Predicting drug response of tumors from integrated genomic profiles by deep neural networks

The study of high-throughput genomic profiles from a pharmacogenomics viewpoint has provided unprecedented insights into the oncogenic features modulating drug response. A recent screening of ~1,000 cancer cell lines to a collection of anti-cancer drugs illuminated the link between genotypes and vulnerability. However, due to essential differences between cell lines and tumors, the translation into predicting drug response in tumors remains challenging. Here we proposed a DNN model to predict drug response based on mutation and expression profiles of a cancer cell or a tumor. The model contains a mutation and an expression encoders pre-trained using a large pan-cancer dataset to abstract core representations of high-dimension data, followed by a drug response predictor network. 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 and four analog DNNs of our model. We then applied the model to predict drug response of 9,059 tumors of 33 cancer types. The model predicted both known, including EGFR inhibitors in non-small cell lung cancer and tamoxifen in ER+ breast cancer, and novel drug targets. 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. Overall, our model and findings improve the prediction of drug response and the identification of novel therapeutic options.Comment: Accepted for presentation in the International Conference on Intelligent Biology and Medicine (ICIBM 2018) at Los Angeles, CA, USA. Currently under consideration for publication in a Supplement Issue of BMC Genomic

Flavin-containing monooxygenases: mutations, disease and drug response

NOTICE: this is the author’s version of a work that was accepted for publication in Trends in Pharmacological Sciences. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Trends in Pharmacological Sciences, [VOL 29, ISSUE 6, (2008)] DOI: 10.1016/j.tips.2008.03.00

Stromal cell effects on melanoma cell drug response

Thesis (M.A.)--Boston UniversityObjective: Melanoma is currently one of the deadliest forms of skin disease in the United States. However in the past decade there have been significant advances in treatment. Among the most promising recent developments, inhibitors of the serine/threonine-protein kinase B-Raf (BRAF inhibitors) such as vemurafenib show great promise and have been shown to increase the median survival of patients with melanoma cells that harbor a mutation of the BRAF gene. While BRAF inhibitors and other treatment therapies have much potential, more needs to be done to improve treatment. As with other cancers, a major hurdle in the treatment of melanoma is the eventual tumor resistance to drug therapy. Accessory cells are thought to play a large role in mediating tumor resistance to drug treatment. Stromal cells have been known to release cytokines and growth factors that aid in cancer proliferation. They can also expression adhesion molecules that further help to aid cell growth and tumor development. It has also been demonstrated that these accessory cells can significantly alter cancer cell drug response as a result of the factors they release or express on their surface. In this study we hypothesize that certain anti-cancer drugs will behave differently against melanoma cell line A375 in the presence versus the absence of stromal cells. Methods: Melanoma cell line A375 was grown on 384 well plates in the presence or absence of different stromal cell lines. A number of different drugs were screened using Compartment-Specific Bioluminescence Imaging to determine if there was a difference in A375 proliferation after drug treatment in the presence versus absence of accessory cells. After an initial screen, a few drugs were chosen to generate dose-response curves to determine if different drugs had different effects at various doses in the presence or absence of stromal cells. [TRUNCATED

Azathioprine-Induced Peripheral T Cell Apoptosis And Drug Response In Patients With Crohn’s Disease

Background and Aim: the long time interval for a trial of thioupurine therapy and the potential side effects in spite of the proven efficacy, do not encourage their use as early therapeutic option in Crohn’s Disease (CD). The development of tests predictive of responsiveness represents a major attempt in the clinical management of CD patients. Azathioprine (AZA) is able to induce apoptosis of T cells; therefore we analyzed the “in vitro” thiopurine-induced T cells apoptosis in a group of CD patients with known response to a previous treatment with AZA. Methods: peripheral CD4+ T cells from 16 CD patients were stimulated with antiCD3/CD28 mAbs in the presence or absence of AZA or 6-MP or 6-thioguanine; apoptosis was assessed using Annexin V staining. Results: Apoptosis stimulation index (% of apoptotic cells in the presence of thiopurine / % of apoptotic cells in their absence) was significantly lower in non responder when compared to responder patients (1.46 (0.97-1.8) vs. 2.19 (1.58-2.65) median (range), respectively; p=0.002 by Mann Whitney test). Conclusions: evaluation of apoptosis stimulation index of peripheral CD4+T cell induced by AZA might represent a parameter useful for a proper selection of CD patients candidate to thiopurine treatment

In Vitro Chemosensitivity Using the Histoculture Drug Response Assay in Human Epithelial Ovarian Cancer

The choice of chemotherapeutic drugs to treat patients with epithelial ovarian cancer has not depended on individual patient characteristics. We have investigated the correlation between in vitro chemosensitivity, as determined by the histoculture drug response assay (HDRA), and clinical responses in epithelial ovarian cancer. Fresh tissue samples were obtained from 79 patients with epithelial ovarian cancer. The sensitivity of these samples to 11 chemotherapeutic agents was tested using the HDRA method according to established methods, and we analyzed the results retrospectively. HDRA showed that they were more chemosensitive to carboplatin, topotecan and belotecan, with inhibition rates of 49.2%, 44.7%, and 39.7%, respectively, than to cisplatin, the traditional drug of choice in epithelial ovarian cancer. Among the 37 patients with FIGO stage Ⅲ/Ⅳ serous adenocarcinoma who were receiving carboplatin combined with paclitaxel, those with carboplatin-sensitive samples on HDRA had a significantly longer median disease-free interval than patients with carboplatin- resistant samples (23.2 vs. 13.8 months, p＜0.05), but median overall survival did not differ significantly (60.4 vs. 37.3 months, p＝0.621). In conclusion, this study indicates that HDRA could provide useful information for designing individual treatment strategies in patients with epithelial ovarian cancer

Distinct genes related to drug response identified in ER positive and ER negative breast cancer cell lines

Breast cancer patients have different responses to chemotherapeutic treatments. Genes associated with drug response can provide insight to understand the mechanisms of drug resistance, identify promising therapeutic opportunities, and facilitate personalized treatment. Estrogen receptor (ER) positive and ER negative breast cancer have distinct clinical behavior and molecular properties. However, to date, few studies have rigorously assessed drug response genes in them. In this study, our goal was to systematically identify genes associated with multidrug response in ER positive and ER negative breast cancer cell lines. We tested 27 human breast cell lines for response to seven chemotherapeutic agents (cyclophosphamide, docetaxel, doxorubicin, epirubicin, fluorouracil, gemcitabine, and paclitaxel). We integrated publicly available gene expression profiles of these cell lines with their in vitro drug response patterns, then applied meta-analysis to identify genes related to multidrug response in ER positive and ER negative cells separately. One hundred eighty-eight genes were identified as related to multidrug response in ER positive and 32 genes in ER negative breast cell lines. Of these, only three genes (DBI, TOP2A, and PMVK) were common to both cell types. TOP2A was positively associated with drug response, and DBI was negatively associated with drug response. Interestingly, PMVK was positively associated with drug response in ER positive cells and negatively in ER negative cells. Functional analysis showed that while cell cycle affects drug response in both ER positive and negative cells, most biological processes that are involved in drug response are distinct. A number of signaling pathways that are uniquely enriched in ER positive cells have complex cross talk with ER signaling, while in ER negative cells, enriched pathways are related to metabolic functions. Taken together, our analysis indicates that distinct mechanisms are involved in multidrug response in ER positive and ER negative breast cells. © 2012 Shen et al

Sex Differences in Cardiovascular Drug Response

In the early sixties, a prominent professor in Clinical Pharmacology at the University College in London, D.R. Laurence, stated: “There are no clinically important sex differences in drug action, except, of course, to sex steroid hormones, but the subject is poorly documented. Women are said to be more liable to become excited by morphine than are men; in this respect they resemble cats.” It was thought that study results in men could easily be extrapolated to women, and women were excluded from clinical studies for simplicity and protection from harmfu

An integrative method for scoring candidate genes from association studies: application to warfarin dosing

BackgroundA key challenge in pharmacogenomics is the identification of genes whose variants contribute to drug response phenotypes, which can include severe adverse effects. Pharmacogenomics GWAS attempt to elucidate genotypes predictive of drug response. However, the size of these studies has severely limited their power and potential application. We propose a novel knowledge integration and SNP aggregation approach for identifying genes impacting drug response. Our SNP aggregation method characterizes the degree to which uncommon alleles of a gene are associated with drug response. We first use pre-existing knowledge sources to rank pharmacogenes by their likelihood to affect drug response. We then define a summary score for each gene based on allele frequencies and train linear and logistic regression classifiers to predict drug response phenotypes.ResultsWe applied our method to a published warfarin GWAS data set comprising 181 individuals. We find that our method can increase the power of the GWAS to identify both VKORC1 and CYP2C9 as warfarin pharmacogenes, where the original analysis had only identified VKORC1. Additionally, we find that our method can be used to discriminate between low-dose (AUROC=0.886) and high-dose (AUROC=0.764) responders.ConclusionsOur method offers a new route for candidate pharmacogene discovery from pharmacogenomics GWAS, and serves as a foundation for future work in methods for predictive pharmacogenomics