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Published on: February 25, 2020
Emerging Techniques of Translational Research in Immuno-Oncology: A Focus on Non-Small Cell Lung Cancer
Mora Guardamagna1,2, Eduardo Zamorano2, Victor Albarrán-Artahona1
1Department of Cancer Medicine, Gustave Roussy, Paris-Saclay University, 94805 Villejuif, France.
Abstract:
The advent of personalized medicine and novel therapeutic strategies has transformed the treatment landscape of non-small cell lung cancer (NSCLC), significantly improving patient survival. However, only a minority of patients experience a durable benefit, as intrinsic or acquired resistance remains a major challenge. Understanding the complex mechanisms of resistance-linked to tumor biology, the tumor microenvironment (TME), and host factors-is crucial to overcoming these barriers. Recent innovations in diagnostics, including artificial intelligence and liquid biopsy, offer promising tools to refine therapeutic decisions. Machine Learning and Deep Learning provide predictive algorithms that enhance diagnostic accuracy and prognostic assessment. Techniques like single-cell RNA sequencing and pathomics offer deeper insights into the role of the TME. Liquid biopsy, as a minimally invasive method, enables real-time detection of circulating tumor components, facilitating the identification of predictive and prognostic biomarkers and illuminating tumor heterogeneity. These translational research advances are revolutionizing the understanding of cancer biology and are key to optimizing personalized treatment strategies. This review highlights emerging tools aimed at improving diagnostic and therapeutic precision in NSCLC, underscoring their role in decoding the interplay between tumor cells, the TME, and the host to ultimately improve patient outcomes.
Insights
Personalized medicine improves non-small cell lung cancer (NSCLC) survival, but resistance is a challenge. New diagnostic tools like AI and liquid biopsy help overcome resistance and optimize NSCLC treatments.
Area of Science:
- Oncology
- Translational Research
- Medical Diagnostics
Background:
- Personalized medicine and novel therapies have advanced non-small cell lung cancer (NSCLC) treatment, improving survival rates.
- However, intrinsic or acquired resistance limits durable benefits for many patients, necessitating a deeper understanding of resistance mechanisms.
- Tumor biology, the tumor microenvironment (TME), and host factors are critical components influencing treatment response and resistance in NSCLC.
Purpose of the Study:
- To review emerging diagnostic and therapeutic tools for improving precision in non-small cell lung cancer (NSCLC) treatment.
- To highlight how these tools aid in understanding the complex interplay between tumor cells, the TME, and host factors.
- To emphasize the role of advanced diagnostics in overcoming resistance and optimizing personalized treatment strategies for improved patient outcomes.
Main Methods:
- Review of recent innovations in diagnostics, including artificial intelligence (AI), machine learning (ML), and deep learning (DL) for predictive algorithms.
- Exploration of advanced techniques such as single-cell RNA sequencing and pathomics for deeper insights into the TME.
- Discussion of liquid biopsy as a minimally invasive method for real-time detection of circulating tumor components and biomarkers.
Main Results:
- AI, ML, and DL enhance diagnostic accuracy and prognostic assessment in NSCLC.
- Single-cell RNA sequencing and pathomics provide granular insights into TME composition and function.
- Liquid biopsy facilitates real-time monitoring of tumor heterogeneity and identification of predictive/prognostic biomarkers.
Conclusions:
- Emerging diagnostic tools are revolutionizing the understanding of NSCLC biology and resistance mechanisms.
- These advancements are crucial for optimizing personalized treatment strategies and overcoming therapeutic barriers.
- Decoding the complex interactions within the tumor ecosystem is key to improving patient outcomes in NSCLC.
