Browsing by Author "Khan, Suleiman A."

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Comprehensive data-driven analysis of the impact of chemoinformatic structure on the genome-wide biological response profiles of cancer cells to 1159 drugs
(2012) Khan, Suleiman A.; Faisal, Ali; Mpindi, John Patrick; Parkkinen, Juuso A.; Kalliokoski, Tuomo; Poso, Antti; Kallioniemi, Olli P.; Wennerberg, Krister; Kaski, Samuel
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Drug response prediction by inferring pathway-response associations with kernelized Bayesian matrix factorization
(2016-09-03) Ammad-ud-din, Muhammad; Khan, Suleiman A.; Malani, Disha; Murumägi, Astrid; Kallioniemi, Olli; Aittokallio, Tero; Kaski, Samuel
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Motivation: A key goal of computational personalized medicine is to systematically utilize genomic and other molecular features of samples to predict drug responses for a previously unseen sample. Such predictions are valuable for developing hypotheses for selecting therapies tailored for individual patients. This is especially valuable in oncology, where molecular and genetic heterogeneity of the cells has a major impact on the response. However, the prediction task is extremely challenging, raising the need for methods that can effectively model and predict drug responses.Results: In this study, we propose a novel formulation of multi-task matrix factorization that allows selective data integration for predicting drug responses. To solve the modeling task, we extend the state-of-the-art kernelized Bayesian matrix factorization (KBMF) method with component-wise multiple kernel learning. In addition, our approach exploits the known pathway information in a novel and biologically meaningful fashion to learn the drug response associations. Our method quantitatively outperforms the state of the art on predicting drug responses in two publicly available cancer datasets as well as on a synthetic dataset. In addition, we validated our model predictions with lab experiments using an in-house cancer cell line panel. We finally show the practical applicability of the proposed method by utilizing prior knowledge to infer pathway-drug response associations, opening up the opportunity for elucidating drug action mechanisms. We demonstrate that pathway-response associations can be learned by the proposed model for the well-known EGFR and MEK inhibitors.Availability and implementation: The source code implementing the method is available at http://research.cs.aalto.fi/pml/software/cwkbmf/.Contacts: muhammad.ammad-ud-din@aalto.fi or samuel.kaski@aalto.fiSupplementary information: Supplementary data are available at Bioinformatics online.
Global proteomics profiling improves drug sensitivity prediction : results from a multi-omics, pan-cancer modeling approach
(2018-04-15) Ali, Mehreen; Khan, Suleiman A.; Wennerberg, Krister; Aittokallio, Tero
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Motivation: Proteomics profiling is increasingly being used for molecular stratification of cancer patients and cell-line panels. However, systematic assessment of the predictive power of large-scale proteomic technologies across various drug classes and cancer types is currently lacking. To that end, we carried out the first pan-cancer, multi-omics comparative analysis of the relative performance of two proteomic technologies, targeted reverse phase protein array (RPPA) and global mass spectrometry (MS), in terms of their accuracy for predicting the sensitivity of cancer cells to both cytotoxic chemotherapeutics and molecularly targeted anticancer compounds. Results: Our results in two cell-line panels demonstrate how MS profiling improves drug response predictions beyond that of the RPPA or the other omics profiles when used alone. However, frequent missing MS data values complicate its use in predictive modeling and required additional filtering, such as focusing on completely measured or known oncoproteins, to obtain maximal predictive performance. Rather strikingly, the two proteomics profiles provided complementary predictive signal both for the cytotoxic and targeted compounds. Further, information about the cellular-abundance of primary target proteins was found critical for predicting the response of targeted compounds, although the non-target features also contributed significantly to the predictive power. The clinical relevance of the selected protein markers was confirmed in cancer patient data. These results provide novel insights into the relative performance and optimal use of the widely applied proteomic technologies, MS and RPPA, which should prove useful in translational applications, such as defining the best combination of omics technologies and marker panels for understanding and predicting drug sensitivities in cancer patients.
Systematic identification of feature combinations for predicting drug response with Bayesian multi-view multi-task linear regression
(2017-07-15) Ammad-Ud-Din, Muhammad; Khan, Suleiman A.; Wennerberg, Krister; Aittokallio, Tero
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Motivation: A prime challenge in precision cancer medicine is to identify genomic and molecular features that are predictive of drug treatment responses in cancer cells. Although there are several computational models for accurate drug response prediction, these often lack the ability to infer which feature combinations are the most predictive, particularly for high-dimensional molecular datasets. As increasing amounts of diverse genome-wide data sources are becoming available, there is a need to build new computational models that can effectively combine these data sources and identify maximally predictive feature combinations. Results: We present a novel approach that leverages on systematic integration of data sources to identify response predictive features of multiple drugs. To solve the modeling task we implement a Bayesian linear regression method. To further improve the usefulness of the proposed model, we exploit the known human cancer kinome for identifying biologically relevant feature combinations. In case studies with a synthetic dataset and two publicly available cancer cell line datasets, we demonstrate the improved accuracy of our method compared to the widely used approaches in drug response analysis. As key examples, our model identifies meaningful combinations of features for the well known EGFR, ALK, PLK and PDGFR inhibitors.