Role of Chemotherapy and Targeted Therapy in Early-Stage Non-Small Cell Lung Cancer

BUB1 inhibition sensitizes lung cancer cell lines to radiotherapy and chemoradiotherapy

Background

Lung cancer is a major public health concern, with high incidence and mortality. Despite advances in targeted therapy and immunotherapy, microtubule stabilizers (paclitaxel, docetaxel), DNA intercalating platinum drugs (cisplatin) and radiation therapy continue to play a critical role in the management of locally advanced and metastatic lung cancer. Novel molecular targets would provide opportunities for improving the efficacies of radiotherapy and chemotherapy.

Hypothesis

We hypothesize that BUB1 (Ser/Thr kinase) is over-expressed in lung cancers and that its inhibition will sensitize lung cancers to chemoradiation.

Methods

BUB1 inhibitor (BAY1816032) was combined with platinum (cisplatin), microtubule poison (paclitaxel), a PARP inhibitor (olaparib) and radiation in cell proliferation and radiation sensitization assays. Biochemical and molecular assays were used to evaluate their impact on DNA damage signaling and cell death mechanisms.

Results

BUB1 expression assessed by immunostaining of lung tumor microarrays (TMAs) confirmed higher BUB1 expression in NSCLC and SCLC compared to that of normal tissues. BUB1 overexpression in lung cancer tissues correlated directly with expression of TP53 mutations in non-small cell lung cancer (NSCLC). Elevated BUB1 levels correlated with poorer overall survival in NSCLC and small cell lung cancer (SCLC) patients. A BUB1 inhibitor (BAY1816032) synergistically sensitized lung cancer cell lines to paclitaxel and olaparib. Additionally, BAY1816032 enhanced cell killing by radiation in both NSCLC and SCLC. Molecular changes following BUB1 inhibition suggest a shift towards pro-apoptotic and anti-proliferative states, indicated by altered expression of BAX, BCL2, PCNA, and Caspases 9 and 3.

Conclusion

A direct correlation between BUB1 protein expression and overall survival was shown. BUB1 inhibition sensitized both NSCLC and SCLC to various chemotherapies (cisplatin, paclitaxel) and targeted therapy (PARPi). Furthermore, we present the novel finding that BUB1 inhibition sensitized both NSCLC and SCLC to radiotherapy and chemoradiation. Our results demonstrate BUB1 inhibition as a promising strategy to sensitize lung cancers to radiation and chemoradiation therapies.

Progress in mechanism-based diagnosis and treatment of tuberculosis comorbid with tumor

Tuberculosis (TB) and tumor, with similarities in immune response and pathogenesis, are diseases that are prone to produce autoimmune stress response to the host immune system. With a symbiotic relationship between the two, TB can facilitate the occurrence and development of tumors, while tumor causes TB reactivation. In this review, we systematically sorted out the incidence trends and influencing factors of TB and tumor, focusing on the potential pathogenesis of TB and tumor, to provide a pathway for the co-pathogenesis of TB comorbid with tumor (TCWT). Based on this, we summarized the latest progress in the diagnosis and treatment of TCWT, and provided ideas for further exploration of clinical trials and new drug development of TCWT.

Economic evaluation of adjuvant therapy with osimertinib in patients with early-stage non-small cell lung cancer and mutated EGFR

PURPOSE

The ADAURA trial demonstrated the superiority of osimertinib over a placebo with regard to disease-free survival, showing it to be indicated as an adjuvant therapy for treatment of non-small cell lung cancer with mutated epidermal growth factor receptor (EGFR). The aim of the present study was to conduct a cost-utility analysis and an analysis of the budgetary impact of adjuvant therapy with osimertinib in patients with non-small cell lung cancer with mutated EGFR who had undergone resection surgery with curative intent.

METHODS

Analyses were based on the outcomes of the ADAURA clinical trial and were conducted through a Spanish National Health Service perspective. The outcome measures used were quality-adjusted life years (QALY).

RESULTS

The average overall cost of adjuvant treatment with osimertinib over a period of 100 months in the overall sample of trial patients (stages IB-IIIA) was 220,961 €, compared with 197,849 € in the placebo group. Effectiveness, estimated according to QALY, was 6.26 years in the osimertinib group and 5.96 years in the placebo group, with the incremental cost-utility ratio being 77,040 €/QALY. With regard to the budgetary impact, it was estimated that, in 2021, approximately 1130 patients would be subsidiaries to receive osimertinib. This pertains to a difference of 17,375,330 € over 100 months to fund this treatment relative to no treatment.

CONCLUSION

Taking into account a Spanish threshold of 24,000 €/QALY, the reduction in the acquisition cost of osimertinib will have to be greater than 10%, to obtain a cost-effective alternative.

High-risk characteristics of pathological stage I lung adenocarcinoma after resection: patients for whom adjuvant chemotherapy should be performed

Background

The objective of the present study was to identify patients with pathologic stage I lung adenocarcinoma (LUAD) who are at high risk of recurrence and assess the efficacy of adjuvant chemotherapy (ACT) in these individuals.

Methods

A retrospective study was conducted on 1504 patients with pathologic stage I LUAD who underwent surgical resection at Shanghai Pulmonary Hospital and Sun Yat-sen University Cancer Center. Cox proportional hazard regression analyses were performed to identify indicators associated with a high risk of recurrence, while the Kaplan-Meier method and Log-rank test were employed to compare recurrence-free survival (RFS) and overall survival (OS) between patients with ACT and those without it.

Results

Four independent indicators, including age (≥62 years), visceral pleural invasion (VPI), predominant pattern (micropapillary/solid), and lymphovascular invasion (LVI), were identified to be significantly related with RFS. Subsequently, patients were classified into high-risk and low-risk groups by LVI, VPI, and predominant pattern. The administration of ACT significantly increased both RFS (P < 0.001) and OS (P = 0.03) in the high-risk group (n = 250). Conversely, no significant difference was observed in either RFS (P = 0.45) or OS (P = 0.063) between ACT and non-ACT patients in the low-risk group (n = 1254).

Conclusions

Postoperative patients with stage I LUAD with factors such as LVI, VPI, and micropapillary/solid predominant pattern may benefit from ACT.

Nomograms integrating CT radiomic and deep learning signatures to predict overall survival and progression-free survival in NSCLC patients treated with chemotherapy

OBJECTIVES

This study aims to establish nomograms to accurately predict the overall survival (OS) and progression-free survival (PFS) in patients with non-small cell lung cancer (NSCLC) who received chemotherapy alone as the first-line treatment.

MATERIALS AND METHODS

In a training cohort of 121 NSCLC patients, radiomic features were extracted, selected from intra- and peri-tumoral regions, and used to build signatures (S1 and S2) using a Cox regression model. Deep learning features were obtained from three convolutional neural networks and utilized to build signatures (S3, S4, and S5) that were stratified into over- and under-expression subgroups for survival risk using X-tile. After univariate and multivariate Cox regression analyses, a nomogram incorporating the tumor, node, and metastasis (TNM) stages, radiomic signature, and deep learning signature was established to predict OS and PFS, respectively. The performance was validated using an independent cohort (61 patients).

RESULTS

TNM stages, S2 and S3 were identified as the significant prognosis factors for both OS and PFS; S2 (OS: (HR (95%), 2.26 (1.40-3.67); PFS: (HR (95%), 2.23 (1.36-3.65)) demonstrated the best ability in discriminating patients with over- and under-expression. For the OS nomogram, the C-index (95% CI) was 0.74 (0.70-0.79) and 0.72 (0.67-0.78) in the training and validation cohorts, respectively; for the PFS nomogram, the C-index (95% CI) was 0.71 (0.68-0.81) and 0.72 (0.66-0.79). The calibration curves for the 3- and 5-year OS and PFS were in acceptable agreement between the predicted and observed survival. The established nomogram presented a higher overall net benefit than the TNM stage for predicting both OS and PFS.

CONCLUSION

By integrating the TNM stage, CT radiomic signature, and deep learning signatures, the established nomograms can predict the individual prognosis of NSCLC patients who received chemotherapy. The integrated nomogram has the potential to improve the individualized treatment and precise management of NSCLC patients.

Prediction of bone metastasis in non-small cell lung cancer based on machine learning

Objective

The purpose of this paper was to develop a machine learning algorithm with good performance in predicting bone metastasis (BM) in non-small cell lung cancer (NSCLC) and establish a simple web predictor based on the algorithm.

Methods

Patients who diagnosed with NSCLC between 2010 and 2018 in the Surveillance, Epidemiology and End Results (SEER) database were involved. To increase the extensibility of the research, data of patients who first diagnosed with NSCLC at the First Affiliated Hospital of Nanchang University between January 2007 and December 2016 were also included in this study. Independent risk factors for BM in NSCLC were screened by univariate and multivariate logistic regression. At this basis, we chose six commonly machine learning algorithms to build predictive models, including Logistic Regression (LR), Decision tree (DT), Random Forest (RF), Gradient Boosting Machine (GBM), Naive Bayes classifiers (NBC) and eXtreme gradient boosting (XGB). Then, the best model was identified to build the web-predictor for predicting BM of NSCLC patients. Finally, area under receiver operating characteristic curve (AUC), accuracy, sensitivity and specificity were used to evaluate the performance of these models.

Results

A total of 50581 NSCLC patients were included in this study, and 5087(10.06%) of them developed BM. The sex, grade, laterality, histology, T stage, N stage, and chemotherapy were independent risk factors for NSCLC. Of these six models, the machine learning model built by the XGB algorithm performed best in both internal and external data setting validation, with AUC scores of 0.808 and 0.841, respectively. Then, the XGB algorithm was used to build a web predictor of BM from NSCLC.

Conclusion

This study developed a web predictor based XGB algorithm for predicting the risk of BM in NSCLC patients, which may assist doctors for clinical decision making.

Deep multiple instance learning for predicting chemotherapy response in non-small cell lung cancer using pretreatment CT images

The individual prognosis of chemotherapy is quite different in non-small cell lung cancer (NSCLC). There is an urgent need to precisely predict and assess the treatment response. To develop a deep multiple-instance learning (DMIL) based model for predicting chemotherapy response in NSCLC in pretreatment CT images. Two datasets of NSCLC patients treated with chemotherapy as the first-line treatment were collected from two hospitals. Dataset 1 (163 response and 138 nonresponse) was used to train, validate, and test the DMIL model and dataset 2 (22 response and 20 nonresponse) was used as the external validation cohort. Five backbone networks in the feature extraction module and three pooling methods were compared. The DMIL with a pre-trained VGG16 backbone and an attention mechanism pooling performed the best, with an accuracy of 0.883 and area under the curve (AUC) of 0.982 on Dataset 1. While using max pooling and convolutional pooling, the AUC was 0.958 and 0.931, respectively. In Dataset 2, the best DMIL model produced an accuracy of 0.833 and AUC of 0.940. Deep learning models based on the MIL can predict chemotherapy response in NSCLC using pretreatment CT images and the pre-trained VGG16 with attention mechanism pooling yielded better predictions.

Dysregulated Immune and Metabolic Microenvironment Is Associated with the Post-Operative Relapse in Stage I Non-Small Cell Lung Cancer

Simple Summary

The underlying mechanism of post-operative relapse of non-small cell lung cancer (NSCLC) remained poorly understood. This study highlights that both tumors and adjacent tissues from stage I NSCLC with relapse show a dysregulated immune and metabolic environment. Immune response shifts from an active state in primary tumors to a suppressive state in recurrent tumors. A model based on the enriched biological features in the primary tumors with relapse could effectively predict recurrence for stage I NSCLC. These results provide insights into the underpinning of the post-operative relapse and suggest that identifying NSCLC patients with a high risk of relapse could help the clinical decision of applying appropriate therapeutic interventions.

Abstract

The underlying mechanism of post-operative relapse of non-small cell lung cancer (NSCLC) remains poorly understood. We enrolled 57 stage I NSCLC patients with or without relapse and performed whole-exome sequencing (WES) and RNA sequencing (RNA-seq) on available primary and recurrent tumors, as well as on matched tumor-adjacent tissues (TATs). The WES analysis revealed that primary tumors from patients with relapse were enriched with USH2A mutation and 2q31.1 amplification. RNA-seq data showed that the relapse risk was associated with aberrant immune response and metabolism in the microenvironment of primary lesions. TATs from the patients with relapse showed an immunosuppression state. Moreover, recurrent lesions exhibited downregulated immune response compared with their paired primary tumors. Genomic and transcriptomic features were further subjected to build a prediction model classifying patients into groups with different relapse risks. We show that the recurrence risk of stage I NSCLC could be ascribed to the altered immune and metabolic microenvironment. TATs might be affected by cancer cells and facilitate the invasion of tumors. The immune microenvironment in the recurrent lesions is suppressed. Patients with a high risk of relapse need active post-operative intervention.

Identification of Prognostic Markers of N6-Methylandenosine-Related Noncoding RNAs in Non-Small-Cell Lung Cancer

Background

Non-small-cell lung cancer (NSCLC) is a major type of lung carcinoma that threatens the health and life of humans worldwide. We aimed to establish an n6-methyladenosine (m6A)-relevant ncRNA model to effectively evaluate the outcome of patients.

Methods

m6A-Related ncRNAs (lncRNA/miRNA) were acquired from the UCSC Xena database. Pearson's correlation analysis among 21 m6A regulatory factors and ncRNAs were implemented to explore m6A-relevant ncRNAs. Weighted gene co-expression network analysis (WGCNA) identified hub modules of gene associated with prognosis of NSCLC patients. Univariate Cox regression analysis identified 80 m6A-related ncRNAs. Least absolute shrinkage and selector operation (LASSO) filtered out redundant factors and established a risk score model (m6A-NSCLC) in the TCGA training data set. Validation of prognostic ability was performed using testing data sets from the TCGA database. We also conducted a correlation analysis among the risk score and different clinical traits. Both univariate and multivariate Cox analyses were combined to verify prognostic factors which have independent value, and a nomogram on the basis of m6A-NSCLC risk scores and clinical traits was constructed to assess the prognosis of patients. In addition, we screened differentially expressed genes (DEGs) based on different risk scores and performed enrichment analysis. Finally, 21 m6A regulators were detected to be differentially expressed between two risk groups.

Results

An m6A-NSCLC risk model with 18 ncRNAs was constructed. By comparison with low-risk patients, high-risk score patients had poor prognosis. The distribution of risk score in the tumor size and extent (T), number of near lymph nodes (N), clinical stage, sex, and tumor types was significantly different. The risk score could act as an independent prognostic factor with the nomogram assessing overall survival in NSCLC. DEGs inherent to cell movement and immune regulation were involved in NSCLC development. Furthermore, 18 of 21 m6A regulators were differentially expressed, implying their correlation to survival prognosis.

Conclusion

The m6A-NSCLC could be effectively utilized for evaluation of prognosis of patients.

Modified Sijunzi Decoction Inhibits Epithelial-Mesenchymal Transition of Non-Small Cell Lung Cancer by Attenuating AKT/GSK3β Pathway in vitro and in vivo

Modified Sijunzi Decoction (MSJZD) is an empirical prescription of Traditional Chinese Medicine (TCM) and has been corroborated to be effective in multiple human diseases, but its role in non-small cell lung cancer (NSCLC) is enigmatic. Here we mainly analyze the function and mechanism of MSJZD in NSCLC. In this study, we used a method that coupled ultra-performance liquid chromatography to quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS) to investigate the major constituents in MSJZD with positive and negative ion modes. Additionally, in in vitro experiments, the effects of serum-containing MSJZD on the biological behavior of NSCLC cells induced by TGF-β1 were assessed by cell function experiments. Then, the influences of serum-containing MSJZD on epithelial-mesenchymal transition (EMT)-related markers were examined by immunofluorescence and western blot assays. Also, the AKT/GSK3β pathway and apoptosis-related markers were estimated by western blotting. Tumor xenografts were generated by subcutaneously injecting A549 cells into BALB/c nude mice to determine the effects of MSJZD in vivo. We first analyzed the composition of MSJZD. In positive ion mode, 47 kinds of components were identified. In negative ion mode, 45 kinds of components were identified. We also found that TGF-β1 contributed to inducing cell morphological changes and EMT progression. In vitro, surprisingly, cell proliferation, migration as well as invasion in NSCLC cells induced by TGF-β1, could be weakened by serum-containing MSJZD, and apoptosis was intensified. Moreover, serum-containing MSJZD weakened EMT passage and AKT/GSK3β pathway activation and induced apoptosis-related markers in NSCLC cells triggered by TGF-β1. In vivo, we discovered that MSJZD attenuated the tumor growth, promoted histopathological damage, and induced apoptosis in A549 tumor-bearing mice. Importantly, MSJZD has also restrained the development of EMT, AKT/GSK3β pathway, and TGF-β1 expression levels in nude mice. These findings demonstrated that MSJZD significantly weakened NSCLC progression by modulating EMT and AKT/GSK3β pathway.

Delivery of gefitinib in synergism with thymoquinone via transferrin-conjugated nanoparticle sensitizes gefitinib-resistant non-small cell lung carcinoma to control metastasis and stemness

Epidermal growth factor receptor (EGFR) normally over-expresses in non-small cell lung cancer (NSCLC) cells. Its mutations act as oncogenic drivers in the cellular signal transduction pathway, and induce the downstream activation of numerous key cellular events involved in cellular proliferation and survival. EGFR tyrosine kinase inhibitors (EGFR-TK inhibitors), such as gefitinib and erlotinib, have been used for a long time in the treatment of NSCLC. However, they fail to overcome the EGFR-TK mutation due to the acquisition of drug resistance. It is strongly believed that the epithelial-to-mesenchymal transition (EMT) is a key player for acquired resistance and consequent limitation of the clinical efficiency of EGFR-TKIs. Therefore, a new strategy needs to be developed to overcome the resistance in NSCLC. In this current study, we have disclosed for the first time the efficiency of transferrin-modified PLGA-thymoquinone-nanoparticles in combination with gefitinib (NP-dual-1, NP-dual-2 and NP-dual-3) towards gefitinib-resistant A549 cells. The gefitinib-resistant A549 cells (A549/GR) showed 12.3-fold more resistance to gefitinib in comparison to non-resistant A549 cells. The phenotypic alteration resembling spindle-cell shape and increased pseudopodia integuments featured the EMT phenomena in A549/GR cells. EMT in A549/GR was later coupled with the loss of Ecad and expansion of Ncad, along with upregulated vimentin expression, as compared to the control A549 cells. Moreover, the invasive nature and migration potential are more amplified in A549/GR cells. Pre-incubation of A549 cells with TGFβ1 also initiated EMT, leading to drug resistance. Conversely, treatment of A549 or A549/GR cells with NP-dual-3 effectively retrieved the sensitivity to gefitinib, restricted the EMT phenomenon, and impaired the TGFβ1-induced EMT. On unveiling the underlying mechanism of therapeutic action, we found that STAT3 and miR-21 were individually overexpressed in the A549/GR cells by transfection, and followed by treatment with NP-dual-3. Simultaneously, NP-dual-3 fragmented HIF1-α induced EMT in A549/GR cells and reduced the CSCs markers, viz., Oct-4, Sox-2, Nanog, and Aldh1. These data are self-sufficient to suggest that NP-dual-3 re-sensitizes the drug-resistant A549/GR cells to gefitinib, possibly by retrieving MET phenomena via modulation of STAT3/mir-21/Akt/PTEN/HIF1-α axis. Thus, TQ nanoparticles combined with TKI gefitinib may provide an effective platform to treat NSCLC.

STAT3 promotes peritoneal metastasis of gastric cancer by enhancing mesothelial-mesenchymal transition

Signal transducer and activator of transcription 3 (STAT3) is a widely-reported oncogene in many human cancers, but its role in the peritoneal metastasis of gastric cancer (GC) has yet to be studied. The expression level of STAT3 in GC patient tissues was assessed. Stable shRNA knockdown (KD) of STAT3 was established in GC cell line AGS, followed by examination of its effect on AGC cell viability and proliferation, xenograft tumor growth, metastatic potential, mesothelial-to-mesenchymal transition (MMT)-related properties and peritoneal metastasis in a mouse model. The specific STAT3 inhibitor BP1-102 was also employed to verify findings from STAT3 KD experiments. Expression of activated STAT3 was upregulated in GC patient tumor tissues, and further elevated among patients diagnosed with peritoneal metastasis. STAT3 deactivation suppressed viability and proliferation of GC cells in vitro, as well as GC tumorigenesis in vivo. Furthermore, the metastatic properties and production of MMT-inducing factors of GC cells in vitro were also dependent on STAT3 activation. Importantly, STAT3 KD significantly compromised peritoneal metastasis of GC in vivo. STAT3 activation contributes to peritoneal metastasis of GC by promoting MMT, warranting further investigation to explore its potential for GC treatment, in particular among peritoneal metastasis patients.

Molecular biomarkers in early stage lung cancer

Low dose computed tomography (LDCT) screening, together with the recent advances in targeted and immunotherapies, have shown to improve non-small cell lung cancer (NSCLC) survival. Furthermore, screening has increased the number of early stage-detected tumors, allowing for surgical resection and multimodality treatments when needed. The need for improved sensitivity and specificity of NSCLC screening has led to increased interest in combining clinical and radiological data with molecular data. The development of biomarkers is poised to refine inclusion criteria for LDCT screening programs. Biomarkers may also be useful to better characterize the risk of indeterminate nodules found in the course of screening or to refine prognosis and help in the management of screening detected tumors. The clinical implications of these biomarkers are still being investigated and whether or not biomarkers will be included in further decision-making algorithms in the context of screening and early lung cancer management still needs to be determined. However, it seems clear that there is much room for improvement even in early stage lung cancer disease-free survival (DFS) rates; thus, biomarkers may be the key to refine risk-stratification and treatment of these patients. Clinicians’ capacity to register, integrate, and analyze all the available data in both high risk individuals and early stage NSCLC patients will lead to a better understanding of the disease’s mechanisms, and will have a direct impact in diagnosis, treatment, and follow up of these patients. In this review, we aim to summarize all the available data regarding the role of biomarkers in LDCT screening and early stage NSCLC from a multidisciplinary perspective. We have highlighted clinical implications, the need to combine risk stratification, clinical data, radiomics, molecular information and artificial intelligence in order to improve clinical decision-making, especially regarding early diagnostics and adjuvant therapy. We also discuss current and future perspectives for biomarker implementation in routine clinical practice.