# Colorectal Cancer With High Tumor Mutational Burden and Mesenchymal-Epithelial Transition (MET) Amplification With Hyperprogressive Disease After Pembrolizumab Treatment

## TL;DR

A patient with colorectal cancer and specific genetic features experienced rapid tumor growth after treatment with pembrolizumab, highlighting a potential risk.

## Contribution

First report of hyperprogressive disease in a TMB-H colorectal cancer patient with MET amplification after pembrolizumab.

## Key findings

- Patient with MET amplification and TMB-H developed hyperprogressive disease after pembrolizumab.
- MET amplification may be linked to the development of hyperprogressive disease in TMB-H tumors.
- The case highlights the need for caution in selecting immune checkpoint inhibitor candidates.

## Abstract

Pembrolizumab, an immune checkpoint inhibitor, has shown efficacy in tumor mutational burden-high (TMB-H) solid tumors and has been approved for the treatment of these diseases. Following immune checkpoint inhibitor administration, rapid tumor progression, known as hyperprogressive disease (HPD), has been observed. This report presents the case of a 60-year-old woman diagnosed with mesenchymal-epithelial transition (MET) amplification and TMB-H colorectal cancer. The patient was initially administered chemotherapy for MET amplification in a clinical trial but was considered refractory following one treatment cycle. Subsequently, she was treated with pembrolizumab for the TMB-H solid tumor. However, she developed HPD one month after starting pembrolizumab treatment and later died in the hospital. To the best of our knowledge, this is the first report of HPD in a patient with colorectal cancer harboring both MET amplification and TMB-H. It suggests that MET amplification may be involved in HPD development. These findings underscore the need for vigilance regarding HPD risk when selecting immune checkpoint inhibitor candidates and highlight the importance of future research, such as exploring MET-targeted combination strategies, in the optimization of treatment outcomes.