scholarly journals Personalized drug-response prediction model for lung cancer patients using machine learning

2020 ◽  
Author(s):  
Rizwan Qureshi
Keyword(s):  
Lung Cancer ◽  
Cancer Patients ◽  
Targeted Therapies ◽  
Drug Response ◽  
Kinase Inhibitors ◽  
Binding Free Energy ◽  
Survival Rates ◽  
Drug Binding ◽  
Geometrical Properties ◽  
Lung Cancer Patients

Lung cancer caused by mutations in the epidermal growth factor receptor (EGFR) is a major cause of cancer deaths worldwide. EGFR Tyrosine kinase inhibitors (TKIs) have been developed, and have shown increased survival rates and quality of life in clinical studies. However, drug resistance is a major issue, and treatment efficacy is lost after about an year. Therefore, predicting the response to targeted therapies for lung cancer patients is a significant research problem. In this work, we address this issue and propose a personalized model to predict the drug-response of lung cancer patients. This model uses clinical information, geometrical properties of the drug binding site, and the binding free energy of the drug-protein complex. The proposed model achieves state of the art performance with 97.5% accuracy, 100% recall, 95% precision, and 96.3% F1-score with a random forest classifier. This model can also be tested on other types of cancer and diseases, and we believe that it may help in taking optimal clinical decisions for treating patients with targeted therapies

scholarly journals Personalized drug-response prediction model for lung cancer patients using machine learning

2020 ◽  
Author(s):  
Rizwan Qureshi
Keyword(s):  
Lung Cancer ◽  
Cancer Patients ◽  
Targeted Therapies ◽  
Drug Response ◽  
Kinase Inhibitors ◽  
Binding Free Energy ◽  
Survival Rates ◽  
Drug Binding ◽  
Geometrical Properties ◽  
Lung Cancer Patients

Lung cancer caused by mutations in the epidermal growth factor receptor (EGFR) is a major cause of cancer deaths worldwide. EGFR Tyrosine kinase inhibitors (TKIs) have been developed, and have shown increased survival rates and quality of life in clinical studies. However, drug resistance is a major issue, and treatment efficacy is lost after about an year. Therefore, predicting the response to targeted therapies for lung cancer patients is a significant research problem. In this work, we address this issue and propose a personalized model to predict the drug-response of lung cancer patients. This model uses clinical information, geometrical properties of the drug binding site, and the binding free energy of the drug-protein complex. The proposed model achieves state of the art performance with 97.5% accuracy, 100% recall, 95% precision, and 96.3% F1-score with a random forest classifier. This model can also be tested on other types of cancer and diseases, and we believe that it may help in taking optimal clinical decisions for treating patients with targeted therapies

scholarly journals Untangling the KRAS mutated lung cancer subsets and its therapeutic implications

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
Kulshrestha Ritu ◽  
Pawan Kumar ◽  
Amit Singh ◽  
K. Nupur ◽  
Sonam Spalgias ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Cancer Patients ◽  
Targeted Therapies ◽  
Kinase Inhibitors ◽  
Prognostic Impact ◽  
Kras Mutations ◽  
Lung Cancers ◽  
Therapeutic Implications ◽  
Lung Cancer Patients ◽  
Response To Chemotherapy

AbstractThe Kirsten rat sarcoma virus transforming protein (KRAS) mutations (predominate in codons 12, 13, and 61) and genomically drive nearly one-third of lung carcinomas. These mutations have complex functions in tumorigenesis, and influence the tumor response to chemotherapy and tyrosine kinase inhibitors resulting in a poorer patient prognosis. Recent attempts using targeted therapies against KRAS alone have met with little success. The existence of specific subsets of lung cancer based on KRAS mutations and coexisting mutations are suggested. Their interactions need further elaboration before newer promising targeted therapies for KRAS mutant lung cancers can be used as earlier lines of therapy. We summarize the existing knowledge of KRAS mutations and their coexisting mutations that is relevant to lung cancer treatment, in this review. We elaborate on the prognostic impact of clinical and pathologic characteristics of lung cancer patients associated with KRAS mutations. We briefly review the currently available techniques for KRAS mutation detection on biopsy and cytology samples. Finally, we discuss the new therapeutic strategies for targeting KRAS-mutant non-small cell lung cancer (NSCLC). These may herald a new era in the treatment of KRASG12Cmutated NSCLC as well as be helpful to develop demographic subsets to predict targeted therapies and prognosis of lung cancer patients.

Using Naïve Bayes Algorithm to Estimate the Response to Drug in Lung Cancer Patients

2019 ◽  
Vol 21 (10) ◽  
pp. 734-748 ◽  
Author(s):  
Baoling Guo ◽  
Qiuxiang Zheng
Keyword(s):  
Lung Cancer ◽  
Side Effects ◽  
Cancer Patients ◽  
Drug Response ◽  
Treatment Options ◽  
Clinical Manifestations ◽  
Treatment Strategies ◽  
Cancer Resistance ◽  
Lung Cancer Patients ◽  
Bayes Algorithm

Aim and Objective: Lung cancer is a highly heterogeneous cancer, due to the significant differences in molecular levels, resulting in different clinical manifestations of lung cancer patients there is a big difference. Including disease characterization, drug response, the risk of recurrence, survival, etc. Method: Clinical patients with lung cancer do not have yet particularly effective treatment options, while patients with lung cancer resistance not only delayed the treatment cycle but also caused strong side effects. Therefore, if we can sum up the abnormalities of functional level from the molecular level, we can scientifically and effectively evaluate the patients' sensitivity to treatment and make the personalized treatment strategies to avoid the side effects caused by over-treatment and improve the prognosis. Result & Conclusion: According to the different sensitivities of lung cancer patients to drug response, this study screened out genes that were significantly associated with drug resistance. The bayes model was used to assess patient resistance.

scholarly journals Expression signature, prognosis value and immune characteristics of cathepsin F in non-small cell lung cancer identified by bioinformatics assessment

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Liyuan Song ◽  
Xianhui Wang ◽  
Wang Cheng ◽  
Yi Wu ◽  
Min Liu ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Molecular Markers ◽  
Cancer Patients ◽  
Targeted Therapies ◽  
Poor Prognosis ◽  
Immune Cell ◽  
Clinicopathological Parameters ◽  
Cancer Specific Survival ◽  
Lung Cancer Patients ◽  
Favorable Prognosis

Abstract Background In recent years, immunotherapies and targeted therapies contribute to population-level improvement in NSCLC cancer-specific survival, however, the two novel therapeutic options have mainly benefit patients containing mutated driven genes. Thus, to explore other potential genes related with immunity or targeted therapies may provide novel options to improve survival of lung cancer patients without mutated driven genes. CTSF is unique in human cysteine proteinases. Presently, CTSF has been detected in several cell lines of lung cancer, but its role in progression and prognosis of lung cancer remains unclear. Methods CTSF expression and clinical datasets of lung cancer patients were obtained from GTEx, TIMER, CCLE, THPA, and TCGA, respectively. Association of CTSF expression with clinicopathological parameters and prognosis of lung cancer patients was analyzed using UALCAN and Kaplan–Meier Plotter, respectively. LinkedOmics were used to analyze correlation between CTSF and CTSF co-expressed genes. Protein–protein interaction and gene–gene interaction were analyzed using STRING and GeneMANIA, respectively. Association of CTSF with molecular markers of immune cells and immunomodulators was analyzed with Immunedeconv and TISIDB, respectively. Results CTSF expression was currently only available for patients with NSCLC. Compared to normal tissues, CTSF was downregulated in NSCLC samples and high expressed CTSF was correlated with favorable prognosis of NSCLC. Additionally, CTSF expression was correlated with that of immune cell molecular markers and immunomodulators both in LUAD and LUSC. Noticeably, high expression of CTSF-related CTLA-4 was found to be associated with better OS of LUAD patients. Increased expression of CTSF-related LAG-3 was related with poor prognosis of LUAD patients while there was no association between CTSF-related PD-1/PD-L1 and prognosis of LUAD patients. Moreover, increased expression of CTSF-related CD27 was related with poor prognosis of LUAD patients while favorable prognosis of LUSC patients. Conclusions CTSF might play an anti-tumor effect via regulating immune response of NSCLC.

scholarly journals Role of cMET expression in non-small-cell lung cancer patients treated with EGFR tyrosine kinase inhibitors

2008 ◽  
Vol 19 (9) ◽  
pp. 1605-1612 ◽  
Author(s):  
P.A. Zucali ◽  
M.G. Ruiz ◽  
E. Giovannetti ◽  
A. Destro ◽  
M. Varella-Garcia ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Tyrosine Kinase ◽  
Small Cell Lung Cancer ◽  
Cancer Patients ◽  
Kinase Inhibitors ◽  
Small Cell ◽  
Small Cell Lung ◽  
Lung Cancer Patients ◽  
Egfr Tyrosine Kinase Inhibitors
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