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.

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/

Deep learning for drug response prediction in cancer

2020 ◽  
Author(s):  
Delora Baptista ◽  
Pedro G Ferreira ◽  
Miguel Rocha
Keyword(s):  
Deep Learning ◽  
Cell Lines ◽  
Cancer Cell ◽  
High Throughput Screening ◽  
Drug Response ◽  
Prediction Models ◽  
Response Prediction ◽  
Cancer Cell Lines ◽  
Cancer Treatments ◽  
Novel Drugs

Abstract Predicting the sensitivity of tumors to specific anti-cancer treatments is a challenge of paramount importance for precision medicine. Machine learning(ML) algorithms can be trained on high-throughput screening data to develop models that are able to predict the response of cancer cell lines and patients to novel drugs or drug combinations. Deep learning (DL) refers to a distinct class of ML algorithms that have achieved top-level performance in a variety of fields, including drug discovery. These types of models have unique characteristics that may make them more suitable for the complex task of modeling drug response based on both biological and chemical data, but the application of DL to drug response prediction has been unexplored until very recently. The few studies that have been published have shown promising results, and the use of DL for drug response prediction is beginning to attract greater interest from researchers in the field. In this article, we critically review recently published studies that have employed DL methods to predict drug response in cancer cell lines. We also provide a brief description of DL and the main types of architectures that have been used in these studies. Additionally, we present a selection of publicly available drug screening data resources that can be used to develop drug response prediction models. Finally, we also address the limitations of these approaches and provide a discussion on possible paths for further improvement. Contact:[email protected]

Clinical drug response prediction from preclinical cancer cell lines by logistic matrix factorization approach

2021 ◽  
Author(s):  
Akram Emdadi ◽  
Changiz Eslahchi
Keyword(s):  
Cell Line ◽  
Cell Lines ◽  
Cancer Cell ◽  
Matrix Factorization ◽  
Drug Response ◽  
Drug Sensitivity ◽  
Cancer Cell Lines ◽  
Tissue Type ◽  
Factorization Approach ◽  
Clinical Drug

Predicting tumor drug response using cancer cell line drug response values for a large number of anti-cancer drugs is a significant challenge in personalized medicine. Predicting patient response to drugs from data obtained from preclinical models is made easier by the availability of different knowledge on cell lines and drugs. This paper proposes the TCLMF method, a predictive model for predicting drug response in tumor samples that was trained on preclinical samples and is based on the logistic matrix factorization approach. The TCLMF model is designed based on gene expression profiles, tissue type information, the chemical structure of drugs and drug sensitivity (IC 50) data from cancer cell lines. We use preclinical data from the Genomics of Drug Sensitivity in Cancer dataset (GDSC) to train the proposed drug response model, which we then use to predict drug sensitivity of samples from the Cancer Genome Atlas (TCGA) dataset. The TCLMF approach focuses on identifying successful features of cell lines and drugs in order to calculate the probability of the tumor samples being sensitive to drugs. The closest cell line neighbours for each tumor sample are calculated using a description of similarity between tumor samples and cell lines in this study. The drug response for a new tumor is then calculated by averaging the low-rank features obtained from its neighboring cell lines. We compare the results of the TCLMF model with the results of the previously proposed methods using two databases and two approaches to test the model’s performance. In the first approach, 12 drugs with enough known clinical drug response, considered in previous methods, are studied. For 7 drugs out of 12, the TCLMF can significantly distinguish between patients that are resistance to these drugs and the patients that are sensitive to them. These approaches are converted to classification models using a threshold in the second approach, and the results are compared. The results demonstrate that the TCLMF method provides accurate predictions across the results of the other algorithms. Finally, we accurately classify tumor tissue type using the latent vectors obtained from TCLMF’s logistic matrix factorization process. These findings demonstrate that the TCLMF approach produces effective latent vectors for tumor samples. The source code of the TCLMF method is available in https://github.com/emdadi/TCLMF.

Small Molecular Leads Differentially Active Against HER2 Positive and Triple Negative Breast Cancer Cell Lines

2019 ◽  
Vol 15 (7) ◽  
pp. 738-742 ◽  
Author(s):  
Adnan Badran ◽  
Atia-tul-Wahab ◽  
Sharmeen Fayyaz ◽  
Elias Baydoun ◽  
Muhammad Iqbal Choudhary
Keyword(s):  
Breast Cancer ◽  
Cancer Cells ◽  
Cell Lines ◽  
Cancer Cell ◽  
Breast Cancer Cell ◽  
Triple Negative ◽  
Cancer Cell Lines ◽  
Breast Cancer Cell Lines ◽  
Cancer Type

Background:Breast cancer is the most prevalent cancer type in women globally. It is characterized by distinct subtypes depending on different gene expression patterns. Oncogene HER2 is expressed on the surface of cell and is responsible for cell growth regulation. Increase in HER2 receptor protein due to gene amplification, results in aggressive growth, and high metastasis in cancer cells.Methods:The current study evaluates and compares the anti-breast cancer effect of commercially available compounds against HER2 overexpressing BT-474, and triple negative MDA-MB-231 breast cancer cell lines.Results:Preliminary in vitro cell viability assays on these cell lines identified 6 lead molecules active against breast cancer. Convallatoxin (4), a steroidal lactone glycoside, showed the most potent activity with IC50 values of 0.63 ± 0.56, and 0.69 ± 0.59 µM against BT-474 and MDA-MB-231, respectively, whereas 4-[4-(Trifluoromethyl)-phenoxy] phenol (3) a phenol derivative, and Reserpine (5) an indole alkaloid selectively inhibited the growth of BT-474, and MDA-MB-231 breast cancer cells, respectively.Conclusion:These results exhibited the potential of small molecules in the treatment of HER2 amplified and triple negative breast cancers in vitro.

Network Propagation Reveals Novel Features Predicting Drug Response of Cancer Cell Lines

2016 ◽  
Vol 11 (2) ◽  
pp. 203-210 ◽  
Author(s):  
Jiguang Wang ◽  
Judith Kribelbauer ◽  
Raul Rabadan
Keyword(s):  
Cell Lines ◽  
Cancer Cell ◽  
Drug Response ◽  
Cancer Cell Lines ◽  
Network Propagation

scholarly journals Antiplatelet Therapy Combined with Anastrozole Induces Features of Partial EMT in Breast Cancer Cells and Fails to Mitigate Breast-Cancer Induced Hypercoagulation

2021 ◽  
Vol 22 (8) ◽  
pp. 4153
Author(s):  
Kutlwano R. Xulu ◽  
Tanya N. Augustine
Keyword(s):  
Breast Cancer ◽  
Cancer Cells ◽  
Cancer Patients ◽  
Antiplatelet Therapy ◽  
Cell Lines ◽  
Cancer Cell ◽  
Breast Cancer Cell ◽  
Breast Cancer Cells ◽  
Cancer Cell Lines ◽  
Breast Cancer Cell Lines

Thromboembolic complications are a leading cause of morbidity and mortality in cancer patients. Cancer patients often present with an increased risk for thrombosis including hypercoagulation, so the application of antiplatelet strategies to oncology warrants further investigation. This study investigated the effects of anastrozole and antiplatelet therapy (aspirin/clopidogrel cocktail or atopaxar) treatment on the tumour responses of luminal phenotype breast cancer cells and induced hypercoagulation. Ethical clearance was obtained (M150263). Blood was co-cultured with breast cancer cell lines (MCF7 and T47D) pre-treated with anastrozole and/or antiplatelet drugs for 24 h. Hypercoagulation was indicated by thrombin production and platelet activation (morphological and molecular). Gene expression associated with the epithelial-to-mesenchymal transition (EMT) was assessed in breast cancer cells, and secreted cytokines associated with tumour progression were evaluated. Data were analysed with the PAST3 software. Our findings showed that antiplatelet therapies (aspirin/clopidogrel cocktail and atopaxar) combined with anastrozole failed to prevent hypercoagulation and induced evidence of a partial EMT. Differences in tumour responses that modulate tumour aggression were noted between breast cancer cell lines, and this may be an important consideration in the clinical management of subphenotypes of luminal phenotype breast cancer. Further investigation is needed before this treatment modality (combined hormone and antiplatelet therapy) can be considered for managing tumour associated-thromboembolic disorder.

scholarly journals Pharmaceutical immunoglobulin G impairs anti-carcinoma activity of oxaliplatin in colon cancer cells

2021 ◽  
Author(s):  
Yuru Shang ◽  
Xianbin Zhang ◽  
Lili Lu ◽  
Ke Jiang ◽  
Mathias Krohn ◽  
...  
Keyword(s):  
Colon Cancer ◽  
Cell Viability ◽  
Cancer Patients ◽  
Cell Lines ◽  
Cancer Cell ◽  
Immunoglobulin G ◽  
Cancer Cell Lines ◽  
Human Igg ◽  
Western Blots ◽  
Igg Receptors

Abstract Background Recent evidence proves that intravenous human immunoglobulin G (IgG) can impair cancer cell viability. However, no study evaluated whether IgG application benefits cancer patients receiving chemotherapeutics. Methods Influence of pharmaceutical-grade human IgG on the viability of a series of patient-derived colon cancer cell lines with and without chemotherapeutic intervention was determined. Cell death was analysed flow cytometrically. In addition, the influence of oxaliplatin and IgG on the ERK1/2-signalling pathway was evaluated by western blots. Results We evaluated the effects of pharmaceutical IgG, such as PRIVIGEN® IgG and Tonglu® IgG, in combination with chemotherapeutics. We did not observe any significant effects of IgG on tumour cell viability directly; however, human IgG significantly impaired the anti-tumoral effects of oxaliplatin. Primary cancer cell lines express IgG receptors and accumulate human IgG intracellularly. Moreover, while oxaliplatin induced the activation of ERK1/2, the pharmaceutical IgG inhibited ERK1/2 activity. Conclusions The present study demonstrates that pharmaceutical IgG, such as PRIVIGEN® IgG and Tonglu® IgG, can impair the anti-carcinoma activity of oxaliplatin. These data strongly suggest that therapeutic IgG as co-medication might have harmful side effects in cancer patients. The clinical significance of these preclinical observations absolutely advises further preclinical, as well as epidemiological and clinical research.

scholarly journals Predicting tumor response to drugs based on gene-expression biomarkers of sensitivity learned from cancer cell lines

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Yuanyuan Li ◽  
David M. Umbach ◽  
Juno M. Krahn ◽  
Igor Shats ◽  
Xiaoling Li ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Lines ◽  
Cancer Cell ◽  
Predictive Models ◽  
Drug Sensitivity ◽  
Human Cancer ◽  
Cancer Cell Lines ◽  
The Cancer Genome Atlas ◽  
Differential Sensitivity ◽  
Tumor Type

Abstract Background Human cancer cell line profiling and drug sensitivity studies provide valuable information about the therapeutic potential of drugs and their possible mechanisms of action. The goal of those studies is to translate the findings from in vitro studies of cancer cell lines into in vivo therapeutic relevance and, eventually, patients’ care. Tremendous progress has been made. Results In this work, we built predictive models for 453 drugs using data on gene expression and drug sensitivity (IC50) from cancer cell lines. We identified many known drug-gene interactions and uncovered several potentially novel drug-gene associations. Importantly, we further applied these predictive models to ~ 17,000 bulk RNA-seq samples from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) database to predict drug sensitivity for both normal and tumor tissues. We created a web site for users to visualize and download our predicted data (https://manticore.niehs.nih.gov/cancerRxTissue). Using trametinib as an example, we showed that our approach can faithfully recapitulate the known tumor specificity of the drug. Conclusions We demonstrated that our approach can predict drugs that 1) are tumor-type specific; 2) elicit higher sensitivity from tumor compared to corresponding normal tissue; 3) elicit differential sensitivity across breast cancer subtypes. If validated, our prediction could have relevance for preclinical drug testing and in phase I clinical design.

scholarly journals Increased Levels of Erythropoietin in Nipple Aspirate Fluid and in Ductal Cells from Breast Cancer Patients

2008 ◽  
Vol 30 (1) ◽  
pp. 51-61
Author(s):  
Ferdinando Mannello ◽  
Laura Fabbri ◽  
Eleonora Ciandrini ◽  
Gaetana A. Tonti
Keyword(s):  
Breast Cancer ◽  
Cancer Patients ◽  
Cell Lines ◽  
Cancer Cell ◽  
Intracellular Signaling ◽  
Tnm Classification ◽  
Cancer Cell Lines ◽  
Breast Cancer Cell Lines ◽  
Serum Samples ◽  
Ductal Cells

Background: Erythropoietin (Epo) is an important regulator of erythropoiesis, and controls proliferation and differentiation of both erythroid and non-erythroid tissues. Epo is actively synthesized by breast cells during lactation, and also plays a role in breast tissues promoting hypoxia-induced cancer initiation. Our aims are to perform an exploratory investigation on the Epo accumulation in breast secretions from healthy and cancer patients and its localization in breast cancer cells.Methods: Epo was determined by ELISA, immunoprecipitation, western blot and immunocytochemical analyses in 130 Nipple Aspirate Fluids (NAF) from 102 NoCancer and 28 Breast Cancer (BC) patients, comparing results with those found in 10 milk, 45 serum samples and breast cancer cell lines.Results: Epo levels in NAFs were significantly higher than those in milk and serum. No difference in Epo electrophoretic mobility was found among NAF, milk and serum samples, and conditioned cell culture medium. Immunolocalization of intracellular Epo in ductal cells floating in BC NAFs was similar to those of cancer cell lines. No significant correlation between TNM classification and Epo in NAFs from BC patients was found. Significantly higher Epo concentration was found in NAF from BC patients compared to NoCancer.Conclusion: We demonstrate that breast epithelial cells are a source of Epo in breast microenvironment, suggesting the presence of a paracrine/autocrine Epo function in NAFs, triggering off intracellular signaling cascade with subsequent BC initiation.

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