Artificial intelligence-guided tissue analysis combined with immune infiltrate assessment predicts stage III colon cancer outcomes in PETACC08 study

Abstract

Objective: Diagnostic tests, such as Immunoscore, predict prognosis in patients with colon cancer. However, additional prognostic markers could be detected on pathological slides using artificial intelligence tools.

Design: We have developed a software to detect colon tumour, healthy mucosa, stroma and immune cells on CD3 and CD8 stained slides. The lymphocyte density and surface area were quantified automatically in the tumour core (TC) and invasive margin (IM). Using a LASSO algorithm, DGMate (DiGital tuMor pArameTErs), we detected digital parameters within the tumour cells related to patient outcomes.

Results: Within the dataset of 1018 patients, we observed that a poorer relapse-free survival (RFS) was associated with high IM stromal area (HR 5.65; 95% CI 2.34 to 13.67; p<0.0001) and high DGMate (HR 2.72; 95% CI 1.92 to 3.85; p<0.001). Higher CD3+ TC, CD3+ IM and CD8+ TC densities were significantly associated with a longer RFS. Analysis of variance showed that CD3+ TC yielded a similar prognostic value to the classical CD3/CD8 Immunoscore (p=0.44). A combination of the IM stromal area, DGMate and CD3, designated 'DGMuneS', outperformed Immunoscore when used in estimating patients' prognosis (C-index=0.601 vs 0.578, p=0.04) and was independently associated with patient outcomes following Cox multivariate analysis. A predictive nomogram based on DGMuneS and clinical variables identified a group of patients with less than 10% relapse risk and another group with a 50% relapse risk.

Conclusion: These findings suggest that artificial intelligence can potentially improve patient care by assisting pathologists in better defining stage III colon cancer patients' prognosis.

Keywords: adjuvant treatment; colorectal cancer; computerised image analysis; immunohistopathology.

Figure 1

Tissue classification methodology and immune cell quantification. (A) Slides are segmented in thousands tiles using QuPath. Each tile is then classified with ColoClass R software. CD3 or CD8 staining is simultaneously evaluated with QuPath. All information are gathered to predict colon cancer relapse. (B) Representative pictures of a tiled slide at low magnification (left panel, scale bar 1 mm) and at high magnification (right panel, scale bar 250 µm). (C) Representative pictures of tissue classification from native slide (left panel) to ColoClass (right panel). Healthy mucosa is displayed in yellow, tumour in red, stroma in blue and immune cells in purple. The dotted line represents ColoClass IM estimation (scale bar 1 mm). (D) Validation of ColoClass versus pathologists. (E) Detection of positive cells on native slide (left panel) and using QuPath (right panel). Positive cells are displayed in green and negative cells in red (scale bar 100 µm). Hthy, healthy mucosa; IC, immune cells; IM, invasivemargin; Other, gathers white spaces and necrosis; Stro, stroma; WS, whole slide.

Figure 2

Predictive value of interest areas and digital features. (A) Forest plot representing the predictive value of stroma area (IM, TC and WSI), immune area (IM, TC and WSI), total tumour area and total healthy area on RFS. (B) Kaplan-Meier survival curve on discovery dataset (n=713) using DGMate split at median. (C) Kaplan-Meier survival curve on validation dataset (n=305) using DGMate split at median. IM, invasive margin; RFS, relapse-free survival; TC, tumour core; WSI, whole slide imaging.

Figure 3

Prognostic value of immune T-cell analysis. (A) Forest plot representing the predictive value of CD3 and CD8 in IM and TC on RFS. (B) Kaplan-Meier RFS curved using CD3+ IM split at median. (C) Kaplan-Meier RFS curved using TC CD3 split at median. (D) Kaplan-Meier RFS curve using Immunoscore split at three risk groups (low 20%, intermediate 60% and high 20%). (E) Immunoscore predictive accuracy compared with CD3+ IM, CD3+ TC, CD8+ IM, CD8+ TC alone using a 1000× bootstrap strategy. AUC, area under the receiver operating characteristic curve; IM, invasive margin; ISlike, Immunoscore like; RFS, relapse-free survival; TC, tumour core; TC CD3, CD3 tumour-infiltrating lymphocytes present in the TC.

Figure 4

Composite variables improve prognosis prediction. (A) Kaplan-Meier relapse-free survival curve on discovery dataset (n=713) using DGMuneS split at third quartile. (B) Kaplan-Meier relapse-free survival curve on validation dataset (n=305) using DGMuneS split at third quartile. (C) Kaplan-Meier relapse-free survival curve on low-risk clinical stage (T1–3, (N1) (n=549) and high-risk clinical stage (T4 or N2) patients (n=469), both split at median, using DGMuneS. (D) Kaplan-Meier relapse-free survival curve on low-risk clinical stage (T1–3, (N1) (n=549) and high-risk clinical stage (T4 or N2) (n=469), using dichotomic ISlike score. (E) Predictive accuracy on patients’ relapse depending on clinical parameters (blue shading), staining parameters (green shading) or combined parameters (red shading) based on 1018 patients from PETACC08 study using a 1000× bootstrap strategy. *p<0.05, **p<0.01, ***p<0.001, ****p<0.0001. AUC, area under the receiver operating characteristic curve; IM, invasive margin; ISlike, Immunoscore like; MMR, mismatch repair; N stage, node stage; T stage, tumour stage; TC, tumour core; TC CD3, CD3 tumour-infiltrating lymphocytes present in the TC.

Figure 5

Nomogram tool based on variable retained in the multivariate model and relapse-free survival according to total score. (A) Nomogram representation of the multivariate model. Each parameter gives a number of points indicated on the upper line. The sum is indicated on total line and provides a 5-year survival probability. (B) Kaplan-Meier survival curve on discovery dataset (n=713) when nomogram score is split depending on relapse risk (low, light red line; high, dark red line or intermediate, red line), grey dotted line displays survival of whole discovery dataset. (C) Kaplan-Meier survival curve on validation dataset (n=305) when nomogram score is split depending on relapse risk (low, light red line; high, dark red line or intermediate, red line), grey dotted line displays survival of whole validation dataset. N stage, node stage; T stage, tumour stage.