Patients with metastatic GI cancers who failed standard therapy and had received anti-programmed cell death 1 (PD-1)/PD-L1 therapy or in combination with a cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) inhibitor between January 2016 and January 2018 were recruited. The tumor response was measured by imaging studies or physical examinations according to the Response Evaluation Criteria in Solid Tumors (RECIST) V.1.1 and iRECIST. Durable clinical benefit (DCB) was defined as complete response, partial response, or stable disease (SD) lasting for ≥24 weeks, and no durable benefit (NDB) was defined as progressive disease or SD that lasted for <24 weeks.8

Total RNA was isolated from 5 μm thick pretreatment archival formalin-fixed, paraffin-embedded (FFPE) sections of tumors. Gene expression profiling was performed using a panel sequencing platform that determines the expression of 395 human genes belonging to the following functional categories9: immunological response to immunotherapy, infiltrating immune cell markers, tumor-specific antigens, tumor markers and essential signaling pathways. Detailed information on the RNA profiling method and the data processing pipeline is provided in the online supplementary methods.

In the discovery cohort, the linear support vector machine (linear SVM) model was applied to distinguish 25 individuals with DCB from 47 patients with NDB based on the 395-gene RNA profiling data (online supplementary methods). Briefly, after data standardization and normalization, recursive feature elimination with cross-validation was performed to select features. We used the sigmoid function to scale the distance to the SVM classifier boundary into the range [0–1], which was designated as the IO-score. The prediction of the independent validation cohort was generated with the training test set procedure. The area under the receiver operating characteristic curve (AUC) was then calculated from the IO-score and the binary group labels (DCB and NDB). We used the Youden index as the cut-off point (high IO-score >0.52) to stratify patients with different prognoses and DCB rates.

We extracted genomic DNA from FFPE specimens and matched white blood cell samples using the blackPREP FFPE DNA Kit (Analytik Jena AG, Jena, Germany) and the Tiangen Whole Blood DNA Kit (Tiangen, Beijing, PRC) according to the manufacturer’s instructions. Whole exome sequencing was then performed to detect genomic alterations. The TMB was determined by analyzing the number of somatic mutations per megabase (mb). The cut-off value for TMB-high and TMB-low was defined as 5 mutations/mb as previously reported.10

PD-L1 status was assessed by immunohistochemical (IHC)-stained archival FFPE sections using an anti-PD-L1 (rabbit, clone SP142, 1:100; Spring Bioscience, California, USA) antibody. PD-L1 expression was evaluated in tumor cells and tumor-infiltrating immune cells. PD-L1 positivity was defined as ≥1% of the tumor/stromal cell membrane staining.

To determine the MMR status, IHC was performed on archival FFPE sections using monoclonal anti-mutL homolog 1, anti-mutS homolog 2, anti-mutS homolog 6 and PMS1 homolog 2. Tumors lacking expression of any one of the four proteins were considered deficient MMR (dMMR); otherwise, they were considered proficient MMR (pMMR). In some cases, to determine MSI status, PCR-based molecular testing was employed, which assesses five microsatellite loci comprising BAT-25, BAT-26, D2S123, D5S346, and D17S250.11 12 MSI-high (MSI-H) tumors were defined as instability at two or more of these markers, whereas MSI-low and microsatellite stability (MSS) were defined as instability at a single locus and no instability at any locus, respectively.13

Statistical tests included the χ² test, Student’s t-test and the Mann-Whitney U test. A receiver operating characteristic curve was generated to evaluate the diagnostic accuracy. The AUC was calculated to measure the discriminatory ability of the potential biomarkers. Progression-free survival (PFS) and overall survival (OS) were analyzed with the Kaplan-Meier method and log-rank test.

A total of 96 patients with metastatic GI cancers were randomly assigned into a discovery cohort (n=72) and a validation cohort (n=24; figure 1A). Clinical characteristics of the subjects are summarized in online supplementary table S1. All patients had received anti-PD-1/anti-PD-L1 therapy or in combination with anti-CTLA4 antibodies. The clinical end points include OS and PFS, and the therapeutic response.

Based on the expression levels of 395-genes in patients with DCB and NDB in the discovery cohort, a linear SVM classification algorithm was adopted to construct a prediction model (figure 1A and online supplementary figure S1). The best-performing features and hyperparameters were determined by 13-fold cross-validation within the discovery cohort. Intriguingly, a linear SVM classifier composed of 24 IO-related genes was developed, and the IO-score of each sample was subsequently derived (figure 1B and online supplementary table S2).

The performance of the IO-score was comprehensively evaluated, and overall accuracies of 94% and 83% were achieved for discriminating DCB and NDB in the discovery (AUC=0.97; 95% CI 0.93 to 1.00) and validation (AUC=0.74; 95% CI 0.51 to 0.97) cohorts, respectively (figure 2A and online supplementary table S3). Moreover, a higher DCB rate was observed in patients with a high IO-score than in patients with a low IO-score (discovery cohort: 92.0% (23 of 25) vs 4.3% (2 of 47), p<0.001; validation cohort: 85.7% (6 of 7) vs 17.6% (3 of 17), p=0.004, figure 2B). Furthermore, a favorable prognosis was identified in the high IO-score subgroup when compared with that in the low IO-score subgroup (figure 2C–D).

We then evaluated the predictive and prognostic values of other candidate biomarkers in the combined cohort (online supplementary table S4 and online supplementary figures S2-S4), with AUCs of 0.69 (95% CI 0.57 to 0.80), 0.59 (95% CI 0.45 to 0.73), and 0.52 (95% CI 0.37 to 0.67) for TMB, MSI/MMR, and PD-L1 positivity, respectively. As expected, a better prognosis was also identified in the TMB-high and MSI-H/dMMR subgroups than in the TMB-low and MSS/pMMR subgroups (online supplementary figures S2 and S3). However, the IO-score tended to have greater power for patient stratification, as revealed by the high odds ratio (OR) for DCB (figure 3 and online supplementary table S4) and the low HR of the survival analysis (figure 2C and D).

Notably, the waterfall plot of maximal tumor reduction in the two independent cohorts also sustained the superiority of the IO-score predictive model based on the IO-score (figure 4A and B). Furthermore, we analyzed the relationship between the IO-score and other potential biomarkers in the subgroup with a complete data set (n=52). Higher percentages of MSI-H and/or TMB-high patients were identified in the high IO-score subgroup than in the low IO-score subgroup (online supplementary figure S5).