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.
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.
Authors: Agnieszka M Mazurek; Tomasz Rutkowski; Anna Fiszer-Kierzkowska; Ewa Małusecka; Krzysztof Składowski Journal: Oral Oncol Date: 2016-01-11 Impact factor: 5.337
Authors: Chetan Bettegowda; Mark Sausen; Rebecca J Leary; Isaac Kinde; Yuxuan Wang; Nishant Agrawal; Bjarne R Bartlett; Hao Wang; Brandon Luber; Rhoda M Alani; Emmanuel S Antonarakis; Nilofer S Azad; Alberto Bardelli; Henry Brem; John L Cameron; Clarence C Lee; Leslie A Fecher; Gary L Gallia; Peter Gibbs; Dung Le; Robert L Giuntoli; Michael Goggins; Michael D Hogarty; Matthias Holdhoff; Seung-Mo Hong; Yuchen Jiao; Hartmut H Juhl; Jenny J Kim; Giulia Siravegna; Daniel A Laheru; Calogero Lauricella; Michael Lim; Evan J Lipson; Suely Kazue Nagahashi Marie; George J Netto; Kelly S Oliner; Alessandro Olivi; Louise Olsson; Gregory J Riggins; Andrea Sartore-Bianchi; Kerstin Schmidt; le-Ming Shih; Sueli Mieko Oba-Shinjo; Salvatore Siena; Dan Theodorescu; Jeanne Tie; Timothy T Harkins; Silvio Veronese; Tian-Li Wang; Jon D Weingart; Christopher L Wolfgang; Laura D Wood; Dongmei Xing; Ralph H Hruban; Jian Wu; Peter J Allen; C Max Schmidt; Michael A Choti; Victor E Velculescu; Kenneth W Kinzler; Bert Vogelstein; Nickolas Papadopoulos; Luis A Diaz Journal: Sci Transl Med Date: 2014-02-19 Impact factor: 17.956
Authors: Mark W Lingen; Weihong Xiao; Alessandra Schmitt; Bo Jiang; Robert Pickard; Paul Kreinbrink; Bayardo Perez-Ordonez; Richard C Jordan; Maura L Gillison Journal: Oral Oncol Date: 2012-07-28 Impact factor: 5.337
Authors: Paul P Anglim; Janice S Galler; Michael N Koss; Jeffrey A Hagen; Sally Turla; Mihaela Campan; Daniel J Weisenberger; Peter W Laird; Kimberly D Siegmund; Ite A Laird-Offringa Journal: Mol Cancer Date: 2008-07-10 Impact factor: 27.401
Authors: Vasudha Mishra; Alka Singh; Xiangying Chen; Ari J Rosenberg; Alexander T Pearson; Alex Zhavoronkov; Peter A Savage; Mark W Lingen; Nishant Agrawal; Evgeny Izumchenko Journal: Br J Cancer Date: 2021-12-07 Impact factor: 7.640