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. 2021 Oct 15;27(20):5557-5565.
doi: 10.1158/1078-0432.CCR-21-0325. Epub 2021 Jun 4.

An Open-Source, Automated Tumor-Infiltrating Lymphocyte Algorithm for Prognosis in Triple-Negative Breast Cancer

Yalai Bai  1 , Kimberly Cole  1 , Sandra Martinez-Morilla  1 , Fahad Shabbir Ahmed  1 , Jon Zugazagoitia  1 , Johan Staaf  2 , Ana Bosch  2   3 , Anna Ehinger  4 , Emma Nimeus  2   5 , Johan Hartman  6   7 , Balazs Acs #  8   6   7 , David L Rimm #  8   9
Affiliations

An Open-Source, Automated Tumor-Infiltrating Lymphocyte Algorithm for Prognosis in Triple-Negative Breast Cancer

Yalai Bai et al. Clin Cancer Res. .

Abstract

Purpose: Although tumor-infiltrating lymphocytes (TIL) assessment has been acknowledged to have both prognostic and predictive importance in triple-negative breast cancer (TNBC), it is subject to inter and intraobserver variability that has prevented widespread adoption. Here we constructed a machine-learning based breast cancer TIL scoring approach and validated its prognostic potential in multiple TNBC cohorts.

Experimental design: Using the QuPath open-source software, we built a neural-network classifier for tumor cells, lymphocytes, fibroblasts, and "other" cells on hematoxylin-eosin (H&E)-stained sections. We analyzed the classifier-derived TIL measurements with five unique constructed TIL variables. A retrospective collection of 171 TNBC cases was used as the discovery set to identify the optimal association of machine-read TIL variables with patient outcome. For validation, we evaluated a retrospective collection of 749 TNBC patients comprised of four independent validation subsets.

Results: We found that all five machine TIL variables had significant prognostic association with outcomes (P ≤ 0.01 for all comparisons) but showed cell-specific variation in validation sets. Cox regression analysis demonstrated that all five TIL variables were independently associated with improved overall survival after adjusting for clinicopathologic factors including stage, age, and histologic grade (P ≤ 0.0003 for all analyses).

Conclusions: Neural net-driven cell classifier-defined TIL variables were robust and independent prognostic factors in several independent validation cohorts of TNBC patients. These objective, open-source TIL variables are freely available to download and can now be considered for testing in a prospective setting to assess clinical utility.See related commentary by Symmans, p. 5446.

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Figures

Figure 1.
Figure 1.
Flowchart of algorithm training, developing to algorithm validation. A. Estimate stain vector was first defined after uploading H&E images. This is followed by cell segmentation using standardized watershed cell detection parameters. Next, a CNN was trained using neural network with tumor cell, TILs, fibroblast and other type or background cells with color coding of each type. A temporary classifier was built and applied to rest of images in classifier training set. After many rounds of cell classification review and correction, a trained classifier (CNN11) was locked once it was morphologically judged to be at least 95% accurate on most images. B: Application of trained classifier resulted in TIL measurements which were calculated as following TIL variables: eTILs%, etTILs%, esTILs%, eaTILs (mm2) and easTILs (see methods section for definition of variables). Associations between TIL variables and patient outcome were identified in WTS Yale (discovery set) using the optimal cut-points determined by X-tile software. All TIL variables were subsequently tested in validation sets including TMA Yale1, TMA Yale2, WTS TCGA and WTS Sweden. C: Workflow explaining how TIL quantification is performed in H&E image-based whole tissue image. Step of tumor region definition is followed by estimate stain vector to normalize hematoxylin and eosin colors. Then, cell segmentation is performed using standardized watershed cell detection parameters and cell classification using the trained classifier. At last, TIL measurements were analyzed into constructed variables. * Pathologist’s supervision is required.
Figure. 2.
Figure. 2.
Representative images of four sample cases showing the H&E images (A, C, E and G) and the cell classification masks (B, D, F and H). E and F: representative image of sample cases with inaccurate cell classification, these rare fields are ultimately censored. G and H: only invasive breast cancer regions were selected and analyzed. Color code of cell classification mask: tumor cells (red), TILs (purple), fibroblasts (green) and others (yellow). Scale bar from A to F: 20um; scale bar from G to H: 200um.
Figure 3.
Figure 3.
Identification of QuPath TIL prognostic role in discovery set (WTS Yale). Kaplan-Meier curves of overall survival (OS) in WTS Yale Discovery set by eTILs% dichotomized at the value of 18.2% (A), etTILs% dichotomized at the value at 16.9%. (B) esTILs% dichotomized at the value 57.4%. (C) eaTILs (mm2) dichotomized at the value #1195.6/mm2. (D) easTILs dichotomized at the value 19.9% (E) and pathologist sTILs% at 19.9% (F). Corresponding Hazard Ratio with 95% Cl and P values are illustrated. Note, P values in this figure are not corrected for multiple testing as occurs in optimal cut-point discovery
Figure 4.
Figure 4.
Validation of QuPath TIL algorithms in WTS TCGA. Kaplan-Meier curves of overall survival (OS) in WTS TCGA set by eTILs% dichotomized at the value of 18.2% (A), etTILs% dichotomized at the value at 16.9% (B), esTILs% dichotomized at the value 57.4% (C), eaTILs (mm2) dichotomized at the value #1195.6/mm2(D) and easTILs dichotomized at the value 19.9% (E). Corresponding Hazard Ratio with 95% Cl and P values are illustrated.
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