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Review: PD-L1 as an emerging target in the treatment and prevention of keratinocytic skin cancer.

ABSTRACT: Recent advances in the understanding and targeting of immune checkpoints have led to great progress in immune therapies against many forms of cancer. While many types of immune checkpoints are currently targeted in the clinic, this review will focus on recent research implicating the programmed cell death protein 1/programmed death-ligand 1 (PD-1/PD-L1) axis as an emerging focus for the treatment of keratinocytic tumors. PD-L1 is of particular interest in nonmelanoma skin cancer (NMSC), as it is not only upregulated in these tumors but is stimulated by environmental ultraviolet exposure. This response may also make PD-L1 an excellent target for photochemoprevention using topically applied small molecule inhibitors. Here, we summarize recent investigations on PD-L1 expression and clinically relevant immune checkpoint inhibitor treatment in cutaneous squamous cell carcinoma, basal cell carcinoma, and head and neck squamous cell carcinoma, as well as small molecule agents targeting PD-L1 that may be useful for clinical development aiming at treatment or prevention of NMSC.

SUBMITTER: Vaishampayan P

PROVIDER: S-EPMC9771956 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature

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Review: PD-L1 as an emerging target in the treatment and prevention of keratinocytic skin cancer.

Vaishampayan Prajakta P Curiel-Lewandrowski Clara C Dickinson Sally E SE

Molecular carcinogenesis 20220919 1

Recent advances in the understanding and targeting of immune checkpoints have led to great progress in immune therapies against many forms of cancer. While many types of immune checkpoints are currently targeted in the clinic, this review will focus on recent research implicating the programmed cell death protein 1/programmed death-ligand 1 (PD-1/PD-L1) axis as an emerging focus for the treatment of keratinocytic tumors. PD-L1 is of particular interest in nonmelanoma skin cancer (NMSC), as it is ...[more]

PMID: 36121318

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Project description:ImportanceDetection of cutaneous cancer on the face using deep-learning algorithms has been challenging because various anatomic structures create curves and shades that confuse the algorithm and can potentially lead to false-positive results.ObjectiveTo evaluate whether an algorithm can automatically locate suspected areas and predict the probability of a lesion being malignant.Design, setting, and participantsRegion-based convolutional neural network technology was used to create 924 538 possible lesions by extracting nodular benign lesions from 182 348 clinical photographs. After manually or automatically annotating these possible lesions based on image findings, convolutional neural networks were trained with 1 106 886 image crops to locate and diagnose cancer. Validation data sets (2844 images from 673 patients; mean [SD] age, 58.2 [19.9] years; 308 men [45.8%]; 185 patients with malignant tumors, 305 with benign tumors, and 183 free of tumor) were obtained from 3 hospitals between January 1, 2010, and September 30, 2018.Main outcomes and measuresThe area under the receiver operating characteristic curve, F1 score (mean of precision and recall; range, 0.000-1.000), and Youden index score (sensitivity + specificity -1; 0%-100%) were used to compare the performance of the algorithm with that of the participants.ResultsThe algorithm analyzed a mean (SD) of 4.2 (2.4) photographs per patient and reported the malignancy score according to the highest malignancy output. The area under the receiver operating characteristic curve for the validation data set (673 patients) was 0.910. At a high-sensitivity cutoff threshold, the sensitivity and specificity of the model with the 673 patients were 76.8% and 90.6%, respectively. With the test partition (325 images; 80 patients), the performance of the algorithm was compared with the performance of 13 board-certified dermatologists, 34 dermatology residents, 20 nondermatologic physicians, and 52 members of the general public with no medical background. When the disease screening performance was evaluated at high sensitivity areas using the F1 score and Youden index score, the algorithm showed a higher F1 score (0.831 vs 0.653 [0.126], P < .001) and Youden index score (0.675 vs 0.417 [0.124], P < .001) than that of nondermatologic physicians. The accuracy of the algorithm was comparable with that of dermatologists (F1 score, 0.831 vs 0.835 [0.040]; Youden index score, 0.675 vs 0.671 [0.100]).Conclusions and relevanceThe results of the study suggest that the algorithm could localize and diagnose skin cancer without preselection of suspicious lesions by dermatologists.

Dataset Information

Review: PD-L1 as an emerging target in the treatment and prevention of keratinocytic skin cancer.

Publications

Review: PD-L1 as an emerging target in the treatment and prevention of keratinocytic skin cancer.

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