Combined Treatment with Immunotherapy-Based Strategies for MSS Metastatic Colorectal Cancer

Simple Summary Given that most patients with MSS mCRC do not respond to immunotherapy agents or do not have durable clinical responses, there is an unmet clinical need to obtain predictors of response to immunotherapy and rational immunotherapy-based combination therapies for this entity. In this review, we present the efforts that have been made to date in the clinical setting to develop immunotherapy-based combinations for MSS mCRC and the rationale that lies behind each strategy. In recent years, deepening knowledge of the complex interactions between the immune system and cancer cells has led to the advent of effective immunotherapies that have revolutionized the therapeutic paradigm of several cancer types. However, colorectal cancer (CRC) is one of the tumor types in which immunotherapy has proven less effective. While there is solid clinical evidence for the therapeutic role of immune checkpoint inhibitors in mismatch repair-deficient (dMMR) and in highly microsatellite instable (MSI-H) metastatic CRC (mCRC), blockade of CTLA-4 or PD-L1/PD-1 as monotherapy has not conferred any major clinical benefit to patients with MMR-proficient (pMMR) or microsatellite stable (MSS) mCRC, reflecting 95% of the CRC population. There thus remains a high unmet medical need for the development of novel immunotherapy approaches for the vast majority of patients with pMMR or MSS/MSI-low (MSI-L) mCRC. Defining the molecular mechanisms for immunogenicity in mCRC and mediating immune resistance in MSS mCRC is needed to develop predictive biomarkers and effective therapeutic combination strategies. Here we review available clinical data from combinatorial therapeutic approaches using immunotherapy-based strategies for MSS mCRC.

Automated summary

The review provides insights into the challenges of immunotherapy in MSS mCRC and highlights the need for developing predictive biomarkers and effective therapeutic combination strategies for this patient population.