Predictive and Prognostic Biomarkers in Non-Small Cell Lung Cancer

Predictive value of immediate early response 5 like (IER5L) in the prognosis and immune checkpoint blockade therapy of non-small cell lung cancer patients

BACKGROUND

Non-small cell lung cancer (NSCLC) is a malignancy with high mortality. Immediate early response 5 like (IER5L) has been found to associate with worse prognosis in colorectal cancer patients. However, its role in the prognosis prediction of NSCLC has remained largely unknown.

METHODS

The IER5L expression in NSCLC and normal tissues was analyzed in two public cohorts: TCGA-LUAD-LUSC and GSE159857. Additionally, functional enrichment, survival analysis, CIBERSORT and tumor mutation burden (TMB) were investigated between low- and high-IER5L level groups. The in vitro IER5L mRNA and protein levels were determined using RT-qPCR and western blot, respectively.

RESULTS

The data from TCGA-LUAD-LUSC and GSE159857 cohorts showed a high IER5L mRNA expression in NSCLC tissue samples compared to normal controls. The increased expression of IER5L in NSCLC cells were also validated by RT-qPCR and western blot analysis. Additionally, NSCLC patients with high-IER5L level had significantly worse prognosis and IER5L could be used as an independent prognostic factor for NSCLC patients. Meanwhile, patients in the high-IER5L group had higher TMB level. IER5L expression was negatively correlated with the proportion of Monocytes and T cells CD4 memory resting, and was positively related to the proportion of Tregs and M0 macrophages in tumor tissues. Besides, transcription factors TFAP4 and ZNF692 may bind to the promoter region of IER5L, and then modulate IER5L gene transcription, thereby affecting IER5L gene expression. Furthermore, GSEA results showed that IER5L gene was closely related to MAPK, PI3K-Akt, NF-kappaB signaling pathways in NSCLC.

CONCLUSION

Collectively, high IER5L expression may be a promising unfavorable prognostic biomarker and therapeutic target for NSCLC patients.

Corynoxine triggers cell death via activating PP2A and regulating AKT-mTOR/GSK3β axes in NSCLC

This study investigates the anticancer activity and pharmacological mechanisms of Corynoxine (Cory) in non-small cell lung cancer (NSCLC). Cory, a natural product derived from the Chinese herbal medicine Uncaria rhynchophylla, demonstrates promising pharmacological activity. Cell proliferation and viability were evaluated via MTT and colony formation assays. Flow cytometry was employed to analyze cell apoptosis, cycle distribution, and mitochondrial membrane potential. Autophagy was detected using fluorescence microscopy and electron microscopy. Western blotting, protein overexpression, gene knockdown, co-immunoprecipitation, and bioinformatics characterized Cory's impact on signaling pathways. The research indicates that Cory inhibits the proliferation of NSCLC cells in vivo and in vitro. Cory enhances PP2A activity, inhibits the AKT/mTOR signaling pathway triggering autophagy, while suppressing the AKT/GSK3β signaling pathway to induce cellular apoptosis in NSCLC. Notably, the activation of PP2A plays a crucial role in Cory's antitumor effects by inhibiting AKT. In vivo experiments validated Cory's efficacy in NSCLC treatment. These findings highlight the promising role of Cory as a lead compound for drug development in NSCLC therapy, providing a viable option for addressing this challenging disease.

Prognostic and clinicopathological value of osteopontin expression in non-small cell lung cancer: a meta-analysis and systematic review

BACKGROUND

Although Osteopontin (OPN) has been reported to be associated with many different human cancers, the data on non-small cell lung cancer (NSCLC) are not definitive. This study aimed to explore the prognostic effect of OPN expression and clinicopathological characteristics in patients with NSCLC.

METHODS

This study followed all aspects of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) report. PubMed, Embase and the Cochrane Library were searched to identify the relative studies. The pooled hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated to estimate the prognostic value of the OPN in patients with NSCLC. The odds ratio (OR) was calculated to represent the relationship between OPN expression and clinicopathological parameters.

RESULTS

A total of fifteen studies with 2173 participants were finally included. The results revealed that high expression of OPN was significantly associated with poorer overall survival (OS) (HR = 1.89; 95%CI = 1.68-2.11; p < 0.001). Moreover, a significant correlation was observed between increased OPN expression and poorly differentiated (well and moderately differentiated vs. poorly differentiated; pooled OR = 0.38; 95% CI = 0.23-0.64; p < 0.001), lymph node metastasis (absence vs. presence; pooled OR = 0.49; 95%CI = 0.32-0.74; p < 0.001), and distant metastasis (absence vs. presence; pooled OR = 0.18; 95%CI = 0.11-0.29; p < 0.001).

CONCLUSION

This meta-analysis implies that OPN might be a valuable biomarker for a poor prognosis and poor clinicopathological outcomes for patients with NSCLC.

Preoperative splenic area as a prognostic biomarker of early-stage non-small cell lung cancer

BACKGROUND

The correlation between the preoperative splenic area measured on CT scans and the overall survival (OS) of early-stage non-small cell lung cancer (NSCLC) patients remains unclear.

METHODS

A retrospective discovery cohort and validation cohort consisting of consecutive NSCLC patients who underwent resection and preoperative CT scans were created. The patients were divided into two groups based on the measurement of their preoperative splenic area: normal and abnormal. The Cox proportional hazard model was used to analyse the correlation between splenic area and OS.

RESULTS

The discovery and validation cohorts included 2532 patients (1374 (54.27%) males; median (IQR) age 59 (52-66) years) and 608 patients (403 (66.28%) males; age 69 (62-76) years), respectively. Patients with a normal splenic area had a 6% higher 5-year OS (n = 727 (80%)) than patients with an abnormal splenic area (n = 1805 (74%)) (p = 0.007) in the discovery cohort. A similar result was obtained in the validation cohort. In the univariable analysis, the OS hazard ratios (HRs) for the patients with abnormal splenic areas were 1.32 (95% confidence interval (CI): 1.08, 1.61) in the discovery cohort and 1.59 (95% CI: 1.01, 2.50) in the validation cohort. Multivariable analysis demonstrated that abnormal splenic area was independent of shorter OS in the discovery (HR: 1.32, 95% CI: 1.08, 1.63) and validation cohorts (HR: 1.84, 95% CI: 1.12, 3.02).

CONCLUSION

Preoperative CT measurements of the splenic area serve as a prognostic indicator for early-stage NSCLC patients, offering a novel metric with potential implications for personalized therapeutic strategies in top-tier oncology research.

MicroRNA in lung cancer-a novel potential way for early diagnosis and therapy

Lung cancer is the most common cause of cancer-related deaths in the world. One of the reasons of poor prognosis and high mortality of lung cancer patients is the diagnosis of the disease in its advanced stage. Despite innovative diagnostic methods and multiple completed and ongoing clinical trials aiming at therapy improvement, no significant increase in patients' long-term survival has been noted over last decades. Patients would certainly benefit from early detection of lung cancer. Therefore, it is crucial to find new biomarkers that can help predict outcomes and tumor responses in order to maximize therapy effectiveness and avoid over- or under-treating patients with lung cancer. Nowadays, scientists' attention is mainly dedicated to so-called liquid biopsy, which is fully non-invasive and easily available method based on simple blood draw. Among common liquid biopsy elements, circulating tumor nucleic acids are worth mentioning. Epigenetic biomarkers, particularly miRNA expression, have several distinct features that make them promising prognostic markers. In this review, we described miRNA's involvement in tumorigenesis and present it as a predictor of cancer development and progression, potential indicator of treatment efficacy, and most importantly promising therapeutic target.

Replication of Real-World Evidence in Oncology Using Electronic Health Record Data Extracted by Machine Learning

Meaningful real-world evidence (RWE) generation requires unstructured data found in electronic health records (EHRs) which are often missing from administrative claims; however, obtaining relevant data from unstructured EHR sources is resource-intensive. In response, researchers are using natural language processing (NLP) with machine learning (ML) techniques (i.e., ML extraction) to extract real-world data (RWD) at scale. This study assessed the quality and fitness-for-use of EHR-derived oncology data curated using NLP with ML as compared to the reference standard of expert abstraction. Using a sample of 186,313 patients with lung cancer from a nationwide EHR-derived de-identified database, we performed a series of replication analyses demonstrating some common analyses conducted in retrospective observational research with complex EHR-derived data to generate evidence. Eligible patients were selected into biomarker- and treatment-defined cohorts, first with expert-abstracted then with ML-extracted data. We utilized the biomarker- and treatment-defined cohorts to perform analyses related to biomarker-associated survival and treatment comparative effectiveness, respectively. Across all analyses, the results differed by less than 8% between the data curation methods, and similar conclusions were reached. These results highlight that high-performance ML-extracted variables trained on expert-abstracted data can achieve similar results as when using abstracted data, unlocking the ability to perform oncology research at scale.

Prognostic value of Beclin 1, EGFR and ALK in non-squamous non-small cell lung cancer

Non-small cell lung cancer (NSCLC) is one of the most malignant tumors. The study was carried out to investigate the prognostic value of Beclin 1, EGFR and ALK for this cancer. Patients diagnosed with non-squamous NSCLC and admitted to our hospital from January 2011 to September 2016 were analyzed. Expression of Beclin 1 and mutation of EGFR and ALK were assessed using polymerase chain reaction (PCR) and fluorescent in situ hybridization (FISH) and analyzed for their relationship with demographic and clinical characteristics of the patients. Multivariate Cox regression models were applied to analyze the risk factors associated with survival and receiver response curves (ROC) were plotted to determine the prognostic value of Beclin 1, EGFR and ALK for patients with non-squamous NSCLC. Compared with adjacent normal tissue, Beclin 1 expression was elevated in the cancer tissue significantly; assessments of EGFR and ALK mutations showed that out of the 480 patients, 233 (48.5%) and 75 (12.6%) patients had EGFR and ALK mutations. Univariate analysis revealed that Beclin 1 level, EGFR and ALK mutations were associated with lymph node metastasis, TNM stage, tumor differentiation and prognosis, but not with gender, age and smoking status. The Kaplan-Meier survival analysis indicated that low Beclin 1 expression and positive EGFR and ALK rearrangements were associated with higher survival rate and longer progress-free survival (PFS). Multivariate Cox regression analysis showed that Beclin 1, EGFR, ALK mutations, tumor differentiation grade, TNM stage and lymph node metastasis were independently associated with PFS. ROC analysis showed that Beclin 1, EGFR and ALK were significant predictors for PFS; the areas under curve (AUC) for Beclin 1, EGFR and ALK were 0.812 (P = 0.018, cut-off value: 1.2), 0.781 (P = 0.011, cut-off value: 15%) and 0.722 (P = 0.010, cut-off value: 11%), respectively, suggesting that they have significant prognostic value for lung cancer patients. Our data indicate that Beclin 1, EGFR and ALK genes are associated with the prognosis of patients with non-squamous NSCLC. High Beclin 1 expression and negative EGFR and ALK mutations predict a poor prognosis with PFS.

Screening for Potential Therapeutic Agents for Non-Small Cell Lung Cancer by Targeting Ferroptosis

Ferroptosis is a form of non-apoptotic and iron-dependent cell death originally identified in cancer cells. Recently, emerging evidence showed that ferroptosis-targeting therapy could be a novel promising anti-tumour treatment. However, systematic analyses of ferroptosis-related genes for the prognosis of non-small cell lung cancer (NSCLC) and the development of antitumor drugs exploiting the ferroptosis process remain rare. This study aimed to identify genes related to ferroptosis and NSCLC and to initially screen lead compounds that induce ferroptosis in tumor cells. We downloaded mRNA expression profiles and NSCLC clinical data from The Cancer Genome Atlas database to explore the prognostic role of ferroptosis-related genes. Four prognosis-associated ferroptosis-related genes were screened using univariate Cox regression analysis and the lasso Cox regression analysis, which could divide patients with NSCLC into high- and low-risk groups. Then, based on differentially expressed risk- and ferroptosis-related genes, the negatively correlated lead compound flufenamic acid (FFA) was screened through the Connective Map database. This project confirmed that FFA induced ferroptosis in A549 cells and inhibited growth and migration in a dose-dependent manner through CCK-8, scratch, and immunofluorescence assays. In conclusion, targeting ferroptosis might be a therapeutic alternative for NSCLC.

Immune Subtypes in LUAD Identify Novel Tumor Microenvironment Profiles With Prognostic and Therapeutic Implications

The six transcriptomic immune subtypes (ISs) (C1 - C6) were reported to have complex and different interplay between TME and cancer cells in TCGA (The Cancer Genome Atlas) pan-cancer cohort. Our study specifically explored how the consequence of interplay determines the prognosis and the response to therapy in LUAD cohorts. Clinical and molecular information of LUAD patients were from TCGA and Gene Expression Omnibus (GEO). The immune cell populations and gene/pathway enrichment analysis were performed to explore the molecular differences among the C3 IS and other ISs in the LUAD population. The proportion of C3 inflammatory IS was identified as the most common IS in both TCGA (N = 457) and GEO (N = 901) cohorts. The C3 IS was also found to be the most accurate prognostic subtype, which was associated with significantly longer OS (p <0.001) and DFS (p <0.001). The C3 IS presented higher levels of CD8 T, M1 macrophage, and myeloid dendritic cells, while lower levels of M2 macrophages and cancer-associated fibroblast cells. Moreover, the C3 subtype was enriched in the antigen process and presenting, interferon-gamma response, T cell receptor signaling, and natural killer cell-mediated cytotoxicity pathways than C1/C2. In contrast, the C1/C2 presented greater activation of pathways related to the cell cycles, DNA repair, and p53 signaling pathways. The immune-related C3 IS had a great ability to stratify the prognosis of LUAD, providing clues for further pathogenic research. This classification might help direct precision medicine screenings of LUAD patients, thus possibly improving their prognoses.

The Value of Dysregulated LncRNAs on Clinicopathology and Survival in Non-Small-Cell Lung Cancer: A Systematic Review and Meta-Analysis

Background: There is growing evidence that a number of lncRNAs are involved in the pathogenesis of non-small-cell lung cancer (NSCLC). However, studies on lncRNA expression in NSCLC patients are far from conclusive. Therefore, we performed a systematic review of such studies to collect and examine the evidence on the potential role of lncRNAs in the development of NSCLC.

Methods: We systematically searched seven literature databases to identify all published studies that evaluated the expression of one or more lncRNAs in human samples with NSCLC (cases) and without NSCLC (controls) from January 1, 1995 to May 24, 2021. Quality assessment of studies was conducted by using the “Quality in Prognosis Studies” (QUIPS) tool, and the heterogeneity across studies was analyzed with the I-squared statistic and chi-square-based Q-tests. Either fixed or random-effect meta-analysis was performed to summarize effect size to investigate the association between lncRNA expression and overall survival (OS), disease-free survival (DFS), progression-free survival (PFS), and clinicopathological features. The R statistical software program was used to conduct standard meta-analysis.

Results: We finally obtained 48 studies with 5,211 patients included in this review after screening. Among the 48 lncRNAs, 38 lncRNAs were consistently upregulated, and 10 were deregulated in patients with NSCLC compared with the control groups. The upregulated lncRNAs were positively associated with histological type: study number (n) = 18, odds ratio (OR) = 0.78, 95% CI: 0.65–0.95 and OR = 1.30, 95% CI: 1.08–1.57, p < 0.01; TNM stages: n = 20, OR = 0.41, 95% CI: 0.29–0.57 and OR = 2.44, 95% CI: 1.73–3.44, p < 0.01; lymph node metastasis: n = 29, OR = 0.49, 95% CI: 0.34–0.71 and OR = 2.04, 95% CI: 1.40–2.96, p < 0.01; differentiation grade: n = 6, OR = 0.61, 95% CI: 0.38–0.99 and OR = 1.63, 95% CI: 1.01–2.64, p < 0.01; distant metastasis: n = 9, OR = 0.37, 95% CI: 0.26–0.53 and OR = 2.72, 95% CI: 1.90–3.90, p < 0.01; tumor size: n = 16, OR = 0.52, 95% CI: 0.43–0.64 and OR = 1.92, 95% CI: 1.57–2.34, p < 0.01; and overall survival [n = 38, hazard ratio (HR) = 1.79, 95% CI = 1.59–2.02, p < 0.01]. Especially, five upregulated lncRNAs (linc01234, ZEB1-AS1, linc00152, PVT1, and BANCR) were closely associated with TNM Ⅲa stage (n = 5, OR = 4.07, 95% CI: 2.63–6.28, p < 0.01). However, 10 deregulated lncRNAs were not significantly associated with the pathogenesis and overall survival in NSCLC in the meta-analysis (p ≥ 0.05).

Conclusion: This systematic review suggests that the upregulated lncRNAs could serve as biomarkers for predicting promising prognosis of NSCLC. The prognostic value of downregulated lncRNA in NSCLC needs to be further explored.

Systematic Review Registration: (http://www.crd.york.ac.uk/PROSPERO).identifier CRD42021240635.

Construction and evaluation of a nomogram for predicting survival in patients with lung cancer

BACKGROUND

Lung cancer is a heterogeneous disease with a severe disease burden. Because the prognosis of patients with lung cancer varies, it is critical to identify effective biomarkers for prognosis prediction.

METHODS

A total of 2325 lung cancer patients were integrated into four independent sets (training set, validation set I, II and III) after removing batch effects in our study. We applied the microarray data algorithm to screen the differentially expressed genes in the training set. The most robust markers for prognosis were identified using the LASSO-Cox regression model, which was then used to create a Cox model and nomogram.

RESULTS

Through LASSO and multivariate Cox regression analysis, eight genes were identified as prognosis-associated hub genes, followed by the creation of prognosis-associated risk scores (PRS). The results of the Kaplan-Meier analysis in the three validation sets demonstrate the good predictive performance of PRS, with hazard ratios of 2.38 (95% confidence interval (CI), 1.61-3.53) in the validation set I, 1.35 (95% CI, 1.06-1.71) in the validation set II, and 2.71 (95% CI, 1.77-4.18) in the validation set III. Additionally, the PRS demonstrated superior survival prediction in subgroups by age, gender, p-stage, and histologic type (p < 0.0001). The complex model integrating PRS and clinical risk factors also have a good predictive performance for 3-year overall survival.

CONCLUSIONS

In this study, we developed a PRS signature to help predict the survival of lung cancer. By combining it with clinical risk factors, a nomogram was established to quantify the individual risk assessments.

Predictive Role of Tumor-Stroma Ratio for Survival of Patients With Non-Small Cell Lung Cancer: A Meta-Analysis

Background: Role of tumor-stroma ratio (TSR) as a predictor of survival in patients with non-small cell lung cancer (NSCLC) remains not clear. A systematic review and meta-analysis was conducted to summarize current evidence for the role of TSR in NSCLC.

Methods: Relevant cohort studies were retrieved via search of Medline, Embase, and Web of Science databases. The data was combined with a random-effect model by incorporating the between-study heterogeneity. Specifically, subgroup and meta-regression analyses were performed to explore the association between TSR and survival in patients with squamous cell carcinoma (SCC) or adenocarcinoma (AC).

Results: Nine cohort studies with 2031 patients with NSCLC were eligible for the meta-analysis. Pooled results showed that compared to those stroma-poor tumor, patients with stroma rich NSCLC were associated with worse recurrence-free survival (RFS, hazard ratio [HR] = 1.52, 95% confidence interval [CI]: 1.07 to 2.16, p = 0.02) and overall survival (OS, HR = 1.48, 95% CI: 1.20 to 1.82, p < 0.001). Subgroup analyses showed that stroma-rich tumor may be associated with a worse survival of SCC (HR = 1.89 and 1.47 for PFS and OS), but a possibly favorable survival of AC (HR = 0.28 and 0.69 for PFS and OS). Results of meta-regression analysis also showed that higher proportion of patients with SCC was correlated with higher HRs for RFS (Coefficient = 0.012, p = 0.03) and OS (Coefficient = 0.014, p = 0.02) in the included patients, while higher proportion of patients with AC was correlated with lower HRs for RFS (Coefficient = −0.012, p = 0.03) and OS (Coefficient = −0.013, p = 0.04), respectively.

Conclusion: Tumor TSR could be used as a predictor of survival in patients with NSCLC. The relative proportion of patients with SCC/AC in the included NSCLC patients may be an important determinant for the association between TSR and survival in NSCLC. Stroma richness may be a predictor of poor survival in patients with lung SCC, but a predictor of better survival in patients with lung AC.

A narrative review of prognosis prediction models for non-small cell lung cancer: what kind of predictors should be selected and how to improve models?

Objective

To discover potential predictors and explore how to build better models by summarizing the existing prognostic prediction models of non-small cell lung cancer (NSCLC).

Background

Research on clinical prediction models of NSCLC has experienced explosive growth in recent years. As more predictors of prognosis are discovered, the choice of predictors to build models is particularly important, and in the background of more applications of next-generation sequencing technology, gene-related predictors are widely used. As it is more convenient to obtain samples and follow-up data, the prognostic model is preferred by researchers.

Methods

PubMed and the Cochrane Library were searched using the items “NSCLC”, “prognostic model”, “prognosis prediction”, and “survival prediction” from 1 January 1980 to 5 May 2021. Reference lists from articles were reviewed and relevant articles were identified.

Conclusions

The performance of gene-related models has not obviously improved. Relative to the innovation and diversity of predictors, it is more important to establish a highly stable model that is convenient for clinical application. Most of the prevalent models are highly biased and referring to PROBAST at the beginning of the study may be able to significantly control the bias. Existing models should be validated in a large external dataset to make a meaningful comparison.

circ_0002346 Suppresses Non-Small-Cell Lung Cancer Progression Depending on the Regulation of the miR-582-3p/STXBP6 Axis

Background

Accumulating articles have reported the pivotal regulatory roles of circular RNAs (circRNAs) in non-small-cell lung cancer (NSCLC) tumorigenesis. Here, our purpose was to explore the role of circ_0002346 in NSCLC progression and its associated mechanism.

Methods

Cell proliferation ability was assessed by a 5-ethynyl-2′-deoxyuridine (EDU) assay and a colony formation assay. Transwell assays were conducted to analyze cell migration and invasion abilities. Cell apoptosis was analyzed by flow cytometry and by using a caspase3 activity assay kit. The glycolysis of NSCLC cells was analyzed using a fluorescence-based glucose/lactate assay kit. A dual-luciferase reporter assay and an RNA pull-down assay were performed to verify the binding relationship between microRNA-582-3p (miR-582-3p) and circ_0002346 or syntaxin-binding protein 6 (STXBP6).

Results

circ_0002346 level was prominently downregulated in NSCLC tissues and cell lines. circ_0002346 overexpression significantly suppressed the proliferation, migration, invasion, and glycolysis and triggered the apoptosis of NSCLC cells. circ_0002346 directly interacted with miR-582-3p, and circ_0002346 overexpression-mediated antitumor effects in NSCLC cells were partly reversed by miR-582-3p overexpression. miR-582-3p directly interacted with the 3′ untranslated region (3′UTR) of STXBP6, and STXBP6 silencing partly counteracted circ_0002346 overexpression-mediated antitumor influences in NSCLC cells. circ_0002346 can upregulate the expression of STXBP6 by acting as a miR-582-3p sponge in NSCLC cells. circ_0002346 overexpression suppressed xenograft tumor growth in vivo.

Conclusion

circ_0002346 overexpression suppressed the malignant properties of NSCLC cells by binding to miR-582-3p to induce the expression of STXBP6.

Histopathological Images and Multi-Omics Integration Predict Molecular Characteristics and Survival in Lung Adenocarcinoma

Histopathological images and omics profiles play important roles in prognosis of cancer patients. Here, we extracted quantitative features from histopathological images to predict molecular characteristics and prognosis, and integrated image features with mutations, transcriptomics, and proteomics data for prognosis prediction in lung adenocarcinoma (LUAD). Patients obtained from The Cancer Genome Atlas (TCGA) were divided into training set (n = 235) and test set (n = 235). We developed machine learning models in training set and estimated their predictive performance in test set. In test set, the machine learning models could predict genetic aberrations: ALK (AUC = 0.879), BRAF (AUC = 0.847), EGFR (AUC = 0.855), ROS1 (AUC = 0.848), and transcriptional subtypes: proximal-inflammatory (AUC = 0.897), proximal-proliferative (AUC = 0.861), and terminal respiratory unit (AUC = 0.894) from histopathological images. Moreover, we obtained tissue microarrays from 316 LUAD patients, including four external validation sets. The prognostic model using image features was predictive of overall survival in test and four validation sets, with 5-year AUCs from 0.717 to 0.825. High-risk and low-risk groups stratified by the model showed different survival in test set (HR = 4.94, p < 0.0001) and three validation sets (HR = 1.64–2.20, p < 0.05). The combination of image features and single omics had greater prognostic power in test set, such as histopathology + transcriptomics model (5-year AUC = 0.840; HR = 7.34, p < 0.0001). Finally, the model integrating image features with multi-omics achieved the best performance (5-year AUC = 0.908; HR = 19.98, p < 0.0001). Our results indicated that the machine learning models based on histopathological image features could predict genetic aberrations, transcriptional subtypes, and survival outcomes of LUAD patients. The integration of histopathological images and multi-omics may provide better survival prediction for LUAD.

Nano-Particulated Erlotinib Compound System in Alleviation of Lung Cancer

To produce an effective nanoparticle-loaded delivery system for the tumor drug erlotinib for non-small cell lung cancer (NSCLC) therapy, we loaded poly(lactic co glycolic acid) (PLGA) nanoparticles with erlotinib and used them to transport the drug to a target area. NCI-H1650 cells were cultured to test the permeability, efficiency, and anti-tumor capacity of PLGA and polyethyleneimine (PEI) drug delivery systems, and an NSCLC mouse model was prepared to further test the anti-tumor efficiency of PLGA. In tests using NCI-H1650 cells, we found that PLGA could effectively transport erlotinib into tumor cells, and release the loaded drug instantly. The infiltration efficiency was significantly higher than that of the PEI delivery system, and the same results were obtained in animal tests. PLGA-erlotinib could promote apoptosis and inhibit the migration of tumor cells more effectively than PEI-erlotinib. In the NSCLC mouse model, PLGA could more effectively reduce the tumor volume and the extent of tumor markers than the PEI delivery system. Immune function was also better rescued with the use of the PLGA system. We concluded that PLGA-erlotinib may be a good choice for lung cancer therapy in the future.

Metabolic active tumour volume quantified on [18F]FDG PET/CT further stratifies TNM stage IV non-small cell lung cancer patients

PURPOSE

This study aimed to assess whether the whole body metabolic active tumour volume (MTV_WB), quantified on staging [¹⁸F]FDG PET/CT, could further stratify stage IV non-small cell lung cancer (NSCLC) patients.

METHODS

A group of 160 stage IV NSCLC patients, submitted to staging [¹⁸F]FDG PET/CT between July 2010 and May 2020, were retrospectively evaluated. MTV_WB was quantified. Univariate and multivariate Cox regressions were carried out to assess correlation with overall survival (OS). C-statistic was used to test predictive power. Kaplan-Meier survival curves with Log-Rank tests were performed to compute statistical differences between strata from dichotomized variables and to calculate the estimated mean survival times (EMST). Survival rates at 1 and 5 years were calculated.

RESULTS

MTV_WB was a statistically significant predictor of OS on univariate (p < 0.0001) and multivariate analyses (p < 0.0001). The multivariate model with MTV_WB (Cindex ± SE = 0.657 ± 0.024) worked significantly better as an OS predictor than the cTNM model (Cindex ± SE = 0.544 ± 0.028) (p = 0.003). An EMST of 29.207 ± 3.627(95% CI 22.099-36.316) months and an EMST of 10.904 ± 1.171(95% CI 8.609-13.199) months (Log-Rank p < 0.0001) were determined for patients with MTV_WB < 104.3 and MTV_WB ≥ 104.3, respectively. In subsamples of stage IVA (cut-off point = 114.5) and IVB patients (cut-off point = 191.1), statistically significant differences between EMST were also reported, with p-values of 0.0001 and 0.0002, respectively. In both substages and in the entire cohort, patients with MTV_WB ≥ cut-off points had lower EMST and survival rates.

CONCLUSION

Baseline MTV_WB, measured on staging [¹⁸F]FDG PET/CT, further stratifies stage IV NSCLC patients. This parameter is an independent predictor of OS and provides valuable prognostic information over the 8th edition of cTNM staging.

Molecular profiling and characteristics of non-small-cell lung cancer patients in Georgia

Aims: In patients with advanced non-small-cell lung cancer, the correlation between histopathology, smoking status, driver oncogene mutations and PD-L1 overexpression were investigated. Patients and methods: A total of 202 patients were identified. Research was done in Georgia. Results: EGFR mutations were detected in 6% of the tested cases (12/187) and five out of 12 EGFR+ cases had histology consistent with squamous cell carcinoma. No statistically significant correlation was observed between PD-L1 expression, smoking status and clinicopathological characteristics. However, the correlation between smoking status and histology was statistically significant (p = 0.0264), as never-smokers had a higher incidence of adenocarcinoma histology. Conclusion: The study showed a small percentage of EGFR mutations associated with adenocarcinoma histology and revealed a solid existence of this mutation in squamous cell carcinoma histology. A higher incidence of adenocarcinoma histology was observed in never-smokers.

Prognostic Value of Ki-67 Expression in Advanced Lung Squamous Cell Carcinoma Patients Treated with Chemotherapy

Background

The relationship between the Ki-67 expression level and chemotherapy response and survival prognosis in advanced lung squamous cell carcinoma (SCC) remains unclear.

Methods

A total of 101 patients were included in the study. All patients received systemic first-line platinum-based chemotherapy. The Ki-67 expression level was determined by immunohistochemistry analysis.

Results

The Ki-67 expression level was positively correlated with an increase in tumor T stage (P = 0.0140), N stage (P < 0.0001), and M stage (P < 0.0001) in advanced lung SCC. High Ki-67 expression could predict chemotherapy response (area under the curve = 0.7524, P < 0.0001). Patients with tumors that expressed high levels of Ki-67 had shorter overall survival (OS) (18.8 months vs 25.5 months, P = 0.0002) and progression-free survival (PFS) (4.8 months vs 6.7 months, P < 0.0001). Cox analysis found Ki-67 expression to be an independent prognostic biomarker of shortened OS (P = 0.009) and PFS (P = 0.008).

Conclusion

Ki-67 expression may affect chemotherapy response and thus has prognostic value. Ki-67 expression may be a promising prognostic biomarker for advanced lung SCC.

miR-765 Acts as a Tumor Promoter and Indicates Poor Prognosis in Non-Small Cell Lung Cancer

PURPOSE

Non-small cell lung cancer (NSCLC) is one of the leading causes of cancer-related death worldwide with poor prognosis. Accumulating evidence indicates that miR-765 is an important regulator in the progression and prognosis of various cancers. In this study, the function in the progression and prognosis of NSCLC was investigated.

PATIENTS AND METHODS

The expression of miR-765 in NSCLC was analyzed by qRT-PCR. The effect of miR-765 on cell proliferation, migration, and invasion of NSCLC was evaluated by CCK8 and Transwell assay. Kaplan-Meier analysis and Cox regression analysis were employed to assess the prognostic value of miR-765.

RESULTS

The results demonstrated the significant upregulation of miR-765 in NSCLC tissues and cell lines relative to normal tissues and cells. High miR-765 expression was significantly correlated with the TNM stage of patients. Patients with high miR-765 expression showed a poorer prognosis than that of patients with low miR-765 expression. Cox analysis indicated that miR-765 could be considered as an independent prognostic factor for NSCLC. Additionally, the upregulation of miR-765 was revealed to promote NSCLC cell proliferation, migration, and invasion by targeting BMP6.

CONCLUSION

The overexpression of miR-765 in NSCLC was associated with TNM stage and poor prognosis of patients. miR-765 served as a tumor promoter of NSCLC by regulating BMP6. These findings provide a potential biomarker and therapeutic target for the prognosis and treatment of NSCLC.

Influencing Factors and Prognostic Value of 18F-FDG PET/CT Metabolic and Volumetric Parameters in Non-Small Cell Lung Cancer

Objective

This study aims to explore factors influencing metabolic and volumetric parameters of [¹⁸F]fluorodeoxyglucose positron emission tomography/computed tomography (¹⁸F-FDG PET/CT) imaging in non-small cell lung cancer (NSCLC) and the predictive value for prognosis of NSCLC.

Methods

Retrospective analysis was performed on 133 NSCLC patients who received ¹⁸F-FDG PET/CT imaging. After ¹⁸F-FDG injection at 3.7 MBq/kg, 1 h early imaging and 2 h delayed imaging were performed. The metabolic and volumetric parameters such as SUVmax, SUVpeak, SULmax, SULpeak, MTV and TLG were measured. The tumor markers including CFYRA21-1, NSE, SCC-ag and the immunohistochemical biomarkers including Ki-67, P53 and CK-7 were examined. All patients were followed up for 24 months, and the 1-year and 2-year overall survival rate (OS) were recorded.

Results

There were significant differences in metabolic and volumetric parameters (SUVmax, SUVpeak, SULmax, SULpeak and TLG) between adenocarcinoma and squamous cell carcinoma of NSCLC. SUVmax, SUVpeak, SULmax, SULpeak, MTV and TLG were correlated with tumor marker NSE and TNM stage. MTV and TLG were related to CYFRA21-1, and only MTV was associated with SCC-ag. SUVpeak and SULmax were related to P53. In addition, early SULpeak and delayed MTV were significant prognostic factors of 1-year OS, while early SUVpeak, delayed TLG and delayed MTV were predictive factors of 2-year OS in NSCLC.

Conclusion

The metabolic and volumetric parameters of ¹⁸F-FDG PET/CT were related to a variety of factors such as NSE, CFYRA21-1, SCC-ag, P53 and TNM stage, and have a predictive value in prognosis of NSCLC.

Effect of Sintilimab combined with Chemotherapy on Tumor Markers and Immune Function of advanced non-small cell lung cancer

Objective

To evaluate the effect of sintilimab combined with chemotherapy on tumor markers and immune function in advanced non-small cell lung cancer.

Methods

The study was conducted at Xi'an Medical University, China. The 120 patients with advanced NSCLC who were treated in our hospital from January 2016 to January 2020 were randomly divided into two groups, with 60 cases in each group. Patients in the control group received conventional GP chemotherapy, while those in the experimental group received intravenous injection of sindilimab on the basis of conventional GP chemotherapy. The changes of serum tumor markers CYFRA211, CEA, CA125 and T lymphocyte subsets CD3+, CD4+, CD8+, CD4+/CD8+ in the two groups prior to and after treatment were compared and analyzed. At the same time, the clinical efficacy at six months was compared between the two groups.

Results

The serum tumor markers CYFRA211, CEA and CA125 in the two groups after treatment were lower than those before treatment, and the difference was statistically significant (P=0.00). Specifically, the above-mentioned markers in the experimental group decreased more significantly than those in the control group, and the difference was statistically significant (CYFRA211, CA125, p=0.00; CEA, p=0.01; the levels of CD3+ and CD4+ in the experimental group were higher than those in the control group after treatment, with statistical significance (CD3+, p=0.00; CD4+, p=0.01)). No significant change can be seen in CD8+ (p=0.14), and the level of CD4+/CD8+ in the experimental group was higher than that in the control group, with a significant difference (p=0.02). The complete remission rate (CR) was 22% in the experimental group and 8% in the control group (P=0.04), which was statistically significant. The progress rate (PD) of the experimental group was significantly lower than that of the control group, with statistical significance (p=0.02). The overall response rate (RR) of the experimental group was more advantageous than that of the control group, with a statistically significant difference (p=0.01).

Conclusion

Compared with chemotherapy alone, significant therapeutic effects can be obtained in the treatment of advanced non-small cell lung cancer with sintilimab combined with chemotherapy. With this combination regimen, the level of serum tumor markers can be significantly reduced, the cellular immune function of patients can be improved, with the overall response rate of treatment increased, and the risk of progressive disease of patients reduced.

PD-(L)1 Inhibitors as Monotherapy for the First-Line Treatment of Non-Small-Cell Lung Cancer Patients with High PD-L1 Expression: A Network Meta-Analysis

Programmed cell death-ligand 1 (PD-L1) has emerged as a potential biomarker for selection of patients more likely to respond to immunotherapy and as a prognostic factor in non-small cell lung cancer (NSCLC). In this network meta-analysis, we aimed to evaluate the efficacy of first-line anti-PD-(L)1 monotherapy in advanced NSCLC patients with high PD-L1 expression (≥50%) compared to platinum-based chemotherapy. We also evaluated efficacy outcomes according to tumor mutational burden (TMB). To that end, we conducted a systematic review. Six clinical trials with 2111 patients were included. In head-to-head comparisons, immunotherapy showed a significant improvement in progression-free survival (PFS: HRpooled = 0.69, 95% CI: 0.52-0.90, p = 0.007), overall survival (OS: HRpooled = 0.69, 95% CI: 0.61-0.78; p < 0.001) and overall response rate (ORR) (Risk ratio (RR)pooled = 1.354, 95% CI: 1.04-1.762, p = 0.024). In the assessment of relative efficacy for PFS through indirect comparisons, pembrolizumab (results from KEYNOTE-024) ranked highest followed by cemiplimab and atezolizumab, with statistical significance determined for some of the drugs. In terms of OS, cemiplimab ranked highest followed by atezolizumab and pembrolizumab, although non-significant OS was determined for these drugs. In conclusion, PD-(L)1 inhibitor monotherapy improves efficacy outcomes in the first line setting of advanced NSCLC patients with high PD-L1 expression. Evaluations with longer follow up are still needed to determine the superiority of any specific drug.

The prognostic value of the tumor-stroma ratio in squamous cell lung cancer, a cohort study

OBJECTIVES

The tumor-stroma ratio (TSR) is based on the relative amount of stroma in the primary tumor and has proven to be an independent prognostic factor in various solid tumors. The prognosis of patients and adjuvant treatment decision making in lung squamous cell carcinomas (SqCC) is based on the TNM classification. Currently, no other prognostic biomarkers are available. In this study we evaluated the prognostic value of the TSR in lung SqCC.

MATERIAL AND METHODS

Patients undergoing lung surgery because of lung SqCC between 2000 and 2018 at the Leiden University Medical Center were included. The TSR was scored on hematoxylin & eosin stained tissue sections. Based on the amount of tumor-stroma, two groups were defined: ≤50% was classified as a stroma-low tumor and >50% as stroma-high. The prognostic value of the TSR was determined with survival analysis.

RESULTS

A total of 174 stage I-III patients were included. Of them, 79 (45%) were stroma-low and 95 (55%) stroma-high. Separately analyzed for tumor stages, the TSR showed to be an independent prognostic biomarker in stage II (n = 68) for 5-year overall survival (HR=3.0; 95% CI, 1.1-8.6; p = 0.035) and 5-year disease free survival (DFS) (HR=3.6; 95% CI, 1.3-9.9; p = 0.014). Patients with a stroma-high tumor had a worse 5-year DFS in the whole cohort (HR 1.6; 95% CI, 1.0-2.4; p = 0.048), but no independent prognostic value was found.

CONCLUSION

In stage II lung SqCC patients, stroma-low tumors have a better prognosis compared to stroma-high tumors. Moreover, adjuvant chemotherapy could be spared for these stroma-low patients.

A multi-parametric prognostic model based on clinical features and serological markers predicts overall survival in non-small cell lung cancer patients with chronic hepatitis B viral infection

BACKGROUND

To establish and validate a multi-parametric prognostic model based on clinical features and serological markers to estimate the overall survival (OS) in non-small cell lung cancer (NSCLC) patients with chronic hepatitis B viral (HBV) infection.

METHODS

The prognostic model was established by using Lasso regression analysis in the training cohort. The incremental predictive value of the model compared to traditional TNM staging and clinical treatment for individualized survival was evaluated by the concordance index (C-index), time-dependent ROC (tdROC) curve, and decision curve analysis (DCA). A prognostic model risk score based nomogram for OS was built by combining TNM staging and clinical treatment. Patients were divided into high-risk and low-risk subgroups according to the model risk score. The difference in survival between subgroups was analyzed using Kaplan-Meier survival analysis, and correlations between the prognostic model, TNM staging, and clinical treatment were analysed.

RESULTS

The C-index of the model for OS is 0.769 in the training cohorts and 0.676 in the validation cohorts, respectively, which is higher than that of TNM staging and clinical treatment. The tdROC curve and DCA show the model have good predictive accuracy and discriminatory power compare to the TNM staging and clinical treatment. The prognostic model risk score based nomogram show some net clinical benefit. According to the model risk score, patients are divided into low-risk and high-risk subgroups. The difference in OS rates is significant in the subgroups. Furthermore, the model show a positive correlation with TNM staging and clinical treatment.

CONCLUSIONS

The prognostic model showed good performance compared to traditional TNM staging and clinical treatment for estimating the OS in NSCLC (HBV+) patients.

A new immune signature for survival prediction and immune checkpoint molecules in lung adenocarcinoma

Background

Lung adenocarcinoma (LUAD) is the most frequent subtype of lung cancer. The prognostic signature could be reliable to stratify LUAD patients according to risk, which helps the management of the systematic treatments. In this study, a systematic and reliable immune signature was performed to estimate the prognostic stratification in LUAD.

Methods

The profiles of immune-related genes for patients with LUAD were used as one TCGA training set: n = 494, other validation set 1: n = 226 and validation set 2: n = 398. Univariate Cox survival analysis was used to identify the candidate immune-related genes from each cohort. Then, the immune signature was developed and validated in the training and validation sets.

Results

In this study, functional analysis showed that immune-related genes involved in immune regulation and MAPK signaling pathway. A prognostic signature based on 10 immune-related genes was established in the training set and patients were divided into high-risk and low-risk groups. Our 10 immune-related gene signature was significantly related to worse survival, especially during early-stage tumors. Further stratification analyses revealed that this 10 immune-related gene signature was still an effective tool for predicting prognosis in smoking or nonsmoking patients, patients with KRAS mutation or KRAS wild-type, and patients with EGFR mutation or EGFR wild-type. Our signature was negatively correlated with B cell, CD4+ T cell, CD8+ T cell, neutrophil, dendritic cell (DC), and macrophage immune infiltration, and immune checkpoint molecules PD-1 and CTLA-4 (P < 0.05).

Conclusions

These findings suggested that our signature was a promising biomarker for prognosis prediction and can facilitate the management of immunotherapy in LUAD.

Elevated TOP2A and UBE2C expressions correlate with poor prognosis in patients with surgically resected lung adenocarcinoma: a study based on immunohistochemical analysis and bioinformatics

PURPOSE

Lung cancer has the highest morbidity and mortality among all cancer types. Reliable prognostic biomarkers are needed to identify high-risk patients apart from TNM system for precision medicine. The present study is designed to identify robust prognostic biomarkers in lung adenocarcinoma (LUAD) based on integration of multiple GEO datasets, The Cancer Genome Atlas (TCGA) database and Clinical Proteomic Tumor Analysis Consortium (CPTAC) database.

METHODS

Four LUAD GEO datasets (GSE10072, GSE2514, GSE43458, and GSE32863) and TCGA database were implemented to analyze the differently expressed genes (DEGs). Gene ontology, KEGG pathway, and protein-protein interaction network (PPI) were conducted based on the above DEGs. Hub genes were selected based on connectivity degree in the PPI network. Expression analysis and Kaplan-Meier survival analysis were conducted in CPTAC lung adenocarcinomas cohort. Kaplan-Meier survival analysis and Cox proportional hazards regression were performed on these hub genes using TCGA and our own cohort.

RESULTS

A total of 430 shared genes in all five datasets were identified as DEGs. Based on their PPI network, nine hub genes were selected and all of them were significantly associated with overall survival using GEPIA analysis. Two hub genes, TOP2A and UBE2C, were further combined and showed poorer prognosis in both TCGA dataset and our validated cohort. Analysis in CPTAC revealed that TOP2A and UBE2C were significantly highly expressed in tumor sample. Multivariable analysis suggested TOP2A and UBE2C as independent prognostic factors in LUAD.

CONCLUSION

Using data mining approach, we identified TOP2A and UBE2C as two robust prognostic factors in LUAD. We also demonstrated the TOP2A/UBE2C co-expression status in LUAD, and TOP2A/UBE2C co-expression correlated with poorer prognosis. More in-depth research is needed for transforming this result into clinical setting.

CT radiomics and PET radiomics: ready for clinical implementation?

INTRODUCTION

Today, rapid technical and clinical developments result in an increasing number of treatment options for oncological diseases. Thus, decision support systems are needed to offer the right treatment to the right patient. Imaging biomarkers hold great promise in patient-individual treatment guidance. Routinely performed for diagnosis and staging, imaging datasets are expected to hold more information than used in the clinical practice. Radiomics describes the extraction of a large number of meaningful quantitative features from medical images, such as computed tomography (CT) and positron emission tomography (PET). Due to the non-invasive nature and ability to capture 3D image-based heterogeneity, radiomic features are potential surrogate markers of the cancer phenotype. Several radiomic studies are published per day, owing to encouraging results of many radiomics-based patient outcome models. Despite this comparably large number of studies, radiomics is mainly studied in proof of principle concept. Hence, a translation of radiomics from a hot topic research field into an essential clinical decision-making tool is lacking, but of high clinical interest.

EVIDENCE ACQUISITION

Herein, we present a literature review addressing the clinical evidence of CT and PET radiomics. An extensive literature review was conducted in PubMed, including papers on robustness and clinical applications.

EVIDENCE SYNTHESIS

We summarize image-modality related influences on the robustness of radiomic features and provide an overview of clinical evidence reported in the literature. Today, more evidence has been provided for CT imaging, however, PET imaging offers the promise of direct imaging of biological processes and functions. We provide a summary of future research directions, which needs to be addressed in order to successfully introduce radiomics into clinical medicine. In comparison to CT, more focus should be directed towards harmonization of PET acquisition and reconstruction protocols, which is important for transferable modelling.

CONCLUSIONS

Both CT and PET radiomics are promising pre-treatment and intra-treatment biomarkers for outcome prediction. Most studies are performed in retrospective setting, however their validation in prospective data collections is ongoing.

Preoperative D-dimer level is an independent prognostic factor for non-small cell lung cancer after surgical resection: a systematic review and meta-analysis

Background

Whether high preoperative D-dimer level has any impact on long-term survival of patients with surgically treated non-small cell lung cancer (NSCLC) remains unclear. Therefore, we conducted the first meta-analysis focusing specifically on prognostic value of high preoperative D-dimer level in NSCLC patients after surgical resection comprehensively.

Methods

We conducted a systematic search for relevant studies in PubMed, Embase, and Web of Science on January 28, 2019. Data for analysis consisted of hazard ratio (HR) with 95% confidence interval (CI) of overall survival (OS) and disease-free survival (DFS) from multivariate analysis and were analyzed by using the STATA 12.0 package.

Results

Finally, we included a total of 6 cohort studies consisting of 1,817 patients with surgically treated NSCLC for analysis. Our meta-analysis found that NSCLC patients with high preoperative D-dimer level had a significantly worse OS (random effects: HR =2.04; 95% CI: 1.30-3.20; P=0.002; I²=67.4%) and DFS (fixed effects: HR =1.98; 95% CI: 1.41-2.78; P<0.001; I²=0.0%) than these with normal preoperative D-dimer level after surgery. However, potential heterogeneity and publication bias was observed during analysis.

Conclusions

High pretreatment level of D-dimer remains to be an independent predictor of poor prognosis in NSCLC patients after surgery. Further well-conducted studies with appropriate adjustments are needed to confirm and update our conclusions.

Defining aggressive or early progressing nononcogene-addicted non-small-cell lung cancer: a separate disease entity?

A substantial proportion of patients with nononcogene-addicted non-small-cell lung cancer (NSCLC) has 'aggressive disease', as reflected in short time to progression or lack of disease control with initial platinum-based chemotherapy. Recently, clinical correlates of aggressive disease behavior during first-line therapy have been shown to predict greater benefit from addition of nintedanib to second-line docetaxel in adenocarcinoma NSCLC. Positive predictive effects of aggressive disease have since been reported with other anti-angiogenic agents (ramucirumab and bevacizumab), while such features may negatively impact on outcomes with nivolumab in nonsquamous NSCLC with low PD-L1 expression. Based on a review of the clinical data, we recommend aggressive nonsquamous NSCLC should be defined by progression within <6-9 months of first-line treatment initiation.

The role of CEA, CYFRA21-1 and NSE in monitoring tumor response to Nivolumab in advanced non-small cell lung cancer (NSCLC) patients

Background

CEA, CYFRA21-1 and NSE are tumor markers used for monitoring the response to chemotherapy in advanced adenocarcinoma, squamous cell carcinoma and small-cell lung cancer, respectively. Their role in cancer immunotherapy needs to be elucidated.

Methods

Patients with advanced non-small cell lung cancer (NSCLC) were treated with nivolumab 3 mg/kg every 2 weeks within the Italian Nivolumab Expanded Access Program. Blood samples were collected at baseline, at each cycle up to cycle 5 and then every two cycles until patient’s withdrawn from the study. All patients underwent a CT-scan after every 4 cycles of treatment and responses were classified according to RECIST 1.1. The biomarkers serum levels were measured with a chemiluminescent microparticle immunoassay for CEA and with an immuno radiometric assay for CYFRA21-1 and NSE. The markers values at baseline and after 4 cycles were used to analyze the relationship between their variation over baseline and the tumor response, evaluated as disease control rate (DCR: CR + PR + SD), and survival (PFS and OS).

Results

A total of 70 patients were evaluable for the analysis. Overall, a disease control was obtained in 24 patients (35.8%, 4 PR + 20 SD). After 4 cycles of nivolumab a CEA or CYFRA21-1 reduction ≥ 20% over the baseline was significantly associated with DCR (CEA, p = 0.021; CYFRA21-1, p < 0.001), PFS (CEA, p = 0.028; CYFRA21-1, p < 0.001) and OS (CEA, p = 0.026; CYFRA21-1, p = 0.019). Multivariate analysis confirmed the ability of CYFRA21-1 reduction ≥ 20% to predict DCR (p = 0.002) and PFS (p < 0.001).

Conclusion

The reduction in serum level of CYFRA21-1 or CEA might be a reliable biomarker to predict immunotherapy efficacy in NSCLC patients. NSE was not significant for monitoring the efficacy of nivolumab.

Ex Vivo Interferon Gamma Production by Peripheral Immune Cells Predicts Survival in Lung Adenocarcinoma

BACKGROUND

Lung cancer is one of the most lethal malignancies, with a 5-year survival rate < 20% in patients with stage IV lung cancer. Impaired host immunity is associated with lung cancer pathogenesis, and interferon gamma (IFN-γ) plays an important role in antitumor immune surveillance. We evaluated the clinical significance of ex vivo production of IFN-γ in patients with lung adenocarcinoma.

PATIENTS AND METHODS

We reviewed the medical records of 109 treatment-naive patients with lung adenocarcinoma who had undergone IFN-γ releasing assay. Differences in the IFN-γ level in nil and mitogen tubes were defined as ex vivo IFN-γ production. Correlation analysis was performed to evaluate the correlation between ex vivo IFN-γ production, cancer staging, and Eastern Cooperative Oncology Group performance status. The optimal cutoff values of low and high ex vivo IFN-γ production were estimated using receiver operator characteristic curve analysis. Cox proportional hazard analyses were used to evaluate the prognostic factors of 1-year overall patient survival.

RESULTS

Ex vivo IFN-γ production correlated with N stage, M stage, cancer staging, and Eastern Cooperative Oncology Group performance status. Low ex vivo IFN-γ production (ex vivo IFN-γ production ≤ 7.79 IU/mL) was independently associated with 1-year overall survival (odds ratio = 3.289; 95% confidence interval, 1.573-6.872; P = .002). Additionally, low ex vivo IFN-γ production was an independent predictor of 1-year overall survival in patients with stage IV cancer (odds ratio = 3.156; 95% confidence interval, 1.473-6.760; P = .003).

CONCLUSION

Ex vivo IFN-γ production before treatment might be a useful biomarker for predicting prognosis in patients with lung adenocarcinoma.

Significance of the epidermal growth factor receptor mutation status and differences among molecular subgroups in surgically resected lung microinvasive adenocarcinoma

Lung microinvasive adenocarcinoma (MIA) is a newly-defined subtype of early stage non-small cell lung cancer (NSCLC). However, its epidermal growth factor receptor (EGFR) mutation status and clinical significance remain unclear. The present study aimed to determine EGFR mutation characteristics and identify their significance in patients with resected lung MIA. The present study also analyzed clinicopathological differences between EGFR molecular subgroups defined as 19Del and L858R. The present study examined EGFR mutations in 79 consecutive lung MIA resection specimens and compared the differences in clinicopathological features between the EGFR wild-type and mutation groups, as well as between the 19Del and L858R subgroups. EGFR mutations were detected in 60 (75.95%) tumors. The most common mutations were 19Del (28 cases; 35.44%) and L858R (30 cases; 37.97%). Two patients harbored rare mutations and one of them had a concomitant double mutation. EGFR mutations were significantly associated with microinvasion component, thyroid transcription factor 1 (TTF-1) expression, intratumoral fibrosis and inflammatory cell infiltration. Subgroup evaluation indicated that there was a significant association between 19Del and tumor size, maximum diameter of microinvasion, presence of intratumoral fibrosis and inflammatory cell infiltration. Similar associations were observed for the L858R subgroup, and L858R was associated with TTF-1 expression. In particular, 19Del occurred more frequently in MIA with a smaller size, with a smaller microinvasive area, without TTF-1 expression, and lacking intratumoral fibrosis and inflammatory cell infiltration. By contrast, L858R was detected more frequently in MIA with entirely different tumor features. In conclusion, the results of the present study indicated that surgically resected MIA cases harboring different EGFR gene statuses exhibit distinct clinicopathological features. Significant differences in pathological features associated with the tumor microenvironment were identified in MIA with 19Del or L858R mutations. Therefore, the present study proposed that MIA should be classified into molecular subgroups based on EGFR mutation subtypes. The molecular sub-classification should be taken into account for prognostic evaluation and clinical management of MIA.

Deep learning for lung cancer prognostication: A retrospective multi-cohort radiomics study

BACKGROUND

Non-small-cell lung cancer (NSCLC) patients often demonstrate varying clinical courses and outcomes, even within the same tumor stage. This study explores deep learning applications in medical imaging allowing for the automated quantification of radiographic characteristics and potentially improving patient stratification.

METHODS AND FINDINGS

We performed an integrative analysis on 7 independent datasets across 5 institutions totaling 1,194 NSCLC patients (age median = 68.3 years [range 32.5-93.3], survival median = 1.7 years [range 0.0-11.7]). Using external validation in computed tomography (CT) data, we identified prognostic signatures using a 3D convolutional neural network (CNN) for patients treated with radiotherapy (n = 771, age median = 68.0 years [range 32.5-93.3], survival median = 1.3 years [range 0.0-11.7]). We then employed a transfer learning approach to achieve the same for surgery patients (n = 391, age median = 69.1 years [range 37.2-88.0], survival median = 3.1 years [range 0.0-8.8]). We found that the CNN predictions were significantly associated with 2-year overall survival from the start of respective treatment for radiotherapy (area under the receiver operating characteristic curve [AUC] = 0.70 [95% CI 0.63-0.78], p < 0.001) and surgery (AUC = 0.71 [95% CI 0.60-0.82], p < 0.001) patients. The CNN was also able to significantly stratify patients into low and high mortality risk groups in both the radiotherapy (p < 0.001) and surgery (p = 0.03) datasets. Additionally, the CNN was found to significantly outperform random forest models built on clinical parameters-including age, sex, and tumor node metastasis stage-as well as demonstrate high robustness against test-retest (intraclass correlation coefficient = 0.91) and inter-reader (Spearman's rank-order correlation = 0.88) variations. To gain a better understanding of the characteristics captured by the CNN, we identified regions with the most contribution towards predictions and highlighted the importance of tumor-surrounding tissue in patient stratification. We also present preliminary findings on the biological basis of the captured phenotypes as being linked to cell cycle and transcriptional processes. Limitations include the retrospective nature of this study as well as the opaque black box nature of deep learning networks.

CONCLUSIONS

Our results provide evidence that deep learning networks may be used for mortality risk stratification based on standard-of-care CT images from NSCLC patients. This evidence motivates future research into better deciphering the clinical and biological basis of deep learning networks as well as validation in prospective data.

Auxiliary variable-enriched biomarker-stratified design

Clinical trials in the era of precision medicine require assessment of biomarkers to identify appropriate subgroups of patients for targeted therapy. In a biomarker-stratified design (BSD), biomarkers are measured on all patients and used as stratification variables. However, such a trial can be both inefficient and costly, especially when the prevalence of the subgroup of primary interest is low and the cost of assessing the biomarkers is high. Efficiency can be improved and costs reduced by using enriched biomarker-stratified designs, in which patients of primary interest, typically the biomarker-positive patients, are oversampled. We consider a special type of enrichment design, an auxiliary variable-enriched design (AEBSD), in which enrichment is based on some inexpensive auxiliary variable that is positively correlated with the true biomarker. The proposed AEBSD reduces the total cost of the trial compared with a standard BSD when the prevalence rate of true biomarker positivity is small and the positive predictive value (PPV) of the auxiliary biomarker is larger than the prevalence rate. In addition, for an AEBSD, we can immediately randomize the patients selected in the screening process without waiting for the result of the true biomarker test, reducing the treatment waiting time. We propose an adaptive Bayesian method to adjust the assumed PPV while the trial is ongoing. Numerical studies and an example illustrate the approach. An R package is available.

International pilot external quality assessment scheme for analysis and reporting of circulating tumour DNA

BACKGROUND

Molecular analysis of circulating tumour DNA (ctDNA) is becoming increasingly important in clinical treatment decisions. A pilot External Quality Assessment (EQA) scheme for ctDNA analysis was organized by four European EQA providers under the umbrella organization IQN Path, in order to investigate the feasibility of delivering an EQA to assess the detection of clinically relevant variants in plasma circulating cell-free DNA (cfDNA) and to analyze reporting formats.

METHODS

Thirty-two experienced laboratories received 5 samples for EGFR mutation analysis and/or 5 samples for KRAS and NRAS mutation analysis. Samples were artificially manufactured to contain 3 mL of human plasma with 20 ng/mL of fragmented ctDNA and variants at allelic frequencies of 1 and 5%.

RESULTS

The scheme error rate was 20.1%. Higher error rates were observed for RAS testing when compared to EGFR analysis, for allelic frequencies of 1% compared to 5%, and for cases including 2 different variants. The reports over-interpreted wild-type results and frequently failed to comment on the amount of cfDNA extracted.

CONCLUSIONS

The pilot scheme demonstrated the feasibility of delivering a ctDNA EQA scheme and the need for such a scheme due to high error rates in detecting low frequency clinically relevant variants. Recommendations to improve reporting of cfDNA are provided.

PD-L1 expression in pleural effusions of pulmonary adenocarcinoma and survival prediction: a controlled study by pleural biopsy

PD-L1 expression in pleural effusions (PE) of lung adenocarcinoma (ADC) was compared with pleural biopsies and the positive expression in PE was correlated with survival time. The matched slices from same patient’s pleura and PE were collected which both were pathologically verified positive. Immunohistochemistry (IHC) was used to detect PD-L1 expression. A total of 51 eligible cases were enrolled, including 30 males and 21 females. The average age was (67.06 ± 12.10) years. PD-L1 expression wasn’t statistically significant in pleura and cell masses (P > 0.05) and the correlation was statistically significant (r = 0.585, P = 0.000). Using an IHC scores of 5 point as a cutoff, positive PD-L1 expression in the pleura was 11.63% and that in the cell masses was 23.26%, and difference was significant (P < 0.05). The correlation coefficient was 0.605. Among 35 cases underwent systemic anti-tumor treatment, the mean survival time with positive PD-L1 expression in PE was 17.370 ± 1.827 months, which was significantly shorter than that with the negative (29.944 ± 2.671 months) (χ² = 4.507, P = 0.034). Positive PD-L1 expression in PE is higher than in the pleura and their correlation is well. It may predict the short survival time after systemic anti-tumor treatment.

An Update on Predictive Biomarkers for Treatment Selection in Non-Small Cell Lung Cancer

It is now widely established that management of lung cancer is much more complex and cannot be centered on the binary classification of small-cell versus non-small cell lung cancer (NSCLC). Lung cancer is now recognized as a highly heterogeneous disease that develops from genetic mutations and gene expression patterns, which initiate uncontrolled cellular growth, proliferation and progression, as well as immune evasion. Accurate biomarker assessment to determine the mutational status of driver mutations such as EGFR, ALK and ROS1, which can be targeted by specific tyrosine kinase inhibitors, is now essential for treatment decision making in advanced stage NSCLC and has shifted the treatment paradigm of NSCLC to more individualized therapy. Rapid advancements in immunotherapeutic approaches to NSCLC treatment have been paralleled by development of a range of potential predictive biomarkers that can enrich for patient response, including PD-L1 expression and tumor mutational burden. Here, we review the key biomarkers that help predict response to treatment options in NSCLC patients.

MicroRNAs: A novel potential biomarker for diagnosis and therapy in patients with non-small cell lung cancer

BACKGROUND

Lung cancer is still one of the most serious causes of cancer-related deaths all over the world. MicroRNAs (miRNAs) are defined as small non-coding RNAs which could play a pivotal role in post-transcriptional regulation of gene expression. Increasing evidence demonstrated dysregulation of miRNA expression associates with the development and progression of NSCLC.

AIMS

To emphasize a variety of tissue-specific miRNAs, circulating miRNAs and miRNA-derived exosomes could be used as potential diagnostic and therapeutic biomarkers in NSCLC patients.

MATERIALS & METHODS

In the current review, we paid attention to the significant discoveries of preclinical and clinical studies, which performed on tissue-specific miRNA, circulating miRNA and exosomal miRNA. The related studies were obtained through a systematic search of Pubmed, Web of Science, Embase.

RESULTS

A variety of tissue-specific miRNAs and circulating miRNAs with high sensitivity and specificity which could be used as potential diagnostic and therapeutic biomarkers in NSCLC patients. In addition, we emphasize that the miRNA-derived exosomes become novel diagnostic biomarkers potentially in these patients with NSCLC.

CONCLUSION

MiRNAs have emerged as non-coding RNAs, which have potential to be candidates for the diagnosis and therapy of NSCLC.

Common TDP1 Polymorphisms in Relation to Survival among Small Cell Lung Cancer Patients: A Multicenter Study from the International Lung Cancer Consortium

Purpose: DNA topoisomerase inhibitors are commonly used for treating small-cell lung cancer (SCLC). Tyrosyl-DNA phosphodiesterase (TDP1) repairs DNA damage caused by this class of drugs and may therefore influence treatment outcome. In this study, we investigated whether common TDP1 single-nucleotide polymorphisms (SNP) are associated with overall survival among SCLC patients.Experimental Design: Two TDP1 SNPs (rs942190 and rs2401863) were analyzed in 890 patients from 10 studies in the International Lung Cancer Consortium (ILCCO). The Kaplan-Meier method and Cox regression analyses were used to evaluate genotype associations with overall mortality at 36 months postdiagnosis, adjusting for age, sex, race, and tumor stage.Results: Patients homozygous for the minor allele (GG) of rs942190 had poorer survival compared with those carrying AA alleles, with a HR of 1.36 [95% confidence interval (CI): 1.08-1.72, P = 0.01), but no association with survival was observed for patients carrying the AG genotype (HR = 1.04, 95% CI, 0.84-1.29, P = 0.72). For rs2401863, patients homozygous for the minor allele (CC) tended to have better survival than patients carrying AA alleles (HR = 0.79; 95% CI, 0.61-1.02, P = 0.07). Results from the Genotype Tissue Expression (GTEx) Project, the Encyclopedia of DNA Elements (ENCODE), and the ePOSSUM web application support the potential function of rs942190.Conclusions: We found the rs942190 GG genotype to be associated with relatively poor survival among SCLC patients. Further investigation is needed to confirm the result and to determine whether this genotype may be a predictive marker for treatment efficacy of DNA topoisomerase inhibitors. Clin Cancer Res; 23(24); 7550-7. ©2017 AACR.