Clonal dominance defines metastatic dissemination in pancreatic cancer.
I-Lin HoChieh-Yuan LiFuchenchu WangLi ZhaoJingjing LiuEr-Yen YenCharles A DykeRutvi ShahZhaoliang LiuAli Osman ÇetinYanshuo ChuFrancesca CitronSergio AttanasioDenise CortiFaezeh DarbaniyanEdoardo Del PoggettoSara LoponteJintan LiuMelinda SoeungZiheng ChenShan JiangHong JiangAkira InoueSisi GaoAngela DeemNingping FengHaoqiang YingMichael P KimVirginia GiulianiGiannicola GenoveseJianhua ZhangP Andrew FutrealAnirban MaitraTimothy P HeffernanLinghua WangKim-Anh DoCiro Gargiulo IsaccoGiulio F DraettaAlessandro CarugoRuitao LinAndrea VialePublished in: Science advances (2024)
Tumors represent ecosystems where subclones compete during tumor growth. While extensively investigated, a comprehensive picture of the interplay of clonal lineages during dissemination is still lacking. Using patient-derived pancreatic cancer cells, we created orthotopically implanted clonal replica tumors to trace clonal dynamics of unperturbed tumor expansion and dissemination. This model revealed the multifaceted nature of tumor growth, with rapid changes in clonal fitness leading to continuous reshuffling of tumor architecture and alternating clonal dominance as a distinct feature of cancer growth. Regarding dissemination, a large fraction of tumor lineages could be found at secondary sites each having distinctive organ growth patterns as well as numerous undescribed behaviors such as abortive colonization. Paired analysis of primary and secondary sites revealed fitness as major contributor to dissemination. From the analysis of pro- and nonmetastatic isogenic subclones, we identified a transcriptomic signature able to identify metastatic cells in human tumors and predict patients' survival.
Keyphrases
- squamous cell carcinoma
- small cell lung cancer
- end stage renal disease
- single cell
- physical activity
- endothelial cells
- body composition
- machine learning
- newly diagnosed
- chronic kidney disease
- induced apoptosis
- ejection fraction
- climate change
- deep learning
- multidrug resistant
- molecular dynamics simulations
- young adults
- patient reported outcomes
- risk assessment
- cell death
- childhood cancer