Validation of a Machine Learning Model to Predict Immunotherapy Response in Head and Neck Squamous Cell Carcinoma

• Published in: Cancers Vol. 16; no. 1; p. 175
• Main Authors: Lee, Andrew Sangho, Valero, Cristina, Yoo, Seong-Keun, Vos, Joris L, Chowell, Diego, Morris, Luc G T
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 29-12-2023; MDPI
• Subjects: Apoptosis; Biomarkers; Blood platelets; Body mass index; Cancer therapies; Chemotherapy; Decision making; Genomics; Head and neck carcinoma; Immune checkpoint inhibitors; Immunohistochemistry; Immunotherapy; Learning algorithms; Machine learning; Medical prognosis; Medical research; Medicine, Experimental; Metastases; Metastasis; Patients; Prediction models; Prognosis; Squamous cell carcinoma; Tumors; prediction; head and neck squamous-cell carcinoma; machine learning; checkpoint inhibition; validation
• ISSN: 2072-6694; 2072-6694
• DOI: 10.3390/cancers16010175

Head and neck squamous-cell carcinoma (HNSCC) is a disease with a generally poor prognosis; half of treated patients eventually develop recurrent and/or metastatic (R/M) disease. Patients with R/M HNSCC generally have incurable disease with a median survival of 10 to 15 months. Although immune-checkpoint blockade (ICB) has improved outcomes in patients with R/M HNSCC, identifying patients who are likely to benefit from ICB remains a challenge. Biomarkers in current clinical use include tumor mutational burden and immunohistochemistry for programmed death-ligand 1, both of which have only modest predictive power. Machine learning (ML) has the potential to aid in clinical decision-making as an approach to estimate a tumor's likelihood of response or a patient's likelihood of experiencing clinical benefit from therapies such as ICB. Previously, we described a random forest ML model that had value in predicting ICB response using 11 or 16 clinical, laboratory, and genomic features in a pan-cancer development cohort. However, its applicability to certain cancer types, such as HNSCC, has been unknown, due to a lack of cancer-type-specific validation. Here, we present the first validation of a random forest ML tool to predict the likelihood of ICB response in patients with R/M HNSCC. The tool had adequate predictive power for tumor response (area under the receiver operating characteristic curve = 0.65) and was able to stratify patients by overall (HR = 0.53 [95% CI 0.29-0.99],
= 0.045) and progression-free (HR = 0.49 [95% CI 0.27-0.87],
= 0.016) survival. The overall accuracy was 0.72. Our study validates an ML predictor in HNSCC, demonstrating promising performance in a novel cohort of patients. Further studies are needed to validate the generalizability of this algorithm in larger patient samples from additional multi-institutional contexts.