Predictive Molecular Biomarkers for Human Health Risk

Abstract

Molecular biomarkers play vital roles in disease risk assessment, personalized treatment selection and therapy response monitoring. This thesis explores the use of diverse molecular biomarkers for the assessment of human health risks, primarily in cancers. MiRNAs and their isoforms (isomiR) are promising biomarker candidates due to their comprehensive regulation of gene expression and involvement in physiology and pathological processes. The first study demonstrates that genetic variations in miRNA precursor regions influence the biogenesis of isomiRs in 95 SNP-isomiR pairs. Notably, we identified a SNP (rs6505162) impacting hsa-miR-423 isomiRs, potentially linked to breast cancer pathogenesis, suggesting their potential as biomarkers in disease assessment. The findings also highlight the mechanism of genetic regulation of isomiR generation and advance our understanding of miRNA mediated post-transcriptional regulation. Secondly, we explored the predictive capacity of aberrant intron-retention neoantigen burden (INB) in predicting the response to immune checkpoint inhibitors (ICI) in metastatic cancers. Both INB and tumor mutation burden (TMB) were strong predictors of ICI therapy duration (p = 0.019 and 0.038, respectively), with patients exhibiting elevated levels demonstrating exceptional treatment duration. Patients with high INB or TMB had improved overall survival (OS) (p = 1.1×10-4). Importantly, INB and TMB were uncorrelated, indicating that they capture distinct aspects of tumor neoantigen. Together, the combined assessment of INB and TMB offers improved accuracy in predicting clinical response to ICI therapies. Finally, we extend the application of molecular biomarkers to the assessment of minimal residual disease for risk stratification in triple negative breast cancer (TNBC) with residual disease after neoadjuvant chemotherapy. Detection of circulating tumor DNA (ctDNA) was a significant predictor of inferior distant disease-free survival (DDFS) (p = 0.006), disease-free survival (DFS) (p = 0.009) and OS (p = 0.002). The combination of circulating tumor cell (CTC) and ctDNA markers provided superior sensitivity and prognostic value. In conclusion, the studies provide compelling evidence for the utility of diverse molecular biomarkers – including miRNA isoforms, abnormal splicing-based neoantigen metrics and circulating tumor DNA in disease prediction and treatment efficacy assessment. By elucidating the roles of diverse biomarkers in predicting cancer pathogenesis and therapeutic response, we pave the way towards more personalized and effective approaches to managing human health risks.