Building flexible and robust analysis frameworks for molecular subtyping of cancers

Area of Science:

• Bioinformatics
• Cancer Genomics
• Clinical Diagnostics

Background:

• Molecular subtyping is crucial for determining tumor aggressiveness and patient prognosis.
• Tumor profiling necessitates expertise in bioinformatics tools for data processing and analysis.
• Data incompatibility and biological variance present challenges in sample classification.

Purpose of the Study:

• To provide a roadmap for implementing bioinformatics frameworks for molecular profiling of human cancers in clinical settings.
• To address challenges in data processing, analysis, and classification for accurate tumor profiling.
• To develop a practical framework applicable to clinical diagnostic settings.

Main Methods:

• Developing a bioinformatics framework integrating quality control, normalization, and batch correction methods.
• Implementing classification and reporting modules within the framework.
• Applying the framework to a breast cancer use case for validation.

Main Results:

• The proposed framework integrates multiple bioinformatics methods for comprehensive molecular profiling.
• The breast cancer use case demonstrates the framework's utility in a clinical diagnostic context.
• The roadmap facilitates the implementation of robust bioinformatics pipelines for cancer diagnostics.

Conclusions:

• The developed bioinformatics framework enables reliable molecular profiling for cancer diagnostics.
• Addressing data challenges is key to accurate tumor classification and prognosis prediction.
• This roadmap supports the clinical integration of advanced bioinformatics for personalized cancer care.