Dataset Information

Profiles of Basal and stimulated receptor signaling networks predict drug response in breast cancer lines.

ABSTRACT: Identifying factors responsible for variation in drug response is essential for the effective use of targeted therapeutics. We profiled signaling pathway activity in a collection of breast cancer cell lines before and after stimulation with physiologically relevant ligands, which revealed the variability in network activity among cells of known genotype and molecular subtype. Despite the receptor-based classification of breast cancer subtypes, we found that the abundance and activity of signaling proteins in unstimulated cells (basal profile), as well as the activity of proteins in stimulated cells (signaling profile), varied within each subtype. Using a partial least-squares regression approach, we constructed models that significantly predicted sensitivity to 23 targeted therapeutics. For example, one model showed that the response to the growth factor receptor ligand heregulin effectively predicted the sensitivity of cells to drugs targeting the cell survival pathway mediated by PI3K (phosphoinositide 3-kinase) and Akt, whereas the abundance of Akt or the mutational status of the enzymes in the pathway did not. Thus, basal and signaling protein profiles may yield new biomarkers of drug sensitivity and enable the identification of appropriate therapies in cancers characterized by similar functional dysregulation of signaling networks.

SUBMITTER: Niepel M

PROVIDER: S-EPMC3845839 | biostudies-literature | 2013 Sep

REPOSITORIES: biostudies-literature

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Profiles of Basal and stimulated receptor signaling networks predict drug response in breast cancer lines.

Niepel Mario M Hafner Marc M Pace Emily A EA Chung Mirra M Chai Diana H DH Zhou Lili L Schoeberl Birgit B Sorger Peter K PK

Science signaling 20130924 294

Identifying factors responsible for variation in drug response is essential for the effective use of targeted therapeutics. We profiled signaling pathway activity in a collection of breast cancer cell lines before and after stimulation with physiologically relevant ligands, which revealed the variability in network activity among cells of known genotype and molecular subtype. Despite the receptor-based classification of breast cancer subtypes, we found that the abundance and activity of signalin ...[more]

PMID: 24065145

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Project description:BACKGROUND:The study of high-throughput genomic profiles from a pharmacogenomics viewpoint has provided unprecedented insights into the oncogenic features modulating drug response. A recent study screened for the response of a thousand human cancer cell lines to a wide collection of anti-cancer drugs and illuminated the link between cellular genotypes and vulnerability. However, due to essential differences between cell lines and tumors, to date the translation into predicting drug response in tumors remains challenging. Recently, advances in deep learning have revolutionized bioinformatics and introduced new techniques to the integration of genomic data. Its application on pharmacogenomics may fill the gap between genomics and drug response and improve the prediction of drug response in tumors. RESULTS:We proposed a deep learning model to predict drug response (DeepDR) based on mutation and expression profiles of a cancer cell or a tumor. The model contains three deep neural networks (DNNs), i) a mutation encoder pre-trained using a large pan-cancer dataset (The Cancer Genome Atlas; TCGA) to abstract core representations of high-dimension mutation data, ii) a pre-trained expression encoder, and iii) a drug response predictor network integrating the first two subnetworks. Given a pair of mutation and expression profiles, the model predicts IC50 values of 265 drugs. We trained and tested the model on a dataset of 622 cancer cell lines and achieved an overall prediction performance of mean squared error at 1.96 (log-scale IC50 values). The performance was superior in prediction error or stability than two classical methods (linear regression and support vector machine) and four analog DNN models of DeepDR, including DNNs built without TCGA pre-training, partly replaced by principal components, and built on individual types of input data. We then applied the model to predict drug response of 9059 tumors of 33 cancer types. Using per-cancer and pan-cancer settings, the model predicted both known, including EGFR inhibitors in non-small cell lung cancer and tamoxifen in ER+ breast cancer, and novel drug targets, such as vinorelbine for TTN-mutated tumors. The comprehensive analysis further revealed the molecular mechanisms underlying the resistance to a chemotherapeutic drug docetaxel in a pan-cancer setting and the anti-cancer potential of a novel agent, CX-5461, in treating gliomas and hematopoietic malignancies. CONCLUSIONS:Here we present, as far as we know, the first DNN model to translate pharmacogenomics features identified from in vitro drug screening to predict the response of tumors. The results covered both well-studied and novel mechanisms of drug resistance and drug targets. Our model and findings improve the prediction of drug response and the identification of novel therapeutic options.

Project description:BackgroundSearches for differentially expressed genes in tumours have made extensive use of array technology. Most samples have been obtained from tumour biopsies or from established tumour-derived cell lines. Here we compare cultures of non-immortalized breast cancer cells, normal non-immortalized breast cells and immortalized normal and breast cancer cells to identify which elements of a defined set of well-known cancer-related genes are differentially expressed.MethodsCultures of cells from pleural effusions or ascitic fluids from breast cancer patients (MSSMs) were used in addition to commercially-available normal breast epithelial cells (HMECs), established breast cancer cell lines (T-est) and established normal breast cells (N-est). The Atlas Human Cancer 1.2 cDNA expression array was employed. The data obtained were analysed using widely-available statistical and clustering software and further validated through real-time PCR.ResultsAccording to Significance Analysis of Microarray (SAM) and AtlasImage software, 48 genes differed at least 2-fold in adjusted intensities between HMECs and MSSMs (p < 0.01). Some of these genes have already been directly linked with breast cancer, metastasis and malignant progression, whilst others encode receptors linked to signal transduction pathways or are otherwise related to cell proliferation. Fifty genes showed at least a 2.5-fold difference between MSSMs and T-est cells according to AtlasImage, 2-fold according to SAM. Most of these classified as genes related to metabolism and cell communication.ConclusionThe expression profiles of 1176 genes were determined in finite life-span cultures of metastatic breast cancer cells and of normal breast cells. Significant differences were detected between the finite life-span breast cancer cell cultures and the established breast cancer cell lines. These data suggest caution in extrapolating information from established lines for application to clinical cancer research.

Dataset Information

Profiles of Basal and stimulated receptor signaling networks predict drug response in breast cancer lines.

Publications

Profiles of Basal and stimulated receptor signaling networks predict drug response in breast cancer lines.

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