, FSzulc, DMi, ELiu, GKessler, BMSyed, JLiu, PJCancer is a leading cause of mortality worldwide. Over 50% of cancers are diagnosed late, rendering many treatments ineffective. Existing liquid biopsy studies demonstrate a minimally invasive and inexpensive approach for disease detection but lack parsimonious biomarker selection, exhibit poor cancer detection performance and lack appropriate validation and testing. We established a tailored machine learning pipeline, DEcancer, for liquid biopsy analysis that addresses these limitations and improved performance. In a test set from a published cohort of 1,005 patients including 8 cancer types and 812 cancer-free individuals, DEcancer increased stage 1 cancer detection sensitivity across cancer types from 48 to 90%. In addition, with a test set cohort of patients from a high dimensional proteomics dataset of 61 lung cancer patients and 80 cancer-free individuals, DEcancer’s performance using a 14-43 protein panel was comparable to 1,000 original proteins. DEcancer is a promising tool which may facilitate improved cancer detection and management. |
| spellingShingle | Halner, A Hankey, L Liang, Z Pozzetti, F Szulc, D Mi, E Liu, G Kessler, BM Syed, J Liu, PJ DEcancer: Machine learning framework tailored to liquid biopsy based cancer detection and biomarker signature selection |
| title | DEcancer: Machine learning framework tailored to liquid biopsy based cancer detection and biomarker signature selection |
| title_full | DEcancer: Machine learning framework tailored to liquid biopsy based cancer detection and biomarker signature selection |
| title_fullStr | DEcancer: Machine learning framework tailored to liquid biopsy based cancer detection and biomarker signature selection |
| title_full_unstemmed | DEcancer: Machine learning framework tailored to liquid biopsy based cancer detection and biomarker signature selection |
| title_short | DEcancer: Machine learning framework tailored to liquid biopsy based cancer detection and biomarker signature selection |
| title_sort | decancer machine learning framework tailored to liquid biopsy based cancer detection and biomarker signature selection |
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