Redefining precision radiotherapy through liquid biopsy

McLaren, D. B.; Aitman, T. J.

doi:10.1038/s41416-023-02398-5

Cited by 7 publications

(4 citation statements)

References 39 publications

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“…The kinetics of ctDNA under radiation treatment are summarized in a review by McLaren and Aitman [17] and generally agree with predictions from our mechanistic model. As in targeted therapy and chemotherapy, radiotherapy patients have exhibited transient rises in ctDNA followed by eventual decreases within two weeks of treatment initiation [14,29,19].…”

Section: Radiotherapysupporting

confidence: 76%

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Early ctDNA kinetics as a dynamic biomarker of cancer treatment response

Li,

Lou,

Leder

et al. 2024

Preprint

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Circulating tumor DNA assays are promising tools for the prediction of cancer treatment response. Here, we build a framework for the design of ctDNA biomarkers of therapy response that incorporate variations in ctDNA dynamics driven by specific treatment mechanisms. We develop mathematical models of ctDNA kinetics driven by tumor response to several therapy classes, and utilize them to simulate randomized virtual patient cohorts to test candidate biomarkers. Using this approach, we propose specific biomarkers, based on ctDNA longitudinal features, for targeted therapy, chemotherapy and radiation therapy. We evaluate and demonstrate the efficacy of these biomarkers in predicting treatment response within a randomized virtual patient cohort dataset. These biomarkers are based on novel proposals for ctDNA sampling protocols, consisting of frequent sampling within a compact time window surrounding therapy initiation – which we hypothesize to hold valuable prognostic information on longer-term treatment response. This study highlights a need for tailoring ctDNA sampling protocols and interpretation methodology to specific biological mechanisms of therapy response, and it provides a novel modeling and simulation framework for doing so. In addition, it highlights the potential of ctDNA assays for making early, rapid predictions of treatment response within the first days or weeks of treatment, and generates hypotheses for further clinical testing.

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Section: Radiotherapysupporting

confidence: 76%

“…For each patient, we generated a survival rate S in (0.7, 1.0) that represented the proportion of tumor cells that survived each dose. We also generate a lag parameter ℓ in (0.2, 0.3) to capture the 3 − 5 day lag time described by [17].…”

Section: Simulations Of Patient Cohortsmentioning

confidence: 99%

Early ctDNA kinetics as a dynamic biomarker of cancer treatment response

Li,

Lou,

Leder

et al. 2024

Preprint

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“…For example, the expression of circulating miR-221/222 correlates with response to and resistance to tamoxifen in the luminal subtype of breast cancer patients ( Patellongi et al, 2023 ), circRNA_047733 can be used as a biomarker for risk assessment of lymph node metastasis in patients with oral squamous cell carcinoma ( Deng et al, 2023 ), and baseline ctDNA mutation frequency can be used as a prognostic marker in patients with metastatic colorectal cancer ( Bachet et al, 2023 ). In addition, measurement of ctDNA at specific time points by liquid biopsy can also be used as a biomarker of efficacy and toxicity to guide the dose and schedule of radiotherapy in cancer patients ( McLaren and Aitman, 2023 ).…”

Section: Omics Data In Cancer Personalized Therapymentioning

confidence: 99%

Machine learning in onco-pharmacogenomics: a path to precision medicine with many challenges

Mondello,

Dal Bo,

Toffoli

et al. 2024

Front. Pharmacol.

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Over the past two decades, Next-Generation Sequencing (NGS) has revolutionized the approach to cancer research. Applications of NGS include the identification of tumor specific alterations that can influence tumor pathobiology and also impact diagnosis, prognosis and therapeutic options. Pharmacogenomics (PGx) studies the role of inheritance of individual genetic patterns in drug response and has taken advantage of NGS technology as it provides access to high-throughput data that can, however, be difficult to manage. Machine learning (ML) has recently been used in the life sciences to discover hidden patterns from complex NGS data and to solve various PGx problems. In this review, we provide a comprehensive overview of the NGS approaches that can be employed and the different PGx studies implicating the use of NGS data. We also provide an excursus of the ML algorithms that can exert a role as fundamental strategies in the PGx field to improve personalized medicine in cancer.

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“…Altogether, cfDNA analysis is of utmost importance for the RT precision that needs personalized irradiation dose adjustment during a fractionated scheme for maximizing therapy efficacy, while limiting radiotoxicity to levels that do not produce prolonged impairment of the life quality [181]. Moreover, the cfDNA approach is also informative on the radiation-induced resistance of tumors, requiring a dose increase in the next irradiation session or an adjunctive therapy to increase the radiosensitivity of tumor cells [182][183][184].…”

Section: Cell-free Dna-based Biomarkersmentioning

confidence: 99%

Molecular Biomarkers for Predicting Cancer Patient Radiosensitivity and Radiotoxicity in Clinical Practice

Gkikoudi,

Kalospyros,

Triantopoulou

et al. 2023

Applied Sciences

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Radiotherapy (RT) is a major part of cancer treatment. The reported variability in patient response to this modality can interfere with the continuation of best-possible care, promote side effects, and lead to long-term morbidity. Tools to predict a patient’s response to radiation could be highly useful in improving therapeutic outcomes while minimizing unnecessary and toxic exposure to radiation. This study investigates the potential of using molecular biomarkers as predictors of radiosensitivity in clinical practice. We review relative studies researching the positive correlation between various molecular biomarkers and patient radiosensitivity, including DNA damage response and repair proteins, inflammation and apoptosis markers, cell cycle regulators, and other biological markers. The clinical perspectives and applicability of these biomarkers in the prediction of radiosensitivity are also critically discussed. Conclusively, we underline the dynamics of molecular biomarkers to improve the efficacy and safety of radiotherapy in clinical practice and highlight the need for further research in this field. Identification of the most prominent markers is crucial for the personalization of therapies entailing ionizing radiation.

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Redefining precision radiotherapy through liquid biopsy

Cited by 7 publications

References 39 publications

Early ctDNA kinetics as a dynamic biomarker of cancer treatment response

Early ctDNA kinetics as a dynamic biomarker of cancer treatment response

Machine learning in onco-pharmacogenomics: a path to precision medicine with many challenges

Molecular Biomarkers for Predicting Cancer Patient Radiosensitivity and Radiotoxicity in Clinical Practice

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