Dataset Information

Leveraging Genomics for Head and Neck Cancer Treatment.

ABSTRACT: The genomic landscape of head and neck squamous cell carcinoma (HNSCC) has been recently elucidated. Key epigenetic and genetic characteristics of this cancer have been reported and substantiated in multiple data sets, including those distinctive to the growing subset of human papilloma virus (HPV)-associated tumors. This increased understanding of the molecular underpinnings of HNSCC has not resulted in new approaches to treatment. Three Food and Drug Administration-approved molecular targeting agents are currently available to treat recurrent/metastatic disease, but these have exhibited efficacy only in subsets of HNSCC patients, and thus surgery, chemotherapy, and/or radiation remain as standard approaches. The lack of predictive biomarkers to any therapy represents an obstacle to achieving the promise of precision medicine. This review aims to familiarize the reader with current insights into the HNSCC genomic landscape, discuss the currently approved and promising molecular targeting agents under exploration in laboratories and clinics, and consider precision medicine approaches to HNSCC.

SUBMITTER: Kemmer JD

PROVIDER: S-EPMC5960879 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature

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Publications

Leveraging Genomics for Head and Neck Cancer Treatment.

Kemmer J D JD Johnson D E DE Grandis J R JR

Journal of dental research 20180208 6

The genomic landscape of head and neck squamous cell carcinoma (HNSCC) has been recently elucidated. Key epigenetic and genetic characteristics of this cancer have been reported and substantiated in multiple data sets, including those distinctive to the growing subset of human papilloma virus (HPV)-associated tumors. This increased understanding of the molecular underpinnings of HNSCC has not resulted in new approaches to treatment. Three Food and Drug Administration-approved molecular targeting ...[more]

PMID: 29420101

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Project description:ImportancePostoperative chemoradiation is the standard of care for cancers with positive margins or extracapsular extension, but the benefit of chemotherapy is unclear for patients with other intermediate risk features.ObjectiveTo evaluate whether machine learning models could identify patients with intermediate-risk head and neck squamous cell carcinoma who would benefit from chemoradiation.Design, setting, and participantsThis cohort study included patients diagnosed with squamous cell carcinoma of the oral cavity, oropharynx, hypopharynx, or larynx from January 1, 2004, through December 31, 2016. Patients had resected disease and underwent adjuvant radiotherapy. Analysis was performed from October 1, 2019, through September 1, 2020. Patients were selected from the National Cancer Database, a hospital-based registry that captures data from more than 70% of newly diagnosed cancers in the United States. Three machine learning survival models were trained using 80% of the cohort, with the remaining 20% used to assess model performance.ExposuresReceipt of adjuvant chemoradiation or radiation alone.Main outcomes and measuresPatients who received treatment recommended by machine learning models were compared with those who did not. Overall survival for treatment according to model recommendations was the primary outcome. Secondary outcomes included frequency of recommendation for chemotherapy and chemotherapy benefit in patients recommended for chemoradiation vs radiation alone.ResultsA total of 33 527 patients (24 189 [72%] men; 28 036 [84%] aged ≤70 years) met the inclusion criteria. Median follow-up in the validation data set was 43.2 (interquartile range, 19.8-65.5) months. DeepSurv, neural multitask logistic regression, and survival forest models recommended chemoradiation for 17 589 (52%), 15 917 (47%), and 14 912 patients (44%), respectively. Treatment according to model recommendations was associated with a survival benefit, with a hazard ratio of 0.79 (95% CI, 0.72-0.85; P < .001) for DeepSurv, 0.83 (95% CI, 0.77-0.90; P < .001) for neural multitask logistic regression, and 0.90 (95% CI, 0.83-0.98; P = .01) for random survival forest models. No survival benefit for chemotherapy was seen for patients recommended to receive radiotherapy alone.Conclusions and relevanceThese findings suggest that machine learning models may identify patients with intermediate risk who could benefit from chemoradiation. These models predicted that approximately half of such patients have no added benefit from chemotherapy.

Dataset Information

Leveraging Genomics for Head and Neck Cancer Treatment.

Publications

Leveraging Genomics for Head and Neck Cancer Treatment.

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