Reinventing diagnostics for personalized therapy in oncology

Area of Science:

• Oncology
• Genomics
• Personalized Medicine

Background:

• Human cancers are diagnosed via light microscopy, a method prone to interobserver variation and diagnostic inaccuracy, particularly in non-small cell lung cancers.
• Current cancer therapies benefit only about 25% of patients, as treatments often fail to consider individual factors influencing response, leading to significant healthcare costs.

Purpose of the Study:

• To highlight the limitations of current cancer classification and grading systems.
• To advocate for the transition to functional classification systems that incorporate tumor heterogeneity.
• To emphasize the need for personalized therapy selection based on individual patient and tumor characteristics.

Main Methods:

• Review of existing literature on cancer diagnosis, classification, and therapy response.
• Analysis of the impact of genomic sequencing and targeted therapies on personalized medicine.
• Illustrative examples from lung and breast cancer research to demonstrate current challenges and emerging solutions.

Main Results:

• Microscopic cancer diagnosis shows poor concordance, even among specialists.
• A significant portion of cancer patients do not benefit from current therapies, resulting in substantial global economic waste.
• Advancements in genomics and targeted therapies are enabling the correlation of tumor characteristics with treatment response.

Conclusions:

• Static cancer classification systems are inadequate for addressing tumor heterogeneity and guiding personalized treatment.
• Emerging technologies offer promise for developing functional classification systems for accurate chemotherapeutic compound selection.
• Personalized therapy, informed by individual factors, is crucial for optimizing resource allocation and improving patient outcomes in oncology.