Background and objective: Lung cancer remains a leading cause of cancer-related mortality globally, with drug resistance posing a significant challenge to effective treatment. The advent of clustered regularly interspaced short palindromic repeats (CRISPR) and CRISPR-associated protein 9 (CRISPR-Cas9) technology offers a novel and precise gene-editing technology for targeting and negating drug resistance mechanisms in lung cancer. This review summarizes the research progress in the use of CRISPR-Cas9 technology for investigating and managing drug resistance in lung cancer treatment.
Methods: A literature search was conducted using the Web of Science and PubMed databases, with the following keywords: [CRISPR-Cas9], [lung cancer], [drug resistance], [gene editing], and [gene therapy]. The search was limited to articles published in English from 2002 to September 2023. From the search results, studies that utilized CRISPR-Cas9 technology in the context of lung cancer drug resistance were selected for further analysis and summarize.
Key content and findings: CRISPR-Cas9 technology enables precise DNA-sequence editing, allowing for the targeted addition, deletion, or modification of genes. It has been applied to investigate drug resistance in lung cancer by focusing on key genes such as epidermal growth factor receptor (EGFR), Kirsten rat sarcoma viral oncogene homolog (KRAS), tumor protein 53 (TP53), and B-cell lymphoma/leukemia-2 (BCL2), among others. The technology has shown potential in inhibiting tumor growth, repairing mutations, and enhancing the sensitivity of cancer cells to chemotherapy. Additionally, CRISPR-Cas9 has been used to identify novel key genes and molecular mechanisms contributing to drug resistance, offering new avenues for therapeutic intervention. The review also highlights the use of CRISPR-Cas9 in targeting immune escape mechanisms and the development of strategies to improve drug sensitivity.
Conclusions: The CRISPR-Cas9 technology holds great promise for advancing lung cancer treatment, particularly in addressing drug resistance. The ability to precisely target and edit genes involved in resistance pathways offers a powerful tool for developing more effective and personalized therapies. While challenges remain in terms of delivery, safety, and ethical considerations, ongoing research and technological refinements are expected to further enhance the role of CRISPR-Cas9 in improving patient outcomes in lung cancer treatment.
Background: Stereotactic body radiotherapy (SBRT) combined immunotherapy has a synergistic effect on patients with stage IV tumors. However, the efficacy and prognostic factors analysis of SBRT combined immunotherapy for patients with pulmonary oligometastases have rarely been reported in the studies. The purpose of this study is to explore the efficacy and prognostic factors analysis of SBRT combined immunotherapy for patients with oligometastatic lung tumors.
Methods: A retrospective analysis was conducted on 43 patients with advanced tumors who received SBRT combined with immunotherapy for pulmonary oligometastases from October 2018 to October 2021. Local control (LC), progression-free survival (PFS), and overall survival (OS) were assessed using the Kaplan-Meier method. Univariate and multivariate analyses of OS were performed using the Cox regression model, and the P value <0.05 was considered statistically significant. The receiver operating characteristic (ROC) curve of neutrophil-to-lymphocyte ratio (NLR) after SBRT was generated. Spearman correlation analysis was used to determine the relationship of planning target volume (PTV) with absolute lymphocyte count (ALC) before and after SBRT and with neutrophil count (NE) after SBRT. Additionally, linear regression was used to examine the relationship between ALC after SBRT and clinical factors.
Results: A total of 43 patients with pulmonary oligometastases receiving SBRT combined with immunotherapy were included in the study. The change in NLR after SBRT was statistically significant (P<0.001). At 1 and 2 years, respectively, the LC rates were 90.3% and 87.5%, the OS rates were 83.46% and 60.99%, and the PFS rates were 69.92% and 54.25%, with a median PFS of 27.00 (17.84-36.13) months. Univariate and multivariate Cox regression analyses showed that a shorter interval between radiotherapy and immunization [≤21 days; hazard ratio (HR) =1.10, 95% confidence interval (CI): 0.06-0.89; P=0.02] and a low NLR after SBRT (HR =0.24, 95% CI: 1.01-1.9; P=0.03) were associated with improved OS. The ROC curve identified 4.12 as the cutoff value for predicting OS based on NLR after SBRT. NLR after SBRT ≤4.12 significantly extended OS compared to NLR after SBRT >4.12 (log-rank P=0.001). Spearman correlation analysis and linear regression analysis showed that PTV was negatively correlated with ALC after SBRT.
Conclusions: Our preliminary research shows that SBRT combined with immunotherapy has a good effect, and NLR after SBRT is a poor prognostic factor for OS. Larger PTV volume is associated with decreased ALC after SBRT.
Background: The risk and risk factors of extrathoracic metastasis at initial diagnosis in T≤3cmN0 lung cancer patients are not fully understood. We aimed to develop a model to predict the risk of extrathoracic metastasis in those patients.
Methods: Clinicopathological data of patients were collected from Surveillance, Epidemiology, and End Results (SEER) database. Univariable and multivariable analyses using logistic regression were conducted to identify risk factors. A predictive model and corresponding nomogram were developed based on the risk factors. The model was assessed using the area under the receiver operating characteristic curve (AUC), Hosmer-Lemeshow test, and decision curve.
Results: A total of 20,057 T≤3cmN0 patients were enrolled, of whom 251 (1.25%) were diagnosed with extrathoracic metastasis at the initial diagnosis. Aged ≤50 [odds ratio (OR): 2.05, 95% confidence interval (CI): 1.19-3.53, P=0.01] and aged ≥81 [1.65 (1.05-2.58), P=0.03], Hispanic [1.81 (1.20-2.71), P=0.004], location of bronchus [3.18 (1.08-9.35), P=0.04], larger tumor size, pleural invasion, and a history of colorectal cancer [2.01 (1.01-4.00), P=0.046] were independent risk factors. In the training cohort and validation cohort, the AUCs of the developed model were 0.727, 0.728 respectively, and the results of Hosmer-Lemeshow test were P=0.47, P=0.61 respectively. The decision curve showed good clinical meaning of the model.
Conclusions: Extrathoracic metastasis at initial diagnosis in T≤3cmN0 lung cancer patients was not rare. The model based on the risk factors showed good performance in predicting the risk of extrathoracic metastasis.
Background: Early-stage invasive lung adenocarcinoma (ADC) characterized by a predominant micropapillary or solid pattern exhibit an elevated risk of recurrence following sub-lobar resection, thus determining histological subtype of early-stage invasive ADC prior surgery is important for formulating lobectomy or sub-lobar resection. This study aims to develop a deep learning algorithm and assess its clinical capability in distinguishing high-risk or low-risk histologic patterns in early-stage invasive ADC based on preoperative computed tomography (CT) scans.
Methods: Two retrospective cohorts were included: development cohort 1 and external test cohort 2, comprising patients diagnosed with T1 stage invasive ADC. Electronic medical records and CT scans of all patients were documented. Patients were stratified into two risk groups. High-risk group: comprising cases with a micropapillary component ≥5% or a predominant solid pattern. Low-risk group: encompassing cases with a micropapillary component <5% and an absence of a predominant solid pattern. The overall segmentation model was modified based on Mask Region-based Convolutional Neural Network (Mask-RCNN), and Residual Network 50 (ResNet50)_3D was employed for image classification.
Results: A total of 432 patients participated in this study, with 385 cases in cohort 1 and 47 cases in cohort 2. The fine-outline results produced by the auto-segmentation model exhibited a high level of agreement with manual segmentation by human experts, yielding a mean dice coefficient of 0.86 [95% confidence interval (CI): 0.85-0.87] in cohort 1 and 0.84 (95% CI: 0.82-0.85) in cohort 2. Furthermore, the deep learning model effectively differentiated the high-risk group from the low-risk group, achieving an area under the curve (AUC) of 0.89 (95% CI: 0.88-0.90) in cohort 1. In the external validation conducted in cohort 2, the deep learning model displayed an AUC of 0.87 (95% CI: 0.84-0.88) in distinguishing the high-risk group from the low-risk group. The average diagnostic time was 16.00±3.2 seconds, with an accuracy of 0.82 (95% CI: 0.81-0.83).
Conclusions: We have developed a deep learning algorithm, LungPath, for the automated segmentation of pulmonary nodules and prediction of high-risk histological patterns in early-stage lung ADC based on CT scans.
Background and objective: Lung cancer stands as the main cause of cancer-related deaths worldwide. With the advent of immunotherapy and the discovery of targetable oncogenic driver genes, although prognosis has changed in the last few years, survival rates remain dismal for most patients. This emphasizes the urgent need for new strategies that could enhance treatment in precision medicine. The role of the microbiota in carcinogenesis constitutes an evolving landscape of which little is known. It has been suggested these microorganisms may influence in responses, resistance, and adverse effects to cancer treatments, particularly to immune checkpoint blockers. However, evidence on the impact of microbiota composition in oncogene-addicted tumors is lacking. This review aims to provide an overview of the relationship between microbiota, daily habits, the immune system, and oncogene-addicted tumors, focusing on lung cancer.
Methods: A PubMed and Google Scholar search from 2013 to 2024 was conducted. Relevant articles were reviewed in order to guide our research and generate hypothesis of clinical applicability.
Key content and findings: Microbiota is recognized to participate in immune reprogramming, fostering inflammatory, immunosuppressive, or anti-tumor responses. Therefore, identifying the microbiota that impact response to treatment and modulating its composition by interventions such as dietary modifications, probiotics or antibiotics, could potentially yield better outcomes for cancer patients. Additionally, targeted therapies that modulate molecular signaling pathways may impact both immunity and microbiota. Understanding this intricate interplay could unveil new therapeutic strategies.
Conclusions: By comprehending how microbiota may influence efficacy of targeted therapies, even though current evidence is scarce, we may generate interesting hypotheses that could improve clinical practice.
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