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  • An Analytical Review of Multi-Omics Integration and Bioinformatics Tools in Precision Oncology

  • Farzaneh Mohamadi Farsani,1,* Zahra Maravandi,2 Mansoore Hosseini-Koupaei,3
    1. Department of Biology, Naghshejahan Higher Education Institute
    2. Department of Biology, Naghshejahan Higher Education Institute
    3. Department of Biology, Naghshejahan Higher Education Institute


  • Introduction: Traditional pathological markers fail to address the spatiotemporal heterogeneity of tumors. Precision oncology has shifted towards Next-Generation Sequencing (NGS) and multi-omics analysis to address this challenge. This study aims to conduct a structured review and analysis of computational pipelines presented in scientific literature, evaluate the efficacy of standard variant calling and interpretation tools, and provide a critical analysis of the translational barriers involved in integrating these data into clinical decision-making.
  • Methods: The current literature review has been done by systematic search in PubMed, Web of Science, and Scopus. The key emphasis has been placed on the review of research papers that use conventional clinical bioinformatics approaches, i.e., alignment and somatic variant callings (BWA-MEM, GATK Mutect2), genome annotation tools (ANNOVAR, Ensembl VEP), as well as mutation databases (COSMIC, ClinVar, CIViC). Moreover, the implementation of multi-omics analysis and drug gene interaction databases (cBioPortal, DGIdb) to analyze drug resistance in the recent years was reviewed and compared.
  • Results: Examination of empirical and methodological evidence reveals that while implementing standard computational pipelines has largely systematized the identification of driver mutations in genes such as EGFR, KRAS, and BRAF, the direct translation of these genomic findings into effective therapeutic strategies still faces structural challenges. The synthesis of available data demonstrates that relying solely on DNA mutation profiles is often insufficient for determining cancer cell fate. Accurate prediction of biomarkers, such as Tumor Mutation Burden (TMB) and immunotherapy response, essentially requires integrating genomic data with RNA-seq expression profiles. Furthermore, the heavy reliance of clinical pathology reference databases, such as ClinVar, on European populations has exacerbated the exponential increase in Variants of Uncertain Significance (VUS) in other populations. This phenomenon, coupled with inconsistencies in the outputs of various annotation algorithms, elevates the error rate of Clinical Decision Support Systems (CDSS) and hinders definitive treatment selection based on drug-gene interactions.
  • Conclusion: Achieving the goals of precision oncology requires a transition from one-dimensional genomic assessments to integrated multi-omics models and rigorous standardization of bioinformatics tool outputs. Addressing current bottlenecks, particularly in under-represented populations, depends on enriching clinical databases with population-specific data and developing Clinical Decision Support Systems tailored to unique genetic characteristics.
  • Keywords: Precision Oncology, Personalized Medicine, Clinical Bioinformatics, Computational Pipeline, NGS

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