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Adding New Dimensions to 3D Cancer Models.

Kevan ChuLukas E Dow
Published in: Cancer research (2024)
Understanding patient-specific responses to anticancer therapies and how individual tumors interact with their tumor microenvironment (TME) is a challenging task. To measure the impact of the TME on diverse and clinically relevant treatments, Ramos Zapatero and colleagues coupled patient-derived organoid (PDO) and cancer-associated fibroblast (CAF) cocultures with high-throughput mass cytometry-based assessment of cell state. Using a newly developed "Trellis" algorithm enabled integration and analysis of highly complex, multidimensional treatment response data. This work showed that tumor cell response to chemotherapy was associated with both intrinsic and nonintrinsic signaling states, whereby proliferative rate, growth factor signaling, and CAFs interaction influenced chemoprotection. Furthermore, the work suggests a potential role for the TME in promoting lineage plasticity associated with drug resistance. In all, the pipeline described provides a blueprint for exploring the intricate interplay of factors influencing cancer treatment response.
Keyphrases
  • single cell
  • growth factor
  • high throughput
  • papillary thyroid
  • squamous cell
  • cell therapy
  • machine learning
  • squamous cell carcinoma
  • stem cells
  • deep learning
  • big data
  • locally advanced
  • childhood cancer
  • young adults