Clinical Management Of Lung Cancer Research Articles

A novel therapeutic outlook: Classification, applications and challenges of inhalable micron/nanoparticle drug delivery systems in lung cancer (Review).

Lung cancer represents a marked global public health concern. Despite existing treatment modalities, the average 5‑year survival rate for patients with patients with lung cancer is only ~20%. As there are numerous adverse effects of systemic administration routes, there is an urgent need to develop a novel therapeutic strategy tailored specifically for patients with lung cancer. Non‑invasive aerosol inhalation, as a route of drug administration, holds unique advantages in the context of respiratory diseases. Nanoscale materials have extensive applications in the field of biomedical research in recent years. The present study provides a comprehensive review of the classification, applications summarized according to existing clinical treatment modalities for lung cancer and challenges associated with inhalable micron/nanoparticle drug delivery systems (DDSs) in lung cancer. Achieving localized treatment of lung cancer preclinical models through inhalation is deemed feasible. However, further research is required to substantiate the efficacy and long‑term safety of inhalable micron/nanoparticle DDSs in the clinical management of lung cancer.

A novel feature selection algorithm for identifying hub genes in lung cancer

Lung cancer, a life-threatening disease primarily affecting lung tissue, remains a significant contributor to mortality in both developed and developing nations. Accurate biomarker identification is imperative for effective cancer diagnosis and therapeutic strategies. This study introduces the Voting-Based Enhanced Binary Ebola Optimization Search Algorithm (VBEOSA), an innovative ensemble-based approach combining binary optimization and the Ebola optimization search algorithm. VBEOSA harnesses the collective power of the state-of-the-art classification models through soft voting. Moreover, our research applies VBEOSA to an extensive lung cancer gene expression dataset obtained from TCGA, following essential preprocessing steps including outlier detection and removal, data normalization, and filtration. VBEOSA aids in feature selection, leading to the discovery of key hub genes closely associated with lung cancer, validated through comprehensive protein–protein interaction analysis. Notably, our investigation reveals ten significant hub genes—ADRB2, ACTB, ARRB2, GNGT2, ADRB1, ACTG1, ACACA, ATP5A1, ADCY9, and ADRA1B—each demonstrating substantial involvement in the domain of lung cancer. Furthermore, our pathway analysis sheds light on the prominence of strategic pathways such as salivary secretion and the calcium signaling pathway, providing invaluable insights into the intricate molecular mechanisms underpinning lung cancer. We also utilize the weighted gene co-expression network analysis (WGCNA) method to identify gene modules exhibiting strong correlations with clinical attributes associated with lung cancer. Our findings underscore the efficacy of VBEOSA in feature selection and offer profound insights into the multifaceted molecular landscape of lung cancer. Finally, we are confident that this research has the potential to improve diagnostic capabilities and further enrich our understanding of the disease, thus setting the stage for future advancements in the clinical management of lung cancer. The VBEOSA source codes is publicly available at https://github.com/TEHNAN/VBEOSA-A-Novel-Feature-Selection-Algorithm-for-Identifying-hub-Genes-in-Lung-Cancer.

Analysis of survival benefit from primary tumor resection and individualized prediction of overall survival for lung cancer patients with bone metastasis: Worth it or not?

BackgroundThe clinical management of lung cancer (LC) patients with bone metastasis (BM) is still a significant challenge. This study aimed to explore the role of primary tumor resection (PTR) on survival outcome of LC patients with BM and to develop two web-based nomograms for predicting the overall survival (OS) of LC patients with BM who received PTR and those who did not. MethodsWe enrolled LC patients with BM from the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2015. Propensity score matching (PSM) was then conducted to balance the baseline characteristics of covariates between patients in surgery and non-surgery groups. Next, Kaplan–Meier analysis with log-rank test was performed to evaluate the survival benefit of PTR before and after PSM methods and to explore the impact of surgical resection extent on the prognosis of LC patients with BM and clinical outcomes in patients with different metastatic patterns. Cox proportional hazard regression analysis was then applied to determine the independent prognostic factors for OS of patients receiving PTR and did not receiving PTR, respectively. Subsequently, we constructed two individualized nomograms for predicting the 12-, 18- and 24-months OS. Finally, receiver operating characteristic (ROC) curve, calibration curve and decision curve analysis (DCA) were generated to evaluate discrimination, accuracy and clinical utility of the nomograms. ResultsA total of 7747 eligible patients were included in this analysis. The survival analysis revealed that PTR was closely associated with better survival outcome among LC patients with BM(P < 0.05), while the survival benefit of PTR was suboptimal in patients presented with multiple metastases(P > 0.05). Besides, lobectomy shows best survival benefit. Two nomograms were then constructed based on independent prognostic factors of patients in the surgery group and the non-surgery group. The ROC curves showed good discrimination of the two nomograms, with the area under curve (AUC) of each time point being higher than 0.7 in both the training set and testing set. The calibration curves also demonstrated satisfactory consistency between actual survival and nomogram-predicted OS of both nomograms. The DCA showed high benefit of nomogram in a clinical context. Moreover, the study population was stratified into three groups based on the scores of the nomogram, and the survival analysis showed that this prognostic stratification was statistically significant (p < 0.05). ConclusionsThis study showed that surgical resection of the primary site strategy can prolong survival of LC patients with BM to some extent, depending on different sites of metastasis and highly selected patients. Furthermore, the web-based nomograms showed significant accuracy in predicting OS for patients with or without surgery, which may provide valuable insights for patients’ counseling and individualized decision-making for clinicians.

TRNA-derived fragments: mechanism of gene regulation and clinical application in lung cancer.

Lung cancer, being the most widespread and lethal form of cancer globally, has a high incidence and mortality rate primarily attributed to challenges associated with early detection, extensive metastasis, and frequent recurrence. In the context of lung cancer development, noncoding RNA molecules have a crucial role in governing gene expression and protein synthesis. Specifically, tRNA-derived fragments (tRFs), a subset of noncoding RNAs, exert significant biological influences on cancer progression, encompassing transcription and translation processes as well as epigenetic regulation. This article primarily examines the mechanisms by which tRFs modulate gene expression and contribute to tumorigenesis in lung cancer. Furthermore, we provide a comprehensive overview of the current bioinformatics analysis of tRFs in lung cancer, with the objective of offering a systematic and efficient approach for studying the expression profiling, functional enrichment, and molecular mechanisms of tRFs in this disease. Finally, we discuss the clinical significance and potential avenues for future research on tRFs in lung cancer. This paper presents a comprehensive systematic review of the existing research findings on tRFs in lung cancer, aiming to offer improved biomarkers and drug targets for clinical management of lung cancer.

Chinese Medical Association guideline for clinical diagnosis and treatment of lung cancer (2023 edition)

To standardize the prevention and clinical management of lung cancer, improve patients' survival outcomes, and offer professional insight for clinicians, the Oncology Society of Chinese Medical Association has summoned experts from departments of pulmonary medicine, oncology, thoracic surgery, radiotherapy, imaging, and pathology to formulate the Oncology Society of Chinese Medical Association guideline for clinical diagnosis and treatment of lung cancer in China (2023 edition) through consensus meetings. Updates in this edition include 1) cancer screening: deletion of high-risk traits of lung cancer based on epidemiological investigations in the Caucasian population, while preserving features confirmed by research on the Chinese population. Advice on screening institutions is also added to raise awareness of the merits and demerits of lung cancer screening through detailed illustrations. 2) Principles of histopathologic evaluation: characteristics of four types of neuroendocrine tumors (typical carcinoid, atypical carcinoid, large cell carcinoma, and small cell carcinoma) are reviewed. 3) Surgical intervention: more options of resection are available for certain peripheral lesions based on several clinical studies (CALGB140503, JCOG0802, JCOG1211). 4) neoadjuvant/adjuvant therapy: marked improvement in the prognosis of non-small cell lung cancer (NSCLC) patients receiving neoadjuvant immunotherapy are reviewed; more options for consolidation immunotherapy after radiochemotherapy have also emerged. 5) Targeted and immune therapy: tyrosine kinase inhibitors of sensitive driver mutations such as EGFR and ALK as well as rare targets such as MET exon 14 skipping, RET fusion, ROS1 fusion, and NTRK fusion have been approved, offering more treatment options for clinicians and patients. Furthermore, multiple immune checkpoint inhibitors have been granted for the treatment of NSCLC and SCLC, resulting in prolonged survival of late-stage lung cancer patients. This guideline is established based on the current availability of domestically approved medications, recommendations of international guidelines, and present clinical practice in China as well as integration of the latest medical evidence of pathology, genetic testing, immune molecular biomarker detection, and treatment methods of lung cancer in recent years, to provide recommendations for professionals in clinical oncology, radiology, laboratory, and rehabilitation.

Chinese Medical Association guideline for clinical diagnosis and treatment of lung cancer (2023 edition)

To standardize the prevention and clinical management of lung cancer, improve patients' survival outcomes, and offer professional insight for clinicians, the Oncology Society of Chinese Medical Association has summoned experts from departments of pulmonary medicine, oncology, thoracic surgery, radiotherapy, imaging, and pathology to formulate the Chinese Medical Association Guideline for Clinical Diagnosis and Treatment of Lung Cancer in China (2023 edition) through consensus meetings. Updates in this edition include 1) cancer screening: deletion of high-risk traits of lung cancer based on epidemiological investigations in the Caucasian population, while preserving features confirmed by research on the Chinese population. Advice on screening institutions is also added to raise awareness of the merits and demerits of lung cancer screening through detailed illustrations. 2) Principles of histopathologic evaluation: characteristics of four types of neuroendocrine tumors (typical carcinoid, atypical carcinoid, large cell carcinoma, and small cell carcinoma) are reviewed. 3) Surgical intervention: more options of resection are available for certain peripheral lesions based on several clinical studies (CALGB140503, JCOG0802, JCOG1211). 4) neoadjuvant/adjuvant therapy: marked improvement in the prognosis of non-small cell lung cancer (NSCLC) patients receiving neoadjuvant immunotherapy are reviewed; more options for consolidation immunotherapy after radiochemotherapy have also emerged. 5) Targeted and immune therapy: tyrosine kinase inhibitors of sensitive driver mutations such as EGFR and ALK as well as rare targets such as MET exon 14 skipping, RET fusion, ROS1 fusion, and NTRK fusion have been approved, offering more treatment options for clinicians and patients. Furthermore, multiple immune checkpoint inhibitors have been granted for the treatment of NSCLC and SCLC, resulting in prolonged survival of late-stage lung cancer patients. This guideline is established based on the current availability of domestically approved medications, recommendations of international guidelines, and present clinical practice in China as well as integration of the latest medical evidence of pathology, genetic testing, immune molecular biomarker detection, and treatment methods of lung cancer in recent years, to provide recommendations for professionals in clinical oncology, radiology, laboratory, and rehabilitation.

Developing a Pan-European Technical Standard for a Comprehensive High-quality Lung Cancer CT Screening Program. An ERS Technical Standard.

A collaborative group was convened to include members of multiple European societies (see below). Topics were identified during a scoping review and a systematic review of the literature was conducted. Full text was provided to members of the group for each topic. The final document was approved by all members and the ERS Scientific Advisory Committee. Ten topics were identified representing key components of a screening program. The action on findings from the LDCT were not included as they are addressed by separate international guidelines (nodule management and clinical management of lung cancer) and by a linked taskforce (incidental findings). Other than smoking cessation, other interventions that are not part of the core screening process were not included (e.g. pulmonary function measurement). Fifty-three statements were produced and areas for further research identified. This European collaborative group has produced a technical standard that is a timely contribution to implementation of LCS. It will serve as a standard that can be used, as recommended by the European Council, to ensure a high quality and effective program.

Recent advances in the electrochemical sensing of lung cancer biomarkers

In the current scenario, about 25% of all cancer deaths reported globally are related to lung cancer. Despite the development of different diagnosing techniques, including X-ray, magnetic resonance imaging, biopsy, etc., there is still a significant challenge in dealing with lung cancer mortality and morbidity. Therefore, the early diagnosis and treatment of lung cancer is essential and holds a considerable place in lung cancer research. Biomarkers, an indicator, are overexpressed in the malignant tissues and are found in the body fluids. The existence of these biomolecules beyond the cut-off level can be used as an indicator for sensing tumour markers in the diagnosis, prognosis, and clinical management of lung cancer. Hence, fast and precise detection of biomarkers is helpful in the early detection of cancer. Moreover, the redox materials used for sensing biomarkers play a critical role in the sensitivity of electroanalytical devices. Therefore, we critically review the early diagnosis of lung cancer using an electrochemical biosensor. First, the importance of biomarkers and their application in determining various stages are discussed. A detailed section is devoted to recent electrochemical electrode materials, including graphene (Gr), carbon nanotube (CNT), and metal-based nanomaterials. Finally, the limitations and future prospects of electrochemical sensors are elaborated.

Blood-based liquid biopsy: Insights into early detection and clinical management of lung cancer.

Currently, early detection of lung cancer relies on the characterisation of images generated from computed tomography (CT). However, lung tissue biopsy, a highly invasive surgical procedure, is required to confirm CT-derived diagnostic results with very high false-positive rates. Hence, a non-invasive or minimally invasive biomarkers is essential to complement the existing low-dose CT (LDCT) for early detection, improve responses to a certain treatment, predict cancer recurrence, and to evaluate prognosis. In the past decade, liquid biopsies (e.g., blood) have been demonstrated to be highly effective for lung cancer biomarker discovery. In this review, the roles of emerging liquid biopsy-derived biomarkers such as circulating nucleic acids, circulating tumour cells (CTCs), long non-coding RNA (lncRNA), and microRNA (miRNA), as well as exosomes, have been highlighted. The advantages and limitations of these blood-based minimally invasive biomarkers have been discussed. Furthermore, the current progress of the identified biomarkers for clinical management of lung cancer has been summarised. Finally, a potential strategy for the early detection of lung cancer, using a combination of LDCT scans and well-validated biomarkers, has been discussed.

Genotoxic Treatment Enhances Immune Response in a Genetic Model of Lung Cancer.

Simple SummaryImmunotherapy has yielded exciting results against lung cancer, but its efficacy is limited to a small percentage of patients, highlighting the necessity to develop new experimental approaches. The currently available models for pre-clinical studies fail to reproduce the biological features of human cancers. Genetically engineered murine models (GEMMs) are driven by key mutations identified in patients, but they do not recapitulate the complex mutational landscape of human cancers, thus failing to activate the immune system appropriately. On the other side, carcinogen-induced models have appropriate mutational burden, but they require much longer experimental times and have inconsistency of results. We developed a hybrid model in which lung tumors are driven by genetically engineered oncogenic mutations in mice, with increased mutational load induced by in vivo treatment with a carcinogen. This model more closely mimics the complexity of human lung cancer and is suitable for pre-clinical immunotherapy studies.Recent advances in immunotherapy have reshaped the clinical management of lung cancer, and immune checkpoint inhibitors (ICIs) are now first-line treatment for advanced lung cancer. However, the majority of patients do not respond to ICIs as single agents, and many develop resistance after initial responses. Therefore, there is urgent need to improve the current ICI strategies. Murine models currently available for pre-clinical studies have serious limitations for evaluating novel immunotherapies. GEMMs are reliable and predictable models driven by oncogenic mutations mirroring those found in cancer patients. However, they lack the mutational burden of human cancers and thus do not elicit proper immune surveillance. Carcinogen-induced models are characterized by mutational burden that more closely resembles human cancer, but they often require extremely long experimental times with inconsistent results. Here, we present a hybrid model in which genetically engineered mice are exposed to the carcinogen N-Methyl-N-Nitrosourea (MNU) to increase tumor mutational burden (TMB), induce early-stage immune responses, and enhance susceptibility to ICIs. We anticipate that this model will be useful for pre-clinical evaluation of novel immunotherapies.

Clinicopathological models for predicting lymph node metastasis in patients with early-stage lung adenocarcinoma: the application of machine learning algorithms.

BackgroundLymph node metastasis (LNM) status can be a critical decisive factor for clinical management of lung cancer. Accurately evaluating the risk of LNM during or after the surgery can be helpful for making clinical decisions. This study aims to incorporate clinicopathological characteristics to develop reliable machine learning (ML)-based models for predicting LNM in patients with early-stage lung adenocarcinoma.MethodsA total of 709 lung adenocarcinoma patients with tumor size ≤2 cm were enrolled for analysis and modeling by multiple ML algorithms. The receiver operating characteristic (ROC) curve and decision curve were used for evaluating model’s predictive performance and clinical usefulness. Feature selection based on potential models was performed to identify most-contributed predictive factors.ResultsLNM occurred in 11.3% (80/709) of patients with lung adenocarcinoma. Most models reached high areas under the ROC curve (AUCs) >0.9. In the decision curve, all models performed better than the treat-all and treat-none lines. The random forest classifier (RFC) model, with a minimal number of five variables introduced (including carcinoembryonic antigen, solid component, micropapillary component, lymphovascular invasion and pleural invasion), was identified as the optimal model for predicting LNM, because of its excellent performance in both ROC and decision curves.ConclusionsThe cost-efficient application of RFC model could precisely predict LNM during or after the operation of early-stage adenocarcinomas (sensitivity: 87.5%; specificity: 82.2%). Incorporating clinicopathological characteristics, it is feasible to predict LNM intraoperatively or postoperatively by ML algorithms.

The function of LncRNAs and their role in the prediction, diagnosis, and prognosis of lung cancer.

Lung cancer remains a major threat to human health. Low dose CT scan (LDCT) has become the main method of early screening for lung cancer due to the low sensitivity of chest X‐ray. However, LDCT not only has a high false positive rate, but also entails risks of overdiagnosis and cumulative radiation exposure. In addition, cumulative radiation by LDCT screening and subsequent follow‐up can increase the risk of lung cancer. Many studies have shown that long noncoding RNAs (lncRNAs) remain stable in blood, and profiling of blood has the advantages of being noninvasive, readily accessible and inexpensive. Serum or plasma assay of lncRNAs in blood can be used as a novel detection method to assist LDCT while improving the accuracy of early lung cancer screening. LncRNAs can participate in the regulation of various biological processes. A large number of researches have reported that lncRNAs are key regulators involved in the progression of human cancers through multiple action models. Especially, some lncRNAs can affect various hallmarks of lung cancer. In addition to their diagnostic value, lncRNAs also possess promising potential in other clinical applications toward lung cancer. LncRNAs can be used as predictive markers for chemosensitivity, radiosensitivity, and sensitivity to epidermal growth factor receptor (EGFR)‐targeted therapy, and as well markers of prognosis. Different lncRNAs have been implicated to regulate chemosensitivity, radiosensitivity, and sensitivity to EGFR‐targeted therapy through diverse mechanisms. Although many challenges need to be addressed in the future, lncRNAs have bright prospects as an adjunct to radiographic methods in the clinical management of lung cancer.

The Role of the Liquid Biopsy in Decision-Making for Patients with Non-Small Cell Lung Cancer.

Liquid biopsy is a rapidly emerging tool of precision oncology enabling minimally invasive molecular diagnostics and longitudinal monitoring of treatment response. For the clinical management of advanced stage lung cancer patients, detection and quantification of circulating tumor DNA (ctDNA) is now widely adopted into clinical practice. Still, interpretation of results and validation of ctDNA-based treatment decisions remain challenging. We report here our experience implementing liquid biopsies into the clinical management of lung cancer. We discuss advantages and limitations of distinct ctDNA assay techniques and highlight our approach to the analysis of recurrent molecular alterations found in lung cancer. Moreover, we report three exemplary clinical cases illustrating the complexity of interpreting liquid biopsy results in clinical practice. These cases underscore the potential and current limitations of liquid biopsy, focusing on the difficulty of interpreting discordant findings. In our view, despite all current limitations, the analysis of ctDNA in lung cancer patients is an essential and highly versatile complementary diagnostic tool for the clinical management of lung cancer patients in the era of precision oncology.

A New Automated Approach for Early Lung Cancer Detection with Improved Diagnostic Performance – A Preliminary Analysis

Lung cancer is becoming the major cause of cancer-related deaths in human worldwide and Saudi Arabia is not an exception. Therein the identification of potentially malignant lung nodules is essential for the diagnosis and clinical management of lung cancer. Unfortunately, in clinical practice, however, interpretation of Computed Tomography (CT) images is challenging for radiologists due to the large number of cases. It is therefore extremely important task to develop computer aided diagnosis (CAD) systems that can aid and enhance the radiologist to potentially reduce false positive (FP) findings. Even though numerous methodologies are proposed for CAD system in the literatures, the one proposed in this work will definitely stand out in improving the sensitivity and specificity for the detection of small nodules particularly in low dose CT images. This work attempts to employ the powerful tool, radiomics quantitative imaging features within curvelet domain to detect and characterize lung nodules with improved sensitivity and specificity. Subsequently, support vector machine (SVM) is used to learn the 2D stochastic and 3D anatomic features of curvelet coefficients and classifies suspected regions either as malignant or benign based on geometric, texture and intensity. A preliminary analysis of the proposed methodology is presented and compared against the metrics, sensitivity, specificity and accuracy on publicly available LIDC database to serve as a benchmark for future research efforts.

Clinical Utility of Targeted Sequencing in Lung Cancer: Experience From an Autonomous Swedish Health Care Center

ObjectivesMutation analysis by massive parallel sequencing (MPS) is routinely performed in the clinical management of lung cancer in Sweden. We describe the clinical and mutational profiles of lung cancer patients subjected to the first 1.5 years of treatment predictive MPS testing in an autonomous regional health care region. MethodsTumors from all patients with lung cancer who had an MPS test from January 2015 to June 2016 in the Skåne health care region in Sweden (1.3 million citizens) were included. Six hundred eleven tumors from 599 patients were profiled using targeted sequencing with a 26-gene exon-focused panel. Data on disease patterns and characteristics of the patients subjected to testing were assembled, and correlations between mutational profiles and clinical features were analyzed. ResultsMPS with the 26-gene panel revealed alterations in 92% of the 611 lung tumors, with the most frequent mutations detected in the nontargetable genes TP53 (62%) and KRAS (37%). Neither KRAS nor TP53 mutations were associated with disease pattern, chemotherapy response, progression-free survival, or overall survival in advanced-stage disease treated with platinum-based doublet chemotherapy as a first-line treatment. Among targetable genes, EGFR driver mutations were detected in 10% of the tumors, and BRAF p.V600 variants in 2.3%. For the 71 never smokers (12%), targetable alterations (EGFR mutations, BRAF p.V600, MET exon 14 skipping, or ALK/ROS1 rearrangement) were detected in 59% of the tumors. ConclusionAlthough the increasing importance of MPS as a predictor of response to targeted therapies is indisputable, its role in prognostics or as a predictor of clinical course in nontargetable advanced stage lung cancer requires further investigation.

Overview of Computer Aided Detection and Computer Aided Diagnosis Systems for Lung Nodule Detection in Computed Tomography.

Lung cancer has become a major cause of cancer-related deaths. Detection of potentially malignant lung nodules is essential for the early diagnosis and clinical management of lung cancer. In clinical practice, the interpretation of Computed Tomography (CT) images is challenging for radiologists due to a large number of cases. There is a high rate of false positives in the manual findings. Computer aided detection system (CAD) and computer aided diagnosis systems (CADx) enhance the radiologists in accurately delineating the lung nodules. The objective is to analyze CAD and CADx systems for lung nodule detection. It is necessary to review the various techniques followed in CAD and CADx systems proposed and implemented by various research persons. This study aims at analyzing the recent application of various concepts in computer science to each stage of CAD and CADx. This review paper is special in its own kind because it analyses the various techniques proposed by different eminent researchers in noise removal, contrast enhancement, thorax removal, lung segmentation, bone suppression, segmentation of trachea, classification of nodule and nonnodule and final classification of benign and malignant nodules. A comparison of the performance of different techniques implemented by various researchers for the classification of nodule and non-nodule has been tabulated in the paper. The findings of this review paper will definitely prove to be useful to the research community working on automation of lung nodule detection.

SIRT1 deacetylated and stabilized XRCC1 to promote chemoresistance in lung cancer

Chemoresistance is one of the most important challenges in the clinical management of lung cancer. SIRT1 is a NAD dependent protein deacetylase and implicated in diverse cellular processes such as DNA damage repair, and cancer progression. SIRT1 is upregulated in chemoresistant lung cancer cells, genetic knockdown or chemical inhibition of SIRT1 reversed chemoresistance by enhancing DNA damage and apoptosis activation, accompanied with XRCC1 degradation. E3 ligase β-TrCP catalyzed the poly-ubiquitination of XRCC1 to promote its proteasome-dependent degradation. SIRT1 bound and deacetylated XRCC1 at lysine K260, K298 and K431, preventing it from β-TrCP-dependent ubiquitination. Mutations of these three lysine sites in XRCC1 abrogated the interaction with β-TrCP and prolonged the half-life of XRCC1 protein. Here, we describes SIRT1 confers chemoresistance to lung cancer cells by deacetylating and stabilizing XRCC1. Therefore, targeting SIRT1 might be a new strategy to manage the chemoresistance of lung cancer, and probably other cancers.

Lung Nodule Detection in Computed Tomography: A Review

Lung cancer is becoming the major cause of cancerrelated deaths in human worldwide. Detection of potentially malignant lung nodules is essential for the diagnosis and clinical management of lung cancer. In clinical practice, interpretation of computed tomography (CT) images is challenging for radiologists due to the large number of cases. There are higher rates of false positives in the manual findings. Computer-aided detection (CADe) and computer-aided diagnosis (CADx) systems enhance the radiologists in accurately delineating the lung nodules. It is extremely important task to analyze CADe and CADx for lung nodule detection. Therefore, it is necessary to review the various techniques in different CADe and CADx proposed and implemented by various researchers. This study aims at analyzing the recent application of various concepts in computer science to each stage of CADe and CADx. This review paper is special in its own kind because it analyses the various techniques proposed by different eminent researchers in noise removal, contrast enhancement, thorax removal, lung segmentation, bone suppression, segmentation of trachea, classification of nodule and non-nodule and finally classification of benign and malignant nodules. A comparison of performance of different techniques implemented by various researchers for the classification of nodule and non-nodule has been specified in the paper. The findings of this review paper will definitely prove to be useful to the research community working on automation of lung nodule detection.

Prognostic and predictive biomarkers post curative intent therapy

Large-scale screening trials have demonstrated that early diagnosis of lung cancer results in a significant reduction in lung cancer mortality. Despite improvements in detecting more lung cancers at early stages, the 5-year survival rates of lung cancers diagnosed before widespread disease is only 30-50%. High rates of recurrence, despite early diagnosis, suggest the need to improve treatment strategies based on the likelihood of recurrence in patient subsets, as well as explore the role of predictive markers for therapy selection in the adjuvant setting. In the era of personalized medicine, there have been a wide array of molecular alterations and signatures studied for their potential prognostic and predictive utility, however most have failed to translate into clinical tools. This review will discuss progress made in clinical management of lung cancer, and recent progress in the development of patient selection tools for the refinement of early stage lung cancers.

Expanded molecular interrogation for potential actionable targets in non-squamous non-small cell lung cancer.

The advent of targeted therapies has established new standards of care for defined molecular subsets of non-small cell lung cancer (NSCLC). Not only has this led to significant changes in the routine clinical management of lung cancer e.g., multiplexed genomic testing, but it has provided important principles and benchmarks for determining "actionability". At present, the clinical paradigms are most evolved for EGFR mutations and ALK rearrangements, where multiple randomized phase III trials have determined optimal treatment strategies in both treatment naïve and resistant settings. However, this may not always be feasible with low prevalence alterations e.g., ROS1 and BRAF mutations. Another emerging observation is that not all targets are equally "actionable", necessitating a rigorous preclinical, clinical and translational framework to prosecute new targets and drug candidates. In this review, we will cover the role of targeted therapies for NSCLC harbouring BRAF, MET, HER2 and RET alterations, all of which have shown promise in non-squamous non-small cell lung cancer (ns-NSCLC). We further review some early epigenetic targets in NSCLC, an area of emerging interest. With increased molecular segmentation of lung cancer, we discuss the upcoming challenges in drug development and implementation of precision oncology approaches, especially in light of the complex and rapidly evolving therapeutic landscape.