Discriminate the response of Acute Myeloid Leukemia patients to treatment by using proteomics data and Answer Set Programming

BackgroundDuring the last years, several approaches were applied on biomedical data to detect disease specific proteins and genes in order to better target drugs. It was shown that statistical and machine learning based methods use mainly clinical data and improve later their results by adding omics data. This work proposes a new method to discriminate the response of Acute Myeloid Leukemia (AML) patients to treatment. The proposed approach uses proteomics data and prior regulatory knowledge in the form of networks to predict cancer treatment outcomes by finding out the different Boolean networks specific to each type of response to drugs. To show its effectiveness we evaluate our method on a dataset from the DREAM 9 challenge.ResultsThe results are encouraging and demonstrate the benefit of our approach to distinguish patient groups with different response to treatment. In particular each treatment response group is characterized by a predictive model in the form of a signaling Boolean network. This model describes regulatory mechanisms which are specific to each response group. The proteins in this model were selected from the complete dataset by imposing optimization constraints that maximize the difference in the logical response of the Boolean network associated to each group of patients given the omic dataset. This mechanistic and predictive model also allow us to classify new patients data into the two different patient response groups.ConclusionsWe propose a new method to detect the most relevant proteins for understanding different patient responses upon treatments in order to better target drugs using a Prior Knowledge Network and proteomics data. The results are interesting and show the effectiveness of our method.

[1]  Gustavo Henrique Goulart Trossini,et al.  Use of machine learning approaches for novel drug discovery , 2016, Expert opinion on drug discovery.

[2]  Julio Saez-Rodriguez,et al.  Revisiting the Training of Logic Models of Protein Signaling Networks with ASP , 2012, CMSB.

[3]  Chitta Baral,et al.  Knowledge Representation, Reasoning and Declarative Problem Solving , 2003 .

[4]  Jing Chen,et al.  NDEx, the Network Data Exchange. , 2015, Cell systems.

[5]  Nikos A. Vlassis,et al.  The global k-means clustering algorithm , 2003, Pattern Recognit..

[6]  P. Shannon,et al.  Cytoscape: a software environment for integrated models of biomolecular interaction networks. , 2003, Genome research.

[7]  Julio Saez-Rodriguez,et al.  caspo: a toolbox for automated reasoning on the response of logical signaling networks families , 2016, Bioinform..

[8]  Kihyun Kim,et al.  BCL2 gene polymorphism could predict the treatment outcomes in acute myeloid leukemia patients. , 2010, Leukemia research.

[9]  Yuri Fedoriw,et al.  Genetic tests to evaluate prognosis and predict therapeutic response in acute myeloid leukemia. , 2010, The Journal of molecular diagnostics : JMD.

[10]  Lincoln Stein,et al.  Reactome: a database of reactions, pathways and biological processes , 2010, Nucleic Acids Res..

[11]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.

[12]  Guanming Wu,et al.  ReactomeFIViz : a Cytoscape app for pathway and network-based data analysis , 2022 .

[13]  Jieping Ye,et al.  Evolution‐informed modeling improves outcome prediction for cancers , 2016, Evolutionary applications.

[14]  Julio Saez-Rodriguez,et al.  OmniPath: guidelines and gateway for literature-curated signaling pathway resources , 2016, Nature Methods.

[15]  Gisbert Schneider,et al.  Deep Learning in Drug Discovery , 2016, Molecular informatics.

[16]  Hiroyuki Ogata,et al.  KEGG: Kyoto Encyclopedia of Genes and Genomes , 1999, Nucleic Acids Res..

[17]  Yuanyuan Wang,et al.  Statistical Methods for High Throughput Screening Drug Discovery Data , 2005 .

[18]  Miroslaw Truszczynski,et al.  Answer set programming at a glance , 2011, Commun. ACM.

[19]  Chris Sander,et al.  Perturbation biology nominates upstream–downstream drug combinations in RAF inhibitor resistant melanoma cells , 2015, eLife.

[20]  Li Liu,et al.  A Crowdsourcing Approach to Developing and Assessing Prediction Algorithms for AML Prognosis , 2016, PLoS Comput. Biol..

[21]  Henning Hermjakob,et al.  The Reactome pathway knowledgebase , 2013, Nucleic Acids Res..

[22]  F. Lo‐Coco,et al.  Early prediction of treatment outcome in acute myeloid leukemia by measurement of WT1 transcript levels in peripheral blood samples collected after chemotherapy , 2008, Haematologica.

[23]  R. Russell,et al.  Illuminating drug discovery with biological pathways , 2005, FEBS letters.

[24]  Gary D. Bader,et al.  Pathway Commons, a web resource for biological pathway data , 2010, Nucleic Acids Res..

[25]  Max Kuhn,et al.  Statistical Methods for Drug Discovery , 2016 .

[26]  Robert F Murphy,et al.  An active role for machine learning in drug development. , 2011, Nature chemical biology.