A paradigm shift in the prevention and diagnosis of oral squamous cell carcinoma

BACKGROUND

Oral squamous cell carcinoma (OSCC) is a widespread disease with only 50%-60% 5-year survival. Individuals with potentially malignant precursor lesions are at high risk.

METHODS

Survival could be increased by effective, affordable, and simple screening methods, along with a shift from incisional tissue biopsies to non-invasive brush biopsies for cytology diagnosis, which are easy to perform in primary care. Along with the explainable, fast, and objective artificial intelligence characterisation of cells through deep learning, an easy-to-use, rapid, and cost-effective methodology for finding high-risk lesions is achievable. The collection of cytology samples offers the further opportunity of explorative genomic analysis.

RESULTS

Our prospective multicentre study of patients with leukoplakia yields a vast number of oral keratinocytes. In addition to cytopathological analysis, whole-slide imaging and the training of deep neural networks, samples are analysed according to a single-cell RNA sequencing protocol, enabling mapping of the entire keratinocyte transcriptome. Mapping the changes in the genetic profile, based on mRNA expression, facilitates the identification of biomarkers that predict cancer transformation.

CONCLUSION

This position paper highlights non-invasive methods for identifying patients with oral mucosal lesions at risk of malignant transformation. Reliable non-invasive methods for screening at-risk individuals bring the early diagnosis of OSCC within reach. The use of biomarkers to decide on a targeted therapy is most likely to improve the outcome. With the large-scale collection of samples following patients over time, combined with genomic analysis and modern machine-learning-based approaches for finding patterns in data, this path holds great promise.

Effects of metabolic traits, lifestyle factors, and pharmacological interventions on liver fat: mendelian randomisation study

Objective

To investigate the effects of metabolic traits, lifestyle factors, and drug interventions on liver fat using the mendelian randomisation paradigm.

Design

Mendelian randomisation study.

Setting

Publicly available summary level data from genome-wide association studies.

Participants

Genome-wide association studies of 32 974 to 1 407 282 individuals who were predominantly of European descent.

Exposures

Genetic variants predicting nine metabolic traits, six lifestyle factors, four lipid lowering drug targets, three antihypertensive drug targets, and genetic association estimates formagnetic resonance imaging measured liver fat.

Main outcome measures

Mendelian randomisation analysis was used to investigate the effects of these exposures on liver fat, incorporating sensitivity analyses that relaxed the requisite modelling assumptions.

Results

Genetically predicted liability to obesity, type 2 diabetes, elevated blood pressure, elevated triglyceride levels, cigarette smoking, and sedentary time watching television were associated with higher levels of liver fat. Genetically predicted lipid lowering drug effects were not associated with liver fat; however, β blocker and calcium channel blocker antihypertensive drug effects were associated with lower levels of liver fat.

Conclusion

These analyses provide evidence of a causal effect of various metabolic traits, lifestyle factors, and drug targets on liver fat. The findings complement existing epidemiological associations, further provide mechanistic insight, and potentially supports a role for drug interventions in reducing the burden of hepatic steatosis and related disease. Further clinical study is now warranted to investigate the relevance of these genetic analyses for patient care.