{"doi":"10.1002/cncr.33393","title":"Ultrasensitive detection of tumor‐specific mutations in saliva of patients with oral cavity squamous cell carcinoma","abstract":"BACKGROUND: Oral cavity squamous cell carcinoma (OCSCC) is the most common head and neck malignancy. Although the survival rate of patients with advanced-stage disease remains approximately 20% to 60%, when detected at an early stage, the survival rate approaches 80%, posing a pressing need for a well validated profiling method to assess patients who have a high risk of developing OCSCC. Tumor DNA detection in saliva may provide a robust biomarker platform that overcomes the limitations of current diagnostic tests. However, there is no routine saliva-based screening method for patients with OCSCC. METHODS: The authors designed a custom next-generation sequencing panel with unique molecular identifiers that covers coding regions of 7 frequently mutated genes in OCSCC and applied it on DNA extracted from 121 treatment-naive OCSCC tumors and matched preoperative saliva specimens. RESULTS: By using stringent variant-calling criteria, mutations were detected in 106 tumors, consistent with a predicted detection rate ≥88%. Moreover, mutations identified in primary malignancies were also detected in 93% of saliva samples. To ensure that variants are not errors resulting in false-positive calls, a multistep analytical validation of this approach was performed: 1) re-sequencing of 46 saliva samples confirmed 88% of somatic variants; 2) no functionally relevant mutations were detected in saliva samples from 11 healthy individuals without a history of tobacco or alcohol; and 3) using a panel of 7 synthetic loci across 8 sequencing runs, it was confirmed that the platform developed is reproducible and provides sensitivity on par with droplet digital polymerase chain reaction. CONCLUSIONS: The current data highlight the feasibility of somatic mutation identification in driver genes in saliva collected at the time of OCSCC diagnosis.","journal":"Cancer","year":2021,"id":162276,"datarank":0.5837730447165941,"base_score":3.8918202981106265,"endowment":3.8918202981106265,"self_citation_contribution":0.5837730447165941,"citation_network_contribution":0.0,"self_endowment_contribution":0.5837730447165941,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":48,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9617,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2021-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":679489,"name":"Arun K Hariharan","orcid":null,"position":1,"is_corresponding":false},{"id":678777,"name":"Rifat Hasina","orcid":"0000-0002-5800-5344","position":2,"is_corresponding":false},{"id":678778,"name":"Jayalakshmi Nair","orcid":"0000-0001-9450-9737","position":3,"is_corresponding":false},{"id":678779,"name":"Shanmukh Katragadda","orcid":"0000-0002-4536-2705","position":4,"is_corresponding":false},{"id":679490,"name":"Sivaraj Irusappan","orcid":null,"position":5,"is_corresponding":false},{"id":679491,"name":"Aarthi Ravichandran","orcid":null,"position":6,"is_corresponding":false},{"id":679492,"name":"Vamsi Veeramachaneni","orcid":null,"position":7,"is_corresponding":false},{"id":679493,"name":"Radhakrishna Bettadapura","orcid":null,"position":8,"is_corresponding":false},{"id":679494,"name":"Muddasir Bhati","orcid":null,"position":9,"is_corresponding":false},{"id":679495,"name":"Veena Ramaswamy","orcid":null,"position":10,"is_corresponding":false},{"id":678780,"name":"Vishal Rao","orcid":"0000-0001-6167-5114","position":11,"is_corresponding":false},{"id":678781,"name":"Ritvi K. 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