Precision oncology has changed the management of advanced non-small-cell lung cancer (NSCLC). Biomarker-matched therapies now improve outcomes in an increasing number of molecularly defined subgroups. In this second paper in a Series on therapeutics in lung cancer, we summarise recent advances in NSCLC with established alterations, including EGFR, ALK, ROS1, KRAS, BRAF, RET, HER2, MET, and NTRK, and discuss emerging targets, such as NRG1 fusions, MTAP loss, and SMARCA4 deficiency. Newer generations of tyrosine kinase inhibitors, the introduction of bispecific antibodies, and antibody-drug conjugates have improved response durability, intracranial disease control, and in some settings, overall survival. However, durable benefit can remain limited by acquired resistance, tumour heterogeneity, lineage plasticity, and off-target escape, supporting repeat tissue biopsy and circulating-tumour DNA profiling to guide subsequent treatment. With several options available such as monotherapy and combination approaches, individualised treatment selection is becoming increasingly complex. We discuss these choices, including the management of CNS disease and oligoprogression, and long-term tolerability. As drug development extends beyond canonical drivers to rarer alterations and adverse co-mutations, the range of targetable disease is increasing. Further progress will depend on more effective and adaptive treatment strategies together with equitable access to comprehensive molecular profiling, timely biomarker testing, and next-generation targeted therapies.
L. Hendriks, Jessica J. Lin, D. S. Tan et al.· The Lancet Respiratory Medic...· 2 citations
Background The AI-HOPE Lung Cancer study is a multicenter initiative designed to integrate artificial intelligence (AI) and real-world data to improve outcome prediction in patients with metastatic non-small-cell lung cancer treated with first-line immunotherapy-based regimens. AI-HOPE aims to leverage machine learning (ML) models to generate individualized predictions of progression-free survival (PFS), overall survival (OS), and treatment-related toxicity in a broad, unselected population. Materials and methods Clinical and imaging data are harmonized and stored within a privacy-compliant infrastructure (San Raffaele Ai CEnter [S-RACE] platform), promoting FAIR (Findable, Accessible, Interoperable and Reusable) data principles and minimizing manual workload. The primary objective is the development of time-to-event models for PFS and OS. Complementary binary classification models will explore early progression, long-term survival, and clinically relevant toxicities. Results The study includes retrospective (from 2017) and prospective (until 2027) phases across 21 European centers. So far, 920 patients have been recruited for the study, of whom 621 have baseline imaging scans available for centralized analysis. In the AI-HOPE study, a flexible methodological approach integrates multiple ML models tailored to specific clinical questions, complemented by explainable AI tools. Multimodal models combining clinical variables with computed tomography and [18F]2-fluoro-2-deoxy-d-glucose–positron emission tomography imaging features (when available) are supported through the S-RACE platform, which provides a partially automated imaging analysis workflow. Conclusions By combining structured clinical variables and multimodal imaging data, the AI-HOPE Lung Cancer study aims to support refined risk stratification and treatment personalization, ultimately facilitating the responsible integration of AI into routine thoracic oncology practice.
F. Ogliari, M. Ferrara, J. Huijs et al.· ESMO real world data and dig...· 0 citations
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