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Locoregional recurrences and distant metastases are major problems for patients with squamous cell carcinoma of the head and neck (SCCHN). Because SCCHN is a heterogeneous group of tumours with varying characteristics, the present study concentrated on the subgroup of squamous cell carcinoma of the oral tongue (SCCOT) to investigate the use of machine learning approaches to predict the risk of recurrence from routine clinical data available at diagnosis. The approach also identified the most important parameters that identify and classify recurrence risk. A total of 66 patients with SCCOT were included. Clinical data available at diagnosis were analysed using statistical analysis and machine learning approaches. Tumour recurrence was associated with T stage (P=0.001), radiological neck metastasis (P=0.010) and diabetes (P=0.003). A machine learning model based on the random forest algorithm and with attendant explainability was used. Whilst patients with diabetes were overrepresented in the SCCOT cohort, diabetics had lower recur‑ rence rates (P=0.015 after adjusting for age and other clinical features) and an improved 2‑year survival (P=0.025) compared with non‑diabetics. Clinical, radiological and histological data available at diagnosis were used to establish a prognostic model for patients with SCCOT. Using machine learning to predict recurrence produced a classification model with 71.2% accuracy. Notably, one of the findings of the feature importance rankings of the model was that diabetics exhibited less recur‑ rence and improved survival compared with non‑diabetics, even after accounting for the independent prognostic variables of tumour size and patient age at diagnosis. These data imply that the therapeutic manipulation of glucose levels used to treatdiabetes may be useful for patients with SCCOT regardless of their diabetic status. Further studies are warranted to investigatethe impact of diabetes in other SCCHN subtypes.

Background: Interpretable machine learning (ML) for early detection of cancer has the potential to improve risk assessment and early intervention.

Methods: Data from 261 proteins related to inflammation and/or tumor processes in 123 blood samples collected from healthy persons, but of whom a sub-group later developed squamous cell carcinoma of the oral tongue (SCCOT), were analyzed. Samples from people who developed SCCOT within less than 5 years were classified as tumor-to-be and all other samples as tumor-free. The optimal ML algorithm for feature selection was identified and feature importance computed by the SHapley Additive exPlanations (SHAP) method. Five popular ML algorithms (AdaBoost, Artificial neural networks [ANNs], Decision Tree [DT], eXtreme Gradient Boosting [XGBoost], and Support Vector Machine [SVM]) were applied to establish prediction models, and decisions of the optimal models were interpreted by SHAP.

Results: Using the 22 selected features, the SVM prediction model showed the best performance (sensitivity = 0.867, specificity = 0.859, balanced accuracy = 0.863, area under the receiver operating characteristic curve [ROC-AUC] = 0.924). SHAP analysis revealed that the 22 features rendered varying person-specific impacts on model decision and the top three contributors to prediction were Interleukin 10 (IL10), TNF Receptor Associated Factor 2 (TRAF2), and Kallikrein Related Peptidase 12 (KLK12).

Conclusion: Using multidimensional plasma protein analysis and interpretable ML, we outline a systematic approach for early detection of SCCOT before the appearance of clinical signs.

Oral cancer is a paradigm of Slaughter's concept of field cancerization, where tumors are thought to originate within an area of cells containing genetic alterations that predispose to cancer development. The field size is unclear but may represent a large area of tissue, and the origin of mutations is also unclear. Here, we analyzed whole exome and transcriptome features in contralateral tumor-distal tongue (i.e. distant from the tumor, not tumor-adjacent) and corresponding tumor tissues of 15 patients with squamous cell carcinoma of the oral tongue. The number of point mutations ranged from 41 to 237 in tumors and from one to 78 in tumor-distal samples. Tumor-distal samples showed mainly clock-like (associated with aging) or tobacco smoking mutational signatures. Tumors additionally showed mutations that associate with cytidine deaminase AID/APOBEC enzyme activities or a UV-like signature. Importantly, no point mutations were shared between a tumor and the matched tumor-distal sample in any patient. TP53 was the most frequently mutated gene in tumors (67%), whereas a TP53 mutation was detected in only one tumor-distal sample, and this mutation was not shared with the matched tumor. Arm-level copy number variation (CNV) was found in 12 tumors, with loss of chromosome (Chr) 8p or gain of 8q being the most frequent events. Two tumor-distal samples showed a gain of Chr8, which was associated with increased expression of Chr8-located genes in these samples, although gene ontology did not show a role for these genes in oncogenic processes. In situ hybridization revealed a mixed pattern of Chr8 gain and neutral copy number in both tumor cells and adjacent nontumor epithelium in one patient. We conclude that distant field cancerization exists but does not present as tumor-related mutational events. The data are compatible with etiologic field effects, rather than classical monoclonal field cancerization theory. © 2022 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Transporter associated with antigen processing 1 (TAP1) and TAP2 serve pivotal roles in adaptive immunity. Tumor cells often show reduced antigen presentation on their surface as one mechanism to escape immune recognition. Whether downregulation of TAPs is a common mechanism of tumor immune evasion in squamous cell carcinoma of the oral tongue (SCCOT) is unclear. In the present study, samples from 78 patients with SCCOT and 17 patients with benign hyperplastic tongue lesions were analyzed for TAP1 and TAP2 expression by immunohistochemistry. The percentage of positive cells and staining intensity were scored. Associations with clinicopathological variables and survival outcome were also investigated. The results demonstrated that TAP1 and TAP2 levels were highly associated with each other in individual samples and were upregulated in SCCOT compared with benign lesions (P<0.001). The proportion of TAP1‐ or TAP2‐positive tumor cells was >80% in all but two of the tumors, whereas 25.6 and 23.0% of the tumors showed weak intensity of TAP1 and TAP2, respectively. There were no significant associations with clinicopathological variables or survival outcomes between TAP‐intermediate/strong and TAP‐weak tumors. However, in patients <70 years old and with early stage SCCOT, male patients had better outcomes than female patients (log‐rank P<0.05), and the best outcome was observed in male patients with intermediate/strong TAP expression. In conclusion, loss of TAP was not a frequent event in SCCOT and stronger TAP expression in male patients was associated with improved survival, providing further evidence for sex‐specific immune modulation in cancer.

Purpose: Apart from tobacco and alcohol, viral infections are proposed as risk factors for laryngeal cancer. The occurrence of oncogenic viruses including human papilloma virus (HPV) and Epstein–Barr virus (EBV), in laryngeal squamous cell carcinoma (LSCC) varies in the world. Carcinogenesis is a multi-step process, and the role of viruses in LSCC progression has not been clarified. We aimed to analyze the presence and co-expression of HPV, EBV, human cytomegalovirus (HCMV) and human adenovirus (HAdV) in LSCC. We also investigated if p16 can act as surrogate marker for HPV in LSCC.

Methods: Combined PCR/microarrays (PapilloCheck®) were used for detection and genotyping of HPV DNA, real-time PCR for EBV, HCMV and HAdV DNA detection, and EBER in situ hybridization (EBER-ISH) for EBV detection in tissue from 78 LSCC patients. Additionally, we analyzed p16 expression with immunohistochemistry.

Results: Thirty-three percent (26/78) of LSCC tumor samples were EBV positive, 9% (7/78) HCMV positive and 4% (3/78) HAdV positive. Due to DNA fragmentation, 45 samples could not be analyzed with PapilloCheck®; 9% of the remaining (3/33) were high-risk HPV16 positive and also over-expressed p16. A total of 14% (11/78) of the samples over-expressed p16.

Conclusion: These findings present a mapping of HPV, EBV, HCMV and HAdV, including the HPV surrogate marker p16, in LSCC in this cohort. Except for EBV, which was detected in a third of the samples, data show viral infection to be uncommon, and that p16 does not appear to be a specific surrogate marker for high-risk HPV infection in LSCC.

Background: Patients with squamous cell carcinoma of the head and neck (SCCHN) have a high-risk of recurrence. We aimed to develop machine learning methods to identify transcriptomic and proteomic features that provide accurate classification models for predicting risk of early recurrence in SCCHN patients.

Methods: Clinical, genomic, transcriptomic and proteomic features distinguishing recurrence risk were examined in SCCHN patients from The Cancer Genome Atlas (TCGA). Recurrence within one year after treatment was classified as high-risk and no recurrence as low-risk.

Results: No significant differences in individual clinicopathological characteristics, mutation profiles or mRNA expression patterns were seen between the groups using conventional statistical analysis. Using the machine learning algorithm, extreme gradient boosting (XGBoost), ten proteins (RAD50, 4E-BP1, MYH11, MAP2K1, BECN1, NF2, RAB25, ERRFI1, KDR, SERPINE1) and five mRNAs (PLAUR, DKK1, AXIN2, ANG and VEGFA) made the greatest contribution to classification. These features were used to build improved models in XGBoost, achieving the best discrimination performance when combining transcriptomic and proteomic data, providing an accuracy of 0.939 and an Area Under the ROC Curve (AUC) of 0.951.

Conclusions: This study highlights machine learning to identify transcriptomic and proteomic factors that play important roles in predicting risk of recurrence in patients with SCCHN and to develop such models by iterative cycles to enhance their accuracy, thereby aiding the introduction of personalized treatment regimens.

Circulating lipoproteins as risk factors or prognostic indicators for various cancers have been investigated previously; however, no clear consensus has been reached. In this study, we aimed at evaluating the impact of serum lipoproteins on the prognosis of patients with squamous cell carcinoma of the head and neck (SCCHN). Levels of total cholesterol, low-density lipoproteins (LDL), high-density lipoproteins (HDL), triglycerides and lipoprotein(a) were measured in serum samples from 106 patients and 28 healthy controls. We found that HDL was the only lipoprotein exhibiting a significant difference in concentration between healthy controls and patients (p = 0.012). Kaplan–Meier survival curves indicated that patients with high levels of total cholesterol or LDL had better overall survival than patients with normal levels (p = 0.028 and p = 0.007, respectively). Looking at patients without lipid medication (n = 89) and adjusting for the effects of TNM stage and weight change, multivariate Cox regression models indicated that LDL was an independent prognostic factor for both overall (p = 0.005) and disease-free survival (p = 0.013). In summary, our study revealed that high LDL level is beneficial for survival outcome in patients with SCCHN. Use of cholesterol-lowering medicines for prevention or management of SCCHN needs to be evaluated carefully.

Background: Circulating markers are attractive molecules for prognosis and management of cancer that allow sequential monitoring of patients during and after treatment. Based on previous protein profiling data, circulating interleukin 1 receptor antagonist (IL-1Ra) was evaluated as a potential diagnostic and prognostic marker for squamous cell carcinomas of the head and neck (SCCHN). In this study, we aimed at confirming the clinical relevance of plasma IL-1Ra in SCCHN and exploring its potential as a prediction marker for SCCHN.

Methods: Plasma from 87 patients with SCCHN, control plasma from 28 healthy individuals and pre-diagnostic plasma from 44 patients with squamous cell carcinoma of the oral tongue (SCCOT) and 88 matched controls were analysed with IL-1Ra electrochemiluminescence immunoassays from mesoscale diagnostics.

Results: Plasma IL-1Ra was found to be up-regulated in patients with oral tongue, gingiva and base of tongue tumours compared to healthy individuals (p < 0.01). IL-1Ra levels positively correlated with tumour size (p < 0.01) and body mass index (p = 0.013). Comparing pre-diagnostic plasma to the matched controls, similar IL1-Ra levels were seen (p = 0.05).

Conclusion: The anti-inflammatory cytokine IL-1Ra could be a diagnostic marker for SCCHN, whereas its potential as a cancer prediction marker was not supported by our data.

As early detection is crucial for improvement of cancer prognosis, we searched for biomarkers in plasma from individuals who later developed squamous cell carcinoma of the oral tongue (SCCOT) as well as in patients with an already established SCCOT. Levels of 261 proteins related to inflammation and/or tumor processes were measured using the proximity extension assay (PEA) in 179 plasma samples (42 collected before diagnosis of SCCOT with 81 matched controls; 28 collected at diagnosis of SCCOT with 28 matched controls). Statistical modeling tools principal component analysis (PCA) and orthogonal partial least square - discriminant analysis (OPLS-DA) were applied to provide insights into separations between groups. PCA models failed to achieve group separation of SCCOT patients from controls based on protein levels in samples taken prior to diagnosis or at the time of diagnosis. For pre-diagnostic samples and their controls, no significant OPLS-DA model was identified. Potentials for separating pre-diagnostic samples collected up to five years before diagnosis (n = 15) from matched controls (n = 28) were seen in four proteins. For diagnostic samples and controls, the OPLS-DA model indicated that 21 proteins were important for group separation. TNF receptor associated factor 2 (TRAF2), decreased in pre-diagnostic plasma (< 5 years) but increased at diagnosis, was the only protein showing altered levels before and at diagnosis of SCCOT (p-value < 0.05). Taken together, changes in plasma protein profiles at diagnosis were evident, but not reliably detectable in pre-diagnostic samples taken before clinical signs of tumor development. Variation in protein levels during cancer development poses a challenge for the identification of biomarkers that could predict SCCOT development.

Le papillome inversé naso-sinusien (PINS) est une tumeur relativement rare dont l’étiologie est mal connue. Elle se caractérise par une agressivité locale et un fort potentiel de récidive en dépit d’une histologie bénigne.

Objectif: L’objectif de cette étude était d’identifier la présence du papillomavirus humain (HPV) et de son marqueur de substitution, la protéine p16, dans des prélèvements tissulaires de PINS issus d’une cohorte régionale.

Matériels et méthodes: À partir de notre cohorte régionale de 88 patients atteints de PINS traités entre 1984 et 2014, 54 sujets ont été sélectionnés et inclus dans cette étude. La technologie PCR a été réalisée sur 53 prélèvements et la coloration immunohistochimique pour recherche de p16 a été réalisée sur 54 prélèvements. L’ADN a été extrait après confirmation histopathologique du PINS. Un génotypage pour 13 types de HPV à risque élevé, 5 types de HPV à risque oncogène et 6 types de HPV à faible risque a été réalisé à l’aide du test de dépistage HPV PapilloCheck®.

Résultats: L’analyse HPV a été réalisable sur 38 des 53 prélèvements. Sur ces 38 prélèvements, seuls 2 étaient positifs pour HPV 11. L’analyse immunohistochimique a montré que p16 était présent dans l’épithélium de tous les prélèvements, et dans les régions papillomateuses de 37 prélèvements.

Conclusion: Étant donné que seuls 2 sur 38 PINS étaient positifs pour HPV (type 11) et que, dans le même temps, p16 était positif dans l’épithélium de tous les prélèvements et dans 37 des 38 régions papillomateuses, nous avons conclu que p16 ne peut pas être utilisé comme marqueur de substitution pour l’infection HPV à risque élevé dans le PINS. Nous préparons actuellement une étude multicentrique prospective afin d’augmenter la puissance de l’étude et de pouvoir mieux évaluer les implications cliniques de HPV et de p16 dans le PINS.