Background: The recent success of immunotherapy in treating tumors has attracted increasing interest in research\nrelated to the adaptive immune system in the tumor microenvironment. Recent advances in next-generation\nsequencing technology enabled the sequencing of whole T-cell receptors (TCRs) and B-cell receptors\n(BCRs)/immunoglobulins (Igs) in the tumor microenvironment. Since BCRs/Igs in tumor tissues have high affinities for\ntumor-specific antigens, the patterns of their amino acid sequences and other sequence-independent features such\nas the number of somatic hypermutations (SHMs) may differ between the normal and tumor microenvironments.\nHowever, given the high diversity of BCRs/Igs and the rarity of recurrent sequences among individuals, it is far more\ndifficult to capture such differences in BCR/Ig sequences than in TCR sequences. The aim of this study was to explore\nthe possibility of discriminating BCRs/Igs in tumor and in normal tissues, by capturing these differences using\nsupervised machine learning methods applied to RNA sequences of BCRs/Igs.\nResults: RNA sequences of BCRs/Igs were obtained from matched normal and tumor specimens from 90 gastric\ncancer patients. BCR/Ig-features obtained in Rep-Seq were used to classify individual BCR/Ig sequences into normal or\ntumor classes. Different machine learning models using various features were constructed as well as gradient\nboosting machine (GBM) classifier combining these models. The results demonstrated that BCR/Ig sequences\nbetween normal and tumor microenvironments exhibit their differences. Next, by using a GBM trained to classify\nindividual BCR/Ig sequences, we tried to classify sets of BCR/Ig sequences into normal or tumor classes. As a result, an\narea under the curve (AUC) value of 0.826 was achieved, suggesting that BCR/Ig repertoires have distinct\nsequence-level features in normal and tumor tissues.\nConclusions: To the best of our knowledge, this is the first study to show that BCR/Ig sequences derived from tumor\nand normal tissues have globally distinct patterns, and that these tissues can be effectively differentiated using BCR/Ig\nrepertoires.
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