Clinical Outcome In Lung Cancer Research Articles

Hospitalization outcome of patients with lung cancer with COVID-19 infection: NIS 2020 analysis.

212 Background: Lung cancer is the most commonly diagnosed cancer and the leading cause of cancer-related deaths globally. Recent data showed the detrimental effects of COVID-19 infection on other neoplasm conditions, causing increased mortality rates and complications. However, data on the precise effects of COVID-19 on hospitalized individuals with lung cancer are scarce. Methods: We analyzed the 2020 U.S. National Inpatient Sample (NIS) to investigate the effects of COVID-19 infection on cases primarily admitted due to lung and bronchus cancer. Adjusted odds ratios (aORs) for specified outcomes were calculated through multivariable logistic and linear regression analyses. The primary outcome was inpatient mortality, with secondary outcomes including system-based complications. Statistical significance was established at a p-value of 0.05. Results: We identified 103,334 patients with a primary discharge diagnosis of lung and bronchus cancer. The mean age was 68.9 years; 50.5% were female. In the non-COVID group, Caucasians accounted for 75%, followed by Hispanics (12%). Of these, 0.36% (374/103,334) had a concurrent diagnosis of COVID-19 infection. In a survey multivariable logistic and linear regression model adjusting for patient and hospital factors, COVID-19 infection was associated with higher in-hospital mortality (aOR 5.39; 95% CI (3.01, 9.64), p < 0.001), higher mean length of stay (b 5.51; 95% CI (2.59, 8.43) p < 0.001), mean total hospital cost (b 10,683; 95% CI (784.32, 20,581.93) p = 0.034), shock (aOR 4.37; 95% CI (1.96, 9.91), p < 0.001), acute respiratory failure (aOR 2.23; 95% CI (1.30, 3.85), p = 0.004), mechanical ventilation usage (aOR 3.12; 95% CI (1.46, 6.66), p = 0.003) and acute kidney injury (aOR 2.71; 95% CI (1.54, 4.74), p < 0.001). We observed non-significant, but increased, risks of sepsis, SIRS, and coagulopathy in COVID-19-positive patients. Conclusions: In conclusion, our study underscores that COVID-19 infection is associated with higher in-hospital mortality and other major clinical outcomes in lung cancer, as well as increased economic burden and cost of stay. To definitively establish causal relationships between the observed factors and the reported clinical outcomes in this population, further longitudinal research is necessary.

Spatial Architecture of Myeloid and T Cells Orchestrates Immune Evasion and Clinical Outcome in Lung Cancer.

This study provides novel insights into the spatial organization of the lung cancer TME in the context of tumor immunogenicity, tumor heterogeneity, and cancer evolution. Pairing the tumor evolutionary history with the spatially resolved TME suggests mechanistic hypotheses for tumor progression and metastasis with implications for patient outcome and treatment. This article is featured in Selected Articles from This Issue, p. 897.

Linking EMT Status of Circulating Tumor Cells to Clinical Outcomes in Lung Cancer.

Lung cancer (LC) remains a leading cause of cancer-related mortality worldwide, with its prognosis influenced by complex biological factors. This study delves into the clinical relevance of circulating tumor cells (CTCs) and their Epithelial-Mesenchymal Transition (EMT) status in LC patients. We enrolled 30 newly diagnosed LC patients and utilized the CanPatrol technique for the separation and categorization of CTCs from peripheral blood samples. Immunofluorescent staining identified epithelial (CK8/18/19, EpCAM), mesenchymal (Vimentin, Twist), and leukocyte (CD45) markers in these cells. Fluorescence microscopy analyzed the slides, and RECIST 1.1 criteria assessed treatment response. Spearman's method was used to correlate CTCs' EMT states with their count and clinical characteristics. Our findings reveal three distinct CTC groups: epithelial (E-CTCs), hybrid epithelial/mesenchymal (E/M-CTCs), and mesenchymal (M-CTCs). Significant statistical differences were observed in stages III-IV vs I-II, tumor sizes T3-T4 vs T1-T2, and in the presence or absence of distant metastasis and lymph node involvement. Notably, the count of E/M-CTCs was positively correlated with TNM staging, tumor size, lymph node, and distant metastasis. Changes in M-CTC count pre- and post-treatment closely mirrored disease progression and control, showing considerable consistency with RECIST criteria. In conclusion, the EMT status of CTCs, especially E/M-CTCs, holds predictive value for LC staging, tumor size, and metastasis. Dynamic monitoring of M-CTCs can accurately reflect disease progression.

The use of generative adversarial networks for multi-site one-class follicular lymphoma classification

Recent advances in digital technologies have lowered the costs and improved the quality of digital pathology Whole Slide Images (WSI), opening the door to apply Machine Learning (ML) techniques to assist in cancer diagnosis. ML, including Deep Learning (DL), has produced impressive results in diverse image classification tasks in pathology, such as predicting clinical outcomes in lung cancer and inferring regional gene expression signatures. Despite these promising results, the uptake of ML as a common diagnostic tool in pathology remains limited. A major obstacle is the insufficient labelled data for training neural networks and other classifiers, especially for new sites where models have not been established yet. Recently, image synthesis from small, labelled datasets using Generative Adversarial Networks (GAN) has been used successfully to create high-performing classification models. Considering the domain shift and complexity in annotating data, we investigated an approach based on GAN that minimized the differences in WSI between large public data archive sites and a much smaller data archives at the new sites. The proposed approach allows the tuning of a deep learning classification model for the class of interest to be improved using a small training set available at the new sites. This paper utilizes GAN with the one-class classification concept to model the class of interest data. This approach minimizes the need for large amounts of labelled data from the new site to train the network. The GAN generates synthesized one-class WSI images to jointly train the classifier with WSIs available from the new sites. We tested the proposed approach for follicular lymphoma data of a new site by utilizing the data archives from different sites. The synthetic images for the one-class data generated from the data obtained from different sites with minimum amount of data from the new site have resulted in a significant improvement of 15% for the Area Under the curve (AUC) for the new site that we want to establish a new follicular lymphoma classifier. The test results have shown that the classifier can perform well without the need to obtain more training data from the test site, by utilizing GAN to generate the synthetic data from all existing data in the archives from all the sites.

The Effect of Timeliness of Care on Lung Cancer Survival - A Population-Based Approach.

Timeliness of care is an important dimension of healthcare quality but it's unclear whether it improves clinical outcomes in lung cancer (LC) patients. This study aims to analyze treatment patterns, time-to-treatment (TTT) and the impact of treatment timeliness (TT) in overall survival (OS) of patients diagnosed with LC in 2009-2014 in a population-based registry from Southern Portugal. We estimated median TTT for overall population, treatment type and stage. The impact of treatment and TT on five-year OS was analyzed using the Kaplan-Meier method and Cox regression modelling to determine the hazard ratio (HR) of death associated with treatment and TT. From the 11,308 cases diagnosed, 61.7% received treatment. Treatment rate decreased with increasing stage from 88% in stage I to 66.1% in stage IV. Overall median TTT was 49 days (IQR: 28-88) and 43.3% received TT. Surgery had a longer TTT than radiotherapy and systemic treatment. Patients in earlier stages had lower TT rates and longer TTT compared to more advanced, 24.7% and 80 days in stage I versus 51.3% and 42 days in stage IV (p < 0.0001). OS was 14.9% for total population and 19.6% and 7.1% for patients with and without treatment registered, respectively. TT had no observed impact on OS for stages I/II but a negative effect for stages III/IV. Relative to treated, the adjusted mortality risk was higher in untreated patients (HR = 2.240; 95%CI: 2.293-2.553). Contrary to treatment, TT had a negative impact on survival, with 11.3% in timely vs. 21.5% in untimely treated. Compared to untimely treated, the risk of death in TT patients was 46.6% higher (HR = 1.465; 95%CI: 1.381-1.555). LC survival is highly dependent on early diagnosis and adequate treatment. Time-to-treatment was longer than recommended for all treatment types but particularly for surgery. Overall TT results were paradoxical, as better survival was observed in patients untimely treated. The factors associated with TT were not possible to analyze and its impact on patient outcomes remains unclear. However, it is important to assess quality-of-care to improved LC management.

Interstitial Lung Abnormalities in Patients With Locally Advanced Esophageal Cancer: Prevalence, Risk Factors, and Clinical Implications.

Interstitial lung abnormalities (ILAs) represent nondependent abnormalities on chest computed tomography (CT) indicating lung parenchymal damages due to inflammation and fibrosis. Interstitial lung abnormalities have been studied as a predictor of clinical outcome in lung cancer, but not in other thoracic malignancies. The present study investigated the prevalence of ILA in patients with esophageal cancer and identified risk factors and clinical implications of ILA in these patients. The study included 208 patients with locally advanced esophageal cancer (median age, 65.6 years; 166 males, 42 females). Interstitial lung abnormality was scored on baseline CT scans before treatment using a 3-point scale (0 = no evidence of ILA, 1 = equivocal for ILA, 2 = ILA). Clinical characteristics and overall survival were compared in patients with ILA (score 2) and others. An ILA was present in 14 of 208 patients (7%) with esophageal cancer on pretreatment chest CT. Patients with ILA were significantly older (median age, 69 vs 65, respectively; P = 0.011), had a higher number of pack-years of smoking ( P = 0.02), and more commonly had T4 stage disease ( P = 0.026) than patients with ILA score of 1 or 0. Interstitial lung abnormality on baseline scan was associated with a lack of surgical resection after chemoradiotherapy (7/14, 50% vs 39/194, 20% respectively; P = 0.016). Interstitial lung abnormality was not associated with overall survival (log-rank P = 0.75, Cox P = 0.613). An ILA was present in 7% of esophageal cancer patients, which is similar to the prevalence in general population and in smokers. Interstitial lung abnormality was strongly associated with a lack of surgical resection after chemoradiotherapy, indicating an implication of ILA in treatment selection in these patients, which can be further studied in larger cohorts.

Abstract 3663: Allostatic load, tumor genomics, and outcomes in NSCLC

Abstract Introduction: Allostatic load (AL) is a multi-system measure of chronic stress shown to be an emerging framework for understanding disparities in cancer outcomes, but there have been no AL studies in lung cancer. Methods: We conducted a retrospective review of patients diagnosed with stage IV lung adenocarcinoma from 2016 - 2020 at the University of Illinois Hospital and Health System. An AL index included 9 biomarkers (systolic blood pressure, diastolic blood pressure, resting heart rate, BMI, albumin, eGFR, creatinine, glucose, and use of medications for hypertension, diabetes, or hypocholesteremia) with 1 point assigned for each biomarker meeting a pre-determined cutoff. AL was dichotomized between AL-high (5-9) and AL-low (0-4) with median progression-free survival of first-line therapy (mPFS1) and median overall survival (mOS) estimated for both groups with Kaplan-Meier curves. Rates of clinically actionable driver mutations (driver-POS), including EGFR, ALK, ROS1, BRAF V600e, RET, MET, NTRK, and KRAS G12C, were compared for both groups using chi-squared test. Mean AL was compared between those who were driver-POS and those who did not have a clinically actionable driver mutation (driver-NEG) using independent samples t-test. Results: A total of 61 patients were identified with 56% female and 74% former or current tobacco users. The cohort was 49% Black, 21% White, and 13% Hispanic or Latino. Mean AL at diagnosis was 3.3 (+/- 1.4). AL-high patients comprised 20% (12) of the cohort compared with 80% (49) AL-low. Between AL-high and AL-low cohorts, there was no statistically significant difference in mPFS1 (15 vs. 13 months, P = 0.98) and mOS (34 vs. 18 months, P = 0.33). Rates of driver-POS were lower in AL-high compared with AL-low (25% vs 69%, P &amp;lt;0.01). Mean AL was higher in the driver-NEG population compared with driver-POS population (3.79 vs 3.03, P = 0.04). Conclusions: Patients whose tumors did not have a clinically actionable driver mutation were associated with a higher AL, suggestive of higher chronic stress playing a role in more aggressive tumor biology. No statistically significant survival difference was observed in AL-high versus AL-low populations for mPFS1 and mOS. Larger, prospective trials are planned to correlate AL at diagnosis with clinical and biomarker data. AL, which serves as a measure of physiological dysfunction due to chronic stress associated with social disadvantage, may have implications for tumor biology and clinical outcomes in lung cancer. Citation Format: Ryan H. Nguyen, Michael Weinfeld, Mary Pasquinelli, Frank Weinberg, Natalie Reizine, Lawrence Feldman, Vijayakrishna K. Gadi. Allostatic load, tumor genomics, and outcomes in NSCLC [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3663.

Platelet Activation in High D-Dimer Plasma Plays a Role in Acquired Resistance to Epidermal Growth Factor Receptor Tyrosine Kinase Inhibitors in Patients with Mutant Lung Adenocarcinoma.

ObjectivePlatelet activation and adhesion to cancer cells increase the release of multiple factors that contribute to EMT and chemoresistance. Elevated levels of D-dimer have been associated with poor clinical outcomes in lung cancer. Platelets in high D-dimer plasma may be activated and implicated in acquired resistance to EGFR TKI in advanced lung adenocarcinoma with mutant EGFR.Materials and MethodsClinical responsive rate (RR), progression-free survival (PFS), and overall survival (OS) were prospectively measured in treatment-naïve lung adenocarcinoma patients with activation mutation. Plasma or platelets from patients with high or low D-dimer level were obtained to investigate the cytotoxic effects of TKIs on mutant cancer cells, and the mechanistic pathways were also explored.ResultsPatients with high D-dimer had worse RR, PFS, and OS. High D-dimer plasma induced resistance to gefitinib, erlotinib, afatinib, or osimertinib in EGFR mutant lung cancer cells. Depletion of platelets in high D-dimer plasma reversed the resistance to TKI. Platelets of high D-dimer plasma had higher adherence capacity to cancer cells, and induced EGFR and Akt activation as well as EMT through Src activation. Inhibition of platelet adherence or activation of Src or Akt conquered the resistance to TKI. The acquired resistance to TKI by high D-dimer plasma was less attributed to secondary gene mutation.ConclusionIncreased platelet activation in the high D-dimer plasma may contribute to first-line acquired EGFR TKI resistance. Thus, therapeutic strategy against platelet activation in patients with high D-dimer levels may improve the efficacy of first-line treatment with EGFR TKI.

Impact of travel burden on clinical outcomes in lung cancer.

Our study explores the influence of travel burden (measured as travel distance and travel time) on clinical outcomes in lung cancer patients. A retrospective analysis of a single Bulgarian center was performed. A total of 9240 lung cancer patients were included in the study. Travel distance and travel time between patients' city of residence and the treating facility were calculated with an online tool to determine the shortest route for travel using the existing road network. The probability of survival was estimated using the Kaplan-Meier method, and differences in survival in each subgroup were evaluated with a log-rank test. About one third of all included patients were living in the same city as the treating facility (n = 2746, 29.7%). Overall survival in our patient population was significantly lower with increasing travel distance (p < 0.001, Mantel-Cox log rank) and travel time (p < 0.001, Mantel-Cox log rank). The 1-year OS rate according to travel distance was 27.1% in the same city group, 22.4% in < 50-km group, and 20.5% in ≥ 50-km group (p < 0.001). The corresponding values for the 5-year OS rate were 2.9%, 2.6%, and 1.4% (p < 0.001). In this retrospective study, we discovered significant differences in the overall survival of patients with lung cancer depending on travel distance and travel time to the treating oncological facility. Despite having similar clinical and pathological characteristics (age, sex, stage at initial diagnosis, histologic subtype), the median overall survival was significantly lower in those subgroups of patients with a higher travel burden.

Clinicopathological and Prognostic Significance of Inhibitor of Apoptosis Protein (IAP) Family Members in Lung Cancer: A Meta-Analysis.

Simple SummaryLung cancer is the leading cause of cancer-related death worldwide. Although novel therapy regimens using immuno- and targeted therapy have improved survival for a subgroup of patients with lung cancer, the five-year survival rate is still poor. The inhibitor of apoptosis protein (IAP) family represents a heterogeneous group of anti-apoptotic proteins that are highly expressed in a variety of human malignancies. Despite conflicting results regarding the prognostic significance of IAPs, high expression of some members of this family have been extensively reported to be associated with poor prognosis in lung cancer patients. Therefore, there might be a subgroup of patients that could benefit from a targeted therapy against specific IAP family members in lung cancer. The aim of this study was to perform a meta-analysis to investigate the prognostic value of IAP family members and their association with clinicopathological features in lung cancer.Lung cancer is the most common cause of cancer-related death worldwide. Approximately 85% is non-small-cell and 15% is small-cell lung cancer. The inhibitor of apoptosis proteins (IAPs) represent a heterogeneous family of anti-apoptotic proteins, some members of which have been reported to correlate with clinical outcome in lung cancer. We screened PubMed, Web of Science, and Scopus for studies that investigated the prognostic value and clinicopathological features of IAPs in lung cancer. Forty-five eligible studies with 4428 patients assessed the expression of the IAPs survivin, XIAP, livin, and BRUCE. The pooled hazard ratio (HR) of 33 studies that analyzed overall survival (OS) revealed a positive correlation between survivin expression and poor prognosis. Seven studies displayed a strong association between survivin and disease recurrence. Two studies that assessed the expression of XIAP and livin, respectively, proved a significant relationship of these IAPs with poor OS. Meta-analyses of clinicopathological variables revealed a significant association between survivin and T stage, UICC stage, the presence of lymph node metastasis, and grade of differentiation. In conclusion, high expression of distinct IAPs significantly correlates with prognosis in lung cancer. Therefore, lung cancer patients might benefit from a targeted therapy against specific IAPs.

Personalizing Radiotherapy Prescription Dose Using Genomic Markers of Radiosensitivity and Normal Tissue Toxicity in NSCLC.

Cancer sequencing efforts have revealed that cancer is the most complex and heterogeneous disease that affects humans. However, radiation therapy (RT), one of the most common cancer treatments, is prescribed on the basis of an empirical one-size-fits-all approach. We propose that the field of radiation oncology is operating under an outdated null hypothesis: that all patients are biologically similar and should uniformly respond to the same dose of radiation. We have previously developed the genomic-adjusted radiation dose, a method that accounts for biological heterogeneity and can be used to predict optimal RT dose for an individual patient. In this article, we use genomic-adjusted radiation dose to characterize the biological imprecision of one-size-fits-all RT dosing schemes that result in both over- and under-dosing for most patients treated with RT. To elucidate this inefficiency, and therefore the opportunity for improvement using a personalized dosing scheme, we develop a patient-specific competing hazards style mathematical model combining the canonical equations for tumor control probability and normal tissue complication probability. This model simultaneously optimizes tumor control and toxicity by personalizing RT dose using patient-specific genomics. Using data from two prospectively collected cohorts of patients with NSCLC, we validate the competing hazards model by revealing that it predicts the results of RTOG 0617. We report how the failure of RTOG 0617 can beexplained by the biological imprecision of empirical uniform dose escalation which results in 80% of patients being overexposed to normal tissue toxicity without potential tumor control benefit. Our data reveal a tapestry of radiosensitivity heterogeneity, provide a biological framework that explains the failure of empirical RT dose escalation, and quantify the opportunity to improve clinical outcomes in lung cancer by incorporating genomics into RT.

Histopathological prognostic markers in metastatic lung adenocarcinoma, validation of stromal fibrosis and immunoscore as easy to interpret findings and their correlation with survival.

e21566 Background: Several histopathological markers have been associated with immune system activity and its relationship with clinical outcomes in lung cancer. Determination of tumor infiltrating lymphocytes as well as specific subpopulations could reveal cytotoxic potential. Additionally, changes in stromal composition and fibrosis could also reflect potential cellular interactions with the tumor lesions. In this study we validate these two findings as potential prognostic factors. Methods: A retrospective cohort analysis of patients with advanced lung adenocarcinoma treated in a reference center in Bogota, Colombia was conducted. Demographic as well as treatment variables were collected between January 2018 and December 2019. In addition to clinical outcomes, the percentage of stromal fibrosis and immunoscore were estimated for all biopsy samples by a trained oncological pathologist. Results: A total of 133 patients were included. Male predominance (51.1%) with Karnofsky performance scores (KPS) greater than 80 and 19% of prior cigarette exposure (26 patients) was identified in the cohort. Median overall survival of the cohort was 27.1 months (95%CI 22.1-NA) and progression free survival of 19.4 months (95%CI 18-24.5 months). Percentage of fibrous stroma and immunoscore were associated with a benefit in both OS and PFS. For patients who presented with a low stromal fibrosis, defined as harboring less than 10%, OS reached a median of 25.1 months (95%CI 21.5- 40.5 months) compared to 38.7 months for patients with high fibrosis (95%CI 27.7 months – NA), (p = 0.02). In terms of immunoscore, values under 40% were associated with a longer median OS (38.7 months, [95%CI 31.3- 58.6 months] vs 22.1 months [95%CI19.8- 42.3 months], p &lt; 0.001). Immunoscore and stromal fibrosis were not codependent variables (p &lt; 0.001), suggesting that these markers offer independent prognostic value with regards to OS. Conclusions: Stromal fibrosis and immunoscore serve as prognostic factors that could be easily interpreted and evaluated in order to offer prognostic classification of patients with metastatic adenocarcinoma of the lung.

Automated abstraction of real-world clinical outcome in lung cancer: A natural language processing and artificial intelligence approach from electronic health records.

e14062 Background: Real world evidence generated from electronic health records (EHRs) is playing an increasing role in health care decisions. It has been recognized as an essential element to assess cancer outcomes in real-world settings. Automatically abstracting outcomes from notes is becoming a fundamental challenge in medical informatics. In this study, we aim to develop a system to automatically abstract outcomes (Progression, Response, Stable Disease) from notes in lung cancer. Methods: A lung cancer cohort (n = 5,003) was obtained from the Mount Sinai Data Warehouse. The progress, pathology and radiology notes of patients were used. We integrated various techniques of Natural Language Processing (NLP) and Artificial Intelligence (AI) and developed a system to automatically abstract outcomes. The corresponding images, biopsies and lines of treatments (LOTs) were abstracted as attributes of outcomes. This system includes four information models: 1. Customized NLP annotator model: preprocessor, section detector, sentence splitter, named entity recognition, relation detector; CRF and LSTM methods were applied to recognize entities and relations. 2. Clinical Outcome container model: biopsy evidence extractor, lines of treatment detector, image evidence extractor, clinical outcome event recognizer, date detector, and temporal reasoning; Domain-specific rules were crafted to automatically infer outcomes. 3. Document Summarizer; 4. Longitudinal Outcome Summarizer. Results: To evaluate the outcomes abstracted, we curated a subset (n = 792) from patient cohort for which LOTs were available. About 61% of the outcomes identified were supported by radiologic images (time window = ±14 days) or biopsy pathology results (time window = ±100 days). In 91% (720/792) of patients, Progression was abstracted within a time window of 90 days prior to first-line treatment. Also, 72% of the Progression events identified were accompanied by a downstream event (e.g., treatment change or death). We randomly selected 250 outcomes for manual curation, and 197 outcomes were assessed to be correct (precision = 79%). Moreover, our automated abstraction system improved human abstractor efficiency to curate outcomes, reducing curation time per patient by 90%. Conclusions: We have demonstrated the feasibility and effectiveness of NLP and AI approaches to abstract outcomes from lung cancer EHR data. It promises to automatically abstract outcomes and other clinical entities from notes across all cancers.

An individualized immune prognostic signature in lung adenocarcinoma

BackgroundTumor immune infiltration is closely associated with clinical outcome in lung cancer. We aimed to develop an immune signature to improve the prognostic predictions of lung adenocarcinoma (LUAD).MethodsWe applied “Cell type Identification by Estimating Relative Subsets of RNA Transcripts” method to quantify the fraction of 22 leukocyte cells from six public microarray datasets. Four datasets from GPL570 were treated as the training cohort and two datasets from GPL96 and GPL10379 as the validation cohorts. An immune risk score (IRS) based on leukocyte cell fraction was established by least absolute shrinkage and selection operator cox regression model.ResultsIRS consisting of 6 types of leukocytes was constructed in the training dataset. In the training cohort (520 patients), the IRS stratified patients into high-IRS group (215 patients) and low-IRS group (305 patients) with significant differences in overall survival (OS) (HR: 2.77, 95% CI 2.08–3.06). Multivariate analysis including age, gender, stage, IRS and tumor purity revealed the IRS to be an independent prognostic factor in all datasets (training: HR: 10.71, 95% CI 5.72–20.07; validation-1: HR 2.68, 95% CI 1.15–6.27; validation-2: HR 3.71, 95% CI 1.33–10.33); all p < 0.05). IRS was significantly positively correlated to the expression levels of PD1, PDL1, CTLA and LAG3 (all p < 0.001). When integrated with clinical characteristics including stage and age, the composite immune and clinical signature presented with improved prognostic accuracy than IRS (mean C-index 0.66 vs. 0.60).ConclusionsThe proposed immune-clinical signature could predict OS in patients with LUAD effectively.

Putative stemness markers octamer-binding transcription factor 4, sex-determining region Y-box 2, and NANOG in non-small cell lung carcinoma: A clinicopathological association.

The promising improvement in the clinical outcome of lung cancer can be possibly achieved by identification of the molecular events that underlie its pathogenesis. Cancer stem cell (CSC) being one of the subsets of tumor majorly participates in drug resistance and treatment failure because of the moderate cell cycle, lower proliferation, and increased expression of DNA repair and anti-apoptosis genes. Although many putative CSC markers exist, a precise characterization for non-small cell lung cancer is of utmost importance due to increased mortality rate and lack of targeted therapies. Hence, the article focuses on the expression of stemness-associated markers, namely octamer-binding transcription factor 4 (OCT4), NANOG, and sex-determining region Y-box 2 (SOX2) in non-small cell lung cancer (NSCLC) patients. The expression of OCT4, NANOG, and SOX2 were evaluated in 32 histopathologically confirmed NSCLC tissues using real-time polymerase chain reaction. The obtained expression was correlated with clinical and pathological manifestations using the statistical test such as Student's t-test and Pearson correlation in varied statistical software. Results showed a significantly higher expression of OCT4 and NANOG compared to SOX2 in the tumor tissues. When the expression of these markers was correlated with the clinical parameters, higher expression was seen in males, patients with age above 60 years, and in adenocarcinoma subtype. In correlation with the habit, higher expression of OCT4 and SOX2 was observed in habituated patients. Expression of NANOG and OCT4 was higher even in patients with poor differentiation. The expression and prognostic significance of CSC markers obviously vary depending on histological NSCLC subtype. Importantly, our findings suggest that OCT4, SOX2, and NANOG network together may be promising for ongoing targeted therapies in specific NSCLC subgroups.

Expression of potassium channel genes predicts clinical outcome in lung cancer.

Lung cancer is the most common cause of cancer deaths worldwide and several molecular signatures have been developed to predict survival in lung cancer. Increasing evidence suggests that proliferation and migration to promote tumor growth are associated with dysregulated ion channel expression. In this study, by analyzing high-throughput gene expression data, we identify the differentially expressed K+ channel genes in lung cancer. In total, we prioritize ten dysregulated K+ channel genes (5 up-regulated and 5 down-regulated genes, which were designated as K-10) in lung tumor tissue compared with normal tissue. A risk scoring system combined with the K-10 signature accurately predicts clinical outcome in lung cancer, which is independent of standard clinical and pathological prognostic factors including patient age, lymph node involvement, tumor size, and tumor grade. We further indicate that the K-10 potentially predicts clinical outcome in breast and colon cancers. Molecular signature discovered through K+ gene expression profiling may serve as a novel biomarker to assess the risk in lung cancer.

The methylation induced by protein arginine methyltransferase 5 promotes tumorigenesis and progression of lung cancer.

Arginine methylation as a common pattern of post-translational modification is involved in many cellular biological processes. Protein arginine methyltransferase 5 (PRMT5) is a primary enzyme in charge of symmetric dimethylation (me2s) of arginine residues. Increasing literatures lead to the belief that PRMT5, as a potential oncogene, plays crucial roles in the tumorigenesis and progression of cancers. First of all, PRMT5 is overexpressed in several cancer cells, with various sub-cellular localization in different type of cells and different phases. Besides, PRMT5 participates in controlling cellular proliferation, differentiation, invasion, migration as well apoptosis through histone and other protein methylation. Moreover, PRMT5 is essential for growth and metastasis of lung cancer cells, and its overexpression indicates a poor clinical outcome of lung cancer. Therefore, in this review, we reviewed the substantial new literatures on PRMT5 and its functions, in order to highlight the significance of understanding this essential enzyme in lung cancer tumorigenesis and progression.

Inequities of Access, Utilization and Clinical Outcome of Lung Cancer in Indonesia

Background: Indonesia integrated all social health insurance and social assistance program into the National Health Insurance Scheme (NHIS) aiming to achieve universal health coverage by 2019. Currently, 194 million people are registered in the NHIS. Lung cancer absorbed a significant amount of the NHIS Indonesia and one of the leading cause of deaths among men in Indonesia. Lung cancer is rising major public health concern due to a high prevalence of smokers yet most lung cancer is diagnosed at late stage leading to short survival rate in Indonesia. Aim: This study aimed at investigating access, utilization, and clinical outcomes of lung cancer's patients. The second aim is to investigate differences in access to treatments of lung cancer patients among different member groups of the NHIS. Methods: The data were drawn from a 2014-2015 claim of the NHIS. The number of lung cancer patients of the outpatient's care was 5939 in 2014 and was 6595 in 2015. The number of inpatients diagnosed with lung cancer was 8969 and 9455 respectively for 2014 and 2015. Descriptive analysis was performed to investigate the differences in access, utilization (consumption), and in clinical outcome across class and membership. Results: Access to treatment of lung cancer patients was 0.5 for outpatient care and 0.7 for inpatient care per 10,000 members. This prevalence was higher compared with the national reported prevalence of lung cancer which was 0.3 per 10,000 populations. However, access and utilization among poorer members (third class and PBI membership) were only 0.1 for outpatient and 0.2 for inpatient per 10,000 members. The quality of treatment mortality and self-discharges were 6.04% and 5.68% compared with the higher class of members which account for 0.48% and 2.31% in 2015. Conclusion: There were inequities in access, utilization, and clinical outcomes among lung cancer patients suggesting disparities of supplies side and nonfinancial barriers among lower socioeconomic groups. The authors suggest the national and local governments should accelerate to produce oncologists and subnational cancer centers to ensure access among NHIS members.

Prognostic value of prognostic nutritional index in lung cancer: a meta-analysis.

Previous studies reported pretreatment prognostic nutritional index (PNI) was associated with clinical outcome of lung cancer. However, the result was not conclusive. We conducted a comprehensive meta-analysis to clarify the impact of PNI in lung cancer prognosis. We identified eligible studies by searching PubMed, EMBASE, and Web of Science, up to August 15, 2017. Overall survival (OS) and clinicopathological characteristics were collected from included studies. Pooled hazard ratios (HR) and corresponding 95% confidence intervals (CIs) were used to estimate clinical and prognostic value of PNI in lung cancer. Ten studies including 5,085 patients were enrolled in the meta-analysis. The result demonstrated that low PNI was correlated with unfavorable OS in lung cancer (HR =1.72; 95% CI, 1.43-2.06; P=0.000), especially among non-small cell lung cancer patients (HR =1.93; 95% CI, 1.56-2.37; P=0.000). As for clinical characteristics, low PNI status was found related to gender (female versus male, HR =0.68; 95% CI, 0.554-0.857; P=0.001) and histology (adenocarcinoma versus non-adenocarcinoma, HR =0.59; 95% CI, 0.47-0.74, P=0.000), but not smoking status (smoker versus never smoker, HR =1.49; 95% CI, 0.99-2.25, P=0.056). No significant publication bias was found (P=0.210). PNI was an independent prognostic indicator for lung cancer, and can serve as a novel biomarker to help guide clinical practice and promote clinical outcomes of lung cancer patients.

Using deep-learning radiomics to predict lung cancer histology.

8545Background: Histologic phenotype is an important predictor of clinical outcomes in lung cancer. Tissue diagnosis is the most definitive approach to categorization, however, this is often techni...