A consistent two-mutation model of lung cancer for different data sets of radon-exposed rats

IntroductionThe International Cancer Benchmarking Partnership (ICBP) identified significant international differences in lung cancer survival. Differing levels of comorbid disease across ICBP countries has been suggested as a potential explanation of this variation but, to date, no studies have quantified its impact. This study investigated whether comparable, robust comorbidity scores can be derived from the different routine population-based cancer data sets available in the ICBP jurisdictions and, if so, use them to quantify international variation in comorbidity and determine its influence on outcome.MethodsLinked population-based lung cancer registry and hospital discharge data sets were acquired from nine ICBP jurisdictions in Australia, Canada, Norway and the UK providing a study population of 233 981 individuals. For each person in this cohort Charlson, Elixhauser and inpatient bed day Comorbidity Scores were derived relating to the 4–36 months prior to their lung cancer diagnosis. The scores were then compared to assess their validity and feasibility of use in international survival comparisons.ResultsIt was feasible to generate the three comorbidity scores for each jurisdiction, which were found to have good content, face and concurrent validity. Predictive validity was limited and there was evidence that the reliability was questionable.ConclusionThe results presented here indicate that interjurisdictional comparability of recorded comorbidity was limited due to probable differences in coding and hospital admission practices in each area. Before the contribution of comorbidity on international differences in cancer survival can be investigated an internationally harmonised comorbidity index is required.

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Aldo-keto reductases are biomarkers of NRF2 activity and are co-ordinately overexpressed in non-small cell lung cancer

British Journal of Cancer ◽

10.1038/bjc.2016.363 ◽

2016 ◽

Vol 115 (12) ◽

pp. 1530-1539 ◽

Cited By ~ 19

Author(s):

A Kenneth MacLeod ◽

Lourdes Acosta-Jimenez ◽

Philip J Coates ◽

Michael McMahon ◽

Frank A Carey ◽

...

Keyword(s):

Lung Cancer ◽

Cell Lines ◽

Mutant Cell ◽

Immunohistochemical Analysis ◽

Lung Tumour ◽

Data Sets ◽

Related Factor ◽

Nrf2 Pathway ◽

Mutational Status ◽

Somatic Alterations

Abstract Background: Although the nuclear factor-erythroid 2-related factor 2 (NRF2) pathway is one of the most frequently dysregulated in cancer, it is not clear whether mutational status is a good predictor of NRF2 activity. Here we utilise four members of the aldo-keto reductase (AKR) superfamily as biomarkers to address this question. Methods: Twenty-three cell lines of diverse origin and NRF2-pathway mutational status were used to determine the relationship between AKR expression and NRF2 activity. AKR expression was evaluated in lung cancer biopsies and Cancer Genome Atlas (TCGA) and Oncomine data sets. Results: AKRs were expressed at a high basal level in cell lines carrying mutations in the NRF2 pathway. In non-mutant cell lines, co-ordinate induction of AKRs was consistently observed following activation of NRF2. Immunohistochemical analysis of lung tumour biopsies and interrogation of TCGA data revealed that AKRs are enriched in both squamous cell carcinomas (SCCs) and adenocarcinomas that contain somatic alterations in the NRF2 pathway but, in the case of SCC, AKRs were also enriched in most other tumours. Conclusions: An AKR biomarker panel can be used to determine NRF2 status in tumours. Hyperactivation of the NRF2 pathway is far more prevalent in lung SCC than previously predicted by genomic analyses.

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Application of Machine Learning for Prediction of Lung Cancer using Omics Data

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.f3625.049620 ◽

2020 ◽

Vol 9 (6) ◽

pp. 230-236

Keyword(s):

Machine Learning ◽

Lung Cancer ◽

Data Mining ◽

Survival Analysis ◽

Early Stage ◽

Image Data ◽

Machine Learning Algorithms ◽

Machine Learning Techniques ◽

Data Sets ◽

Omics Data

Cancer is one of the deadly diseases across many countries. However, cancer can be cured, if detected at an early stage. Researchers are working on healthcare for early detection and prevention of cancer. Medical data has reached its utmost potential by providing researchers with huge data sets collected from all over the globe. In the present scenario, Machine Learning has been widely used in the area of cancer diagnosis and prognosis. Survival analysis may help in the prediction of the early onset of disease, relapse, re-occurrence of diseases and biomarker identification. Applications of machine learning and data mining methods in medical field are currently the most widespread in cancer detection and survival analysis. In this survey, different ways to detect and predict lung cancer using latest Machine learning algorithms combined with data mining has been analyzed. Comparative study of various machine learning techniques and technologies has been done over different types of data such as clinical data, omics data, image data etc.

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Lysocardiolipin acyltransferase regulates NSCLC cell proliferation and migration by modulating mitochondrial dynamics

Journal of Biological Chemistry ◽

10.1074/jbc.ra120.012680 ◽

2020 ◽

Vol 295 (38) ◽

pp. 13393-13406

Author(s):

Long Shuang Huang ◽

Sainath R. Kotha ◽

Sreedevi Avasarala ◽

Michelle VanScoyk ◽

Robert A. Winn ◽

...

Keyword(s):

Lung Cancer ◽

Cell Proliferation ◽

Cell Lines ◽

Mitochondrial Dynamics ◽

Nsclc Cell ◽

Human Lung Cancer ◽

Data Sets ◽

Cancer Data ◽

And Migration

Lysocardiolipin acyltransferase (LYCAT), a cardiolipin (CL)-remodeling enzyme, is crucial for maintaining normal mitochondrial function and vascular development. Despite the well-characterized role for LYCAT in the regulation of mitochondrial dynamics, its involvement in lung cancer, if any, remains incompletely understood. In this study, in silico analysis of TCGA lung cancer data sets revealed a significant increase in LYCAT expression, which was later corroborated in human lung cancer tissues and immortalized lung cancer cell lines via indirect immunofluorescence and immunoblotting, respectively. Stable knockdown of LYCAT in NSCLC cell lines not only reduced CL and increased monolyso-CL levels but also reduced in vivo tumor growth, as determined by xenograft studies in athymic nude mice. Furthermore, blocking LYCAT activity using a LYCAT mimetic peptide attenuated cell migration, suggesting a novel role for LYCAT activity in promoting NSCLC. Mechanistically, the pro-proliferative effects of LYCAT were mediated by an increase in mitochondrial fusion and a G1/S cell cycle transition, both of which are linked to increased cell proliferation. Taken together, these results demonstrate a novel role for LYCAT in promoting NSCLC and suggest that targeting LYCAT expression or activity in NSCLC may provide new avenues for the therapeutic treatment of lung cancer.

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Integration of protein phosphorylation, acetylation, and methylation data sets to outline lung cancer signaling networks

Science Signaling ◽

10.1126/scisignal.aaq1087 ◽

2018 ◽

Vol 11 (531) ◽

pp. eaaq1087 ◽

Cited By ~ 21

Author(s):

Mark Grimes ◽

Benjamin Hall ◽

Lauren Foltz ◽

Tyler Levy ◽

Klarisa Rikova ◽

...

Keyword(s):

Lung Cancer ◽

Protein Phosphorylation ◽

Signaling Networks ◽

Data Sets ◽

Methylation Data ◽

Cancer Signaling

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SU-FF-T-573: Tracking Versus Gating for Lung Cancer Radiotherapy. A Retrospective Study Involving 5 Patient 4DCT Data Sets

Medical Physics ◽

10.1118/1.3182071 ◽

2009 ◽

Vol 36 (6Part18) ◽

pp. 2656-2656

Author(s):

Teboh Roland ◽

C Shi ◽

Yaxi Liu ◽

S Stathakis ◽

P Mavroidis ◽

...

Keyword(s):

Lung Cancer ◽

Retrospective Study ◽

Data Sets ◽

Cancer Radiotherapy

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Daily set up variation in patients receiving radiation with tomotherapy for lung tumors

Journal of Clinical Oncology ◽

10.1200/jco.2006.24.18_suppl.17005 ◽

2006 ◽

Vol 24 (18_suppl) ◽

pp. 17005-17005

Author(s):

N. Mohammed ◽

M. P. Mehta ◽

S. M. Bentzen ◽

D. Khuntia ◽

W. A. Tome

Keyword(s):

Lung Cancer ◽

Standard Deviation ◽

Rank Correlation ◽

Data Sets ◽

Karnofsky Performance Score ◽

Error Minimization ◽

Lung Cancer Patients ◽

Spearman’S Rank Correlation ◽

The Mean ◽

Set Up

17005 Background: The on-board megavoltage (MV) computed tomography (CT) capabilities of a Tomotherapy unit were used to obtain the daily MVCT images of lung cancer patients. For daily patient alignment, differences between the MVCT scan and planning CT were resolved by calculating the necessary couch shifts in the X = mediolateral, Y = craniocaudal, and Z = anteriorposterior directions. Daily shifts were analyzed. Methods: 583 alignments from 36 patients with lung cancer were available for analysis. The systematic (Σ) and random (σ) errors were calculated and a covariate analysis was performed with tumor size, Karnofsky Performance Score (KPS), and presence of atelectasis. Two error minimization strategies were applied to the data - 1) shifts from fraction 1 were subtracted from subsequent shifts, and 2) the average of shifts 1–3 were subtracted from shifts 4 onward. Σ and σ were calculated for each of the 3 data sets and applied to van Herk’s margin recipe 2.5 Σ + 0.7σ. The mean, standard deviation, and standard error of the magnitude shifts for 13 patients who each received 23 fractions were analyzed by Spearman’s rank correlation test for the relationship between shift magnitude and fraction number. Results: The presence of atelectasis was significantly related to a smaller σ in millimeters, 2.8 ± 0.08 vs. 3.5 ± 0.09 (p = 1.1 × 10−8). The other covariates were not significantly related to set-up error. The 2nd error minimization strategy decreased Σ in the X, Y, and Z directions from 4.7 ± 0.6, 5.8 ± 0.9, 4.9 ± 0.6 to 2.1 ± 0.1, 4.2 ± 0.5, 3.4 ± 0.3 (p = 2.0 × 10−5, 0.13, 0.02) respectively. Calculated margins from van Herk’s equation for all data reported as (x, y, z) in mm were (13.8, 19.6, and 15.9). For strategies 1 and 2 respectively, calculated margins were reduced by (27.2%, 11.5%, 10.6%) and (46.7%, 21.5%, 23.2%). The mean magnitude of isocenter shift and the standard deviation were found to increase with fraction number (p = 1.0 × 10−6 and 5.0 × 10−5 respectively). Conclusion: The error correction strategies significantly reduced Σ but did not reduce the margins dramatically. Drift in accuracy during a long treatment course and an inability to identify subgroups of patients based on our covariates who may not need daily imaging suggests that daily image verification + correction will help reduce error and margins. No significant financial relationships to disclose.

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Does Motion Assessment With 4-Dimensional Computed Tomographic Imaging for Non–Small Cell Lung Cancer Radiotherapy Improve Target Volume Coverage?

Clinical Medicine Insights Oncology ◽

10.1177/1179554917698461 ◽

2017 ◽

Vol 11 ◽

pp. 117955491769846 ◽

Cited By ~ 4

Author(s):

Naseer Ahmed ◽

Sankar Venkataraman ◽

Kate Johnson ◽

Keith Sutherland ◽

Shaun K Loewen

Keyword(s):

Lung Cancer ◽

Gross Tumor Volume ◽

Tumor Volume ◽

Target Volume ◽

Data Sets ◽

Nodal Disease ◽

Dice Coefficient ◽

Data Set ◽

4D Ct ◽

Ct Data

Introduction: Modern radiotherapy with 4-dimensional computed tomographic (4D-CT) image acquisition for non–small cell lung cancer (NSCLC) captures respiratory-mediated tumor motion to provide more accurate target delineation. This study compares conventional 3-dimensional (3D) conformal radiotherapy (3DCRT) plans generated with standard helical free-breathing CT (FBCT) with plans generated on 4D-CT contoured volumes to determine whether target volume coverage is affected. Materials and methods: Fifteen patients with stage I to IV NSCLC were enrolled in the study. Free-breathing CT and 4D-CT data sets were acquired at the same simulation session and with the same immobilization. Gross tumor volume (GTV) for primary and/or nodal disease was contoured on FBCT (GTV_3D). The 3DCRT plans were obtained, and the patients were treated according to our institution’s standard protocol using FBCT imaging. Gross tumor volume was contoured on 4D-CT for primary and/or nodal disease on all 10 respiratory phases and merged to create internal gross tumor volume (IGTV)_4D. Clinical target volume margin was 5 mm in both plans, whereas planning tumor volume (PTV) expansion was 1 cm axially and 1.5 cm superior/inferior for FBCT-based plans to incorporate setup errors and an estimate of respiratory-mediated tumor motion vs 8 mm isotropic margin for setup error only in all 4D-CT plans. The 3DCRT plans generated from the FBCT scan were copied on the 4D-CT data set with the same beam parameters. GTV_3D, IGTV_4D, PTV, and dose volume histogram from both data sets were analyzed and compared. Dice coefficient evaluated PTV similarity between FBCT and 4D-CT data sets. Results: In total, 14 of the 15 patients were analyzed. One patient was excluded as there was no measurable GTV. Mean GTV_3D was 115.3 cm3 and mean IGTV_4D was 152.5 cm3 ( P = .001). Mean PTV_3D was 530.0 cm3 and PTV_4D was 499.8 cm3 ( P = .40). Both gross primary and nodal disease analyzed separately were larger on 4D compared with FBCT. D95 (95% isodose line) covered 98% of PTV_3D and 88% of PTV_4D ( P = .003). Mean dice coefficient of PTV_3D and PTV_4D was 84%. Mean lung V20 was 24.0% for the 3D-based plans and 22.7% for the 4D-based plans ( P = .057). Mean heart V40 was 12.1% for the 3D-based plans and 12.7% for the 4D-based plans ( P = .53). Mean spinal cord Dmax was 2517 and 2435 cGy for 3D-based and 4D-based plans, respectively ( P = .019). Mean esophageal dose was 1580 and 1435 cGy for 3D and 4D plans, respectively ( P = .13). Conclusions: IGTV_4D was significantly larger than GTV_3D for both primary and nodal disease combined or separately. Mean PTV_3D was larger than PTV_4D, but the difference was not statistically significant. The PTV_4D coverage with 95% isodose line was inferior, indicating the importance of incorporating the true size and shape of the target volume. Relatively less dose was delivered to spinal cord and esophagus with plans based on 4D data set. Dice coefficient analysis for degree of similarity revealed that 16% of PTVs from both data sets did not overlap, indicating different anatomical positions of the PTV due to tumor/nodal motion during a respiratory cycle. All patients with lung cancer planned for radical radiotherapy should have 4D-CT simulation to ensure accurate coverage of the target volumes.

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Comparison of COPD and Lung Cancer Mechanisms for Identifying MicroRNA Biomarkers By The Reconstruction of miRNA-mRNA Co-Expression Network

10.21203/rs.3.rs-239948/v1 ◽

2021 ◽

Author(s):

Amirhossein Fathinavid ◽

Zaynab Mousavian ◽

Ali Najafi ◽

Ali Masoudi-Nejad

Keyword(s):

Lung Cancer ◽

Molecular Mechanisms ◽

Differential Expression Analysis ◽

Chronic Obstructive ◽

Data Sets ◽

Obstructive Pulmonary Disease ◽

Correlation Matrices ◽

Mrna Expression Data ◽

Target Interaction ◽

And Control

Abstract Background: The association between lung cancer and chronic obstructive pulmonary disease (COPD) is now well established; as people with COPD are more likely to develop lung carcinoma. However, the evidence for this relationship is inconclusive and there is currently little information on the underlying molecular mechanisms. MicroRNAs (miRNAs) are one of the regulatory factors in lung cancer and COPD that their functions are widely studied in many chronic diseases and cancers. Rationally, determining common miRNAs for both of diseases could provide a more detailed picture of this association and the involved molecular mechanisms. In this study, we applied systems biology approaches to identify and predict miRNAs that potentially play regulatory roles between COPD and lung cancer. Results: We performed differential expression analysis on public miRNA and mRNA expression data sets, for both of diseases, and calculated two correlation matrices between miRNA and mRNA for case and control samples. Then we constructed two miRNA-mRNA co-expression networks and merged these two co-expression networks into a community co-expression network. Results indicated the existence of very common miRNAs (ex. hsa-miR-326 and hsa-miR-1293) and mRNAs (such as FAT2, ALOX5AP, and LDB2) between the two mentioned diseases. Moreover, we discovered specific miRNAs (hsa-miR-574-3p) that targeted common mRNAs. We utilized drug-target interaction networks to identify candidate drugs (e.g. iloperidone) for common mRNAs that could be considered in treatment both of diseases.Conclusions: Generally, our study highlighted common miRNAs between COPD and lung cancer that could be used as new signatures or biomarkers for therapeutic purposes. Moreover, discovered candidate drugs may be applied in the treatment of both mentioned diseases. Investigating the miRNA biomarkers in this study improves our understanding about the shared mechanisms between COPD and lung cancer.

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