scholarly journals Network-Based Matching of Patients and Targeted Therapies for Precision Oncology*

2019 ◽  
Author(s):  
Qingzhi Liu ◽  
Min Jin Ha ◽  
Rupam Bhattacharyya ◽  
Lana Garmire ◽  
Veerabhadran Baladandayuthapani
Keyword(s):  
Lung Cancer ◽  
Cancer Patients ◽  
Cell Lines ◽  
Cancer Cell ◽  
Drug Sensitivity ◽  
Cancer Cell Lines ◽  
Precision Oncology ◽  
Large Set ◽  
Sensitivity Data ◽  
The Right

The extensive acquisition of high-throughput molecular profiling data across model systems (human tumors and cancer cell lines) and drug sensitivity data, makes precision oncology possible – allowing clinicians to match the right drug to the right patient. Current supervised models for drug sensitivity prediction, often use cell lines as exemplars of patient tumors and for model training. However, these models are limited in their ability to accurately predict drug sensitivity of individual cancer patients to a large set of drugs, given the paucity of patient drug sensitivity data used for testing and high variability across different drugs. To address these challenges, we developed a multilayer network-based approach to impute individual patients’ responses to a large set of drugs. This approach considers the triplet of patients, cell lines and drugs as one inter-connected holistic system. We first use the omics profiles to construct a patient-cell line network and determine best matching cell lines for patient tumors based on robust measures of network similarity. Subsequently, these results are used to impute the “missing link” between each individual patient and each drug, called Personalized Imputed Drug Sensitivity Score (PIDS-Score), which can be construed as a measure of the therapeutic potential of a drug or therapy. We applied our method to two subtypes of lung cancer patients, matched these patients with cancer cell lines derived from 19 tissue types based on their functional proteomics profiles, and computed their PIDS-Scores to 251 drugs and experimental compounds. We identified the best representative cell lines that conserve lung cancer biology and molecular targets. The PIDS-Score based top sensitive drugs for the entire patient cohort as well as individual patients are highly related to lung cancer in terms of their targets, and their PIDS-Scores are significantly associated with patient clinical outcomes. These findings provide evidence that our method is useful to narrow the scope of possible effective patient-drug matchings for implementing evidence-based personalized medicine strategies.Data and code availabilityhttps://github.com/bayesrx/bayesrx.github.io/tree/master/authors/liu-q./ Shiny app (data and results visualization tool): https://qingzliu.shinyapps.io/psb-app/

scholarly journals Gimap5 Inhibits Lung Cancer Growth by Interacting With M6PR

2021 ◽  
Vol 11 ◽  
Author(s):  
Pei Dai ◽  
Zhongxiang Tang ◽  
Pinglang Ruan ◽  
Ousman Bajinka ◽  
Dan Liu ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Cancer Patients ◽  
Cell Lines ◽  
Cancer Cell ◽  
Cancer Cell Lines ◽  
Arginine Deiminase ◽  
Lung Cancer Cell ◽  
Mesenchymal Transition ◽  
Lung Cancer Patients ◽  
Potential Biomarker

ObjectiveSeveral studies have demonstrated the impacts of GTPases of immunity-associated proteins (GIMAPs) on malignant cells. However, the mechanisms through which Gimap5 regulates lung cancer cells are yet to be thoroughly investigated in the literature. Our study aimed to investigate the function of Gimap5 in the development of lung cancer.MethodsThe expression levels of the GIMAP family were analyzed in lung cancer patients of various cancer databases and lung cancer cell lines. After the survival rates of the cells were analyzed, we constructed Gimap5 over-expressed lung cancer cell lines and assessed the effects of Gimap5 on cell migration, cell invasion, cell proliferation and the epithelial-mesenchymal transition (EMT). We later screened the interacting proteins of Gimap5 using Co-IP combined with mass spectrometry and then analyzed the expression and distribution of M6PR, including its impacts on protein-arginine deiminase type-4 (PADI4).ResultsFindings indicated that GIMAP family expression decreased significantly in lung cancer cell lines. We also noticed that the downregulation of the GIMAP family was related to the poor prognosis of lung cancer patients. Our experimental results showed that Gimap5 could inhibit the migration, invasion, proliferation and EMT of lung cancer cell lines. Moreover, we found that Gimap5 promoted the transport of M6PR from the cytoplasm to the cell membrane, thereby inhibiting the enhancement of EMT-related PADI4.ConclusionOur research suggested that Gimap5 could inhibit the growth of lung cancer by interacting with M6PR and that it could be a potential biomarker for the diagnosis and prognosis of lung cancer.

Participation of poly(ADP-robose) polymerase in the drug sensitivity in human lung cancer cell lines

1992 ◽  
Vol 118 (4) ◽  
pp. 244-248 ◽  
Author(s):  
Sachiyo Kubo ◽  
Mitsuko Matsutani ◽  
Kazuhiko Nakagawa ◽  
Tsutomu Ogura ◽  
Hiroyasu Esumi ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Cell Lines ◽  
Cancer Cell ◽  
Drug Sensitivity ◽  
Human Lung ◽  
Cancer Cell Lines ◽  
Human Lung Cancer ◽  
Lung Cancer Cell ◽  
Human Lung Cancer Cell

scholarly journals Drug Sensitivity Prediction From Cell Line-Based Pharmacogenomics Data: Guidelines for Developing Machine Learning Models

2021 ◽  
Author(s):  
Hossein Sharifi-Noghabi ◽  
Soheil Jahangiri-Tazehkand ◽  
Casey Hon ◽  
Petr Smirnov ◽  
Anthony Mammoliti ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cell Line ◽  
Cell Lines ◽  
Cancer Cell ◽  
Drug Sensitivity ◽  
Cancer Cell Lines ◽  
Precision Oncology ◽  
Learning Models ◽  
Drug Sensitivity Prediction ◽  
Machine Learning Models

ABSTRACTThe goal of precision oncology is to tailor treatment for patients individually using the genomic profile of their tumors. Pharmacogenomics datasets such as cancer cell lines are among the most valuable resources for drug sensitivity prediction, a crucial task of precision oncology. Machine learning methods have been employed to predict drug sensitivity based on the multiple omics data available for large panels of cancer cell lines. However, there are no comprehensive guidelines on how to properly train and validate such machine learning models for drug sensitivity prediction. In this paper, we introduce a set of guidelines for different aspects of training a predictor using cell line datasets. These guidelines provide extensive analysis of the generalization of drug sensitivity predictors, and challenge many current practices in the community including the choice of training dataset and measure of drug sensitivity. Application of these guidelines in future studies will enable the development of more robust preclinical biomarkers.

Evaluation of CD73 in lung cancer.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. e14525-e14525
Author(s):  
Hui Yu ◽  
Maya Amar ◽  
Christopher J Rivard ◽  
Kim Ellison ◽  
Leslie Rozeboom ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Protein Expression ◽  
Cancer Patients ◽  
Cell Lines ◽  
Cancer Cell ◽  
Genomic Data ◽  
Cancer Cell Lines ◽  
Lung Cancer Cell ◽  
Lung Cancer Patients ◽  
Cd73 Expression

e14525 Background: Immunotherapy has shown promising results in multiple forms of cancer including lung cancer patients. Treatment targeting alternative immune checkpoints may demonstrate value for specific cancers or in combination therapy with other immunotherapies or chemotherapeutic agents. While adenosine has physiological importance in preventing excess inflammatory reactions and inhibiting autoimmunity, it is generated through an enzymatic cascade in the tumor microenvironment whereby AMP is ultimately dephosphorylated to adenosine by CD73 on tumor cells. Previous studies have shown that increased tumor CD73 expression correlated with increased metastasis, worse prognosis and chemotherapy resistance. Methods: Genomic data from the CCLE and TCGA database were evaluated for CD73 expression in lung cancer. The specificity of an anti-CD73 antibody (Cell Signaling) was confirmed by Western blotting in lung cancer cell lines. Immunohistochemistry (IHC) was performed for FFPE lung cancer cell lines and a NSCLC patient cohort. CD73 expression data was correlated with patient demographic data including outcomes. Results: CD73 mRNA was expressed in lung cancer cell lines (70/126, 55.6%) and did not correlate with other immune checkpoint ligands including PD-L1, GAL-9, and B7H4 in NSCLC. Review of the TCGA database identified CD73 expression is highest in thyroid carcinoma and significant in lung cancer. The prevalence of CD73 protein expression by IHC in NSCLC cell lines was 84.8% (39/46) and 15.4% (6/39) in SCLC cell lines. We also evaluated CD73 protein expression in a NSCLC cohort with a prevalence of 32.1% (36/112), with no significant correlation to patient clinical characteristics or outcomes. Conclusions: We evaluated the expression of the immune checkpoint, CD73, in lung cancer cell lines and tissues. Both genomic data and protein expression by IHC demonstrated CD73 was significantly expressed. However, there was no correlation with other immune biomarkers or patient demographics or outcomes. CD73 is a new target for immunotherapy and current clinical studies with CD73 inhibitors may prove beneficial to lung cancer patients.

scholarly journals A Deep Learning Framework for Prediction of Clinical Drug Response of Cancer Patients and Identification of Drug Sensitivity Biomarkers using Preclinical Samples

2021 ◽  
Author(s):  
David Earl Hostallero ◽  
Lixuan Wei ◽  
Liewei Wang ◽  
Junmei Cairns ◽  
Amin Emad
Keyword(s):  
Deep Learning ◽  
Cancer Patients ◽  
Cell Lines ◽  
Cancer Cell ◽  
Drug Response ◽  
Drug Sensitivity ◽  
Cancer Cell Lines ◽  
Cancer Type ◽  
Learning Framework ◽  
Multiple Drugs

Background: Prediction of the response of cancer patients to different treatments and identification of biomarkers of drug sensitivity are two major goals of individualized medicine. In this study, we developed a deep learning framework called TINDL, completely trained on preclinical cancer cell lines, to predict the response of cancer patients to different treatments. TINDL utilizes a tissue-informed normalization to account for the tissue and cancer type of the tumours and to reduce the statistical discrepancies between cell lines and patient tumours. In addition, this model identifies a small set of genes whose mRNA expression are predictive of drug response in the trained model, enabling identification of biomarkers of drug sensitivity. Results: Using data from two large databases of cancer cell lines and cancer tumours, we showed that this model can distinguish between sensitive and resistant tumours for 10 (out of 14) drugs, outperforming various other machine learning models. In addition, our siRNA knockdown experiments on 10 genes identified by this model for one of the drugs (tamoxifen) confirmed that all of these genes significantly influence the drug sensitivity of the MCF7 cell line to this drug. In addition, genes implicated for multiple drugs pointed to shared mechanism of action among drugs and suggested several important signaling pathways. Conclusions: In summary, this study provides a powerful deep learning framework for prediction of drug response and for identification of biomarkers of drug sensitivity in cancer.

Expression of cytokeratin 8 in lung cancer cell lines and measurement of serum cytokeratin 8 in lung cancer patients

Lung Cancer ◽  
2002 ◽  
Vol 38 (1) ◽  
pp. 31-38 ◽  
Author(s):  
Yoko Fukunaga ◽  
Shuji Bandoh ◽  
Jiro Fujita ◽  
Yu Yang ◽  
Yutaka Ueda ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Cancer Patients ◽  
Cell Lines ◽  
Cancer Cell ◽  
Cancer Cell Lines ◽  
Lung Cancer Cell ◽  
Lung Cancer Patients
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