Dataset Information

Prognostic modeling of oral cancer by gene profiles and clinicopathological co-variables.

ABSTRACT: Accurate staging and outcome prediction is a major problem in clinical management of oral cancer patients, hampering high precision treatment and adjuvant therapy planning. Here, we have built and validated multivariable models that integrate gene signatures with clinical and pathological variables to improve staging and survival prediction of patients with oral squamous cell carcinoma (OSCC). Gene expression profiles from 249 human papillomavirus (HPV)-negative OSCCs were explored to identify a 22-gene lymph node metastasis signature (LNMsig) and a 40-gene overall survival signature (OSsig). To facilitate future clinical implementation and increase performance, these signatures were transferred to quantitative polymerase chain reaction (qPCR) assays and validated in an independent cohort of 125 HPV-negative tumors. When applied in the clinically relevant subgroup of early-stage (cT1-2N0) OSCC, the LNMsig could prevent overtreatment in two-third of the patients. Additionally, the integration of RT-qPCR gene signatures with clinical and pathological variables provided accurate prognostic models for oral cancer, strongly outperforming TNM. Finally, the OSsig gene signature identified a subpopulation of patients, currently considered at low-risk for disease-related survival, who showed an unexpected poor prognosis. These well-validated models will assist in personalizing primary treatment with respect to neck dissection and adjuvant therapies.

SUBMITTER: Mes SW

PROVIDER: S-EPMC5601734 | biostudies-literature | 2017 Aug

REPOSITORIES: biostudies-literature

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Prognostic modeling of oral cancer by gene profiles and clinicopathological co-variables.

Mes Steven W SW Te Beest Dennis D Poli Tito T Rossi Silvia S Scheckenbach Kathrin K van Wieringen Wessel N WN Brink Arjen A Bertani Nicoletta N Lanfranco Davide D Silini Enrico M EM van Diest Paul J PJ Bloemena Elisabeth E Leemans C René CR van de Wiel Mark A MA Brakenhoff Ruud H RH

Oncotarget 20170726 35

Accurate staging and outcome prediction is a major problem in clinical management of oral cancer patients, hampering high precision treatment and adjuvant therapy planning. Here, we have built and validated multivariable models that integrate gene signatures with clinical and pathological variables to improve staging and survival prediction of patients with oral squamous cell carcinoma (OSCC). Gene expression profiles from 249 human papillomavirus (HPV)-negative OSCCs were explored to identify a ...[more]

PMID: 28938638

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Project description:BackgroundIntegrating phenotypic and genotypic information to improve prognostic prediction is under active investigation for lung adenocarcinoma (LUAD). In this study, we developed a new prognostic model for event-free survival (EFS) and recurrence-free survival (RFS) based on the combination of clinicopathologic variables, gene expression, and mutation data.MethodsWe enrolled a total of 408 patients from the Cancer Genome Atlas Lung Adenocarcinoma (TCGA-LUAD) project for the study. We pre-selected gene expression or mutation features and constructed 14 different input feature sets for predictive model development. We assessed model performance with multiple evaluation metrics including the distribution of C-index on testing dataset, risk score significance, and time-dependent AUC under competing risks scenario. We stratified patients into higher- and lower-risk subgroups by the final risk score and further investigated underlying immune phenotyping variations associated with the differential risk.ResultsThe model integrating all three types of data achieved the best prediction performance. The resultant risk score provided a higher-resolution risk stratification than other models within pathologically defined subgroups. The score could account for extra EFS-related variations that were not captured by clinicopathologic scores. Being validated for RFS prediction under a competing risks modeling framework, the score achieved a significantly higher time-dependent AUC as compared to that of the conventional clinicopathologic variables-based model (0.772 vs. 0.646, p value < 0.001). The higher-risk patients were characterized with transcriptional aberrations of multiple immune-related genes, and a significant depletion of mast cells and natural killer cells.ConclusionsWe developed a novel prognostic risk score with improved prediction accuracy, using clinicopathologic variables, gene expression and mutation profiles as input, for LUAD. Such score was a significant predictor of both EFS and RFS.Trial registrationThis study was based on public open data from TCGA and hence the study objects were retrospectively registered.

Project description:BACKGROUND: Dicer is aberrantly expressed in several types of cancers. Applying real-time PCR, we detected the expression of Dicer mRNA in normal mucosa (n = 162), primary colorectal cancer (CRC) (n = 162) and liver metastasis (n = 37), and analysed the relationship between Dicer expression and clinicopathological features. We also correlated the expression of Dicer mRNA to the miRNA expression of miR-141, miR-200a, miR-200b, mir-200c and miR-429 in liver metastases. METHODS: RT-PCR and qPCR were used to analyse the Dicer expression in normal mucosa, primary tumour and liver metastasis by using the High Capacity cDNA Reverse Transcription Kit and TaqMan™® Gene Expression assays for Dicer and GAPDH. RT-PCR and qPCR were used to detect miRNA expression in liver metastases by utilizing TaqMan® MicroRNA Reverse Transcription Kit and TaqMan® miRNA Assays. Statistical analyses were performed with STATISTICA. RESULTS: Dicer expression in rectal cancer (3.146 ± 0.953) was higher than in colon cancer (2.703 ± 1.204, P = 0.018). Furthermore the Dicer expression was increased in primary tumours (3.146 ± 0.952) in comparison to that in normal mucosa from rectal cancer patients (2.816 ± 1.009, P = 0.034) but this is not evident in colon cancer patients. Dicer expression in liver metastases was decreased in comparison to that of either normal mucosa or primary tumour in both colon and rectal cancers (P < 0.05). Patients with a high Dicer expression in normal mucosa had a worse prognosis compared to those with a low Dicer expression, independently of gender, age, tumour site, stage and differentiation (P < 0.001, RR 3.682, 95% CI 1.749 - 7.750). In liver metastases, Dicer was positively related to miR-141 (R = 0.419, P = 0.015). CONCLUSION: Dicer is up-regulated in the early development of rectal cancers. An increased expression of Dicer mRNA in normal mucosa from CRC patients is significantly related to poor survival independently of gender, age, tumour site, stage and differentiation.

Dataset Information

Prognostic modeling of oral cancer by gene profiles and clinicopathological co-variables.

Publications

Prognostic modeling of oral cancer by gene profiles and clinicopathological co-variables.

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