Cancer Res Treat.  2022 Oct;54(4):1175-1190. 10.4143/crt.2021.1133.

Histopathologic and Molecular Biomarkers of PD-1/PD-L1 Inhibitor Treatment Response among Patients with Microsatellite Instability‒High Colon Cancer

• Hyung J
• Cho EJ
• Kim J
• Kim JH
• Kim JE
• Hong YS
• Kim TW
• Sung CO
• Kim SY

• KMID: 2534197
• DOI: http://doi.org/10.4143/crt.2021.1133

Abstract

Purpose
Recent clinical trials have reported response rates < 50% among patients treated with programmed death-1 (PD-1)/programmed death-ligand 1 (PD-L1) inhibitors for microsatellite instability‒high (MSI-H) colorectal cancer (CRC), and factors predicting treatment response have not been fully identified. This study aimed to identify potential biomarkers of PD-1/PD-L1 inhibitor treatment response among patients with MSI-H CRC.
Materials and Methods
MSI-H CRC patients enrolled in three clinical trials of PD-1/PD-L1 blockade at Asan Medical Center (Seoul, Republic of Korea) were screened and classified into two groups according to treatment response. Their histopathologic features and expression of 730 immune-related genes from the NanoString platform were evaluated, and a machine learning–based classification model was built to predict treatment response among MSI-H CRCs patients.
Results
A total of 27 patients (15 responders, 12 non-responders) were included. A high degree of lymphocytic/neutrophilic infiltration and an expansile tumor border were associated with treatment response and prolonged progression-free survival (PFS), while mucinous/signet-ring cell carcinoma was associated with a lack of treatment response and short PFS. Gene expression profiles revealed that the interferon-γ response pathway was enriched in the responder group. Of the top eight differentially expressed immune-related genes, PRAME had the highest fold change in the responder group. Higher expression of PRAME was independently associated with better PFS along with histologic subtypes in the multivariate analysis. The classification model using these genes showed good performance for predicting treatment response.
Conclusion
We identified histologic and immune-related gene expression characteristics associated with treatment response in MSI-H CRC, which may contribute to optimal patient stratification.

Keyword

Microsatellite instability; Colonic neoplasms; Immune checkpoint inhibitors; Biomarker; Transcriptome profiles; Histology; Machine learning

Figure

• 

  Fig. 1 Case selection and study design according to the response to immune checkpoint inhibitor treatment among patients with deficient mismatch repair (dMMR)/microsatellite instability–high (MSI-H) metastatic colorectal cancers. CR, complete response; MSS, microsatellite stable; PD-1, programmed death-1; PD-L1, programmed death-ligand 1; PR, partial response; SD, stable disease.

• 

  Fig. 2 Histopathologic features associated with ICI response among patients with dMMR/MSI-H CRC. (A) Representative histologic features of MSI-H CRCs among responders and non-responders. High degree of lymphocytic infiltration along the tumor border in the responder group (upper left, ×200). High degree of neutrophil infiltration grade along the tumor-stroma interface in the responder group (middle left, ×200). Expansile tumor border and surrounding of tumor cells by inflammatory cell infiltrates in the responder group (lower left, ×40). Infiltrative tumor border in the non-responder group (upper right, ×40). Mucinous adenocarcinoma with abundant extracellular mucin separating tumor cells from adjacent stroma in the non-responder group (middle right, ×100). Signet-ring cell carcinoma without signs of inflammatory cell infiltration along the tumor-stroma interface in the non-responder group (lower right, ×200). (B) Response status after ICI during follow-up and histopathologic features in patients with MSI-H CRCs. (C) Progression-free survival (log-rank test) of patients with MSI-H CRCs after ICI according to tumor histology, lymphocyte infiltration, and tumor border. (D) Multivariable logistic regression analysis of treatment response with histopathologic variables. CR, complete response; CRC, colorectal cancer; dMMR, deficient mismatch repair; ICI, immune checkpoint inhibitor; LA, lymphoid aggregate; MSI-H, microsatellite instability–high; ND, not determined; PD, progressive disease; PR, partial response; SD, stable disease.

• 

  Fig. 3 Differential expression analysis of immune genes between the two groups (11 responders vs. 8 non-responders). (A) Heatmap of differentially expressed immune genes (absolute log2FC > 0.5 and p < 0.05 by Wilcoxon rank-sum test) between the two groups. (B) Enriched pathways in gene ontology enrichment analysis of responders and non-responders. (C) Volcano plots highlighting genes with significantly higher expression in the responder group and non-responder group (absolute log2FC > 0.5 and p < 0.05 by Wilcoxon rank-sum test). (D) The comparative expression levels of genes with significantly higher expression in the responder group (BST2, CCL18, CCL28, CXCL1, CXCL11, and PRAME) and the non-responder group (ABCB1 and CD99). CRC, colorectal cancer; DEG, differentially expressed genes; MSI-H, microsatellite instability–high.

• 

  Fig. 4 Association between PRAME expression and response to ICIs. (A) Representative IHC results of PRAME with nuclear expression in CRC cells (×100) and significant correlation between protein expression and mRNA expression (p=0.0036, Wilcoxon rank-sum test). (B) Spider plot of the changes in the sum of target lesions from the baseline along ICI treatment with annotation of the PRAME IHC results. (C) Swimmer plot showing the clinical response and duration of ICI treatment with PRAME IHC results and PRAME mRNA expression levels. (D) Progression-free survival outcomes according to PRAME protein expression and PRAME mRNA expression among patients with MSI-H CRCs after ICI treatment (log-rank test). (E) Multivariable Cox regression analysis for progression-free survival. CI, confidence interval; CR, complete response; CRC, colorectal cancer; ICI, immune checkpoint inhibitor; IHC, immunohistochemistry; MSI-H, microsatellite instability–high; PD, progressive disease; PR, partial response; SD, stable disease.

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  Fig. 5 Prediction modeling for treatment response to ICIs among patients with MSI-H CRCs. (A) Overview of the processes of prediction model building using RF based on immune-related gene expression and internal validation of the model using the LOOCV method. (B) Feature importance of input genes in modeling by RF. (C) Performance of the prediction model. AUC, area under the curve; CRC, colorectal cancer; DEG, differentially expressed genes; ICI, immune checkpoint inhibitor; LOOCV, leave-one-out cross-validation; MSI-H, microsatellite instability–high; NPV, negative predictive value; PPV, positive predictive value; RF, random forest; ROC, receiver operating characteristic.

Reference

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