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bioRxiv · 10.1101/2021.09.06.459161

Quantifying Intratumor Heterogeneity by Key Genes Selected Using Concrete Autoencoder

Tanvir, R. B.·Sobhan, M.·Mamun, A. A.·Mondal, A. M.
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Abstract

The tumor cell population in cancer tissue has distinct molecular characteristics and exhibits different phenotypes, thus, resulting in different subpopulations. This phenomenon is known as Intratumor Heterogeneity (ITH), a major contributor to drug resistance, poor prognosis, etc. Therefore, quantifying the levels of ITH in cancer patients is essential, and many algorithms do so in different ways, using different types of omics data. DEPTH (Deviating gene Expression Profiling Tumor Heterogeneity) is the latest algorithm that uses transcriptomic data to evaluate the ITH score. It shows promising performance, has strong similarity with six other algorithms and has an advantage over two algorithms that uses the same type of data (tITH, sITH). However, it has a major drawback since it uses expression values of all the genes ([~]20K genes) in quantifying ITH levels. We hypothesize that a subset of key genes is sufficient to quantify the ITH level. To prove our hypothesis, we developed a deep learning-based computational framework using unsupervised Concrete Autoencoder (CAE) to select a set of cancer-specific key genes that can be used to evaluate the ITH score. For the experiment, we used gene expression profile data of tumor cohorts of breast, kidney, and lung cancer from the TCGA repository. Using multi-run CAE, we selected three sets of key genes, each set related to breast, kidney, and lung tumor cohorts. For the three cancers stated and three molecular subtypes of lung cancer, we calculated the ITH level using all genes and key genes selected by CAE and performed a side-by-side comparison. We could reach similar conclusions for survival and prognostic outcomes based on ITH scores derived from all genes and the sets of key genes. Additionally, for subtypes of lung cancer, the comparative distribution of ITH scores derived from all and key genes remains similar. Based on these observations, it can be stated that a subset of key genes, instead of all genes, is sufficient for ITH quantification. Our results also showed that many key genes are prognostically significant, which can be used as possible therapeutic targets.

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Tanvir, R. B., Sobhan, M., Mamun, A. A., Mondal, A. M.. 2021-09-06. Quantifying Intratumor Heterogeneity by Key Genes Selected Using Concrete Autoencoder. https://doi.org/10.1101/2021.09.06.459161

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