Bioinformatics roadmap for therapy selection in cancer genomics

Area of Science:

• Oncology
• Bioinformatics
• Genomics

Background:

• Tumour heterogeneity, encompassing inter- and intratumour variations, is a primary driver of cancer treatment failure and relapse.
• Precision oncology aims to personalize cancer treatment by integrating diverse patient data, including clinical, epidemiological, and omics information.
• Bioinformatics tools are crucial for analyzing complex cancer data to guide therapeutic decisions.

Purpose of the Study:

• To review current bioinformatics approaches for selecting cancer therapies.
• To address how different types of tumour heterogeneity (inter- and intratumour) influence therapy selection strategies.
• To discuss the integration of these data-driven workflows into clinical practice.

Main Methods:

• Review of existing literature on tumour heterogeneity and precision oncology.
• Analysis of bioinformatics tools for analyzing genomics, expression, and multi-omics data.
• Examination of methods for clonal inference and single-cell transcriptomics for intratumour heterogeneity.

Main Results:

• Different bioinformatics strategies are employed for intertumour heterogeneity (e.g., genomics variants, expression data, multi-omics) and intratumour heterogeneity (e.g., clonal inference, single-cell transcriptomics).
• These methods provide computational scores to prioritize drugs for tailored treatment selection.
• The review highlights specific bioinformatics tools applicable to each approach.

Conclusions:

• Bioinformatics tools are essential for navigating tumour heterogeneity in precision oncology.
• Tailored therapy selection can be achieved by leveraging diverse omics data and advanced analytical methods.
• Integrating these advanced computational workflows into clinical practice holds promise for improving cancer treatment outcomes.