Identification and validation of gene expression models that predict clinical outcome in patients with early-stage laryngeal cancer

Fountzilas, Elena; Markou, Konstantinos; Vlachtsis, Konstantinos; Nikolaou, Angelos; Arapantoni-Dadioti, Petroula; Ntoula, E.; Tassopoulos, Georgios; Bobos, Mattheos; Konstantinopoulos, Panagiotis A.; Fountzilas, George; Spentzos, Dimitrios

doi:10.1093/annonc/mdr576

Cited by 24 publications

(19 citation statements)

References 49 publications

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“…To further validate the prognostic significance of these profiles, we applied our 28-gene model to a publicly available cohort of patients with early stage laryngeal cancer [28]. Due to technical variation between the two datasets, 23 out of the 28 genes of our model were used to stratify patients based on their risk for recurrence.…”

Section: Resultsmentioning

confidence: 99%

“…Our group has previously identified prognostic gene models in patients with early stage (T1N0M0, T2N0M0) laryngeal cancer [28] by using Illumina expression profiling (Illumina, CA) in 56 FFPE tissue samples. While this former study provided promising information on the genetic profile of early stage laryngeal cancer, herein we sought to expand our research to operable laryngeal cancer beyond stage.…”

Section: Discussionmentioning

confidence: 99%

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Identification and Validation of a Multigene Predictor of Recurrence in Primary Laryngeal Cancer

et al. 2013

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PurposeLocal recurrence is the major manifestation of treatment failure in patients with operable laryngeal carcinoma. Established clinicopathological factors cannot sufficiently predict patients that are likely to recur after treatment. Additional tools are therefore required to accurately identify patients at high risk for recurrence. This study attempts to identify and independently validate gene expression models, prognostic of disease-free survival (DFS) in operable laryngeal cancer.Materials and MethodsUsing Affymetrix U133A Genechips, we profiled fresh-frozen tumor tissues from 66 patients with laryngeal cancer treated locally with surgery. We applied Cox regression proportional hazards modeling to identify multigene predictors of recurrence. Gene models were then validated in two independent cohorts of 54 and 187 patients (fresh-frozen and formalin-fixed tissue validation sets, respectively).ResultsWe focused on genes univariately associated with DFS (p<0.01) in the training set. Among several models comprising different numbers of genes, a 30-probe set model demonstrated optimal performance in both the training (log-rank, p<0.001) and 1st validation (p = 0.010) sets. Specifically, in the 1st validation set, median DFS as predicted by the 30-probe set model, was 34 and 80 months for high- and low-risk patients, respectively. Hazard ratio (HR) for recurrence in the high-risk group was 3.87 (95% CI 1.28–11.73, Wald's p = 0.017). Testing the expression of selected genes from the above model in the 2nd validation set, with qPCR, revealed significant associations of single markers, such as ACE2, FLOT1 and PRKD1, with patient DFS. High PRKD1 remained an unfavorable prognostic marker upon multivariate analysis (HR = 2.00, 95% CI 1.28–3.14, p = 0.002) along with positive nodal status.ConclusionsWe have established and validated gene models that can successfully stratify patients with laryngeal cancer, based on their risk for recurrence. It seems worthy to prospectively validate PRKD1 expression as a laryngeal cancer prognostic marker, for routine clinical applications.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Identification and Validation of a Multigene Predictor of Recurrence in Primary Laryngeal Cancer

et al. 2013

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“…In a recent seminal work in the field [22] a multigene predictor of recurrence in early laryngeal cancer was developed and validated. In that study, a panel of genes coding for members of the IGFR pathway emerged as a potent prognostic tool able to discriminate patients with poor and favorable prognosis (p<0.0001 in the training set and p = 0.0001 in the first validation set) [22].…”

Section: Introductionmentioning

confidence: 99%

“…In that study, a panel of genes coding for members of the IGFR pathway emerged as a potent prognostic tool able to discriminate patients with poor and favorable prognosis (p<0.0001 in the training set and p = 0.0001 in the first validation set) [22]. These results generated the hypothesis that protein expression and mRNA levels of the most important effectors of the IGFR pathway may be associated with clinical outcome in patients with early laryngeal cancer and may thus serve as predictive biological markers of relapse and survival in this setting.…”

Section: Introductionmentioning

confidence: 99%

Insulin-Like Growth Factor 1 Receptor (IGF1R) Expression and Survival in Operable Squamous-Cell Laryngeal Cancer

et al. 2013

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IntroductionPrognosis of patients with operable laryngeal cancer is highly variable and therefore potent prognostic biomarkers are warranted. The insulin-like growth factor receptor (IGFR) signaling pathway plays a critical role in laryngeal carcinogenesis and progression.Patients and MethodsWe identified all patients with localized TNM stage I–III laryngeal cancer managed with potentially curative surgery between 1985 and 2008. Immunohistochemical (IHC) expression of IGF1R-alpha, IGF1R-beta and IGF2R was evaluated using the immunoreactive score (IRS) and mRNA levels of important effectors of the IGFR pathway were assessed, including IGF1R, IGF-binding protein 3 (IGFBP3), suppressor of cytokine signaling 2 (SOCS2) and members of the MAP-kinase (MAP2K1, MAPK9) and phosphatidyl-inositol-3 kinase (PIK3CA, PIK3R1) families. Cox-regression models were applied to assess the predictive value of biomarkers on disease-free survival (DFS) and overall survival (OS).ResultsAmong 289 eligible patients, 95.2% were current or ex smokers, 75.4% were alcohol abusers, 15.6% had node-positive disease and 32.2% had received post-operative irradiation. After a median follow-up of 74.5 months, median DFS was 94.5 months and median OS was 106.3 months. Using the median IRS as the pre-defined cut-off, patients whose tumors had increased IGF1R-alpha cytoplasm or membrane expression experienced marginally shorter DFS and significantly shorter OS compared to those whose tumors had low IGF1R-alpha expression (91.1 vs 106.2 months, p = 0.0538 and 100.3 vs 118.6 months, p = 0.0157, respectively). Increased mRNA levels of MAPK9 were associated with prolonged DFS (p = 0.0655) and OS (p = 0.0344). In multivariate analysis, IGF1R-alpha overexpression was associated with a 46.6% increase in the probability for relapse (p = 0.0374). Independent predictors for poor OS included node-positive disease (HR = 2.569, p<0.0001), subglottic/transglottic localization (HR = 1.756, p = 0.0438) and IGF1R-alpha protein overexpression (HR = 1.475, p = 0.0504).ConclusionIGF1R-alpha protein overexpression may serve as an independent predictor of relapse and survival in operable laryngeal cancer. Prospective evaluation of the IGF1R-alpha prognostic utility is warranted.

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“…For this reason, integrated analysis of multiple transcriptomic studies is necessary for identifying consistent, fundamental gene expression patterns that indicate HNSCC status. Previous predictive modeling studies have applied gene expression data to various problems related to HNSCC, including predicting metastatic disease [7, 8], the development of cancer in patients with oral premalignant lesions [9, 10], and the risk of recurrence and relapse [11, 12]. A key aspect of HNSCC research is early detection: if the cancer is detected at an early stage, patient response to treatment is relatively high, and five year survival rates for multiple disease subsites exceeds 80% [13, 14].…”

Section: Introductionmentioning

confidence: 99%

Developing robust predictive models for head and neck cancer across microarray and RNA-seq data

Kaddi

Wang

2015

Proceedings of the 6th ACM Conference on Bioinformatics, Computational Biology and Health Informatics

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Increased understanding of the transcriptomic patterns underlying head and neck squamous cell carcinoma (HNSCC) can facilitate earlier diagnosis and better treatment outcomes. Integrating knowledge from multiple studies is necessary to identify fundamental, consistent gene expression signatures that distinguish HNSCC patient samples from disease-free samples, and particularly for detecting HNSCC at an early pathological stage. This study utilizes feature integration and heterogeneous ensemble modeling techniques to develop robust models for predicting HNSCC disease status in both microarray and RNAseq datasets. Several alternative models demonstrated good performance, with MCC and AUC values exceeding 0.8. These models were also applied to discriminate between early pathological stage HNSCC and normal RNA-seq samples, showing encouraging results. The predictive modeling workflow was integrated into a software tool with a graphical user interface. This tool enables HNSCC researchers to harness frequently observed transcriptomic features and ensembles of previously developed models when investigating new HNSCC gene expression datasets.

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Identification and validation of gene expression models that predict clinical outcome in patients with early-stage laryngeal cancer

Cited by 24 publications

References 49 publications

Identification and Validation of a Multigene Predictor of Recurrence in Primary Laryngeal Cancer

Identification and Validation of a Multigene Predictor of Recurrence in Primary Laryngeal Cancer

Insulin-Like Growth Factor 1 Receptor (IGF1R) Expression and Survival in Operable Squamous-Cell Laryngeal Cancer

Developing robust predictive models for head and neck cancer across microarray and RNA-seq data

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