Primary liver cancer ranks third among lethal cancer cases globally, with China being a high-incidence country, where over half of the deaths occur. Due to its extensive tumor heterogeneity, the latest targeted combined immunotherapy regimens are effective in only 20% to 30% of patients. There is a lack of molecular subtyping to guide precise drug use, and there is an urgent need to research molecular subtyping that can guide precise drug use in liver cancer and reveal new drug-resistant targets.
The study and its significance:
A research paper was recently published in Cancer Cell by a team led by Zhang Ning from Peking University First Hospital, in collaboration with Wu Jianmin's team from Peking University Cancer Hospital and Yang Xing's team from Peking University First Hospital, along with cooperation from Zhang Jiangong's team from Henan Cancer Hospital.
"This study, based on a liver cancer-like organ biobank, further elucidates the heterogeneity within liver cancer tumors. Through large-scale targeted drug screening, it reveals molecular subtypes of drug sensitivity in liver cancer, elucidates key resistance targets and their mechanisms to lenvatinib, and synthesizes new drugs, providing important clues for the precise diagnosis and treatment of liver cancer," said corresponding author Zhang Ning to Chinese Science Bulletin.
Zhang Ning's interdisciplinary team, based on large-scale organ-like studies, combined multi-omics analysis, machine learning techniques, and pharmacological research to establish a drug-sensitive molecular classification for clinical drugs, laying the foundation for the precise diagnosis and treatment of liver cancer.
The team sampled multiple points of liver cancer patients' surgical specimens and successfully established a biobank covering 144 patients and 399 tumor sites. Phenotypic heterogeneity of organ-like structures and tissues was confirmed through hematoxylin-eosin staining, immunofluorescence, and immunohistochemistry. Exome and transcriptome sequencing validated the heterogeneity of highly preserved tissue samples from organ-like structures across multiple dimensions, including somatic mutations, copy number variations, and transcriptomic similarities.
To further study intratumoral heterogeneity, the team found significant differences in tumor cell mutations and copy number variations among multiple regions of some patients' organ-like structures and constructed evolutionary trees of multiple regions. At the transcriptomic level, the team found differential expression of targets of first-line liver cancer drugs sorafenib and lenvatinib across multiple regions, with drug-sensitive heterogeneity between different sites.
The team selected commonly used first-line liver cancer drugs sorafenib and lenvatinib, second/third-line drugs regorafenib, apatinib, and bevacizumab, as well as cholangiocarcinoma targeted drugs pemigatinib and ivosidenib, to screen 376 organ-like structures for drug sensitivity, exploring molecular subtypes of drug sensitivity in liver cancer. Combining patient clinical information, the team further confirmed the consistency between organ-like structure drug sensitivity and patient treatment outcomes. Furthermore, the team used machine learning models to analyze the drug sensitivity molecular subtypes of clinical liver cancer targeted drugs lenvatinib, sorafenib, regorafenib, and apatinib.
Using lenvatinib as an example of a first-line liver cancer drug, the team revealed key resistance targets and resistance mechanisms, formulated a new combined drug therapy scheme, synthesized new drugs, and confirmed their good tumor killing effects at the organ-like structure level and in mouse models. Finally, the team revealed the drug-sensitive molecular subtypes of the new drugs, with high expression of stemness genes in drug-resistant organ-like structures, suggesting a possible association between drug resistance and tumor stemness.
This research was supported and funded by the National Natural Science Foundation of China, the Ministry of Science and Technology, Baidu Fund, and the Beijing Municipal Science and Technology Commission.
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