Method for diagnosing cancer using transcriptome-based immune repertoire profiling

A method of diagnosing cancer diagnosis based on immunological profiling using transcriptomes, the method comprising:

• (a) obtaining whole transcriptome sequencing (WTS) information from a biological sample;
• (b) aligning the obtained WTS information to a reference genome database;
• (c) extracting immune cell receptor features and transcriptome features from the aligned WTS information;
• and
• (d) inputting the extracted features to an artificial intelligence model trained to distinguish between a cancer patient and a normal subject and comparing the analyzed output value with a cut-off value to determine whether or not there is cancer.

A device for diagnosing cancer based on immunological profiling, the device comprising:

• a decoder configured to decode whole transcriptome sequencing (WTS) information from a biological sample;
• an aligner configured to align the decoded WTS information to a reference genome database;
• a feature detector configured to extract immune cell receptor features and transcript features from the aligned WTS information;
• and
• a cancer diagnostic unit configured to compare the analyzed output obtained by inputting the extracted features to an artificial intelligence model trained to distinguish between a cancer patient and a normal subject, with a cut-off value, and determine whether or not cancer is present.

A computer-readable storage medium including an instruction configured to be executed by a processor for providing information for diagnosis of cancer, through the following steps comprising:

• (a) obtaining whole transcriptome sequencing (WTS) information from a biological sample;
• (b) aligning the obtained WTS information to a reference genome database;
• (c) extracting immune cell receptor features and transcriptome features from the aligned WTS information;
• (d) inputting the extracted features to an artificial intelligence model trained to distinguish between a cancer patient and a normal subject, comparing the analyzed output value with a cut-off value, and primarily determining whether or not there is cancer;
• and
• (e) inputting the immune cell receptor features and transcriptome features of a sample primarily determined that there is cancer into a second artificial intelligence model trained to distinguish between an immunoactive state and cancer, comparing the analyzed output value with a cut-off value, and finally determining whether or not there is cancer.