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Introduction: Metabolic reprogramming is a hallmark of cancer. Plasma metabolomics offers a minimally invasive approach for identifying metabolic alterations that may provide insights into tumor progression.

Objectives: We aimed to characterize plasma metabolomic profiles in patients with oral squamous cell carcinoma (OSCC) and evaluate their clinical relevance.

Methods: Plasma samples from 43 OSCC patients and 129 cancer-free controls, matched at a 1:3 ratio based on age, sex, and body mass index, were analyzed using gas chromatography-mass spectrometry (GC-MS). A random forest algorithm was applied to identify key metabolic features distinguishing OSCC from controls. The clinical significance of the top metabolites was assessed and validated in another OSCC cohort (n = 27).

Results: A total of 113 compounds were putatively annotated and analyzed based on relative abundances. A ten-feature panel demonstrated good classification performance (area under the curve = 0.87; Matthews correlation coefficient = 0.703). The ten features are maltose, glucose, xylulose, δ-gluconolactone, fructose, indoleacetic acid, α-hydroxybutyric acid, glutamic acid, cysteine, and the monoacylglyceride MG(18:1(9Z)/0:0/0:0), suggesting dysregulated carbohydrate metabolism and oxidative stress as the major plasma metabolomic alterations in OSCC. Notably, α-hydroxybutyric acid levels were elevated in patients with regional lymph node metastasis compared with those without.

Conclusion: Our findings underscore the intricate interplay between altered glucose metabolism, redox imbalance, and OSCC. α-hydroxybutyric acid, a marker of oxidative stress and an indicator of insulin resistance, may be associated with metastatic progression.

Background: The incidence of squamous cell carcinoma of the oral tongue (SCCOT) among young adults is increasing in several regions of the world. Age-dependent differences in the biology of SCCOT have been suspected.

Methods: We used the Olink Explore 3072 high-throughput platform to comprehensively quantify plasma proteins in 24 young (≤ 40 years of age) and 50 old (> 50 years of age) individuals. Eight young and 20 old individuals were diagnosed with SCCOT, four young and nine old individuals with SCC at other oral subsites (SCCOO), and the remaining 12 young and 21 old individuals were healthy controls. Dimension reduction analysis, differential expression analysis, and functional enrichment analysis were performed to characterize young patient-specific biological signatures.

Results: Plasma levels of 2923 proteins were obtained. Principal component analysis indicated age-related expression patterns. Comparing young patients to young controls/old patients/old controls, differential abundance analysis showed that increases in protein levels of Peroxiredoxin 2 (PRDX2) and C-C motif chemokine ligand 26 (CCL26) and a decrease in Kallikrein related peptidase 4 (KLK4) were young patient-specific. Reactome pathway enrichment analysis identified “Cellular response to chemical stress,” “Detoxification of reactive oxygen species” and “Cellular responses to stimuli” as the top altered pathways in young patients with SCCOT.

Conclusions: Abnormal cellular stress and aberrant immune regulation could thus be linked to cancer development in young patients. The unique plasma proteomic signature observed in young patients with SCCOT suggests that they constitute a specific group with distinct underlying pathophysiological processes.

Squamous cell carcinoma of the oral tongue (SCCOT) represents an aggressive malignancy characterized by high metastatic potential and significant heterogeneity in its tumor microenvironment. The tumor-stroma ratio (TSR) has emerged as a prognostic biomarker, with higher stromal content frequently correlating with worse survival outcomes. Traditional approaches using the standard 50% TSR cutoff may not be optimal for SCCOT, and visual TSR estimation introduces variability during TSR region annotation. This study aimed to develop and validate a dedicated TSR estimation model for SCCOT by incorporating representative TSR regions from the invasive tumor front of whole slide images and to determine the optimal TSR threshold for prognostic stratification. Using hematoxylin and eosin-stained images from The Cancer Genome Atlas as a discovery cohort and whole slide images from Norrland's University Hospital Umea, Sweden (NUS) as a validation cohort, we developed a computational model to estimate TSR. The model demonstrated a high correlation with pathologist-based TSR estimation in both discovery (R = 0.848, p < 0.01) and validation (R = 0.783, p < 0.01) cohorts. The optimal 55% cutoff identified by the model improved prognostic accuracy over the traditional 50% threshold, with patients having high stroma within the tumor invasive front showing worse overall (log-rank p = 0.006) and disease-specific (log-rank p = 0.016) survival. Our computational TSR model for SCCOT demonstrates that automated TSR estimation enhances prognostic accuracy at an optimal cutoff of 55%, contributing to more precise risk stratification and potentially enabling personalized treatment strategies in SCCOT management.

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.

PURPOSE: The field cancerization concept indicates the presence of pre-cancerous changes in clinically normal tissue surrounding the tumor. In squamous cell carcinoma of the oral tongue (SCCOT) which is infrequently linked to human papillomavirus infection, we have previously reported that clinically normal tongue contralateral to tumor (NTCT) is molecularly abnormal. Here, combining our transcriptomic and genomic data, we aimed to investigate the contribution of molecular changes in NTCT to cancer development.

METHODS: Microarray gene expression data of 14 healthy controls, 23 NTCT and 29 SCCOT samples were investigated to characterize transcriptional profiles in NTCT. Whole exome sequencing and RNA-sequencing data of paired NTCT and tumor samples from 15 SCCOT patients were used to study correlation between copy number variation and differential gene expression.

RESULTS: Using supervised multivariate partial least squares discriminant analysis, a total of 61 mRNAs that distinguish NTCT from healthy tongue were selected. Functional enrichment analysis of the 22 upregulated genes showed increased "positive regulation of nitrogen compound metabolic process" in NTCT. All 12 genes involved in this process have roles in apoptosis (anti- and/or pro-apoptotic). Compared to healthy controls, Zinc Finger Protein 395 (ZNF395), a pro-apoptotic tumor suppressor located on chromosome 8p, was the only gene showing increased mRNA level in NTCT whereas decreased in SCCOT. Given the frequent loss of chromosome 8p in SCCOT, the impact of ZNF395 copy number variation on gene expression was further examined, revealing a positive correlation between copy number and mRNA level (correlation coefficient = 0.572, p < 0.001).

CONCLUSION: NTCT is susceptible to malignant transformation, where tissue homeostasis is maintained at least partly through regulation of apoptosis. Loss of the pro-apoptotic gene ZNF395 could thus initiate cancer development.

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.

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.