7 Flexible probabilistic methods to unlock the clinical potential of liquid biopsy sampling
OBJECTIVES/GOALS: Decoding the origins of cell-free DNA (cfDNA) released from dying cells in a liquid biopsy sample (e.g. blood) offers the potential to provide insight into the dynamic, organism-wide changes reflective of health and disease. Thus, making cfDNA an ideal target for serial, minimally...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Cambridge University Press
2024-04-01
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| Series: | Journal of Clinical and Translational Science |
| Online Access: | https://www.cambridge.org/core/product/identifier/S205986612400030X/type/journal_article |
| _version_ | 1797226913231536128 |
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| author | Arthur Patrick McDeed Siddarth Jain Megan McNamara Anton Wellstein Ming Tan Jaeil Ahn |
| author_facet | Arthur Patrick McDeed Siddarth Jain Megan McNamara Anton Wellstein Ming Tan Jaeil Ahn |
| author_sort | Arthur Patrick McDeed |
| collection | DOAJ |
| description | OBJECTIVES/GOALS: Decoding the origins of cell-free DNA (cfDNA) released from dying cells in a liquid biopsy sample (e.g. blood) offers the potential to provide insight into the dynamic, organism-wide changes reflective of health and disease. Thus, making cfDNA an ideal target for serial, minimally invasive monitoring of disease-related changes. METHODS/STUDY POPULATION: We develop a probabilistic method that leverages the co-regulation of neighboring CpG sites on individual methylome-wide sequencing (WGBS) reads to more flexibly model cell-specific methylation compared to prior methods that focus on the methylation rate of a single CpG site. We then extend our cross-sectional model to account for sequential sampling within the same subject. The increased sampling frequency is critical to identifying the evolutionary dynamics of disease progression influencing treatment response and resistance, and disease recurrence. We utilize Bayesian inference techniques to model patient-specific longitudinal profiles of cell-type turnover in simulated serial samples. RESULTS/ANTICIPATED RESULTS: We found our model more effective at capturing a range of methylation patterns on cfDNA fragments with lower Root Mean Square Error across simulations compared to a single CpG model. We apply our model to detect significant (p < 0.05, Friedman’s test) increases in cellular contributions from lung and cardiac tissue in breast cancer patients (n=15) undergoing radiation therapy compared to baseline. We also identify signals of radiation induced toxicity to the liver in right-sided breast cancer patients (n=8) receiving radiation treatment compared to left-sided breast cancer patients (n=7). Finally, we show our extended model results in more efficient estimates of simulated cell-type turnover profiles compared to analyzing serial samples cross-sectionally, ignoring the longitudinal nature of the data. DISCUSSION/SIGNIFICANCE: Here we address an unmet need in developing novel statistical methodologies to decode the origins of methylated cfDNA obtained from liquid biopsy samples. We demonstrate the far-ranging clinical utility of serial liquid biopsy sampling to complement and advance the standards of clinical care in oncology and other pathologies. |
| first_indexed | 2024-04-24T14:32:27Z |
| format | Article |
| id | doaj.art-d254eb944f5c48b28f8234b797253ca8 |
| institution | Directory Open Access Journal |
| issn | 2059-8661 |
| language | English |
| last_indexed | 2024-04-24T14:32:27Z |
| publishDate | 2024-04-01 |
| publisher | Cambridge University Press |
| record_format | Article |
| series | Journal of Clinical and Translational Science |
| spelling | doaj.art-d254eb944f5c48b28f8234b797253ca82024-04-03T02:00:25ZengCambridge University PressJournal of Clinical and Translational Science2059-86612024-04-0182210.1017/cts.2024.307 Flexible probabilistic methods to unlock the clinical potential of liquid biopsy samplingArthur Patrick McDeed0Siddarth Jain1Megan McNamara2Anton Wellstein3Ming Tan4Jaeil Ahn5Georgetown-HowardGeorgetown-HowardGeorgetown-HowardGeorgetown-HowardGeorgetown-HowardGeorgetown-HowardOBJECTIVES/GOALS: Decoding the origins of cell-free DNA (cfDNA) released from dying cells in a liquid biopsy sample (e.g. blood) offers the potential to provide insight into the dynamic, organism-wide changes reflective of health and disease. Thus, making cfDNA an ideal target for serial, minimally invasive monitoring of disease-related changes. METHODS/STUDY POPULATION: We develop a probabilistic method that leverages the co-regulation of neighboring CpG sites on individual methylome-wide sequencing (WGBS) reads to more flexibly model cell-specific methylation compared to prior methods that focus on the methylation rate of a single CpG site. We then extend our cross-sectional model to account for sequential sampling within the same subject. The increased sampling frequency is critical to identifying the evolutionary dynamics of disease progression influencing treatment response and resistance, and disease recurrence. We utilize Bayesian inference techniques to model patient-specific longitudinal profiles of cell-type turnover in simulated serial samples. RESULTS/ANTICIPATED RESULTS: We found our model more effective at capturing a range of methylation patterns on cfDNA fragments with lower Root Mean Square Error across simulations compared to a single CpG model. We apply our model to detect significant (p < 0.05, Friedman’s test) increases in cellular contributions from lung and cardiac tissue in breast cancer patients (n=15) undergoing radiation therapy compared to baseline. We also identify signals of radiation induced toxicity to the liver in right-sided breast cancer patients (n=8) receiving radiation treatment compared to left-sided breast cancer patients (n=7). Finally, we show our extended model results in more efficient estimates of simulated cell-type turnover profiles compared to analyzing serial samples cross-sectionally, ignoring the longitudinal nature of the data. DISCUSSION/SIGNIFICANCE: Here we address an unmet need in developing novel statistical methodologies to decode the origins of methylated cfDNA obtained from liquid biopsy samples. We demonstrate the far-ranging clinical utility of serial liquid biopsy sampling to complement and advance the standards of clinical care in oncology and other pathologies.https://www.cambridge.org/core/product/identifier/S205986612400030X/type/journal_article |
| spellingShingle | Arthur Patrick McDeed Siddarth Jain Megan McNamara Anton Wellstein Ming Tan Jaeil Ahn 7 Flexible probabilistic methods to unlock the clinical potential of liquid biopsy sampling Journal of Clinical and Translational Science |
| title | 7 Flexible probabilistic methods to unlock the clinical potential of liquid biopsy sampling |
| title_full | 7 Flexible probabilistic methods to unlock the clinical potential of liquid biopsy sampling |
| title_fullStr | 7 Flexible probabilistic methods to unlock the clinical potential of liquid biopsy sampling |
| title_full_unstemmed | 7 Flexible probabilistic methods to unlock the clinical potential of liquid biopsy sampling |
| title_short | 7 Flexible probabilistic methods to unlock the clinical potential of liquid biopsy sampling |
| title_sort | 7 flexible probabilistic methods to unlock the clinical potential of liquid biopsy sampling |
| url | https://www.cambridge.org/core/product/identifier/S205986612400030X/type/journal_article |
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