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Prediction of Radiosensitivity in Head and Neck Squamous Cell Carcinoma Based on Multiple Omics Data

ABSTRACT: Head and neck squamous cell carcinoma (HNSCC) is a malignant tumor. Radiotherapy (RT) is an important treatment for HNSCC, but not all patients derive survival benefit from RT due to the individual differences on radiosensitivity. A prediction model of radiosensitivity based on multiple omics data might solve this problem. Compared with single omics data, multiple omics data can illuminate more systematical associations between complex molecular characteristics and cancer phenotypes. In this study, we obtained 122 differential expression genes by analyzing the gene expression data of HNSCC patients with RT (N = 287) and without RT (N = 189) downloaded from The Cancer Genome Atlas. Then, HNSCC patients with RT were randomly divided into a training set (N = 149) and a test set (N = 138). Finally, we combined multiple omics data of 122 differential genes with clinical outcomes on the training set to establish a 12-gene signature by two-stage regularization and multivariable Cox regression models. Using the median score of the 12-gene signature on the training set as the cutoff value, the patients were divided into the high- and low-score groups. The analysis revealed that patients in the low-score group had higher radiosensitivity and would benefit from RT. Furthermore, we developed a nomogram to predict the overall survival of HNSCC patients with RT. We compared the prognostic value of 12-gene signature with those of the gene signatures based on single omics data. It suggested that the 12-gene signature based on multiple omics data achieved the best ability for predicting radiosensitivity. In conclusion, the proposed 12-gene signature is a promising biomarker for estimating the RT options in HNSCC patients.

SUBMITTER: Liu J

PROVIDER: S-EPMC7461877 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

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Project description:A critical risk factor for head and neck squamous cell carcinoma (HNSCC), particularly of the oropharynx, and the response to radiotherapy is human papillomavirus (HPV) type-16/18 infection. Specifically, HPV-positive HNSCC display increased radiosensitivity and improved outcomes, which has been linked with defective signalling and repair of DNA double-strand breaks (DSBs). This differential response to radiotherapy has been recapitulated in vitro using cell lines, although studies utilising appropriate 3D models that are more reflective of the original tumour are scarce. Furthermore, strategies to enhance the sensitivity of relatively radioresistant HPV-negative HNSCC to radiotherapy are still required. We have analysed the comparative response of in vitro 3D spheroid models of oropharyngeal squamous cell carcinoma to x-ray (photon) irradiation and provide further evidence that HPV-positive cells, in this case now grown as spheroids, show greater inherent radiosensitivity compared to HPV-negative spheroids due to defective DSB repair. We subsequently analysed these and an expanded number of spheroid models, with a particular focus on relatively radioresistant HPV-negative HNSCC, for impact of poly(ADP-ribose) polymerase (PARP) inhibitors (olaparib and talazoparib) in significantly inhibiting spheroid growth in response to photons but also proton beam therapy. We demonstrate that in general, PARP inhibition can further radiosensitise particularly HPV-negative HNSCC spheroids to photons and protons leading to significant growth suppression. The degree of enhanced radiosensitivity was observed to be dependent on the model and on the tumour site (oropharynx, larynx, salivary gland, or hypopharynx) from which the cells were derived. We also provide evidence suggesting that PARP inhibitor effectiveness relates to homologous recombination repair proficiency. Interestingly though, we observed significantly enhanced effectiveness of talazoparib versus olaparib specifically in response to proton irradiation. Nevertheless, our data generally support that PARP inhibition in combination with radiotherapy (photons and protons) should be considered further as an effective treatment for HNSCC, particularly for relatively radioresistant HPV-negative tumours.

Project description:OBJECTIVES:Head and neck squamous cell carcinoma (HNSCC) shows a remarkable heterogeneity between tumors, which may be captured by a variety of quantitative features extracted from diagnostic images, termed radiomics. The aim of this study was to develop and validate MRI-based radiomic prognostic models in oral and oropharyngeal cancer. MATERIALS AND METHODS:Native T1-weighted images of four independent, retrospective (2005-2013), patient cohorts (n = 102, n = 76, n = 89, and n = 56) were used to delineate primary tumors, and to extract 545 quantitative features from. Subsequently, redundancy filtering and factor analysis were performed to handle collinearity in the data. Next, radiomic prognostic models were trained and validated to predict overall survival (OS) and relapse-free survival (RFS). Radiomic features were compared to and combined with prognostic models based on standard clinical parameters. Performance was assessed by integrated area under the curve (iAUC). RESULTS:In oral cancer, the radiomic model showed an iAUC of 0.69 (OS) and 0.70 (RFS) in the validation cohort, whereas the iAUC in the oropharyngeal cancer validation cohort was 0.71 (OS) and 0.74 (RFS). By integration of radiomic and clinical variables, the most accurate models were defined (iAUC oral cavity, 0.72 (OS) and 0.74 (RFS); iAUC oropharynx, 0.81 (OS) and 0.78 (RFS)), and these combined models outperformed prognostic models based on standard clinical variables only (p < 0.001). CONCLUSIONS:MRI radiomics is feasible in HNSCC despite the known variability in MRI vendors and acquisition protocols, and radiomic features added information to prognostic models based on clinical parameters. KEY POINTS:• MRI radiomics can predict overall survival and relapse-free survival in oral and HPV-negative oropharyngeal cancer. • MRI radiomics provides additional prognostic information to known clinical variables, with the best performance of the combined models. • Variation in MRI vendors and acquisition protocols did not influence performance of radiomic prognostic models.

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Prediction of Radiosensitivity in Head and Neck Squamous Cell Carcinoma Based on Multiple Omics Data

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