Clinical perspectives on serum tumor marker use in predicting prognosis and treatment response in advanced non-small cell lung cancer.

The optimal positioning and usage of serum tumor markers (STMs) in advanced non-small cell lung cancer (NSCLC) care is still unclear. This review aimed to provide an overview of the potential use and value of STMs in routine advanced NSCLC care for the prediction of prognosis and treatment response. Radiological imaging and clinical symptoms have shown not to capture a patient's entire disease status in daily clinical practice. Since STM measurements allow for a rapid, minimally invasive, and safe evaluation of the patient's tumor status in real time, STMs can be used as companion decision-making support tools before start and during treatment. To overcome the limited sensitivity and specificity associated with the use of STMs, tests should only be applied in specific subgroups of patients and different test characteristics should be defined per clinical context in order to answer different clinical questions. The same approach can similarly be relevant when developing clinical applications for other (circulating) biomarkers. Future research should focus on the approaches described in this review to achieve STM test implementation in advanced NSCLC care.

Comparing modeling strategies combining changes in multiple serum tumor biomarkers for early prediction of immunotherapy non-response in non-small cell lung cancer.

BACKGROUND: Patients treated with immune checkpoint inhibitors (ICI) are at risk of adverse events (AEs) even though not all patients will benefit. Serum tumor markers (STMs) are known to reflect tumor activity and might therefore be useful to predict response, guide treatment decisions and thereby prevent AEs. OBJECTIVE: This study aims to compare a range of prediction methods to predict non-response using multiple sequentially measured STMs. METHODS: Nine prediction models were compared to predict treatment non-response at 6-months (n = 412) using bi-weekly CYFRA, CEA, CA-125, NSE, and SCC measurements determined in the first 6-weeks of therapy. All methods were applied to six different biomarker combinations including two to five STMs. Model performance was assessed based on sensitivity, while model training aimed at 95% specificity to ensure a low false-positive rate. RESULTS: In the validation cohort, boosting provided the highest sensitivity at a fixed specificity across most STM combinations (12.9% -59.4%). Boosting applied to CYFRA and CEA achieved the highest sensitivity on the validation data while maintaining a specificity >95%. CONCLUSIONS: Non-response in NSCLC patients treated with ICIs can be predicted with a specificity >95% by combining multiple sequentially measured STMs in a prediction model. Clinical use is subject to further external validation.

Combining variant detection and fragment length analysis improves detection of minimal residual disease in postsurgery circulating tumour DNA of stage II-IIIA NSCLC patients.

Stage II-IIIA nonsmall cell lung cancer (NSCLC) patients receive adjuvant chemotherapy after surgery as standard-of-care treatment, even though only approximately 5.8% of patients will benefit. Identifying patients with minimal residual disease (MRD) after surgery using tissue-informed testing of postoperative plasma circulating cell-free tumour DNA (ctDNA) may allow adjuvant therapy to be withheld from patients without MRD. However, the detection of MRD in the postoperative setting is challenging, and more sensitive methods are urgently needed. We developed a method that combines variant calling and a novel ctDNA fragment length analysis using hybrid capture sequencing data. Among 36 stage II-IIIA NSCLC patients, this method distinguished patients with and without recurrence of disease in a 20 times repeated 10-fold cross validation with 75% accuracy (P = 0.0029). In contrast, using only variant calling or only fragment length analysis, no signification distinction between patients was shown (P = 0.24 and P = 0.074 respectively). In addition, a variant-level fragmentation score was developed that was able to classify variants detected in plasma cfDNA into tumour-derived or white-blood-cell-derived variants with 84% accuracy. The findings in this study may help drive the integration of various types of information from the same data, eventually leading to cheaper and more sensitive techniques to be used in this challenging clinical setting.

Cell-Free DNA at Diagnosis for Stage IV Non-Small Cell Lung Cancer: Costs, Time to Diagnosis and Clinical Relevance.

Tissue biopsies can be burdensome and are only effective in 10-30% of patients with metastasized non-small-cell lung cancer (mNSCLC). Next-generation sequencing (NGS) on cell-free DNA (cfDNA) might be an attractive alternative. We evaluated the costs, throughput time, and diagnostic yield of two diagnostic scenarios with tissue and cfDNA for mNSCLC patients, compared to diagnostics based on tissue biopsy alone. Data were retrieved from 209 stage IV NSCLC patients included in 10 hospitals in the Netherlands in the observational Lung cancer Early Molecular Assessment (LEMA) trial. Discrete event simulation was developed to compare three scenarios, using LEMA data as input where possible: (1) diagnostics with "tissue only"; (2) diagnostics with "cfDNA first", and subsequent tissue biopsy if required (negative for EGFR, BRAF ALK, ROS1); (3) cfDNA if tissue biopsy failed ("tissue first"). Scenario- and probabilistic analyses were performed to quantify uncertainty. In scenario 1, 84% (Credibility Interval [CrI] 70-94%) of the cases had a clinically relevant test result, compared to 93% (CrI 86-98%) in scenario 2, and 93% (CrI 86-99%) in scenario 3. The mean throughput time was 20 days (CrI 17-23) pp in scenario 1, 9 days (CrI 7-11) in scenario 2, and 19 days (CrI 16-22) in scenario 3. Mean costs were €2304 pp (CrI €2067-2507) in scenario 1, compared to €3218 (CrI €3071-3396) for scenario 2, and €2448 (CrI €2382-2506) for scenario 3. Scenarios 2 and 3 led to a reduction in tissue biopsies of 16% and 9%, respectively. In this process-based simulation analysis, the implementation of cfDNA for patients with mNSCLC resulted in faster completion of molecular profiling with more identified targets, with marginal extra costs in scenario 3.

Immune defects in patients with pulmonary Mycobacterium abscessus disease without cystic fibrosis.

The prevalence of Mycobacterium abscessus infections in non-cystic fibrosis (CF) patients has increased in recent years. In this study, we investigate whether immune defects explain the apparent susceptibility to this opportunistic infection in non-CF patients. We performed stimulations of peripheral blood mononuclear cells and whole blood from 13 patients with M. abscessus pulmonary disease and 13 healthy controls to investigate their cytokine production after 24 h and 7 days. Patients were predominantly women (54%) with a mean age of 59 years; 62% had nodular bronchiectatic disease. Many patients had predisposing pulmonary diseases, such as COPD (46%), and asthma (23%). Patients with COPD showed an impaired interleukin (IL)-6 response to M. abscessus and a reduced IL-17 response to Candida, together with a M. abscessus-specific enhanced IL-22 production. Patients without COPD showed higher levels of interleukin-1 receptor antagonist (IL-1Ra), an anti-inflammatory molecule. Within the non-COPD patients, those with bronchiectasis showed defective interferon (IFN)-γ production in response to Candida albicans. In conclusion, susceptibility to M. abscessus is likely determined by a combination of immunological defects and predisposing pulmonary disease. The main defect in the innate immune response was a shift of the ratio of IL-1ß to IL-1Ra, which decreased the bioactivity of this pathway in the adaptive immune response. In the adaptive immune response there was defective IL-17 and IFN-γ production. Patients with COPD and bronchiectasis showed different cytokine defects. It is therefore crucial to interpret the immunological results within the clinical background of the patients tested.