Validation of a machine learning model to predict immunotherapy response in head and neck squamous cell carcinoma Journal Article
| Authors: | Lee, A. S.; Valero, C.; Yoo, S. K.; Vos, J. L.; Chowell, D.; Morris, L. G. T. |
| Article Title: | Validation of a machine learning model to predict immunotherapy response in head and neck squamous cell carcinoma |
| Abstract: | 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], p = 0.045) and progression-free (HR = 0.49 [95% CI 0.27–0.87], p = 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. © 2023 by the authors. |
| Keywords: | immunohistochemistry; adult; controlled study; treatment response; aged; gene mutation; major clinical study; overall survival; area under the curve; validation process; adjuvant therapy; cancer staging; prospective study; sensitivity and specificity; genetic analysis; tumor associated leukocyte; cancer immunotherapy; progression free survival; cohort analysis; genotype; retrospective study; prediction; histology; body mass; amino acid sequence; algorithm; heterozygosity; microsatellite instability; dna fingerprinting; clinical decision making; validation; predictive value; receiver operating characteristic; extracorporeal oxygenation; head and neck squamous cell carcinoma; diagnostic test accuracy study; clinical outcome; machine learning; immune checkpoint inhibitor; human; male; female; article; median survival time; random forest; neutrophil lymphocyte ratio; tumor mutational burden; checkpoint inhibition; head and neck squamous-cell carcinoma; neoplastic cell transformation |
| Journal Title: | Cancers |
| Volume: | 16 |
| Issue: | 1 |
| ISSN: | 2072-6694 |
| Publisher: | MDPI |
| Date Published: | 2024-01-01 |
| Start Page: | 175 |
| Language: | English |
| DOI: | 10.3390/cancers16010175 |
| PROVIDER: | scopus |
| PMCID: | PMC10778506 |
| PUBMED: | 38201602 |
| DOI/URL: | |
| Notes: | The MSK Cancer Center Support Grant (P30 CA008748) is acknowledged in the PDF -- Corresponding author is MSK author: Luc G. T. Morris -- Source: Scopus |
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