Future Lung Cancer Research Requires Precision Medicine Ecosystem

Author(s):

There is a growing need to share information across research settings and the community, with the rapid introduction of new biomarkers, cancer detection strategies, immunotherapies, and targeted therapies. This synchronization of system biology tool datasets could help create a new digital ecosystem focused on precision medicine.

Giorgio V. Scagliotti, MD

“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. “Not only by clinical decision support tools, and to not only have curated knowledge base. This will be informative for clinical decision making and research studies.”

Precision Medicine Ecosystem

A precision medicine ecosystem could bridge clinicians, laboratories, research enterprises, and clinical-information-system developers in novel ways.¹ This can include clinical-decision support from various parties and case-level databases and biobanks that receive data from clinical and research workflows. In this ecosystem, researchers and clinical labs are able to utilize all of these data sources while also contributing new information.

In this precision medicine ecosystem, factors should be considered to assess the impact of clinical development trends, Scagliotti said. 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.² The biobank and patient samples would comprise established primary lines, short-term cultures, malignant pleural mesothelioma tissue, immuno-organoids, and patient-derived xenografts. Through that, researchers evaluate iomics layers and molecular subtypes, exome NGS, global RNA sequencing, methylation EPIC, and marker analysis for drug screening and be able to gain a deeper understanding of genomics, for example.

Artificial Intelligence

Scagliotti emphasized the important role of AI, which is being specifically applied to medical imaging by highlighting suspicious regions in images, detecting indeterminate nodules, and addressing high false-positive rates and overdiagnosis. It can also be contributive to characterization of tumors and tumor monitoring that go behind traditional techniques.

“AI promises to make great strides in the qualitative interpretation of cancer imaging, including volumetric assessment of tumors overtime, extrapolation of the tumor genotype and biological course from its radiographic phenotype and prediction of clinical outcomes.”

With the advent of AI, there can be improved detection of incidental pulmonary nodules, which can be used to predict future risk of cancer development, differentiate between benign and cancerous nodules, and/or differentiate between aggressive tumors and indolent cancers.

Another step forward includes the International Association for the Study of Lung Cancer (IASLC)’s cloud-based screening registry, Early Lung Imaging Confederation (ELIC), which was developed in 2018.

The registry, which is still in its early phases, stems from prior studies that demonstrated an association between lung cancer screening and reduced lung cancer mortality.³ The system connects lung cancer screening images with biomedical data to assist with less screening backlogs, but with a larger picture to improve reliability of clinical decision-support with CT images and better the development of precise quantitative disease biomarkers.

“This is just the beginning of the story,” Scagliotti concluded.

References

Aronson SJ and Rehm HL. Building the foundation for genomics in precision medicine. Nature. 2015;526(7573):336-342. doi: 10.1038/nature15816.
TRANSCAN-2. TOPMESO. Published October 22, 2018. https://bit.ly/2ZbAMSD. Accessed July 25, 2019.
International Association for the Study of Lung Cancer. Breaking Through Limits in Lung Cancer Survival: The Lung Ambition Alliance. https://bit.ly/2S2YDkG. Accessed July 25, 2019.

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