Immunotherapeutic treatment represents a highly efficacious approach to controlling the growth of many tumors, and can improve the quality of life for advanced CRC patients. However, many patients fail to respond to immunotherapies, and the Association for Cancer Immunotherapy has emphasized the importance of identifying those patients most likely to benefit from these therapeutic regimens [25].
Herein, we developed a quantitative approach to evaluating the immunological TME associated with CRC patient tumors. The results of this study indicate that ICI scores represent a valuable prognostic biomarker that can be used to predict outcomes in these patients, and we found that higher ICI scores as well as increased intratumoral CD4+, CD8+, plasma cell, and macrophage infiltration were linkedto better patient outcomes, in line with prior research [26–27]. This underscores the potential for already extant immune responses to achieve anti-tumor effects and to thereby affect the way cancer patients respond to immunotherapy. CRC tumors are thought to exhibit among the highest levels of immune cell infiltration on average [28]. We thus hypothesized that comprehensively characterizing ICI profiles and related patterns of gene expression would represent a novel approach to developing patient-specific evaluation and treatment strategies. We began by characterizing the TME associated with CRC tumors at a molecular level, enabling us to identify immune-related genes based upon ICI gene clusters. ICI gene cluster B was associated with lower immune scores, matrix scores, and with decreased immune cell infiltration consistent with what is often referred to as a ‘cold’ immune phenotype. In contrast, ICI gene cluster A was associated with higher immune scores and inflammatory cell infiltration, and these patients had a more favorable prognosis as well as increased CD8 + T cell, activated CD4 + T cell, and plasma cell infiltration [29–30]. Indeed, many prior studies have highlighted the effects of the TME on cancer patient OS [31]. The anti-tumor immune responses observed for patients in ICI gene cluster A suggest that they are likely to attain more benefits from immunotherapeutic treatment, and these clusters may thus offer value for the development of more efficacious immunotherapies. However, given the significant heterogeneity associated with the immunological TME in a given patient, it is vital that ICI patterns be evaluated on a patient-by-patient basis. Consistent with such an approach, one research group has reported to use of an individually-tailored tumor-specific biomarker model that can more reliably gauge breast cancer patient prognosis [32].
Using the Boruta algorithm, we established an ICI score that we were then able to analyze as a biomarker of CRC patient prognosis. GSEA analyses of these genes composing these ICI-related patterns revealed the regulation and vascular signaling pathways to be significantly enriched among samples from those with low ICI scores, while butanoate and retinol signaling pathways were enriched among those with high ICI scores. Recent wor has clarified the relationship between genetic mutations and patient sensitivity to immunotherapy [33–34]. By leveraging this fact and evaluating key immunological parameters including consensus ICI scores, tumor driver mutation analyses [35], TMB measurements [36], LOH HLA (loss of heterozygosity at the HLA locus) assessments [37–38], PD-L1 expression analyses [39], and the detection of key immune gene expression-related signatures [40–41], it is possible to more reliably classify particular cancers. Integrated ICI scores offered new insights regarding differences in variant frequencies in many genes when comparing samples from the low and high ICI score groups, with some of these genes being directly linked to therapeutic sensitivity or resistance. When we performed a stratified analysis, we determined the prognostic value of ICI scores to be independent of TMB status for CRC patients, suggesting that these two metrics offer distinct insights into the immunobiology of patient tumors, enabling the more robust assessment of patient outcomes. After employing two different algorithms to classify 712 CRC patients based upon their ICI profiles, we identified two ICI patterns and were able to employ a PCA approach to derive ICI scores therefrom. Higher ICI scores were associated with significantly higher patient OS relative to lower ICI scores, and this remained true in patients with advanced disease and in those ≤ 65-years-old. High MARCO expression in those with low ICI score subtype disease was correlated with increased Treg infiltration and reduced levels of NK and effector T cells, which may be linked to poorer patient prognosis.
Lastly, we explored ICI scores and associated patient characteristics, and found that simultaneous analyses of ICI scores and TMB status may improve our ability to reliably gauge CRC patient prognosis, offering a means of potentially identifying patients at a higher risk of tumor recurrence, although further research is required to test this possibility. ICI scores nonetheless represent a powerful tool for estimating tumor-specific immune fitness, and can be used to predict which patients are most likely to benefit from immunotherapy, thereby helping to improve CRC patient survival.