Biomarker-guided therapy for colorectal cancer: strength in complexity

Abstract

The number of molecularly stratified treatment options available to patients with colorectal cancer (CRC) is increasing, with a parallel rise in the use of biomarkers to guide prognostication and treatment decision-making. The increase in both the number of biomarkers and their use has resulted in a progressively complex situation, evident both from the extensive interactions between biomarkers and from their sometimes complex associations with patient prognosis and treatment benefit. Current and emerging biomarkers also reflect the genomic complexity of CRC, and include a wide range of aberrations such as point mutations, amplifications, fusions and hypermutator phenotypes, in addition to global gene expression subtypes. In this Review, we provide an overview of current and emerging clinically relevant biomarkers and their role in the management of patients with CRC, illustrating the intricacies of biomarker interactions and the growing treatment opportunities created by the availability of comprehensive molecular profiling.

Figure 1.. Clinical implications of biomarker interactions in CRC.

Interactions between established and emerging clinical biomarkers suggest that more comprehensive molecular profiling would improve patient outcomes a. Detection of, and stratification based on genetic and/or clinical features associated with primary resistance, such as alterations in the MAPK signalling pathway, might improve responsiveness to targeted therapies and/or enable the identification of more effective drug combinations. b. The accumulation of low-prevalence ‘actionable’ alterations has the potential to increase the total use of biomarker-guided therapies c. The co-occurrence of more than one ‘actionable’ alteration might enable new treatment options when resistance develops, although the most appropriate treatment sequence and/or drug combinations need to be determined. BRAFi, BRAF inhibitor; ERBB2^amp, amplification of ERBB2/HER2; Fusion+, positive for kinase gene fusions; MSI-H, microsatellite instability-high; RASwt, RAS wild-type.

Figure 2.. Optimization of immunotherapy in CRC.

Many of the genetic and/or clinical features that determine responsiveness to immune-checkpoint inhibitors (ICIs) (inside circle) are associated with genotypes and phenotypes (outside circle) that can be modulated. In patients with CRC, clinical (red and blue text) or pre-clinical (white text) data are available on a few biomarkers and/or mechanisms that might enable the modification of treatment responses. The best-described mechanisms of resistance in patients with hypermutated and/or immunogenic cancers include loss of IFNγ response owing to JAK1 mutations. The potential to promote immune-cell infiltration is strongest in tumours with an immunosuppressive phenotype, although limited clinical data are available on this possibility in patients with CRC. Experimental data suggest that chemotherapies, inhibition of TGFβ, as well as epigenetic modifiers that target MDSCs might all promote immune-cell infiltration. The expansion of this simplified model is an important task for the optimization of ICIs in the coming years. CMS, consensus molecular subtypes; CTL, CD8+ cytotoxic lymphocyte; IFNγ, interferon-gamma; MDSC, myeloid-derived suppressor cells; MSI-H, microsatellite instability-high; MSS, microsatellite stable; TGFβ-i, TGFβ inhibition; Th1, T helper 1 cell; T-reg, regulatory T cell; white arrow up/down; upregulation or increased levels/down-regulation or decreased levels.

Figure 3.. Treatment options and biomarker interactions in metastatic CRCs.

EGFR targeted therapies, guided by RAS mutation status remain the foundation of biomarker stratified medicine in patients with metastatic CRC, although treatment options are expanding, guided by several low-prevalence biomarkers and biomarker combinations. CRCs of the CMS4 subtype and/or those harbouring RAS mutations are important target populations for the development of new treatment strategies. BRAFi, BRAF inhibition; CMS, consensus molecular subtypes; MEKi, MEK inhibition; MSI, microsatellite instability; MSS, microsatellite stability; RAS, KRAS/NRAS.