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.

Serum tumor markers for response prediction and monitoring of advanced lung cancer: A review focusing on immunotherapy and targeted therapies.

BACKGROUND: The value of serum tumor markers (STMs) in the current therapeutic landscape of lung cancer is unclear. OBJECTIVE: This scoping review gathered evidence of the predictive, prognostic, and monitoring value of STMs for patients with advanced lung cancer receiving immunotherapy (IT) or targeted therapy (TT). METHODS: Literature searches were conducted (cut-off: May 2022) using PubMed and Cochrane CENTRAL databases. Medical professionals advised on the search strategies. RESULTS: Study heterogeneity limited the evidence and inferences from the 36 publications reviewed. While increased baseline levels of serum cytokeratin 19 fragment antigen (CYFRA21-1) and carcinoembryonic antigen (CEA) may predict IT response, results for TT were less clear. For monitoring IT-treated patients, STM panels (including CYFRA21-1, CEA, and neuron-specific enolase) may surpass the power of single analyses to predict non-response. CYFRA21-1 measurement could aid in monitoring TT-treated patients, but the value of CEA in this context requires further investigation. Overall, baseline and dynamic changes in individual or combined STM levels have potential utility to predict treatment outcome and for monitoring of patients with advanced lung cancer. CONCLUSIONS: In advanced lung cancer, STMs provide additional relevant clinical information by predicting treatment outcome, but further standardization and validation is warranted.

Pre-analytical stability of the CEA, CYFRA 21.1, NSE, CA125 and HE4 tumor markers.

BACKGROUND: For lung cancer, circulating tumor markers (TM) are available to guide clinical treatment decisions. To ensure adequate accuracy, pre-analytical instabilities need to be known and addressed in the pre-analytical laboratory protocols. OBJECTIVE: This study investigates the pre-analytical stability of CA125, CEA, CYFRA 21.1, HE4 and NSE for the following pre-analytical variables and procedures; i) whole blood stability, ii) serum freeze-thaw cycles, iii) electric vibration mixing and iv) serum storage at different temperatures. METHODS: Left-over patient samples were used and for every investigated variable six patient samples were used and analysed in duplicate. Acceptance criteria were based on analytical performance specifications based on biological variation and significant differences with baseline. RESULTS: Whole blood was stable for at least 6 hours for all TM except for NSE. Two freeze-thaw cycles were acceptable for all TM except CYFRA 21.1. Electric vibration mixing was allowed for all TM except for CYFRA 21.1. Serum stability at 4°C was 7 days for CEA, CA125, CYFRA 21.1 and HE4 and 4 hours for NSE. CONCLUSIONS: Critical pre-analytical processing step conditions were identified that, if not taken into account, will result in reporting of erroneous TM results.

Prognostic Value of Nivolumab Clearance in Non-Small Cell Lung Cancer Patients for Survival Early in Treatment.

INTRODUCTION: Immune checkpoint inhibitors improved survival of advanced stage non-small cell lung cancer patients, but the overall response rate remains low. A biomarker that identifies non-responders would be helpful to allow treatment decisions. Clearance of immune checkpoint inhibitors is related to treatment response, but its prognostic potential early in treatment remains unknown. Our primary aim was to investigate the prognostic potential of nivolumab clearance for overall survival early in treatment. Our secondary aim was to evaluate the performance of nivolumab clearance as prognostic biomarker. PATIENTS AND METHODS: Individual estimates of nivolumab clearances at first dose, 6 and 12 weeks after treatment initiation were obtained via nonlinear mixed-effects modelling. Prognostic value of nivolumab clearance was estimated using univariate Cox regression at first dose and for the ratios between 6 and 12 weeks to first dose. The performance of nivolumab clearance as biomarker was assessed by calculating sensitivity and specificity. RESULTS: During follow-up of 75 months, 69 patients were included and 865 died. Patients with a nivolumab clearance ≥ 7.3 mL/h at first dose were more likely to die compared to patients with a nivolumab clearance < 7.3 mL/h at first dose (hazard ratio [HR] = 3.55, 955 CI 1.75-7.20). The HRs of dose nivolumab clearance ratios showed similar results with a HR of 3.93 (955 CI 1.66-9.32) for 6 weeks to first-dose clearance ratio at a 0.953 cut-point and a HR of 2.96 (955 CI 1.32-6.64) for 12 weeks to first-dose clearance ratio at a cut-point of 0.814. For nivolumab clearance at all early time points, sensitivity was high (≥ 0.95) but specificity was low (0.11-0.29). CONCLUSION: Nivolumab clearance is indicative of survival early in treatment. Our results encourage to further assess the prognostic potential of immunotherapy clearance.

Diagnosing Non-Small Cell Lung Cancer by Exhaled Breath Profiling Using an Electronic Nose: A Multicenter Validation Study.

BACKGROUND: Despite the potential of exhaled breath analysis of volatile organic compounds to diagnose lung cancer, clinical implementation has not been realized, partly due to the lack of validation studies. RESEARCH QUESTION: This study addressed two questions. First, can we simultaneously train and validate a prediction model to distinguish patients with non-small cell lung cancer from non-lung cancer subjects based on exhaled breath patterns? Second, does addition of clinical variables to exhaled breath data improve the diagnosis of lung cancer? STUDY DESIGN AND METHODS: In this multicenter study, subjects with non-small cell lung cancer and control subjects performed 5 min of tidal breathing through the aeoNose, a handheld electronic nose device. A training cohort was used for developing a prediction model based on breath data, and a blinded cohort was used for validation. Multivariable logistic regression analysis was performed, including breath data and clinical variables, in which the formula and cutoff value for the probability of lung cancer were applied to the validation data. RESULTS: A total of 376 subjects formed the training set, and 199 subjects formed the validation set. The full training model (including exhaled breath data and clinical parameters from the training set) were combined in a multivariable logistic regression analysis, maintaining a cut off of 16% probability of lung cancer, resulting in a sensitivity of 95%, a specificity of 51%, and a negative predictive value of 94%; the area under the receiver-operating characteristic curve was 0.87. Performance of the prediction model on the validation cohort showed corresponding results with a sensitivity of 95%, a specificity of 49%, a negative predictive value of 94%, and an area under the receiver-operating characteristic curve of 0.86. INTERPRETATION: Combining exhaled breath data and clinical variables in a multicenter, multi-device validation study can adequately distinguish patients with lung cancer from subjects without lung cancer in a noninvasive manner. This study paves the way to implement exhaled breath analysis in the daily practice of diagnosing lung cancer. CLINICAL TRIAL REGISTRATION: The Netherlands Trial Register; No.: NL7025; URL: https://trialregister.nl/trial/7025.

Modeling strategies to analyse longitudinal biomarker data: An illustration on predicting immunotherapy non-response in non-small cell lung cancer.

Serum tumor markers acquired through a blood draw are known to reflect tumor activity. Their non-invasive nature allows for more frequent testing compared to traditional imaging methods used for response evaluations. Our study aims to compare nine prediction methods to accurately, and with a low false positive rate, predict progressive disease despite treatment (i.e. non-response) using longitudinal tumor biomarker data. Bi-weekly measurements of CYFRA, CA-125, CEA, NSE, and SCC were available from a cohort of 412 advanced stage non-small cell lung cancer (NSCLC) patients treated up to two years with immune checkpoint inhibitors. Serum tumor marker measurements from the first six weeks after treatment initiation were used to predict treatment response at 6 months. Nine models with varying complexity were evaluated in this study, showing how longitudinal biomarker data can be used to predict non-response to immunotherapy in NSCLC patients.

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.

Biological and technical factors in the assessment of blood-based tumor mutational burden (bTMB) in patients with NSCLC.

BACKGROUND: Patients treated with immunotherapy are at risk of considerable adverse events, and the ongoing struggle is to accurately identify the subset of patients who will benefit. Tumor mutational burden (TMB) has emerged as a promising predictive biomarker but requires tumor tissue which is not always available. Blood-based TMB (bTMB) may provide a minimally invasive assessment of mutational load. However, because of the required sequencing depth, bTMB analysis is costly and prone to false negative results. This study attempted to design a minimally sized bTMB panel, examined a counting-based method for bTMB in patients with stage I to IV non-small cell lung cancer (NSCLC) and evaluated both technical factors such as bTMB and tissue-based TMB (tTMB) cut-off, as well as sample-related factors such as cell-free DNA input mass which influence the correlation between bTMB and tTMB. METHODS: Tissue, plasma, and whole blood samples collected as part of the LEMA trial (NCT02894853) were used in this study. Samples of 185 treatment naïve patients with stage I to IV NSCLC were sequenced at the Roche Sequencing Solutions with a custom panel designed for TMB, using reagents and workflows derived from the AVENIO Tumor Tissue and circulating tumor DNA Analysis Kits. RESULTS: A TMB panel of 1.1 Mb demonstrated highly accurate TMB high calls with a positive predictive value of 95% when using a tTMB cut-off of 16 mut/Mb, corresponding with 42 mut/Mb for bTMB. The positive per cent agreement (PPA) of bTMB was relatively low at 32%. In stage IV samples with at least 20 ng of cfDNA input, PPA of bTMB improved to 63% and minimizing the panel to a subset of 577 kb was possible while maintaining 63% PPA. CONCLUSION: Plasma samples with high bTMB values are highly correspondent with tTMB, whereas bTMB low results may also be the result of low tumor burden at earlier stages of disease as well as poorly shedding tumors. For advanced stages of disease, PPA (sensitivity) of bTMB is satisfactory in comparison to tTMB, even when using a panel of less than 600 kb, warranting consideration of bTMB as a predictive biomarker for patients with NSCLC eligible for immunotherapy in the future.

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.