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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.

Cellular stress conditions activate p53-dependent pathways to counteract the inflicted damage. To achieve the required functional diversity, p53 is subjected to numerous post-translational modifications and the expression of isoforms. Little is yet known how p53 has evolved to respond to different stress pathways. The p53 isoform p53/47 (p47 or ΔNp53) is linked to aging and neural degeneration and is expressed in human cells via an alternative cap-independent translation initiation from the 2nd in-frame AUG at codon 40 (+118) during endoplasmic reticulum (ER) stress. Despite an AUG codon in the same location, the mouse p53 mRNA does not express the corresponding isoform in either human or mouse-derived cells. High-throughput in-cell RNA structure probing shows that p47 expression is attributed to PERK kinase-dependent structural alterations in the human p53 mRNA, independently of eIF2α. These structural changes do not take place in murine p53 mRNA. Surprisingly, PERK response elements required for the p47 expression are located downstream of the 2nd AUG. The data show that the human p53 mRNA has evolved to respond to PERK-mediated regulation of mRNA structures in order to control p47 expression. The findings highlight how p53 mRNA co-evolved with the function of the encoded protein to specify p53-activities under different cellular conditions.

DNA and RNA binding proteins (DRBPs) are a broad class of molecules that regulate numerous cellular processes across all living organisms, creating intricate dynamic multilevel networks to control nucleotide metabolism and gene expression. These interactions are highly regulated, and dysregulation contributes to the development of a variety of diseases, including cancer. An increasing number of proteins with DNA and/or RNA binding activities have been identified in recent years, and it is important to understand how their activities are related to the molecular mechanisms of cancer. In addition, many of these proteins have overlapping functions, and it is therefore essential to analyze not only the loss of function of individual factors, but also to group abnormalities into specific types of activities in regard to particular cancer types. In this review, we summarize the classes of DNA-binding, RNA-binding, and DRBPs, drawing particular attention to the similarities and differences between these protein classes. We also perform a cross-search analysis of relevant protein databases, together with our own pipeline, to identify DRBPs involved in cancer. We discuss the most common DRBPs and how they are related to specific cancers, reviewing their biochemical, molecular biological, and cellular properties to highlight their functions and potential as targets for treatment.

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.

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.

Human cells are subjected to continuous challenges by different genotoxic stress attacks. DNA damage leads to erroneous mutations, which can alter the function of oncogenes or tumor suppressors, resulting in cancer development. To circumvent this, cells activate the DNA damage response (DDR), which mainly involves cell cycle regulation and DNA repair processes. The tumor suppressor p53 plays a pivotal role in the DDR by halting the cell cycle and facilitating the DNA repair processes. Various pathways and factors participating in the detection and repair of DNA have been described, including scores of RNA-binding proteins (RBPs) and RNAs. It has become increasingly clear that p53’s role is multitasking, and p53 mRNA regulation plays a prominent part in the DDR. This review is aimed at covering the p53 RNA metabolism linked to the DDR and highlights the recent findings.

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.

Oral cancers are surrounded by epithelium that histologically might seem normal, but genetically has aberrations. In patients with squamous cell carcinoma of the oral tongue (SCCOT), it is therefore important to study not only the tumor but also the clinically tumor-free contralateral tongue tissue that remains in the patient after treatment to map changes of prognostic and/or diagnostic value. The transporter associated with antigen processing (TAP) dimer is a key factor in the process of activating cytotoxic T cells. By downregulating the expression of TAP, tumor cells can escape cytotoxic T cell recognition. Biopsies from tumor and clinically tumor-free contralateral tongue tissue in 21 patients with SCCOT were analyzed together with tongue biopsies from 14 healthy individuals, which served as the control group. Dividing patients into TAP1-high and TAP1-low groups according to the median TAP1 level in tumor-free samples showed that patients with lower TAP1 mRNA levels in tumor-free samples had better overall (p = 0.003) and disease-free survival (p = 0.002). The results showing that TAP1 levels in tumor-free tongue tissue contralateral to the SCCOT correlate with survival is an important contribution to early diagnosis and follow up of SCCOT.