Implementation of a Precision Pathology Program Focused on Oncology-Based Prognostic and Predictive Outcomes

Area of Science:

• Oncology
• Pathology
• Biomarker Discovery

Background:

• Personalized medicine, introduced 10-15 years ago, relies on biomarker discovery for treatment selection.
• Companion diagnostics in oncology face challenges in development and implementation.
• Predictive pathology, assessing microscopic morphology, is crucial for patient selection.

Purpose of the Study:

• To review the evolution of predictive pathology from subjective assessment to quantitative science.
• To discuss components of precise pathology platforms for next-generation patient phenotyping.
• To highlight the role of protein biomarker panels and advanced analytics in personalized cancer treatment.

Main Methods:

• Review of literature on predictive pathology and companion diagnostics.
• Discussion of advanced image analysis and biomarker quantitation.
• Integration of tissue-based and fluid-based analytics for patient phenotyping.

Main Results:

• Histologic attributes in breast cancer (e.g., tubular, cribriform, lobular carcinoma) guide treatment decisions.
• Protein biomarker panels offer a higher level of personalized pathology for tumor characterization.
• Quantitative approaches enhance the accuracy and reliability of predictive pathology.

Conclusions:

• Predictive pathology is transitioning to a quantitative science, improving patient selection for targeted therapies.
• Advanced image analysis, biomarker quantitation, and integrated analytics are key to next-generation precise patient phenotyping.
• Personalized medicine, driven by precise pathology, promises better patient outcomes, reduced toxicity, and improved health economics.