Multi-source data approach for personalized outcome prediction in lung cancer screening: update from the NELSON trial.

Trials show that low-dose computed tomography (CT) lung cancer screening in long-term (ex-)smokers reduces lung cancer mortality. However, many individuals were exposed to unnecessary diagnostic procedures. This project aims to improve the efficiency of lung cancer screening by identifying high-risk participants, and improving risk discrimination for nodules. This study is an extension of the Dutch-Belgian Randomized Lung Cancer Screening Trial, with a focus on personalized outcome prediction (NELSON-POP). New data will be added on genetics, air pollution, malignancy risk for lung nodules, and CT biomarkers beyond lung nodules (emphysema, coronary calcification, bone density, vertebral height and body composition). The roles of polygenic risk scores and air pollution in screen-detected lung cancer diagnosis and survival will be established. The association between the AI-based nodule malignancy score and lung cancer will be evaluated at baseline and incident screening rounds. The association of chest CT imaging biomarkers with outcomes will be established. Based on these results, multisource prediction models for pre-screening and post-baseline-screening participant selection and nodule management will be developed. The new models will be externally validated. We hypothesize that we can identify 15-20% participants with low-risk of lung cancer or short life expectancy and thus prevent ~140,000 Dutch individuals from being screened unnecessarily. We hypothesize that our models will improve the specificity of nodule management by 10% without loss of sensitivity as compared to assessment of nodule size/growth alone, and reduce unnecessary work-up by 40-50%.

Systemic alterations in neutrophils and their precursors in early-stage chronic obstructive pulmonary disease.

Systemic inflammation is established as part of late-stage severe lung disease, but molecular, functional, and phenotypic changes in peripheral immune cells in early disease stages remain ill defined. Chronic obstructive pulmonary disease (COPD) is a major respiratory disease characterized by small-airway inflammation, emphysema, and severe breathing difficulties. Using single-cell analyses we demonstrate that blood neutrophils are already increased in early-stage COPD, and changes in molecular and functional neutrophil states correlate with lung function decline. Assessing neutrophils and their bone marrow precursors in a murine cigarette smoke exposure model identified similar molecular changes in blood neutrophils and precursor populations that also occur in the blood and lung. Our study shows that systemic molecular alterations in neutrophils and their precursors are part of early-stage COPD, a finding to be further explored for potential therapeutic targets and biomarkers for early diagnosis and patient stratification.

Decoding circulating tumor DNA to identify durable benefit from immunotherapy in lung cancer.

OBJECTIVES: Predicting the outcome of immunotherapy-treated non-small cell lung cancer (NSCLC) patients is challenging. Measuring circulating tumor DNA (ctDNA) in plasma is promising, but its application for outcome delineation needs further refinement. Since most information from the next-generation sequencing (NGS) panel is typically left unused, we aim to integrate more information. MATERIALS AND METHODS: Patient and ctDNA data were compiled from five published studies involving advanced NSCLC. Plasma samples collected prior (t0) and early during (t1) immunotherapy were selected, tracking the changes of the highest t0 variant per gene. Durable benefit (DB, defined as progression free survival ≥ ½ year) was predicted. Performance was quantified using the integrated receiver operating characteristic curve (ROC AUC) and compared with the traditional molecular response (MR). RESULTS: A total of 365 patients were pooled. Seven recurrently mutated genes were selected which optimally predicted DB (ROC AUC: 0.77-0.11+0.10), outperforming the MR predictor (with a ROC AUC: 0.64-0.11+0.11). Inclusion of patient characteristics led to a slight further improvement (ROC AUC: 0.80-0.10+0.09). The model performed satisfactory across all ctDNA platforms despite differences in panel size and content. CONCLUSION: Relative to a non-informative classifier (ROC AUC: 0.5), a twofold improvement in predictive value was achieved compared to MR by an integration of changes across seven selected genes in immunotherapy-treated NSCLC patients, whilst being broadly applicable across ctDNA NGS panels.

Dynamic Changes of Circulating Tumor DNA Predict Clinical Outcome in Patients With Advanced Non-Small-Cell Lung Cancer Treated With Immune Checkpoint Inhibitors.

PURPOSE: Immune checkpoint inhibitors (ICIs) are increasingly being used in non-small-cell lung cancer (NSCLC), yet biomarkers predicting their benefit are lacking. We evaluated if on-treatment changes of circulating tumor DNA (ctDNA) from ICI start (t0) to after two cycles (t1) assessed with a commercial panel could identify patients with NSCLC who would benefit from ICI. PATIENTS AND METHODS: The molecular ctDNA response was evaluated as a predictor of radiographic tumor response and long-term survival benefit of ICI. To maximize the yield of ctDNA detection, de novo mutation calling was performed. Furthermore, the impact of clonal hematopoiesis (CH)-related variants as a source of biologic noise was investigated. RESULTS: After correction for CH-related variants, which were detected in 75 patients (44.9%), ctDNA was detected in 152 of 167 (91.0%) patients. We observed only a fair agreement of the molecular and radiographic response, which was even more impaired by the inclusion of CH-related variants. After exclusion of those, a ≥ 50% molecular response improved progression-free survival (10 v 2 months; hazard ratio [HR], 0.55; 95% CI, 0.39 to 0.77; P = .0011) and overall survival (18.4 v 5.9 months; HR, 0.44; 95% CI, 0.31 to 0.62; P < .0001) compared with patients not achieving this end point. After adjusting for clinical variables, ctDNA response and STK11/KEAP1 mutations (HR, 2.08; 95% CI, 1.4 to 3.0; P < .001) remained independent predictors for overall survival, irrespective of programmed death ligand-1 expression. A landmark survival analysis at 2 months (n = 129) provided similar results. CONCLUSION: On-treatment changes of ctDNA in plasma reveal predictive information for long-term clinical benefit in ICI-treated patients with NSCLC. A broader NSCLC patient coverage through de novo mutation calling and the use of a variant call set excluding CH-related variants improved the classification of molecular responders, but had no significant impact on survival.