Impact of the tumor immune contexture in microsatellite-stable metastatic colorectal cancer treated with avelumab, cetuximab, and irinotecan

Abstract

The treatment of patients with microsatellite-stable (MSS) metastatic colorectal cancer (mCRC) remains a significant clinical challenge. Cetuximab, an anti-epidermal growth factor receptor (EGFR) monoclonal antibody (mAb), induces immunogenic cell death, potentially synergizing with immune checkpoint inhibitors. The phase 2, proof-of-concept, single-arm AVETUXIRI trial (ClinicalTrials.gov: NCT03608046) evaluates the safety and efficacy of cetuximab, irinotecan (a topoisomerase I inhibitor), and avelumab (an anti-programmed cell death ligand 1 [PD-L1]) in 57 patients with RAS wild-type or mutated MSS mCRC refractory to chemotherapy and anti-EGFR mAbs. Exploratory objectives include investigating the tumor immune microenvironment within mCRC biopsies performed during the trial and correlating it with treatment activity. A manageable safety profile is observed. Although the overall efficacy endpoints are not met, biomarkers associated with clinical efficacy are identified. Patients exhibiting a high Immunoscore, strong cytotoxic and T cell proximity to tumor cells, and a high genetic immunoediting score within mCRC biopsies before treatment demonstrate significant therapeutic survival benefit, independent of RAS tumor mutation status.

Keywords: Immunoscore; RAS mutation; avelumab; biomarkers; cetuximab; immunoediting score; immunofluorescence; immunotherapy; metastatic colorectal cancer; transcriptomics.

Graphical abstract

Figure 1

Clinical trial design, patient cohorts, and omics profiling workflow (A) Clinical trial design and patient cohorts. Diagram illustrating the study treatment regimen. Treatment with cetuximab (500 mg/m² every 2 weeks) and irinotecan (150 mg/m² every 2 weeks) started 2 weeks prior to the initiation of avelumab (10 mg/kg every 2 weeks). This combined treatment continued until progression, with the first radiological evaluation at week 11. Metastasis biopsies were collected at baseline (week 0), before avelumab initiation (week 3), and at the first radiological evaluation (week 11). Patients were split into three cohorts (cohort A: RAS WT and cohorts B and C: RAS MUT), with safety, efficacy, and survival (progression-free survival [PFS] and overall survival [OS]) as clinical objectives. Translational objectives included extensive tumor immune microenvironment analysis and its correlation with clinical efficacy results. (B) Omics profiling and integrative analysis workflow. Flowchart depicting sample collection, omics profiling, and analysis pipeline. Out of 57 mCRC patients assessed, 55 initiated treatment and contributed a total of 145 biopsies at weeks 0, 3, and 11. Multiplex immunofluorescence (mIF) enabled spatial characterization of the immune microenvironment, utilizing biopsy-adapted (ISb) and distance-based biopsy-adapted (ISb_20) Immunoscores as well as immune cell profiling. RNA-seq supported differential gene expression analysis, deconvolution analysis for immune cell-type inference within the tumor microenvironment, and immunologic constant of rejection (ICR) score. Whole-exome sequencing (WES) data enabled genomic landscape characterization, including mutational profiling, assessment of tumor mutational burden, neoantigen, and genetic immunoediting (GIE). Integrative analysis synthesized findings from mIF, RNA-seq, and WES to elucidate immune infiltration dynamics, transcriptomic changes, and mutational signatures over the course of treatment and correlated this with treatment response and patient survival.

Figure 2

Efficacy analyses of RAS-MUT and RAS-WT cohorts (A) Waterfall plot depicting the best overall response (BOR) of target lesions (RECIST1.1) in percentage, by patient ID. Bars indicate individual patient responses, with red for RAS-MUT and blue for RAS-WT cohorts. The dashed lines represent thresholds for partial response (+20%) and progressive disease (−30%). (B) Spider plot showing the percentage change in the sum of targeted lesions diameters over time for individual patients in both RAS-MUT (red) and RAS-WT (blue) cohorts. Dashed lines represent aggregated data for each cohort. (C) Swimmer plot illustrating the duration of treatment and clinical outcomes for each patient. Each bar represents one patient, with red bars corresponding to the RAS-MUT cohort and blue bars to RAS-WT. Symbols on the bars indicate patient status (all deceased at analysis): progressive disease (PD), stable disease (SD), partial response (PR), and patient withdrawal due to adverse events (AEs). (D and E) Kaplan-Meier survival curves for progression-free survival (PFS) and overall survival (OS) comparing RAS-MUT (red) and RAS-WT (blue) cohorts. Tick marks indicate censored patients, and the number of patients at risk is displayed below the plot. Log rank test p values are displayed. (F–H) Forest plots from Cox proportional hazards (CoxPH) and linear regression multivariate models showing hazard ratios (HRs and 95% CI), regression coefficients (coef. and 95% CI), and corresponding p values for OS, PFS, and BOR, respectively, across various clinical covariates for the overall cohort. (I–K) Forest plots depicting CoxPH and linear regression multivariate models specific to the RAS-WT cohort for OS, PFS, and BOR, respectively, with HRs (HR and 95% CI) or regression coefficients (coef. and 95% CI) and corresponding p values.

Figure 3

Biopsy-adapted Immunoscore and efficacy results (A–C) Representative immunofluorescence images of liver metastasis biopsies illustrating the 3 classes of biopsy-adapted Immunoscore (ISb) with blue for Hoechst (nuclei), pink for CD3⁺, and orange for CD8⁺: (A) ISb high, (B) ISb medium, and (C) ISb low. Scale bars: 100 μm. (D and E) Kaplan-Meier survival curves for progression-free survival (PFS) and overall survival (OS) stratified by ISb levels (high, medium, and low). Tick marks represent censored patients. Log rank test p values are displayed. (F–I) Bar plots showing the frequency distribution of patients based on ISb levels (high, medium, and low) and their (F) PFS status (≤6 vs. >6 months), (G) OS status (≤12 vs. >12 months), (H) cohort (RAS-MUT vs. RAS-WT), and (I) best overall response (BOR) status (tumor growth: increase of the size of the sum of target lesions; tumor shrinkage: decrease of the size of the sum of target lesions) across time points. Fisher’s exact test p values are displayed. (J–L) Forest plots from Cox proportional hazards (CoxPH) and linear regression multivariate models for (J) OS, (K) PFS, and (L) BOR, displaying hazard ratios (HRs and 95% CI) and regression coefficients (coef. and 95% CI) with p values for clinical covariates, including ISb.

Figure 4

Distance-based biopsy-adapted Immunoscore (ISb_20) and clinical outcomes (A) Representative immunofluorescence images of liver metastasis biopsies analyzed by HALO artificial intelligence module for the detection of tumor nuclei (red) and non-tumoral nuclei (green). Scale bars: 200 μm. (B) Representative dot plot reconstitution of a liver metastasis biopsy with tumor cells in yellow, CD3⁺CD8⁻ T cells in maroon, CD3⁺CD8⁺ T cells in pink, and stromal unstained cells in gray. (C) Illustrated representative G-cross analysis of the probability distribution of CD3⁺ T cells near tumor cells, with the y axis representing the probability of a CD3⁺ T cells being at a given radius (r) (x axis) of a tumor cell. G^km (solid black line) represents the interaction function for tumor cells measured against CD3⁺ T cells, G^bord (dashed red line) indicates the border-corrected function, and G^pois (green line) illustrates the function under the assumption of a homogeneous Poisson process (independent, random distribution), serving as baseline comparison. The probability value of the G^km function at 20 μm of distance for CD3⁺ T and CD8⁺ T cells has been used to compute a distance-based biopsy-adapted Immunoscore (ISb_20). (D and E) Kaplan-Meier survival curves for progression-free survival (PFS) and overall survival (OS) stratified by ISb_20 levels (high, medium, and low). Tick marks represent censored patients. Log rank test p values are displayed. (F–I) Bar plots showing the frequency distribution of patients based on ISb_20 levels (high, medium, and low) and their (F) PFS status (≤6 vs. >6 months), (G) OS status (≤12 vs. >12 months), (H) cohort (RAS-MUT vs. RAS-WT), and (I) best overall response (BOR) status (tumor growth: increase of the size of the sum of target lesions; tumor shrinkage: decrease of the size of the sum of target lesions) across time points. Fisher’s exact test p values are displayed. (J–L) Forest plots from Cox proportional hazards (CoxPH) and linear regression multivariate models for (J) OS, (K) PFS, and (L) BOR, displaying hazard ratios (HRs and 95% CI) and regression coefficients (coef. and 95% CI) with p values for clinical covariates, including ISb_20.

Figure 5

Comprehensive analysis of the tumor immune microenvironment (A) Heatmap of clinical and immune characteristics across baseline metastasis biopsies. The heatmap annotations show clinical variables including best overall response (BOR), displayed as both a continuous and discrete variable (tumor growth: increase in the size of target lesions; tumor shrinkage: decrease in target lesions), RAS mutation status, iRECIST criteria, progression-free survival (PFS), overall survival (OS), ISb, ISb_20, site of biopsy, HLA⁺CK⁺ double-positive staining (HLA loss defined as staining below the median; no HLA loss above the median), and PD-L1 status (negative when there are <1% PD-L1⁺ cells, positive when there are ≥1% PD-L1⁺ cells). The bottom image presents immune-related density and distance probability at 20-μm features as Z scores for multiple cell populations. Biopsies were stratified into two clusters using unsupervised Euclidean distance clustering. (B and C) Kaplan-Meier survival curves for (B) PFS and (C) OS, stratified by clusters with tick marks indicating censored patients. The number of patients at risk is displayed below each plot. Log rank test p values are indicated. (D and E) Forest plots from univariate Cox proportional hazards (CoxPH) analysis of immune cell populations. (D) shows immune features associated with PFS and (E) with OS. Hazard ratios (HRs) and 95% CI are displayed. (F and G) Bulk RNA-seq gene set variation analysis (GSVA) scores of immune cell types from the consensus tumor microenvironment (consensus TME) colon adenocarcinoma (COAD) dataset. (F) Violin plot comparing GSVA scores for all cell types combined between mIF clusters and (G) boxplot displaying GSVA scores for individual cell types. ∗p < 0.1, ∗∗p < 0.05, and ∗∗∗p < 0.001. Non-significant differences are marked “ns.” Statistical analysis was performed using t tests and Wilcoxon tests.

Figure 6

Immunoediting score and clinical outcomes (A and B) Kaplan-Meier survival curves for progression-free survival (PFS) and overall survival (OS) stratified by immunoediting score (IES), based on both genetic immunoediting (GIE) and immunologic constant of rejection (ICR) (IES1 = ICR low and no GIE; IES2 = ICR low and GIE; IES3 = ICR high and no GIE; and IES4 = ICR high and GIE). Tick marks represent censored patients. The number of patients at risk is displayed below each plot. Log rank test p values are displayed. (C–F) Bar plots showing the frequency distribution of patients based on IES and their (C) PFS status (≤6 vs. >6 months), (D) OS status (≤12 vs. >12 months), (E) cohort, and (F) best overall response (BOR) status (tumor growth: increase of the size of the sum of target lesions; tumor shrinkage: decrease of the size of the sum of target lesions) across time points. Fisher’s exact test p values are displayed. (G–I) Forest plots from Cox proportional hazards (CoxPH) and linear regression multivariate models for (G) OS, (H) PFS, and (I) BOR (as a continuous variable), displaying hazard ratios (HRs and 95% CI) and regression coefficients (coef. and 95% CI) with p values for clinical covariates, including IES.

Figure 7

Mutational landscape and tumor mutational burden across time points in RAS-MUT and RAS-WT cohorts (A) Oncoplot displaying the top 20 mutated genes and RTK-RAS pathway genes across baseline (week 0), week 3, and week 11 in RAS-MUT and RAS-WT cohorts. Each column represents a patient, with mutations color coded according to mutation type: nonsense mutation (red), in-frame deletion (black), multi-hit mutation (yellow), missense mutation (green), splice site mutation (blue), and frameshift deletion (orange). The upper annotation bars display patient cohort (RAS-MUT and RAS-WT), time point, progression-free survival (PFS; ≤6 vs. >6 months), overall survival (OS; ≤12 vs. >12 months), and best overall response (BOR; tumor growth or shrinkage). The pathway row represents the most altered pathway with gray when at least one gene is mutated in the pathway. The bar plot on the right shows the mutation frequency of each gene. (B) Line plot of tumor mutational burden (TMB) per Mb across patients, ranked by TMB levels. TMB values are shown on a log scale. Patients are grouped by cohort (RAS-MUT and RAS-WT) and time point (weeks 0, 3, and 11), with color shading indicating the time point.