scholarly journals MOLI: Multi-Omics Late Integration with deep neural networks for drug response prediction

2019 ◽  
Author(s):  
Hossein Sharifi-Noghabi ◽  
Olga Zolotareva ◽  
Colin C. Collins ◽  
Martin Ester

AbstractMotivationHistorically, gene expression has been shown to be the most informative data for drug response prediction. Recent evidence suggests that integrating additional omics can improve the prediction accuracy which raises the question of how to integrate the additional omics. Regardless of the integration strategy, clinical utility and translatability are crucial. Thus, we reasoned a multi-omics approach combined with clinical datasets would improve drug response prediction and clinical relevance.ResultsWe propose MOLI, a Multi-Omics Late Integration method based on deep neural networks. MOLI takes somatic mutation, copy number aberration, and gene expression data as input, and integrates them for drug response prediction. MOLI uses type-specific encoding subnetworks to learn features for each omics type, concatenates them into one representation and optimizes this representation via a combined cost function consisting of a triplet loss and a binary cross-entropy loss. The former makes the representations of responder samples more similar to each and different from the non-responders, and the latter makes this representation predictive of the response values. We validate MOLI on in vitro and in vivo datasets for five chemotherapy agents and two targeted therapeutics. Compared to state-of-the-art single-omics and early integration multi-omics methods, MOLI achieves higher prediction accuracy in external validations. Moreover, a significant improvement in MOLI’s performance is observed for targeted drugs when training on a pan-drug input, i.e. using all the drugs with the same target compared to training only on drug-specific inputs. MOLI’s high predictive power suggests it may have utility in precision oncology.Availability of the implemented codeshttps://github.com/hosseinshn/[email protected] and [email protected]

2019 ◽  
Vol 35 (14) ◽  
pp. i501-i509 ◽  
Author(s):  
Hossein Sharifi-Noghabi ◽  
Olga Zolotareva ◽  
Colin C Collins ◽  
Martin Ester

Abstract Motivation Historically, gene expression has been shown to be the most informative data for drug response prediction. Recent evidence suggests that integrating additional omics can improve the prediction accuracy which raises the question of how to integrate the additional omics. Regardless of the integration strategy, clinical utility and translatability are crucial. Thus, we reasoned a multi-omics approach combined with clinical datasets would improve drug response prediction and clinical relevance. Results We propose MOLI, a multi-omics late integration method based on deep neural networks. MOLI takes somatic mutation, copy number aberration and gene expression data as input, and integrates them for drug response prediction. MOLI uses type-specific encoding sub-networks to learn features for each omics type, concatenates them into one representation and optimizes this representation via a combined cost function consisting of a triplet loss and a binary cross-entropy loss. The former makes the representations of responder samples more similar to each other and different from the non-responders, and the latter makes this representation predictive of the response values. We validate MOLI on in vitro and in vivo datasets for five chemotherapy agents and two targeted therapeutics. Compared to state-of-the-art single-omics and early integration multi-omics methods, MOLI achieves higher prediction accuracy in external validations. Moreover, a significant improvement in MOLI’s performance is observed for targeted drugs when training on a pan-drug input, i.e. using all the drugs with the same target compared to training only on drug-specific inputs. MOLI’s high predictive power suggests it may have utility in precision oncology. Availability and implementation https://github.com/hosseinshn/MOLI. Supplementary information Supplementary data are available at Bioinformatics online.


2018 ◽  
Vol 51 (5) ◽  
pp. 2073-2084 ◽  
Author(s):  
Hai-Hui Huang ◽  
Jing-Guo Dai ◽  
Yong Liang

Background/Aims: One of the most important impacts of personalized medicine is the connection between patients’ genotypes and their drug responses. Despite a series of studies exploring this relationship, the predictive ability of such analyses still needs to be strengthened. Methods: Here we present the Lq penalized network-constrained logistic regression (Lq-NLR) method to meet this need, in which the predictors are integrated into the gene expression data and biological network knowledge and are combined with a more aggressive penalty function. Response prediction models for two cancer targeting drugs (erlotinib and sorafenib) were developed from gene expression data and IC50 values from a large panel of cancer cell lines by utilizing the proposed approach. Then the drug responders were tested with the baseline tumor gene expression data, yielding an in vivo drug sensitivity prediction. Results: These results demonstrated the high effectiveness of this approach. One of the best results achieved by our method was a correlation of 0.841 between the cell line in vitro drug response and patient’s in vivo drug response. We then applied these two drug prediction models to develop a personalized medicine approach in which the subsequent treatment depends on each patient’s gene-expression profile. Conclusion: The proposed method is much better than the existing approach and can capture a more accurate reflection of the relationship between genotypes and phenotypes.


RNA Biology ◽  
2019 ◽  
Vol 16 (8) ◽  
pp. 1044-1054 ◽  
Author(s):  
Haopeng Yu ◽  
Wenjing Meng ◽  
Yuanhui Mao ◽  
Yi Zhang ◽  
Qing Sun ◽  
...  

2020 ◽  
Vol 15 ◽  
Author(s):  
Sheraz Naseer ◽  
Waqar Hussain ◽  
Yaser Daanial Khan ◽  
Nouman Rasool

Background: Among all the major Post-translational modification, lipid modifications possess special significance due to their widespread functional importance in eukaryotic cells. There exist multiple types of lipid modifications and Palmitoylation, among them, is one of the broader types of modification, having three different types. The N-Palmitoylation is carried out by attachment of palmitic acid to an N-terminal cysteine. Due to the association of N-Palmitoylation with various biological functions and diseases such as Alzheimer’s and other neurodegenerative diseases, carrying out important processes in the life cycle of various pathogens, its identification is very important. Objective: The in vitro, ex vivo and in vivo identification of Palmitoylation is laborious, time-taking and costly. There is a dire need of an efficient and accurate computational model to help researchers and biologists identifying these sites, in an easy manner. Herein, we propose a novel prediction model for identification of N-Palmitoylation sites in proteins. Method: Proposed prediction model is developed by combining the Chou’s Pseudo Amino Acid Composition (PseAAC) with deep neural networks. We used well-known deep neural networks (DNNs) for both the tasks of learning a feature representation of peptide sequences and developing prediction model to perform classification. Results: Among different DNNs, Gated Recurrent Unit (GRU) based RNN model showed highest scores in terms of accuracy, and all other computed measures, and outperforms all the previously reported predictors. Conclusion: The proposed GRU based RNN model can help identifying N-Palmitoylation in a very efficient and accurate manner which can help scientists understand the mechanism of this modification in proteins.


Blood ◽  
2014 ◽  
Vol 124 (21) ◽  
pp. 2160-2160
Author(s):  
Jarno Kivioja ◽  
Mika Kontro ◽  
Angeliki Thanasopoulou ◽  
Muntasir Mamun Majumder ◽  
Bhagwan Yadav ◽  
...  

Abstract Background The t(5;11)(q35;p15.5) translocation resulting in fusion of the nucleoporin NUP98 and methyltransferase NSD1 (NUP98-NSD1) genes is a recurrent aberration observed in pediatric and adult AML. The NUP98-NSD1 fusion often co-occurs with the FLT3-ITD mutation and characterizes a group of cytogenetically normal AML patients with very poor prognosis. Despite advances in the understanding of the biology of NUP98-NSD1-positive AML, its therapeutic success rate has remained low. We aimed to identify novel candidate drugs for NUP98-NSD1-positive AML by testing primary patient cells and in vitro cell models with a high-throughput drug sensitivity platform. Methods Leukemic blasts were Ficoll separated from bone marrow (BM) aspirates of an AML patient positive for t(5;11)(q35;p15.5) and FLT3-ITD. RNA extracted from primary cells was used for RNA sequencing and gene expression analysis. NUP98-NSD1 cDNA was amplified from primary cell RNA and expressed from a lentiviral vector (LeGO-iCer2) also encoding the cerulean fluorescent marker. The NUP98-NSD1/LeGo-iCer2 and empty LeGo-iCer2 viruses were used to establish stably expressing Ba/F3 cell lines. Primary murine (BALB/c) BM cells were transduced with NUP98-NSD1 and FLT3-ITD retroviruses alone or in combination (NNF) in vitro (“preleukemic”) or passaged in vivo (“leukemic”) as previously described (Thanasopoulou et al, 2014). For screening, 309 small molecule inhibitors including FDA/EMA-approved and investigational oncology drugs were plated on 384-well plates in a 10,000-fold concentration range. Cells were dispensed on the pre-drugged plates and incubated at 37°C for 72h, and then cell viability measured using the CellTiter-Glo® luminescent assay. Drug response curves were generated and a drug sensitivity score determined (Yadav et al, 2014). Select drug sensitivity was calculated for each drug by comparing results between primary leukemic and healthy donor BM cells or between the cell constructs and empty vector transduced controls cells. Results Primary patient cells and murine BM cells expressing FLT3-ITD alone or in combination with NUP98-NSD1 were selectively sensitive to specific FLT3 inhibitors (e.g. quizartinib, sorafenib and lestaurtinib), and broad-spectrum receptor tyrosine kinase inhibitors targeting FLT3-ITD (e.g. cabozantinib, crenolanib, foretinib, midostaurin, MGCD-265 and ponatinib). Furthermore, these cells were highly sensitive to checkpoint kinase 1/2- inhibitor AZD7762. The primary murine cells expressing both NUP98-NSD1 and FLT3-ITD showed higher sensitivity to all of the above-mentioned drugs compared to cells expressing either of the events alone indicating functional synergy. A very distinct drug response pattern was observed in the leukemic NNF cells cultured in vivo compared to the same cells cultured in vitro suggesting that microenvironment may also affect the observed drug responses. Interestingly, the preleukemic murine cells expressing NUP98-NSD1 with or without FLT3-ITD as well as the primary patient cells showed extreme vulnerability to BCL2/BCL-xL inhibitor navitoclax. Furthermore, primary murine cells expressing NUP98-NSD1 alone showed high select sensitivity to JAK-inhibitors ruxolitinib, BMS-911543, AZD1480 and tofacitinib indicating the fusion may stimulate JAK/STAT-signaling. Similar sensitivity was also observed in the Ba/F3-cells expressing NUP98-NSD1. In support of these findings, gene expression analyses showed high expression of anti-apoptotic factors BCL2, BCL-xL and MCL1 in the patient cells. MCL1 is regulated by STAT3 while BCL-xL is regulated by STAT5, which were also highly expressed. Conclusions In summary, we have observed an enhanced response to specific and non-specific FLT3 inhibitors in cells expressing NUP98-NSD1 and FLT3-ITD together compared to cells expressing either of the two alone. This coincides with previous findings that functional co-operation between NUP98-NSD1 and FLT3-ITD is important in AML (Thanasopoulou et al, 2014). We have seen high in-vitro-in-vivo correlation between primary patient cells and murine cells expressing NUP98-NSD1 and FLT3-ITD. Moreover, we have identified potential candidate compounds targeting oncogenic signaling activated by these two events. These data form a basis for clinical evaluation of candidate compounds for NUP98-NSD1-positive AML. Disclosures Porkka: Bristol-Myers Squibb: Honoraria, Research Funding; Novartis: Honoraria, Research Funding. Heckman:Celgene: Research Funding.


2017 ◽  
Author(s):  
Žiga Avsec ◽  
Mohammadamin Barekatain ◽  
Jun Cheng ◽  
Julien Gagneur

AbstractMotivationRegulatory sequences are not solely defined by their nucleic acid sequence but also by their relative distances to genomic landmarks such as transcription start site, exon boundaries, or polyadenylation site. Deep learning has become the approach of choice for modeling regulatory sequences because of its strength to learn complex sequence features. However, modeling relative distances to genomic landmarks in deep neural networks has not been addressed.ResultsHere we developed spline transformation, a neural network module based on splines to flexibly and robustly model distances. Modeling distances to various genomic landmarks with spline transformations significantly increased state-of-the-art prediction accuracy of in vivo RNA-binding protein binding sites for 114 out of 123 proteins. We also developed a deep neural network for human splice branchpoint based on spline transformations that outperformed the current best, already distance-based, machine learning model. Compared to piecewise linear transformation, as obtained by composition of rectified linear units, spline transformation yields higher prediction accuracy as well as faster and more robust training. As spline transformation can be applied to further quantities beyond distances, such as methylation or conservation, we foresee it as a versatile component in the genomics deep learning toolbox.AvailabilitySpline transformation is implemented as a Keras layer in the CONCISE python package: https://github.com/gagneurlab/concise. Analysis code is available at goo.gl/[email protected]; [email protected]


Blood ◽  
2004 ◽  
Vol 104 (11) ◽  
pp. 2273-2273
Author(s):  
Kai Uwe Chow ◽  
Daniel Nowak ◽  
Soo-Zin Kim ◽  
Bernd Schneider ◽  
Martina Komor ◽  
...  

Abstract Only a few approaches are available to address the mechanisms of cell death in vivo which are induced by anticancer treatment in patients with malignancies. In this study in vitro chemosensitivity testing of primary peripheral blood leukemic cells of five patients suffering from different leukemic Non-Hodgkin’s lymphomas (atypical CLL, typical CLL, Immunocytoma, Mantle Cell Lymphoma, Prolymphocytic Leukemia (PLL)) was combined with the analysis of the in vivo rate of apoptosis by flow-cytometry (Annexin V and depolarisation of mitochondrial membrane potential (MMP) by JC-1). Furthermore, changes in expression patterns of apoptosis related proteins during chemotherapeutic treatment were detected by Western Blot. Gene expression profiling (HG-U133A, Affymetrix, Santa Clara, CA) was employed to identify common marker genes of in vivo drug response. In vitro chemosensitivity was tested using the cytotoxic agents which the patients were scheduled to receive and was strongly correlated with effective reduction of leukemic lymphoma cells in patients resulting in complete remissions in all five cases. Due to the rapid clearance of apoptotic tumor cells in vivo neither the analysis of the in vivo rate of apoptosis and depolarisation of MMP nor the assessment of expression of regulators of apoptosis showed concordant results concerning the drug response. However, assessment of gene expression during therapy could identify a set of 30 genes to significant discriminate between samples from patients before treatment compared to samples from the same patients after receiving cytotoxic therapy. Among these 30 genes we found a high proportion of genes associated with apoptotic cell death and cell proliferation signalling including complement lysis inhibitor (clusterin, CLU, SP40), beta-catenin interacting protein (ICAT), peroxisome proliferator activated receptor alpha (PPARα), TNF alpha converting enzyme (ADAM 17 / TACE), homeo box A3 (HOXA), inositol polyphosphate 5 phophatase (PPI 5 PIV, SHIP1), FK 506 binding protein (FKBP 38) and inhibitor of p53 induced apoptosis alpha (NME 6). Clusterin is able to mediate apoptosis via p53 and increases drug-induced cell death when overexpressed as detected in our treated samples. The downregulation of NME 6 during chemotherapeutic treatment may enhance this effect. These results indicate that in vitro chemosensitivity testing and gene expression profiling can successfully be utilised to predict in vivo drug response in patients with leukemic NHL’s and can be used to explore new pathway models of drug-induced cell death in vivo which are independent of different lymphoma subtypes and different treatment regimens.


2022 ◽  
Vol 12 (1) ◽  
Author(s):  
Sheraz Naseer ◽  
Rao Faizan Ali ◽  
Suliman Mohamed Fati ◽  
Amgad Muneer

AbstractIn biological systems, Glutamic acid is a crucial amino acid which is used in protein biosynthesis. Carboxylation of glutamic acid is a significant post-translational modification which plays important role in blood coagulation by activating prothrombin to thrombin. Contrariwise, 4-carboxy-glutamate is also found to be involved in diseases including plaque atherosclerosis, osteoporosis, mineralized heart valves, bone resorption and serves as biomarker for onset of these diseases. Owing to the pathophysiological significance of 4-carboxyglutamate, its identification is important to better understand pathophysiological systems. The wet lab identification of prospective 4-carboxyglutamate sites is costly, laborious and time consuming due to inherent difficulties of in-vivo, ex-vivo and in vitro experiments. To supplement these experiments, we proposed, implemented, and evaluated a different approach to develop 4-carboxyglutamate site predictors using pseudo amino acid compositions (PseAAC) and deep neural networks (DNNs). Our approach does not require any feature extraction and employs deep neural networks to learn feature representation of peptide sequences and performing classification thereof. Proposed approach is validated using standard performance evaluation metrics. Among different deep neural networks, convolutional neural network-based predictor achieved best scores on independent dataset with accuracy of 94.7%, AuC score of 0.91 and F1-score of 0.874 which shows the promise of proposed approach. The iCarboxE-Deep server is deployed at https://share.streamlit.io/sheraz-n/carboxyglutamate/app.py.


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