Identification and evolution of PDK-1-like involving lamprey innate immunity.

3-phosphoinositide-dependent protein kinase-1 (PDK-1) is a key kinase regulating the activity of the PI3K/AKT pathway and a major regulator of the AGC protein kinase family. It is essential in the physiological activities of cells, embryonic development, individual development and immune response. In this study, we have identified for the first time an analogue of PDK-1 in the most primitive vertebrate, lamprey, and named it PDK-1-like. The protein sequence similarity of lamprey PDK-1-like to human, mouse, chicken, African xenopus and zebrafish PDK-1 were 64.4 %, 64.5 %, 65.0 %, 61.3 % and 63.2 %, respectively. The phylogenetic tree showed that PDK-1-like of lamprey were located at the base of the vertebrate branch, in line with the trend of biological evolution. Meanwhile, homology analysis showed that PDK-1 proteins across species shared a conserved kinase structural domain and a Pleckstrin Homology (PH) domain. Genomic synteny analysis revealed that the large-scale duplication blocks were not found in lamprey genome and neighbor genes of lamprey PDK-1-like presented dramatic differences compared with jawed vertebrates. More importantly, qPCR analysis showed that PDK-1-like was widely expressed in lamprey. Its mRNA expression levels varied in response to different pathogenic stimuli, and its expression was generally up-regulated under Polyinosinic-Polycytidylic acid (Poly(I:C)) stimulation. Pearson's correlation analysis showed that PDK-1-like was involved in co-expressed with MyD88-independent TLR-3 pathway during the immune response of lamprey, instead of MyD88-dependent TLR-3 pathway. In summary, our composite results offer valuable clues to the origin and evolution of PDK-1, and imply that PDK-1 s are among the most ancestral immune regulators in vertebrates.

An Incremental-Self-Training-Guided Semi-Supervised Broad Learning System.

The broad learning system (BLS) has recently been applied in numerous fields. However, it is mainly a supervised learning system and thus not suitable for specific practical applications with a mixture of labeled and unlabeled data. Despite a manifold regularization-based semi-supervised BLS, its performance still requires improvement, because its assumption is not always applicable. Therefore, this article proposes an incremental-self-training-guided semi-supervised BLS (ISTSS-BLS). Distinctive to traditional self-training, where all unlabeled data are labeled simultaneously, incremental self-training (IST) obtains unlabeled data incrementally from an established sorted list based on the distance between the data and their cluster center. During iterative learning, a small portion of labeled data is first used to train BLS. The system recursively self-updates its structure and meta-parameters using: 1) the double-restricted mechanism and 2) the dynamic neuron-incremental mechanism. The double-restricted mechanism is beneficial to preventing the introduction of incorrect pseudo-labeled samples, and the dynamic neuron-incremental mechanism guides the self-updating of the network structure effectively based on the training accuracy of the labeled data. These strategies guarantee a parsimonious model during the update. Besides, a novel metric, the accuracy-time ratio (A/T), is proposed to evaluate the model's performance comprehensively regarding time and accuracy. In experimental verifications, ISTSS-BLS performs outstandingly on 11 datasets. Specifically, the IST is compared with the traditional one on three scales data, saving up to 52.02% learning time. In addition, ISTSS-BLS is compared with different state-of-the-art alternatives, and all results indicate that it possesses significant advantages in performance.

Adaptive Subspace Optimization Ensemble Method for High-Dimensional Imbalanced Data Classification.

It is hard to construct an optimal classifier for high-dimensional imbalanced data, on which the performance of classifiers is seriously affected and becomes poor. Although many approaches, such as resampling, cost-sensitive, and ensemble learning methods, have been proposed to deal with the skewed data, they are constrained by high-dimensional data with noise and redundancy. In this study, we propose an adaptive subspace optimization ensemble method (ASOEM) for high-dimensional imbalanced data classification to overcome the above limitations. To construct accurate and diverse base classifiers, a novel adaptive subspace optimization (ASO) method based on adaptive subspace generation (ASG) process and rotated subspace optimization (RSO) process is designed to generate multiple robust and discriminative subspaces. Then a resampling scheme is applied on the optimized subspace to build a class-balanced data for each base classifier. To verify the effectiveness, our ASOEM is implemented based on different resampling strategies on 24 real-world high-dimensional imbalanced datasets. Experimental results demonstrate that our proposed methods outperform other mainstream imbalance learning approaches and classifier ensemble methods.

First-Line Osimertinib in Patients With EGFR-Mutated Non-Small Cell Lung Cancer: Effectiveness, Resistance Mechanisms, and Prognosis of Different Subsequent Treatments.

Background: Although the clinical application of osimertinib, a third-generation epidermal growth factor receptor-tyrosine kinase inhibitor (EGFR-TKI), has been a new step forward in the first-line treatment of non-small cell lung cancer (NSCLC), an increasing number of patients with progression on osimertinib represents a great challenge clinically. The patterns of resistance mechanisms and subsequent treatment strategies after first-line osimertinib resistance are not well established. Methods: Between January 1, 2016 and October 31, 2020, a consecutive of 56 EGFR-mutant lung cancer patients treated with osimertinib as first-line therapy at Daping Hospital (Chongqing, China) were retrospective screened. The samples of pre-osimertinib and osimertinib-resistance were all detected by next-generation sequencing (NGS) panels. Statistical analyses were carried out using SPSS 23.0 software. Survival analyses were performed using the Kaplan-Meier method and compared using a log-rank test between groups. Results: Among 47 patients with osimertinib effectiveness analysis, the median progression free survival (mPFS) was 15.4 months (95% confidence interval [CI]: 12.2-24.9 months), and median overall survival (mOS) was 35.5 months (95% CI: 23.9 months -NA). A total of 21 patients underwent repeated NGS tests upon osimertinib resistance. MET amplification was the most common resistance mechanism (6/21, 28.6%), followed by C797S mutation (5/21, 23.8%). A total of 15 patients received subsequent treatments, with mPFS of 7.3 months (95% CI 5.0 months -NA). Among them, 7 patients with EGFR C797 S or/and MET amplification received subsequent second-line targeted therapy, achieving mPFS of 7.3 months (95% CI 4.5 months -NA). Of note, 3 patients received immunotherapy as second- or third-line treatment after osimertinib resistance, achieving median clinical benefit of 37.3 months. Conclusions: MET amplification and C797S mutation are main resistance mechanisms, which could be targeted by crizotinib and gefitinib, respectively. More than 50% patients could receive subsequent anticancer targetable therapies after first-line osimertinib resistance. Immunotherapy may also be an acceptable choice after osimertinib resistance.

Genotyping of cerebrospinal fluid in lung cancer patients with leptomeningeal metastasis.

BACKGROUND: The prognosis of non-small-cell lung cancer (NSCLC) with leptomeningeal metastasis (LM) is poor. Detection of cell-free DNA (cfDNA) by next generation sequencing (NGS) in cerebrospinal fluid (CSF) may facilitate diagnosis of LM and identification of drug resistance mechanisms, yet its clinical use needs to be further verified. METHODS: We performed a retrospective cohort study to assess the genetic profiles of paired CSF and plasma samples in lung cancer patients with LM. Of 17 patients screened, a total of 14 patients with LM and paired NGS tests were enrolled. RESULTS: All patients harbor driver gene mutations, including 12 epidermal growth factor receptor (EGFR) activating mutations, 1 anaplastic lymphoma kinase (ALK) rearrangement, and 1 ROS-1 fusion. Genetic mutations were detected in CSF cfDNA from 92.9% patients (13/14), which was significantly higher than that from the plasma (9/14, 64.2%). The mutations were highly divergent between CSF and plasma cfDNA, with a concordance rate of 24.38% and 10 mutations shared by the two media. CSF cfDNA could also benefit the analysis of resistance mechanisms to targeted therapies. In five patients who experienced progression on 1st or 2nd generation EGFR-tyrosine kinase inhibitors (TKIs), RB1 mutation, and amplification of MET and EGFR were detected in CSF cfDNA only. In eight patients with LM progression on osimertinib resistance, EGFR amplification was detected in CSF cfDNA from four patients, whereas no CNVs were detected in the matched plasma samples. CONCLUSIONS: In conclusion, CSF could be superior to plasma in providing a more comprehensive genetic landscape of LM to find out drug resistance mechanisms and guide subsequent treatments.

Case Report: An "Immune-Cold" EGFR Mutant NSCLC With Strong PD-L1 Expression Shows Resistance to Chemo-Immunotherapy.

Long-term survival benefit has been noticed in non-small-cell lung cancer (NSCLC) patients treated with immune checkpoint inhibitors (ICIs), such as PD-1 inhibitors. However, it is still controversial whether patients with EGFR-activating mutations may benefit from ICIs. Recently, in stage IIIA NSCLC, chemo-immunotherapy has led to significant pathological response, yet patients with the presence of known EGFR mutations were excluded from some randomized trials of neoadjuvant therapy. Herein, we report a case of a 50-year-old female patient, who was initially diagnosed as stage IIIA lung squamous cell carcinoma. Immunohistochemistry analysis showed that the patient presented with high PD-L1 expression. Then, chemo-immunotherapy was given to the patient but the disease progressed quickly with distant metastasis. A re-biopsy revealed a poorly differentiated lung adenocarcinoma together with EGFR p.L858R mutation. Then the patient received gefitinib, which resulted in significant regression of primary lung lesion. A detailed examination of pre-treatment tumor sections demonstrated rare infiltration of CD8+ T cells, indicating that the current patient presented with an "immune-cold" microenvironment, which might explain the primary resistance to chemo-immunotherapy. Taken together, our case indicated that comprehensive detection of PD-L1 expression, driver gene status, together with tumor immune microenvironment, may offer a better prediction of treatment efficacy.

Ibrutinib reverses IL-6-induced osimertinib resistance through inhibition of Laminin α5/FAK signaling.

Osimertinib, a 3rd generation epidermal growth factor receptor tyrosine kinase inhibitor (EGFR-TKI), is the first-line standard-of-care for EGFR-mutant non-small cell lung cancer (NSCLC) patients, while acquired drug resistance will inevitably occur. Interleukin-6 (IL-6) is a keystone cytokine in inflammation and cancer, while its role in osimertinib efficacy was unknown. Here we show that clinically, plasma IL-6 level predicts osimertinib efficacy in EGFR mutant NSCLC patients. Highly increased IL-6 levels are found in patients with acquired resistance to osimertinib. Addition of IL-6 or exogenous overexpression of IL-6 directly induces osimertinib resistance. Proteomics reveals LAMA5 (Laminin α5) and PTK2, protein tyrosine kinase 2, also called focal adhesion kinase (FAK), are activated in osimertinib-resistant cells, and siRNA knockdown of LAMA5 or PTK2 reverses IL-6-mediated osimertinib resistance. Next, using a large-scale compound screening, we identify ibrutinib as a potent inhibitor of IL-6 and Laminin α5/FAK signaling, which shows synergy with osimertinib in osimertinib-resistant cells with high IL-6 levels, but not in those with low IL-6 levels. In vivo, this combination inhibits tumor growth of xenografts bearing osimertinib-resistant tumors. Taken together, we conclude that Laminin α5/FAK signaling is responsible for IL-6-induced osimertinib resistance, which could be reversed by combination of ibrutinib and osimertinib.

Research Review for Broad Learning System: Algorithms, Theory, and Applications.

In recent years, the appearance of the broad learning system (BLS) is poised to revolutionize conventional artificial intelligence methods. It represents a step toward building more efficient and effective machine-learning methods that can be extended to a broader range of necessary research fields. In this survey, we provide a comprehensive overview of the BLS in data mining and neural networks for the first time, focusing on summarizing various BLS methods from the aspects of its algorithms, theories, applications, and future open research questions. First, we introduce the basic pattern of BLS manifestation, the universal approximation capability, and essence from the theoretical perspective. Furthermore, we focus on BLS's various improvements based on the current state of the theoretical research, which further improves its flexibility, stability, and accuracy under general or specific conditions, including classification, regression, semisupervised, and unsupervised tasks. Due to its remarkable efficiency, impressive generalization performance, and easy extendibility, BLS has been applied in different domains. Next, we illustrate BLS's practical advances, such as computer vision, biomedical engineering, control, and natural language processing. Finally, the future open research problems and promising directions for BLSs are pointed out.

Progressive Ensemble Kernel-Based Broad Learning System for Noisy Data Classification.

The broad learning system (BLS) is an algorithm that facilitates feature representation learning and data classification. Although weights of BLS are obtained by analytical computation, which brings better generalization and higher efficiency, BLS suffers from two drawbacks: 1) the performance depends on the number of hidden nodes, which requires manual tuning, and 2) double random mappings bring about the uncertainty, which leads to poor resistance to noise data, as well as unpredictable effects on performance. To address these issues, a kernel-based BLS (KBLS) method is proposed by projecting feature nodes obtained from the first random mapping into kernel space. This manipulation reduces the uncertainty, which contributes to performance improvements with the fixed number of hidden nodes, and indicates that manually tuning is no longer needed. Moreover, to further improve the stability and noise resistance of KBLS, a progressive ensemble framework is proposed, in which the residual of the previous base classifiers is used to train the following base classifier. We conduct comparative experiments against the existing state-of-the-art hierarchical learning methods on multiple noisy real-world datasets. The experimental results indicate our approaches achieve the best or at least comparable performance in terms of accuracy.

Successful osimertinib rechallenge following subsequent chemotherapy regimen in a patient with metastatic non-small cell lung carcinoma: a case report.

Although tyrosine kinase inhibitors (TKIs) targeting epidermal growth factor receptor (EGFR) have a favorable and durable treatment response, almost all patients will eventually acquire resistance and develop disease progression. Re-administration of first and second-generation EGFR TKIs has been successfully executed in advanced non-small cell lung cancer (NSCLC) subsequent to EGFR-TKI resistance. However, osimertinib rechallenge following osimertinib resistance in EGFR T790M-negative patient is less explored. Herein, we describe a metastatic adenocarcinoma NSCLC patient with exon 19 deletion in EGFR (19del) who acquired resistance to initial gefitinib and second-line osimertinib but was successfully rechallenged with osimertinib following treatment failure with chemotherapy. The osimertinib rechallenge, despite the absence of EGFR T790M, was considered after the development of multiple small pulmonary lesions and an increase in EGFR exon 19 deletion. After a month of osimertinib rechallenge, pulmonary and brain lesions significantly reduced achieving partial response. The success of osimertinib rechallenge following previous osimertinib resistance in a metastatic NSCLC patient with EGFR 19del in the absence of T790M suggests that re-administration of osimertinib can be a treatment option in similar situations. In addition, this case also highlights the importance of mutational profiling for treatment monitoring to understand the mutational landscape of the patient and guide subsequent treatment including treatment rechallenge.

Multifocal pleural capillary hemangioma: a rare cause of hemorrhagic pleural effusion-case report.

BACKGROUND: Capillary hemangioma can be found in many organs, but rarely in pleura. Previously, only localized pleural capillary hemangioma cases have been reported. Corticosteroids are the most commonly recommended drugs in capillary hemangioma. CASE PRESENTATION: Here, we present a case of a young woman with recurrent hemorrhagic pleural effusion. Despite repeatedly thoracentesis, the routine examinations, including chest computed tomography (CT) scan, pleural effusion biochemical test, and cytology all failed to make a definite diagnosis. Thus, single port video-assisted thoracoscopy (VATS) was then performed. Numerous nodules arising from the parietal pleura were found, and biopsies showed multifocal pleural capillary. However, recurrent pleural effusion was successfully managed by oral azathioprine, after failure of dexamethasone treatment. CONCLUSIONS: To our knowledge, this is the first case of a patient with recurrent hemorrhagic pleural effusion masquerading as malignant pleurisy, but in fact caused by multifocal pleural capillary hemangioma.

Lazarus type response to immunotherapy in three patients with poor performance status and locally advanced NSCLC: a case series and literature review.

BACKGROUND: Immune checkpoint inhibitors (ICIs) have become the standard treatment for patients with advanced non-small cell lung cancer (NSCLC). However, the safety and efficacy of ICIs in severe advanced NSCLC patients with poor performance status (PS) are still unclear. METHODS: In the current study, we report a retrospective case series of three critically ill NSCLC patients with poor PS treated with immunotherapy in our hospital, and discussed these cases with reference to the existing literature and guidelines. RESULTS: Before treatment, the Eastern Cooperative Oncology Group (ECOG) PS scores of all three patients were 4, while programmed cell death protein ligand-1 (PD-L1) was strongly expressed (over 50%). After initiating anti-programmed cell death 1 (PD-1)/PD-L1 agents, the PS score of the three patients improved rapidly to 0-1 in a short time. A Lazarus type response was observed in all patients. There were no grade 3-4 immune-related adverse events (irAEs) in any of the patients, and only one patient developed rash (grade 2 irAE) and hypothyroidism (grade 2 irAE). The best response across all three patients was partial response (PR). As of the latest follow-up date on June 10, 2020, two patients are still alive, with the other having died on January 14, 2020, whose progression-free survival (PFS) and overall survival (OS) were 11 and 16 months, respectively. CONCLUSIONS: Immunotherapy is still an effective and low-toxicity option for severe advanced NSCLC patients with poor PS. Lazarus type response may occur, especially in patients whose PD-L1 is strongly expressed (≥50%). However, a greater amount of real-world data or randomized clinical trials are needed in this setting.

Preference of cis-Thioamide Structure in N-Thioacyl-N-methylanilines.

The thioamide group represents a highly attractive isostere of the amide bond. We report a combined structural and computational study on cis-thioamide conformation of N-thioacyl-N-methylanilines. Amide to thioamide replacement in a class of anilides that are highly valuable as conformational locks results in a higher preference for cis conformation in a unique compacted template intrinsic to the thioamide structure. The study strongly supports the use of N-methylthioanilides as cis-conformational locks in various facets of chemistry.

Genetic Heterogeneity Between Paired Primary and Brain Metastases in Lung Adenocarcinoma.

PURPOSE: About one-third of nonsmall cell lung cancer (NSCLC) patients develop brain metastases (BM). However, there is an unmet need for early diagnosis and treatment of BM. The precise mechanism for BM is still unknown. However, the genetic heterogeneity between primary tumor and paired BM indicates that sampling from the primary tumor may not be able to fully represent the mutational status in metastases. In this study, the genetic heterogeneity of primary lung adenocarcinoma and paired BM was analyzed. PATIENTS AND METHODS: A total of 11 paired samples of primary tumors and BM from lung cancer patients were included, in which 7 paired samples of patients were finally analyzed. Samples were sequenced by whole-exome sequencing (WES) to investigate the common and unique mutations in the primary tumors and BM, and the similarities and differences in copy number variation (CNV). RESULTS: The consistency of gene mutation between primary lung adenocarcinoma and paired BM was 33% to 86%. FAM129C and ADAMTSs specifically mutated in BM, along with NKX2-1 high amplification and SAMD2/4 copy number deletion. CONCLUSION: The consistency of gene mutation between primary lung adenocarcinoma and corresponding BM is relatively high, while the individual differences were significant. FAM129C and ADAMTSs mutations and high amplification of NKX2-1 may be related to BM of lung cancer. The loss of copy number of SAMD2/4 may be a potential therapeutic target for BM from lung adenocarcinoma.

Serial ultra-deep sequencing of circulating tumor DNA reveals the clonal evolution in non-small cell lung cancer patients treated with anti-PD1 immunotherapy.

BACKGROUND: Immune-therapy with anti-PD1 inhibitors, such as pembrolizumab, is revolutionizing the treatment of non-small cell lung cancers (NSCLC). However, identifying patients for the potential therapeutic response and predicting therapy resistance and early relapse remains a challenge. METHODS: Between 2016 and 2018, 60 patients were treated with pembrolizumab, among who 12 NSCLC patients had both baseline (before treatment) and serial (on treatment) periodical circulating tumor DNA (ctDNA) samples. Those samples were sequenced on a 329 pan cancer-related gene panel. Analyses of tumor burden, blood tumor mutational burden (bTMB), maximum somatic allele frequency (MSAF), and tumor clonal structure were performed in association with clinical response. Candidate resistance mutations involved in relapse and metastases were further investigated. RESULTS: ctDNA was detected and mutational profiling was performed for each patient. Those with a high baseline bTMB level showed significantly improved progression-free survival (PFS) after pembrolizumab treatment. Tumor burden and therapeutic response significantly correlated with the MSAF instead of the bTMB. Clone analysis detected tumor progression about 2-4 months ahead of computed tomography (CT) scan. One mutation in gene PTCH1 (Protein patched homolog 1) and two acquired anti-PD1 candidate resistance mutations of gene B2M (ß2 microglobulin) were identified in association with distant metastasis. The evolutionary tree of a representative patient was also described. CONCLUSION: This pilot study showed that MSAF could be another good indicator of therapeutic response, and clonal analysis could be clinically useful in monitoring clonal dynamics and detecting remote metastasis and early relapse.

Universal Approximation Capability of Broad Learning System and Its Structural Variations.

After a very fast and efficient discriminative broad learning system (BLS) that takes advantage of flatted structure and incremental learning has been developed, here, a mathematical proof of the universal approximation property of BLS is provided. In addition, the framework of several BLS variants with their mathematical modeling is given. The variations include cascade, recurrent, and broad-deep combination structures. From the experimental results, the BLS and its variations outperform several exist learning algorithms on regression performance over function approximation, time series prediction, and face recognition databases. In addition, experiments on the extremely challenging data set, such as MS-Celeb-1M, are given. Compared with other convolutional networks, the effectiveness and efficiency of the variants of BLS are demonstrated.

5,6-Difluorobenzothiazole-Based Conjugated Polymers with Large Band Gaps and Deep Highest Occupied Molecular Orbital Levels.

A 5,6-difluorobenzothiazole-based dibromo monomer was successfully synthesized, from which new fluorinated conjugated polymers PF-ffBTz and PFN-ffBTz were prepared via copolymerizations with two fluorene-based diboronic ester monomers. Twisted fluorene-ffBTz backbones enable PF-ffBTz and PFN-ffBTz with large band gaps up to 3.10 eV and deep-lying highest occupied molecular orbital levels down to -6.2 eV. The chemical structures of PF-ffBTz and PFN-ffBTz impart some new functionalities of fluorinated conjugated polymers. PF-ffBTz can show deep blue electroluminescent emission, with high external quantum efficiency of 3.71%. PFN-ffBTz, with amino-functionalized side chains on the fluorene unit, can serve as an efficient cathode interlayer in inverted polymer solar cells (PSCs), showing better photovoltaic performances if compared with a ZnO interlayer. In addition, it is found that using an optical filter to cut off the short wavelength section (≤380 nm) of incident light can significantly elevate photostability of PSCs under continuous illumination.

Broad Learning System: An Effective and Efficient Incremental Learning System Without the Need for Deep Architecture.

Broad Learning System (BLS) that aims to offer an alternative way of learning in deep structure is proposed in this paper. Deep structure and learning suffer from a time-consuming training process because of a large number of connecting parameters in filters and layers. Moreover, it encounters a complete retraining process if the structure is not sufficient to model the system. The BLS is established in the form of a flat network, where the original inputs are transferred and placed as "mapped features" in feature nodes and the structure is expanded in wide sense in the "enhancement nodes." The incremental learning algorithms are developed for fast remodeling in broad expansion without a retraining process if the network deems to be expanded. Two incremental learning algorithms are given for both the increment of the feature nodes (or filters in deep structure) and the increment of the enhancement nodes. The designed model and algorithms are very versatile for selecting a model rapidly. In addition, another incremental learning is developed for a system that has been modeled encounters a new incoming input. Specifically, the system can be remodeled in an incremental way without the entire retraining from the beginning. Satisfactory result for model reduction using singular value decomposition is conducted to simplify the final structure. Compared with existing deep neural networks, experimental results on the Modified National Institute of Standards and Technology database and NYU NORB object recognition dataset benchmark data demonstrate the effectiveness of the proposed BLS.

Bone metastasis from lung cancer identified by genetic profiling.

Cancer metastasis remains responsible for the vast majority of cases of cancer-related morbidity and mortality. Metastasis, by its definition, is the spread of cancer from the primary site to the distant tissues. Advancing the scientific and clinical understanding of cancer metastasis is a high priority. The prerequisite requirement for pathological consistency may be compromised during metastasis. The present study reports the case of a cancer patient with different pathological types. The patient presented with pain in the neck and right hip, as well as weight loss. He underwent whole-body positron emission tomography-computed tomography, which identified a mass in the lung and abnormal metabolism of the bone. Biopsies of the ilium and lung were performed and he was shown to have lung adenocarcinoma and bone squamous carcinoma. The morphology and immunohistochemical patterns were completely different, while each lesion harbored an identical genetic profile. The bone lesion was identified to be a metastasis from the lung cancer. The patient was prescribed an epithelial growth factor receptor inhibitor, which resulted in a partial response in the lung mass and alleviation of the patient's bone pain. Through this case study, we advocate the importance of using genetic testing in addition to pathological assessment.

Neutrophil-lymphocyte ratio as a prognostic marker for chemotherapy in advanced lung cancer.

BACKGROUND: Lung cancer ranks first both in morbidity and mortality in malignancies, but prognostic biological markers are lacking. The neutrophil-lymphocyte ratio (NLR) was proposed as a convenient biological marker. This study aimed to explore the prognostic value of NLR in advanced non-small cell lung cancer (NSCLC). METHODS: This retrospective study screened patients admitted from October 2007 to October 2014. Patients had histopathologically confirmed, treatment-naïve, metastatic NSCLC, and were prescribed platinum doublet chemotherapy. NLR and demographic data were collected, together with the outcome of chemotherapy. Progression-free survival (PFS) and overall survival (OS) were analyzed using the Kaplan-Meier method and Cox regression model. RESULTS: A total of 325 patients were enrolled. The cutoff value for NLR (3.19) was determined by receiver operator characteristic analysis. Patients were dichotomized into high (≥3.19) and low (<3.19) NLR groups. Both groups had similar demographic features. However, the low-NLR group had longer PFS (6.1 months) and OS (22.3 months) than the high-NLR group (5.1 months, p = 0.002; 13.1 months, p<0.001, respectively). Multivariate analysis confirmed that NLR was inversely related to the prognosis of these patients (HR = 1.684, 95%: 1.297-2.185, p<0.001). CONCLUSIONS: This study argues that NLR is a convenient prognostic biological marker for advanced NSCLC patients treated with first-line chemotherapy and warrants further validation.