Liquid Biopsies in Oncology and the Current Regulatory Landscape

Strotman, Lindsay N.; Millner, Lori M.; Valdes, Roland; Linder, Mark W.

doi:10.1007/s40291-016-0220-5

Cited by 19 publications

(11 citation statements)

References 68 publications

(77 reference statements)

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“…The need for “liquid biopsy” arose from the fact that molecular profiling of tumours relies on invasive surgical procedures, often associated with procedural risks/complications, and tissue collection is unfeasible for many cancer patients. The identification of circulating biomarkers such as circulating tumour cells (CTCs) or circulating tumour-derived nucleic acids thus provided a potential solution for cancer characterization and management [ 38 ].…”

Section: Liquid Biopsymentioning

confidence: 99%

Radiomics and liquid biopsy in oncology: the holons of systems medicine

Neri

Paiar

et al. 2018

Insights Imaging

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Radiomics is a process of extraction and analysis of quantitative features from diagnostic images. Liquid biopsy is a test done on a sample of blood to look for cancer cells or for pieces of tumourigenic DNA circulating in the blood. Radiomics and liquid biopsy have great potential in oncology, since both are minimally invasive, easy to perform, and can be repeated in patient follow-up visits, enabling the extraction of valuable information regarding tumour type, aggressiveness, progression, and response to treatment. Both methods are in their infancy, with major evidence of application in lung and gastrointestinal cancer, while still undergoing evaluation in other cancer types. In this paper, the main oncologic applications of radiomics and liquid biopsy are reviewed, and a synergistic approach incorporating both tests for cancer diagnosis and follow-up is discussed within the context of systems medicine.Teaching Points• Radiomics is a process of extraction and analysis of quantitative features from diagnostic images.• Most clinical applications of radiomics are in the field of oncologic imaging.• Radiomics applies to all imaging modalities.• A cluster of radiomic features is a “radiomic signature”.• Machine learning may improve the efficacy of radiomics analysis.

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Section: Liquid Biopsymentioning

confidence: 99%

Radiomics and liquid biopsy in oncology: the holons of systems medicine

Neri

Paiar

et al. 2018

Insights Imaging

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“…Another promising avenue to reduce cancer burden is to enable the detection of cancer cells at the earliest possible time. Recently, sizable research efforts have been dedicated to the field of ‘liquid biopsies’—the detection of small amounts of circulating tumor DNA in the blood of patients—before tumors are visible though imaging [ 14 – 16 ]. Although the accuracy of detecting cancer-related events in liquid biopsies requires further improvement, the concept of cancer early detection and treatment outcome prediction from a simple blood test has prompted significant hope for many cancer patients and interest from the private sector; Illumina has recently launched a spinoff company, GRAIL [ 17 ], that aims to market a simple blood test for early detection of cancer in asymptomatic patients.…”

Section: The Contribution Of Genomics To Pcm Practicementioning

confidence: 99%

Integrating precision cancer medicine into healthcare—policy, practice, and research challenges

et al. 2016

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Precision medicine (PM) can be defined as a predictive, preventive, personalized, and participatory healthcare service delivery model. Recent developments in molecular biology and information technology make PM a reality today through the use of massive amounts of genetic, ‘omics’, clinical, environmental, and lifestyle data. With cancer being one of the most prominent public health threats in developed countries, both the research community and governments have been investing significant time, money, and efforts in precision cancer medicine (PCM). Although PCM research is extremely promising, a number of hurdles still remain on the road to an optimal integration of standardized and evidence-based use of PCM in healthcare systems. Indeed, PCM raises a number of technical, organizational, ethical, legal, social, and economic challenges that have to be taken into account in the development of an appropriate health policy framework. Here, we highlight some of the more salient issues regarding the standards needed for integration of PCM into healthcare systems, and we identify fields where more research is needed before policy can be implemented. Key challenges include, but are not limited to, the creation of new standards for the collection, analysis, and sharing of samples and data from cancer patients, and the creation of new clinical trial designs with renewed endpoints. We believe that these issues need to be addressed as a matter of priority by public health policymakers in the coming years for a better integration of PCM into healthcare.

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“…Liquid biopsy [ 7 , 11 ], which surmounts the limitation of tissue biopsy, is evaluated as a potential tool for early cancer detection and monitoring [ 12 ]. By sampling from blood, stool, urine, saliva, and other fluid samples, liquid biopsy provides a non-invasive and feasible cancer detection service [ 13 , 14 , 15 , 16 ]. Compared with tissue biopsy, liquid biopsy is also more comprehensive to evaluate tumor heterogeneity since tumor sites can release aberrant signals into body fluid [ 17 , 18 ].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Protocols in Early Cancer Detection Based on Liquid Biopsy: A Survey

Liu

Chen

Petinrin

et al. 2021

Life

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With the advances of liquid biopsy technology, there is increasing evidence that body fluid such as blood, urine, and saliva could harbor the potential biomarkers associated with tumor origin. Traditional correlation analysis methods are no longer sufficient to capture the high-resolution complex relationships between biomarkers and cancer subtype heterogeneity. To address the challenge, researchers proposed machine learning techniques with liquid biopsy data to explore the essence of tumor origin together. In this survey, we review the machine learning protocols and provide corresponding code demos for the approaches mentioned. We discuss algorithmic principles and frameworks extensively developed to reveal cancer mechanisms and consider the future prospects in biomarker exploration and cancer diagnostics.

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Liquid Biopsies in Oncology and the Current Regulatory Landscape

Cited by 19 publications

References 68 publications

Radiomics and liquid biopsy in oncology: the holons of systems medicine

Radiomics and liquid biopsy in oncology: the holons of systems medicine

Integrating precision cancer medicine into healthcare—policy, practice, and research challenges

Machine Learning Protocols in Early Cancer Detection Based on Liquid Biopsy: A Survey

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