# TCRpred: incorporating T-cell receptor repertoire for clinical outcome prediction

## TL;DR

This paper introduces TCRpred, a tool that uses T-cell receptor data to predict clinical outcomes, showing it works better than other methods.

## Contribution

The novel contribution is TCRpred, a method that effectively uses complex TCR data for improved clinical predictions.

## Key findings

- TCRpred performs well in predicting clinical outcomes in simulations.
- The method outperforms alternative approaches in most cases.
- TCRpred shows practical utility when applied to real cancer datasets.

## Abstract

T-cell receptor (TCR) plays critical roles in recognizing antigen peptides and mediating adaptive immune response against disease. High-throughput technologies have enabled the sequencing of TCR repertoire at the single nucleotide level, allowing researchers to characterize TCR sequences with high resolutions. The TCR sequences provide important information about patients’ adaptive immune system, and have the potential to improve clinical outcome prediction. However, it is challenging to incorporate the TCR repertoire data for prediction, because the data is unstructured, highly complex, and TCR sequences vary widely in their compositions and abundances across different individuals. We introduce TCRpred, an analytic tool for incorporating TCR repertoire for clinical outcome prediction. The TCRpred is able to utilize features that can be extracted from the TCR amino acid sequences, as well as features that are hidden in the TCR amino acid sequences and are hard to extract. Simulation studies show that the proposed approach has a good performance in predicting clinical outcome and tends to be more powerful than potential alternative approaches. We apply the TCRpred to real cancer datasets and demonstrate its practical utility in clinical outcome prediction.