Independent component analysis improves characterization of cancer patients
Creators: name: Nazarov, Petr V.
name: Muller, Arnaud
name: Eveno, Thomas
name: Kaoma, Tony
name: Azuaje, Francisco
Publisher: name: F1000 Research Limited
Year: 2019

description: 1. Introduction<br>Characterization of cancer patients and comparison of their transcriptomics data to large publicly available datasets, such as TCGA, is limited by several factors. First, the evolving technology introduces technical biases into observations. Thus, newly obtained datasets and reference (or training) data come, in general, from different distributions. Second, data obtained from a bulk sample represent a mixture of transcriptomes of various cell types and may change from one biopsy to another. Third, cancers naturally develop inter and intra-tumour heterogeneity of malignant cells. We recently proposed to use a data-driven deconvolution method – consensus independent component analysis (ICA) to decompose heterogeneous transcriptomics data end extract information relevant for patient diagnostics and prognostics. We applied our approach to investigate melanoma patients, and showed that it was able to predict tumour subtype and patient survival [1]. Here we confirmed the validity of the approach on 2 large cohorts of brain tumour patients.<br><br>2. Approach<br>ICA [2] performs a matrix decomposition, dividing experimentally observed gene expression matrix <i>X<sub>gs</sub></i> into <i>k</i> independent signals <i>S<sub>gk</sub></i> mixed with the coefficients of <i>M<sub>ks</sub></i>. Matrix <i>S</i> shows contribution of genes in different components and its biological meaning can be extracted using gene set enrichment analysis. The weight matrix <i>M</i> can be linked to clinical outcomes, such as tumour subtype and patient survival. In order to capture the technical biases, we combined the new and the reference datasets and applied ICA to the joint dataset. Next, the predictive models for tumour subtypes [3] (such as random forest) and survival (Cox regression) were built and trained on the reference dataset. We developed a hazard score <i>HS</i> that combines stability of the <i>i</i>-th independent component <i>R<sup>2</sup><sub>i</sub></i>, Cox log-hazard ratio <i>H<sub>i</sub></i> and centred/scaled weight matrix <i>M<sup>*</sup></i>.<br><br>3. Results and discussion<br>The proposed approach was applied to 2 TCGA datasets: GBM (glioblastoma multiform, 171 samples) and LGG (low grade glioma, 530 samples). As an independent cohort we used 325 samples from Chinese Glioma Cohort (CGGA). Despite the fact that the data showed a significant difference in distributions , the proposed method was able to predict patient survival in the new dataset. Cox regression between the estimated HS and the actual survival gave p-value = 3.4e-18.<br>Next, we investigated potential of predicting tumour subtype and IDH1 mutation status that are vital for the patient therapy [3]. First, we trained a classifier exclusively on TCGA and applied to CGGA. The accuracy of such classification was lower then expected by cross-validation on TCGA cohort: Acc<sub>subtype</sub>=0.62 (expected 0.9) and Acc<sub>IDH1</sub>=0.8 (expected 0.99). However, if at least a part of CGGA cohort was included into the training set, the accuracy was strongly improved: Acc<sub>subtype</sub> = 0.87, Acc<sub>IDH1</sub>=0.97. Therefore, the diagnostics of the patients requires that at least some patients in the training set come from the same distribution as the testing set.<br><br>4. Conclusions<br>(1) Consensus ICA corrects technical biases and improves comparability of the new data to the reference dataset.<br>(2) Weights of the ICA-derived components can be used to predict patient survival and classes. The later was demonstrated on a new CGGA dataset.<br>(3) ICA provides information about biological processes in the new samples and allows scoring them.<br><br>References

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