Abstract
The paper represents a novel approach for individual medical treatment in oncology, based on machine learning with transferring gene expression data, obtained on cell lines, onto individual cancer patients for drug efficiency prediction. We give a detailed analysis how to build drug response classifiers, on the example of three experimental pairs of data “kind of cancer/chosen drug for treatment”. The main hardness of the problem was the meager size of patient training data: it is many many hundred times smaller than a dimensionality of original feature space.
The core feature of our transfer technique is to avoid extrapolation in the feature space when make any predictions of the clinical outcome of the treatment for a patient using gene expression data for cell lines. We can assure that there is no extrapolation by special selection of dimensions of the feature space, which provide sufficient number, say M, of cell line points both below and above any point that correspond to a patient. Additionally, in a manner that is a little similar to the k nearest neighbor (kNN) method, after the selection of feature subspace, we take into account only K cell line points that are closer to a patient’s point in the selected subspace. Having varied different feasible values of K and M, we showed that the predictor’s accuracy considered AUC, for all three cases of cancer-like diseases are equal or higher than 0.7.
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Notes
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In that one could find an analogy between the situation and very popular now case, called “domain adaptation” [5].
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This work was supported by the Russian Science Foundation grant 18-15-00061.
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Appendices: Materials and Methods
Appendices: Materials and Methods
Transcriptome Profiling for Renal Cancer Samples
The details of experimental procedure at Illumina HumanHT-12v4 and CustomArray ECD 4X2K/12K platform were reported previously [19]. Raw expression data were deposited in the GEO database (http://www.ncbi.nlm.nih.gov/geo/), accession numbers GSE52519 and GSE65635.
Harmonization of Illumina and Custom Array Expression Profiles for Renal Cancer
To cross-harmonize the results for the Illumina and CustomArray gene expression profiling, all expression profiles were transformed with the XPN method [28] using the R package CONOR [29].
SVM, Binary Tree and Random Forest Machine Learning Procedures
All the SVM calculations were performed using the R package ‘e1071’ [30], that employs the C++ library ‘libsvm’ [31]. Calculations according to binary tree [14] and random forest [15] methods were done with the R packages ‘rpart’ and ‘randomForest’, respectively.
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Borisov, N., Tkachev, V., Buzdin, A., Muchnik, I. (2018). Prediction of Drug Efficiency by Transferring Gene Expression Data from Cell Lines to Cancer Patients. In: Rozonoer, L., Mirkin, B., Muchnik, I. (eds) Braverman Readings in Machine Learning. Key Ideas from Inception to Current State. Lecture Notes in Computer Science(), vol 11100. Springer, Cham. https://doi.org/10.1007/978-3-319-99492-5_9
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