Fecal microbiota transplantation plus tislelizumab and fruquintinib in refractory microsatellite stable metastatic colorectal cancer: an open-label, single-arm, phase II trial (RENMIN-215)

Abstract

Background: Immunotherapy has revolutionized the treatment of cancer. However, microsatellite stable (MSS) metastatic colorectal cancer (mCRC) shows a low response to PD-1 inhibitors. Antiangiogenic therapy can enhance anti-PD-1 efficacy, but it still cannot meet clinical needs. Increasing evidence supported a close relationship between gut microbiome and anti-PD-1 efficacy. This study aimed to explore the efficacy and safety of the combination of fecal microbiota transplantation (FMT) and tislelizumab and fruquintinib in refractory MSS mCRC.

Methods: In the phase II trial, MSS mCRC patients were administered FMT plus tislelizumab and fruquintinib as a third-line or above treatment. The primary endpoint was progression-free survival (PFS). Secondary endpoints were overall survival (OS), objective response rate (ORR), disease control rate (DCR), duration of response (DoR), clinical benefit rate (CBR), safety and quality of life. Feces and peripheral blood were collected for exploratory biomarker analysis. This study is registered with Chictr.org.cn, identifier ChiCTR2100046768.

Findings: From May 10, 2021 to January 17, 2022, 20 patients were enrolled. Median follow-up was 13.7 months. Median PFS was 9.6 months (95% CI 4.1-15.1). Median OS was 13.7 months (95% CI 9.3-17.7). Median DoR was 8.1 months (95% CI 1.7-10.6). ORR was 20% (95% CI 5.7-43.7). DCR was 95% (95% CI 75.1-99.9). CBR was 60% (95% CI 36.1-80.9). Nineteen patients (95%) experienced at least one treatment-related adverse event (TRAE). Six patients (30%) had grade 3-4 TRAEs, with the most common being albuminuria (10%), urine occult blood (10%), fecal occult blood (10%), hypertension (5%), hyperglycemia (5%), liver dysfunction (5%), hand-foot skin reaction (5%), and hypothyroidism (5%). No treatment-related deaths occurred. Responders had a high-abundance of Proteobacteria and Lachnospiraceae family and a low-abundance of Actinobacteriota and Bifidobacterium. The treatment did not change the structure of peripheral blood TCR repertoire. However, the expanded TCRs exhibited the characteristics of antigen-driven responses in responders.

Interpretation: FMT plus tislelizumab and fruquintinib as third-line or above treatment showed improved survival and manageable safety in refractory MSS mCRC, suggesting a valuable new treatment option for this patient population.

Funding: This study was supported by the National Natural Science Foundation of China (82102954 to Wensi Zhao) and the Special Project of Central Government for Local Science and Technology Development of Hubei Province (ZYYD2020000169 to Yongshun Chen).

Keywords: Fecal microbiota transplantation; Fruquintinib; Metastatic colorectal cancer; Microsatellite stable; Tislelizumab.

Fig. 1

CONSORT diagram of the study.

Fig. 2

Flowchart of the trial.

Fig. 3

Tumor response and Kaplan–Meier curves. (A) PFS and (B) OS were assessed in the ITT population (n = 20). (C) DoR was assessed in PR patients (n = 4). (D) Spider plot of tumor response in target lesions by month (Red line was defined as a patient who had survived without disease progression for more than 6 months). Data cut-off date for survival results was July 10, 2023. CI, confidence interval; DoR, duration of response; ITT, intention-to-treat; NR, non-responders; OS, overall survival; PFS, progression-free survival; R, responders.

Fig. 4

Antitumor activity. (A) Waterfall plot showing the best percent change in the size of target lesions from baseline. The dashed lines at +20% and −30% indicate thresholds for PD and PR, respectively, according to RECIST 1.1. (B) Swimming plot for the onset of response, duration of response, and outcome. ECOG PS, Eastern Cooperative Oncology Group performance status; PD, progressive disease; PR, partial response; Rego, regorafenib; SD, stable disease; TRAE, treatment-related adverse event.

Fig. 5

Typical case presentation. Representative images of two patients and the dynamic changes of serum tumor markers. Bev, bevacizumab; C, cycle; CAPEOX/XELOX, capecitabine/oxaliplatin; CEA, carcinoembryonic antigen; FMT: fecal microbiota transplantation; irAEs: immune-related adverse events; XELIRI, capecitabine/irinotecan; PD, progressive disease; Rego, regorafenib; SD, stable disease.

Fig. 6

The gut microbiome after study treatment in responders significantly differed from that in non-responders. (A) Alpha diversity indices as measured by Shannon index (P = 0.036), Simpson index (P = 0.18), and Chao1 index (P = 0.021), demonstrated significantly higher diversity and a trend towards higher unevenness in post-treatment fecal samples in responders than non-responders. (B) Beta diversity in post-treatment samples by response status was statistically significant in PCoA based on Bray Curtis distance (P = 0.0010). (C) Differentially abundant taxa analyzed by LefSe are projected as cladogram (left) and histogram (right). All listed taxa were significantly (Kruskal–Wallis test, P < 0.05; logarithmic LDA score >2) enriched for their respective groups (postNR, red and postR, green). (D) Stacked bar plot showing composition of common bacteria at the phylum level. (E–F) Box plot of the relative abundance of bacteria between groups at phylum and genus level, respectively. (G–H) Bar plots of the top 20 enriched differential KEGG and metabolic pathways in MetaCyc (postR, red and postNR, green). (I) Network analysis and visualization of interactions between species using Gephi. (J–K) Random forest classification for response based on the gut microbiome profiles at the genus level. ROC curve of the random forest classifier. Feature importance dot plot of the random forest classifier. ∗, P < 0.05; ∗∗, P < 0.01; ∗∗∗, P < 0.001. AUC, area under the curve; KEGG, Kyoto Encyclopedia of Genes and Genomes; LDA, linear discriminant analysis; PCoA, principal coordinate analysis; PostR, post-treatment samples from responders; PostNR, post-treatment samples from non-responder; ROC, receiver operator characteristic.

Fig. 7

The expanded TCRs in responders show more cluster structures. (A) The frequency of clonotypes distribution range in each sample. (B) Correlated clone sizes in PBMCs samples. Scatterplots of clonotypes size pre- and post-treatment in the top thousand clonotypes. Red indicates expanded clones (≥2-fold increase), blue indicates contracted clones (≥2-fold decrease). (C) Diagram describes the construction process of similar CDR3 sequences. (D) Clusters formed from CDR3 sequences of expanded clones and contracted clones. (E) CDR3 amino acid physicochemical properties from clustered clones and non-clustered clones in responders. (F) Estimation of the specificity and sensitivity by the logistic regression model with ROC curve. (G) The line plot of the correlation between T cell response score and amino acid physicochemical features of CDR3 middle region position. ∗∗, P < 0.01; ∗∗∗, P < 0.001. CDR, complementarity-determining region; PBMC, Peripheral blood mononuclear cells; ROC, receiver operating characteristic.