Future Lung Cancer Research Requires Precision Medicine Ecosystem

Future Lung Cancer Research Requires Precision Medicine Ecosystem

Publication date: Jul 26, 2019

This synchronization of system biology tool datasets could help create a new digital ecosystem focused on precision medicine, explained Giorgio V. Scagliotti, MD, PhD. -In the digital era, the ‘5 P’s’ of healthcare will predominate-preventive, predictive, participatory, personalized, [and] pertinent,” said Scagliotti, adding that a more personalized research strategy can lead to greater evolutions in research for biomedicine and prevention, as well as clinical medicine. In his presentation, -Changing Translational Research in NSCLC: Future Prospects,” during the 20th Annual International Lung Cancer Congress, Scagliotti, a professor of oncology of the University of Torino, Italy, discussed the promising, yet limited, evolution of therapeutic options, pointing to steps being taken to create a larger precision medicine ecosystem. -All of these kinds of things are exciting, but we need to think ahead, and we think to look for a new sort of precision medicine ecosystem,” he explained.

This includes digital health and mobile technologies, an increased focus on patient-reported outcomes (PROs), the emergence of curated real-world data sources, the use of predictive analytics and artificial intelligence (AI), shifts in the classes of agents being evaluated, availability of biomarker assays, changes in the regulatory landscape, and the availability of pools of pre-screened patients or direct-to-patient recruitment. Mobile technologies will also impact clinical trial designs, as they have already transformed clinical development, Scagliotti explained, citing telemedicine and virtual physician visits, connected biometric sensors, consumer mobile apps, disease management apps, consumer wearables, in-home connected virtual assessments, and web-based interactive programs. These tools have led to increased data sources-continuous data, contextual metadata, real-time data, and electronic PRO data-before evolving to facets like novel endpoints, digital biomarkers, companion apps, virtual trials and patient-centric designs, patient safety and centralized monitoring, virtual electronic consent, direct-to-patient recruitment, and work burden. One forward-thinking strategy is the TranslatiOnal Platform for de-orphaning malignant pleural MESOthelioma (TOPMESO), a biobank and patient samples that, through analyses, could lead to more effective therapeutic strategies for clinical studies and also determine minimally invasive biomarkers. The biobank and patient samples would comprise established primary lines, short-term cultures, malignant pleural mesothelioma tissue, immuno-organoids, and patient-derived xenografts.

Concepts Keywords
AI Immunotherapies
Artificial Intelligence Volumetric assessment tumors
Biobank NSCLC
Biobanks Malignant pleural mesothelioma
Biomarker Overtime extrapolation tumor
Biomarkers Digital mobile technologies
Biomedicine Healthcare
Biometric Virtual assessments web
Bridge Imaging
Clinical Medicine Clinical tools
Clinical Trial Drug screening
Congress Artificial intelligence
Digital Telemedicine
Ecosystem Genotype
Evolution Digital mobile technologies
Exome Biobank
Extrapolation Genomics
False Positive Precision medicine
Genomics Mesothelioma
Genotype Biomarkers
Great Strides Clinical medicine
Healthcare Branches of biology
Immunotherapies
Italy
Malignant
Malignant Pleural Mesothelioma
Mesothelioma
Metadata
Methylation
Minimally Invasive
Mobile
Mobile Technologies
Nodules
Overdiagnosis
Overtime
Patient Reported Outcomes
Patient Safety
PhD
Phenotype
Physician
Pleural
Precision Medicine
Predictive Analytics
Radiographic
Sequencing
Synchronization
Targeted Therapies
Telemedicine
Torino
Tumor
Wearables
Xenografts

Semantics

Type Source Name
disease MESH Lung Cancer
disease DOID Lung Cancer
disease MESH community
disease MESH cancer
disease DOID cancer
gene UNIPROT PDC
gene UNIPROT ESR1
gene UNIPROT ERAL1
disease DOID NSCLC
gene UNIPROT NR4A2
gene UNIPROT ALG3
gene UNIPROT IMPACT
disease MESH development
gene UNIPROT PROS1
gene UNIPROT CTSB
gene UNIPROT SMIM10L2B
gene UNIPROT SMIM10L2A
disease DOID malignant pleural MESOthelioma
pathway BSID Methylation
disease MESH overdiagnosis
gene UNIPROT RXFP2

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