Evolving Clinical Pathways for Perioperative Strategies in Resectable NSCLC
In this interview, Sandip Patel, MD, discusses the evolution of clinical pathways for non-small cell lung cancer (NSCLC), including the integration of perioperative strategies, the potential of immunotherapy in earlier stages, the role of artificial intelligence (AI) in treatment decision-making, and the impact of real-world data and emerging therapeutic targets.
Please state your name, title, and any relevant clinical experience.
Sandip Patel, MD: I'm Sandip Patel, MD, a medical oncologist and professor of medicine at the University of California, San Diego. I've been working in medical oncology—specifically thoracic oncology—and phase 1 trials for a little over a decade now.
How should clinical pathways evolve to incorporate perioperative strategies, such as immunotherapy or antibody-drug conjugates (ADCs), for resectable non-small cell lung cancer (NSCLC)?
Dr Patel: Standardized workflows and testing for EGFR, ALK, and, in some parts of the world, PD-L1 could be started by pathology on initial diagnosis (prior to the medical oncologist visit) to reduce the overall time to initiation of systemic treatment for chemoimmunotherapy. The use of multidisciplinary tumor boards, with involvement from surgery, radiation oncology, medical oncology, radiology, pathology, pulmonology, and others, is important to ensure that appropriate pathologic staging has been performed and that resectability has been evaluated.
What is the impact of introducing immunotherapy in earlier stages of NSCLC, such as neoadjuvant chemoimmunotherapy, and how does this have the potential to improve patient outcomes?
Dr Patel: The use of immunotherapy in earlier stages of NSCLC can improve cure rates for patients. Neoadjuvant, adjuvant, and perioperative approaches all have the potential to benefit patients and improve cure rates.
What potential do you see for artificial intelligence (AI) and machine learning in streamlining clinical pathways and improving decision-making for NSCLC treatments?
Dr Patel: I think AI assistance in oncology will be important going forward. One practical way AI prompting could have a major positive impact is by appropriately prompting primary care providers to perform low-dose CT screening on the relevant National Lung Screening Trial (NLST) population, thereby aiding early detection and improving NSCLC cure rates.
What role does real-world data play in refining pathways for both early and advanced NSCLC, particularly in diverse patient populations?
Dr Patel: Real-world evidence plays an important role in understanding patient populations who were often not included or not fit enough for clinical trials but whom we routinely treat in the real-world setting, such as end organ dysfunction, borderline pulmonary status, or ECOG 2. Understanding the benefit of interventions in these populations using real-world evidence is important.
Beyond current treatments, what novel targets or therapeutic strategies are on the horizon for NSCLC, and how might they influence future clinical pathways?
Dr Patel: Multiple ADCs and novel small-molecule inhibitors are currently in development and will likely be approved over the next several years. While biomarkers for many of the ADCs have remained elusive, AI-assisted digital pathology may uncover novel predictive biomarkers for patient selection. Novel small-molecular inhibitors targeting specific and multiple KRAS mutations, as well as particular aberrations in EGFR and HER2, are exciting but require appropriate molecular testing with next-generation sequencing (NGS) which can be performed using tissue or blood samples.
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