Resistance to immune checkpoint inhibitors in colorectal cancer with deficient mismatch repair/microsatellite instability: misdiagnosis, pseudoprogression and/or tumor heterogeneity?
Nicola NormannoVincenza CaridiAngelo Paolo Dei TosAntonio AvalloneFortunato CiardielloCarmine PintoPublished in: Exploration of targeted anti-tumor therapy (2024)
Colorectal carcinoma (CRC) with deficiency of the deficient mismatch repair (dMMR) pathway/microsatellite instability (MSI) is characterized by a high mutation load and infiltration of immune cells in the tumor microenvironment. In agreement with these findings, clinical trials have demonstrated a significant activity of immune checkpoint inhibitors (ICIs) in dMMR/MSI metastatic CRC (mCRC) patients and, more recently, in CRC patients with early disease undergoing neoadjuvant therapy. However, despite high response rates and durable clinical benefits, a fraction of mCRC patients, up to 30%, showed progressive disease when treated with single agent anti-programmed cell death 1 (PD-1) antibody. This article discusses the three main causes that have been associated with early progression of dMMR/MSI mCRC patients while on treatment with ICIs, i.e., misdiagnosis, pseudoprogression and tumor heterogeneity. While pseudoprogression probably does not play a relevant role, data from clinical studies demonstrate that some dMMR/MSI CRC cases with rapid progression on ICIs may be misdiagnosed, underlining the importance of correct diagnostics. More importantly, evidence suggests that dMMR/MSI mCRC is a heterogeneous group of tumors with different sensitivity to ICIs. Therefore, we propose novel diagnostic and therapeutic strategies to improve the outcome of dMMR/MSI CRC patients.
Keyphrases
- end stage renal disease
- newly diagnosed
- ejection fraction
- chronic kidney disease
- clinical trial
- prognostic factors
- squamous cell carcinoma
- multiple sclerosis
- small cell lung cancer
- rectal cancer
- patient reported outcomes
- machine learning
- mesenchymal stem cells
- artificial intelligence
- deep learning
- electronic health record
- phase iii
- double blind