by Christos Evangelou, MSc, PhD – Medical Writer and Editor

In a new study, researchers at the National Taiwan University Hospital and JelloX Biotech Inc. developed a novel computer-assisted tool for three-dimensional (3D) imaging of PD-L1 expression in immunofluorescence-stained and optically cleared breast cancer specimens. The proposed 3D framework showed high overall agreement with traditional, clinical-grade two-dimensional (2D) staining techniques in terms of morphological characteristics and PD-L1 expression assessment.¹

Moreover, the computer-assisted algorithm for detecting tumor-infiltrating immune cells in the 3D immunofluorescence images achieved satisfactory results compared to traditional techniques. This proof-of-concept study demonstrates the feasibility and potential clinical utility of a novel 3D imaging tool for assessing PD-L1 expression in breast cancer specimens, which could help refine patient selection for immunotherapy.

The uneven distribution of PD-L1 in breast cancer tissues in 3D space could be clearly visualized with our new methodology. With novel methodologies demonstrating comparable characteristics of breast cancer cells, phenotypic characteristics of immune cells, and PD-L1 expression patterns to conventional 2D methods, physicians can identify more suitable breast cancer patients for immunotherapy,

said Yen-Shen Lu, MD, PhD, the corresponding author of the study.

The report was published in BMC Cancer.

Rationale: Developing a Digital Pathology Framework for Spatial Analysis of PD-L1 Expression

Breast cancer affects millions of women worldwide, with the triple-negative subtype presenting treatment challenges because of its aggressive nature and limited treatment options. However, advances in cancer immunotherapy have offered renewed hope for patients with triple-negative breast cancer (TNBC), with the development of therapies targeting the programmed death-1 (PD-1) and programmed death-ligand 1 (PD-L1) immune checkpoints.

Identifying patients who are most likely to benefit from these immunotherapies is crucial, and PD-L1 expression has emerged as a key biomarker for patient selection. However, PD-L1 expression in TNBC tissues is heterogeneous, posing challenges to the accurate assessment of PD-L1 levels in tumor samples using traditional 2D staining techniques, such as immunohistochemistry (IHC), which fail to capture the spatial heterogeneity in PD-L1 expression.

The current treatment guideline has a threshold of 1% tumor-infiltrating immune cells by dividing the area of immune cells expressing PD-L1 over that of the whole tumor using conventional pathology diagnosis with planar or 2D single slide staining assays. However, the intrinsic heterogeneous expression of PD-L1 on tumor-infiltrating immune cells in 3D tumor volume is difficult to accurately measure or represent by a single section, raising the need for novel pathology sampling methodology coupled with digital pathology analysis to solve the problems while considering the feasibility in clinical workflow, 

explained Dr. Lu.

Computer-Assisted 3D Imaging

The researchers collected primary tumor specimens from 33 patients with breast cancer, including six with TNBC. The specimens were divided into distinct sets for various analyses.¹ The team then used immunofluorescence staining and optical clearing techniques to generate high-resolution 3D images of breast cancer samples, followed by the implementation of a computer-assisted algorithm to detect and quantify PD-L1 expression on tumor-infiltrating immune cells.

Led by experienced oncologists, our team comprised clinicians, pathologists, scientists, and engineers to address the challenges of accurately assessing PD-L1 expression in breast cancer tissues using 2D techniques. Our collaborative approach enables a volume-based comprehensive evaluation of PD-L1 positivity on tumor-infiltrating immune cells within each intact tissue. We conducted a comparative analysis of our novel approach with traditional clinical-grade 2D staining to demonstrate overall agreement,

noted Dr. Lu.

The 3D imaging framework demonstrated a high overall agreement with traditional 2D IHC, with a concordance rate of 90% compared to traditional IHC. “In this study comprising 20 cases, two experienced pathologists classified PD-L1 expression from digital fluorescent images with identical diagnosis, demonstrating higher inter-observer reliability than the traditional method,” Dr. Lu explained. He added that 5 of 20 (25%) cases showed no sign of PD-L1, representing true negatives.

High Spatial Heterogeneity in PD-L1 Expression in Breast Tumors

When the researchers assessed the spatial distribution of PD-L1 expression within the 3D tumor microenvironment, they found that the levels of PD-L1 expression varied significantly across different tissue layers within the same tumor. In 6 of 20 (30%) cases, the PD-L1 expression crossed the crucial 1% threshold for patient eligibility to PD-1/PD-L1 inhibitors, depending on which layer of the tumor was examined.

Dr. Lu noted that 3 of the 6 (50%) cases of TNBC exhibited inter-layer heterogeneity of PD-L1 expression crossing the 1% threshold.

One case was reclassified from PD-L1 negative to positive by our methodology and two experienced pathologists,

he said.

This finding highlights the limitations of traditional 2D IHC, which may fail to capture the true heterogeneity of PD-L1 expression within the complex 3D architecture of the tumor. By providing a more comprehensive and accurate assessment of this critical biomarker, the researchers believe their 3D imaging approach could have a profound impact on patient selection for immunotherapy. “This suggests that early and precise diagnosis may significantly impact the patient’s strategy with suitable immunotherapy options,” noted Dr. Lu.

The researchers also found that the computer-assisted algorithm was able to effectively detect and quantify tumor-infiltrating immune cells in the 3D images, further enhancing the tool’s potential clinical utility. Moreover, the average PD-L1 expression levels obtained from the 3D analysis differed from the traditional 2D IHC in 40% (8 of 20) of the cases, underscoring the potential clinical implications of this approach.

Need for Further Validation

Although this proof-of-concept study has laid the foundation for a more sophisticated and comprehensive assessment of PD-L1 expression in breast cancer, the cohort size was small. The authors acknowledge the need for further validation, refinement, and standardization of the 3D imaging workflow before it can be readily adopted in clinical practice. Addressing potential artifacts, optimizing staining protocols, and validating the approach in larger patient cohorts will be crucial next steps.

Dr. Lu added that this method could be adapted to detect and quantify other clinically relevant biomarkers, such as estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2). This would allow for more accurate patient selection for targeted therapies.

In our future research, we aim to demonstrate that 3D pathology can overcome cancer heterogeneity and prove whether they are superior to traditional case slides in providing accurate clinical outcomes. Identifying more suitable patients through a refined biomarker-guided stratification strategy, which combines 3D pathology imaging and computer-assisted analysis, fulfills the unmet medical need for precision diagnosis of breast cancer,

Dr. Lu concluded.

The study was funded by JelloX Biotech Inc.

References

1. Lee YH, Huang CY, Hsieh YH, et al. A novel computer-assisted tool for 3D imaging of programmed death-ligand 1 expression in immunofluorescence-stained and optically cleared breast cancer specimens. BMC Cancer. 2024;24(1):121. Published 2024 Jan 24. doi:10.1186/s12885-023-11748-8