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Classification of the Tumor Immune Microenvironment Using Machine-Learning-Based CD8 Immunophenotyping As a Potential Biomarker for Immunotherapy and TGF-β Blockade in Nonsmall Cell Lung Cancer

AI in Precision Oncology


Background: The cellular composition of the tumor immune microenvironment (TIME) is a key contributor to the response of the tumor to immunotherapy. Transforming growth factor-beta (TGF-β) signaling is known to promote immune exclusion, where CD8+ T cells are in the surrounding stromal tissue but not within the tumor itself. To better identify patients with an immune-excluded phenotype, we developed two machine learning (ML) models to quantify CD8+ cell positivity and classify the immunophenotype of a histological cancer specimen.

Methods: Immunohistochemistry against CD8 was performed on nonsmall cell lung cancer (NSCLC) samples (N=200) and digitized whole slide images (WSIs) were then generated. ML models, trained on these WSIs, identified relevant tissue regions (cancer epithelium, stroma) and cell types (CD8+ lymphocytes). Features related to CD8, including overall CD8+ count proportion, CD8+ count proportion in cancer epithelium, and CD8+ count proportion in cancer-associated stroma, were extracted for the ML-based approaches to predict immunophenotypes. In the cutoff model, data-driven cutoffs were applied to model-generated human interpretable features of CD8+ count proportion within cancer epithelium and cancer-associated stroma, whereas in the spatial model, all tissue and cell model predictions within the TIME were used to train a graph neural network to classify immunophenotypes.

Results: An inverse correlation was observed between TGF-β signaling and manually determined CD8+ cell levels. CD8 quantification model predictions showed high concordance with pathologist consensus annotations for all model classes. Concordance of model-derived immunophenotype predictions with ground truth pathologist-derived immunophenotype consensus labels was comparable with concordance of an average pathologist with the same ground truth consensus. Significant associations were seen between immunophenotypes (derived from ground truth and model predictions) and relative abundance of T cell populations and PD-L1 activity gene signature scores, while a trend was observed between immunophenotypes and a gene signature indicative of TGF-β signaling.

Conclusions: We developed a digital pathology approach that can characterize and classify the cancer immunophenotypes in a reproducible and scalable manner, paving the road for the application of such a method to identify patients who may benefit from immunotherapy and/or TGF-β blockade in NSCLC.
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