High red blood cell distribution width attenuates the effectiveness of Immune checkpoint inhibitor therapy: An exploratory study using a clinical data warehouse.

BACKGROUND: Immune checkpoint inhibitors (ICIs) have improved outcomes in cancer treatment but are also associated with adverse events and financial burdens. Identifying accurate biomarkers is crucial for determining which patients are likely to benefit from ICIs. Current markers, such as PD-L1 expression and tumor mutation burden, exhibit limited predictive accuracy. This study utilizes a Clinical Data Warehouse (CDW) to explore the prognostic significance of novel blood-based factors, such as the neutrophil-to-lymphocyte ratio and red cell distribution width (RDW), to enhance the prediction of ICI therapy benefit. METHODS: This retrospective study utilized an exploratory cohort from the CDW that included a variety of cancers to explore factors associated with pembrolizumab treatment duration, validated in a non-small cell lung cancer (NSCLC) patient cohort from electronic medical records (EMR) and CDW. The CDW contained anonymized data on demographics, diagnoses, medications, and tests for cancer patients treated with ICIs between 2017-2022. Logistic regression identified factors predicting ≤2 or ≥5 pembrolizumab doses as proxies for progression-free survival (PFS), and Receiver Operating Characteristic analysis was used to examine their predictive ability. These factors were validated by correlating doses with PFS in the EMR cohort and re-testing their significance in the CDW cohort with other ICIs. This dual approach utilized the CDW for discovery and EMR/CDW cohorts for validating prognostic biomarkers before ICI treatment. RESULTS: A total of 609 cases (428 in the exploratory cohort and 181 in the validation cohort) from CDW and 44 cases from EMR were selected for study. CDW analysis revealed that elevated red cell distribution width (RDW) correlated with receiving ≤2 pembrolizumab doses (p = 0.0008), with an AUC of 0.60 for predicting treatment duration. RDW's correlation with PFS (r = 0.80, p<0.0001) and its weak association with RDW (r = -0.30, p = 0.049) were confirmed in the EMR cohort. RDW also remained significant in predicting short treatment duration across various ICIs (p = 0.0081). This dual methodology verified pretreatment RDW elevation as a prognostic biomarker for shortened ICI therapy. CONCLUSION: This study suggests the utility of CDWs in identifying prognostic biomarkers for ICI therapy in cancer treatment. Elevated RDW before treatment initiation emerged as a potential biomarker of shorter therapy duration.

DNA methylation analysis in plasma for early diagnosis in lung adenocarcinoma.

BACKGROUND: Lung adenocarcinoma (LUAD) represents the most prevalent type of lung cancer. SHOX2 and RASSF1A methylation have been identified as important biomarkers for diagnosis and prognosis of lung cancer. Bronchoalveolar lavage fluid (BALF) exhibits good specificity and sensitivity in diagnosing pulmonary diseases, but its acquisition is challenging and may cause discomfort to patients. In clinical, plasma samples are more convenient to obtain than BALF; however, there is little research on the concurrent detection of SHOX2 and RASSF1A methylation in plasma. This study aims to assess the diagnostic value of a combined promoter methylation assay for SHOX2 and RASSF1A in early-stage LUAD using plasma samples. METHODS: BALF and blood samples were obtained from 36 early-stage LUAD patients, with a control group of nineteen non-tumor individuals. The promoter methylation levels of SHOX2 and RASSF1A in all subjects were assessed using the human SHOX2 and RASSF1A gene methylation kit. RESULTS: The methylation detection rate of SHOX2 and RASSF1A in plasma was 61.11%, slightly lower than that in BALF (66.7%). The Chi-square test revealed no significant difference in the methylation rate between BALF and plasma (P > 0.05). The area under the receiver operating characteristic (ROC) curve analysis for blood was 0.806 (95% CI, 0.677 to 0.900), while for BALF it was 0.781 (95% CI, 0.649 to 0.881). Additionally, we conducted an analysis on the correlation between SHOX2 and RASSF1A methylation levels in plasma with gender, age, tumor differentiation, pathologic classification, and other clinicopathological variables; however, no significant correlations were observed. CONCLUSIONS: Measurement of SHOX2 and RASSF1A methylation for early diagnosis of LUAD can be achieved with high sensitivity and specificity by using plasma as a substitute for BALF samples.

Proteome Fishing for CRISPR/Cas12a-Based Orthogonal Multiplex Aptamer Sensing.

Detection of serum protein biomarkers is extremely challenging owing to the superior complexity of serum. Here, we report a method of proteome fishing from the serum. It uses a magnetic nanoparticle-protein corona and a multiplexed aptamer panel, which we incubated with the nanoparticle-protein corona for biomarker recognition. To transfer protein biomarker detection to aptamer detection, we established a CRISPR/Cas12a-based orthogonal multiplex aptamer sensing (COMPASS) platform by profiling the aptamers of protein corona with clinical nonsmall cell lung cancer (NSCLC) serum samples. Furthermore, we determined the four out of nine (FOON) panel (including HE4, NSE, AFP, and VEGF165) to be the most cost-effective and accurate panel for COMPASS in NSCLC diagnosis. The diagnostic accuracy of NSCLC by the FOON panel with internal and external cohorts was 95.56% (ROC-AUC = 99.40%) and 89.58% (ROC-AUC = 95.41%), respectively. Our developed COMPASS technology circumvents the otherwise challenging multiplexed serum protein amplification problem and avoids aptamer degradation in serum. Therefore, this novel COMPASS could lead to the development of a facile, cost-effective, intelligent, and high-throughput diagnostic platform for large-cohort cancer screening.

Promising microRNAs in pre-diagnostic serum associated with lung cancer up to eight years before diagnosis: a HUNT study.

INTRODUCTION: Blood biomarkers for early detection of lung cancer (LC) are in demand. There are few studies of the full microRNome in serum of asymptomatic subjects that later develop LC. Here we searched for novel microRNA biomarkers in blood from non-cancer, ever-smokers populations up to eight years before diagnosis. METHODS: Serum samples from 98,737 subjects from two prospective population studies, HUNT2 and HUNT3, were considered initially. Inclusion criteria for cases were: ever-smokers; no known cancer at study entrance; 0-8 years from blood sampling to LC diagnosis. Each future LC case had one control matched to sex, age at study entrance, pack-years, smoking cessation time, and similar HUNT Lung Cancer Model risk score. A total of 240 and 72 serum samples were included in the discovery (HUNT2) and validation (HUNT3) datasets, respectively, and analysed by next-generation sequencing. The validated serum microRNAs were also tested in two pre-diagnostic plasma datasets from the prospective population studies NOWAC (n = 266) and NSHDS (n = 258). A new model adding clinical variables was also developed and validated. RESULTS: Fifteen unique microRNAs were discovered and validated in the pre-diagnostic serum datasets when all cases were contrasted against all controls, all with AUC > 0.60. In combination as a 15-microRNAs signature, the AUC reached 0.708 (discovery) and 0.703 (validation). A non-small cell lung cancer signature of six microRNAs showed AUC 0.777 (discovery) and 0.806 (validation). Combined with clinical variables of the HUNT Lung Cancer Model (age, gender, pack-years, daily cough parts of the year, hours of indoor smoke exposure, quit time in years, number of cigarettes daily, body mass index (BMI)) the AUC reached 0.790 (discovery) and 0.833 (validation). These results could not be validated in the plasma samples. CONCLUSION: There were a few significantly differential expressed microRNAs in serum up to eight years before diagnosis. These promising microRNAs alone, in concert, or combined with clinical variables have the potential to serve as early diagnostic LC biomarkers. Plasma is not suitable for this analysis. Further validation in larger prospective serum datasets is needed.

The Diagnostic and Prognostic Value of the Combination of Tumor M2-Pyruvate Kinase, Carcinoembryonic Antigen, and Cytokeratin 19 Fragment in Non-Small Cell Lung Cancer.

Objective: Finding biomarkers related to non-small cell lung cancer (NSCLC) is helpful for the diagnosis and precise treatment of lung cancer. The relationship between serum tumor M2-pyruvate kinase (TuM2-PK), carcinoembryonic antigen (CEA), and cytokeratin 19 fragment (CYFRA21-1) and NSCLC was analyzed. Methods: The serum levels of TuM2-PK, CEA, and CYFRA21-1 in 184 patients with the NSCLC group, 60 patients with the benign lung disease (BLD) group, and 90 healthy controls (HC) group were detected. The levels of TuM2-PK were measured by using an enzyme-linked immunosorbent assay. The detection methods of CEA and CYFRA21-1 were electrochemiluminescence. The receiver operating characteristic (ROC) curve was drawn to evaluate the diagnostic value of TuM2-PK, CEA, and CYFRA21-1 on NSCLC. The Kaplan-Meier survival curve was drawn to evaluate the survival status in NSCLC patients with different serum levels of TuM2-PK, CEA, and CYFRA21-1. Results: Serum levels of TuM2-PK, CEA, and CYFRA21-1 in the NSCLC group were significantly higher than those in the BLD group and the HC group (P < .01). Serum levels of TuM2-PK, CEA, and CYFRA21-1 in NSCLC patients were associated with the tumor lymph node metastasis stage (P < .05), lymph node metastasis (P < .05), and distant metastasis (P < .05). The ROC curve showed that the area under the curve of serum levels of TuM2-PK, CEA, and CYFRA21-1 was 0.814, 0.638, and 0.719, respectively, and that the combination of the above 3 was 0.918. The Kaplan-Meier survival curve showed that the 1-, 3- and 5-year survival rate in NSCLC patients with positive TuM2-PK, CEA, and CYFRA21-1 was significantly lower than that in NSCLC patients with negative TuM2-PK, CEA, and CYFRA21-1, respectively (P < .05). Conclusions: Serum TuM2-PK, CEA, and CYFRA21-1 levels have high clinical values in the diagnosis of NSCLC, and can effectively judge the prognosis of patients.

Trends in primary care blood tests prior to lung and colorectal cancer diagnosis-A retrospective cohort study using linked Australian data.

INTRODUCTION: Abnormal results in common blood tests may occur several months before lung cancer (LC) and colorectal cancer (CRC) diagnosis. Identifying early blood markers of cancer and distinct blood test signatures could support earlier diagnosis in general practice. METHODS: Using linked Australian primary care and hospital cancer registry data, we conducted a cohort study of 855 LC and 399 CRC patients diagnosed between 2001 and 2021. Requests and results from general practice blood tests (six acute phase reactants [APR] and six red blood cell indices [RBCI]) were examined in the 2 years before cancer diagnosis. Poisson regression models were used to estimate monthly incidence rates and examine pre-diagnostic trends in blood test use and abnormal results prior to cancer diagnosis, comparing patterns in LC and CRC patients. RESULTS: General practice blood test requests increase from 7 months before CRC and 6 months before LC diagnosis. Abnormalities in many APR and RBCI tests increase several months before cancer diagnosis, often occur prior to or in the absence of anaemia (in 51% of CRC and 81% of LC patients with abnormalities), and are different in LC and CRC patients. CONCLUSIONS: This study demonstrates an increase in diagnostic activity in Australian general practice several months before LC and CRC diagnosis, indicating potential opportunities for earlier diagnosis. It identifies blood test abnormalities and distinct signatures that are early markers of LC and CRC. If combined with other pre-diagnostic information, these blood tests have potential to support GPs in prioritising patients for cancer investigation of different sites to expedite diagnosis.

Plasma metabolomics of immune-related adverse events for patients with lung cancer treated with PD-1/PD-L1 inhibitors.

BACKGROUND: Metabolomics has the characteristics of terminal effects and reflects the physiological state of biological diseases more directly. Several current biomarkers of multiple omics were revealed to be associated with immune-related adverse events (irAEs) occurrence. However, there is a lack of reliable metabolic biomarkers to predict irAEs. This study aims to explore the potential metabolic biomarkers to predict risk of irAEs and to investigate the association of plasma metabolites level with survival in patients with lung cancer receiving PD-1/PD-L1 inhibitor treatment. METHODS: The study collected 170 plasmas of 85 patients with lung cancer who received immune checkpoint inhibitors (ICIs) treatment. 58 plasma samples of 29 patients with irAEs were collected before ICIs treatment and at the onset of irAEs. 112 plasma samples of 56 patients who did not develop irAEs were collected before ICIs treatment and plasma matched by treatment cycles to onset of irAEs patients. Untargeted metabolomics analysis was used to identify the differential metabolites before initiating ICIs treatment and during the process that development of irAEs. Kaplan-Meier curves analysis was used to detect the associations of plasma metabolites level with survival of patients with lung cancer. RESULTS: A total of 24 differential metabolites were identified to predict the occurrence of irAEs. Baseline acylcarnitines and steroids levels are significantly higher in patients with irAEs, and the model of eight acylcarnitine and six steroid metabolites baseline level predicts irAEs occurrence with area under the curve of 0.91. Patients with lower concentration of baseline decenoylcarnitine(AcCa(10:1) 2, decenoylcarnitine(AcCa(10:1) 3 and hexanoylcarnitine(AcCa(6:0) in plasma would have better overall survival (OS). Moreover, 52 differential metabolites were identified related to irAEs during ICIs treatment, dehydroepiandrosterone sulfate, corticoserone, cortisol, thyroxine and sphinganine 1-phaosphate were significantly decreased in irAEs group while oxoglutaric acid and taurocholic acid were significantly increased in irAEs group. CONCLUSIONS: High levels of acylcarnitines and steroid hormone metabolites might be risk factor to development of irAEs, and levels of decenoylcarnitine (AcCa(10:1) 2, decenoylcarnitine (AcCa(10:1) 3 and hexanoylcarnitine (AcCa(6:0) could be used to predict OS for patients with lung cancer received ICIs treatment.

Dual-signal output biosensor for the detection of program death-ligand 1 and therapy progress monitoring of cancer.

A disposable dual-output biosensor to detect program death-ligand 1 (PD-L1) was developed for immunotherapy progress monitoring and early cancer detection in a single experimental setup. The aptamer probe was assembled on rGO composited with carboxylated terthiophene polymer (rGO-pTBA) to specifically capture PD-L1 protein labeled with a new redox mediator, ortho-amino phenol para sulphonic acid, for amperometric detection. Each sensing layer was characterized through electrochemical and surface analysis experiments, then confirmed the sensing performance. The calibration plots for the standard PD-L1 protein detection revealed two dynamic ranges of 0.5-100.0 pM and 100.0-500.0 pM, where the detection limit was 0.20 ± 0.001 pM (RSD ≤5.2%) by amperometry. The sensor reliability was evaluated by detecting A549 lung cancer cell-secreted PD-L1 and clinically relevant serum levels of soluble PD-L1 (sPD-L1) using both detection methods. In addition, therapeutic trials were studied through the quantification of sPD-L1 levels for a small cohort of lung cancer patients. A significantly higher level of sPD-L1 was observed for patients (221.6-240.4 pM) compared to healthy individuals (16.2-19.6 pM). After immunotherapy, the patients' PD-L1 level decreased to the range of 126.7-141.2 pM. The results indicated that therapy monitoring was successfully done using both the proposed methods. Additionally, based on a comparative study on immune checkpoint-related proteins, PD-L1 is a more effective biomarker than granzyme B and interferon-gamma.

The associations between dysregulation of human blood metabolites and lung cancer risk: evidence from genetic data.

BACKGROUND: Metabolic dysregulation is recognized as a significant hallmark of cancer progression. Although numerous studies have linked specific metabolic pathways to cancer incidence, the causal relationship between blood metabolites and lung cancer risk remains unclear. METHODS: Genomic data from 29,266 lung cancer patients and 56,450 control individuals from the Transdisciplinary Research in Cancer of the Lung and the International Lung Cancer Consortium (TRICL-ILCCO) were utilized, and findings were replicated using additional data from the FinnGen consortium. The analysis focused on the associations between 486 blood metabolites and the susceptibility to overall lung cancer and its three major clinical subtypes. Various Mendelian randomization methods, including inverse-variance weighting, weighted median estimation, and MR-Egger regression, were employed to ensure the robustness of our findings. RESULTS: A total of 19 blood metabolites were identified with significant associations with lung cancer risk. Specifically, oleate (OR per SD = 2.56, 95% CI: 1.51 to 4.36), 1-arachidonoylglyceropholine (OR = 1.79, 95% CI: 1.22 to 2.65), and arachidonate (OR = 1.67, 95% CI: 1.16 to 2.40) were associated with a higher risk of lung cancer. Conversely, 1-linoleoylglycerophosphoethanolamine (OR = 0.57, 95% CI: 0.40 to 0.82), ADpSGEGDFXAEGGGVR, a fibrinogen cleavage peptide (OR = 0.60, 95% CI: 0.47 to 0.77), and isovalerylcarnitine (OR = 0.62, 95% CI: 0.49 to 0.78) were associated with a lower risk of lung cancer. Notably, isoleucine (OR = 9.64, 95% CI: 2.55 to 36.38) was associated with a significantly higher risk of lung squamous cell cancer, while acetyl phosphate (OR = 0.11, 95% CI: 0.01 to 0.89) was associated with a significantly lower risk of small cell lung cancer. CONCLUSION: This study reveals the complex relationships between specific blood metabolites and lung cancer risk, highlighting their potential as biomarkers for lung cancer prevention, screening, and treatment. The findings not only deepen our understanding of the metabolic mechanisms of lung cancer but also provide new insights for future treatment strategies.

Metabolomics-based search for lung cancer markers among patients with different smoking status.

Tobacco smoking is the main etiological factor of lung cancer (LC), which can also cause metabolome disruption. This study aimed to investigate whether the observed metabolic shift in LC patients was also associated with their smoking status. Untargeted metabolomics profiling was applied for the initial screening of changes in serum metabolic profile between LC and chronic obstructive pulmonary disease (COPD) patients, selected as a non-cancer group. Differences in metabolite profiles between current and former smokers were also tested. Then, targeted metabolomics methods were applied to verify and validate the proposed LC biomarkers. For untargeted metabolomics, a single extraction-dual separation workflow was applied. The samples were analyzed using a liquid chromatograph-high resolution quadrupole time-of-flight mass spectrometer. Next, the selected metabolites were quantified using liquid chromatography-triple-quadrupole mass spectrometry. The acquired data confirmed that patients' stratification based on smoking status impacted the discriminating ability of the identified LC marker candidates. Analyzing a validation set of samples enabled us to determine if the putative LC markers were truly robust. It demonstrated significant differences in the case of four metabolites: allantoin, glutamic acid, succinic acid, and sphingosine-1-phosphate. Our research showed that studying the influence of strong environmental factors, such as tobacco smoking, should be considered in cancer marker research since it reduces the risk of false positives and improves understanding of the metabolite shifts in cancer patients.

Exploring the causal association between uric acid and lung cancer in east Asian and European populations: a mendelian randomization study.

BACKGROUND: Lung cancer still ranks first in the mortality rate of cancer. Uric acid is a product of purine metabolism in humans. Its presence in the serum is controversial; some say that its high levels have a protective effect against tumors, others say the opposite, that is, high levels increase the risk of cancer. Therefore, the aim of this study was to investigate the potential causal association between serum uric acid levels and lung cancer. METHODS: Mendelian randomization was used to achieve our aim. Sensitivity analyses was performed to validate the reliability of the results, followed by reverse Mendelian analyses to determine a potential reverse causal association. RESULTS: A significant causal association was found between serum uric acid levels and lung cancer in East Asian and European populations. Further sublayer analysis revealed a significant causal association between uric acid and small cell lung cancer, while no potential association was observed between uric acid and non-small cell lung cancer, squamous lung cancer, and lung adenocarcinoma. The sensitivity analyses confirmed the reliability of the results. Reverse Mendelian analysis showed no reverse causal association between uric acid and lung cancer. CONCLUSIONS: The results of this study suggested that serum uric acid levels were negatively associated with lung cancer, with uric acid being a potential protective factor for lung cancer. In addition, uric acid level monitoring was simple and inexpensive. Therefore, it might be used as a biomarker for lung cancer, promoting its wide use clinical practice.

Integrative analysis of blood transcriptome profiles in small-cell lung cancer patients for identification of novel chemotherapy resistance-related biomarkers.

Introduction: Chemoresistance constitutes a prevalent factor that significantly impacts thesurvival of patients undergoing treatment for smal-cell lung cancer (SCLC). Chemotherapy resistance in SCLC patients is generally classified as primary or acquired resistance, each governedby distinct mechanisms that remain inadequately researched. Methods: In this study, we performed transcriptome screening of peripheral blood plasma obtainedfrom 17 patients before and after receiving combined etoposide and platinum treatment. We firs testimated pseudo-single-cell analysis using xCell and ESTIMATE and identified differentially expressed genes (DEGs), then performed network analysis to discover key hub genes involved in chemotherapy resistance. Results: Our analysis showed a significant increase in class-switched memory B cell scores acrossboth chemotherapy resistance patterns, indicating their potential crucial role in mediatingresistance. Moreover, network analysis identifed PRICKLE3, TNFSFI0, ACSLl and EP300 as potential contributors to primary resistance, with SNWl, SENP2 and SMNDCl emerging assignificant factors in acquired resistance, providing valuable insights into chemotherapy resistancein SCLC. Discussion: These findings offer valuable insights for understanding chemotherapy resistance and related gene signatures in SCLC, which could help further biological validation studies.

Rapid detection of lung cancer based on serum Raman spectroscopy and a support vector machine: a case-control study.

BACKGROUND: Early screening and detection of lung cancer is essential for the diagnosis and prognosis of the disease. In this paper, we investigated the feasibility of serum Raman spectroscopy for rapid lung cancer screening. METHODS: Raman spectra were collected from 45 patients with lung cancer, 45 with benign lung lesions, and 45 healthy volunteers. And then the support vector machine (SVM) algorithm was applied to build a diagnostic model for lung cancer. Furthermore, 15 independent individuals were sampled for external validation, including 5 lung cancer patients, 5 benign lung lesion patients, and 5 healthy controls. RESULTS: The diagnostic sensitivity, specificity, and accuracy were 91.67%, 92.22%, 90.56% (lung cancer vs. healthy control), 92.22%,95.56%,93.33% (benign lung lesion vs. healthy) and 80.00%, 83.33%, 80.83% (lung cancer vs. benign lung lesion), repectively. In the independent validation cohort, our model showed that all the samples were classified correctly. CONCLUSION: Therefore, this study demonstrates that the serum Raman spectroscopy analysis technique combined with the SVM algorithm has great potential for the noninvasive detection of lung cancer.

Improving Prediction of Survival and Progression in Metastatic Non-Small Cell Lung Cancer After Immunotherapy Through Machine Learning of Circulating Tumor DNA.

PURPOSE: To use modern machine learning approaches to enhance and automate the feature extraction from the longitudinal circulating tumor DNA (ctDNA) data and to improve the prediction of survival and disease progression, risk stratification, and treatment strategies for patients with 1L non-small cell lung cancer (NSCLC). MATERIALS AND METHODS: Using IMpower150 trial data on patients with untreated metastatic NSCLC treated with atezolizumab and chemotherapies, we developed a machine learning algorithm to extract predictive features from ctDNA kinetics, improving survival and progression prediction. We analyzed kinetic data from 17 ctDNA summary markers, including cell-free DNA concentration, allele frequency, tumor molecules in plasma, and mutation counts. RESULTS: Three hundred and ninety-eight patients with ctDNA data (206 in training and 192 in validation) were analyzed. Our models outperformed existing workflow using conventional temporal ctDNA features, raising overall survival (OS) concordance index to 0.72 and 0.71 from 0.67 and 0.63 for C3D1 and C4D1, respectively, and substantially improving progression-free survival (PFS) to approximately 0.65 from the previous 0.54-0.58, a 12%-20% increase. Additionally, they enhanced risk stratification for patients with NSCLC, achieving clear OS and PFS separation. Distinct patterns of ctDNA kinetic characteristics (eg, baseline ctDNA markers, depth of ctDNA responses, and timing of ctDNA clearance, etc) were revealed across the risk groups. Rapid and complete ctDNA clearance appears essential for long-term clinical benefit. CONCLUSION: Our machine learning approach offers a novel tool for analyzing ctDNA kinetics, extracting critical features from longitudinal data, improving our understanding of the link between ctDNA kinetics and progression/mortality risks, and optimizing personalized immunotherapies for 1L NSCLC.

'Plasma first' approach for detecting epidermal growth factor receptor mutation in advanced non-small cell lung carcinoma.

INTRODUCTION: The treatment approach for recently diagnosed advanced non-small cell lung cancer (NSCLC) with EGFR mutations primarily relies on confirming the tissue diagnosis as non-squamous NSCLC. This routine clinical practice of tissue diagnosis imposes several barriers and delays in turnaround time (TAT) for biomarker testing, significantly delaying the time to treatment. The objective of this study is to investigate the 'plasma first' approach for detection of EGFR mutation in advanced stage treatment naïve NSCLC patients. METHODS: We prospectively collected blood samples of treatment naïve patients with clinical and radiological suspicion of advanced stage NSCLC prior to obtaining tissue biopsy. Plasma cfDNA was tested for EGFR mutation using two different methods. We compared the sensitivity and TAT of liquid biopsy with tissue biopsy. RESULTS: In total, we analyzed plasma cell-free DNA (cfDNA) of 236 patients suspected of having advanced NSCLC for EGFR mutations. We observed a notably shorter turnaround time (TAT) of 3 days, which was significantly quicker compared to the 12-day TAT for tissue biopsy (p < 0.05). The ddPCR method had a sensitivity of 82.8%, which was higher than 66.34% sensitivity of ARMS-PCR. The current study also highlights that there is no significant difference in the clinical outcome of the patients whether treated based on liquid biopsy only or tissue biopsy (median progression-free survival of 11.56 vs. 11.9 months; p = 0.94). CONCLUSIONS: Utilizing a 'plasma first' strategy, given its shorter turnaround time, strong positive concordance and comparable outcomes to tissue biopsy, emerges as a highly specific and reliable method for detecting EGFR mutations in advanced-stage NSCLC.

C-reactive protein as robust laboratory value associated with prognosis in patients with stage III non-small cell lung cancer (NSCLC) treated with definitive radiochemotherapy.

To evaluate the prognostic value of biomarkers from peripheral blood obtained as routine laboratory assessment for overall survival in a cohort of stage III non-small cell lung cancer (NSCLC) patients treated with definitive radiochemotherapy at a high-volume cancer center. Seven blood biomarkers from 160 patients treated with definitive radiochemotherapy for stage III NSCLC were analyzed throughout the course treatment. Parameters were preselected using univariable and multivariable proportional hazards analysis and were assessed for internal validity using leave-one-out cross validation. Cross validated classifiers including biomarkers in addition to important clinical parameters were compared with classifiers containing the clinical parameters alone. An increased C-reactive protein (CRP) value in the final week of radiotherapy was found as a prognostic factor for overall survival, both as a continuous (HR 1.099 (1.038-1.164), p < 0.0012) as well as categorical variable splitting data at the median value of 1.2 mg/dl (HR 2.214 (1.388-3.531), p < 0.0008). In the multivariable analysis, the CRP value-maintained significance with an HR of 1.105 (1.040-1.173) and p-value of 0.0012. The cross validated classifier using CRP at the end of radiotherapy in addition to clinical parameters separated equally sized high and low risk groups more distinctly than a classifier containing the clinical parameters alone (HR = 2.786 (95% CI 1.686-4.605) vs. HR = 2.287 (95% CI 1.407-3.718)). Thus, the CRP value at the end of radiation therapy has successfully passed the crucial cross-validation test. The presented data on CRP levels suggests that inflammatory markers may become increasingly important during definitive radiochemotherapy, particularly with the growing utilization of immunotherapy as a consolidation therapy for stage III NSCLC.

Serum albumin and derived neutrophil-to-lymphocyte ratio are potential predictive biomarkers for immune checkpoint inhibitors in small cell lung cancer.

Background: Immune checkpoint inhibitors (ICIs) have reshaped the treatment landscape of small cell lung cancer (SCLC), but only a minority of patients benefit from this therapy. Therefore, it is critical to identify potential risk factors that could predict the efficacy of ICI treatment in SCLC patients and identify patient subgroups who may benefit the most from ICI therapy. Methods: Our study included a total of 183 SCLC patients who had received at least one dose of ICI treatment. We utilized both logistic regression and Cox proportional hazard regression to evaluate whether various patient clinical factors and serum biomarkers could serve as predictors of patient response to treatment and overall survival (OS) during ICI therapy. Results: Logistic regression showed that patients with a history of surgery (p=0.003, OR 9.06, 95% CI: (2.17, 37.9)) and no metastasis (p=0.008, OR 7.82, 95% CI: (1.73, 35.4)) exhibited a higher odds of response to ICI treatment. Cox regression analyses demonstrated that pretreatment blood albumin (p=0.003, HR 1.72, 95% CI: (1.21, 2.45)) and derived neutrophil to lymphocyte ratio (dNLR) (p=0.003, HR 1.71, 95% CI: (1.20-2.44)) were independent predictors for OS in SCLC patients. By establishing a pre-treatment prognostic scoring system based on baseline albumin and dNLR, we found that patients with high albumin and low dNLR exhibited a significantly better prognosis than those with low albumin and high dNLR in both the full (P<.0001, HR 0.33, 95% CI: 0.20-0.55) and the metastatic cohort (P<.0001, HR 0.28, 95% CI: 0.15-0.51). The better prognostic group also had younger age, higher BMI and lower systemic inflammatory biomarker values than the unfavorable group (P<.0001). Conclusion: Our data reveals the significant role of metastasis status and treatment history in predicting the initial response of SCLC patients to ICI treatment. However, baseline serum albumin and dNLR provide a more precise prognostic prediction for patient OS. The scoring system based on albumin and dNLR enhances the ability to stratify patient prognosis and holds the potential to guide clinical decision-making for SCLC patients undergoing ICI therapy.

Assessment of serum tumor markers CEA, CA-125, and CA19-9 as adjuncts in non-small cell lung cancer management.

Conventional tumor markers may serve as adjuncts in non-small cell lung cancer (NSCLC) management. This study analyzed whether three tumor markers (CEA, CA19-9, and CA-125) held associations with radiographic and clinical outcomes in NSCLC. It constituted a single-center study of NSCLC patients treated with systemic therapy at the London Regional Cancer Program. Serum tumor markers were analyzed for differences in radiographic responses (RECIST v1.1 or iRECIST), associations with clinical characteristics, and all-cause mortality. A total of 533 NSCLC patients were screened, of which 165 met inclusion criteria. A subset of 92 patients had paired tumor markers and radiographic scans. From the latter population, median (IQR) fold-change from nadir to progression was 2.13 (IQR 1.24-3.02; p < 0.001) for CEA, 1.46 (IQR 1.13-2.18; p < 0.001) for CA19-9, and 1.53 (IQR 0.96-2.12; p < 0.001) for CA-125. Median (IQR) fold-change from baseline to radiographic response was 0.50 (IQR 0.27, 0.95; p < 0.001) for CEA, 1.08 (IQR 0.74, 1.61; p = 0.99) for CA19-9, and 0.47 (IQR 0.18, 1.26; p = 0.008) for CA-125. In conclusion, tumor markers are positioned to be used as adjunct tools in clinical decision making, especially for their associations with radiographic response (CEA/CA-125) or progression (CEA/CA-125/CA-19-9).

Risk Factors of Severe Postoperative Complication in Lung Cancer Patients with Diabetes Mellitus.

PURPOSE: Clinically, postoperative complications are occasionally observed in lung cancer patients with diabetes mellitus (DM). The increased risk of postoperative complications in DM patients has been reported in other fields. This study aims to identify risk factors for severe postoperative complications in lung cancer patients with DM. METHODS: Of 2756 consecutive patients who underwent complete resection for lung cancer between 2008 and 2018 in our hospital, 475 patients (20%) were complicated by DM. Clinical factors and diabetic factors (HbA1c, preoperative fasting blood glucose [FBG], postoperative mean FBG on 1, 3 postoperative days [PODs], and use of insulin) were evaluated by univariable and multivariable analyses to identify independent risk factors of severe complication. RESULTS: The 349 (73%) patients were male. Their median age was 71 years. Severe perioperative complications occurred in 128 (27%) patients. In the multivariable analysis, male (p <0.01), age (≥75 years) (p = 0.04), preoperative FBG (≥140 mg/dL) (p = 0.03), and increased mean FBG on 1, 3 PODs (≥180 mg/dL) (p <0.01) were significantly associated with severe perioperative complications. CONCLUSION: Increased FBG on 1, 3 PODs (≥180 mg/dL) was an independent risk factor for severe perioperative complications in lung cancer with DM. Postoperative hyperglycemia may be correlated to severe perioperative complications.

Prognostic significance of programmed cell death ligand 1 blood markers in non-small cell lung cancer treated with immune checkpoint inhibitors: a systematic review and meta-analysis.

Background: Immune checkpoint inhibitors (ICIs) are effective for non-small cell lung cancer (NSCLC) treatment, but the response rate remains low. Programmed cell death ligand 1 (PD-L1) in peripheral blood, including soluble form (sPD-L1), expression on circulating tumor cells (CTCs PD-L1) and exosomes (exoPD-L1), are minimally invasive and promising markers for patient selection and management, but their prognostic significance remains inconclusive. Here, we performed a meta-analysis for the prognostic value of PD-L1 blood markers in NSCLC patients treated with ICIs. Methods: Eligible studies were obtained by searching PubMed, EMBAS, Web of Science, and Cochrane Library prior to November 30, 2023. The associations between pre-treatment, post-treatment and dynamic changes of blood PD-L1 levels and progression-free survival (PFS)/over survival (OS) were analyzed by estimating hazard ratio (HR) and 95% confidence interval (CI). Results: A total of 26 studies comprising 1606 patients were included. High pre- or post-treatment sPD-L1 levels were significantly associated with worse PFS (pre-treatment: HR=1.49, 95%CI 1.13-1.95; post-treatment: HR=2.09, 95%CI 1.40-3.12) and OS (pre-treatment: HR=1.83, 95%CI 1.25-2.67; post-treatment: HR=2.60, 95%CI 1.09-6.20, P=0.032). High pre-treatment exoPD-L1 levels predicted a worse PFS (HR=4.24, 95%CI 2.82-6.38, P<0.001). Pre-treatment PD-L1+ CTCs tended to be correlated with prolonged PFS (HR=0.63, 95%CI 0.39-1.02) and OS (HR=0.58, 95%CI 0.36-0.93). Patients with up-regulated exoPD-L1 levels, but not sPD-L1, after ICIs treatment had significantly favorable PFS (HR=0.36, 95%CI 0.23-0.55) and OS (HR=0.24, 95%CI 0.08-0.68). Conclusion: PD-L1 blood markers, including sPD-L1, CTCs PD-L1 and exoPD-L1, can effectively predict prognosis, and may be potentially utilized for patient selection and treatment management for NSCLC patients receiving ICIs.