RESUMO
Adjuvant chemotherapy (ACT) is usually used to reduce the risk of disease relapse and improve survival for stage II/III colorectal cancer (CRC). However, only a subset of patients could benefit from ACT. Thus, there is an urgent need to identify improved biomarkers to predict survival and stratify patients to refine the selection of ACT. We used high-throughput proteomics to analyze tumor and adjacent normal tissues of stage II/III CRC patients with /without relapse to identify potential markers for predicting prognosis and benefit from ACT. The machine learning approach was applied to identify relapse-specific markers. Then the artificial intelligence (AI)-assisted multiplex IHC was performed to validate the prognostic value of the relapse-specific markers and construct a proteomic-derived classifier for stage II/III CRC using 3 markers, including FHL3, GGA1, TGFBI. The proteomics profiling-derived signature for stage II/III CRC (PS) not only shows good accuracy to classify patients into high and low risk of relapse and mortality in all three cohorts, but also works independently of clinicopathologic features. ACT was associated with improved disease-free survival (DFS) and overall survival (OS) in stage II (pN0) patients with high PS and pN2 patients with high PS. This study demonstrated the clinical significance of proteomic features, which serve as a valuable source for potential biomarkers. The PS classifier provides prognostic value for identifying patients at high risk of relapse and mortality and optimizes individualized treatment strategy by detecting patients who may benefit from ACT for survival.
RESUMO
Purpose: Deficient mismatch repair (dMMR) is an established biomarker for the response to the programmed cell death (PD)-1 inhibitors in metastatic colorectal cancer (mCRC). Although patients with dMMR mCRC could achieve a high incidence of disease control and favorable progression-free survival (PFS), reported response rates to PD-1 inhibitors are variable from 28% to 52%. We aimed to explore the additional predictive biomarkers associated with response to anti-PD-1 immunotherapy in patients with dMMR mCRC. Methods: This multicenter cohort study enrolled patients with dMMR mCRC receiving anti-PD-1 immunotherapy at the Sixth Affiliated Hospital of Sun Yat-sen University and Sun Yat-sen University Cancer Center between December 2016 and December 2019. The total information of 20 peripheral blood biomarkers, including T cells (frequency of CD4+ T cell, frequency of CD8+ T cell, and ratio of CD4+/CD8+), carcinoembryonic antigen (CEA), inflammatory markers, and lipid metabolism markers, was collected. The association between response or survival and peripheral blood parameters was analyzed. Results: Among the tested parameters, the ratio of CD4+/CD8+ and frequency of CD4+ T cell were significantly associated with PFS (p = 0.023, p = 0.012) and overall survival (OS; p = 0.027, p = 0.019) in a univariate analysis. A lower level of CD4+/CD8+ ratio or frequency of CD4+ T cell showed a significant association with better overall response rates (ORRs; p = 0.03, p = 0.01). The ratio of CD4+/CD8+ and frequency of CD4+ T cell maintained significance in multivariate Cox model for PFS (HR = 9.23, p = 0.004; HR = 4.83, p = 0.02) and OS (HR = 15.22, p = 0.009; HR = 16.21, p = 0.025). Conclusion: This study indicated that the ratio of CD4+/CD8+ and the frequency of CD4+ T cell might be crucial independent biomarkers within dMMR mCRC to better identify patients for anti-PD-1 immunotherapy. If validated in prospective clinical trials, the ratio of CD4+/CD8+ and the frequency of CD4+ T cell might aid in guiding the treatment of PD-1 inhibitors among patients with dMMR mCRC.