Treatment Of Lung Cancer Patients Research Articles

A semantic fidelity interpretable-assisted decision model for lung nodule classification.

Early diagnosis of lung nodules is important for the treatment of lung cancer patients, existing capsule network-based assisted diagnostic models for lung nodule classification have shown promising prospects in terms of interpretability. However, these models lack the ability to draw features robustly at shallow networks, which in turn limits the performance of the models. Therefore, we propose a semantic fidelity capsule encoding and interpretable (SFCEI)-assisted decision model for lung nodule multi-class classification. First, we propose multilevel receptive field feature encoding block to capture multi-scale features of lung nodules of different sizes. Second, we embed multilevel receptive field feature encoding blocks in the residual code-and-decode attention layer to extract fine-grained context features. Integrating multi-scale features and contextual features to form semantic fidelity lung nodule attribute capsule representations, which consequently enhances the performance of the model. We implemented comprehensive experiments on the dataset (LIDC-IDRI) to validate the superiority of the model. The stratified fivefold cross-validation results show that the accuracy (94.17%) of our method exceeds existing advanced approaches in the multi-class classification of malignancy scores for lung nodules. The experiments confirm that the methodology proposed can effectively capture the multi-scale features and contextual features of lung nodules. It enhances the capability of shallow structure drawing features in capsule networks, which in turn improves the classification performance of malignancy scores. The interpretable model can support the physicians' confidence in clinical decision-making.

Abstract A037: Mechanisms of resistance to BAY 2927088, the first reversible inhibitor targeting EGFR exon 20 insertion mutations in non-small cell lung cancer

Abstract Molecular therapies targeting EGFR-mutant non-small cell lung cancer (NSCLC) have dramatically improved prognosis for patients with the classical activating mutations, L858R and exon 19 deletion. However, tumors harboring EGFR exon 20 insertions have been historically more difficult to target. Agents such as amivantamab and mobocertinib have recently been granted accelerated approval for treatment of lung cancer patients with exon 20 insertions, but agents with an improved selectivity profile versus wild-type EGFR are still needed. BAY 2927088, currently in phase I clinical trials, is a novel reversible inhibitor that targets EGFR exon 20 insertion mutations with decreased activity towards wild-type EGFR. Although most patients with advanced EGFR-mutant NSCLC eventually progress due to acquired resistance to EGFR inhibitors, the mechanisms of resistance to BAY 2927088 are currently unknown. Using two different methods of in vitro resistance generation by inhibitor treatment of sensitive cell lines, we identified mechanisms of intrinsic and acquired resistance to BAY 2927088 and other EGFR inhibitors. Incubation with a high concentration of inhibitor selected for drug-tolerant persister cells that underwent a transcriptional state transition resembling EMT (epithelial-mesenchymal transition). Conversely, gradual escalation of inhibitor concentrations resulted in acquisition of oncogenic NRAS mutations. In a parallel approach, we performed a deep mutational scanning assay of the EGFR D770_N771insSVD kinase domain, replacing each amino acid with every other possible amino acid, identifying both known and novel mutations that cause resistance to BAY 2927088 and other EGFR inhibitors. As expected, the C797S mutation was enriched as a mechanism of acquired resistance only to irreversible EGFR exon 20 insertion inhibitors. Surprisingly, the T790M gatekeeper mutation was found to cause resistance to all EGFR exon 20 insertion inhibitors tested, including irreversible inhibitors. Several novel resistance mutations clustering predominantly in exons 20 and 21 were also identified. Citation Format: Gizem Karsli Uzunbas, Quinn McVeigh, Akansha Gupta, Stephanie Hoyt, Kristen Doucette, Hasmik Keshishian, Steven Carr, Xiaoping Yang, David Root, Andrew D Cherniack, Franziska Siegel, Stephan Siegel, Matthew Meyerson, Heidi Greulich. Mechanisms of resistance to BAY 2927088, the first reversible inhibitor targeting EGFR exon 20 insertion mutations in non-small cell lung cancer [abstract]. In: Proceedings of the AACR-NCI-EORTC Virtual International Conference on Molecular Targets and Cancer Therapeutics; 2023 Oct 11-15; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2023;22(12 Suppl):Abstract nr A037.

Current status and prospects for thoracic surgery

The world today is undergoing revolutionary changes in science, technology, and industry. These changes have optimized the surgical procedure for lung cancer treatment into a more minimally invasive, precise, comprehensive, industrialized, and patient-centered manner. The definition of minimally invasive treatment for lung cancer has been expanded from simply reducing the size of the skin incision to reserve the lung function. The principle of precision surgery is emphasized throughout the whole process of patient management including diagnosis and treatment. Multimodality therapy helps narrow the gaps between different disciplines and thus provides more personalized treatment for lung cancer patients. Integration of industrial techniques such as visualization, surgical robot, and artificial intelligence into medical practice will potentially lead to a revolution in thoracic surgical procedure. Today, thoracic surgeons are responsible for establishing a self-reliant surgical practice for Chinese patients with lung cancer. It is necessary to attach great importance to patient-centered care, move forward to review minimally invasive surgical procedure, and foster better practice for lung cancer management by keeping up with cutting-edge research on science and technology in the context of changes and challenges.

Abstract A014: Deep learning-driven drug discovery: A breakthrough algorithm and its implication in lung cancer therapy development

Abstract We have developed an algorithm and implemented it in a software platform for the purpose of developing new anti-tumor drugs in the form of small molecules. In this study, we focused on generating molecules specifically for the treatment of lung cancer patients. To begin with, we employed deep learning (DL) techniques to evaluate the genes associated with poor clinical outcomes in lung cancer patients. By utilizing generative adversarial neural networks (GAN), we acquired additional patient data. The results of each experiment were presented as a list of genes ordered by their impact on the desired effect. We then intersected the lists of genes obtained from experiments with overall survival (OS) and progression-free interval (PFI) data. This allowed us to identify a set of genes whose expression was correlated with poor prognosis. In order to enhance the precision, we trained another DL model to distinguish between normal and tumor tissue based on gene expression. By doing so, we were able to identify the smaller set of genes that could be targeted. Subsequently, we developed a module that predicts the interactions between inhibitors and proteins. This involved representing protein amino acid sequences and chemical compound formulas in vector form, and a virtual screening of the Pubchem database. The implementation of the Drug-protein interactions module resulted in a dataset of 118,379 pairs, including 19,250 pairs describing compounds bound to proteins, and 99,129 precedents describing non-bound ones. DLwas applied, yielding a ROC-AUC of 0.86. Following the search for candidate molecules, we obtained 160,000 pairs with a predicted interaction probability above 0.99, as well as 2,921 pairs with probability of 1.0. Additionally, we created a DL-based module to predict the IC50 values in cell line experiments. Virtual pre-clinical trials were conducted using the selected inhibitors to identify relevant cell lines for subsequent laboratory experiments. Through this process, we obtained formulas for several molecules that demonstrated predicted binding to specific proteins. During the cell experiment emulation, our feature importance algorithm selected 129 genes. For the cell experiment emulation stage, we specifically chose interactions with a probability of at least 0.9. We prioritized molecules that acted on the minimum number of cell lines with a higher probability, thus ensuring higher specificity. Ultimately, we selected 5 small molecules as potential candidates, as well as certain cell lines for their validation. The NLP technologies utilized in this study demonstrated their effectiveness in processing tens of thousands of articles. The pipeline of methods presented in this paper lays the groundwork for automated AI-driven drug discovery. We have showcased the application of modern machine learning methods, particularly DL, as well as the methods used to prepare the initial data for the learning algorithms. The performance of these methods has been validated through cross-validation using data from publicly available sources. Citation Format: Dmitrii K Chebanov, Vsevolod A Misyurin, Nadezhda S Tatevosova. Deep learning-driven drug discovery: A breakthrough algorithm and its implication in lung cancer therapy development [abstract]. In: Proceedings of the AACR-NCI-EORTC Virtual International Conference on Molecular Targets and Cancer Therapeutics; 2023 Oct 11-15; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2023;22(12 Suppl):Abstract nr A014.

EE538 Hospital Budget Impact of a Lung Suite Navigation Platform to Diagnose and Treat Lung Cancer Patients

EE538 Hospital Budget Impact of a Lung Suite Navigation Platform to Diagnose and Treat Lung Cancer Patients

Role of rs873601 Polymorphisms in Prognosis of Lung Cancer Patients Treated with Platinum-Based Chemotherapy.

Lung cancer is still the most lethal malignancy in the world, according to the report of Cancer Statistics in 2021. Platinum-based chemotherapy combined with immunotherapy is the first-line treatment in lung cancer patients. However, the 5-year survival rate is always affected by the adverse reactions and drug resistance caused by platinum-based chemotherapy. DNA damage and repair system is one of the important mechanisms that can affect the response to chemotherapy and clinical outcomes in lung cancer patients. The objective of this study is to find the relationship between the polymorphisms of DNA repair genes with the prognosis of platinum-based chemotherapy in lung cancer patients. We performed genotyping in 17 single nucleotide polymorphisms (SNPs) of Excision Repair Cross-Complementation group (ERCC) genes and X-ray Repair Cross-Complementing (XRCC) genes of 345 lung cancer patients via Sequenom MassARRAY. We used Cox proportional hazard models, state, and plink to analyze the associations between SNPs and the prognosis of lung cancer patients. We found that the ERCC5 rs873601 was associated with the overall survival time in lung cancer patients treated with platinum-based chemotherapy (p = 0.031). There were some polymorphisms that were related to the prognosis in specific subgroups of lung cancer. Rs873601 showed a great influence on the prognosis of patients more than 55 years, Small Cell Lung Cancer (SCLC), and smoking patients. Rs2444933 was associated with prognosis in age less than 55 years, SCLC, metastasis, and stage III/IV/ED patients. Rs3740051 played an important role in the prognosis of SCLC and metastasis patients. Rs1869641 was involved in the prognosis of SCLC patients. Rs1051685 was related to the prognosis in non-metastasis patients. The ERCC5 rs873601 (G>A) was a valuable biomarker for predicting the prognosis in lung cancer patients treated with platinum-based chemotherapy.

Assessing the Effectiveness of Montelukast Sodium in Managing Pulmonary Infections in Lung Cancer Patients

Objective: To investigate the efficacy of montelukast sodium in the treatment of lung cancer patients with pulmonary infections. Methods: A total of 330 patients diagnosed with lung cancer and pulmonary infection, who were admitted to the First Affiliated Hospital of Xi’an Medical University from 2020 to 2022, were selected as research subjects. They were randomly divided into two groups: a control group receiving conventional treatment and an observation group receiving conventional treatment combined with montelukast sodium. Each group consisted of 165 cases. The time required for clinical symptom improvement, the resolution of lung infection signs, and the levels of serum inflammatory factors before and after treatment were compared between the two groups. Results: The observation group exhibited significantly faster improvement in clinical symptoms compared to the control group (P < 0.001). ELISA assays were conducted to detect the expression levels of IL-1β, IL-6, IL-8, and TNF-α in the serum of both groups at 1 week and 2 weeks into the treatment. The results indicated that, as the treatment progressed, the observation group displayed significantly lower levels of the four serum inflammatory factors compared to the control group (P < 0.001). Conclusion: Montelukast sodium demonstrates efficacy in the treatment of patients with lung cancer complicated by pulmonary infections. These findings suggest its potential for further verification and clinical application.

LncRNA MIR31HG fosters stemness malignant features of non-small cell lung cancer via H3K4me1- and H3K27Ace-mediated GLI2 expression

Non-coding RNAs are responsible for oncogenesis and the development of stemness features, including multidrug resistance and metastasis, in various cancers. Expression of lncRNA MIR31HG in lung cancer tissues and peripheral sera of lung cancer patients were remarkably higher than that of healthy individuals and indicated a poor prognosis. Functional analysis showed that MIR31HG fosters stemness-associated malignant features of non-small cell lung cancer cells. Further mechanistic investigation revealed that MIR31HG modulated GLI2 expression via WDR5/MLL3/P300 complex-mediated H3K4me and H3K27Ace modification. In vivo MIR31HG repression with an antisense oligonucleotide attenuated tumor growth and distal organ metastasis, whereas MIR31HG promotion remarkably encouraged cellular invasion in lung and liver tissues. Our data suggested that MIR31HG is a potential diagnostic indicator and druggable therapeutic target to facilitate multiple strategic treatments for lung cancer patients.

Association of immune-related adverse events with COVID-19 pneumonia in lung cancer patients receiving immune checkpoint inhibitors: a cross-sectional study in China

BackgroundImmune checkpoint inhibitors (ICIs) are commonly used to treat lung cancer patients, but their use can lead to immune-related adverse events (irAEs), which pose a challenge for treatment strategies. The impact of irAEs on the incidence of COVID-19 pneumonia in lung cancer patients during the ongoing COVID-19 pandemic is unclear. This study aims to investigate the association between irAEs and COVID-19 pneumonia in lung cancer patients receiving ICIs.MethodsWe conducted a cross-sectional study of lung cancer patients who received ICIs and were infected with COVID-19 due to the Omicron variant between December 2022 and February 2023 in China. We collected data on irAEs and COVID-19 outcomes. Logistic regression analyses were used to calculate odds ratios (ORs) and 95% confidence intervals (CIs) for the association between irAEs and the incidence of COVID-19 pneumonia.ResultsA total of 193 patients were enrolled, with 72 patients (37.30%) in the irAEs group and 121 patients (62.70%) in the non-irAEs group. Twenty-six patients (13.47%) developed COVID-19 pneumonia and 6 patients (3.11%) progressed to severe cases after COVID-19 infection. Multivariate logistic regression showed that the lung cancer patients who experienced irAEs was significantly associated with a higher incidence rate of COVID-19 pneumonia (OR = 9.56, 95%CI: 2.21–41.33; P = 0.0025).ConclusionOur study suggests that lung cancer patients receiving ICIs and experiencing irAEs may have a higher risk of developing COVID-19 pneumonia due to the Omicron variant. Therefore, close monitoring of these patients during the COVID-19 pandemic is necessary to mitigate this risk.

DeepXplainer: An interpretable deep learning based approach for lung cancer detection using explainable artificial intelligence

Background and ObjectiveArtificial intelligence (AI) has several uses in the healthcare industry, some of which include healthcare management, medical forecasting, practical making of decisions, and diagnosis. AI technologies have reached human-like performance, but their use is limited since they are still largely viewed as opaque black boxes. This distrust remains the primary factor for their limited real application, particularly in healthcare. As a result, there is a need for interpretable predictors that provide better predictions and also explain their predictions. MethodsThis study introduces “DeepXplainer”, a new interpretable hybrid deep learning-based technique for detecting lung cancer and providing explanations of the predictions. This technique is based on a convolutional neural network and XGBoost. XGBoost is used for class label prediction after “DeepXplainer” has automatically learned the features of the input using its many convolutional layers. For providing explanations or explainability of the predictions, an explainable artificial intelligence method known as “SHAP” is implemented. ResultsThe open-source “Survey Lung Cancer” dataset was processed using this method. On multiple parameters, including accuracy, sensitivity, F1-score, etc., the proposed method outperformed the existing methods. The proposed method obtained an accuracy of 97.43%, a sensitivity of 98.71%, and an F1-score of 98.08. After the model has made predictions with this high degree of accuracy, each prediction is explained by implementing an explainable artificial intelligence method at both the local and global levels. ConclusionsA deep learning-based classification model for lung cancer is proposed with three primary components: one for feature learning, another for classification, and a third for providing explanations for the predictions made by the proposed hybrid (ConvXGB) model. The proposed “DeepXplainer” has been evaluated using a variety of metrics, and the results demonstrate that it outperforms the current benchmarks. Providing explanations for the predictions, the proposed approach may help doctors in detecting and treating lung cancer patients more effectively.

Machine Learning Techniques to Predict Timeliness of Care among Lung Cancer Patients.

Delays in the assessment, management, and treatment of lung cancer patients may adversely impact prognosis and survival. This study is the first to use machine learning techniques to predict the quality and timeliness of care among lung cancer patients, utilising data from the Victorian Lung Cancer Registry (VLCR) between 2011 and 2022, in Victoria, Australia. Predictor variables included demographic, clinical, hospital, and geographical socio-economic indices. Machine learning methods such as random forests, k-nearest neighbour, neural networks, and support vector machines were implemented and evaluated using 20% out-of-sample cross validations via the area under the curve (AUC). Optimal model parameters were selected based on 10-fold cross validation. There were 11,602 patients included in the analysis. Evaluated quality indicators included, primarily, overall proportion achieving "time from referral date to diagnosis date ≤ 28 days" and proportion achieving "time from diagnosis date to first treatment date (any intent) ≤ 14 days". Results showed that the support vector machine learning methods performed well, followed by nearest neighbour, based on out-of-sample AUCs of 0.89 (in-sample = 0.99) and 0.85 (in-sample = 0.99) for the first indicator, respectively. These models can be implemented in the registry databases to help healthcare workers identify patients who may not meet these indicators prospectively and enable timely interventions.

Transcriptional signature of early cisplatin drug-tolerant persister cells in lung adenocarcinoma.

Resistance to cisplatin is the main cause of treatment failure in lung adenocarcinoma. Drug-tolerant-persister (DTP) cells are responsible for intrinsic resistance, since they survive the initial cycles of treatment, representing a reservoir for the emergence of clones that display acquired resistance. Although the molecular mechanisms of DTP cells have been described, few studies have investigated the earliest molecular alterations of DTP cells in intrinsic resistance to cisplatin. In this work, we report a gene expression signature associated with the emergence of cisplatin-DTP cells in lung adenocarcinoma cell lines. After a single exposure to cisplatin, we sequenced the transcriptome of cisplatin-DTPs to identify differentially expressed genes. Bioinformatic analysis revealed that early cisplatin-DTP cells deregulate metabolic and proliferative pathways to survive the drug insult. Interaction network analysis identified three highly connected submodules in which SOCS1 had a significant participation in controlling the proliferation of cisplatin-DTP cells. Expression of the candidate genes and their corresponding protein was validated in lung adenocarcinoma cell lines. Importantly, the expression level of SOCS1 was different between CDDP-susceptible and CDDP-resistant lung adenocarcinoma cell lines. Moreover, knockdown of SOCS1 in the CDDP-resistant cell line partially promoted its susceptibility to CDDP. Finally, the clinical relevance of the candidate genes was analyzed in silico, according to the overall survival of cisplatin-treated patients from The Cancer Genome Atlas. Survival analysis showed that downregulation or upregulation of the selected genes was associated with overall survival. The results obtained indicate that these genes could be employed as predictive biomarkers or potential targets to improve the effectiveness of CDDP treatment in lung cancer patients.

Challenges of Treating Lung Cancer Patients at MR-Linac Using MR-Based Synthetic CT Calculation in the Adaptive Workflow

Magnetic Resonance guided adaptive radiotherapy (MRgART) allows plan adaptation according to the new patient anatomy; the contours of the structures are adjusted based on the patient's daily MRI, and in the adapt to shape (ATS) workflow, the adapted plan is recalculated on the MRI-based synthetic CT (sCT) generated by bulk density assignment. For sites where there is a high electronic density (ED) gradient between the target and surrounding tissues, such as in lung cancer treatments, the assignment of an average ED may not be able to reproduce an accurate dose calculation. This study evaluates the accuracy of the sCT adapted plan calculation for lung cancer patients and assesses whether the assignment of an optimized ED can reduce dosimetric differences should they arise MATERIALS/METHODS: Nine lung cancer patients treated at Unity 1.5 MR-Linac were selected for this retrospective study. The patient's target and organs at risk (OARs) were contoured, and a CT reference plan containing the ED bulk assignment information i.e., the contours to use in the ATS workflow, and their corresponding average ED was generated. To assess the accuracy of the dosimetry of the adapted plan calculated on the sCT, the plan was recalculated on an ideal sCT (sCTref) obtained from the reference CT by forcing the drawn contours to the average ED as defined on the CT reference plan. Targets and OARs dose-volume histogram (DVH) of the CT and sCTref plans and the dose distributions using gamma (γ) analysis with 2%-2mm criteria were compared. In the case of a discrepancy between the DVHs, the average Eds used for the recalculation on the sCTref were adjusted by several attempts to obtain a sCT optimized (sCTopt) for which a superposition of DVHs on CT and sCTopt was achieved. For 7 of the 9 patients CT and sCTref target DVHs were not comparable, with a mean dosimetric difference of 5.55% (range 2.35%-7.46%) in the target volume receiving the prescription dose (VDpre), while OARs DVH dose differences remained below 1% for the nine patients. The adjustment of the ED of the homolateral lung in the sCTopt, reduced the mean target VDpre dosimetric difference between CT and sCTopt to 0.66% (range 0.17%-1.64%). In addition, the results of the gamma analysis increased from values ranging between 39.5%-70.3% to 88.5%-93.2%, as shown in the Table. Dosimetric errors in the use of the sCT calculation for targets in high ED gradient areas may arise; the use of optimized ED for sCT calculation may be a promising strand to investigate in order to proceed with MR-based sCT plan adaptation for lung cancer treatment.

Modern Stereotactic Radiotherapy for Brain Metastases from Lung Cancer: Current Trends and Future Perspectives Based on Integrated Translational Approaches.

Brain metastases (BMs) represent the most frequent metastatic event in the course of lung cancer patients, occurring in approximately 50% of patients with non-small-cell lung cancer (NSCLC) and in up to 70% in patients with small-cell lung cancer (SCLC). Thus far, many advances have been made in the diagnostic and therapeutic procedures, allowing improvements in the prognosis of these patients. The modern approach relies on the integration of several factors, such as accurate histological and molecular profiling, comprehensive assessment of clinical parameters and precise definition of the extent of intracranial and extracranial disease involvement. The combination of these factors is pivotal to guide the multidisciplinary discussion and to offer the most appropriate treatment to these patients based on a personalized approach. Focal radiotherapy (RT), in all its modalities (radiosurgery (SRS), fractionated stereotactic radiotherapy (SRT), adjuvant stereotactic radiotherapy (aSRT)), is the cornerstone of BM management, either alone or in combination with surgery and systemic therapies. We review the modern therapeutic strategies available to treat lung cancer patients with brain involvement. This includes an accurate review of the different technical solutions which can be exploited to provide a "state-of-art" focal RT and also a detailed description of the systemic agents available as effective alternatives to SRS/SRT when a targetable molecular driver is present. In addition to the validated treatment options, we also discuss the future perspective for focal RT, based on emerging clinical reports (e.g., SRS for patients with many BMs from NSCLC or SRS for BMs from SCLC), together with a presentation of innovative and promising findings in translational research and the combination of novel targeted agents with SRS/SRT.

Efficacy and safety of veliparib plus chemotherapy for the treatment of lung cancer: A systematic review of clinical trials.

As a poly-ADP ribose polymerase (PARP) inhibitor, veliparib has been identified as a potential therapeutic agent for lung cancer. The present study aimed to conduct a systematic review of clinical trials investigating the efficacy and safety of veliparib for treating lung cancer. PubMed, Scopus, the Web of Science, and Google Scholar were systematically searched up to October 30, 2022. Only randomized controlled trials (RCTs) evaluating the efficacy or safety of veliparib in the treatment of lung cancer patients were included. Studies were excluded if they were not RCTs, enrolled healthy participants or patients with conditions other than lung cancer, or investigated therapeutic approaches other than veliparib. The Cochrane risk-of-bias tool was used for quality assessment. The seven RCTs (n = 2188) showed that patients treated with a combination of veliparib and chemotherapy had a significantly higher risk of adverse events, when compared to the control arm. There was no statistically significant difference in overall survival (OS) between those treated with veliparib plus chemotherapy and those receiving the standard therapies. Only two trials demonstrated an improvement in progression-free survival (PFS), and only one study found an increase in objective response rate (ORR). Furthermore, adding veliparib to standard chemotherapy showed no benefit in extending the duration of response (DoR) in any of the studies. Only a small number of studies have found veliparib to be effective, in terms of improved OS, PFS, and ORR, while the majority of studies found no benefit for veliparib over standard treatment.

Warburg effect enhanced by AKR1B10 promotes acquired resistance to pemetrexed in lung cancer-derived brain metastasis

BackgroundResistance to pemetrexed (PEM), a rare chemotherapeutic agent that can efficiently cross the blood-brain barrier, limits the therapeutic efficacy for patients with lung cancer brain metastasis (BM). Aldo-keto reductase family 1 B10 (AKR1B10) was recently found to be elevated in lung cancer BM. The link between AKR1B10 and BM-acquired PEM is unknown.MethodsPEM drug-sensitivity was assessed in the preclinical BM model of PC9 lung adenocarcinoma cells and the BM cells with or without AKR1B10 interference in vitro and in vivo. Metabolic reprogramming of BM attributed to AKR1B10 was identified by chromatography-mass spectrometry (GC-MS) metabolomics, and the mechanism of how AKR1B10 mediates PEM chemoresistance via a way of modified metabolism was revealed by RNA sequencing as well as further molecular biology experimental approaches.ResultsThe lung cancer brain metastatic subpopulation cells (PC9-BrM3) exhibited significant resistance to PEM and silencing AKR1B10 in PC9-BrM3 increased the PEM sensitivity in vitro and in vivo. Metabolic profiling revealed that AKR1B10 prominently facilitated the Warburg metabolism characterized by the overproduction of lactate. Glycolysis regulated by AKR1B10 is vital for the resistance to PEM. In mechanism, AKR1B10 promoted glycolysis by regulating the expression of lactate dehydrogenase (LDHA) and the increased lactate, acts as a precursor that stimulates histone lactylation (H4K12la), activated the transcription of CCNB1 and accelerated the DNA replication and cell cycle.ConclusionsOur finding demonstrates that AKR1B10/glycolysis/H4K12la/CCNB1 promotes acquired PEM chemoresistance in lung cancer BM, providing novel strategies to sensitize PEM response in the treatment of lung cancer patients suffering from BM.

Effects of combining immune checkpoint inhibitors and anti-angiogenic agents on bone metastasis in non-small cell lung cancer patients

ABSTRACT This study aimed to evaluate the efficacy of combining immune checkpoint inhibitors (ICIs) and anti-angiogenic agents in treating lung cancer patients with bone metastases (BMs), as it is unclear whether this combination is effective for this condition. Non-small cell lung cancer patients with BMs receiving ICIs were divided into experimental and control groups based on anti-angiogenic treatment. Progression-free survival (PFS) and overall survival (OS) were evaluated using the Kaplan-Meier method, with log-rank test for comparisons. Prognostic factors were determined by univariate and multivariate Cox regression analyses. The study included 95 patients. The experimental group (n = 42) had a higher disease control rate (DCR) (90.5% vs. 68.6%, p = .009), objective response rate (ORR) (35.7% vs. 24.5%, p = .235), and longer median bone PFS (14.3 months vs. 8.3 months, p = .011) for bone metastasis. However, there were no significant differences in overall DCR (92.8% vs. 86.7%, p = .339), ORR (64.3% vs. 62.3%, p = .839), and PFS (12.4 months vs. 11.6 months, p = 0.383) between the 2 groups. The experimental group had a lower incidence of skeleton-related events (SREs) (28.6% vs. 35.8%, p = .425), and SRE patients had shorter PFS (7.7 vs. 14.3 months, p < .001) and OS (12.1 vs. 19.0 months, p = .028). Anti-angiogenic therapy (HR = 0.55, p = .012) and SRE (HR = 2.93, p < .001) were identified as independent prognostic factors for bone metastatic PFS. Adverse events were slightly higher in the experimental group (29.3% vs. 18.9%, p = .238), but not statistically significant. The combination of ICIs and anti-angiogenic agents leads to a significant PFS for BMs and potentially decreases SRE.

Intermittent hypoxia inhibits anti-tumor immune response via regulating PD-L1 expression in lung cancer cells and tumor-associated macrophages

Accumulating evidence has shown an increased tumor incidence and reduced survival rate in cancer patients with obstructive sleep apnea (OSA). Although intermittent hypoxia is known to play a crucial role, the molecular mechanism by which intermittent hypoxia accelerates lung cancer progression remains to be elucidated.A lung cancer xenograft mouse model was established by subcutaneously injecting LLC cells into C57BL/6 mice. The tumor-bearing mice were exposed to either normoxia or intermittent hypoxia and received either IgG2a, anti-programmed death ligand-1 (PD-L1), PX-478, or anti-PD-L1 + PX-478 treatment.A significant upregulation of tumor associated macrophages (TAMs) papulation and PD-L1 levels was observed in lung adenocarcinoma patients with OSA. We further confirmed that hypoxia-inducible factor-1 alpha (HIF-1α) regulates PD-L1 at transcriptional levels, mainly through binding to the hypoxia response element 4. Using a lung cancer xenograft mouse model, we observed that intermittent hypoxia exposed tumors grew faster and bigger with upregulated HIF-1α and PD-L1 expression, enhanced TAMs and Treg populations, and reduced cytotoxic T cells and cytokine secretion. Finally, we found a combination of PX-478 and anti-PD-L1 exerted an encouraging tumor inhibition effect compared to single treatment. Combination therapies based on HIF-1α and PD-L1 blockade might serve as a promising strategy to treat lung cancer patients with OSA.

A Case Report on Zoledronic Acid as Palliative Treatment in Carcinoma of Lung with Bone Metastasis

Abstract Bone metastasis is a general complication of advanced carcinomas, usually seen in breast cancer, prostate cancer, non-small cell lung cancer and multiple myeloma. Zoledronic acid, which belongs to bisphosphonates, is indicated for bone metastasis caused due to carcinomas. This case report is about a patient with carcinoma of lung (Stage 4) with bone metastasis who has been provided with zoledronic acid as a palliative treatment. The patient was a 65-year-old male with the known case of type 2 diabetes mellitus (T2DM) and ischemic heart disease (IHD) status post (s/p) coronary artery angiography (CAG) with left ventricular ejection fraction (LVEF) 30% who was on medication. He presented with chest pain and breathlessness nine months prior and his multi-detected computed tomography (MDCT) pulmonary angiography revealed bronchogenic carcinoma. A computed tomography (CT) scan showed a metabolically active lytic lesion involving the left posterior 2nd rib and degenerative changes were noted in the spine. He has now reported upper back pain for three months localised to the left and right inferior scapular region. His pain scored a nine on the Visual Analogue Scale (VAS), and he was prescribed zoledronic acid 5mg/100ml IV infusion once in 3 weeks. On a revisit, the patient was assessed to be symptomatically better, and the pain score was lowered to 6, indicating reduced severity of discomfort. In conclusion, zoledronic acid is effective in the palliative treatment of lung cancer patients with bone metastasis.

Clinical evaluation of synthetic computed tomography methods in adaptive proton therapy of lung cancer patients.

Efficient workflows for adaptive proton therapy are of high importance. This study evaluated the possibility to replace repeat-CTs (reCTs) with synthetic CTs (sCTs), created based on cone-beam CTs (CBCTs), for flagging the need of plan adaptations in intensity-modulated proton therapy (IMPT) treatment of lung cancer patients. Forty-two IMPT patients were retrospectively included. For each patient, one CBCT and a same-day reCT were included. Two commercial sCT methods were applied; one based on CBCT number correction (Cor-sCT), and one based on deformable image registration (DIR-sCT). The clinical reCT workflow (deformable contour propagation and robust dose re-computation) was performed on the reCT as well as the two sCTs. The deformed target contours on the reCT/sCTs were checked by radiation oncologists and edited if needed. A dose-volume-histogram triggered plan adaptation method was compared between the reCT and the sCTs; patients needing a plan adaptation on the reCT but not on the sCT were denoted false negatives. As secondary evaluation, dose-volume-histogram comparison and gamma analysis (2%/2mm) were performed between the reCT and sCTs. There were five false negatives, two for Cor-sCT and three for DIR-sCT. However, three of these were only minor, and one was caused by tumour position differences between the reCT and CBCT and not by sCT quality issues. An average gamma pass rate of 93% was obtained for both sCT methods. Both sCT methods were judged to be of clinical quality and valuable for reducing the amount of reCT acquisitions.