Assessing PD-L1 Expression Status Using Radiomic Features from Contrast-Enhanced Breast MRI in Breast Cancer Patients: Initial Results.

Roberto Lo Gullo,Sunitha B Thakur,Danny F Martinez,Peter Gibbs,Varadan Sevilimedu,Jeffrey S Reiner,Hannah Wen,Katja Pinker,Raza Hoda,Maxine S Jochelson

doi:10.3390/cancers13246273

Roberto Lo Gullo, Sunitha B Thakur + Show 8 more

Open Access

https://doi.org/10.3390/cancers13246273

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Journal: Cancers	Publication Date: Dec 14, 2021
Citations: 11	License type: CC BY 4.0

Affiliation: Memorial Sloan Kettering Cancer Center

Abstract

Simple SummaryTo our knowledge, this is the first study assessing radiomics coupled with machine learning from MRI-derived features to predict PD-L1 expression status in biopsy-proven triple negative breast cancers and comparing the performance of this approach with the performance of qualitative assessment by two radiologists. This pilot study shows that radiomics analysis coupled with machine learning of DCE-MRI is a promising approach to derive prognostic and predictive information and to select patients who could benefit from anti-PD-1/PD-L1 treatment. This technique could also be used to monitor PD-L1 expression, as it can vary over time and between different regions of the tumor, thus avoiding repeated biopsies.The purpose of this retrospective study was to assess whether radiomics analysis coupled with machine learning (ML) based on standard-of-care dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can predict PD-L1 expression status in patients with triple negative breast cancer, and to compare the performance of this approach with radiologist review. Patients with biopsy-proven triple negative breast cancer who underwent pre-treatment breast MRI and whose PD-L1 status was available were included. Following 3D tumor segmentation and extraction of radiomic features, radiomic features with significant differences between PD-L1+ and PD-L1− patients were determined, and a final predictive model to predict PD-L1 status was developed using a coarse decision tree and five-fold cross-validation. Separately, all lesions were qualitatively assessed by two radiologists independently according to the BI-RADS lexicon. Of 62 women (mean age 47, range 31–81), 27 had PD-L1− tumors and 35 had PD-L1+ tumors. The final radiomics model to predict PD-L1 status utilized three MRI parameters, i.e., variance (FO), run length variance (RLM), and large zone low grey level emphasis (LZLGLE), for a sensitivity of 90.7%, specificity of 85.1%, and diagnostic accuracy of 88.2%. There were no significant associations between qualitative assessed DCE-MRI imaging features and PD-L1 status. Thus, radiomics analysis coupled with ML based on standard-of-care DCE-MRI is a promising approach to derive prognostic and predictive information and to select patients who could benefit from anti-PD-1/PD-L1 treatment.

Highlights

In the last decade, immunotherapy has emerged as a key player in the field of oncology, with encouraging results seen in cancers such as melanoma, renal cell carcinoma, nonsmall-cell lung cancer, and bladder cancer [1,2,3]
The potential of radiomics analysis coupled with machine learning (ML) based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for the prediction of PD-L1 expression status in triple negative breast cancer has not been explored
Of the 62 patients included in the final analysis, 27 were PD-L1 SP142-negative (PD-L1−) and 35 were PD-L1 SP142-positive (PD-L1+)

Summary

Introduction

Immunotherapy has emerged as a key player in the field of oncology, with encouraging results seen in cancers such as melanoma, renal cell carcinoma, nonsmall-cell lung cancer, and bladder cancer [1,2,3]. While tumoral overexpression of PD-L1 has a negative influence on survival and treatment response by decreasing the anti-tumoral inflammatory response, the expression of PD-L1 on immune cells and the re-activation of the host’s antitumor immune response through immunotherapy targeting PD-L1 can improve patient outcomes. Breast cancer subtypes, such as triple negative and HER2-positive breast cancers, can overexpress PD-L1 on either breast cancer cells or on TILs. In patients with triple negative breast cancer in particular, the expression of PD-L1 mainly occurs on tumor-infiltrating immune cells rather than on tumor cells [11,12]. Several other studies have reported that PD-L1 polymorphisms significantly influence the breast cancer stage, and the effectiveness of chemotherapy and overall and disease-free survival after tumor resection [11,14,16]

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