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

ACCESS DATA

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

Similar Datasets

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.

Project description:BACKGROUND:Multidisciplinary management of head and neck cancer (HNC) must reconcile increasingly sophisticated subspecialty care with timeliness of care. Prior studies examined the individual effects of delays in diagnosis-to-treatment interval, postoperative interval, and radiation interval but did not consider them collectively. The objective of the current study was to investigate the combined impact of these interwoven intervals on patients with HNC. METHODS:Patients with HNC who underwent curative-intent surgery with radiation were identified in the National Cancer Database between 2004 and 2013. Multivariable models were constructed using restricted cubic splines to determine nonlinear relations with overall survival. RESULTS:Overall, 15,064 patients were evaluated. After adjustment for covariates, only prolonged postoperative interval (P?<?.001) and radiation interval (P?<?.001) independently predicted for worse outcomes, whereas the association of diagnosis-to-treatment interval with survival disappeared. By using multivariable restricted cubic spline functions, increasing postoperative interval did not affect mortality until 40 days after surgery, and each day of delay beyond this increased the risk of mortality until 70 days after surgery (hazard ratio, 1.14; 95% confidence interval, 1.01-1.28; P?=?.029). For radiation interval, mortality escalated continuously with each additional day of delay, plateauing at 55 days (hazard ratio, 1.25; 95% confidence interval, 1.11-1.41; P?<?.001). Delays beyond these change points were not associated with further survival decrements. CONCLUSIONS:Increasing delays in postoperative and radiation intervals are associated independently with an escalating risk of mortality that plateaus beyond certain thresholds. Delays in initiating therapy, conversely, are eclipsed in importance when appraised in conjunction with the entire treatment course. Such findings may redirect focus to streamlining those intervals that are most sensitive to delays when considering survival burden. Cancer 2018. © 2018 American Cancer Society.

Dataset Information

Leveraging Genomics for Head and Neck Cancer Treatment.

Publications

Leveraging Genomics for Head and Neck Cancer Treatment.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure