Why do cancer therapies stop working even when they are cutting-edge?

Cancer cells evolve and adapt to evade even advanced therapeutics, leading to resistance and disease relapse. This is the critical challenge oncology faces despite 300+ new FDA-approved therapies in two decades.

How can researchers predict which patients will respond to a specific cancer drug?

Organoids, biomarkers, and AI-enhanced models enable prediction of patient-specific drug responses by mimicking human biology more closely than traditional 2D or animal models, allowing personalized therapy development.

What makes KRAS such a difficult target to drug?

KRAS mutations are prevalent in hard-to-treat cancers: nearly 90% of pancreatic cancers and 30-40% of lung and colorectal cancers. Recent FDA-approved KRAS G12C inhibitors show promise but face rising resistance and diverse mutation profiles.

How is immuno-oncology evolving beyond checkpoint inhibitors?

Recent FDA approvals for TIL therapies, bispecific antibodies, and new CAR-T developments are driving combination-based strategies that integrate with standard care and address immune cell exhaustion in the tumor microenvironment.

Can AI meaningfully reduce drug development time and cost?

AI promises to slash drug development timelines by nearly half while improving accuracy in identifying viable compounds, critical as global pharmaceutical R&D spending is projected to exceed $230 billion by 2026.