Risk Groups for Survival in HPV-Positive and HPV-Negative OPSCC.

Over the last three decades, it has become clear that infection with high-risk human papillomavirus (HPV) is etiologically linked to the development of head and neck squamous cell carcinomas, particularly those carcinomas that arise in the oropharyngeal region.

Molecular characterization of p16-immunopositive but HPV DNA-negative oropharyngeal carcinomas.

Recent studies have reported that p16 protein overexpression qualifies as a surrogate marker identifying an oncogenic human papillomavirus (HPV) infection in oropharyngeal squamous cell carcinoma (OPSCC). However, there is still a percentage of OPSCCs that are positive for p16 immunohistochemistry (p16 IHC) but lack HPV DNA. The objective of this study was to characterize this group at the molecular level by performing sensitive HPV DNA- and RNA-based PCR methods and genetic profiling. All patients diagnosed with an OPSCC in the period 2000-2006 in two Dutch university medical centers were included (n = 841). The presence of HPV in a tumor sample was tested by p16 IHC followed by an HPV DNA GP5+/6+ PCR. p16 IHC scored positive in 195 samples, of which 161 were HPV DNA-positive and 34 (17%) HPV DNA-negative. In the latter group, a SPF10-LiPA25 assay, an HPV16 type-specific E7 PCR and an E6 mRNA RT-PCR were performed. Next, ten of these cases were further analyzed for loss of heterozygosity (LOH) of 15 microsatellite markers at chromosome arms 3p, 9p and 17p. Of the 34 p16-positive but PCR-negative OPSCCs, two samples tested positive by SPF10 assay, HPV16 E7 PCR and HPV16 E6 mRNA RT-PCR. Three samples tested positive by SPF10 assay but negative by the HPV16-specific assays. Nine of ten cases that were tested for LOH showed a genetic pattern comparable to that of HPV-negative tumors. This study categorizes p16-positive but HPV DNA-negative OPSCCs as HPV-negative tumors based on genetic profiling. This study highlights the importance of performing HPV testing in addition to p16 IHC for proper identification of HPV-associated OPSCCs.

No evidence for active human papillomavirus (HPV) in fields surrounding HPV-positive oropharyngeal tumors.

BACKGROUND: Patients with human papillomavirus (HPV)-positive oropharyngeal squamous cell carcinomas (OPSCCs) have a better prognosis than patients with HPV-negative OPSCCs. Important factors contributing to this better prognosis are relatively low numbers of local/regional recurrences (LRRs) and second primary tumors (SPTs) in patients with HPV-positive OPSCC. These low numbers may be explained in addition by the absence of a 'field cancerization' effect, which is a cause of LRRs and SPTs in patients with HPV-negative OPSCC. We aimed to detect a possible 'field effect' in patients with HPV-positive OPSCC. As HPV is involved in the early stage of carcinogenesis in OPSCCs, its presence is considered a reliable marker for the detection of such a field effect. Therefore, the presence of transcriptionally active HPV was analyzed in the mucosa surrounding HPV-positive OPSCCs. METHODS: We included 20 patients who were surgically treated for an HPV-positive OPSCC in the period 2000-2006. Of each patient, the formalin-fixed paraffin-embedded tumor sample and all available resection margins were collected. In total, 97 resection margins were investigated with an average of five resection margins per tumor. All samples were analyzed for the presence of tumor and the presence of transcriptionally active HPV by HPV16-E6-mRNA detection. RESULTS: All tumors were HPV16-E6-mRNA positive. HPV16-E6-mRNA could be detected in the resection margins that contained tumor (n = 6). All tumor-negative resection margins (n = 91) scored negative for HPV16-E6-mRNA. CONCLUSIONS: In conclusion, transcriptional active HPV could not be detected in the mucosa surrounding an HPV-positive OPSCC, which suggests the absence of field effect. This observation may explain the lower number of LRRs and SPTs in HPV-positive patients.

Increasing prevalence rates of HPV attributable oropharyngeal squamous cell carcinomas in the Netherlands as assessed by a validated test algorithm.

Human papillomavirus (HPV) infection has been etiologically linked to oropharyngeal squamous cell carcinoma (OPSCC). The prevalence of HPV-positive OPSCC varies between studies, ranging from 20 to 90%. This may be related to the lack of a standardized HPV detection assay as well as to the time period in which HPV prevalence is investigated, as rising incidence rates are reported over the last decades. Here, we validated our previously defined test algorithm for HPV detection in formalin-fixed paraffin-embedded (FFPE) tumor specimen consisting of p16(INK4A) immunostaining followed by high-risk HPV DNA detection by GP5+/6+ PCR on the positive cases (Smeets et al., Int J Cancer 2007;121:2465-72). In addition, we analyzed HPV prevalence rates in OPSCCs in the years 1990-2010. The test algorithm was validated on a consecutive series of 86 OPSCCs collected during 2008-2011, of which both fresh frozen and FFPE samples were available. We performed HPV-E6 RT-PCR on the frozen samples as gold standard and applied the algorithm to the corresponding FFPE samples. The test algorithm showed an accuracy of 98%. Using the validated algorithm, we determined the presence of an oncogenic HPV infection in 240 OPSCCs of patients diagnosed in the years 1990-2010 at our center. A significant increase in the proportion of HPV-positive samples was observed, from 5.1% in 1990 to 29.0% in 2010 (p = 0.001). In conclusion, we confirmed the accuracy of the test algorithm for HPV detection in FFPE tumor specimen and we found a significant increase in the prevalence of HPV in OPSCC over the last two decades at our center.

Radiomic Machine-Learning Classifiers for Prognostic Biomarkers of Head and Neck Cancer.

INTRODUCTION: "Radiomics" extracts and mines a large number of medical imaging features in a non-invasive and cost-effective way. The underlying assumption of radiomics is that these imaging features quantify phenotypic characteristics of an entire tumor. In order to enhance applicability of radiomics in clinical oncology, highly accurate and reliable machine-learning approaches are required. In this radiomic study, 13 feature selection methods and 11 machine-learning classification methods were evaluated in terms of their performance and stability for predicting overall survival in head and neck cancer patients. METHODS: Two independent head and neck cancer cohorts were investigated. Training cohort HN1 consisted of 101 head and neck cancer patients. Cohort HN2 (n = 95) was used for validation. A total of 440 radiomic features were extracted from the segmented tumor regions in CT images. Feature selection and classification methods were compared using an unbiased evaluation framework. RESULTS: We observed that the three feature selection methods minimum redundancy maximum relevance (AUC = 0.69, Stability = 0.66), mutual information feature selection (AUC = 0.66, Stability = 0.69), and conditional infomax feature extraction (AUC = 0.68, Stability = 0.7) had high prognostic performance and stability. The three classifiers BY (AUC = 0.67, RSD = 11.28), RF (AUC = 0.61, RSD = 7.36), and NN (AUC = 0.62, RSD = 10.52) also showed high prognostic performance and stability. Analysis investigating performance variability indicated that the choice of classification method is the major factor driving the performance variation (29.02% of total variance). CONCLUSION: Our study identified prognostic and reliable machine-learning methods for the prediction of overall survival of head and neck cancer patients. Identification of optimal machine-learning methods for radiomics-based prognostic analyses could broaden the scope of radiomics in precision oncology and cancer care.

Radiomic feature clusters and prognostic signatures specific for Lung and Head & Neck cancer.

Radiomics provides a comprehensive quantification of tumor phenotypes by extracting and mining large number of quantitative image features. To reduce the redundancy and compare the prognostic characteristics of radiomic features across cancer types, we investigated cancer-specific radiomic feature clusters in four independent Lung and Head &Neck (H) cancer cohorts (in total 878 patients). Radiomic features were extracted from the pre-treatment computed tomography (CT) images. Consensus clustering resulted in eleven and thirteen stable radiomic feature clusters for Lung and H cancer, respectively. These clusters were validated in independent external validation cohorts using rand statistic (Lung RS = 0.92, p < 0.001, H RS = 0.92, p < 0.001). Our analysis indicated both common as well as cancer-specific clustering and clinical associations of radiomic features. Strongest associations with clinical parameters: Prognosis Lung CI = 0.60 ± 0.01, Prognosis H CI = 0.68 ± 0.01; Lung histology AUC = 0.56 ± 0.03, Lung stage AUC = 0.61 ± 0.01, H HPV AUC = 0.58 ± 0.03, H stage AUC = 0.77 ± 0.02. Full utilization of these cancer-specific characteristics of image features may further improve radiomic biomarkers, providing a non-invasive way of quantifying and monitoring tumor phenotypic characteristics in clinical practice.

Cancer stem cell enrichment marker CD98: a prognostic factor for survival in patients with human papillomavirus-positive oropharyngeal cancer.

PURPOSE: Several hypotheses have been proposed to explain the relatively good prognosis of patients with a human papillomavirus (HPV)-positive oropharyngeal squamous cell carcinoma (OPSCC) and one of these is a higher sensitivity to (chemo)radiation. Previous studies have suggested that treatment failure in OPSCC patients is caused by resistance of cancer stem cells (CSCs). The purpose of this study was to evaluate the association between the number of CSCs and prognosis in HPV-positive OPSCC patients. EXPERIMENTAL DESIGN: All OPSCC patients (n=711) treated between 2000 and 2006 in two Dutch university hospitals were included. Presence of HPV in a tumour tissue specimen was tested by p16-immunostaining followed by HPV DNA GP5+/6+polymerase chain reaction (PCR). The presence and intensity of tumour CSC markers CD44 and CD98 were determined by immunohistochemistry and semiquantitative scoring was performed. Overall survival (OS) and progression-free survival (PFS) rates were compared between patients with low and high CD44/CD98 expression in relation to HPV status. RESULTS: HPV-positive tumours showed a lower percentage of cells with CD44 and CD98 expression than HPV-negative tumours (p<0.001, χ(2)-test). Within the group of patients with HPV-positive OPSCC, a high percentage of CD98-positive tumour cells was associated with a significantly worse 5-year OS and PFS (OS: 36.4% and PFS: 27.3%) compared to patients with a low percentage of CD98-positive cells (OS: 71.9% and PFS: 70.5%, respectively) (p<0.001). CONCLUSIONS: HPV-positive OPSCCs harbour fewer cells expressing the CSC enrichment markers CD44 and CD98. Furthermore, OS and PFS were significantly worse for patients with HPV-positive OPSCC with a high percentage of CD98-positive cells.

Externally validated HPV-based prognostic nomogram for oropharyngeal carcinoma patients yields more accurate predictions than TNM staging.

PURPOSE: Due to the established role of the human papillomavirus (HPV), the optimal treatment for oropharyngeal carcinoma is currently under debate. We evaluated the most important determinants of treatment outcome to develop a multifactorial predictive model that could provide individualized predictions of treatment outcome in oropharyngeal carcinoma patients. METHODS: We analyzed the association between clinico-pathological factors and overall and progression-free survival in 168 OPSCC patients treated with curative radiotherapy or concurrent chemo-radiation. A multivariate model was validated in an external dataset of 189 patients and compared to the TNM staging system. This nomogram will be made publicly available at www.predictcancer.org. RESULTS: Predictors of unfavorable outcomes were negative HPV-status, moderate to severe comorbidity, T3-T4 classification, N2b-N3 stage, male gender, lower hemoglobin levels and smoking history of more than 30 pack years. Prediction of overall survival using the multi-parameter model yielded a C-index of 0.82 (95% CI, 0.76-0.88). Validation in an independent dataset yielded a C-index of 0.73 (95% CI, 0.66-0.79. For progression-free survival, the model's C-index was 0.80 (95% CI, 0.76-0.88), with a validation C-index of 0.67, (95% CI, 0.59-0.74). Stratification of model estimated probabilities showed statistically different prognosis groups in both datasets (p<0.001). CONCLUSION: This nomogram was superior to TNM classification or HPV status alone in an independent validation dataset for prediction of overall and progression-free survival in OPSCC patients, assigning patients to distinct prognosis groups. These individualized predictions could be used to stratify patients for treatment de-escalation trials.

Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach.

Human cancers exhibit strong phenotypic differences that can be visualized noninvasively by medical imaging. Radiomics refers to the comprehensive quantification of tumour phenotypes by applying a large number of quantitative image features. Here we present a radiomic analysis of 440 features quantifying tumour image intensity, shape and texture, which are extracted from computed tomography data of 1,019 patients with lung or head-and-neck cancer. We find that a large number of radiomic features have prognostic power in independent data sets of lung and head-and-neck cancer patients, many of which were not identified as significant before. Radiogenomics analysis reveals that a prognostic radiomic signature, capturing intratumour heterogeneity, is associated with underlying gene-expression patterns. These data suggest that radiomics identifies a general prognostic phenotype existing in both lung and head-and-neck cancer. This may have a clinical impact as imaging is routinely used in clinical practice, providing an unprecedented opportunity to improve decision-support in cancer treatment at low cost.