Multi-omics of the gut microbial ecosystem in patients with microsatellite-instability-high gastrointestinal cancer resistant to immunotherapy

Abstract

Despite the encouraging efficacy of anti-PD-1/PD-L1 immunotherapy in microsatellite-instability-high/deficient mismatch repair (MSI-H/dMMR) advanced gastrointestinal cancer, many patients exhibit primary or acquired resistance. Using multi-omics approaches, we interrogate gut microbiome, blood metabolome, and cytokines/chemokines of patients with MSI-H/dMMR gastrointestinal cancer (N = 77) at baseline and during the treatment. We identify a number of microbes (e.g., Porphyromonadaceae) and metabolites (e.g., arginine) highly associated with primary resistance to immunotherapy. An independent validation cohort (N = 39) and mouse model are used to further confirm our findings. A predictive machine learning model for primary resistance is also built and achieves an accuracy of 0.79 on the external validation set. Furthermore, several microbes are pinpointed that gradually changed during the process of acquired resistance. In summary, our study demonstrates the essential role of gut microbiome in drug resistance, and this can be utilized as a preventative diagnosis tool and therapeutic target in the future.

Keywords: MSI-H/dMMR; gastrointestinal cancer; gut microbiome; immunotherapy; microsatellite instability-high/deficient mismatch repair.

Graphical abstract

Figure 1

Study design and clinical sample collection (A) 18 patients with gastric cancer (GC) and 59 patients with colorectal cancer (CRC) with MSI-H/dMMR were recruited. Patients were classified as being responders (Rs) or non-Rs (NR). Acquired resistance (AR) patients and long Rs (LRs) were further differentiated in Rs. (B) In total, 290 stool and 70 blood plasma samples were collected. CR, complete response; PD, progressive disease; PR, partial response; SD, stable disease. See also Figure S1.

Figure 2

Gut microbiome composition, functional pathways, and blood metabolites associated with drug response (A) PCoA plot of fecal samples arranged by response and diagnosis using Bray-Curtis dissimilarity. The x and y axes show the first and second principal coordinates, along with the percentage of variance explained on each dimension. NR, non-responders; R, responders. (B) A volcano plot of taxa differentially abundant at baseline between Rs (n = 26) and NRs (n = 22) based on MaasLin2 results. Taxa that differed significantly between groups (p < 0.05) were named accordingly. (C) A volcano plot of metabolome differentially abundant between Rs (n = 46) and NRs (n = 24) based on MaasLin2 results. Metabolome that differed significantly between groups (p < 0.05) were named accordingly. The horizontal axis represents the coefficient value. If the coefficient value is greater than zero, then the metabolite is enriched in the Rs. The vertical axis represents the q values (the adjusted p value). (D) Differences in the relative abundance of different short-chain fatty acids between Rs (n = 46) and NRs (n = 24) based on Wilcoxon test. (E) Top 25 functional pathways enriched in Rs based on the untargeted metabolome data, sorted by p value. See also Figures S2–S7 and Tables S1, S2, S3, S4, and S5.

Figure 3

Enhanced anti-tumor effects in mice following fecal microbiota transplantation (FMT) with feces from Rs (A) Experimental design. (B) Tumor growth curve. Graphic representation of the results of a one-way ANOVA followed by Tukey’s multiple comparisons test. Fecal material was obtained from 3 Rs and 3 NRs, with each gavaged to 3 mice. (n = 9 for R/NR group; n = 3 for control group). ∗∗∗ represents p values < 0.01. (C) Helper T cells (CD3⁺CD4⁺CD8⁻), cytotoxic T cells (CD3⁺CD4⁻CD8⁺), and Treg (CD4⁺CD8⁻FoxP3⁺) T cell population in the tumor microenvironment, quantified through mIHC. Graphic representation of the results of a one-way ANOVA followed by Tukey’s multiple comparisons test. ∗ represents p values < 0.05. (D) Representative images of mIHC of tumor tissue samples. NC, negative control; R, responders; NR, non-responders. Yellow: interferon γ (IFN-γ), green: Foxp3, magenta: CD8, red: CD3, and cyan: CD4. (E) Heatmap showing the differential metabolites identified from blood metabolome. (F) Dot plot showing significantly enriched KEGG pathways based on mouse tumor gene expression data. (G) Network analysis of tumor size, blood metabolites, and T cell population in the tumor microenvironment. Spearman correlations were calculated between each feature comparison, and those yielding an adjusted p value (p.adjust-value) <0.05 were retained. Node size indicates feature degree. See also Tables S6 and S7.

Figure 4

Machine learning model based on microbial biosignature (A) Receiver operating characteristic (ROC) curves of machine learning models based on species (AUC = 0.76). (B) Feature contribution represented by the SHAP value in the three above-mentioned machine learning models.

Figure 5

Gut microbiome associated with AR (A) Differential microbes identified during the progression of drug resistance. (B) Microbial dynamics in representative LR patients, focusing on the differential microbes found during the progression of AR. See also Figures S8–S11 and Table S8.