Systems pathology--taking molecular pathology into a new dimension

Area of Science:

• Pathology
• Systems Biology
• Computational Biology

Background:

• Pathology traditionally relies on morphological and molecular data for cancer prognostication.
• While 'omic' technologies offer potential, many molecular signatures lack independent validation and mechanistic insight.
• Current methods often yield correlative associations, limiting predictive power for cellular network behavior in disease.

Purpose of the Study:

• To explore the transformation of pathology into a quantitative systems science.
• To address the challenges of integrating diverse clinical data into robust mathematical models for cancer.
• To review approaches for applying systems biology effectively in clinical cancer research.

Main Methods:

• Integrating morphological, histological, and molecular data.
• Utilizing systems biology principles to model cellular dynamics and intercellular behavior.
• Developing and validating mathematical models for cancer prediction.

Main Results:

• Pathology has the potential to become a quantitative systems science by leveraging rich datasets.
• Challenges exist in validating molecular signatures and establishing mechanistic links for clinical utility.
• Systems biology offers a framework for integrating complex data into predictive cancer models.

Conclusions:

• Quantitative pathology, integrating diverse data, is essential for advancing cancer prediction.
• Robust mathematical and systems biology models are needed to overcome current limitations.
• Successful application of systems biology in the clinic requires addressing data integration and model validation challenges.