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8,187 results for “Lung cancer”
Protein structure files for the paper "Multiplexed identification of RAS paralog imbalance as a driver of lung cancer growth" in Nature Cell Biology by Tang et al.
<p>This archive contains models of HRAS, KRAS, and NRAS homo- and heterodimers with various mutations discussed in the paper, "Multiplexed identification of RAS paralog imbalance as a driver of lung cancer growth" in Nature Cell Biology by Tang et al.<br> as well as crystallographic dimers of these proteins as identified by the ProtCAD database, http://dunbrack2.fccc.edu/ProtCAD/Results/PfamArchClusterInfo.aspx?GroupId=8 (cluster 5). Several of the models are shown in Supp. Figure 11b and the crystallographic dimers of RAS that provide evidence for the possible biological relevance of these models are shown in Supp. Figure 11a.</p> <p>The crystallographic dimers were identified by clustering all possible interfaces generated by symmetry operators in crystals of HRAS, KRAS, and NRAS as described in the paper: Xu, Q., Dunbrack, R.L. ProtCID: a data resource for structural information on protein interactions. <em>Nat Commun</em> <strong>11</strong>, 711 (2020). https://doi.org/10.1038/s41467-020-14301-4.</p> <p>The models were created by superposing monomers of HRAS, KRAS, or NRAS onto the alpha4-alpha5 dimer present in the crystal of PDB entry 3k8y. Mutations were made in PyMOL. The structures were relaxed with the FastRelax protocol and the Ref2015 scoring function in the program Rosetta, which uses the backbone-dependent rotamer library of Shapovalov and Dunbrack to repack side chains.</p> <p>The crystallographic dimers are contained in a zipped PyMOL session. The mmCIF format for all the structures is present in a zip file, Tang_et_al_crystallographic_and_modeled_RAS_dimer_ciffiles.zip. The PyMOL session and zip file contains 87 HRAS dimers, 14 KRAS dimers, and 1 NRAS dimer, all having the interface consisting of the alpha4 and alpha5 helices. The PyMOL session also contains the modeled structures. Only Mg ions and GTP/GNP/GDP ligands are shown. Others are present but hidden and may be displayed by PyMOL ("show sticks, het").</p> <p> </p>
RNA datasets to derive predictors for immune checkpoint inhibitor therapy of non-small cell lung cancer
<p>Nanostring nCounter datasets and corresponding clinical data of tumor samples of patients with advanced NSCLC who received anti-PD-1 immuntherapy. Prospectively divided into a discovery and a validation cohort.</p> <p>Please cite the corresponding publication in Annals of Oncology (10.1093/annonc/mdz049)</p>
Spatial immunophenotyping of the tumor microenvironment in non-small cell lung cancer
<p>A dataset with spatial immune cell information on a lung cancer cohort from Uppsala University Hospital, Sweden, with anonymized clinical data. For more information please refer to the 'readme' file and the original study (https://doi.org/10.1016/j.ejca.2023.02.012).</p>
Identification of biomarkers for the early detection of non-small cell lung cancer: a systematic review and meta-analysis
<p>We sought to identify the best biomarkers for the early diagnosis of LC, using a systematic review of seven databases. We identified 79 articles that focused on the identification and assessment of diagnostic biomarkers and then performed a meta-analysis. This work has been submitted for publication.</p>
Data and scripts for SCLC_CellMiner: Integrated Genomics and Therapeutics Predictors of Small Cell Lung Cancer Cell Lines based on their genomic signatures
<p>This is the repository of data and scripts for the analysis of the CellminerCDB-SCLC manuscript and website (<a href="https://discover.nci.nih.gov/SclcCellMinerCDB/">https://discover.nci.nih.gov/SclcCellMinerCDB/</a>)</p> <p> </p> <p>CellMiner-SCLC (https://discover.nci.nih.gov/SclcCellMinerCDB) integrates 118 patient-derived cell lines with drug sensitivity and genomic datasets, including high resolution methylome and RNAseq data. CellMiner-SCLC provides a new resource for SCLC research for this “recalcitrant cancer”. Of fundamental importance, we demonstrate the reproducibility and stability of the cell line datasets from different institutions (CCLE, GDSC, CTRP, NCI and UTSW). We validate the classification based on four master transcription factors: NEUROD1, ASCL1, POU2F3 and YAP1 and show transcription networks connecting them with the MYC genes (MYC, MYCL1 and MYCN) and the NOTCH and HIPPO pathways. We find that the 4 subsets express specific surface markers for antibody-targeted therapies. The YAP1-driven (SCLC-Y) cell lines differ from the other subsets by expressing the NOTCH pathway, epithelial-mesenchymal-transition (EMT) and antigen-presenting machinery (APM) genes, and by responding to mTOR and AKT inhibitors, suggesting the potential of NOTCH modulators, YAP1 inhibitors and immune checkpoint inhibitors for SCLC-Y tumors.</p>
Metabolomics Analysis for the Identification of Biomarkers in Small Cell Lung Cancer
<p>Small cell lung cancer (SCLC), a highly aggressive malignancy with a poor prognosis is usually detected at the extensive stage of the disease. The demand for early diagnostic methods and reliable biomarkers is increasing, although a number of tumor markers such as NSE and NCAM have already been utilized in clinics. Here, we conducted untargeted metabolomics in 54 plasma samples from 34 patients with SCLC and 20 healthy controls.</p>
PERFORMANCE OF MACHINE LEARNING ALGORITHMS FOR LUNG CANCER PREDICTION: A COMPARATIVE STUDY
<p>This study compares the performance of five machine learning algorithms—logistic regression, support vector machines, random forests, gradient boosting, and neural networks—for lung cancer prediction using demographic, lifestyle, and medical data from the UCI Machine Learning Repository. Gradient boosting and random forests achieved the highest accuracy (89% and 87%, respectively) and AUC-ROC scores (0.93 and 0.92), while neural networks reached 90% accuracy but presented interpretability limitations. Key predictors included smoking history, chronic disease, and respiratory symptoms, aligning with established risk factors. Ensemble methods, particularly gradient boosting and random forests, provided an optimal balance of accuracy and interpretability, highlighting their potential for clinical applications in early lung cancer detection.</p>
Bevacizumab plus erlotinib versus erlotinib alone as first line treatment of patients with EGFR-mutated advanced nonsquamous non-small cell lung cancer. BEVacizumab plus ERLotinib studY (BEVERLY): an academic, multicenter, randomised phase III trial.
<p>Background. Adding bevacizumab to erlotinib prolonged PFS of patients with EGFR-mutated advanced NSCLC in the Japanese NEJ026 trial, but limited data were available in non-Asian patients. BEVERLY is an Italian, multicenter, randomized phase III trial of bevacizumab plus erlotinib versus erlotinib alone as first-line treatment of advanced EGFR-mutated NSCLC.</p> <p>Methods. Eligible patients were randomized 1:1 to erlotinib (150mg daily) plus bevacizumab (15mg/kg iv q3w) or erlotinib alone, until disease progression or unacceptable toxicity. Center, ECOG PS and type of mutation (ex19 deletion vs ex21 L858R vs others) were stratification variables. Investigator-assessed PFS (IA-PFS) and blinded-independent centrally-reviewed PFS (BICR-PFS) were co-primary endpoints. With 80% power in detecting a 0·60 HR and 2–sided α error 0·05, 126 events out of 160 patients were needed. The trial was registered as NCT02633189 and EudraCT 2015-002235-17.</p> <p>Findings. From Apr 11, 2016 to Feb 27, 2019, 160 pts were randomized to erlotinib pus bevacizumab (80) or erlotinib alone (80). Baseline characteristics were balanced between arms; 34 (42·5%) patients in erlotinib plus bevacizumab arm and 43 (53·8%) in erlotinib arm were former or current smokers. At a median follow-up of 36·3 months, 140 PFS events (87·5%) were reported, 68 with erlotinib plus bevacizumab and 72 with erlotinib. Median IA-PFS was 15·4 months (95% CI 12·2–18·6) with erlotinib plus bevacizumab and 9·6 months (95% CI 8·2–10·6) with erlotinib (HR 0·66; 95%CI: 0·47–0·92). BICR-PFS analysis confirmed this result. A significant interaction with treatment effect was found for smoking habit (P=0·0323): former or current smokers receiving erlotinib plus bevacizumab had a longer PFS (16·9 months [95% CI 10·2–21·8] versus 8·8 months [95% CI 5·6–9·6]) than those receiving erlotinib alone.</p> <p>Hypertension (grade≥3: 24% vs 5%), skin rash (grade≥3: 31% vs 14%), thromboembolic events (any grade: 11% vs 4%), and proteinuria (any grade: 23% vs 6%) were more frequent with the combination treatment.</p> <p>Interpretation. The addition of bevacizumab to first-line erlotinib significantly prolonged PFS in Italian patients with EGFR-mutated NSCLC, without unexpected safety issues.</p>
Datasets from "Circulating miRNA and Lung Cancer: - a More Comprehensive Analysis of Available Data"
<p>A collection of datasets on miRNA and lung cancer used in</p> <p>Berg, O.F.B.: Circulating miRNA and Lung Cancer: - a More Comprehensive Analysis of Available Data.<br> NTNU Open (2022)</p> <p> </p> <p>The datasets in this collection are processed and normalized from available raw datasets. The processing code that was used can be found on <a href="https://github.com/OleFredrik1/masterthesis">https://github.com/OleFredrik1/masterthesis</a>. The raw datasets are:</p> <p><strong>Asakura2020:</strong></p> <p>Asakura, K., Kadota, T., Matsuzaki, J., Yoshida, Y., Yamamoto, Y., Nakagawa, K., Takizawa, S., Aoki, Y., Nakamura, E., Miura, J., Sakamoto, H., Kato, K., Watanabe, S.-i., and Ochiya, T. (2020). A miRNA-based diagnostic model predicts resectable lung cancer in humans with high accuracy. <em>Communications Biology</em>, 3(1):1–9.</p> <p>Accession ID: GSE137140</p> <p>Link: <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE137140">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE137140</a></p> <p> </p> <p><strong>Bianchi2011:</strong></p> <p>Bianchi, F., Nicassio, F., Marzi, M., Belloni, E., Dall’Olio, V., Bernard, L., Pelosi, G., Maisonneuve, P., Veronesi, G., and Di Fiore, P. P. (2011). A serum circulating miRNA diagnostic test to identify asymptomatic high-risk individuals with early stage lung cancer. <em>EMBO Molecular Medicine</em>, 3(8):495–503.</p> <p>Link: <a href="https://www.embopress.org/action/downloadSupplement?doi=10.1002%2Femmm.201100154&file=emmm_201100154_sm_suppdata2.xls">https://www.embopress.org/action/downloadSupplement?doi=10.1002%2Femmm.201100154&file=emmm_201100154_sm_suppdata2.xls</a></p> <p> </p> <p><strong>Chen2019:</strong></p> <p>Accession ID: GSE71661</p> <p>Link: <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE71661">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE71661</a></p> <p> </p> <p><strong>Duan2021:</strong></p> <p>Duan, X., Qiao, S., Li, D., Li, S., Zheng, Z., Wang, Q., and Zhu, X. (2021). Circulating miRNAs in Serum as Biomarkers for Early Diagnosis of Non-small Cell Lung Cancer. <em>Frontiers in Genetics</em>, 12:987.</p> <p>Accession ID: GSE137140</p> <p>Link: <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE137140">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE137140</a></p> <p> </p> <p><strong>Fehlmann2020:</strong></p> <p>Fehlmann, T., Kahraman, M., Ludwig, N., Backes, C., Galata, V., Keller, V., Geffers, L., Mercaldo, N., Hornung, D., Weis, T., Kayvanpour, E., Abu-Halima, M., Deuschle, C., Schulte, C., Suenkel, U., von Thaler, A.-K., Maetzler, W., Herr, C., Fähndrich, S., Vogelmeier, C., Guimaraes, P., Hecksteden, A., Meyer, T., Metzger, F., Diener, C., Deutscher, S., Abdul-Khaliq, H., Stehle, I., Haeusler, S., Meiser, A., Groesdonk, H. V., Volk, T., Lenhof, H.-P., Katus, H., Balling, R., Meder, B., Kruger, R., Huwer, H., Bals, R., Meese, E., and Keller, A. (2020). Evaluating the Use of Circulating MicroRNA Profiles for Lung Cancer Detection in Symptomatic Patients. <em>JAMA oncology</em>, 6(5):714–723.</p> <p>Accession ID: E-MTAB-8026</p> <p>Link: <a href="https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-8026/">https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-8026/</a></p> <p> </p> <p><strong>Halvorsen2016:</strong></p> <p>Halvorsen, A. R., Bjaanæs, M., LeBlanc, M., Holm, A. M., Bolstad, N., Rubio, L., Peñalver, J. C., Cervera, J., Mojarrieta, J. C., López-Guerrero, J. A., Brustugun, O. T., and Helland, Å. (2016). A unique set of 6 circulating microRNAs for early detection of non-small cell lung cancer. <em>Oncotarget</em>, 7(24):37250–37259.</p> <p>Accession ID: GSE70080</p> <p>Link: <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE70080">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE70080</a></p> <p> </p> <p><strong>Jin2017:</strong></p> <p>Jin, X., Chen, Y., Chen, H., Fei, S., Chen, D., Cai, X., Liu, L., Lin, B., Su, H., Zhao, L., Su, M., Pan, H., Shen, L., Xie, D., and Xie, C. (2017). Evaluation of Tumor-Derived Exosomal miRNA as Potential Diagnostic Biomarkers for Early-Stage Non–Small Cell Lung Cancer Using Next-Generation Sequencing. <em>Clinical Cancer Research</em>, 23(17):5311–5319.</p> <p>Link: <a href="https://aacrjournals.org/clincancerres/article/23/17/5311/123048/Evaluation-of-Tumor-Derived-Exosomal-miRNA-as">https://aacrjournals.org/clincancerres/article/23/17/5311/123048/Evaluation-of-Tumor-Derived-Exosomal-miRNA-as</a> (table s1)</p> <p> </p> <p><strong>Keller2009:</strong></p> <p>Keller, A., Leidinger, P., Borries, A., Wendschlag, A., Wucherpfennig, F., Scheffler, M., Huwer, H., Lenhof, H.-P., and Meese, E. (2009). miRNAs in lung cancer - Studying complex fingerprints in patient’s blood cells by microarray experiments. <em>BMC Cancer</em>, 9(1):353.</p> <p>Accession ID: GSE17681</p> <p>Link: <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE17681">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE17681</a></p> <p> </p> <p><strong>Keller2014:</strong></p> <p>Keller, A., Leidinger, P., Vogel, B., Backes, C., ElSharawy, A., Galata, V., Mueller, S. C., Marquart, S., Schrauder, M. G., Strick, R., Bauer, A., Wischhusen, J., Beier, M., Kohlhaas, J., Katus, H. A., Hoheisel, J., Franke, A., Meder, B., and Meese, E. (2014). miRNAs can be generally associated with human pathologies as exemplified for miR-144*. <em>BMC Medicine</em>, 12(1):224.</p> <p>Accession ID: GSE61741</p> <p>Link: <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE61741">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE61741</a></p> <p> </p> <p><strong>Keller2020:</strong></p> <p>Keller, A., Fehlmann, T., Backes, C., Kern, F., Gislefoss, R., Langseth, H., Rounge, T. B., Ludwig, N., and Meese, E. (2020). Competitive learning suggests circulating miRNA profiles for cancers decades prior to diagnosis. <em>RNA Biology</em>, 17(10):1416–1426.</p> <p>Link: <a href="https://www.tandfonline.com/doi/full/10.1080/15476286.2020.1771945">https://www.tandfonline.com/doi/full/10.1080/15476286.2020.1771945</a> (Supplemental Table 9)</p> <p> </p> <p><strong>Kryczka2021:</strong></p> <p>Kryczka, J., Migdalska-Sęk, M., Kordiak, J., Kiszałkiewicz, J. M., PastuszakLewandoska, D., Antczak, A., and Brzeziańska-Lasota, E. (2021). Serum Extracellular Vesicle-Derived miRNAs in Patients with Non-Small Cell Lung Cancer—Search for Non-Invasive Diagnostic Biomarkers. <em>Diagnostics</em>, 11(3):425.</p> <p>Link: <a href="https://www.mdpi.com/2075-4418/11/3/425/s1">https://www.mdpi.com/2075-4418/11/3/425/s1</a></p> <p> </p> <p><strong>Leidinger2011:</strong></p> <p>Leidinger, P., Keller, A., Borries, A., Huwer, H., Rohling, M., Huebers, J., Lenhof, H.-P., and Meese, E. (2011). Specific peripheral miRNA profiles for distinguishing lung cancer from COPD. <em>Lung Cancer</em>, 74(1):41–47.</p> <p>Accession ID: GSE24709</p> <p>Link: <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE24709">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE24709</a></p> <p> </p> <p><strong>Leidinger2014:</strong></p> <p>Leidinger, P., Backes, C., Dahmke, I. N., Galata, V., Huwer, H., Stehle, I., Bals, R., Keller, A., and Meese, E. (2014). What makes a blood cell based miRNA expression pattern disease specific? - A miRNome analysis of blood cell subsets in lung cancer patients and healthy controls. <em>Oncotarget</em>, 5(19):9484–9497.</p> <p>Accession ID: GSE55993</p> <p>Link: <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE55993">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE55993</a></p> <p> </p> <p><strong>Leidinger2016:</strong></p> <p>Leidinger, P., Brefort, T., Backes, C., Krapp, M., Galata, V., Beier, M., Kohlhaas, J., Huwer, H., Meese, E., and Keller, A. (2016). High-throughput qRT-PCR validation of blood microRNAs in non-small cell lung cancer. <em>Oncotarget</em>, 7(4):4611–4623.</p> <p>Link: <a href="https://www.oncotarget.com/article/6566/text/">https://www.oncotarget.com/article/6566/text/</a> (supplementary files)</p> <p> </p> <p><strong>Li2017:</strong></p> <p>Li, L.-L., Qu, L.-L., Fu, H.-J., Zheng, X.-F., Tang, C.-H., Li, X.-Y., Chen, J., Wang, W.-X., Yang, S.-X., Wang, L., Zhao, G.-H., Lv, P.-P., Zhang, M., Lei, Y.-Y., Qin, H.-F., Wang, H., Gao, H.-J., and Liu, X.-Q. (2017). Circulating microRNAs as novel biomarkers of ALK-positive non-small cell lung cancer and predictors of response to crizotinib therapy. <em>Oncotarget</em>, 8(28):45399–45414.</p> <p>Accession ID: GSE94536</p> <p>Link: <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE94536">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE94536</a></p> <p> </p> <p><strong>Marzi2016:</strong></p> <p>Marzi, M. J., Montani, F., Carletti, R. M., Dezi, F., Dama, E., Bonizzi, G., Sandri, M. T., Rampinelli, C., Bellomi, M., Maisonneuve, P., Spaggiari, L., Veronesi, G., Bianchi, F., Di Fiore, P. P., and Nicassio, F. (2016). Optimization and Standardization of Circulating MicroRNA Detection for Clinical Application: The miR-Test Case. <em>Clinical Chemistry</em>, 62(5):743–754</p> <p>Accession ID: GSE76462</p> <p>Link: <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE76462">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE76462</a></p> <p> </p> <p><strong>Nigita2018:</strong></p> <p>Nigita, G., Distefano, R., Veneziano, D., Romano, G., Rahman, M., Wang, K., Pass, H., Croce, C. M., Acunzo, M., and Nana-Sinkam, P. (2018). Tissue and exosomal miRNA editing in Non-Small Cell Lung Cancer. <em>Scientific Reports</em>, 8(1):10222.</p> <p>Accession ID: GSE114711</p> <p>Link: <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE114711">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE114711</a></p> <p> </p> <p><strong>Patnaik2012:</strong></p> <p>Patnaik, S. K., Yendamuri, S., Kannisto, E., Kucharczuk, J. C., Singhal, S., and Vachani, A. (2012). MicroRNA Expression Profiles of Whole Blood in Lung Adenocarcinoma. <em>PLOS ONE</em>, 7(9):e46045.</p> <p>Accession ID: GSE27486</p> <p>Link: <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE27486">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE27486</a></p> <p> </p> <p><strong>Patnaik2017:</strong></p> <p>Patnaik, S. K., Kannisto, E. D., Mallick, R., Vachani, A., and Yendamuri, S. (2017). Whole blood microRNA expression may not be useful for screening non-small cell lung cancer. <em>PLOS ONE</em>, 12(7):e0181926.</p> <p>Accession ID: GSE40738</p> <p>Link: <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE40738">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE40738</a></p> <p> </p> <p><strong>Qu2017:</strong></p> <p>Qu, L., Li, L., Zheng, X., Fu, H., Tang, C., Qin, H., Li, X., Wang, H., Li, J., Wang, W., Yang, S., Wang, L., Zhao, G., Lv, P., Lei, Y., Zhang, M., Gao, H., Song, S., and Liu, X. (2017). Circulating plasma microRNAs as potential markers to identify EGFR mutation status and to monitor epidermal growth factor receptor-tyrosine kinase inhibitor treatment in patients with advanced non-small cell lung cancer. <em>Oncotarget</em>, 8(28):45807–45824.</p> <p>Accession ID: GSE93300</p> <p>Link: <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE93300">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE93300</a></p> <p> </p> <p><strong>Reis2020:</strong></p> <p>Reis, P. P., Drigo, S. A., Carvalho, R. F., Lopez Lapa, R. M., Felix, T. F., Patel, D., Cheng, D., Pintilie, M., Liu, G., and Tsao, M.-S. (2020). Circulating miR-16-5p, miR-92a-3p, and miR-451a in Plasma from Lung Cancer Patients: Potential Application in Early Detection and a Regulatory Role in Tumorigenesis Pathways. <em>Cancers</em>, 12(8):2071.</p> <p>Accession ID: GSE152702</p> <p>Link: <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE152702">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE152702</a></p> <p> </p> <p><strong>Wozniak2015:</strong></p> <p>Wozniak, M. B., Scelo, G., Muller, D. C., Mukeria, A., Zaridze, D., and Brennan, P. (2015). Circulating MicroRNAs as Non-Invasive Biomarkers for Early Detection of Non-Small-Cell Lung Cancer. <em>PLOS ONE</em>, 10(5):e0125026.</p> <p>Accession ID: GSE64591</p> <p>Link: <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE64591">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE64591</a></p> <p> </p> <p><strong>Yao2019:</strong></p> <p>Yao, B., Qu, S., Hu, R., Gao, W., Jin, S., Liu, M., and Zhao, Q. (2019). A panel of miRNAs derived from plasma extracellular vesicles as novel diagnostic biomarkers of lung adenocarcinoma. <em>FEBS Open Bio</em>, 9(12):2149–2158.</p> <p>Accession ID: GSE111803</p> <p>Link: <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE111803">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE111803</a></p> <p> </p> <p><strong>Zaporozhchenko2018:</strong></p> <p>Zaporozhchenko, I. A., Morozkin, E. S., Ponomaryova, A. A., Rykova, E. Y., Cherdyntseva, N. V., Zheravin, A. A., Pashkovskaya, O. A., Pokushalov, E. A., Vlassov, V. V., and Laktionov, P. P. (2018). Profiling of 179 miRNA Expression in Blood Plasma of Lung Cancer Patients and Cancer-Free Individuals. <em>Scientific Reports</em>, 8(1):6348.</p> <p>Accession ID: E-MTAB-6304</p> <p>Link: <a href="https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-6304/">https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-6304/</a></p> <p> </p> <p>The Abdollahi2019 and Boeri2011 datasets are not included as I recived them by email and I did not recieve conformation that they were OK with me publishing the datasets.</p>
Genome editing of LKB1 gene by CRISPR/Cas9 in lung cancer cells and evaluation of its role in metformin and cisplatin response.
<pre>LKB1 is an important upstream inhibitor of the mTOR/S6Ks pathway. Loss of LKB1 is often associated with cancer, including in A549 lung cancer cell line, boosting the transformation of pre-malignant neoplasic cells. Metformin, a natural compound derived from Galega officinalis, is mainly used as a treatment for diabetes mellitus type 2 (DM2), but recently, it has been associated to lower incidence of cancer. One of the main mechanisms is by activation of AMPK through different pathways, including LKB1 activation. Activation of AMPK inhibits the mTOR pathway, protein synthesis and thus cell growth. The combination of metformin and cisplatin, a main chemothepeutic drug for lung cancer, has shown to improve treatment of cancer cells to cisplatin and also to hinder the cisplatin associated resistance common in lung cancer. Here we aim to use the CRISPR/Cas9 technology to correct the mutation presented in A549 lung cancer cells and improve their response to metformin or to the combination between metformin and cisplatin. Besides, we will assess the mTOR signaling pathway status, as well as cell growth, proliferation, cell cycle and sensitivity to apoptosis induction by cisplatin. This project may be useful as a proof of principle that in the future therapies must consider the genomic background of cancer cells before administration of a specific anticancer drug.<br><br></pre>
Human lung cancer harbors spatially-organized stem-immunity hubs that associate with response to immunotherapy
<p>Data associated with Chen, Nieman, Spurrell et al "Human lung cancer harbors spatially-organized stem-immunity hubs that associate with response to immunotherapy" Nature Immunology, 2024. </p> <p>(1) Multiplex RNA scope. Processed cell-level data from 3 datasets, separated into manually selected "Stem Immunity" regions and "Tumor" regions, in separate files. </p> <p>(2) GeoMx. Unprocessed GeoMex data, with standard fields provided by Nanostring software. </p> <p>(3) MERFISH. Transcript-level information, including CellPose and Baysor segmentation. </p> <p>(4) Co-registered multispectral RNA and protein images. Processed cell-level data from 46 patients. Patient-level meta data information is available in supplementary table 1, tab B_Patient_Metadata. Fluorophore channel information is Figure 1a. </p> <p>Paper: <a title="https://secure-web.cisco.com/1OQRHTBMPf1SngM0FLdNuKQOPPx7fLOR87kGiirhnF_e0M74duym9zratwJMs0WJXqHJPAlkZoWXBoHafhJ72n9s0edU76bj8nCUblNWpOnoK6c8KGEQgcNuk8OuBqXAooW9yoh6etJ0bq6VGDmoFd95MrgMob6is1GxcOWx-XK996d_j8QDVu1rptbeyI8n08UfqmHA3n0A0P37k_-DYUWMh7VFNSmbLGzVqGG2n-czoFN3vL5JrMDPRvo0LDGJs96rzB6zYiaLdk6UgYXXc6eUV6FI64MTxmuXCbEmVRqONrjLdyG6HJULOrJLmKURM/https%3A%2F%2Fwww.nature.com%2Farticles%2Fs41590-024-01792-2" href="https://secure-web.cisco.com/1OQRHTBMPf1SngM0FLdNuKQOPPx7fLOR87kGiirhnF_e0M74duym9zratwJMs0WJXqHJPAlkZoWXBoHafhJ72n9s0edU76bj8nCUblNWpOnoK6c8KGEQgcNuk8OuBqXAooW9yoh6etJ0bq6VGDmoFd95MrgMob6is1GxcOWx-XK996d_j8QDVu1rptbeyI8n08UfqmHA3n0A0P37k_-DYUWMh7VFNSmbLGzVqGG2n-czoFN3vL5JrMDPRvo0LDGJs96rzB6zYiaLdk6UgYXXc6eUV6FI64MTxmuXCbEmVRqONrjLdyG6HJULOrJLmKURM/https%3A%2F%2Fwww.nature.com%2Farticles%2Fs41590-024-01792-2">https://www.nature.com/articles/s41590-024-01792-2</a></p>
Real-world comprehensive genomic and immune profiling reveals distinct age- and sex-based genomic and immune landscapes in tumors of patients with non-small cell lung cancer
<p>Wallen ZD, Ko H, Nesline MK, Hastings SB, Strickland KC, Previs RA, Zhang S, Pabla S, Conroy J, Jackson JB, Saini KS, Jensen TJ, Eisenberg M, Caveney B, Sathyan P, Severson EA, Ramkissoon SH. <strong>Real-world comprehensive genomic and immune profiling reveals distinct age- and sex-based genomic and immune landscapes in tumors of patients with non-small cell lung cancer.</strong> <em>Front Immunol.</em> 2024 Jun 21;15:1413956. doi: <a href="https://doi.org/10.3389/fimmu.2024.1413956">10.3389/fimmu.2024.1413956</a>. PMID: <a href="https://pubmed.ncbi.nlm.nih.gov/38975340/">38975340</a>; PMCID: <a href="https://pmc.ncbi.nlm.nih.gov/articles/PMC11224431/">PMC11224431</a>.</p> <p><strong>ABSTRACT</strong></p> <p>Younger patients with non-small cell lung cancer (NSCLC) (<50 years) represent a significant patient population with distinct clinicopathological features and enriched targetable genomic alterations compared to older patients. However, previous studies of younger NSCLC suffer from inconsistent findings, few studies have incorporated sex into their analyses, and studies targeting age-related differences in the tumor immune microenvironment are lacking. We performed a retrospective analysis of 8,230 patients with NSCLC, comparing genomic alterations and immunogenic markers of younger and older patients while also considering differences between male and female patients. We defined older patients as those ≥65 years and used a 5-year sliding threshold from <45 to <65 years to define various groups of younger patients. Additionally, in an independent cohort of patients with NSCLC, we use our observations to inform testing of the combinatorial effect of age and sex on survival of patients given immunotherapy with or without chemotherapy. We observed distinct genomic and immune microenvironment profiles for tumors of younger patients compared to tumors of older patients. Younger patient tumors were enriched in clinically relevant genomic alterations and had gene expression patterns indicative of reduced immune system activation, which was most evident when analyzing male patients. Further, we found younger male patients treated with immunotherapy alone had significantly worse survival compared to male patients ≥65 years, while the addition of chemotherapy reduced this disparity. Contrarily, we found younger female patients had significantly better survival compared to female patients ≥65 years when treated with immunotherapy plus chemotherapy, while treatment with immunotherapy alone resulted in similar outcomes. These results show the value of comprehensive genomic and immune profiling (CGIP) for informing clinical treatment of younger patients with NSCLC and provides support for broader coverage of CGIP for younger patients with advanced NSCLC.</p> <p><strong>DATA AVAILABILITY: </strong></p> <p>De-identified, individual-level patient data, genomic variants, and individual immune gene expression data used in the manuscript can be found in this repository (https://zenodo.org/record/11396552). An R markdown file with R code used to perform the analyses and generate figures is also provided in the repository along with the data. All versions of software used are provided in the Methods section of the manuscript. Raw sequencing data were derived from routine clinical testing of real-world patients and cannot be shared publicly. Data for immune gene expression signatures are not publicly available due to a non‑provisional patent filing covering the methods used to generate and analyze these data but are available from the corresponding author on reasonable request.</p>
Processed counts data of cfMeDIP-seq profiles of small cell lung cancer patients
<p>R objects of cfMeDIP-seq profiles of small cell lung cancer patient cfDNA, peripheral blood leukocytes, non-cancer control patients cfDNA, and CDX tumour tissue. The data are whole-genome across 300bp windows after removing ENCODE-blacklisted regions. The data also includes MeDEStrand-converted MeDIP data for peripheral blood leukocytes</p>
ALTA-1L Study: A Study of Brigatinib Versus Crizotinib in Anaplastic Lymphoma Kinase Positive (ALK+) Advanced Non-small Cell Lung Cancer (NSCLC) Participants
ClinicalTrials.gov study NCT02737501. IPD Sharing: YES. Countries: 19. Publications: 5.
Study of DS-1062a in Advanced or Metastatic Non-small Cell Lung Cancer With Actionable Genomic Alterations (TROPION-Lung05)
ClinicalTrials.gov study NCT04484142. IPD Sharing: YES. Countries: 10. Publications: 1.
Study of REGN 2810 Compared to Platinum-Based Chemotherapies in Participants With Metastatic Non-Small Cell Lung Cancer (NSCLC)
ClinicalTrials.gov study NCT03088540. IPD Sharing: YES. Countries: 24. Publications: 4.
Study of Efficacy and Safety of Nivolumab in Combination With EGF816 and of Nivolumab in Combination With INC280 in Patients With Previously Treated Non-small Cell Lung Cancer
ClinicalTrials.gov study NCT02323126. IPD Sharing: YES. Countries: 8. Publications: 1.
Phase 2 Study of Brigatinib in Japanese Participants With Anaplastic Lymphoma Kinase (ALK)-Positive Non-Small Cell Lung Cancer (NSCLC)
ClinicalTrials.gov study NCT03410108. IPD Sharing: YES. Countries: 1. Publications: 2.
Incorporating Veterans' Preferences Into Lung Cancer Screening Decisions
ClinicalTrials.gov study NCT02899754. IPD Sharing: NO. Countries: 1. Publications: 5.
HERTHENA-Lung01: Patritumab Deruxtecan in Subjects With Metastatic or Locally Advanced EGFR-mutated Non-Small Cell Lung Cancer
ClinicalTrials.gov study NCT04619004. IPD Sharing: YES. Countries: 16. Publications: 2.
ScienceDex guides
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.